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 copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig
if TYPE_CHECKING... | 331 |
'''simple docstring'''
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoMode... | 331 | 1 |
'''simple docstring'''
import functools
def _snake_case ( A , A ) -> int:
# Validation
if not isinstance(A , A ) or not all(isinstance(A , A ) for day in days ):
raise ValueError('''The parameter days should be a list of integers''' ... | 228 |
'''simple docstring'''
from collections.abc import Iterable
from typing import Any
class a__ :
'''simple docstring'''
def __init__( self , lowerCamelCase_ = None ) -> List[str]:
lowerCAmelCase__ = value
... | 228 | 1 |
'''simple docstring'''
def lowerCamelCase ( lowerCAmelCase : int , lowerCAmelCase : list ):
"""simple docstring"""
_enforce_args(lowerCAmelCase , lowerCAmelCase )
if n == 0:
return 0
__magic_name__ : List[str] = float('-inf' )
for i in range(1 , n + 1 ):
__... | 331 |
'''simple docstring'''
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCamelCase ( lowercase__ ):
'''simple docstring'''
A_ : Dict = (DDPMScheduler,)
def __lowerCAmelCase ( self : Any ... | 331 | 1 |
from __future__ import annotations
import inspect
import unittest
from typing import List, Tuple
from transformers import RegNetConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_co... | 143 | import os
import zipfile
import pytest
from datasets.utils.extract import (
BzipaExtractor,
Extractor,
GzipExtractor,
LzaExtractor,
SevenZipExtractor,
TarExtractor,
XzExtractor,
ZipExtractor,
ZstdExtractor,
)
from .utils import require_lza, require_pyazr, require_... | 143 | 1 |
'''simple docstring'''
import math
import time
from typing import Dict, List, Optional
from torch.utils.data import Dataset
from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(chec... | 93 |
'''simple docstring'''
from math import isqrt, loga
def snake_case_ ( __SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
lowercase_ : Any = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + ... | 93 | 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,
RobertaTokeniz... | 31 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowercase: str = logging.get_logger(__name__)
__lowercase: Optional[Any] = {
"abeja/gpt-neox-japanese-2.7b": "https://huggingface.co/abeja/gpt-neox-japanese-2.7b/r... | 31 | 1 |
from typing import TYPE_CHECKING
from ..utils import _LazyModule
lowerCAmelCase_ = {
'''config''': [
'''EXTERNAL_DATA_FORMAT_SIZE_LIMIT''',
'''OnnxConfig''',
'''OnnxConfigWithPast''',
'''OnnxSeq2SeqConfigWithPast''',
'''PatchingSpec''',
],
'... | 8 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
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 e... | 190 | 0 |
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_available():
... | 51 |
import gc
import unittest
import numpy as np
import torch
from diffusers import (
AudioDiffusionPipeline,
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
DiffusionPipeline,
Mel,
UNetaDConditionModel,
UNetaDModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.tes... | 51 | 1 |
import os
from pathlib import Path
import numpy as np
import pytest
from pack_dataset import pack_data_dir
from parameterized import parameterized
from save_len_file import save_len_file
from torch.utils.data import DataLoader
from transformers import AutoTokenizer
from transformers.models.mbart.... | 51 | '''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, ra... | 239 | 0 |
import sys
_snake_case : str = (
"73167176531330624919225119674426574742355349194934"
"96983520312774506326239578318016984801869478851843"
"85861560789112949495459501737958331952853208805511"
"12540698747158523863050715693290963295227443043557"
"6689664895044524452316173185640309871... | 134 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class a (_lowerCAmelCase ):
"""simple docstring"""
... | 134 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : str = {
"""configuration_maskformer""": ["""MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MaskFormerConfig"""],
"""configuration_maskfo... | 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 |
import pprint
import requests
a : int = """https://zenquotes.io/api"""
def __lowerCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/today""" ).json()
def __lowerCamelCase ( ) -> list:
return requests.get(API_ENDPOINT_URL + """/random"""... | 370 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
a : List[str] = logging.getLogger(__name__)
class UpperCamelCase_ ( __magic_name__... | 338 | 0 |
import inspect
import unittest
class _lowerCamelCase ( unittest.TestCase ):
"""simple docstring"""
def _snake_case ( self )->Any:
'''simple docstring'''
try:
import diffusers # noqa: F401
except ImportError:
... | 186 |
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
_UpperCAmelCase : List[Any] = log... | 222 | 0 |
"""simple docstring"""
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def lowerCamelCase_ (UpperCamelCase__ : Dict , Upp... | 363 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import flax
import jax.numpy as jnp
from jax import random
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin
@flax... | 68 | 0 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class __SCREAMING_SNAKE_CASE ( metaclass=lowerCamelCase ):
snake_case_ = ["""torch""", """transformers""", """onnx"""]
def __init__( self : str , *__lowercase : ... | 152 |
'''simple docstring'''
import socket
def _a( ):
'''simple docstring'''
SCREAMING_SNAKE_CASE__ : Tuple =socket.socket(socket.AF_INET, socket.SOCK_STREAM )
SCREAMING_SNAKE_CASE__ : str =socket.gethostname()
... | 152 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> np.array:
UpperCamelCase_: Dict = F'''{sampling_rate}'''
UpperCamelCase_: Any = '1'
UpperC... | 363 |
import os
import sys
import tempfile
import torch
from .state import AcceleratorState
from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment
def snake_case (UpperCAmelCase__ , UpperCAmelCase__=() , UpperCAmelCase__=None , UpperCAmelCase__="n... | 292 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
UpperCAmelCase__ : Lis... | 25 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def lowercase_ ( _snake_case ):
... | 25 | 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(... | 311 |
'''simple docstring'''
from __future__ import annotations
def lowerCAmelCase_ ( snake_case__ ):
'''simple docstring'''
A : List[str] = 2
A : Dict = []
while i * i <= n:
if n % i:
i += 1
else:... | 311 | 1 |
"""simple docstring"""
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available
if is_vision_available():
from PIL import Image
from transformers import AutoProcessor, BlipaProcessor,... | 256 |
"""simple docstring"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_flax_utils import FlaxG... | 316 | 0 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> str | Literal[False]:
"""simple docstring"""
a = list(snake_case_ )
a = lis... | 330 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE__ ( snake_case_, snake_case_ ) -> str | Literal[False]:
"""simple docstring"""
a = list(snake_case_ )
a = lis... | 330 | 1 |
'''simple docstring'''
from collections import defaultdict
from pathlib import Path
import pandas as pd
from rouge_cli import calculate_rouge_path
from utils import calculate_rouge
a__ : int = [
'Prosecutor: "No videos were used in the crash investigation" German papers say they saw ... | 80 |
'''simple docstring'''
from typing import Dict
import numpy as np
import torch
from . import residue_constants as rc
from .tensor_utils import tensor_tree_map, tree_map
def snake_case_ ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_SCREAMING_SNAKE_CASE : Tuple ... | 200 | 0 |
"""simple docstring"""
from __future__ import annotations
import math
def _lowerCAmelCase ( lowercase_ ):
if num <= 0:
UpperCAmelCase = F"""{num}: Invalid input, please enter a positive integer."""
raise ValueError(lowercase_ )
U... | 181 |
"""simple docstring"""
import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union
import numpy as np
import torch
from .utils import deprecate, is_transformers_available
if is_transformers_available():
import transformers
def _lowerCAmelCas... | 181 | 1 |
import math
def __magic_name__ ( __a : int ):
'''simple docstring'''
return math.sqrt(__snake_case ) * math.sqrt(__snake_case ) == num
def __magic_name__ ( __a : int ):
'''simple docstring'''
UpperCamelCase__ = 0
... | 244 |
'''simple docstring'''
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... | 190 | 0 |
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 .tokeni... | 330 |
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__ : Optional[int] = pd.read_csv("""sample_data.csv""", header... | 330 | 1 |
'''simple docstring'''
import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request
from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
fr... | 93 |
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class __lowerCAmelCase :
"""simple docstring"""
snake_case_ = 42 # [batch_size x 3]
snake_case_ = 42 # [batch_size x 3]
snake_cas... | 90 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__A = logging.get_logger(__name__)
__A = {
'''facebook/data2vec-text-base''': '''https://huggingface.co/data2vec/... | 278 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__A = logging.get_logger(__name__)
__A = ... | 278 | 1 |
def SCREAMING_SNAKE_CASE__ ( lowerCamelCase__ ) -> int:
__lowerCamelCase : List[Any] = hex_num.strip()
if not hex_num:
raise ValueError('No value was passed to the function' )
__lowerCamelCase : List[Any] = hex_num[0] == '-'
if is_negative:
__lowe... | 73 |
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class A_ ( SCREAMING_SNAKE_CASE ):
... | 73 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__lowerCamelCase = {
"configuration_roformer": ... | 362 | """simple docstring"""
def UpperCAmelCase ( UpperCamelCase__ = 4_000_000 ):
"""simple docstring"""
A__ = [0, 1]
A__ = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1] )
if fib[i + 2] >... | 154 | 0 |
import argparse
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, Dist... | 212 |
import argparse
import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download
from transformers import (
CLIPTokenizer,
CLIPTokenizerFast,
VideoMAEImageProcessor,
XCLIPConfig,
XCLIPModel,
XCLIPProcessor,
XCLIPTextConfig,
XCLIPVisionConfig,
)... | 212 | 1 |
'''simple docstring'''
from copy import deepcopy
from typing import Optional, Union
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available
if is_torch_available():
import to... | 5 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def UpperCAmelCase_ (__a : str = "https://www.worldometers.info/coronavirus" ):
"""simple docstring"""
_a : List[str] = BeautifulSoup(requests.get(__a ).text , 'html.parser' )
_a : ... | 5 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
__SCREAMING_SNAKE_CASE : int = {}
try:
if not is_sentencepiece_available():
... | 31 |
import os
import string
import sys
__UpperCamelCase : List[Any] = 1 << 8
__UpperCamelCase : Union[str, Any] = {
"""tab""": ord("""\t"""),
"""newline""": ord("""\r"""),
"""esc""": 27,
"""up""": 65 + ARROW_KEY_FLAG,
"""down""": 66 + ARROW_KEY_... | 307 | 0 |
import unittest
from transformers import DebertaConfig, 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 ...... | 78 | from json import JSONDecodeError # Workaround for requests.exceptions.JSONDecodeError
import requests
def lowerCAmelCase_ ( __lowerCAmelCase = "isbn/0140328726" )-> dict:
'''simple docstring'''
UpperCAmelCase : Tuple =olid.strip().strip('''/''' ) # Remove leadi... | 78 | 1 |
"""simple docstring"""
def __lowerCAmelCase ( lowercase : int , lowercase : list ) -> int:
"""simple docstring"""
_enforce_args(lowercase , lowercase )
if n == 0:
return 0
snake_case : Dict = float("-inf" )
for i in... | 203 |
"""simple docstring"""
import json
import os
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from requests.exceptions import HTTPError
from transformers.utils import (
CONFIG_NAME,
FLAX_WEIGHTS_NAME,
TF2_WEIGHTS_NAME,
TRANSFORMERS_CACHE,
WEIGHTS_NAME,
... | 203 | 1 |
import torch
from diffusers import DDPMScheduler
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( UpperCAmelCase_ ):
'''simple docstring'''
a_ : Tuple =(DDPMScheduler,)
def UpperCamelCase_ ( self : Optional[int] , **UpperCamelCase : ... | 260 |
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=UpperCAmelCase_ ):
'''simple docstring'''
a_ : Optional[int] =["""speech"""]
def __init__( self : Optional[int] , *UpperCamelCase : int , **UpperCamelCase : str ):
... | 260 | 1 |
'''simple docstring'''
def __lowerCamelCase ( A__ = 10**9 ) -> int:
"""simple docstring"""
UpperCamelCase = 1
UpperCamelCase = 2
UpperCamelCase = 0
UpperCamelCase = 0
UpperCamelCase = 0
while perimeter <= max_perimeter:
... | 28 |
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,
FEATURE_E... | 281 | 0 |
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class ... | 45 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrin... | 45 | 1 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCAmelCase_ ( __snake_case ) -> int:
"""sim... | 5 |
'''simple docstring'''
def snake_case_ (_a : str , _a : str ):
UpperCAmelCase = len(_a ) + 1
UpperCAmelCase = len(_a ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string matches with... | 34 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
MobileViTConfig,
MobileViTForImageClassification,
MobileViTForSemanticSegmentation,
MobileViTIma... | 182 |
"""simple docstring"""
from .glue import glue_convert_examples_to_features, glue_output_modes, glue_processors, glue_tasks_num_labels
from .squad import SquadExample, SquadFeatures, SquadVaProcessor, SquadVaProcessor, squad_convert_examples_to_features
from .utils import DataProcessor, InputExample, InputFeatur... | 182 | 1 |
'''simple docstring'''
import math
def lowerCamelCase ( __lowerCamelCase : int ) ->bool:
_SCREAMING_SNAKE_CASE = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(__lowerCamelCase )
def lowerCamelCase ( __lowerCa... | 58 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, Encoder... | 295 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
from math import sqrt
# 1 for manhattan, 0 for euclidean
a : str = 0
a : Any = [
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obst... | 353 |
"""simple docstring"""
import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type
from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.util... | 79 | 0 |
'''simple docstring'''
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 OptionalDepen... | 89 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A: Optional[Any] = {
"configuration_tapas": ["TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP", "TapasConfig"],
"tokenization_tapas": ["TapasTokenize... | 109 | 0 |
"""simple docstring"""
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
StableDiffu... | 363 |
"""simple docstring"""
_A = range(2, 20 + 1)
_A = [10**k for k in range(ks[-1] + 1)]
_A = {}
def a__ ( lowerCAmelCase , lowerCAmelCase , lowerCAmelCase , lowerCAmelCase ) -> int:
UpperCAmelCase__ : List[str] = sum(a_i[j] for j in... | 166 | 0 |
import math
from datetime import datetime, timedelta
def _a ( UpperCamelCase_ : int ) -> datetime:
"""simple docstring"""
lowerCAmelCase__ = year % 19
lowerCAmelCase__ = year % 4
lowerCAmelCase__ = year % 7
lo... | 340 |
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
a_ = (3, 9, -11, 0, 7, 5, 1, -1)
a_ = (4, 6, 2, 0, 8, 10, 3, -2)
@dataclass
class lowercase__ :
a_ =42
a_ =42
class lowercase__ :
def __init__( ... | 340 | 1 |
'''simple docstring'''
import unittest
from transformers import MraConfig, 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, floats_tensor, ids_ten... | 364 |
'''simple docstring'''
import logging
import os
from typing import List, TextIO, Union
from conllu import parse_incr
from utils_ner import InputExample, Split, TokenClassificationTask
__snake_case =logging.getLogger(__name__)
class UpperCAmelCase_ ( __lowercase ):
... | 55 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
... | 79 |
import numpy as np
def UpperCAmelCase ( a_ , a_ , a_ = 1E-12 , a_ = 1_0_0 , ) -> tuple[float, np.ndarray]:
"""simple docstring"""
assert np.shape(a_ )[0] == np.shape(a_ )[1]
# Ensure proper dimensionality.
assert np.shape(a_ )[0] == np.shape(a_ )[0]
... | 15 | 0 |
from math import asin, atan, cos, radians, sin, sqrt, tan
SCREAMING_SNAKE_CASE :Union[str, Any] = 637_8137.0
SCREAMING_SNAKE_CASE :List[Any] = 635_6752.31_4245
SCREAMING_SNAKE_CASE :Dict = 637_8137
def lowerCAmelCase( SCREAMING_SNAKE_CASE_ , SCR... | 365 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import Confi... | 60 | 0 |
def _A ( SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int , SCREAMING_SNAKE_CASE : int ):
"""simple docstring"""
a__ : int =(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formu... | 95 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
... | 167 | 0 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils im... | 350 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class _UpperCamelCase ( _A , unittest.TestCase ):
'''simple docstring'''
__UpperCamelCase : Tu... | 223 | 0 |
_A = 8.314_462 # Unit - J mol-1 K-1
def lowerCamelCase__ ( __lowerCAmelCase : List[Any] , __lowerCAmelCase : Optional[int] , __lowerCAmelCase : Optional[int] ):
"""simple docstring"""
if moles < 0 or kelvin < 0 or volume < 0:
raise ValueError("Invalid ... | 231 |
"""simple docstring"""
import os
import unittest
from transformers import FunnelTokenizer, FunnelTokenizerFast
from transformers.models.funnel.tokenization_funnel import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import Toke... | 269 | 0 |
"""simple docstring"""
import os
from collections.abc import Iterator
def _A ( _a : str = "." ):
"""simple docstring"""
for dir_path, dir_names, filenames in os.walk(_a ):
A = [d for d in dir_names if d != """s... | 77 |
"""simple docstring"""
import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import ClassLabel, Features, Value
from .base import TaskTemplate
@dataclass(frozen=SCREAMING_SNAKE_CASE )
class lowerCamelCase__ ( SCREAMING_SNAKE_CASE ):
... | 77 | 1 |
import re
import string
import numpy as np
import datasets
_SCREAMING_SNAKE_CASE = """
Returns the rate at which the input predicted strings exactly match their references, ignoring any strings input as part of the regexes_to_ignore list.
"""
_SCREAMING_SNAKE_CASE = """
... | 327 |
import sys
_SCREAMING_SNAKE_CASE = (
"""73167176531330624919225119674426574742355349194934"""
"""96983520312774506326239578318016984801869478851843"""
"""85861560789112949495459501737958331952853208805511"""
"""12540698747158523863050715693290963295227443043557"""
"""6... | 327 | 1 |
"""simple docstring"""
from .imports import is_tqdm_available
if is_tqdm_available():
from tqdm.auto import tqdm as _tqdm
from ..state import PartialState
def _A ( lowercase = True , *lowercase , **lowercase ):
"""simple docstring"""
if not is... | 215 |
"""simple docstring"""
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def _A ( lowercase ):
"""simple docstring"""
a ={}
... | 215 | 1 |
'''simple docstring'''
import unittest
from transformers import SPIECE_UNDERLINE, XLNetTokenizer, XLNetTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase_ = get_tests_... | 309 |
"""simple docstring"""
def _snake_case ( lowercase__ : int = 1_0 ) -> str:
'''simple docstring'''
if not isinstance(lowercase__ , lowercase__ ) or n < 0:
raise ValueError("""Invalid input""" )
lowerCAmelCase_ :List[str] = 1_0**n
... | 84 | 0 |
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
from .transformer_engine import ... | 300 |
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def A ( _lowerCamelCase , _lowerCamelCase , _lowerCamelCase ):
'''simple docstring'''
_lowerCAmelCase : Dict = ("dense.weight", "... | 300 | 1 |
'''simple docstring'''
import tempfile
import torch
from diffusers import (
DEISMultistepScheduler,
DPMSolverMultistepScheduler,
DPMSolverSinglestepScheduler,
UniPCMultistepScheduler,
)
from .test_schedulers import SchedulerCommonTest
class A ( __UpperCAmelCase ):
lowerCamelCase... | 164 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
... | 164 | 1 |
from math import loga
def lowerCamelCase_ ( _UpperCamelCase ) -> int:
"""simple docstring"""
if a < 0:
raise ValueError('''Input value must be a positive integer''' )
elif isinstance(_UpperCamelCase , _UpperCamelCase ):
raise TypeError('''Inp... | 367 |
from math import isclose, sqrt
def lowerCamelCase_ ( _UpperCamelCase , _UpperCamelCase , _UpperCamelCase ) -> tuple[float, float, float]:
"""simple docstring"""
snake_case_ : Dict = point_y / 4 / point_x
snake_case_ : List[str] ... | 279 | 0 |
from __future__ import annotations
import unittest
from transformers import LEDConfig, 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
from ...test_pipeline... | 218 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCAmelCase : Tuple = {
"configuration_llama": ["LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP... | 218 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
SCREAMING_SNAKE_CASE_ = data... | 364 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE_ = {
"""configuration_megatron_bert""": ["""MEGATRON_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegatronBertConfig"""],
}
try:
if not ... | 193 | 0 |
import argparse
import logging
import os
import sys
import numpy as np
import onnxruntime
import torch
from bart_onnx.generation_onnx import BARTBeamSearchGenerator
from bart_onnx.reduce_onnx_size import remove_dup_initializers
import transformers
from transformers import BartForConditionalGener... | 51 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
snake_case_ : Dict = {"configuration_mbart"... | 51 | 1 |
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoConfig,
AutoModelForMaskedLM... | 353 |
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 __a ( __UpperCame... | 28 | 0 |
def UpperCamelCase ( lowerCAmelCase__ , lowerCAmelCase__ ):
'''simple docstring'''
return int((input_a, input_a).count(0 ) == 0 )
def UpperCamelCase ( ):
'''simple docstring'''
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert a... | 101 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from timm import create_model
from timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import BitConfig, Bit... | 101 | 1 |
from ...utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_torch_available,
is_transformers_available,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dumm... | 134 |
from ..utils import DummyObject, requires_backends
class a (metaclass=_lowerCAmelCase ):
"""simple docstring"""
__UpperCAmelCase : int = ["speech"]
def __init__( self : List[Any] , *lowerCamelCase : List[Any] , **lowerCamelCase : Optional[Any] ... | 134 | 1 |
"""simple docstring"""
def __magic_name__ ( lowercase ):
if length <= 0 or not isinstance(_A , _A ):
raise ValueError("""Length must be a positive integer.""" )
return [n * (2 * n - 1) for n in range(_A )]
if __name__ == "__main__":
print(hexag... | 173 | '''simple docstring'''
from __future__ import annotations
from typing import Dict
from ...configuration_utils import PretrainedConfig
__lowercase = {
'''susnato/ernie-m-base_pytorch''': '''https://huggingface.co/susnato/ernie-m-base_pytorch/blob/main/config.json''',
'''susnato/ernie-m-large_py... | 272 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CL... | 357 |
"""simple docstring"""
# Copyright 2021 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/lic... | 79 | 0 |
"""simple docstring"""
from math import sqrt
def _lowerCamelCase ( _UpperCamelCase ):
'''simple docstring'''
__lowerCAmelCase = 0
for i in range(1 , int(sqrt(_UpperCamelCase ) + 1 ) ):
if n % i == 0 and i != sqrt(_UpperCamelCase ):
total += i + n... | 57 |
import torch
from torch import nn
class snake_case ( nn.Module ):
'''simple docstring'''
def __init__( self : int , lowerCAmelCase : Tuple , lowerCAmelCase : int , lowerCAmelCase : Any , lowerCAmelCase : T... | 317 | 0 |
"""simple docstring"""
from __future__ import annotations
from math import pi, sqrt
def __UpperCAmelCase ( snake_case_ : Union[str, Any] , snake_case_ : Dict ) -> Optional[Any]:
"""simple docstring"""
if inductance <= 0:
... | 353 |
"""simple docstring"""
from functools import reduce
SCREAMING_SNAKE_CASE : int = (
'''73167176531330624919225119674426574742355349194934'''
'''96983520312774506326239578318016984801869478851843'''
'''85861560789112949495459501737958331952853208805511'''
'''1254069874715852386305... | 317 | 0 |
'''simple docstring'''
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, p... | 75 |
'''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 | 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__":
SCREAMING_SNAKE_CASE__ = pd.read_csv("""sample_data.csv""", header=None)
SCREAMING_SN... | 353 |
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_mvp import MvpTokenizer
SCREAMING_... | 297 | 0 |
"""simple docstring"""
import torch
from torch import nn
from transformers import CLIPPreTrainedModel, CLIPVisionModel
from ...models.attention import BasicTransformerBlock
from ...utils import logging
lowercase__ = logging.get_logger(__name__) # pylint: disable=invalid... | 96 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class __lowerCAmelCase ( lo... | 330 | 0 |
_A = 8.3_144_598
def __UpperCamelCase ( _A , _A ):
if temperature < 0:
raise Exception('''Temperature cannot be less than 0 K''' )
if molar_mass <= 0:
raise Exception('''Molar mass cannot be less than or equal to 0 kg/mol''' )
else:
... | 167 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _in... | 167 | 1 |
'''simple docstring'''
def A_ ( ):
SCREAMING_SNAKE_CASE:Tuple = 0
for i in range(1 , 1001 ):
total += i**i
return str(snake_case )[-10:]
if __name__ == "__main__":
print(solution())
| 139 |
_lowercase: Dict = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def a( A : str ) -> int:
"""simple docs... | 227 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_SCREAMING_SNAKE_CASE : Any = {
"""configuration_roformer""": ["... | 368 |
"""simple docstring"""
# 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 wo... | 157 | 0 |
from __future__ import annotations
def __lowercase ( a__ , a__ ) -> set[str]:
__SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = set(a__ ), [start]
while stack:
__SCREAMING_SNAKE_CASE = stack.pop()
explored.add(a__... | 257 |
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
IMAGENET_STA... | 257 | 1 |
"""simple docstring"""
import logging
import os
from .state import PartialState
class __lowercase ( logging.LoggerAdapter ):
'''simple docstring'''
@staticmethod
def _lowerCamelCase ( _UpperCAmelCase ):
... | 371 |
"""simple docstring"""
import enum
import warnings
from .. import MODEL_FOR_CAUSAL_LM_MAPPING, TF_MODEL_FOR_CAUSAL_LM_MAPPING
from ..utils import add_end_docstrings, is_tf_available
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
... | 188 | 0 |
"""simple docstring"""
__SCREAMING_SNAKE_CASE =[
'''VerificationMode''',
'''Version''',
'''disable_progress_bar''',
'''enable_progress_bar''',
'''is_progress_bar_enabled''',
'''experimental''',
]
from .info_utils import VerificationMode
from .logging import disable_progress_bar, enab... | 213 |
'''simple docstring'''
import warnings
from ...utils import is_sklearn_available, requires_backends
if is_sklearn_available():
from scipy.stats import pearsonr, spearmanr
from sklearn.metrics import fa_score, matthews_corrcoef
__SCREAMING_SNAKE_CASE :List[str] = (
'''This metric will... | 22 | 0 |
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data import Dataset
from transf... | 357 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
a__ : Optional[Any] = {'''configuration_deit''': ['''DEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeiTConfig''', '''... | 19 | 0 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.... | 205 |
import colorsys
from PIL import Image # type: ignore
def _SCREAMING_SNAKE_CASE ( a , a , a ) -> float:
__A : List[str] = x
__A : str = y
for step in range(a ): # noqa: B007
__A : Union[str, Any] = a * ... | 280 | 0 |
import logging
import numpy as np
import pytest
from scipy.linalg import eigh
logging.basicConfig(level=logging.INFO, format='''%(message)s''')
def lowerCamelCase_ ( _a ):
"""simple docstring"""
return input_array.reshape((input_array.size, 1) )
d... | 211 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_funnel import FunnelTokenizer
lowerCamelCase = logging.get_logger(__name__)
lowerCamelCase ... | 211 | 1 |
from collections.abc import Sequence
def UpperCamelCase ( __lowerCamelCase : Sequence[float] , __lowerCamelCase : bool = False ):
if not arr:
return 0
snake_case : Tuple = 0 if allow_empty_subarrays else float("-inf" )
sna... | 59 |
from __future__ import annotations
def lowerCAmelCase__( lowercase : str , lowercase : list[str] | None = None ) -> list[list[str]]:
__snake_case : List[str] = word_bank or []
# create a table
__snake_case : int = len(lowercase ) + 1
__snake... | 326 | 0 |
import inspect
import unittest
from transformers import ViTMSNConfig
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 ConfigTester
from ...test... | 358 |
from math import isqrt
def _SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE :int ) -> list[int]:
__lowerCAmelCase : Tuple = [True] * max_number
for i in range(2 , isqrt(max_number - 1 ) + 1 ):
if is_prime[i]:
for j in range(i**2 , SCREAMING_SNAKE_CASE , SCR... | 232 | 0 |
'''simple docstring'''
import os
# Precomputes a list of the 100 first triangular numbers
UpperCAmelCase : List[Any] = [int(0.5 * n * (n + 1)) for n in range(1, 1_0_1)]
def a__ ( ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE = os.path.dirname(os.path.rea... | 267 |
'''simple docstring'''
import math
def _lowerCAmelCase ( __snake_case : int ) -> bool:
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 e... | 190 | 0 |
"""simple docstring"""
import os
import unittest
from transformers import LayoutLMTokenizer, LayoutLMTokenizerFast
from transformers.models.layoutlm.tokenization_layoutlm import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin... | 353 |
"""simple docstring"""
class __a :
'''simple docstring'''
def __init__( self , _a ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = arr.split(""",""" )
def _a ( self ) -> Any:
"... | 56 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__: Dict = logging.get_logger(__name__)
A__: Dict = {
'''microsoft/markuplm-base''': '''https://huggingface.co/microsoft/markuplm-base/resolve/mai... | 276 |
'''simple docstring'''
from __future__ import annotations
import requests
def SCREAMING_SNAKE_CASE_ ( _UpperCAmelCase : str ) -> dict:
_a : Any =F"https://hacker-news.firebaseio.com/v0/item/{story_id}.json?print=pretty"
return requests.get(_UpperCAmelCa... | 276 | 1 |
from typing import List, Optional
import numpy as np
from ...processing_utils import ProcessorMixin
from ...utils import to_numpy
class __A( a ):
snake_case_ = '''EncodecFeatureExtractor'''
snake_case_ = ('''T5Tokenizer''', '''T5TokenizerFast''')
def __... | 33 |
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
from ..auto import CONFIG_MAPPING
A : List[Any] = logging.get_logger(__name__)
A : ... | 33 | 1 |
'''simple docstring'''
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 imp... | 321 |
'''simple docstring'''
def lowercase__ ( __UpperCamelCase = 4000000 )-> int:
UpperCamelCase = []
UpperCamelCase ,UpperCamelCase = 0, 1
while b <= n:
if b % 2 == 0:
even_fibs.append(__UpperCamelCase )
Upp... | 321 | 1 |
"""simple docstring"""
import math
def __SCREAMING_SNAKE_CASE ( A_ , A_ = 0 , A_ = 0 ):
lowerCAmelCase__ : Tuple = end or len(A_ )
for i in range(A_ , A_ ):
lowerCAmelCase__ : Dict = i
lowerCAmelCase__ : List[Any] = ... | 74 |
"""simple docstring"""
import qiskit
def __SCREAMING_SNAKE_CASE ( A_ = 2 ):
lowerCAmelCase__ : int = qubits
# Using Aer's simulator
lowerCAmelCase__ : str = qiskit.Aer.get_backend('''aer_simulator''' )
# Creating a Quantum Circuit acting on the q register
low... | 74 | 1 |
def a ( A__ : list ) -> list:
"""simple docstring"""
_lowercase =len(snake_case__ )
for _ in range(snake_case__ ):
for i in range(_ % 2 , arr_size - 1 , 2 ):
if arr[i + 1] < arr[i]:
... | 205 |
"""simple docstring"""
from __future__ import annotations
def lowercase (snake_case__ : list[int] ) -> list[int]: # This function is recursive
'''simple docstring'''
lowerCAmelCase = len(snake_case__ )
# If the array contains only... | 155 | 0 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
__A = logging.get_logger(__name__)
def ... | 358 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from math import * # noqa: F403
from sympy import diff
def a__ ( __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE , __SCREAMING_SNAKE_CASE = 1_0**-1_0 ) -> float:
__lowerCAmelCase: ... | 108 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
f... | 12 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_A = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDepend... | 62 | 0 |
'''simple docstring'''
import sys
from typing import Tuple
import numpy as np
import torch
from PIL import Image
from torch import nn
from transformers.image_utils import PILImageResampling
from utils import img_tensorize
class __lowerCamelCase :
"""simple docstring"""
def __init__( self ... | 227 |
'''simple docstring'''
# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion
import math
from dataclasses import dataclass
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from diffusers.co... | 227 | 1 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
_lowercase : int = "naver-clova-ix/donut-base"
class lowerCAmelCase__ ( unittest.TestCase ):
def _snake_case ( self ):
"""simple docstrin... | 93 |
a_ = 8.314_4598
def lowerCamelCase__ ( _a , _a):
if temperature < 0:
raise Exception("Temperature cannot be less than 0 K")
if molar_mass <= 0:
raise Exception("Molar mass cannot be less than or equal to 0 kg/mol")
else:
return (3 * UNIVERSAL_GAS_CONSTANT * temperature / molar_ma... | 76 | 0 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> np.array:
UpperCamelCase_: Dict = F'''{sampling_rate}'''
UpperCamelCase_: Any = '1'
UpperC... | 292 |
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
while b:
UpperCamelCase_ ,UpperCamelCase_: int = b, a % b
return a
def snake_case (UpperCAmelCase__ , UpperCAmelCase__ ) -> int:
return a if b == 0 else euclidean_gcd_recursive(UpperCAmelCase... | 292 | 1 |
"""simple docstring"""
import itertools
import string
from collections.abc import Generator, Iterable
def UpperCAmelCase__ (snake_case__ : Iterable[str] , snake_case__ : int ):
"""simple docstring"""
_snake_case : Optional[Any] = iter(sna... | 64 |
import logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
SCREAMING_SNAKE_CASE_ = logging.getLogger(__name__)
@dataclass
class UpperCamelCase__ ( lowerCAmel... | 296 | 0 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : str , snake_case_ : int ) -> list:
'''simple docstring'''
UpperCAmelCase_ = word.split()
def justify(snake_case_ : list , snake_case_ : int , snake_case_ : int ) -> str:
UpperC... | 358 | '''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from torch import nn
from transformers import (
SpeechaTextaConfig,
SpeechaTextaForCausalLM,
SpeechaTextaTokenizer,
SpeechEncoderDecoderConfig,
SpeechEncoderDecoderModel,
WavaVecaConfig,
WavaVecaF... | 106 | 0 |
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__nam... | 345 |
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class _snake_case ( nn.Module ):
'''simple docstring'''
A__ : int
A__ : int
A__ : ... | 345 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case : List[str] = {
'''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
'''tokenization_biogpt''': ['... | 356 |
from typing import TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
snake_case : Any = {'''configuration_van''': ['''VAN_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''VanConfig''']}
try:
if not is_torch_available():
raise... | 281 | 0 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def _lowerCAmelCase ( _UpperCamelCase : Callable , _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : float , _UpperCamelCase : floa... | 47 |
'''simple docstring'''
import unittest
import numpy as np
import requests
from transformers.testing_utils import 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... | 47 | 1 |
import math
def __A ( _lowercase ):
'''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 multiples of 3 are not primes
... | 75 |
# Copyright 2023 The HuggingFace Inc. 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 ... | 75 | 1 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging
logging.set_verb... | 213 | """simple docstring"""
import sys
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 m... | 213 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require... | 97 |
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, Dist... | 97 | 1 |
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