code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
from heapq import heappop, heappush
import numpy as np
def SCREAMING_SNAKE_CASE ( snake_case , snake_case , snake_case , snake_case , ) -> tuple[float | int, list[tuple[int, int]]]:
__lowercase = grid.shape
__lowercase = [-1, 1, 0, 0]
... | 375 |
import unittest
from transformers import BertGenerationConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTeste... | 662 | 0 |
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def lowerCamelCase_ ( ):
'''simple docstring'''
UpperCamelCase__ = ArgumentParser(
... | 718 | # Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter
if TYP... | 591 | 0 |
'''simple docstring'''
from typing import Optional, Union
import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...... | 56 |
from typing import TYPE_CHECKING
# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
a_ = {
'''configuration_informer''': [
'''INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''InformerConfig''',
],
}
try:
if no... | 339 | 0 |
from __future__ import annotations
import os
from typing import Any
import requests
UpperCamelCase_ = '''https://api.github.com'''
# https://docs.github.com/en/free-pro-team@latest/rest/reference/users#get-the-authenticated-user
UpperCamelCase_ = BASE_URL + '''/user'''
# https://githu... | 716 |
import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
UpperCamelCase_ = HfApi()
UpperCamelCase_ = {}
# fmt: off
UpperCamelCase_ = torch.tensor([
-0.7_5_1_5, -1.6_8_8_3, 0.2_4_2_0, 0.0_3_0_0, 0.6_3_4_7, 1.3_4_3_3, -1.1_7_4_3, -3... | 322 | 0 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from... | 26 |
'''simple docstring'''
# This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
fro... | 591 | 0 |
import multiprocessing
import time
from arguments import PretokenizationArguments
from datasets import load_dataset
from transformers import AutoTokenizer, HfArgumentParser
def __lowerCamelCase ( UpperCamelCase__ ):
'''simple docstring'''
snake_case_ = {}
... | 702 |
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, prepare_image_inputs
if is_to... | 108 | 0 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class _lowercase :
lowercase = 4_2
lowercase = None
lowercase = None
def __lowercase ( ):
UpperCamelC... | 417 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
from ...utils.dumm... | 348 | 0 |
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
from ..pipeline_params import TEXT_GUIDED_... | 307 |
import argparse
from torch import nn
# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
ProphetNetForConditionalGeneration as P... | 307 | 1 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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, r... | 683 |
'''simple docstring'''
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
_UpperCAmelCase : Optional[int] = pytest.mark.integration
@pytest.mark.para... | 683 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCAmelCase = logging.get_logger(__name__)
UpperCAmelCase = {
"""microsoft/cvt-13""": """https://huggingface.co/microsoft/cvt-13/resolve/main/config.json""",
# See all Cvt models at https://hugging... | 721 |
import requests
def __lowerCAmelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE )-> None:
"""simple docstring"""
snake_case_ = {'''Content-Type''': '''application/json'''}
snake_case_ = requests.post(SCREAMING_SNAKE_CASE , json={'''text''': messag... | 531 | 0 |
'''simple docstring'''
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def _UpperCAmelCase ( _lowerCamelCase : int ) -> int:
_lowerCAmelCase : Any = prime_factors(_lowerCamelCase )
if is_square_free(_lowerCamelCase ):
... | 384 |
'''simple docstring'''
import qiskit
def _UpperCAmelCase ( _lowerCamelCase : int = 2 ) -> qiskit.result.counts.Counts:
_lowerCAmelCase : List[Any] = qubits
# Using Aer's simulator
_lowerCAmelCase : Optional[Any] = qiskit.Aer.get_backend("""aer_simu... | 384 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Dict, Optional, Union
import torch
import torch.nn.functional as F
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
f... | 705 |
"""simple docstring"""
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
UpperCAmelCase: int = ver... | 600 | 0 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow
from ...t... | 465 | """simple docstring"""
import qiskit
def lowercase__( __SCREAMING_SNAKE_CASE : int , __SCREAMING_SNAKE_CASE : int ):
lowercase_ : Tuple = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
lowercase_ : L... | 425 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
import random
from collections.abc import Collection
from typing import overload
class UpperCAmelCase__ :
def __init__(self , _a = None ) -> None:
if components is None:
lowerca... | 713 | '''simple docstring'''
import math
import os
import sys
def _UpperCamelCase ( SCREAMING_SNAKE_CASE_ ):
lowercase_ : List[str] = ''
try:
with open(SCREAMING_SNAKE_CASE_ , 'rb' ) as binary_file:
lowercase_ : Di... | 438 | 0 |
'''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_checkpoint
... | 533 |
'''simple docstring'''
from __future__ import annotations
def snake_case_ ( SCREAMING_SNAKE_CASE__ , SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
if len(SCREAMING_SNAKE_CASE__ ) <= 1 or n <= 1:
return
insert_next(SCREAMING_SNAKE_CASE__ , n - 1 )
rec_in... | 533 | 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... | 97 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import BertTokenizer, BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils i... | 97 | 1 |
"""simple docstring"""
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__":
lowercase__ = pd.read_csv("""sample_data.csv""", header=None)
... | 610 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCamelCase = {
'configuration_pix2struct': [
'PIX2STRUCT_PRETRAINED_CONFIG_ARCHIVE_MAP',
... | 179 | 0 |
import doctest
from collections import deque
import numpy as np
class lowerCAmelCase_ :
"""simple docstring"""
def __init__(self ) -> None:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Any = [2, 1, 2, -1]
SCREAMI... | 700 |
"""simple docstring"""
from manim import *
class lowerCAmelCase_ (a__ ):
"""simple docstring"""
def __magic_name__ (self ) -> Any:
"""simple docstring"""
SCREAMING_SNAKE_CASE__ : Dict = Rectangle(height=0.5 ... | 545 | 0 |
'''simple docstring'''
import unittest
from typing import Dict, List, Optional, Union
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 ImageProcessingSavin... | 41 |
import os
import re
import warnings
from shutil import copyfile
from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
if TYPE_CHECKING:
from ...tokenization_utils_base import TextInput
from ...utils import logging
... | 511 | 0 |
import copy
import inspect
import unittest
from transformers import AutoBackbone
from transformers.configuration_utils import PretrainedConfig
from transformers.testing_utils import require_timm, require_torch, torch_device
from transformers.utils.import_utils import is_torch_available
from ...test_backbone_common ... | 703 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"unc-nlp/lxmert-base-uncased": "https://huggingface.co/unc-nlp/lxmert-base-uncased/resolve/main/config.json",
}
class __magic_name__ (... | 1 | 0 |
"""simple docstring"""
import numpy as np
def UpperCAmelCase ( _lowercase : np.ndarray ) -> np.ndarray:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
def UpperCAmelCase ( _lowercase : np.ndarray ) -> np.ndarray:
"""simple docstr... | 552 |
"""simple docstring"""
from typing import Optional
import numpy as np
import torch
from torch import nn
from transformers import GPTaConfig, GPTaLMHeadModel
from transformers.modeling_utils import ModuleUtilsMixin
from ...configuration_utils import ConfigMixin, register_to_config
from ...models ... | 552 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
lowerCamelCase : str =logging.get_logg... | 237 | """simple docstring"""
from __future__ import annotations
from random import random
from typing import Generic, TypeVar
lowerCamelCase : Tuple =TypeVar('''KT''')
lowerCamelCase : Dict =TypeVar('''VT''')
class __snake_case( Generic[KT, VT] ):
... | 237 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
from transformers import NezhaConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...generation.test_u... | 571 |
"""simple docstring"""
def UpperCAmelCase ( A : int , A : int ):
'''simple docstring'''
return int((input_a, input_a).count(1 ) != 0 )
def UpperCAmelCase ( ):
'''simple docstring'''
assert or_gate(0 , 0 ... | 573 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ..utils import _LazyModule
a_ = {
"config": [
"EXTERNAL_DATA_FORMAT_SIZE_LIMIT",
"OnnxConfig",
"OnnxConfigWithPast",
"OnnxSeq2SeqConfigWithPast",
"PatchingSpec",
],
... | 718 |
"""simple docstring"""
import numpy as np
def a__ ( __lowercase , __lowercase ) -> np.ndarray:
return np.where(vector > 0 , __lowercase , (alpha * (np.exp(__lowercase ) - 1)) )
if __name__ == "__main__":
import doctest
doctest.testmod() | 621 | 0 |
'''simple docstring'''
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
... | 211 |
'''simple docstring'''
import argparse
import requests
import torch
from PIL import Image
from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel
def __A ( _SCREAMING_SNAKE_CASE : Tuple ):
"""simple docstring"""
if "img_enco... | 211 | 1 |
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def _lowerCamelCase ( snake_case , snake_case , snake_case , snake_case ):
_lowerCAmelCase = {
'en': 'Machine learning is great, isn\'t it?',
'ru': 'Машинное обучение - это ... | 225 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase: Any = {
'''configuration_roberta''': ['''ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''... | 225 | 1 |
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 lowercase ( A__ ):
'''simple docstring'''
... | 254 |
from ..utils import DummyObject, requires_backends
class lowercase ( metaclass=A__ ):
'''simple docstring'''
__SCREAMING_SNAKE_CASE = ["""torch""", """scipy"""]
def __init__( self , *_snake_case , **_snake_case )... | 254 | 1 |
from __future__ import annotations
lowerCamelCase_ = {
'''A''': ['''B''', '''C''', '''E'''],
'''B''': ['''A''', '''D''', '''E'''],
'''C''': ['''A''', '''F''', '''G'''],
'''D''': ['''B'''],
'''E''': ['''A''', '''B''', '''D'''],
'''F''': ['''C'''],
'''G''': ['''C'''],
}
... | 701 |
def UpperCamelCase( lowercase_ = 200 ) -> int:
'''simple docstring'''
snake_case_ = [1, 2, 5, 10, 20, 50, 100, 200]
snake_case_ = [0] * (pence + 1)
snake_case_ = 1 # base case: 1 way to make 0 pence
for coin in coins:
for i in range(lowercase_ , pe... | 161 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_UpperCAmelCase : Any = {
'''configuration_clap''': [
'''CLAP_PRETRAINED_MODEL_ARCHIVE_LIST''',
'''ClapAudioConfig''',
'''ClapConfig''',
... | 72 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ = logging.get_logger(__name__)
UpperCamelCase_ = {
'naver-clova-ix/donut-base': 'https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json',
# See all Don... | 625 | 0 |
import re
from typing import Callable, List, Optional, Union
import tensorflow as tf
try:
from tensorflow.keras.optimizers.legacy import Adam
except ImportError:
from tensorflow.keras.optimizers import Adam
class __lowercase ( tf.keras.optimizers.schedules.LearningRateSchedule ):
... | 591 | # This model implementation is heavily inspired by https://github.com/haofanwang/ControlNet-for-Diffusers/
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers imp... | 591 | 1 |
'''simple docstring'''
import argparse
from typing import List
import evaluate
import numpy as np
import torch
from datasets import DatasetDict, load_dataset
# New Code #
# We'll be using StratifiedKFold for this example
from sklearn.model_selection import StratifiedKFold
from torch.optim import AdamW
from torch.u... | 119 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCAmelCase = {
'''configuration_clipseg''': [
'''CLIPSEG_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''CLIPSegConfig... | 90 | 0 |
import warnings
warnings.warn(
'memory_utils has been reorganized to utils.memory. Import `find_executable_batchsize` from the main `__init__`: '
'`from accelerate import find_executable_batch_size` to avoid this warning.',
FutureWarning,
)
| 719 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_p... | 515 | 0 |
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _UpperCAmelCase ( unittest.TestCase ):
"""simple docstring"""
def lowercase ( self ... | 53 |
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... | 53 | 1 |
"""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, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils... | 721 |
"""simple docstring"""
# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional
import numpy as np
import pyarrow as pa
from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .forma... | 16 | 0 |
import torch
from diffusers import DDIMParallelScheduler
from .test_schedulers import SchedulerCommonTest
class lowerCAmelCase_ ( __lowercase ):
UpperCAmelCase = (DDIMParallelScheduler,)
UpperCAmelCase = (("eta", 0.0), ("num_inference_steps", 50))
def Upp... | 10 | import random
import torch
from huggingface_hub import HfApi
from diffusers import UNetaDModel
_lowerCAmelCase = HfApi()
_lowerCAmelCase = {}
# fmt: off
_lowerCAmelCase = torch.tensor([
-0.7515, -1.6883, 0.2420, 0.0300, 0.6347, 1.3433, -1.1743, -3.7467,
1.2342, -2.2485, 0.4636, 0.8076,... | 10 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
UpperCamelCase_ : Optional[int] = logging.get_logger(__name__)
UpperCamelCase_ : List[Any] = {
"""studio-ousia/luke-base""": """https://huggingface.co/studio-ousia/luke-base/resolve/main/config.json"""... | 717 |
"""simple docstring"""
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_u... | 497 | 0 |
import math
def __SCREAMING_SNAKE_CASE ( lowercase_ ) -> bool:
'''simple docstring'''
__UpperCAmelCase : str = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(lowercase_ )
def __SCREAMIN... | 462 |
import unittest
from knapsack import greedy_knapsack as kp
class lowerCamelCase ( unittest.TestCase ):
def A( self):
__UpperCAmelCase : Optional[Any] = [1_0, 2_0, 3_0, 4_0, 5_0, 6_0]
__UpperCAmelCase : str = [2, 4, 6, 8, 1_0, 1_2]
__UpperCAmelCase ... | 462 | 1 |
import tempfile
import unittest
from transformers import TaConfig, is_torch_available
from transformers.testing_utils import (
require_sentencepiece,
require_tokenizers,
require_torch,
slow,
torch_device,
)
from ...generation.test_utils import GenerationTesterMixin
from ...test_modeli... | 712 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
__a = {"""tokenization_herbert""": ["""HerbertTokenizer"""]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotA... | 627 | 0 |
"""simple docstring"""
def lowerCamelCase_( _lowerCamelCase , _lowerCamelCase ) -> float:
'''simple docstring'''
return price * (1 + tax_rate)
if __name__ == "__main__":
print(f'''{price_plus_tax(100, 0.25) = }''')
print(f'''{price_plus_tax(125.50, 0.05) = }''') | 46 |
"""simple docstring"""
from __future__ import annotations
from typing import Any
class snake_case :
def __init__( self :Optional[Any] , _lowerCamelCase :int ):
__SCREAMING_SNAKE_CASE : int = num_of_nodes
__SCREAMING_SNAKE_CASE : list[lis... | 674 | 0 |
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor... | 440 |
def _lowerCAmelCase ( _a : int , _a : bool = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_... | 440 | 1 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pi... | 615 |
import logging
from transformers.configuration_utils import PretrainedConfig
__magic_name__ : Tuple = logging.getLogger(__name__)
class SCREAMING_SNAKE_CASE__ (_a ):
lowercase_ : Tuple = "masked_bert"
def __init__( self : List[Any] , _... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
UpperCAmelCase__ : int ={"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
ex... | 700 |
from __future__ import annotations
UpperCAmelCase__ : str =tuple[int, int, int]
UpperCAmelCase__ : str =tuple[str, str, str]
# used alphabet --------------------------
# from string.ascii_uppercase
UpperCAmelCase__ : int ='''ABCDEFGHIJKLMNOPQRSTUVWXYZ'''
# --------... | 269 | 0 |
"""simple docstring"""
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 = logging.get_logger(__name__)
snake_case ... | 103 |
class UpperCamelCase_ :
'''simple docstring'''
def __init__( self , a ) -> Tuple:
snake_case_ = n
snake_case_ = [None] * self.n
snake_case_ = 0 # index of the first element
snake_case_ = 0
... | 198 | 0 |
"""simple docstring"""
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPImageProcessor, CLIPVisionConfig, CLIPVisionModel
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEImgaImgPipeline
from diffusers.pipelines.shap_e import ShapERe... | 700 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A = logging.get_logger(__name__)
_A = {
'caidas/swin2sr-classicalsr-x2-64': (
'https://huggingface.co/caidas/swin2sr-classicalsr-x2-64/resolve/main/config.json'
),
}
... | 133 | 0 |
import math
def a_ ( __lowercase : 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 even numbers, all multiples of 3 are not primes
return False
# All prim... | 686 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_lowerCamelCase : Dict = logging.get_logger(__name__)
_lowerCamelCase : Union[str, Any] = {
'''caidas/swin2sr-classicalsr-x2-64''': (
'''https://huggingface.co/caidas/swin2sr-classica... | 686 | 1 |
'''simple docstring'''
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
WavaVecaConfig,
WavaVecaCTCTokenizer,
WavaVecaFeatureExtractor,
WavaVecaForCTC,
WavaVecaForPreTraining,
WavaVecaProcessor,
logging,... | 574 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModelWithProjection,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import (
Diffus... | 574 | 1 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
if is_tf_available():
import numpy as np
import tensorflow as tf
from transformers import TFCamembertModel
... | 31 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import FunnelConfig, is_tf_available
from transformers.testing_utils import require_tf
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, random_... | 640 | 0 |
from copy import deepcopy
import torch
import torch.nn.functional as F
from torch.optim import AdamW
from torch.optim.lr_scheduler import LambdaLR
from torch.utils.data import DataLoader
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState
from accelerate.test_utils import Regres... | 3 |
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 A ( lowercase , lowe... | 3 | 1 |
'''simple docstring'''
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFGPTaLMHeadModel, is_keras_nlp_available, is_tf_available
from transformers.models.gpta.tokenization_gpta import GPTaTokenizer
from transformers.testing_utils import require_k... | 405 |
from datetime import datetime
import requests
def _UpperCamelCase ( lowercase__ ):
__SCREAMING_SNAKE_CASE : Optional[int] = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
__SCREAMING_SNAKE_CASE : Tuple = requests.get(base_url + url ).js... | 696 | 0 |
'''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
UpperCamelCase__ : Dict = logging.get_logger(__name__)
class _UpperCamelCase ( ... | 710 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ : List[Any] = {
'''configuration_time_series_transformer''': [
'''TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''',... | 178 | 0 |
'''simple docstring'''
# 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
#
... | 378 |
'''simple docstring'''
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("""Googling.....""")
snake_case = """https://www.google.com/search?q=""" + """ """.join(sys.argv[1:])
snake_case =... | 378 | 1 |
"""simple docstring"""
import unittest
from transformers import JukeboxTokenizer
from transformers.testing_utils import require_torch
class a ( unittest.TestCase ):
UpperCamelCase : List[str] = JukeboxTokenizer
UpperCamelCase : Union[str, Any] = {
... | 36 |
"""simple docstring"""
def __magic_name__ ( lowercase , lowercase ):
return int((input_a, input_a).count(0 ) == 0 )
def __magic_name__ ( ):
assert and_gate(0 , 0 ) == 0
assert and_gate(0 , 1 ) == 0
assert and_gate(1 , 0 ) ==... | 36 | 1 |
"""simple docstring"""
import logging
import os
import sys
import warnings
from dataclasses import dataclass, field
from random import randint
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import DatasetDict, load_dataset
import transformers
from transformers imp... | 163 |
"""simple docstring"""
def __UpperCamelCase ( SCREAMING_SNAKE_CASE ) -> str:
"""simple docstring"""
if not all(char in "01" for char in bin_string ):
raise ValueError("Non-binary value was passed to the function" )
if not bin_string:
raise ... | 163 | 1 |
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__)
_lowerCamelCase = {
... | 707 |
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
_lowerCamelCase = logging.get_logger(__name__)
class _SCREAMING_SNAKE_CASE (UpperCamelCase ):
def __init__( self : int , *UpperCamelCase : Optional[int]... | 447 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
__A = {
"configuration_xlm": ["XLM_PRETRAINED_CONFIG_ARCHIVE_MAP", "XLMConfig", "XLMOnnxConfig"],
"tokenization_xlm": ["XLMTokenizer"],
}
try:
if not is_torch_... | 68 |
'''simple docstring'''
import numpy
class _UpperCamelCase :
'''simple docstring'''
def __init__( self , _a , _a ):
"""simple docstring"""
a__ = input_array
# Random initial weights are assigned where ... | 394 | 0 |
"""simple docstring"""
from collections import deque
class A__ :
'''simple docstring'''
def __init__( self: Optional[int] , _SCREAMING_SNAKE_CASE: str , _SCREAMING_SNAKE_CASE: int , _SCREAMING_SNAKE_CASE: int) -> None:
"""simple docstring"... | 615 |
"""simple docstring"""
import pyarrow.parquet as pq
import pytest
from datasets import Audio, Dataset, DatasetDict, Features, NamedSplit, Sequence, Value, config
from datasets.features.image import Image
from datasets.io.parquet import ParquetDatasetReader, ParquetDatasetWriter, get_writer_ba... | 615 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
_UpperCAmelCase : List[str] = {"""configuration_yolos""": ["""YOLOS_PRETRAINED_CONFIG_ARCHIVE_MAP""", """YolosConfig""", """YolosOnnxConfig"""]}
try:
if n... | 362 |
"""simple docstring"""
def lowercase__(A ) ->bool:
"""simple docstring"""
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 218 | 0 |
'''simple docstring'''
import requests
from bsa import BeautifulSoup
def SCREAMING_SNAKE_CASE( UpperCamelCase = "https://www.worldometers.info/coronavirus" ) -> dict:
UpperCAmelCase_ : List[str] = BeautifulSoup(requests.get(lowerCamelCase_ ).text ,'... | 706 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
"tanreinama/GPTSAN-2.8B-spout_is_uniform": (
"https://huggingface.co/tanreinama/GPTSAN-2.8B-spout... | 471 | 0 |
def lowerCamelCase__ ( lowercase , lowercase ):
"""simple docstring"""
return int(input_a == input_a == 0 )
def lowerCamelCase__ ( ):
"""simple docstring"""
print("Truth Table of NOR Gate:" )
print("| Input 1 | Input 2 | Output |" )
print(F'''| ... | 62 |
'''simple docstring'''
def A__ ( ):
return [list(range(1000 - i , -1000 - i , -1 ) ) for i in range(1000 )]
UpperCamelCase : Dict = generate_large_matrix()
UpperCamelCase : Any = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -... | 50 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase__ : List[str] = {
"""configuration_timesformer""": ["""TIMESFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """TimesformerConfig"""],
}
try:
if not is_tor... | 710 |
from scipy.stats import spearmanr
import datasets
lowerCamelCase__ : Union[str, Any] = """
The Spearman rank-order correlation coefficient is a measure of the
relationship between two datasets. Like other correlation coefficients,
this one varies between -1 and +1 with 0 implying no correlation... | 495 | 0 |
import unittest
from .lib import (
Matrix,
Vector,
axpy,
square_zero_matrix,
unit_basis_vector,
zero_vector,
)
class _UpperCAmelCase ( unittest.TestCase ):
'''simple docstring'''
def __UpperCAmelCase ( self : Any) -> List[str]:
"""simple docstring""... | 547 |
'''simple docstring'''
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def __lowerCamelCase ( SCREAMING_SNAKE_CASE_ : List[Any] ) -> Dict:
"""simple docstring"""
SCRE... | 421 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowerCamelCase = {
'configuration_whisper': ['WHISPER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'Whi... | 59 |
import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutpu... | 59 | 1 |
from __future__ import annotations
def SCREAMING_SNAKE_CASE__ ( _lowercase : float , _lowercase : float , _lowercase : float ) -> Optional[Any]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise Value... | 266 |
def lowerCamelCase__ ( __A :int ,__A :float ,__A :float ):
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def lowerCamelCase__ ( __A :float ,__A :float ,__A :float ):
"""simple docstring"""
... | 268 | 0 |
"""simple docstring"""
import os
import re
import warnings
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_ta ... | 558 | """simple docstring"""
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from ac... | 558 | 1 |
'''simple docstring'''
from ..utils import (
OptionalDependencyNotAvailable,
is_flax_available,
is_scipy_available,
is_torch_available,
is_torchsde_available,
)
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
... | 199 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
SCREAMING_SNAKE_CASE = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyN... | 199 | 1 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {
'''roberta-base''':... | 706 |
import argparse
from transformers import CLIPImageProcessor, CLIPVisionModelWithProjection
from diffusers import UnCLIPImageVariationPipeline, UnCLIPPipeline
if __name__ == "__main__":
_snake_case = argparse.ArgumentParser()
parser.add_argument('''--dump_path''', default=None, type=str, r... | 231 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import flax
import flax.linen as nn
import jax
import jax.numpy as jnp
from flax.core.frozen_dict import FrozenDict
from ..configuration_utils import ConfigMixin, flax_register_to_config
from ..utils import BaseOutput
from .embeddings_flax i... | 501 |
'''simple docstring'''
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
__lowerCamelCase : str = WebClient(token=os.environ["""CI_SLACK_BOT_TOKEN"""])
def ... | 501 | 1 |
'''simple docstring'''
from __future__ import annotations
def _lowercase ( lowerCamelCase__ : list[int], lowerCamelCase__ : int ):
_a = 0
_a = len(lowerCamelCase__ ) - 1
while i < j:
if nums[i] + nums[j] == target:
return [i, j]
... | 691 |
'''simple docstring'''
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 impor... | 691 | 1 |
'''simple docstring'''
from ..utils import DummyObject, requires_backends
class lowerCAmelCase_ ( metaclass=__magic_name__ ):
__lowerCamelCase : int = ["torch"]
def __init__( self , *_lowerCAmelCase , **_lowerCAmelCase ) -> Union[str, Any]:
requires_ba... | 18 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effecti... | 330 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
__lowercase :Optional[int] = logging.get_logger(__name__)
class _a ( lowercase__ ):
"""simple docstring"""
def __init__( self : int , *a :... | 708 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
__lowercase :Tuple = "\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yongh... | 26 | 0 |
'''simple docstring'''
# 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
#
... | 48 |
from math import factorial, radians
def lowercase_ ( __snake_case : float , __snake_case : int = 18 , __snake_case : int = 10 ) -> float:
'''simple docstring'''
snake_case__ :Optional[int] = angle_in_degrees - ((a... | 241 | 0 |
'''simple docstring'''
def SCREAMING_SNAKE_CASE__ ( __A ) -> str:
return "".join([hex(a_ )[2:].zfill(2 ).upper() for byte in list(a_ )] )
def SCREAMING_SNAKE_CASE__ ( __A ) -> bytes:
if (len(a_ ) % 2) != 0:
raise ValueError(
'Base16 encoded data... | 718 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowercase : int = {
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"Mask2FormerCo... | 542 | 0 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import doctest
import sys
import warnings
from os.path import abspath, dirname, join
import _pytest
from transformers.testing_utils import HfDoctestModule, HfDocTestParser
# allow having mul... | 33 |
"""simple docstring"""
from collections import namedtuple
UpperCAmelCase = namedtuple("""from_to""", """from_ to""")
UpperCAmelCase = {
"""cubicmeter""": from_to(1, 1),
"""litre""": from_to(0.001, 1_0_0_0),
"""kilolitre""": from_to(1, 1),
"""gallon""": from_to(0.00_454, 264.... | 535 | 0 |
"""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
snake_case = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
impor... | 716 |
"""simple docstring"""
from __future__ import annotations
def snake_case ( lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ , lowerCAmelCase_ ) -> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[in... | 404 | 0 |
'''simple docstring'''
from __future__ import annotations
import unittest
from transformers import DistilBertConfig, 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_ten... | 51 |
"""simple docstring"""
import string
from math import logaa
def A ( _A, _A ):
"""simple docstring"""
snake_case_ :Union[str, Any] = document.translate(
str.maketrans("", "", string.punctuation ) ).replace("\n", "" )
snake_case_ :Tuple ... | 584 | 0 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_ava... | 309 |
"""simple docstring"""
import qiskit
def A__ ( __lowerCamelCase, __lowerCamelCase ):
"""simple docstring"""
_lowerCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_lowerCAmelCase = qiskit.QuantumCirc... | 309 | 1 |
import argparse
import shutil
from pathlib import Path
from tqdm import tqdm
from transformers import AutoTokenizer
def a__ ( A__, A__, A__, A__=1_0_2_4 ):
SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : List[Any] = [], []
SCREAMING_SN... | 101 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase__ : Union[str, Any] ={
'configuration_blip_2': [
'BLIP_2_PRETRAINED_CONFIG_ARCHIVE_MAP',
'Blip2Config',
'Blip2QFormerConfig',
... | 101 | 1 |
'''simple docstring'''
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import evaluate
import numpy as np
from datasets import load_dataset
import transformers
from transformers import (
AutoConfig,
AutoModelFo... | 644 |
'''simple docstring'''
def lowerCamelCase_ ( SCREAMING_SNAKE_CASE__ : int ) -> str:
if number > 0:
raise ValueError('''input must be a negative integer''' )
UpperCAmelCase_ : Union[str, Any] = len(bin(SCREAMING_SNAKE_CASE__ )[3:] )
... | 644 | 1 |
import argparse
import torch
from transformers import BertConfig, BertForPreTraining, load_tf_weights_in_bert
from transformers.utils import logging
logging.set_verbosity_info()
def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase ):
# Initialise PyTorch... | 671 |
from __future__ import annotations
from math import pi
from typing import Protocol
import matplotlib.pyplot as plt
import numpy as np
class __lowercase ( UpperCAmelCase_ ):
"""simple docstring"""
def _SCREAMING_SNAKE_CASE ( self : List[Any] , lowerCAmelCase__ ... | 671 | 1 |
import argparse
import os
import torch
from transformers.utils import WEIGHTS_NAME
a = ["""small""", """medium""", """large"""]
a = """lm_head.decoder.weight"""
a = """lm_head.weight"""
def UpperCamelCase_( __magic_name__ ... | 382 |
from math import sqrt
def UpperCamelCase_( __magic_name__ : int = 1000000 ):
"""simple docstring"""
_lowerCAmelCase :int = 0
_lowerCAmelCase :int = 0
_lowerCAmelCase :int
while num_cuboids <= limit:
max_cu... | 382 | 1 |
def UpperCAmelCase_ ( UpperCAmelCase__ , UpperCAmelCase__ ):
def get_matched_characters(UpperCAmelCase__ , UpperCAmelCase__ ) -> str:
lowercase_ = []
lowercase_ = min(len(_stra ) , len(_stra ) ) // 2
for i, l in enumerate(_stra ):
... | 412 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import c... | 412 | 1 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_snake_case : int = logging.get_logger(__name__)
_snake_case : Optional[Any] = {}
class _UpperCAmelCase ( _UpperCamelCase ):
"""simple do... | 712 |
def a_ ( lowerCAmelCase_ : str, lowerCAmelCase_ : str ):
if not (isinstance(lowerCAmelCase_, lowerCAmelCase_ ) and isinstance(lowerCAmelCase_, lowerCAmelCase_ )):
raise ValueError('longest_common_substring() takes two strings for inputs' )
__lowerCAmelCase ... | 421 | 0 |
"""simple docstring"""
from jiwer import compute_measures
import datasets
UpperCAmelCase = """\
@inproceedings{inproceedings,
author = {Morris, Andrew and Maier, Viktoria and Green, Phil},
year = {2004},
month = {01},
pages = {},
title = {From WER and RIL to MER and WIL: impro... | 88 |
'''simple docstring'''
snake_case_ = {}
def __lowercase (_SCREAMING_SNAKE_CASE :int , _SCREAMING_SNAKE_CASE :int , _SCREAMING_SNAKE_CASE :int ):
# if we are absent twice, or late 3 consecutive days,
# no further prize strings are possible
if late == 3 or ab... | 507 | 0 |
'''simple docstring'''
from __future__ import annotations
import math
def UpperCAmelCase__ ( UpperCAmelCase__ ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Ne... | 667 |
'''simple docstring'''
__lowerCamelCase = range(2, 20 + 1)
__lowerCamelCase = [10**k for k in range(ks[-1] + 1)]
__lowerCamelCase = {}
def UpperCAmelCase__ ( UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__, UpperCAmelCase__ ) -> Tuple... | 667 | 1 |
"""simple docstring"""
def _snake_case ( _snake_case : list ) -> list:
'''simple docstring'''
_A = False
while is_sorted is False: # Until all the indices are traversed keep looping
_A = True
for i in range(0 , len(_snake_ca... | 7 |
'''simple docstring'''
import unittest
from transformers import BarthezTokenizer, BarthezTokenizerFast, BatchEncoding
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
@req... | 24 | 0 |
'''simple docstring'''
import gc
import unittest
import numpy as np
import torch
from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu
enable_full_determinism()
@slow
... | 705 |
'''simple docstring'''
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2... | 418 | 0 |
def lowerCamelCase__ (__lowerCamelCase ):
if p < 2:
raise ValueError("p should not be less than 2!" )
elif p == 2:
return True
_SCREAMING_SNAKE_CASE : int = 4
_SCREAMING_SNAKE_CASE : str = (1 << p) - 1
f... | 249 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def lowerCamelCase__ (__lowerCamelCase, __lowerCamelCase, __lowerCamelCase ):
_SCREAMING_SNAKE_CASE : Tuple = AutoConfig.from_pretrained(__lowerC... | 249 | 1 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@... | 294 |
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import datasets
import numpy as np
import tensorflow as tf
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoMod... | 294 | 1 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils ... | 33 |
from typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase = '\\n@misc{wu2016googles,\n title={Google\'s Neural Machine Translation System: Bridging the Gap between Human and Machine Translation},\n author={Yonghui ... | 43 | 0 |
def _lowerCAmelCase ( _a : str , _a : str ) -> str:
lowerCAmelCase_ : int = len(_a )
lowerCAmelCase_ : int = len(_a )
lowerCAmelCase_ : int = (
first_str_length if first_str_length > second_str_length else second_s... | 440 |
def _lowerCAmelCase ( _a : int , _a : bool = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3_31_70_44_06_46_79_... | 440 | 1 |
'''simple docstring'''
from __future__ import annotations
def a_ ( _lowerCAmelCase ) -> Any:
__lowerCamelCase : str = 2
__lowerCamelCase : Optional[int] = []
while i * i <= n:
if n % i:
i += 1
else:
n //= i
factors.a... | 459 |
import argparse
import os
import pickle
import sys
import torch
from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl
from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils
from transformers.models.transfo_xl.tokenization_transfo_xl ... | 615 | 0 |
import argparse
import json
from tqdm import tqdm
def _snake_case ():
UpperCamelCase_ = argparse.ArgumentParser()
# Required parameters
parser.add_argument(
'--src_path' , type=__lowercase , default='biencoder-nq-dev.json' , help='Path to raw DPR... | 712 |
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 ..... | 618 | 0 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __magic_name__ ( __a : str = "" ):
'''simple docstring'''
UpperCamelCase__ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
UpperCamelCase__ ... | 513 |
import warnings
from ...utils import logging
from .image_processing_donut import DonutImageProcessor
lowerCamelCase_ = logging.get_logger(__name__)
class __A( __lowerCamelCase ):
"""simple docstring"""
def __init__(self , *SCREAMING_SNAKE_CASE_ , **SCREAMING_SNAKE_... | 513 | 1 |
"""simple docstring"""
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 imp... | 133 |
"""simple docstring"""
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
... | 133 | 1 |
import warnings
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 UpperCamelCase_ ( UpperCAmelCase_... | 87 |
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_dev... | 23 | 0 |
import contextlib
import importlib
import io
import unittest
import transformers
# Try to import everything from transformers to ensure every object can be loaded.
from transformers import * # noqa F406
from transformers.testing_utils import DUMMY_UNKNOWN_IDENTIFIER, require_flax, require_tf, require_torch
... | 704 | """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 _UpperCAmelCase :
"""simple docstring"""
_... | 558 | 0 |
import argparse
import json
import os
import fairseq
import torch
from fairseq.data import Dictionary
from transformers import (
UniSpeechConfig,
UniSpeechForCTC,
UniSpeechForPreTraining,
WavaVecaFeatureExtractor,
WavaVecaPhonemeCTCTokenizer,
WavaVecaProcessor,
logging,
)
logging.set_... | 419 |
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 : Optional[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_... | 419 | 1 |
import argparse
import tensorflow as tf
import torch
from transformers import BertConfig, BertForMaskedLM
from transformers.models.bert.modeling_bert import (
BertIntermediate,
BertLayer,
BertOutput,
BertPooler,
BertSelfAttention,
BertSelfOutput,
)
from transformers.utils import logging
l... | 682 |
from __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor... | 682 | 1 |
# 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
SCREAMING_SNAKE_CASE__ : List[str] = Path(__file__).resolve().parents[3] / """src"""
sys.path.insert(1, str(git_repo_path))
import dataclas... | 112 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
SCREAMING_SNAKE_CASE__ : Tuple = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE__ ... | 112 | 1 |
"""simple docstring"""
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( SCREAMING_SNAKE_CASE__ ):
"""simple docstring"""
_lowe... | 21 |
"""simple docstring"""
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
__lowerCAmelCase : Optional[int] = logging.get_logger(__name__)
def __lowerCAmelCase ( __UpperCamelCas... | 21 | 1 |
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