code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tenso... | 46 |
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked before tokenizers
lower... | 170 | 0 |
"""simple docstring"""
def __lowerCAmelCase ( __lowerCAmelCase : Optional[Any] , __lowerCAmelCase : Dict , __lowerCAmelCase : Dict , __lowerCAmelCase : List[Any] ) -> str:
# Return True if there is node that has not iterated.
_UpperCamelCase : Any = [... | 239 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE = {"""configuration_reformer""": ["""REFORMER_PR... | 239 | 1 |
"""simple docstring"""
import random
from typing import Any
def snake_case ( lowerCAmelCase_ ) -> list[Any]:
for _ in range(len(lowerCAmelCase_ ) ):
_snake_case = random.randint(0 , len(lowerCAmelCase_ ) - 1 )
_snake_case = random.randint(0 ... | 103 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 108 | 0 |
from __future__ import annotations
def a_ ( _A , _A ) -> str:
"""simple docstring"""
# Checks if the entire collection has been sorted
if len(_A ) <= 1 or n <= 1:
return
insert_next(_A , n - 1 )
rec_insertion_sort(_A , ... | 372 |
def a_ ( _A = 4000000 ) -> int:
"""simple docstring"""
snake_case__ = [0, 1]
snake_case__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i += 1
snake_cas... | 372 | 1 |
from collections import namedtuple
import requests
from lxml import html # type: ignore
a_ :Tuple = namedtuple('covid_data', 'cases deaths recovered')
def a ( A__ = "https://www.worldometers.info/coronavirus/" ) -> covid_data:
'''simple docstring'''
SCREAMING_SNAKE_... | 35 |
import unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tens... | 35 | 1 |
# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny model through reduction of a normal pre-trained model, but keeping the
# full vocab, merges file, and t... | 716 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_earl... | 478 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,
)
from... | 1 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case = {
'''configuration_autoformer''': [
'''AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''AutoformerC... | 1 | 1 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="""session""" )
def __lowerCamelCase ( ) -> ... | 718 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__snake_case : Union[str, Any] = {
'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}
try:
if not is_t... | 687 | 0 |
class UpperCamelCase:
def __init__( self : Any ) -> List[str]:
'''simple docstring'''
__snake_case = 0
__snake_case = 0
__snake_case = {}
def SCREAMING_SNAKE_CASE_ ( self : List[str] ... | 371 |
def _lowerCAmelCase ( _lowerCAmelCase = 100 ) -> int:
'''simple docstring'''
__snake_case = n * (n + 1) * (2 * n + 1) / 6
__snake_case = (n * (n + 1) / 2) ** 2
return int(square_of_sum - sum_of_squares )
if __name__ == "__main__"... | 371 | 1 |
def snake_case (UpperCamelCase : int , UpperCamelCase : int ):
'''simple docstring'''
return abs(UpperCamelCase ) if a == 0 else greatest_common_divisor(b % a , UpperCamelCase )
def snake_case (UpperCamelCase : int , UpperCamelCase : ... | 235 |
def snake_case (UpperCamelCase : int , UpperCamelCase : bool = False ):
'''simple docstring'''
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3317044064679887... | 235 | 1 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_poolformer import PoolFormerImageProcessor
A = logging.get_logger(__name__)
class __snake_case ( a__):
def __init__( self, *A, **A ):
"""simple docstri... | 320 |
import unittest
from transformers import DebertaVaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ... | 336 | 0 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
__UpperCamelCase : int = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
"convert": [... | 106 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
__UpperCamelCase : Any = None
try:
import msvcrt
except ImportError:
__UpperCamelCase : Optional[Any] = None
try:
import fcntl
ex... | 106 | 1 |
"""simple docstring"""
def __UpperCAmelCase ( lowercase ):
"""simple docstring"""
_UpperCAmelCase = len(lowercase )
for i in range(lowercase ):
for j in range(i + 1 ,lowercase ):
if numbers[j] < numbers[i]:
_UpperCAmelCase , _UpperCAmelCase = nu... | 277 | """simple docstring"""
from transformers import BertTokenizerFast
from .custom_tokenization import CustomTokenizer
class a ( lowerCAmelCase_ ):
_snake_case : Dict = CustomTokenizer
pass
| 277 | 1 |
"""simple docstring"""
from __future__ import annotations
def _A ( _a : int , _a : int ):
"""simple docstring"""
A = []
create_all_state(1 , _a , _a , [] , _a )
return result
d... | 255 |
"""simple docstring"""
def _A ( _a : float , _a : float , _a : float , _a : float , _a : float , ):
"""simple docstring"""
A = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p i... | 255 | 1 |
def _A ( _lowercase = 10_00 ) -> int:
"""simple docstring"""
__UpperCamelCase = 2**power
__UpperCamelCase = str(_lowercase )
__UpperCamelCase = list(_lowercase )
__UpperCamelCase = 0
for i in list_num:
sum_of_num += int(_lowercase )
... | 1 |
import os
from distutils.util import strtobool
def UpperCamelCase__ ( lowerCAmelCase__ ,lowerCAmelCase__ ):
for e in env_keys:
lowercase = int(os.environ.get(lowerCAmelCase__ ,-1 ) )
if val >= 0:
return val
return default
def UpperCamel... | 428 | 0 |
from __future__ import annotations
from random import random
class __magic_name__ :
'''simple docstring'''
def __init__( self: Tuple , _lowerCamelCase: int | None = None ):
SCREAMING_SNAKE_CASE_ = value
SCREAMING_SNAKE... | 89 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_a... | 89 | 1 |
'''simple docstring'''
import os
from itertools import chain
from random import randrange, shuffle
import pytest
from .sola import PokerHand
_a : str = (
"4S 3H 2C 7S 5H",
"9D 8H 2C 6S 7H",
"2D 6D 9D TH 7D",
"TC 8C 2S JH 6C",
"JH 8S TH AH QH",
"TS KS 5S 9S AC",
"KD... | 56 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 6 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checko... | 703 |
import numpy as np
from transformers import BatchFeature
from transformers.testing_utils import require_tf, require_torch
from .test_feature_extraction_common import FeatureExtractionSavingTestMixin
class UpperCAmelCase__( lowerCamelCase ):
'''simple docstring'''
A : List[Any] ... | 642 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transform... | 99 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[Any] = {
"""configuration_swinv2""": ["""SWINV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Swinv2Config"""],
}
try:
if not ... | 328 | 0 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def _UpperCAmelCase ( a : int , a : int , a : float = 1 / sqrt(2 ) ):
snake_case__ = tau * frequency / samplerate
snake_case__ = sin(snake_case_ )
snake_case__ =... | 718 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import DataLoader, ... | 99 | 0 |
"""simple docstring"""
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging... | 238 |
"""simple docstring"""
import argparse
import datetime
def UpperCamelCase ( _lowerCAmelCase : str ) -> str:
_UpperCAmelCase : List[str] = {
"""0""": """Sunday""",
"""1""": """Monday""",
"""2""": """Tuesday""",
"""3""": """We... | 238 | 1 |
'''simple docstring'''
import cva
import numpy as np
class __lowercase :
'''simple docstring'''
def __init__(self ,_lowerCamelCase ,_lowerCamelCase ) -> str:
'''simple docstring'''
if k in (0.0_4, 0.0... | 56 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers imp... | 56 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class A_ ( __a , __a ):
_A :Optional[int] = 1
@re... | 428 |
import unittest
import torch
from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism
from .test_modeling_common import ModelTesterMixin, UNetTesterMixin
enable_full_determinism()
class A_ ( __a , ... | 428 | 1 |
import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput
def _lowerCAmelCase ( ... | 713 |
def _lowerCAmelCase ( A__ , A__ , A__ ):
if principal <= 0:
raise Exception('Principal borrowed must be > 0' )
if rate_per_annum < 0:
raise Exception('Rate of interest must be >= 0' )
if years_to_repay <= 0 or not isinstance(A__ , A__ ):
... | 642 | 0 |
import unittest
from transformers import (
MODEL_FOR_OBJECT_DETECTION_MAPPING,
AutoFeatureExtractor,
AutoModelForObjectDetection,
ObjectDetectionPipeline,
is_vision_available,
pipeline,
)
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_pytessera... | 36 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
flip_channel_order,
get_resize_output_image_size,
rescale,
resize,
to_channel_dimensio... | 36 | 1 |
def UpperCamelCase_( __magic_name__ : int = 50 ):
"""simple docstring"""
_lowerCAmelCase :Optional[int] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
for tile_start ... | 382 |
def UpperCamelCase_( __magic_name__ : str ):
"""simple docstring"""
_lowerCAmelCase :Optional[Any] = [0 for i in range(len(__magic_name__ ) )]
# initialize interval's left pointer and right pointer
_lowerCAmelCase , _lowerCAmelCase :List[An... | 382 | 1 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCAmelCase_ ( a__ ):
def __init__( self, SCREAMING_SNAKE_CASE_, SCREAMING_SNAKE_CASE_ = None, ... | 40 |
'''simple docstring'''
import unittest
from transformers import AutoTokenizer, NystromformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, ... | 432 | 0 |
"""simple docstring"""
import argparse
import logging
import os
from datetime import datetime
import numpy as np
import torch
from torch import nn
from torch.utils.data import DataLoader, RandomSampler, TensorDataset
from tqdm import tqdm
from transformers import GPTaLMHeadModel
SCREAMING_SNAKE_C... | 715 |
"""simple docstring"""
import numpy as np
# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models
if __name__ == "__main__":
# Initialising the CNN
# (Sequential- Building the model layer by layer)
SCREAMING_SNAKE_CASE = ... | 556 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
SegformerForSemanti... | 91 |
"""simple docstring"""
import gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
AudioLDMPipeline,
... | 91 | 1 |
import re
import jax.numpy as jnp
from flax.traverse_util import flatten_dict, unflatten_dict
from jax.random import PRNGKey
from ..utils import logging
_UpperCamelCase : Any =logging.get_logger(__name__)
def a__ (__lowercase :Union[str, Any] ) -> Any:
_A : ... | 332 |
def a__ (__lowercase :Tuple ) -> Optional[Any]:
# if the collection is empty, returns empty
if collection == []:
return []
# get some information about the collection
_A : List[str] = len(__lowercase )
_A : Optional[Any] ... | 332 | 1 |
import inspect
import unittest
from transformers import ConvNextVaConfig
from transformers.models.auto import get_values
from transformers.models.auto.modeling_auto import MODEL_FOR_BACKBONE_MAPPING_NAMES, MODEL_MAPPING_NAMES
from transformers.testing_utils import require_torch, require_vision, slow, torch_device... | 87 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import Aut... | 539 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
_lowerCAmelCase : List[str] = {"tokenization_byt5": ["ByT5Tokenizer"]}
if TYPE_CHECKING:
from .tokenization_byta import ByTaTokenizer
else:
import sys
_lowerCAmelCase : str = _LazyModule(__name__, globals()["_... | 604 |
import collections
import inspect
import unittest
from transformers import SwinvaConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTe... | 604 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switching between checkout... | 214 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_lowerCAmelCase : Tuple ={
"""configuration_mobilenet_v2""": [
"""MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""MobileNetV2Config""",
"... | 113 | 0 |
'''simple docstring'''
def A ( A_ : str ):
snake_case : List[str] = ''''''
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def A ( A_ : str ... | 555 |
'''simple docstring'''
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForCond... | 555 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql import sql # noqa F40... | 100 |
# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch
import math
from typing import Union
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import randn_tensor
from .scheduling_utils import SchedulerMixin
class A_ ( __U... | 669 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer
... | 410 |
import argparse
import os
import re
import packaging.version
__magic_name__ : Dict = '''examples/'''
__magic_name__ : List[str] = {
'''examples''': (re.compile(r'''^check_min_version\("[^"]+"\)\s*$''', re.MULTILINE), '''check_min_version("VER... | 410 | 1 |
import argparse
import csv
import logging
import os
import random
import numpy as np
import torch
from torch.utils.data import DataLoader, RandomSampler, SequentialSampler, TensorDataset
from tqdm import tqdm, trange
from transformers import (
CONFIG_NAME,
WEIGHTS_NAME,
AdamW,
OpenAIGPTDoubleHeadsM... | 562 |
'''simple docstring'''
import torch
import torch.nn as nn
from transformers.modeling_utils import ModuleUtilsMixin
from transformers.models.ta.modeling_ta import TaBlock, TaConfig, TaLayerNorm
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
... | 331 | 0 |
import argparse
import glob
import logging
import os
import sys
import time
from collections import defaultdict
from pathlib import Path
from typing import Dict, List, Tuple
import numpy as np
import pytorch_lightning as pl
import torch
from callbacks import SeqaSeqLoggingCallback, get_checkpoint_callback, get_ea... | 713 |
from __future__ import annotations
from PIL import Image
# Define glider example
lowerCamelCase__ = [
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0... | 226 | 0 |
'''simple docstring'''
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCamelCase : List[str] = parse(importlib.metadata.version('torch'))
def _SCREAMING_SNAKE_CASE (A , A , A ) -> ... | 460 |
'''simple docstring'''
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def _SCREAMING_SNAKE_CASE (A ) -> Dict:
"""simple docstring"""
lowercase__ = os.path.join(args.tf_model_dir , ''... | 460 | 1 |
"""simple docstring"""
def __snake_case ( SCREAMING_SNAKE_CASE: int = 1000 ):
"""simple docstring"""
return sum(e for e in range(3 , SCREAMING_SNAKE_CASE ) if e % 3 == 0 or e % 5 == 0 )
if __name__ == "__main__":
print(f'{solution() = }')
... | 491 |
"""simple docstring"""
import enum
import warnings
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
from ..models... | 491 | 1 |
import argparse
import requests
import torch
# pip3 install salesforce-lavis
# I'm actually installing a slightly modified version: pip3 install git+https://github.com/nielsrogge/LAVIS.git@fix_lavis
from lavis.models import load_model_and_preprocess
from PIL import Image
from transformers import (
AutoTok... | 73 |
import math
import os
import sys
def lowerCamelCase__ (_UpperCAmelCase):
SCREAMING_SNAKE_CASE = ''
try:
with open(_UpperCAmelCase , 'rb') as binary_file:
SCREAMING_SNAKE_CASE = binary_file.read()
for dat in data:
SCREAMING_SNAKE_CASE ... | 73 | 1 |
from math import ceil
def _A ( __snake_case :int = 1001 ) -> int:
"""simple docstring"""
__SCREAMING_SNAKE_CASE = 1
for i in range(1 , int(ceil(n / 2.0 ) ) ):
__SCREAMING_SNAKE_CASE = 2 * i + 1
__SCREA... | 214 |
import argparse
_snake_case : Union[str, Any] = 'docs/source/_static/js/custom.js'
def _A ( __snake_case :List[Any] ) -> Any:
"""simple docstring"""
with open(__snake_case , encoding="utf-8" , newline="\n" ) as f:
... | 214 | 1 |
from __future__ import annotations
from collections.abc import Iterator
from typing import Generic, TypeVar
snake_case_ = TypeVar('T')
class SCREAMING_SNAKE_CASE__ ( Generic[T] ):
def __init__(self : Union[str, Any] , a__ : T ):
... | 592 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
snake_case_ = logging.get_logger(__name__)
snake_case_ = {
'xlnet-base-cased': 'https://huggingface.co/xlnet-base-cased/resolve/main/config.json',
'xlnet-large-c... | 592 | 1 |
"""simple docstring"""
import heapq
import sys
import numpy as np
lowercase = tuple[int, int]
class SCREAMING_SNAKE_CASE_ :
'''simple docstring'''
def __init__( self) -> List[str]:
'''simple docstring'''
snake_case__ : int = []
... | 715 |
"""simple docstring"""
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowercase = logging.get_logger(__name__)
lowercase = {
"""vocab_file""": """vocab.json""",
... | 150 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase ( __SCREAMING_SNAKE_CASE ):
'''si... | 55 |
"""simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerat... | 680 | 0 |
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
__lowerCAmelCase = logging.get_logger(__name__)
class __a ( __UpperCamelCase ):
__lowercase : Union[str, Any] ... | 335 |
from __future__ import annotations
from dataclasses import dataclass
@dataclass
class __a :
__lowercase : float
__lowercase : TreeNode | None = None
__lowercase : TreeNode | None = None
def snake_case_ ( snake_case )... | 335 | 1 |
'''simple docstring'''
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, tor... | 38 |
'''simple docstring'''
def UpperCamelCase__ ( __magic_name__ : List[Any] ) -> Tuple:
'''simple docstring'''
if not head:
return True
# split the list to two parts
snake_case__ , snake_case__ : Dict = head.next, head
while fast and fast.next:
snake_... | 38 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from transformers.utils import is_torch_available
if is_torch_available():
import torch
from transformers.testing_utils import require_torch
from .test_tools_common import ToolTesterMixin
@re... | 703 |
"""simple docstring"""
import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
fro... | 492 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
_lowerCAmelCase : str =(
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
_lowerCAmelCase : list[int] =[ord(letter) for letter in string.ascii_lowercase... | 113 |
def _A ( SCREAMING_SNAKE_CASE ):
UpperCAmelCase__: Tuple = int(SCREAMING_SNAKE_CASE )
if decimal in (0, 1): # Exit cases for the recursion
return str(SCREAMING_SNAKE_CASE )
UpperCAmelCase__ , UpperCAmelCase__: Union[str, Any] = divmod(SCREAMING_SNAKE_CASE ,2 ... | 113 | 1 |
"""simple docstring"""
import math
def lowercase ( lowerCAmelCase__ : int ) -> list[int]:
__a = []
__a = 2
__a = int(math.sqrt(lowerCAmelCase__ ) ) # Size of every segment
__a = [True] * (end + 1)
__a = []
while start <= ... | 65 |
"""simple docstring"""
import pytest
from datasets import inspect_metric, list_metrics, load_metric
@pytest.fixture
def lowercase ( lowerCAmelCase__ : Optional[int] ) -> int:
monkeypatch.setattr('''datasets.utils.deprecation_utils._emitted_deprecation_warnings''' , s... | 65 | 1 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...tes... | 331 |
'''simple docstring'''
import sys
UpperCamelCase_ : Union[str, Any] = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""1254069874715852386305... | 331 | 1 |
"""simple docstring"""
import math
def _UpperCamelCase ( _A ) -> bool:
"""simple docstring"""
_UpperCAmelCase = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(_A )
def _UpperCamelCase ( _A = 1 / 1_2_3_4_5 ... | 19 |
"""simple docstring"""
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from dat... | 19 | 1 |
"""simple docstring"""
class UpperCamelCase_ :
def __init__( self : str ) -> Optional[Any]:
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : List[Any] = ""
UpperCAmelCase_ : Tuple = []
def _SCREAMING_SNAKE_CASE ( self : Tuple ... | 95 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE ( __UpperCamelCase , __UpperCamelCase) -> set[str]:
a , a = set(__UpperCamelCase), [start]
while stack:
a = stack.pop()
explored.add(__UpperCamelCase)
# Differences from BF... | 515 | 0 |
'''simple docstring'''
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
"""simple docstring"""
return number | (1 << position)
def _lowercase ( lowerCamelCase__ , lowerCamelCase__ ) -> int:
... | 10 | '''simple docstring'''
import json
import re
from typing import TYPE_CHECKING, List, Optional, Tuple, Union
import numpy as np
from ...utils import is_tf_available, is_torch_available, logging
if TYPE_CHECKING:
if is_torch_available():
import torch
if is_tf_available():
impor... | 10 | 1 |
from __future__ import annotations
import math
def _snake_case (__lowercase):
if num <= 0:
UpperCamelCase_ = f"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(__lowercase)
UpperCamelCase_ = [True] * (num + 1)
U... | 23 |
"""simple docstring"""
def _lowerCAmelCase ( ) -> int:
return [
a * b * (1_0_0_0 - a - b)
for a in range(1, 9_9_9 )
for b in range(lowerCamelCase__, 9_9_9 )
if (a * a + b * b == (1_0_0_0 - a - b) ** 2)
][0]
if __name__ == "__main__":
print(F'{solu... | 572 | 0 |
from __future__ import annotations
def lowerCAmelCase__ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ) -> set[str]:
lowerCAmelCase__ , lowerCAmelCase__ : Union[str, Any] = set(SCREAMING_SNAKE_CASE_ ), [start]
while stack:
lowerCAmelCase__ : Optiona... | 69 |
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class A__ ( __magic_name__ , unittest.TestCase ):
... | 69 | 1 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
f... | 94 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator,... | 188 | 0 |
"""simple docstring"""
import comet # From: unbabel-comet
import torch
import datasets
UpperCAmelCase: Union[str, Any] = datasets.logging.get_logger(__name__)
UpperCAmelCase: Tuple = """\
@inproceedings{rei-EtAl:2020:WMT,
author = {Rei, Ricardo and Stewart, Craig and... | 716 |
"""simple docstring"""
import argparse
import os
import jax as jnp
import numpy as onp
import torch
import torch.nn as nn
from music_spectrogram_diffusion import inference
from tax import checkpoints
from diffusers import DDPMScheduler, OnnxRuntimeModel, SpectrogramDiffusionPipeline
from diffusers.pipeline... | 600 | 0 |
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
from .tokenization_wavaveca import WavaVecaCTCTokenizer
class SCREAMING_SNAKE_CASE_ ( _a ):
'''simple docstring'''
... | 305 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
SCREAMING_SNAKE_CASE : Optional[Any] = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP",
"ConditionalDetr... | 89 | 0 |
import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image
if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
_lowerCamelCase : Dict = {
"linear": PIL.Image.Resampling.BILINEAR,
"bilinear": PIL.Image.Resampling.B... | 196 |
from __future__ import annotations
import inspect
import unittest
import numpy as np
from transformers import ResNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_comm... | 196 | 1 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from torchvision import transforms
from transformers import BitImageProcessor, FocalNetConfig, FocalNetForImageClassification
from transformers.image_utils import IMAGENET_DEFAULT_M... | 651 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import load_numpy,... | 651 | 1 |
def _UpperCAmelCase ( UpperCamelCase: list , UpperCamelCase: list ):
"""simple docstring"""
_validate_point(UpperCamelCase )
_validate_point(UpperCamelCase )
if len(UpperCamelCase ) != len(UpperCamelCase ):
raise ValueError("Both points must be in the same n-dimensional space" )... | 376 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( __UpperCAmelCase ):
@staticmethod
@abstractmethod
def UpperCAmelCase__ ( snake_case__ : ArgumentParser ):
"""simple docstring"""
raise NotImplementedError(... | 376 | 1 |
SCREAMING_SNAKE_CASE__ : Dict = [0, 2, 4, 6, 8]
SCREAMING_SNAKE_CASE__ : Optional[Any] = [1, 3, 5, 7, 9]
def _lowerCamelCase ( __lowerCamelCase , __lowerCamelCase , __lowerCamelCase , __lowerCamelCase ) -> int:
'''simple ... | 79 |
'''simple docstring'''
class UpperCAmelCase__ :
def __init__( self : Any,__A : Any,__A : Any,__A : Any ):
_lowerCamelCase : List[Any] = name
_lowerCamelCase : Union[str, Any] = value
_lowerCamelCase : str ... | 44 | 0 |
import unicodedata
from dataclasses import dataclass
from typing import Optional, Union
import numpy as np
from transformers.data.data_collator import DataCollatorMixin
from transformers.file_utils import PaddingStrategy
from transformers.tokenization_utils_base import PreTrainedTokenizerBase
def lowerCamelCase ... | 701 | def lowerCamelCase ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
'''simple docstring'''
return 1 if input_a == input_a else 0
def lowerCamelCase ( ):
'''simple docstring'''
assert xnor_gate(0 , 0 ) == 1
assert xnor_gate(0 , 1 ) == 0... | 452 | 0 |
from __future__ import annotations
def snake_case__ ( lowerCamelCase_ , lowerCamelCase_ ):
A : Tuple = get_failure_array(lowerCamelCase_ )
# 2) Step through text searching for pattern
A , A : Dict = 0, 0 # index in... | 542 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SI... | 542 | 1 |
def snake_case__ ( __lowercase , __lowercase ) -> bool:
"""simple docstring"""
A__ : Any = len(__lowercase ) + 1
A__ : Dict = len(__lowercase ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string... | 715 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
... | 182 | 0 |
'''simple docstring'''
import requests
_snake_case : Union[str, Any] = 'https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='
def snake_case_ (UpperCamelCase : str ):
'''simple docstring'''
_a = requests.get(_NEWS_API ... | 22 | import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_ut... | 140 | 0 |
'''simple docstring'''
import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple
import numpy as np
from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational ... | 701 |
'''simple docstring'''
import math
import sys
def _lowerCamelCase ( lowercase : str ) -> str:
_a = ""
try:
with open(lowercase , "rb" ) as binary_file:
_a = binary_file.read()
for dat in data:
_a ... | 521 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transfo... | 466 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils im... | 466 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCAmelCase ( UpperCamelCase_: Optional[Any] , UpperCamelCase_:... | 702 |
"""simple docstring"""
import importlib
import json
import os
from collections import OrderedDict
from typing import Dict, Optional, Union
# Build the list of all image processors
from ...configuration_utils import PretrainedConfig
from ...dynamic_module_utils import get_class_from_dynamic_module, resolve_t... | 612 | 0 |
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL import Image
from ..image_utils import load_image
if is_torch_a... | 332 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"sayakpaul/vit-msn-base": "https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json",
# See all ViT MSN models at https://hu... | 332 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class UpperCamelCase__ ( tf.keras.layers.Layer ):... | 630 |
'''simple docstring'''
import unittest
from knapsack import greedy_knapsack as kp
class UpperCamelCase__ ( unittest.TestCase ):
"""simple docstring"""
def a ( self ):
'''simple docstring'''
_lowerCAmelCase : Union[str, ... | 630 | 1 |
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
@require_torch_gpu
class UpperC... | 148 |
from __future__ import annotations
import requests
lowerCAmelCase__ : Any =set(
'''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_ut... | 148 | 1 |
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class a ( UpperCAmelCase ):
@staticmethod
@abstractmethod
def _UpperCAmelCase ( A_ ):
'''simple docstring'''
raise NotImplementedError()
@abstractmethod
def ... | 708 |
def __SCREAMING_SNAKE_CASE ( lowerCAmelCase: str , lowerCAmelCase: str ) -> int:
if len(lowerCAmelCase ) != len(lowerCAmelCase ):
raise ValueError("String lengths must match!" )
_UpperCAmelCase : List[Any] = 0
for chara, chara in zip(lowerCAmelCase , ... | 467 | 0 |
from ..utils import DummyObject, requires_backends
class _UpperCamelCase (metaclass=a_ ):
snake_case_ = ["""keras_nlp"""]
def __init__( self , *__UpperCamelCase , **__UpperCamelCase )-> Union[str, Any]:
requires_backends(self , ... | 367 |
'''simple docstring'''
import re
def lowerCAmelCase__ ( lowerCamelCase : str ):
if len(re.findall('[ATCG]' ,lowerCamelCase ) ) != len(lowerCamelCase ):
raise ValueError('Invalid Strand' )
return dna.translate(dna.maketrans('ATCG' ,'TAGC' ) )
if _... | 128 | 0 |
import io
import math
from typing import Dict, Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import convert_to_rgb, normalize, to_channel_dimension_format, to_pil_image
from ...image_u... | 300 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
__a = logging.get_logger(__name__)
__a = {'vocab_fi... | 300 | 1 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator,... | 53 |
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
from flax.training.common_util... | 302 | 0 |
"""simple docstring"""
import os
import sys
import unittest
__UpperCAmelCase = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import get_test_info # noqa: E402
from get_test_info import ( # noqa: ... | 708 |
"""simple docstring"""
import math
from numpy import inf
from scipy.integrate import quad
def _snake_case ( lowercase__ : float ) -> float:
'''simple docstring'''
if num <= 0:
raise ValueError("""math domain error""" )
return quad(lo... | 256 | 0 |
'''simple docstring'''
import numpy as np
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ = 1e-12 , SCREAMING_SNAKE_CASE_ = 1_00 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(UpperCamelCase__ )[0] ... | 591 |
from string import ascii_uppercase
__A = {str(ord(c) - 55): c for c in ascii_uppercase}
def lowerCamelCase_ ( UpperCamelCase__ : int , UpperCamelCase__ : int ) -> str:
"""simple docstring"""
if isinstance(UpperCamelCase__ ... | 469 | 0 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _A ( snake_case ):
'''simple docstring'''
__lowerCamelCase : List[str] = (UnCLIPScheduler,)
def snake_case_ ( self ,**SCREAMING_SNAKE_CASE_ ... | 315 |
import argparse
import json
from collections import OrderedDict
import torch
from huggingface_hub import cached_download, hf_hub_url
from transformers import AutoImageProcessor, CvtConfig, CvtForImageClassification
def lowercase ( __A : Optional[Any] ) -> Optional[Any]:
'''simple ... | 315 | 1 |
from __future__ import annotations
import unittest
from transformers import DebertaVaConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_attention_mask
f... | 632 |
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin
class _UpperCAm... | 632 | 1 |
import warnings
from .generation import TFGenerationMixin
class snake_case__ (_UpperCamelCase ):
"""simple docstring"""
warnings.warn(
"""Importing `TFGenerationMixin` from `src/transformers/generation_tf_utils.py` is deprecated and will """
"""be removed in Transform... | 662 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_big_bird impor... | 662 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase_ = logging.get_logger(__name__)
lowerCAmelCase_ = {
'''google/pix2struct-textcaps-base''': (
'''https://huggingface.co/google/pix... | 60 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__lowerCamelCase = logging.get_logger(__name__)
__lowerCamelCase = {
"roberta-base":... | 608 | 0 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
fro... | 708 |
import argparse
import json
import os
import re
import torch
from transformers import BloomConfig, BloomModel
from transformers.file_utils import CONFIG_NAME, WEIGHTS_NAME
from transformers.utils import logging
logging.set_verbosity_info()
__A : Tuple = [
"word_embeddings_layernorm.weight",
"... | 334 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
import numpy as np
from transformers import OPTConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_commo... | 41 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
... | 366 | 0 |
# Function to print upper half of diamond (pyramid)
def UpperCAmelCase__ ( UpperCAmelCase__ :Dict ):
'''simple docstring'''
for i in range(0 , UpperCAmelCase__ ):
for _ in range(0 , n - i - 1 ): # printing spaces
print(" " , end="" )
f... | 715 |
A_ : Any = [4, 1, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Tuple = [3, 7, 7, 4, 2, 6, 4, 1, 5, 3, 7, 5]
A_ : Optional[int] = {
0: '''Sunday''',
1: '''Monday''',
2: '''Tuesday''',
3: '''Wednesday''',
4: '''Thursday''',
5: '''Friday''',
... | 32 | 0 |
'''simple docstring'''
import os
import socket
from contextlib import contextmanager
import torch
from ..commands.config.default import write_basic_config # noqa: F401
from ..state import PartialState
from .dataclasses import DistributedType
from .imports import is_deepspeed_available, is_tpu_available
... | 427 | '''simple docstring'''
from __future__ import annotations
import math
def UpperCamelCase_ ( snake_case_ : int ) -> bool:
'''simple docstring'''
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negativ... | 427 | 1 |
from __future__ import annotations
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : list[float] ):
"""simple docstring"""
__a = 0.00
__a = 0
for resistor in resistors:
if resistor <= 0:
__a = f"Resistor at index {index} has a neg... | 705 |
from math import sqrt
def lowerCAmelCase__ ( _SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all m... | 547 | 0 |
import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional
import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm
import transformers
from transformers import Au... | 45 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applica... | 164 | 0 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> Optional[int]:
if a < 0 or b < 0:
raise ValueError("the value of both inputs must be positive" )
a_ : List[str] = str(bin(lowerCamelCase_ ) )[2:] # remove the leading "0... | 712 |
"""simple docstring"""
def lowerCAmelCase_ ( SCREAMING_SNAKE_CASE__, SCREAMING_SNAKE_CASE__ ) -> float:
_validate_point(SCREAMING_SNAKE_CASE__ )
_validate_point(SCREAMING_SNAKE_CASE__ )
if len(SCREAMING_SNAKE_CASE__ ) != len(SCREAMING_SNAKE_CASE__ ):
raise ValueError("Both points ... | 370 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__snake_case = logging.get_logger(__name__)
__snake_case = {
"""facebook/xglm-564M""": """https://huggingface.co/facebook/xglm-564M/resolve/main/config.json""",
# See all XGLM... | 178 |
"""simple docstring"""
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def __lowerCAmelCase ( lowercase : List[Any] , lowercase : Dict , lowercase : str ... | 178 | 1 |
'''simple docstring'''
import json
import os
import shutil
import tempfile
import unittest
from transformers import BatchEncoding, CanineTokenizer
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.tokenization_utils import AddedToken
from transformers.utils import cached_pr... | 502 |
'''simple docstring'''
def _a ( __lowerCAmelCase : int ):
"""simple docstring"""
if p < 2:
raise ValueError('''p should not be less than 2!''' )
elif p == 2:
return True
snake_case__ : Any = 4
snake_case__ : int = (1 << p) ... | 502 | 1 |
import copy
import os
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
from datasets.arrow_writer import ArrowWriter, OptimizedTypedSequence, ParquetWriter, TypedSequence
from datasets.features import ... | 105 |
import pytest
import datasets
# Import fixture modules as plugins
__snake_case = ['''tests.fixtures.files''', '''tests.fixtures.hub''', '''tests.fixtures.fsspec''']
def _A ( _lowercase , _lowercase ) -> Tuple:
"""simple docstring"""
for item in ... | 1 | 0 |
'''simple docstring'''
import pytest
from datasets.parallel import ParallelBackendConfig, parallel_backend
from datasets.utils.py_utils import map_nested
from .utils import require_dill_gt_0_3_2, require_joblibspark, require_not_windows
def _SCREAMING_SNAKE_CASE( snake_c... | 411 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffus... | 411 | 1 |
'''simple docstring'''
from collections.abc import Generator
def __A ( ):
_UpperCAmelCase , _UpperCAmelCase : Any = 0, 1
while True:
_UpperCAmelCase , _UpperCAmelCase : Tuple = b, a + b
yield b
def __A ... | 414 |
'''simple docstring'''
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_available():
from PIL impor... | 414 | 1 |
from __future__ import annotations
from math import gcd
def _SCREAMING_SNAKE_CASE ( a , a = 2 , a = 1 , a = 3 , ) -> int | None:
# A value less than 2 can cause an infinite loop in the algorithm.
if num < 2:
raise ValueError('The input value ca... | 77 |
from __future__ import annotations
import math
def _SCREAMING_SNAKE_CASE ( a , a ) -> list:
if len(a ) != 2 or len(a[0] ) != 2 or len(a ) != 2 or len(b[0] ) != 2:
raise Exception('Matrices are not 2x2' )
__A : Optional[int] ... | 77 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_speech_available, is_torch_available
SCREAMING_SNAKE_CASE = {
"""configuration_audio_spectrogram_transformer""": [
"""AUDIO_SPECTROGRAM_TRANSFORMER_PRETRAINED_CONFI... | 199 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=A ):
'''simple docstring'''
lowercase_ : Optional[int] = ["torch", "transformers", "onnx"]
def __init__( self : Tuple , *snake_case__ : str ... | 199 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 718 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def lowerCamelCase__ ( _A , _A , _A ):
'''simple docstring'''
snake_c... | 139 | 0 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision... | 77 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowercase : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXCon... | 116 | 0 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -> bool:
SCREAMING_SNAKE_CASE_ : Union[str, Any] = len(SCREAMING_SNAKE_CASE )
SCREAMING_SNAKE_CASE_ : Optional[Any] = [[False] * (required_sum + 1) for _ in range(arr_len + 1 )]
# for each... | 311 |
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE ) -> Union[str, Any]:
stooge(SCREAMING_SNAKE_CASE , 0 , len(SCREAMING_SNAKE_CASE ) - 1 )
return arr
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ) -... | 311 | 1 |
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