code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
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.t... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
from ..utils import DummyObject, requires_backends
class a ( metaclass=lowercase__ ):
"""simple docstring"""
a : List[str] = ['keras_nlp']
def __init__( self : Optional[int] , *__lowercase : List[str] , **__lowercase... | 63 |
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
@require_sentencepi... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : float , __lowerCamelCase : int ):
if digit_amount > 0:
return round(number - int(__lowerCamelCase ) , __lowerCamelCase )
return number - int(__lowerCamelCase )
if __name__ == "__main__":
... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
from google.protobuf import descriptor as _descriptor
from google.protobuf import descriptor_pool as _descriptor_pool
from google.protobuf import symbol_database as _symbol_database
from google.protobuf.internal import builder as _builder
# @@protoc_insertion_point(imports)
a : Dict ... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
from . import (
albert,
align,
altclip,
audio_spectrogram_transformer,
auto,
autoformer,
bark,
bart,
barthez,
bartpho,
beit,
bert,
bert_generation,
bert_japanese,
bertweet,
big_bird,
bigbird_pegasus,
biogpt,
bit... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
if partitions <= 0:
raise ValueError("""partitions must be a positive number!""" )
if partitions > number_of_bytes:
raise ValueEr... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Iterator
from dataclasses import dataclass
@dataclass
class a :
"""simple docstring"""
a : int
a : int
class a :
"""s... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import TransformeraDModel, VQDiffusionPipeline, VQDiffusionScheduler, VQModel
from diffusers.pipelines.vq_diffusion.pipeline_vq_diffusion import LearnedC... | 63 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
import inspect
import tempfile
import unittest
from huggingface_hub import hf_hub_download
from transformers import is_torch_available
from transformers.testing_utils import is_flaky, require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
a : int = "\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n"
a : Optional[Any... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
a : Optional[Any] = "ABCDEFGHIJKLMNOPQRSTUVWXYZ"
def lowerCamelCase__ ( ):
__UpperCAmelCase : int = input("""Enter message: """ )
__UpperCAmelCase : Any = input("""Enter key [alphanumeric]: """ )
__UpperCAmelCase : ... | 63 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_avai... | 63 | 1 |
from collections import OrderedDict
from ...utils import logging
from .auto_factory import _BaseAutoModelClass, _LazyAutoMapping, auto_class_update
from .configuration_auto import CONFIG_MAPPING_NAMES
a : str = logging.get_logger(__name__)
a : Union[str, Any] = ... | 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
__UpperCAmelCase : List[str] = [1]
__UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase : Any = 0, 0, 0
__UpperCAmelCase : Any = ugly_nums[ia] * 2
__UpperCAmelCase : O... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 1 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_tf_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_tf_available():
import tensorflow as tf
a : Tuple = logging.g... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
import inspect
import re
from hashlib import shaaaa
from typing import Dict, List
from .arrow import arrow
from .audiofolder import audiofolder
from .csv import csv
from .imagefolder import imagefolder
from .json import json
from .pandas import pandas
from .parquet import parquet
from .sql im... | 63 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : Optional[int] , __lowerCamelCase : str ):
print("""\nThe shortest path matrix using Floyd Warshall algorithm\n""" )
for i in range(__lowerCamelCase ):
for j in range(__lowerCamelCase ):
if dist[i][... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
def lowerCamelCase__ ( ):
__UpperCAmelCase : Optional[Any] = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]
__UpperCAmelCase : str = 6
__UpperCAmelCase : List[str] = 1
__UpperCAmelCase : Tuple = 1901
__UpperCAmelCase : ... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 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
a : Dict = Path(__file__).resolve().parents[3] / "src"
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import... | 63 |
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,
)
a : List[str] = {"configuration_xg... | 63 | 1 |
from typing import List, Optional, Union
import numpy as np
import PIL
import torch
from PIL import Image
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMSch... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
class a :
"""simple docstring"""
def __init__( self : Optional[int] , __lowercase : int ) -> None:
__UpperCAmelCase : Any = size
__UpperCAmelCase : Dict = [0] * size
__UpperCAmelCase : Uni... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
import argparse
import torch
from ...utils import logging
from . import AlbertConfig, AlbertForPreTraining, load_tf_weights_in_albert
logging.set_verbosity_info()
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : Any , __lowerC... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 |
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
@require_sentencepi... | 63 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( lowercase__ ):
"""simple docstring"""
a : Optional[Any] = ['image_processor', 'tokenizer']
a : int ... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def lowerCamelCase__ ( ):
__UpperCAmelCase : int = {
"""repo_name""": ["""test_repo1""", """test_repo2""", """test_r... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : list[int] ): # This function is recursive
__UpperCAmelCase : Optional[Any] = len(__lowerCamelCase )
# If the array contains only one element, we return it (it's the stop ... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
import os
import tempfile
import unittest
from transformers import DistilBertConfig, 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 ModelTeste... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
from collections import deque
def lowerCamelCase__ ( __lowerCamelCase : Dict ):
__UpperCAmelCase : List[Any] = len(__lowerCamelCase )
__UpperCAmelCase : List[Any] = deque()
__UpperCAmelCase : int = [False for _ in range(_... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int = 1000 ):
__UpperCAmelCase : str = 2**power
__UpperCAmelCase : List[Any] = 0
while n:
__UpperCAmelCase , __UpperCAmelCase : List[str] = r + n % 10, n // 10
return r
if __name__... | 63 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
import io
import json
import unittest
from parameterized import parameterized
from transformers import FSMTForConditionalGeneration, FSMTTokenizer
from transformers.testing_utils import get_tests_dir, require_torch, slow, torch_device
from utils import calculate_bleu
a : Any = ... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
import inspect
import unittest
from transformers import RegNetConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_configuration_common import Confi... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
from __future__ import annotations
from typing import Generic, TypeVar
a : List[Any] = TypeVar("T")
class a ( Generic[T] ):
"""simple docstring"""
def __init__( self : str , __lowercase : T ) -> None:
__UpperCA... | 63 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_avai... | 63 | 1 |
from collections import namedtuple
a : List[Any] = namedtuple("from_to", "from_ to")
a : Tuple = {
"cubicmeter": from_to(1, 1),
"litre": from_to(0.0_01, 1_000),
"kilolitre": from_to(1, 1),
"gallon": from_to(0.0_04_54, 2_64.1_72),
"cubicyard": ... | 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : tuple[int, int] , __lowerCamelCase : int ):
__UpperCAmelCase , __UpperCAmelCase : Union[str, Any] = position
__UpperCAmelCase : Tuple = [
(y ... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 1 |
import importlib.metadata
import operator
import re
import sys
from typing import Optional
from packaging import version
a : Optional[int] = {
"<": operator.lt,
"<=": operator.le,
"==": operator.eq,
"!=": operator.ne,
">=": operator.ge,
">": operat... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
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 import FlaxT... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
from typing import Any
def lowerCamelCase__ ( __lowerCamelCase : list , __lowerCamelCase : list , __lowerCamelCase : dict , __lowerCamelCase : dict , __lowerCamelCase : dict , ):
_validation(
__lowerCamelCase ... | 63 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
from collections import Counter
from pathlib import Path
from typing import Optional, Tuple
import yaml
class a ( yaml.SafeLoader ):
"""simple docstring"""
def UpperCAmelCase ( self : Tuple , __lowercase : List[Any] ) -> Optional[... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 1 |
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, rand... | 63 |
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,
)
a : List[str] = {"configuration_xg... | 63 | 1 |
from __future__ import annotations
from math import pi
# Define the Reduced Planck Constant ℏ (H bar), speed of light C, value of
# Pi and the function
a : List[Any] = 1.0_5_4_5_7_1_8_1_7e-3_4 # unit of ℏ : J * s
a : Any = 3e8 # unit of c : m * s^-1
... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
a : List[str] = {
"configuration_rembert": ["REMBERT_... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
import collections
import gzip
import os
import urllib
import numpy
from tensorflow.python.framework import dtypes, random_seed
from tensorflow.python.platform import gfile
from tensorflow.python.util.deprecation import deprecated
a : Union[str, Any] = collections.namedtuple("_... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
from __future__ import annotations
from typing import TypedDict
class a ( lowercase__ ):
"""simple docstring"""
a : str
a : int
def lowerCamelCase__ ( __lowerCamelCase : str ):
if not isinstance(__lowe... | 63 |
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
@require_sentencepi... | 63 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
a : Dict = logging.get_logger(__name__)
a : Union[str,... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
class a :
"""simple docstring"""
def __init__( self : str , __lowercase : List[str] ) -> List[Any]:
__UpperCAmelCase : Any = val
__UpperCAmelCase : List[Any] = None
__UpperCAmelCase : Opti... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Union[str, Any] = logging.get_logger(__name__)
a : List[Any] = {
"facebook/timesformer": "https://huggingface.co/facebook/timesformer/resolve/main/config.json",
}
class ... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image
from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils impor... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : list[float] ):
if len(__lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
r... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_co... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
from __future__ import annotations
import queue
class a :
"""simple docstring"""
def __init__( self : Union[str, Any] , __lowercase : Optional[int] ) -> Union[str, Any]:
__UpperCAmelCase : Optional[int] = data
__U... | 63 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_avai... | 63 | 1 |
from torch import nn
def lowerCamelCase__ ( __lowerCamelCase : Optional[int] ):
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f"""Uns... | 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int = 10**12 ):
__UpperCAmelCase : Optional[Any] = 1
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : Any = 1
__UpperCAmelCase : int = 1
while numerator <= 2 * min_total - ... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 1 |
from math import cos, sin, sqrt, tau
from audio_filters.iir_filter import IIRFilter
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : float = 1 / sqrt(2 ) ):
__UpperCAmelCase : Optional[... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
import json
import os
import unittest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizer... | 63 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 1 |
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,
)
a : List[str] = {"configuration_xg... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
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, B... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
a : Any = {
"configuration_graphormer": ["GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP", "GraphormerConfig"],
}
try:
if not is... | 63 |
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,
)
a : List[str] = {"configuration_xg... | 63 | 1 |
import logging
import os
import sys
from pathlib import Path
from unittest.mock import patch
from parameterized import parameterized
from run_eval import run_generate
from run_eval_search import run_search
from transformers.testing_utils import CaptureStdout, TestCasePlus, slow
from utils imp... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import BatchEncoding, MarianTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import is_sentencepiece_available, is_tf_available,... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
import qiskit
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int ):
__UpperCAmelCase : Optional[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
__UpperCAme... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...test_conf... | 63 |
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
@require_sentencepi... | 63 | 1 |
import argparse
from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt
if __name__ == "__main__":
a : int = argparse.ArgumentParser()
parser.add_argument(
"--checkpoint_path", default=None, type=str, r... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
import re
import string
from collections import Counter
import sacrebleu
import sacremoses
from packaging import version
import datasets
a : Any = "\n@inproceedings{xu-etal-2016-optimizing,\n title = {Optimizing Statistical Machine Translation for Text Simplification},\n ... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if not isinstance(__lowerCamelCase , __lowerCamelCase ):
__UpperCAmelCase : Tuple = f"""Input value of [number={number}] must be an integer"""
raise TypeError(__lowerCamelCase )
if numb... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
import gc
import unittest
from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy a... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
import argparse
from collections import defaultdict
import yaml
a : Any = "docs/source/en/_toctree.yml"
def lowerCamelCase__ ( __lowerCamelCase : Union[str, Any] ):
__UpperCAmelCase : List[str] = defaultdict(__lowerCamelCase )... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
import tempfile
import unittest
from make_student import create_student_by_copying_alternating_layers
from transformers import AutoConfig
from transformers.file_utils import cached_property
from transformers.testing_utils import require_torch
a : List[Any] = "sshleifer/bart-t... | 63 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:READM... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : int ):
__UpperCAmelCase : int = (num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow
... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_avai... | 63 | 1 |
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
a : List[str] = "scheduler_config.json"
class a ... | 63 |
import contextlib
import os
import sqlitea
import pytest
from datasets import Dataset, Features, Value
from datasets.io.sql import SqlDatasetReader, SqlDatasetWriter
from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases, require_sqlalchemy
def ... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : int = 100 ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = 0
for i in range(1 , n + 1 ):
sum_of_squares += i**2
sum_of_ints += i
return sum_of_ints**2 - sum_of_squar... | 63 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 | 1 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 |
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
a : Dict = logging.get_logger(__name__)
a : Tuple = [
["attention", "attn"],
... | 63 | 1 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 | 1 |
from itertools import product
from cva import COLOR_BGR2GRAY, cvtColor, imread, imshow, waitKey
from numpy import dot, exp, mgrid, pi, ravel, square, uinta, zeros
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : Tuple ):
__UpperCAmelC... | 63 |
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available() and is_transformers_version(">=", "4.25.0")):
raise OptionalDe... | 63 | 1 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 |
import math
from typing import List, Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput
class a ( lowercase__ , lowercase__ ):
"... | 63 | 1 |
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install -e .[dev]' when switc... | 63 |
from argparse import ArgumentParser
from .add_new_model import AddNewModelCommand
from .add_new_model_like import AddNewModelLikeCommand
from .convert import ConvertCommand
from .download import DownloadCommand
from .env import EnvironmentCommand
from .lfs import LfsCommands
from .pt_to_tf import... | 63 | 1 |
import copy
import os
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Dict, Mapping, Optional, Union
if TYPE_CHECKING:
from ...processing_utils import ProcessorMixin
from ...utils import TensorType
from ...configuration_utils import PretrainedConfig
f... | 63 |
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,
)
a : List[str] = {"configuration_xg... | 63 | 1 |
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
... | 63 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
a : str = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailab... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : list ):
if len(__lowerCamelCase ) < 2:
return collection
def circle_sort_util(__lowerCamelCase : list , __lowerCamelCase : int , __lowerCamelCase : int ) -> bool:
__UpperCAmelCase :... | 63 |
def lowerCamelCase__ ( __lowerCamelCase : int ):
if num <= 0:
raise ValueError("""Input must be a positive integer""" )
__UpperCAmelCase : int = [True] * (num + 1)
__UpperCAmelCase : Tuple = 2
while p * p <= num:
if primes[p]:
... | 63 | 1 |
import itertools
import random
import unittest
import numpy as np
from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow
from ...test_sequence_feature_extraction_common import Seque... | 63 |
import copy
import os
from typing import Union
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : str = logging.get_logger(__name__)
a : Union[str, Any] = {
"microsoft/git-base": "https://huggingface.co/microsoft/git-base... | 63 | 1 |
from maths.is_square_free import is_square_free
from maths.prime_factors import prime_factors
def lowerCamelCase__ ( __lowerCamelCase : int ):
__UpperCAmelCase : Optional[Any] = prime_factors(__lowerCamelCase )
if is_square_free(__lowerCamelCase ... | 63 |
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
@require_sentencepi... | 63 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : list[int] , __lowerCamelCase : int ):
__UpperCAmelCase : str = 0
__UpperCAmelCase : Any = len(__lowerCamelCase ) - 1
while i < j:
if nums... | 63 |
from __future__ import annotations
import math
def lowerCamelCase__ ( __lowerCamelCase : int , __lowerCamelCase : int , __lowerCamelCase : bool , __lowerCamelCase : list[int] , __lowerCamelCase : float ):
if depth <... | 63 | 1 |
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(check_device=False):
i... | 63 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
a : Tuple = logging.get_logger(__name__)
a : List[str] = {"openai-gpt": "https://huggingface.co/openai-gpt/resolve/main/config.json"}
class a ( lowercase__ ):
... | 63 | 1 |
from __future__ import annotations
a : Optional[Any] = [True] * 1_000_001
a : Union[str, Any] = 2
while i * i <= 1_000_000:
if seive[i]:
for j in range(i * i, 1_000_001, i):
a : Optional[Any] = False
... | 63 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaInpaintPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : str , __lowerCamelCase : int ):
__UpperCAmelCase : list[list[str]] = [[] for _ in range(__lowerCamelCase )]
__UpperCAmelCase : Optional[Any] = key - 1
if key <= 0:
raise ValueError("""H... | 63 |
import copy
import fnmatch
import json
import os
import pickle as pkl
import shutil
import sys
import tarfile
import tempfile
from collections import OrderedDict
from contextlib import contextmanager
from functools import partial
from hashlib import shaaaa
from io import BytesIO
from pathlib... | 63 | 1 |
from __future__ import annotations
def lowerCamelCase__ ( __lowerCamelCase : str ):
return [ord(__lowerCamelCase ) - 96 for elem in plain]
def lowerCamelCase__ ( __lowerCamelCase : list[int] ):
return "".join(chr(elem + 96 ... | 63 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
... | 63 | 1 |
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_utils import GenerationTesterMixi... | 63 |
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
a : Optional[int] = logging.get_logger(__name__)
class a ( lowercase__ ):
... | 63 | 1 |
def lowerCamelCase__ ( __lowerCamelCase : Tuple , __lowerCamelCase : List[Any] ):
__UpperCAmelCase : List[str] = 0
__UpperCAmelCase : List[str] = len(__lowerCamelCase ) - 1
while left <= right:
# avoid divided by 0 during inter... | 63 |
from math import pi, sqrt
def lowerCamelCase__ ( __lowerCamelCase : float ):
if num <= 0:
raise ValueError("""math domain error""" )
if num > 1_7_1.5:
raise OverflowError("""math range error""" )
elif num - int(__lowerCamelCase ) not in ... | 63 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LDMTextToImagePipeline, UNetaDConditionModel
from diffusers.utils.testing_utils import (
enable_full_determinis... | 63 |
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class a :
"""simple docstring"""
a : int
a : Node | None = ... | 63 | 1 |
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def lowerCamelCase__ ( __lowerCamelCase : Tuple ):
# This defines a "chinese character" as anything in the CJK Unicode ... | 63 |
import json
import os
import unittest
from transformers.models.gptsan_japanese.tokenization_gptsan_japanese import (
VOCAB_FILES_NAMES,
GPTSanJapaneseTokenizer,
)
from transformers.testing_utils import require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixi... | 63 | 1 |
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_im... | 63 |
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu_avai... | 63 | 1 |
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