code stringlengths 82 53.2k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.generation import DisjunctiveConstraint
@require_torch
class __SCREAMING_SNAKE_CASE (unitte... | 692 |
"""simple docstring"""
import math
def UpperCamelCase (SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE ):
return math.pow(SCREAMING_SNAKE_CASE , 2 ) - a
def UpperCamelCase (SCREAMING_SNAKE_CASE ):
return 2 * x
def UpperCamelC... | 102 | 0 |
def A ( __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase , __UpperCAmelCase ) -> List[str]:
'''simple docstring'''
if index == r:
for j in range(__UpperCAmelCase ):
... | 561 |
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
UpperCamelCase_ = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model"... | 561 | 1 |
def a__ ( A_ ):
'''simple docstring'''
if not isinstance(A_, A_ ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divisor for divisor in range(1, input_num // 2 + 1 ) ... | 529 |
import argparse
import collections
import os
import re
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_table.py
__lowerCAmelCase : Dict = 'src/transformer... | 529 | 1 |
'''simple docstring'''
import webbrowser
from sys import argv
from urllib.parse import parse_qs, quote
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
__lowerCamelCase : Optional[int] = "%20".join(argv[1:]) if len(argv) > 1 else quote... | 459 |
'''simple docstring'''
from __future__ import annotations
from collections import deque
from collections.abc import Sequence
from dataclasses import dataclass
from typing import Any
@dataclass
class UpperCAmelCase :
UpperCAmelCase : int
UpperCAmelCase : Node | None = None
UpperCAmelCase... | 459 | 1 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_UpperCamelCase = {
'configuration_la... | 459 |
'''simple docstring'''
import argparse
from collections import defaultdict
import yaml
_UpperCamelCase = 'docs/source/en/_toctree.yml'
def a_ ( _lowerCAmelCase ) -> Any:
__lowerCamelCase : Optional[int] = defaultdict(_lowerCAmelCase )
__lowerCam... | 459 | 1 |
'''simple docstring'''
def UpperCAmelCase_ ( lowercase__ , lowercase__ , lowercase__ , lowercase__ ):
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not alrea... | 41 |
'''simple docstring'''
from __future__ import annotations
def UpperCAmelCase_ ( lowercase__ ):
'''simple docstring'''
a_ =str(lowercase__ )
return len(lowercase__ ) == 9 and set(lowercase__ ) == set("123456789" )... | 41 | 1 |
import datetime
import platform
import subprocess
from typing import Optional, Tuple, Union
import numpy as np
def _UpperCamelCase ( lowercase__ , lowercase__ ):
__SCREAMING_SNAKE_CASE : Tuple = F'''{sampling_rate}'''
__SCREAMING_SNAKE_CASE : str = '''1'''... | 696 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCAmelCase : Dict =logging.get_logger(__name__)
__lowerCAmelCase : List[Any] ={
'google/switch-base-8': 'https://huggingface.co/google/switch-base-8/blob/main/config.json',
}
class _... | 696 | 1 |
'''simple docstring'''
import unittest
from knapsack import knapsack as k
class _UpperCamelCase ( unittest.TestCase ):
def UpperCamelCase__ ( self : List[Any] ):
"""simple docstring"""
__SCREAMING_SNAKE_CASE : List[Any] = 0
__SCREAMING_S... | 715 |
'''simple docstring'''
def lowerCAmelCase_ ( _lowerCamelCase: int ):
if number > 0:
raise ValueError("""input must be a negative integer""" )
__SCREAMING_SNAKE_CASE : str = len(bin(_lowerCamelCase )[3:] )
__SCREAMING_SNAKE_CASE : Any = bin(abs(_lowerCamel... | 178 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
snake_case_ : List[str] = {
'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
'tokenizatio... | 595 |
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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension,
ImageInput,
... | 57 | 0 |
def __UpperCAmelCase ( ) -> Optional[int]:
"""simple docstring"""
_a : Dict = []
_a : List[Any] = 1
while len(__a ) < 1E6:
constant.append(str(__a ) )
i += 1
_a : List[str] = ''''''.join(__a )
... | 578 |
def __UpperCAmelCase ( __a : int = 2_000_000 ) -> int:
"""simple docstring"""
_a : List[str] = [0 for i in range(n + 1 )]
_a : Tuple = 1
_a : Tuple = 1
for i in range(2 ,int(n**0.5 ) + 1 ):
if primality_... | 578 | 1 |
"""simple docstring"""
from __future__ import annotations
import unittest
from transformers import EsmConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor,... | 289 |
"""simple docstring"""
import collections
import importlib.util
import os
import re
from pathlib import Path
_lowerCAmelCase : Dict = "src/transformers"
# Matches is_xxx_available()
_lowerCAmelCase : List[Any] = re.compile(r"is\_([a-z_]*)_available()")
# Catches a one-line _impor... | 289 | 1 |
# This is the module that test_patching.py uses to test patch_submodule()
import os # noqa: this is just for tests
import os as renamed_os # noqa: this is just for tests
from os import path # noqa: this is just for tests
from os import path as renamed_path # noqa: this is just for tests
from os... | 703 |
from pathlib import Path
from typing import List
from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import get_tests_dir, is_tool_test
from transformers.tools.agent_types import AGENT_TYPE_MAPPING, AgentAudio, AgentImage, AgentText
if is_torch_avail... | 205 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
"""bigcode/gpt_bigcode-santacoder""": """https://huggingface.co/bigcode/gpt_bigcode-santacoder/resolve/main/config.json""",
}
class a__ ... | 77 |
"""simple docstring"""
import os
import sys
from contextlib import contextmanager
# Windows only
if os.name == "nt":
import ctypes
import msvcrt # noqa
class lowerCAmelCase ( ctypes.Structure ):
'''simple docstring'''
SCREAMING_SNAKE_CASE_ : A... | 247 | 0 |
'''simple docstring'''
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
... | 245 |
'''simple docstring'''
from collections.abc import Callable
def UpperCamelCase ( a , a , a ) -> float:
'''simple docstring'''
__magic_name__ = a
__magic_name__ = b
if function(a ) == 0: # one of the a or b is a root for the function
... | 245 | 1 |
from __future__ import annotations
import math
def A__ ( __A : 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 ... | 184 |
def A__ ( __A : str , __A : str ) ->str:
if not (isinstance(__A , __A ) and isinstance(__A , __A )):
raise ValueError('''longest_common_substring() takes two strings for inputs''' )
__A =len(__A )
__A =len(__A )
_... | 184 | 1 |
"""simple docstring"""
def UpperCamelCase_ ( lowerCamelCase : Any ) -> List[str]:
"""simple docstring"""
__magic_name__ : Union[str, Any] = len(lowerCamelCase )
__magic_name__ : Dict = sum(lowerCamelCase )
__magic_name__ ... | 147 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Callable
from typing import Generic, TypeVar
A = TypeVar("""T""")
A = TypeVar("""U""")
class _UpperCamelCase ( Generic[T, U] ):
"""simple docstring"""
def __init__( self : Any , snake_case : ... | 147 | 1 |
'''simple docstring'''
def lowerCAmelCase_ ( snake_case_ : Optional[Any]=2_81_23 ) -> str:
'''simple docstring'''
UpperCAmelCase_ = [1] * (limit + 1)
for i in range(2 , int(limit**0.5 ) + 1 ):
sum_divs[i * i] += i
for... | 78 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableDiffusionUpscalePipeline, UNetaDConditionModel
from diffusers.utils import floa... | 439 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
lowerCamelCase = {
"""configuration_roberta_prelayernorm""": [
"""ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_M... | 708 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def SCREAMING_SNAKE_CASE( __UpperCamelCase ) -> Dict:
if "cls_token" in name:
a__ : Union[str, Any] = name.replace("cls_toke... | 207 | 0 |
'''simple docstring'''
import argparse
import json
from pathlib import Path
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformer... | 150 |
'''simple docstring'''
import argparse
from pathlib import Path
from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration
def __lowercase (_lowercase, _lowercase, _lowercase, _lowercase, _lowercase = None, _lowercase = None, _lowercase... | 150 | 1 |
def UpperCAmelCase_ ( __UpperCamelCase, __UpperCamelCase, __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ =(num_of_terms / 2) * (2 * first_term + (num_of_terms - 1) * common_diff)
# formula for sum of series
return total
def UpperCAmelCase_ ( ):
print(sum_of_s... | 588 |
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModel, PreTrainedModel
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
def UpperCAmelCase_ ( __UpperCamelCase, __UpperCamelCase ):
SCREAMING_SNAKE_CASE__ =nn.... | 588 | 1 |
"""simple docstring"""
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
__A = '▁'
__A = {'vocab_file': 'spiece.model'}
__A = {... | 586 |
import os
import posixpath
import uuid
from dataclasses import dataclass
from typing import TYPE_CHECKING, Iterable, List, Optional, Tuple, Union
import numpy as np
import pyarrow as pa
import datasets
from datasets.arrow_writer import ArrowWriter, ParquetWriter
from datasets.config import MAX_SHARD_SIZE
from datas... | 456 | 0 |
def A ( _UpperCAmelCase : List[Any] , _UpperCAmelCase : Tuple ) -> int:
'''simple docstring'''
while a != 0:
_UpperCAmelCase , _UpperCAmelCase = b % a, a
return b
def A ( _UpperCAmelCase : Dict , _U... | 712 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_xlnet impo... | 639 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
__UpperCamelCase : Optional[int] ... | 4 |
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
SCREAMING_SNAKE_CASE : List[Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : List[Any]... | 141 | 0 |
'''simple docstring'''
import inspect
import os
import re
from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# pytho... | 716 |
'''simple docstring'''
import collections
import inspect
import unittest
from transformers import FocalNetConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
f... | 539 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class a_ ( unittest.TestCase ):
'''simple docstring'''
def _lowercase ( self ) -> i... | 318 |
import unittest
import numpy as np
import torch
from diffusers import ScoreSdeVePipeline, ScoreSdeVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class a_ ( unittest.TestC... | 318 | 1 |
import os
import zipfile
import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def __lowerCamelCase ( snake_case__ ,snake_case__=7 ) -> int:
"""simple docstring"""
_SCREAMING_SNAKE_CASE = None
if token is not None:
... | 702 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky... | 569 | 0 |
"""simple docstring"""
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : str , a_ : Tuple )-> List[str]:
"""simple docstring"""
UpperCAmelCase_ : Optional[Any] = val
UpperCAmelCase_ : List[str] = ... | 470 |
"""simple docstring"""
def A_ ( lowercase ) -> None:
"""simple docstring"""
UpperCAmelCase_ : Union[str, Any] = generate_pascal_triangle(lowercase )
for row_idx in range(lowercase ):
# Print left spaces
for _ in range(num_r... | 470 | 1 |
"""simple docstring"""
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 ( UpperCamelCase , UpperCamelCas... | 487 |
"""simple docstring"""
def _UpperCamelCase ( UpperCamelCase , UpperCamelCase = False ) -> bool:
"""simple docstring"""
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
... | 487 | 1 |
'''simple docstring'''
# Usage:
# ./gen-card-allenai-wmt16.py
import os
from pathlib import Path
def UpperCamelCase ( lowercase_ : Tuple , lowercase_ : Union[str, Any] , lowercase_ : Optional[int] , lowercase_ : Dict ) -> Optional[Any]:
'''s... | 72 |
'''simple docstring'''
from __future__ import annotations
import time
import numpy as np
_UpperCAmelCase : int = [8, 5, 9, 7]
_UpperCAmelCase : List[str] = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
_UpperCAmelCase : Union[str, Any] = [
[3, 2, 1, 4... | 72 | 1 |
"""simple docstring"""
import math
class lowerCAmelCase_ :
'''simple docstring'''
def __init__( self , snake_case_=0 ) -> List[Any]: # a graph with Node 0,1,...,N-1
__lowerCAmelCase = n
__lowerCAmelCa... | 718 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
SCREAMING_SNAKE_CASE_ = {'''configuration_unispeech''': ['''UNISPEECH_PRETRAINED_CONFIG_ARCHI... | 573 | 0 |
"""simple docstring"""
from operator import delitem, getitem, setitem
import pytest
from data_structures.hashing.hash_map import HashMap
def snake_case ( A__ ):
return getitem, k
def snake_case ( A__ ,A__ ):
return setitem, k, v
def snake_case ( A__ ):
return delitem... | 95 |
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import PIL
from PIL import Image
from ...utils import (
BaseOutput,
OptionalDependencyNotAvailable,
is_flax_available,
is_k_diffusion_available,
is_k_diffusion_version,
is_o... | 328 | 0 |
"""simple docstring"""
import unittest
from transformers import AlbertConfig, is_torch_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import M... | 118 |
"""simple docstring"""
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 ut... | 118 | 1 |
from ... import PretrainedConfig
lowerCamelCase : List[str] = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class A( __lowercase ):
'''simple docstring'''
UpperCamelCase = NEZHA_PRE... | 70 |
'''simple docstring'''
import doctest
import logging
import os
import unittest
from pathlib import Path
from typing import List, Union
import transformers
from transformers.testing_utils import require_tf, require_torch, slow
__UpperCamelCase = logging.getLogger()
... | 26 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPooli... | 594 | import os
import torch
from ..logging import get_logger
from .constants import FSDP_PYTORCH_VERSION, MODEL_NAME, OPTIMIZER_NAME
from .versions import is_torch_version
if is_torch_version(">=", FSDP_PYTORCH_VERSION):
import torch.distributed.checkpoint as dist_cp
from torch.distributed.checkpoint.default... | 594 | 1 |
'''simple docstring'''
import os
from typing import Dict, List, Tuple, TypeVar, Union
snake_case = TypeVar('''T''')
snake_case = Union[List[T], Tuple[T, ...]]
snake_case = Union[T, List[T], Dict[str, T]]
snake_case = Union[str, bytes, os.PathLike]
| 309 | '''simple docstring'''
import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class SCREAMING_SNAKE_CASE ( __a ):
"""simple docstring"""
__A = ""
__A = (
None # protocol passed in... | 309 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase_ = logging.get_logger(__name__)
lowercase_ = {
'''funnel-transformer/small''': '''https://huggingface.co/funnel-transformer/small/resolve/main/config.json''',
'''funnel-transformer/small-b... | 336 |
from __future__ import annotations
class A__ :
def __init__( self , lowerCamelCase ) -> None:
"""simple docstring"""
__magic_name__ : List[str] = data
__magic_name__ : Node | None = ... | 336 | 1 |
import inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
torch_device,
)
from transformers.utils impo... | 335 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
)
snake_case : Optional[Any] = {
'''configuration_roberta_prelayernorm''': [
'''ROBERTA_PRELAYERNORM_PRETRAINED_CON... | 335 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import List, Optional, Union
import numpy as np
import torch
from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available
@dataclass
class lowerCAmelCase__ ... | 492 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowercase__ = logging.get_logger(__name__)
lowercase__ = {
"""google/realm-cc-news-pretrained-embedder""": (
"""https://huggingface.co/google/realm-cc-news-pretra... | 492 | 1 |
'''simple docstring'''
UpperCAmelCase_ : str = {
'a': 'AAAAA',
'b': 'AAAAB',
'c': 'AAABA',
'd': 'AAABB',
'e': 'AABAA',
'f': 'AABAB',
'g': 'AABBA',
'h': 'AABBB',
'i': 'ABAAA',
'j': 'BBBAA',
'k': 'ABAAB',
'l': 'ABABA',
'm': 'ABABB',
'n': 'ABBAA'... | 533 |
import os
from collections.abc import Iterator
def _SCREAMING_SNAKE_CASE ( snake_case = "." ) -> Iterator[str]:
for dir_path, dir_names, filenames in os.walk(snake_case ):
_UpperCAmelCase = [d for d in dir_names if d != """scripts""" and d[0... | 518 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
if is_sentencepiece_available():
from ..ta.tok... | 718 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin
if is_torch_available():
import... | 13 | 0 |
from typing import Optional
from .. import Features, NamedSplit
from ..packaged_modules.text.text import Text
from ..utils.typing import NestedDataStructureLike, PathLike
from .abc import AbstractDatasetReader
class _SCREAMING_SNAKE_CASE ( __SCREAMING_SNAKE_CASE ):
'''simple docstring'''... | 59 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__lowerCamelCase : Any = logging.get_logger(__name__)
__lowerCamelCase : Optional[int] = {
"studio-ousia/luke-base": "https://huggingface.co/studio-ousia/luke-base/resolve/mai... | 416 | 0 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _UpperCamelCase ( UpperCamelCase__ ):
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCame... | 113 |
'''simple docstring'''
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def _UpperCamelCase ( UpperCamelCase__ ):
# A local function to see if a dot lands in the circle.
def is_in_circle(UpperCame... | 113 | 1 |
"""simple docstring"""
import torch
def A ( ):
"""simple docstring"""
if torch.cuda.is_available():
snake_case_ :List[str] = torch.cuda.device_count()
else:
snake_case_ :str = 0
print(F'''Successfully ran on {num_gpus} GPUs''' ... | 584 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__UpperCAmelCase : Union[str, Any] = logging.get_logger(__name__)
__UpperCA... | 584 | 1 |
'''simple docstring'''
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
... | 204 | '''simple docstring'''
import requests
from bsa import BeautifulSoup
def __A ( UpperCAmelCase ,UpperCAmelCase ) -> str:
'''simple docstring'''
_UpperCamelCase : Dict = BeautifulSoup(requests.get(UpperCAmelCase ,params=UpperCAmelCase ... | 204 | 1 |
"""simple docstring"""
class __lowerCamelCase :
def __init__(self ):
'''simple docstring'''
_lowerCAmelCase = 0
_lowerCAmelCase = 0
_lowerCAmelCase = {}
def A__ (self , lowerCamelCase ):
'... | 156 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging
SCREAMING_SNAKE_CASE : Union[str, Any] = logging.get_logger(__name__)
SCREAMING_SNAKE_CASE : str = {}
class __lowerCamelCase ( __lowercase ):
__UpperCame... | 156 | 1 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_video_inputs
if is_torch_available():
... | 208 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def SCREAMING_SNAKE_CASE ( __lowerCAmelCase , __lowerCAmelCase ) -> str | Literal[False]:
snake_case__ = list(__lowerCAmelCase )
snake_case__ ... | 208 | 1 |
'''simple docstring'''
import importlib
import json
import os
import sys
import tempfile
import unittest
from pathlib import Path
import transformers
import transformers.models.auto
from transformers.models.auto.configuration_auto import CONFIG_MAPPING, AutoConfig
from transform... | 466 |
# Copyright (c) 2021-, NVIDIA CORPORATION. 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 re... | 257 | 0 |
import functools
import logging
import os
import sys
import threading
from logging import (
CRITICAL, # NOQA
DEBUG, # NOQA
ERROR, # NOQA
FATAL, # NOQA
INFO, # NOQA
NOTSET, # NOQA
WARN, # NOQA
WARNING, # NOQA
)
from typing import Optional
import huggingface_hub.utils as hf_hub_... | 516 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import MgpstrTokenizer
from transformers.models.mgp_str.tokenization_mgp_str import VOCAB_FILES_NAMES
from transformers.testing_utils import require_torch, require_vision
from transformers.utils im... | 516 | 1 |
import argparse
import os
import re
a__: Any = 'src/transformers/models/auto'
# re pattern that matches mapping introductions:
# SUPER_MODEL_MAPPING_NAMES = OrderedDict or SUPER_MODEL_MAPPING = OrderedDict
a__: List[str] = re.compile(r'[A-Z_]+_MAPPING(\s+|_[A-Z_]... | 190 |
import math
def UpperCamelCase__( UpperCamelCase__ : int )->list:
A__ = [True] * n
A__ = False
A__ = False
A__ = True
for i in range(3 , int(n**0.5 + 1 ) , 2 ):
A__ = i * 2
wh... | 190 | 1 |
from ..utils import DummyObject, requires_backends
class __lowerCAmelCase ( metaclass=lowerCAmelCase):
_a = ['''torch''', '''scipy''']
def __init__( self: List[str] , *_lowerCAmelCase: Tuple , **_lowerCAmelCase: str ):
r... | 453 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import... | 453 | 1 |
'''simple docstring'''
from collections.abc import Callable
import numpy as np
def __lowercase ( __lowercase , __lowercase , __lowercase , __lowercase , __lowercase ) -> np.array:
'''simple docstring'''
_A = int(np.ceil((x_end - xa) / step_size ) )
... | 330 |
'''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_utils import Generati... | 330 | 1 |
'''simple docstring'''
import qiskit
def lowercase_ ( lowercase__ , lowercase__ ) ->qiskit.result.counts.Counts:
_snake_case: Tuple = qiskit.Aer.get_backend('aer_simulator' )
# Create a Quantum Circuit acting on the q register
_snake_case: List[... | 703 |
'''simple docstring'''
import warnings
from contextlib import contextmanager
from ...processing_utils import ProcessorMixin
class lowerCamelCase ( __UpperCAmelCase ):
_SCREAMING_SNAKE_CASE = "Speech2TextFeatureExtractor"
_SCREAMING_SNAKE_CASE = "Speech2... | 273 | 0 |
"""simple docstring"""
#
# This a `torch.distributed` diagnostics script that checks that all GPUs in the cluster (one or
# many nodes) can talk to each other via nccl and allocate gpu memory.
#
# To run first adjust the number of processes and nodes:
#
# python -m torch.distributed.run --nproc_per_node 2 --nnodes 1 t... | 657 |
"""simple docstring"""
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=lowerCamelCase ):
lowercase_ : Dict = ['''torch''', '''torchsde''']
def __init__( self , *a_ , **a_ ) -> Optional[int]:
requires_backends(self ,... | 657 | 1 |
'''simple docstring'''
import argparse
import gc
import json
import os
import shutil
import warnings
import torch
from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer
try:
from transformers import LlamaTokenizerFast
except ImportError as e:
warnings.warn(e)
warnings.warn(
"T... | 329 |
'''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_available... | 329 | 1 |
import tempfile
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import MaMaaaTokenizer, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_torch,... | 592 |
import math
def lowerCamelCase__ ( snake_case_ : int ) -> bool:
__snake_case = math.loga(math.sqrt(4 * positive_integer + 1 ) / 2 + 1 / 2 )
return exponent == int(snake_case_ )
def lowerCamelCase__ ( snake_case_ : flo... | 592 | 1 |
'''simple docstring'''
import random
import unittest
from torch.utils.data import BatchSampler, DataLoader, IterableDataset
from accelerate import Accelerator
from accelerate.data_loader import (
BatchSamplerShard,
DataLoaderDispatcher,
DataLoaderShard,
IterableDatasetShard,
SkipBatchS... | 216 |
'''simple docstring'''
from collections import deque
def __UpperCAmelCase ( A : int ) -> Optional[Any]:
UpperCAmelCase_ : Tuple = len(A )
UpperCAmelCase_ : Dict = deque()
UpperCAmelCase_ : Optional[Any] = [False for _ in r... | 216 | 1 |
"""simple docstring"""
from typing import Dict, List, Optional, Union
import numpy as np
from .feature_extraction_utils import BatchFeature, FeatureExtractionMixin
from .utils import PaddingStrategy, TensorType, is_tf_tensor, is_torch_tensor, logging, to_numpy
snake_case = log... | 103 |
import torch
from torch import nn
class A__ ( nn.Module ):
'''simple docstring'''
def __init__( self : List[str] , _SCREAMING_SNAKE_CASE : List[Any] , _SCREAMING_SNAKE_CASE : Optional[int] , _SCREAMING_SNAKE_CA... | 280 | 0 |
'''simple docstring'''
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
import torch
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature... | 631 |
'''simple docstring'''
import unittest
import torch
from diffusers import DDIMScheduler, DDPMScheduler, UNetaDModel
from diffusers.training_utils import set_seed
from diffusers.utils.testing_utils import slow
_A : Union[str, Any] =False
class _lowercase ( ... | 631 | 1 |
"""simple docstring"""
import unittest
from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from ...test_tokenization_common import TokenizerTesterMixin
lowerCAmelCase_ = get_tests_dir('fixtu... | 560 |
"""simple docstring"""
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... | 560 | 1 |
'''simple docstring'''
# limitations under the License.
from typing import Optional, Tuple, Union
import torch
from diffusers import DiffusionPipeline, ImagePipelineOutput
class UpperCAmelCase_ ( lowerCamelCase_ ):
"""simple docstring"""
def __init__( self , lowerCamelCase ... | 435 |
'''simple docstring'''
import contextlib
import csv
import json
import os
import sqlitea
import tarfile
import textwrap
import zipfile
import pyarrow as pa
import pyarrow.parquet as pq
import pytest
import datasets
import datasets.config
@pytest.fixture(scope="session")
def A__ ( ):
'''simple doc... | 435 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, Deco... | 304 |
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_herbert import HerbertTokenizer
lowercase_ : Union[str, Any] = logging.get_logger(__name__)
lowercase_ : Dict ... | 304 | 1 |
'''simple docstring'''
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _co... | 717 |
'''simple docstring'''
import argparse
import torch
from torch import nn
from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration
def _lowerCamelCase (__lowerCamelCase : Any ) -> Optional[int]:
a__ = [
"encoder.version",
"deco... | 289 | 0 |
from string import ascii_lowercase, ascii_uppercase
def lowerCAmelCase__ ( a__: str ) -> str:
'''simple docstring'''
if not sentence:
return ""
_UpperCAmelCase = dict(zip(a__ , a__ ) )
return lower_to_upper.get(sente... | 618 |
from typing import Any
def lowerCAmelCase__ ( a__: list , a__: list , a__: dict , a__: dict , a__: dict , ) -> list:
'''simple docstring'''
_validation(
a__ , a__ , a__ , a__ , ... | 618 | 1 |
'''simple docstring'''
from __future__ import annotations
import random
import unittest
from transformers import TransfoXLConfig, is_tf_available
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTes... | 701 |
'''simple docstring'''
import unittest
from transformers.models.xlm_prophetnet.tokenization_xlm_prophetnet import SPIECE_UNDERLINE, XLMProphetNetTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, slow
from transformers.utils import cached_property
from ...test_tokenizat... | 537 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
A = {
'configuration_tapas': ['TAPAS_PRETRAINED_CONFIG_ARCHIVE_MAP', 'TapasConfig'],
'tokenization_tapas': ['TapasT... | 449 |
"""simple docstring"""
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_IDENTI... | 449 | 1 |
import warnings
from typing import List
import numpy as np
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import is_flax_available, is_tf_available, is_torch_available
class A (SCREAMING_SNAKE_CASE ):
'''simple... | 247 |
# 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 r... | 247 | 1 |
from typing import List, Optional, Union
import numpy as np
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging
lowerCamelCase =logging.get_logger(__name__)
class _lowerCam... | 285 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCamelCase ={
"configuration_luke": ["LUKE_PRETRAINED_CONFIG_ARCHIVE_MAP", "LukeConfig"],
"tokenization_luke": ["LukeTokenizer"],
}
try:
if not is_torch_available():
raise ... | 285 | 1 |
'''simple docstring'''
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_ , lowerCamelCase_) -> Optional[int]:
global f # a global dp table for knapsack
if f[i][j] < 0:
if j < wt[i - 1]:
UpperCamelCase__ ... | 6 |
'''simple docstring'''
def __UpperCAmelCase ( lowerCamelCase_ , lowerCamelCase_) -> list[str]:
return [sentence[i : i + ngram_size] for i in range(len(lowerCamelCase_) - ngram_size + 1)]
if __name__ == "__main__":
from doctest import testmod
testmod(... | 6 | 1 |
'''simple docstring'''
import os
import warnings
from typing import List, Optional
from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig
A_ = logging.get_logger(__name__)
class UpperCAmelCase :
'''simple docstring'''
def __init... | 42 |
'''simple docstring'''
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 OptionalDepend... | 5 | 0 |
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available
if is_datasets_available():
import datasets
class _lowerCAmelC... | 713 |
'''simple docstring'''
import argparse
import json
import torch
from diffusers import DDPMScheduler, LDMPipeline, UNetaDModel, VQModel
def UpperCAmelCase ( a_ , a_=1 ) -> str:
"""simple docstring"""
if n_shave_prefix_segments >= 0:
... | 385 | 0 |
'''simple docstring'''
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_visio... | 199 |
import json
import os
from typing import Dict, List, Optional, Tuple
import regex as re
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
__a: Any = logging.get_logger(__name__)
__a: Dict = {
'''vocab_file''': '''vocab.json''',
'''merge... | 108 | 0 |
'''simple docstring'''
from typing import Callable, List, Optional, Tuple, Union
import torch
from transformers import CLIPTextModel, CLIPTokenizer
from ...configuration_utils import ConfigMixin, register_to_config
from ...models import ModelMixin, TransformeraDModel, VQModel
from ...schedulers im... | 680 |
'''simple docstring'''
from math import atan, cos, radians, sin, tan
from .haversine_distance import haversine_distance
a : Any = 6_378_137.0
a : List[Any] = 6_356_752.314_245
a : Dict = 6_378_137
def __UpperCAmelCase ( _UpperCAmelCase : float... | 680 | 1 |
import tensorflow as tf
from ...tf_utils import shape_list
class lowerCamelCase (tf.keras.layers.Layer ):
"""simple docstring"""
def __init__( self : str, _UpperCAmelCase : List[Any], _UpperCAmelCase : List[str], _UpperCAmelCa... | 663 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
_lowerCamelCase : List[str] = 2_0_0
# Number of elements selected in every generation of evolution. The selection takes
# place from best to wors... | 663 | 1 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = os.path.join(args.tf_model_dir , "parameters.json" )
... | 14 |
"""simple docstring"""
from __future__ import annotations
from decimal import Decimal
from numpy import array
def a__ ( lowerCAmelCase__ ):
UpperCAmelCase_ = Decimal
# Check if the provided matrix has 2 rows and 2 columns
# since this implementati... | 14 | 1 |
'''simple docstring'''
import argparse
import json
import os
import torch
from torch import nn
from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
d... | 26 |
'''simple docstring'''
def _a ( _lowerCamelCase = 100 ) -> int:
"""simple docstring"""
__snake_case : Any = n * (n + 1) * (2 * n + 1) / 6
__snake_case : List[Any] = (n * (n + 1) / 2) ** 2
return int(s... | 26 | 1 |
import unittest
from transformers import AutoTokenizer, FalconConfig, 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 Mode... | 715 |
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_submodules,
)
from accelerate.test_utils import require_m... | 106 | 0 |
"""simple docstring"""
import importlib
import os
import sys
# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append('''.''')
def UpperCAmelCase ( snake_case : Any ):
_lowerCAmelCase:Dict = test_fi... | 227 |
"""simple docstring"""
import absl # noqa: F401 # Here to have a nice missing dependency error message early on
import nltk # noqa: F401 # Here to have a nice missing dependency error message early on
import numpy # noqa: F401 # Here to have a nice missing dependency error message early on
import six # n... | 227 | 1 |
"""simple docstring"""
import argparse
import os
import numpy as np
import tensorflow as tf
import torch
from transformers import BertModel
def lowercase (snake_case__ : int , snake_case__ : List[Any] , snake_case__ : Tuple ) -> int:
'''simple docstri... | 703 |
"""simple docstring"""
from queue import PriorityQueue
from typing import Any
import numpy as np
def lowercase (snake_case__ : dict , snake_case__ : str , snake_case__ : set , snake_case__ : set , snake_case__ : dict , snake_c... | 529 | 0 |
"""simple docstring"""
import argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
ConditionalDetrConfig,
ConditionalDetrForObject... | 259 |
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def a_ ( ) -> Optional[int]:
_snake_case , _snake_case = 9, 14 # noqa: F841
_snake_case = [
[0, 1, 4],
[0, 7, 8],
[1, 2, 8]... | 686 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from tokenizers import processors
from ...tokenization_utils import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logg... | 555 |
'''simple docstring'''
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
UpperCAmelCase = logging.get_logger(__name__)
class a ( __magic_name__ ):
def __init__( self : Union[str, Any], *SCREAMING_SNAKE_CAS... | 555 | 1 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionAttendAndExcitePipeline,
UNetaDConditionModel,
)
from diffusers.utils import l... | 47 |
from ...utils import is_note_seq_available, is_transformers_available, is_torch_available
from ...utils import OptionalDependencyNotAvailable
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except OptionalDependencyNo... | 47 | 1 |
import pytest
import datasets
# Import fixture modules as plugins
SCREAMING_SNAKE_CASE__ = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def __SCREAMING_SNAKE_CASE ( SCREAMING_SNAKE_CASE : List[Any] , SCREAMING_SNAKE_CASE : Tuple ) -... | 688 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def __SCREAMING_SNAK... | 688 | 1 |
'''simple docstring'''
import os
import pickle
import unittest
from transformers import AutoTokenizer
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.models.bert_japanese.tokenization_bert_japanese import (
VOCAB_FILES_NAMES,
BertJapaneseTokenizer,
CharacterTokeni... | 370 | '''simple docstring'''
from __future__ import annotations
import math
def snake_case__ ( _A: int ) -> list[int]:
'''simple docstring'''
if num <= 0:
lowerCAmelCase = f"{num}: Invalid input, please enter a positive integer."
raise ValueError(_A )
low... | 370 | 1 |
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
while b:
lowercase__ , lowercase__ = b, a % b
return a
def __lowerCAmelCase ( SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ ):
return a if b == 0 else euclidean_gcd_recursive(SCREAMING_SNA... | 714 |
import json
from typing import Dict, List, Optional, Tuple, Union
from tokenizers import pre_tokenizers, processors
from ...tokenization_utils_base import AddedToken, BatchEncoding, EncodedInput
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import PaddingStrategy, logging
from .toke... | 37 | 0 |
from manim import *
class SCREAMING_SNAKE_CASE (UpperCAmelCase ):
def SCREAMING_SNAKE_CASE_ ( self : Tuple )-> Optional[int]:
"""simple docstring"""
lowercase__ = Rectangle(height=0.5 , width=0.5 )
lowerca... | 235 |
import requests
lowercase_ = """YOUR API KEY"""
def __UpperCamelCase (_SCREAMING_SNAKE_CASE , _SCREAMING_SNAKE_CASE = giphy_api_key ) -> list:
lowercase__ = '+'.join(query.split() )
lowercase__ = F"""https://api.giphy.co... | 235 | 1 |
from ..utils import DummyObject, requires_backends
class _a ( metaclass=UpperCamelCase__ ):
_lowercase : Any = ['''note_seq''']
def __init__( self: List[str] , *UpperCamelCase_: Optional[int] , **UpperCamelCase_: Tuple ) -> Lis... | 429 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_di... | 429 | 1 |
"""simple docstring"""
from abc import ABC, abstractmethod
from argparse import ArgumentParser
class _lowerCAmelCase ( lowerCamelCase ):
@staticmethod
@abstractmethod
def _a ( a_ ) -> str:
raise NotImplementedError()
@abstractmethod
def _a ( ... | 657 |
"""simple docstring"""
import unittest
from transformers.utils.backbone_utils import (
BackboneMixin,
get_aligned_output_features_output_indices,
verify_out_features_out_indices,
)
class _lowerCAmelCase ( unittest.TestCase ):
def _a ( self ) -> Optional[Any]:
_Uppe... | 657 | 1 |
from ... import PretrainedConfig
UpperCamelCase__ ={
'sijunhe/nezha-cn-base': 'https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json',
}
class lowerCAmelCase__( __lowercase ):
'''simple docstring'''
__snake_case = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MA... | 705 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
UpperCamelCase__ ={
'configuration_lilt': ['LILT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'LiltConfig'],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAva... | 381 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax
import jax.numpy as jnp
class SCREAMING_SNAKE_CASE ( nn.Module ):
snake_case__ = 42
snake_case__ = jnp.floataa
def SCREAMING_SNAKE_CASE ( self : st... | 466 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__lowerCAmelCase = {'configuration_sew': ['SEW_PRETRAINED_CONFIG_ARCHIVE_MAP', 'SEWConfig']}
try:
if not is_torch_available():... | 466 | 1 |
"""simple docstring"""
from math import ceil
def lowercase (_snake_case ,_snake_case ) -> Any:
'''simple docstring'''
__UpperCamelCase = list(range(0 ,__snake_case ) )
__UpperCamelCase = [item for sublist in list(device_map.values() )... | 707 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from ... | 228 | 0 |
lowerCAmelCase__ = {
0: '0',
1: '1',
2: '2',
3: '3',
4: '4',
5: '5',
6: '6',
7: '7',
8: '8',
9: '9',
10: 'a',
11: 'b',
12: 'c',
13: 'd',
14: 'e',
15: 'f',
}
def __lowercase ( _UpperCAmelCase ) -> str:
'''simple do... | 321 | 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
... | 321 | 1 |
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 import (
... | 707 |
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 ShapERenderer
from diffu... | 167 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A = logging.get_logger(__name__)
A = {
'microsoft/cvt-13': 'https://huggingface.co/microsoft/cvt-13/resolve/main/config.json',
# See all Cvt models at https://hug... | 320 |
'''simple docstring'''
A = [
'Audio',
'Array2D',
'Array3D',
'Array4D',
'Array5D',
'ClassLabel',
'Features',
'Sequence',
'Value',
'Image',
'Translation',
'TranslationVariableLanguages',
]
from .audio import Audio
from .features impor... | 320 | 1 |
import numpy as np
def snake_case_ (_a : np.ndarray ):
return 1 / (1 + np.exp(-vector ))
def snake_case_ (_a : np.ndarray ):
return vector * sigmoid(_a )
if __name__ == "__main__":
import doctest
doctest.testmod()
| 701 |
'''simple docstring'''
def snake_case_ (_a : list[list[int]] , _a : int , _a : int , _a : list[int] ):
# 1. Validate that path exists between current and next vertices
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
... | 358 | 0 |
'''simple docstring'''
def __snake_case ( SCREAMING_SNAKE_CASE_ : int ) -> Optional[int]:
"""simple docstring"""
UpperCAmelCase = len(_A )
for i in range(length - 1 ):
UpperCAmelCase = i
for k in range(i + 1 , _A ):
... | 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'''
import collections
import inspect
import unittest
from typing import Dict, List, Tuple
from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available
from .... | 707 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional
import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors
import transformers
from transformers import (
AutoConfig,
AutoModelForMultip... | 156 | 0 |
"""simple docstring"""
import os
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager import xopen, xsplitext
from ..table i... | 95 |
"""simple docstring"""
from ....configuration_utils import PretrainedConfig
from ....utils import logging
lowercase__ :Tuple = logging.get_logger(__name__)
lowercase__ :List[Any] = {
'speechbrain/m-ctc-t-large': 'https://huggingface.co/speechbrain/m-ctc... | 522 | 0 |
'''simple docstring'''
import ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, slow
from acceler... | 713 |
'''simple docstring'''
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
UpperCamelCase =logging.get_logger(__name__) # pylint: disable=invalid-name
def ... | 543 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
... | 495 |
'''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,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 495 | 1 |
from scipy.stats import pearsonr
import datasets
lowerCamelCase : Optional[Any] = '''
Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the a... | 290 |
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 290 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.