code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from typing import List, Optional, Union
import tqdm
from filelock import FileLock
from transformers import (
BartTokenizer,
BartTokenizerFast,
DataProcessor,
PreTrainedTokenizer,
RobertaTok... | 28 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
_lowerCamelCase : List[Any] = {
"configuration_m2m_100": ["M2M_100_PRETRAINED_CONFIG_ARCHIVE_MAP", "M2M100Config", "... | 28 | 1 |
'''simple docstring'''
import faiss # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import requests # noqa: F401 # Here to have a nice missing dependency error message early on
imp... | 334 |
'''simple docstring'''
import requests
UpperCamelCase = '''https://newsapi.org/v1/articles?source=bbc-news&sortBy=top&apiKey='''
def SCREAMING_SNAKE_CASE( __lowercase ) -> None:
# fetching a list of articles in json format
A: Tuple = requests.get(_NE... | 334 | 1 |
'''simple docstring'''
from __future__ import annotations
from functools import lru_cache
from math import ceil
lowerCAmelCase__ = 100
lowerCAmelCase__ = set(range(3, NUM_PRIMES, 2))
primes.add(2)
lowerCAmelCase__ = 42
for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
if prime ... | 104 |
from __future__ import annotations
def _lowerCamelCase( lowercase__ , lowercase__ ) -> bool:
'''simple docstring'''
__lowercase= get_failure_array(lowercase__ )
# 2) Step through text searching for pattern
__lowercase, __lowercase= 0, 0 # index into text, pattern
wh... | 295 | 0 |
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_tf_available, loggi... | 354 |
def _a ( UpperCAmelCase ) -> int:
"""simple docstring"""
if not isinstance(UpperCAmelCase , UpperCAmelCase ) or number < 0:
raise ValueError('''Input must be a non-negative integer''' )
lowerCamelCase__ : List[str] = 0
while number:... | 265 | 0 |
"""simple docstring"""
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD... | 96 |
"""simple docstring"""
from sklearn.metrics import fa_score, matthews_corrcoef
import datasets
from .record_evaluation import evaluate as evaluate_record
lowercase__ = """\
@article{wang2019superglue,
title={SuperGLUE: A Stickier Benchmark for General-Purpose Language ... | 96 | 1 |
"""simple docstring"""
import csv
from collections import defaultdict
from dataclasses import dataclass, field
from typing import List, Optional
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import ScalarFormatter
from transformers import HfArgumentParser
def _snake_case ... | 352 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowercase_ ( unittest.TestCase ):
'''simple docstr... | 271 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_A : Tuple =logging.get_logger(__name__)
class _lowercase ( A__ ... | 41 |
"""simple docstring"""
import argparse
import os
import re
import numpy as np
import PIL
import torch
from timm import create_model
from torch.optim.lr_scheduler import OneCycleLR
from torch.utils.data import DataLoader, Dataset
from torchvision.transforms import Compose, RandomResizedCrop, Resize, To... | 126 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel... | 126 | """simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_a : List[Any] = logging.get_logger(__name__)
class __A ( SCREAMING_SNAKE_CASE_ ,... | 126 | 1 |
def snake_case__ ( lowerCAmelCase_, lowerCAmelCase_ ):
"""simple docstring"""
return int((input_a, input_a).count(1 ) != 0 )
def snake_case__ ( ):
"""simple docstring"""
assert or_gate(0, 0 ) == 0
assert or_gate(0, 1 ) == 1
... | 334 |
import unittest
from transformers import SqueezeBertConfig, 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, r... | 334 | 1 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
lowercase_ = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))
import check_copies # noqa: E402
# This is the reference code that wi... | 194 |
import os
from typing import BinaryIO, Optional, Union
import numpy as np
import pyarrow.parquet as pq
from .. import Audio, Dataset, Features, Image, NamedSplit, Value, config
from ..features.features import FeatureType, _visit
from ..formatting import query_table
from ..packaged_modules import _PACKAGED_DATASETS... | 194 | 1 |
'''simple docstring'''
def __lowerCAmelCase (__lowerCAmelCase = 4_000_000 ):
_UpperCAmelCase : Optional[Any] = [0, 1]
_UpperCAmelCase : int = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] > n:
break
i ... | 234 |
'''simple docstring'''
from __future__ import annotations
import math
class UpperCamelCase_ :
def __init__( self , A ) -> None:
UpperCAmelCase : Optional[int] = size
# approximate the overall size of segment tree with given value
UpperCAmelCase :... | 265 | 0 |
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.layers import LSTM, Dense
from tensorflow.keras.models import Sequential
if __name__ == "__main__":
_UpperCAmelCase = pd.read_csv("""sample_data.csv""", header=None)
_UpperCAmelCase ... | 192 | import os
import shutil
from pathlib import Path
from typing import Optional, Union
import numpy as np
from huggingface_hub import hf_hub_download
from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging
if is_onnx_available():
import onnxruntime as ort
_UpperC... | 192 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase = {
'''configuration_vision_text_dual_encoder''': ['''VisionTextDualEncoderConfig'''],
'''... | 131 |
'''simple docstring'''
from typing import List, Optional, Tuple, Union
import torch
from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase__ ( lowercase__ ):... | 271 | 0 |
def UpperCamelCase_( lowerCamelCase_ ) -> set:
_lowercase : Any = set()
# edges = list of graph's edges
_lowercase : int = get_edges(lowerCamelCase_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add h... | 84 |
from typing import List, Optional, Tuple, Union
import torch
from ...schedulers import DDIMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class _lowerCamelCase( _a ):
def __init__( self, lowerCamelCase, lowerCamelCase) -... | 84 | 1 |
"""simple docstring"""
from typing import Optional
import pyspark
from .. import Features, NamedSplit
from ..download import DownloadMode
from ..packaged_modules.spark.spark import Spark
from .abc import AbstractDatasetReader
class A_ ( A__ ):
"""simple docstring"""
... | 126 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase = logging.get_logger(__name__)
lowerCAmel... | 126 | 1 |
import json
import os
import tempfile
import transformers
import datasets
from utils import generate_example_dataset, get_duration
UpperCamelCase = 50_0000
UpperCamelCase , UpperCamelCase = os.path.split(__file__)
UpperCamelCase = os.path.join(RESULTS_B... | 221 |
from maths.prime_factors import prime_factors
def _A ( lowerCAmelCase_ : int ):
"""simple docstring"""
if not isinstance(lowerCAmelCase_ , lowerCAmelCase_ ):
lowerCAmelCase__ = F'Input value of [number={number}] must be an int... | 221 | 1 |
"""simple docstring"""
def lowerCamelCase__ ( __snake_case ) -> set:
"""simple docstring"""
_UpperCamelCase = set()
# edges = list of graph's edges
_UpperCamelCase = get_edges(__snake_case )
# While there are still elements in ed... | 194 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_a = {
"""configuration_autoformer""": [
"""AUTOFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 194 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowercase__ ={
'configuration_rembert': ['REMBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Rem... | 90 |
import argparse
import os
import re
import packaging.version
lowercase__ ='examples/'
lowercase__ ={
'examples': (re.compile(R'^check_min_version\("[^"]+"\)\s*$', re.MULTILINE), 'check_min_version("VERSION")\n'),
'init': (re.compile(R'^__version__\s+=\s+"([^"]+)"\s*$', re.MULTILINE), '__ve... | 90 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A_ : str = logging.get_logger(__name__)
A_ : str = {
'facebook/dpr-ctx_encoder-single-nq-base': (
'https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'
... | 192 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A_ : Optional[int] = {'configuration_ibert': ['IBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'IBertConfig', 'IBertOnnxConfig']}
try:
if not is_torch_available():
raise OptionalDependenc... | 192 | 1 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import XLMRobertaTokenizer
from diffusers import (
AltDiffusionImgaImgPipeline,
AutoencoderKL,
PNDMScheduler,
UNetaDConditionModel,
)
from diffusers.image_processor import VaeIma... | 363 |
"""simple docstring"""
UpperCAmelCase: str = """
# Transformers installation
! pip install transformers datasets
# To install from source instead of the last release, comment the command above and uncomment the following one.
# ! pip install git+https://github.com/huggingface/transformers.g... | 336 | 0 |
"""simple docstring"""
from math import sqrt
import numpy as np
from sympy import symbols
# Coefficient
# Speed of light (m/s)
__UpperCAmelCase = 2_99_79_24_58
# Symbols
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase = symbols('ct x y z')
... | 84 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def _snake_case ( ) -> Generator[int, None, None]:
'''simple docstring'''
lowerCAmelCase_ :dict[int, int] = {}
lowerCAmelCase_ :int ... | 84 | 1 |
"""simple docstring"""
from collections.abc import Iterable
from typing import Any
class lowerCAmelCase__ :
def __init__( self : List[Any] , snake_case__ : int | None = None ):
'''simple docstring'''
UpperCAmelCase__ : Optional[int] = value
Uppe... | 351 |
"""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, torch... | 298 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCAmelCase ( UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ , UpperCamelCase__ ):
"""simple docstring"""
if (direction == 1 and array[indexa] > array[indexa]) or ... | 221 | """simple docstring"""
from io import BytesIO
from typing import List, Union
import requests
from ..utils import add_end_docstrings, is_decord_available, is_torch_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_decord_available():
import numpy as np
... | 221 | 1 |
'''simple docstring'''
import socket
def _A ( ) -> Union[str, Any]:
_lowercase : Any = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowercase : Optional[Any] = socket.gethostname()
_lowercase : Union[str, Any] ... | 199 |
'''simple docstring'''
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TR... | 199 | 1 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_commo... | 90 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
__A ... | 90 | 1 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''
lowercase_ = (UnCLIPScheduler,)
def SCREAMING_SNAKE_CASE_ (self : Optional[int]... | 273 |
from __future__ import annotations
from typing import Any
class _SCREAMING_SNAKE_CASE :
'''simple docstring'''
def __init__(self : Tuple , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : float = 0) ->None:
'''simple docstr... | 273 | 1 |
def A ( a_ = 1_000_000 ) -> int:
__UpperCamelCase : List[Any] =limit + 1
__UpperCamelCase : Any =[0] * limit
for first_term in range(1 ,a_ ):
for n in range(a_ ,a_ ,a_ ):
... | 71 |
import inspect
import tempfile
from collections import OrderedDict, UserDict
from collections.abc import MutableMapping
from contextlib import ExitStack, contextmanager
from dataclasses import fields
from enum import Enum
from typing import Any, ContextManager, List, Tuple
import numpy as np
from .import_utils impo... | 336 | 0 |
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dim... | 357 |
def __lowerCamelCase ( UpperCAmelCase_ : int = 100 ):
"""simple docstring"""
a :List[Any] = 0
a :List[Any] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_int... | 281 | 0 |
import tempfile
import torch
from diffusers import PNDMScheduler
from .test_schedulers import SchedulerCommonTest
class __lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE = (PNDMScheduler,)
_SCREAMING_SNAK... | 159 |
'''simple docstring'''
def __lowerCAmelCase ( snake_case__ ):
return [
txt[:a] + txt[a].upper() + txt[a + 1 :]
for a in range(len(snake_case__ ) )
if txt[a].isalpha()
]
if __name__ == "__main__":
__import__('''doctest''').testmod(... | 298 | 0 |
from PIL import Image
def UpperCamelCase_( _snake_case : Image , _snake_case : float ):
"""simple docstring"""
def brightness(_snake_case : int ) -> float:
return 128 + level + (c - 128)
if not -255.0 <= level <= 255.0:
... | 308 |
import argparse
import logging
from collections import namedtuple
import torch
from model_bertabs import BertAbsSummarizer
from models.model_builder import AbsSummarizer # The authors' implementation
from transformers import BertTokenizer
logging.basicConfig(level=logging.INFO)
_lowerCAmelCase : ... | 308 | 1 |
# using dfs for finding eulerian path traversal
def a_ ( SCREAMING_SNAKE_CASE__ : Any , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : List[str] , SCREAMING_SNAKE_CASE__ : Optional[int]=None ):
'''simple docstring'''
_low... | 199 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers import (
... | 199 | 1 |
import warnings
from ...utils import logging
from .image_processing_mobilevit import MobileViTImageProcessor
_lowercase : Union[str, Any] =logging.get_logger(__name__)
class snake_case__ (A__ ):
"""simple docstring"""
def __init__( se... | 365 |
import torch
from diffusers import StableDiffusionPipeline
_lowercase : Optional[int] ="path-to-your-trained-model"
_lowercase : Union[str, Any] =StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("cuda")
_lowercase : Any ="A photo of sks... | 266 | 0 |
def __SCREAMING_SNAKE_CASE ( ) -> Optional[Any]:
'''simple docstring'''
UpperCAmelCase = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
UpperCAmelCase = 6
UpperCAmelCase = 1
UpperCAmelCase = 1901
UpperCAmelCase = 0
while year < 200... | 273 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config import PatchingSpec
from ...tokeniz... | 273 | 1 |
"""simple docstring"""
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
lowercase__ : Optional[Any] = logging.get_logger(__name__)
class _UpperCAmelCase :
... | 360 |
"""simple docstring"""
import math
import sys
def __lowercase ( _a ):
if number != int(_a ):
raise ValueError('''the value of input must be a natural number''' )
if number < 0:
raise ValueError('''the value of input must not be a negative number''' )
if number == ... | 155 | 0 |
"""simple docstring"""
import os
import re
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCamelCase__ = logging.get_logger(__name__)
lowerCamelCase__ = {
"vocab_file": "vocab.txt... | 86 |
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 lowerCAmelC... | 281 | 0 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : Any , lowerCAmelCase : Any ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def lowerCamelCase ( ):
"""simple docstring"""
print('Truth Table of NOR Gate:' )
print('| ... | 354 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
lowerCAmelCase :Tuple = logging.get_logger(__name__)
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
def __init__( self : Any , ... | 275 | 0 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.I... | 308 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase_ = logg... | 308 | 1 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
__UpperCAmelCase = 2_00
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be... | 42 |
import json
import os
import torch
from diffusers import UNetaDModel
os.makedirs('''hub/hopper-medium-v2/unet/hor32''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/unet/hor128''', exist_ok=True)
os.makedirs('''hub/hopper-medium-v2/value_function''', exist_ok=True)
def __lowerCamelCase ( __magi... | 42 | 1 |
from ..utils import DummyObject, requires_backends
class _lowercase ( metaclass=_lowerCAmelCase ):
lowercase = ["sentencepiece"]
def __init__( self : int , *snake_case : Optional[Any] , **snake_case : Tuple ) -> Optional[Any]:
"""simple docs... | 175 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, T... | 266 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_available():... | 20 | from .constants import (
MODEL_NAME,
OPTIMIZER_NAME,
RNG_STATE_NAME,
SAFE_WEIGHTS_INDEX_NAME,
SAFE_WEIGHTS_NAME,
SCALER_NAME,
SCHEDULER_NAME,
TORCH_LAUNCH_PARAMS,
WEIGHTS_INDEX_NAME,
WEIGHTS_NAME,
)
from .dataclasses import (
BnbQuantizationConfig,
ComputeEnvironment,
... | 20 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtraction... | 321 |
"""simple docstring"""
import argparse
import gc
import json
import os
import re
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoModelForCausalLM, AutoTokenizer, PreTrainedTokenizerFast, RwkvConfig
from transformers.modeling_utils import WEIGHTS_INDEX_NAME, shard_chec... | 155 | 0 |
import argparse
import collections
import os
import re
import tempfile
import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this ... | 362 |
import json
import logging
import os
import sys
from pathlib import Path
import finetune_rag
from transformers.file_utils import is_apex_available
from transformers.testing_utils import (
TestCasePlus,
execute_subprocess_async,
require_ray,
require_torch_gpu,
require_torch_... | 208 | 0 |
'''simple docstring'''
class _lowercase :
def __init__( self: Dict , UpperCamelCase__: int , UpperCamelCase__: Dict , UpperCamelCase__: List[Any] ):
lowerCamelCase__ : Dict = name
l... | 41 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclass
class __lowercase (_UpperCA... | 275 | 0 |
import gc
import unittest
from parameterized import parameterized
from diffusers import FlaxUNetaDConditionModel
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import load_hf_numpy, require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
@slow
@require... | 119 | from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
OPENAI_CLIP_MEAN,
OPENAI_... | 119 | 1 |
'''simple docstring'''
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( __A ) -> list[int]: # This function is recursive
_snake_case = len(__A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
if array_length <= 1:
... | 42 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : List[str] = {
"configuration_pix2struct": [
"PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Pix2Stru... | 42 | 1 |
import math
class _snake_case :
def A__ ( self : str, __lowercase : list[list[float]], __lowercase : list[int] ):
lowercase__ = 0.0
lowercase__ = 0.0
for i in range(len(__lowercase ) ):
da += math.pow((sample[i] - weights[0][i])... | 224 |
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
lowercase_ = logging.get_logger(__name__)
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase__ = r"\w+[.]\d+"
low... | 224 | 1 |
from __future__ import annotations
import unittest
from transformers import RoFormerConfig, 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
from ... | 20 |
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 _snake_case( SCREAMING_SNAKE_CASE__ ) -> Tuple:
lowercase : U... | 20 | 1 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'facebook/s2t-wav2vec2-large-en-de': (
'https://huggingface.co/facebook/s2t-wav2vec2-large-en-de/resolve/main/config.json'
... | 351 |
"""simple docstring"""
def UpperCamelCase ( UpperCAmelCase , UpperCAmelCase , UpperCAmelCase ) ->int:
"""simple docstring"""
if n == 0:
return 1
elif n % 2 == 1:
return (binary_exponentiation(UpperCAmelCase , n - 1 , UpperCAmelCase ) * a) % mod
else:
... | 303 | 0 |
'''simple docstring'''
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowerCam... | 206 |
'''simple docstring'''
import argparse
import io
import requests
import torch
from omegaconf import OmegaConf
from diffusers import AutoencoderKL
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import (
assign_to_checkpoint,
conv_attn_to_linear,
create_vae_diffuse... | 208 | 0 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRU... | 14 |
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def lowerCamelCase ( a_ , a_ , a_ , a_ , a_ ) -> List[Any]:
# load base model
lowerCAmelCase_ = StableD... | 14 | 1 |
def UpperCamelCase ( snake_case__ : int , snake_case__ : int ) -> str:
if a < 0 or b < 0:
raise ValueError('the value of both inputs must be positive' )
UpperCamelCase : Optional[int] = str(bin(snake_case__ ) )[2:] # remove the leading "0b"
UpperCamelCa... | 119 |
import argparse
import os
from pathlib import Path
import fairseq
import torch
from packaging import version
from torch import nn
from transformers import (
BartConfig,
BartForConditionalGeneration,
BartForSequenceClassification,
BartModel,
BartTokenizer,
)
from transformers.utils import loggi... | 119 | 1 |
"""simple docstring"""
import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock
import torch
from ..state import Acceler... | 353 |
from typing import List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE_ = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE_ = {
'huggingface/autoformer-tourism-monthly': 'https://huggingface.co/huggingface/autoformer-touri... | 189 | 0 |
"""simple docstring"""
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, ... | 224 |
"""simple docstring"""
import math
from enum import Enum
from typing import Optional, Union
from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR
from .utils import logging
lowercase__ : Optional[Any] = logging.get_logger(__name__)
... | 224 | 1 |
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List
from ..utils import logging
_lowerCamelCase = logging.get_logger(__name__)
def a__ ( _SCREAMING_SNAKE_CASE : Union[str, Any]=None , _... | 354 |
'''simple docstring'''
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def a__ ( ) -> tuple[list[int], int]:
"""simple docstring"""
UpperCAmelCase_ : Tuple = [randint(-10_00 ... | 67 | 0 |
'''simple docstring'''
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowercase_ (lowerCamelCase__ ):
"""simple docstring"""
SCREAMING_SNAKE_CASE : Optional[Any] = ['image_processor', 'tokenizer']
... | 104 |
import argparse
import json
import os
import pickle
import shutil
import numpy as np
import torch
from distiller import Distiller
from lm_seqs_dataset import LmSeqsDataset
from transformers import (
BertConfig,
BertForMaskedLM,
BertTokenizer,
DistilBertConfig,
DistilBertForMaskedLM,
DistilBer... | 303 | 0 |
"""simple docstring"""
A_ : Dict = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCamelCase_ ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ... | 370 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
A_ : List[str] = {"configuration_encoder_decoder": ["EncoderDecoderConfig"]}
try:
if n... | 316 | 0 |
from . import __version__
# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
CLOUDFRONT_DISTRIB_PREFIX,
CONFIG_NAME,
DISABLE_TELEMETRY,
DUMMY_INPUTS,
DUMMY_MASK,
ENV_VARS_TRUE_AND_AUTO_VALUES,
ENV_VARS_TRUE_VALUES,
FEATU... | 14 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
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_common ... | 14 | 1 |
'''simple docstring'''
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized, parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sa... | 357 | '''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__UpperCAmelCase =logging.get_logger(__name__)
__UpperCAmelCase ={
"microsoft/unispeech-sat-base-100h-libri-ft": (
"https://huggingface.co/mi... | 237 | 0 |
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
if number > 0:
raise ValueError('''input must be a negative integer''' )
__UpperCamelCase :str = len(bin(SCREAMING_SNAKE_CASE )[3:] )
__UpperCamelCase :Optional[Any] = bin(abs(SCREAMING_SNAKE_CASE ) -... | 43 |
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from... | 189 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import pyarrow as pa
if TYPE_CHECKING:
from .features import FeatureType
@dataclass
class __SCREAMING_SNAKE_CASE :
snake_case_ = 42... | 354 |
"""simple docstring"""
import cva
import numpy as np
class __SCREAMING_SNAKE_CASE :
def __init__( self : Union[str, Any] , snake_case : float , snake_case : int ):
'''simple docstring'''
if k in (0.04, 0.06):
... | 296 | 0 |
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os.path import join # noqa: this is just for tests
from os.path import join as renamed_join # n... | 343 | '''simple docstring'''
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit,
... | 67 | 0 |
"""simple docstring"""
import warnings
from functools import wraps
from typing import Callable
def lowerCAmelCase (__UpperCamelCase : Callable ):
"""simple docstring"""
@wraps(__UpperCamelCase )
def _inner_fn(*__UpperCamelCase : Any , **__UpperCamelCase : List[Any... | 85 | """simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
__lowercase = {
'''albert-base-v1''': '''https://huggingface.co/albert-base-v1/resolve/main/config.json''',
'''albert-... | 85 | 1 |
'''simple docstring'''
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tens... | 56 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
UpperCamelCase : Union[str, Any] = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def ... | 316 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
UpperCAmelCase__ = {
"uclanlp/visualbert-vqa": "https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json",
"uclanlp/visual... | 26 |
def _a ( a :list ) -> list:
if len(a ) <= 1:
return lst
a = 1
while i < len(a ):
if lst[i - 1] <= lst[i]:
i += 1
else:
a , a = lst[i], lst[i - 1]
i -= 1
if i == 0:
a = 1
return lst
if __name__ == "__main_... | 26 | 1 |
"""simple docstring"""
class SCREAMING_SNAKE_CASE__ :
def __init__( self : List[str] ):
lowerCAmelCase = {}
def __lowercase ( self : Optional[int] ):
print(self.vertex )
for i in self.vertex:
pri... | 155 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class UpperCAmelCase ( metaclass=UpperCamelCase__ ):
__lowercase = ["""note_seq"""]
def __init__( self :Optional[Any] , *lowercase_ :List[Any] , **lowercase_ :List[str] ... | 237 | 0 |
'''simple docstring'''
import argparse
import os
import re
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
a_ = 'src/diffusers'
# Matches is_xxx_available()
a_ = re.compile(R'is\_([a-z_]*)_avail... | 354 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
fro... | 222 | 0 |
'''simple docstring'''
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def _lowerCamelCase ( lowercase : Any , ... | 63 |
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class UpperCamelCase__ ( lowerCAmelCase_ ):
'''simple docstring'''
def __init__( self : Un... | 296 | 0 |
from __future__ import annotations
import typing
from collections.abc import Iterable
import numpy as np
a =typing.Union[Iterable[float], Iterable[int], np.ndarray] # noqa: UP007
a =typing.Union[np.floataa, int, float] # noqa: UP007
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ... | 354 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ , lowerCamelCase__ ) -> bool:
if len(lowerCamelCase__ ) == 0:
return False
__lowerCamelCase : List[Any] = len(lowerCamelCase__ ) // 2
if a_list[midpoint] == item:
retu... | 113 | 0 |
'''simple docstring'''
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING
_SCREAMING_SNAKE_CASE : Optional[int] = logging.get_logger(__name__)
class _snake_case ( lowercase_ ):
lower... | 85 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_SCREAMING_SNAKE_CASE : Tuple = {
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 85 | 1 |
'''simple docstring'''
# Imports
import numpy as np
class a__:
def __init__( self : Dict , __snake_case : Union[str, Any]=None , __snake_case : Tuple=None , __snake_case : Optional[Any]=None , __snake_case : int=None , __sn... | 96 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase: Optional[int] = {
'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig']... | 96 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
"XCLIPVisionConfig",
],
... | 26 |
import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils impor... | 26 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase : Tuple = {"configuration_fnet": ["FNET_PRETRAINED_CONFIG_ARCHIVE_MAP... | 114 |
'''simple docstring'''
def _lowerCAmelCase ( _UpperCamelCase : float , _UpperCamelCase : float ) -> float:
"""simple docstring"""
if mass < 0:
raise ValueError('The mass of a body cannot be negative' )
return 0.5 * mass * abs(_UpperCamelCase ... | 114 | 1 |
'''simple docstring'''
def snake_case_ ( _lowerCAmelCase : Optional[Any] = 50 ) -> int:
UpperCAmelCase : int = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 , 5 ):
... | 23 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
_UpperCAmelCase : List[str] = {
"EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
... | 222 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowerCamelCase_ : str = logging.get_logger(__name__)
class __A ( _SCREAMING_SNAKE_CASE ):
"""... | 215 |
"""simple docstring"""
import inspect
import unittest
from transformers import YolosConfig
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_configura... | 215 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
__UpperCamelCase : Optional[Any] = list[list[float | int]]
def __SCREAMING_SNAKE_CASE ( A_ , A_ ):
lowerCAmelCase__ : List[str] = len(SCREAMING_SNAKE_CASE_ )
lowerCAme... | 106 |
"""simple docstring"""
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is... | 113 | 0 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
_snake_case = ['''small''', '''medium''', '''large''']
_snake_case = '''lm_head.decoder.weight'''
_snake_case = '''lm_head.weight'''
def _UpperCamelCase ( snake_case__, sn... | 342 | import argparse
from typing import Dict
import tensorflow as tf
import torch
from tqdm import tqdm
from transformers import BigBirdPegasusConfig, BigBirdPegasusForConditionalGeneration
_snake_case = [
# tf -> hf
('''/''', '''.'''),
('''layer_''', '''layers.'''),
('''kernel''', ... | 342 | 1 |
"""simple docstring"""
import socket
def _snake_case ( ):
_lowerCamelCase : List[Any] = socket.socket(socket.AF_INET , socket.SOCK_STREAM )
_lowerCamelCase : Union[str, Any] = socket.gethostname()
_lowerCamelCase : Li... | 96 |
"""simple docstring"""
# Imports
import numpy as np
class lowerCAmelCase__ :
'''simple docstring'''
def __init__( self , lowercase=None , lowercase=None , lowercase=None , lowercase=None , lowercase=None ):
self.set_m... | 96 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
__snake_case : Dict = {'''configuration_mra''': ['''MRA_PRETRAINED_CONFIG_A... | 357 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__snake_case : Dict = {"""configuration_focalnet""": ["""FOCALNET_PRETRAINED_CONFIG_ARCHIVE_MAP""", """FocalNetConfig"""]}
tr... | 122 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be consid... | 114 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils i... | 114 | 1 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class UpperCAmelCase_ ( metaclass=_lowercase):
snake_case__ = ['''keras_nlp''']
def __init__( self : Any , *__UpperCamelCase : Union[str, Any] , **__UpperCamelCase : int )... | 357 | """simple docstring"""
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers import VQDiffusionSch... | 54 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
A_ : Dict = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def snake_case_ ( lowerCAmelCase_ , lowerCAmelCase_ )-> Optional[int]:
'... | 215 |
'''simple docstring'''
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from tr... | 215 | 1 |
from __future__ import annotations
class UpperCAmelCase_ :
'''simple docstring'''
def __init__( self , _A , _A ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = text, pattern
__SCREA... | 118 |
def __lowercase ( a__ ) -> int:
__SCREAMING_SNAKE_CASE = [[0 for _ in range(a__ )] for _ in range(m + 1 )]
for i in range(m + 1 ):
__SCREAMING_SNAKE_CASE = 1
for n in range(m + 1 ):
for k in range(1 ... | 118 | 1 |
from scipy.stats import spearmanr
import datasets
__magic_name__: str = "\nThe Spearman rank-order correlation coefficient is a measure of the\nrelationship between two datasets. Like other correlation coefficients,\nthis one varies between -1 and +1 with 0 implying no correlation.\nPositive correla... | 342 |
from __future__ import annotations
def UpperCamelCase ( _A ): # This function is recursive
"""simple docstring"""
__magic_name__ : str = len(_A )
# If the array contains only one element, we return it (it's the stop condition of
# recursion)
... | 342 | 1 |
"""simple docstring"""
def __A ( a_ :int) -> bool:
if number < 0:
raise ValueError('''number must not be negative''')
return number & (number - 1) == 0
if __name__ == "__main__":
import doctest
doctest.testmod() | 188 |
"""simple docstring"""
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import ... | 188 | 1 |
'''simple docstring'''
import argparse
import pickle
import numpy as np
import torch
from torch import nn
from transformers import ReformerConfig, ReformerModelWithLMHead
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase__ ( lowerCAm... | 70 |
_A = [0, 2, 4, 6, 8]
_A = [1, 3, 5, 7, 9]
def lowerCamelCase__ ( a__ : int , a__ : int , a__ : list[int] , a__ : int ) -> int:
if remaining_length == 0:
if digits[0] == 0 or digits[-1] == 0:
return ... | 122 | 0 |
'''simple docstring'''
import random
import unittest
import torch
from diffusers import IFImgaImgSuperResolutionPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
... | 164 |
'''simple docstring'''
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
UpperCamelCase__ : Optional[Any] = Path(__file__).resolve().parents[3] / 'src'
sys.path.insert(1, str(git_... | 164 | 1 |
'''simple docstring'''
import unittest
from parameterized import parameterized
from transformers import LlamaConfig, is_torch_available, set_seed
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common im... | 97 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
a__ : Union[str, Any] = log... | 54 | 0 |
'''simple docstring'''
import argparse
import json
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
... | 123 |
'''simple docstring'''
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import TimesformerConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
fr... | 123 | 1 |
from collections import deque
from .hash_table import HashTable
class lowerCamelCase (SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
def __init__( self : Dict , *__magic_name__ : Optional[int] , **__magic_name__ : Dict ) -> Optional[Any]:... | 118 | import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase (... | 118 | 1 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import ... | 143 | import argparse
import gc
import json
import os
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 A... | 143 | 1 |
from __future__ import annotations
import math
from collections import Counter
from string import ascii_lowercase
def UpperCAmelCase__ ( _A : str ):
'''simple docstring'''
a__, a__ =analyze_text(_A )
a__ =list(''' ''' + ascii_lowercase )
# what is our tot... | 188 |
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, SquadDataTrainingArguments
| 188 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotConfig, is_flax_available
from transformers.testing_utils import jax_device, require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeli... | 182 |
"""simple docstring"""
import pytest
import datasets.config
from datasets.utils.info_utils import is_small_dataset
@pytest.mark.parametrize('dataset_size' , [None, 400 * 2**20, 600 * 2**20] )
@pytest.mark.parametrize('input_in_memory_max_size' , ['default', 0, 100 * 2**20, 900 * 2**20] )
def __lowerCAme... | 182 | 1 |
'''simple docstring'''
def _A ( lowercase__ = 100 ):
lowercase__ = set()
lowercase__ = 0
lowercase__ = n + 1 # maximum limit
for a in range(2 , lowercase__ ):
for b in range(2 , lowercase_... | 164 |
'''simple docstring'''
import heapq as hq
import math
from collections.abc import Iterator
class A :
def __init__( self , lowerCamelCase__ ) -> Optional[Any]:
'''simple docstring'''
lowercase__ = str(id_ )
lowercase__ ... | 164 | 1 |
from __future__ import annotations
import unittest
import numpy as np
from transformers import BlipTextConfig
from transformers.testing_utils import require_tf, slow
from transformers.utils import is_tf_available
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 94 |
def lowerCAmelCase__ ( lowerCamelCase_ : str ,lowerCamelCase_ : str):
'''simple docstring'''
lowerCAmelCase__ : Optional[Any] = len(lowerCamelCase_)
lowerCAmelCase__ : str = len(lowerCamelCase_)
lowerCAmelCase__ : Union[str, Any] ... | 94 | 1 |
from collections.abc import Callable
class a :
"""simple docstring"""
def __init__( self : str , lowerCamelCase : Callable | None = None ) -> None:
# Stores actual heap items.
__snake_case : list = []
# Store... | 123 |
import collections
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_snake_case : int = logging.get_logger(__name__)
_snake_case : Union[str, Any] = "▁"
_snake_ca... | 123 | 1 |
from __future__ import annotations
def __lowercase ( _UpperCamelCase ) ->Tuple: # This function is recursive
"""simple docstring"""
lowercase : Dict = len(__SCREAMING_SNAKE_CASE )
# If the array contains only one element, we return it (it's the... | 370 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dummy... | 173 | 0 |
import copy
import os
import cva
import numpy as np
from matplotlib import pyplot as plt
class __snake_case :
def __init__( self ) -> Dict:
'''simple docstring'''
snake_case__ : List[str] = ''
snake_case__ ... | 143 | import json
from typing import List, Optional, Tuple
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer
... | 143 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_SCREAMING_SNAKE_CASE = logging.get_logger(__name__)
_SCREAMING_SNAKE_CAS... | 357 | import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
_SCREAMING_SNAKE_CASE = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
' (KHTML, like Gecko) Chrome/70.0.3538.102 Safari/537.36 Edge/18.195... | 81 | 0 |
import numpy as np
from PIL import Image
def A ( _lowercase , _lowercase , _lowercase ):
SCREAMING_SNAKE_CASE : Any = np.array(_lowercase )
if arr.shape[0] != arr.shape[1]:
raise ValueError('''The input array is not a square matrix''' )
... | 182 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__UpperCamelCase : List[Any] = {'configuration_glpn': ['GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GLPNConfig']}
try:
if not is_vision_available():
... | 182 | 1 |
from collections.abc import Sequence
def UpperCamelCase (lowercase_: Sequence[float] , lowercase_: float ) -> float:
return sum(c * (x**i) for i, c in enumerate(lowercase_ ) )
def UpperCamelCase (lowercase_: Sequence[float] , lowercase_: float ) -> float:
A__ : ... | 355 |
from __future__ import annotations
def UpperCamelCase (lowercase_: float , lowercase_: float , lowercase_: float ) -> dict[str, float]:
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resistance < 0:
raise ValueError... | 141 | 0 |
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