code stringlengths 81 54k | code_codestyle int64 0 721 | style_context stringlengths 91 41.9k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
def _lowerCamelCase ( a_ : int): # noqa: E741
lowerCamelCase :List[Any] = len(a_)
lowerCamelCase :List[str] = 0
lowerCamelCase :Union[str, Any] = [0] * n
lowerCamelCase :Optional[int] = [False] * n
lowerCamelCase :Option... | 711 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {
"""configuration_electra""": ["""ELECTRA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Ele... | 712 | def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_graphormer""": ["""GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GraphormerConfig"""],
}
try:
if not is_torch_ava... | 713 | import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 49 | 0 |
import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch
import datasets
import datasets.config
from .utils import require_beam
class _lowerCAmelCase ( datasets.BeamBasedBuilder ):
def snake_case ( self ... | 714 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import p... | 49 | 0 |
from typing import Any
def _lowerCamelCase ( a_ : list , a_ : list , a_ : dict , a_ : dict , a_ : dict , ):
_validation(
a_ , a_ , a_ , a_ , a_ , )
# Creates data structures and fill init... | 715 | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ''
_UpperCAmelCase = (
None # p... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_upernet""": ["""UperNetConfig"""],
}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()
except OptionalDependency... | 716 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 49 | 0 |
from __future__ import annotations
def _lowerCamelCase ( a_ : list , a_ : int):
# Checks if the entire collection has been sorted
if len(a_) <= 1 or n <= 1:
return
insert_next(a_ , n - 1)
rec_insertion_sort(a_ , n - 1)
def _lowerCamelCase... | 717 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Layo... | 49 | 0 |
def _lowerCamelCase ( a_ : str = "The quick brown fox jumps over the lazy dog" , ):
lowerCamelCase :Dict = set()
# Replace all the whitespace in our sentence
lowerCamelCase :int = input_str.replace(''' ''' , '''''')
for alpha in input_str:
if "a... | 718 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
A__ = {
"""configuration_poolformer""": [
"""POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""PoolFormerConfig""",
"""PoolForm... | 719 | import operator as op
def _lowerCamelCase ( a_ : Tuple):
lowerCamelCase :int = []
lowerCamelCase :List[str] = lambda a_ , a_: int(x / y) # noqa: E731 integer division operation
lowerCamelCase :Optional[int] = {
'''^''': op.... | 49 | 0 |
from ..utils import DummyObject, requires_backends
class _lowerCAmelCase ( metaclass=__SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ['torch', 'transformers', 'onnx']
def __init__( self : Union[str, Any] , *__snake_case : Optional[Any] , **__sna... | 720 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fai... | 49 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import cached_download, hf_hub_download, hf_hub_url
from PIL import Image
from transformers import DetaConfig, DetaForObjectDetection, DetaImageProcessor, SwinConfig
from transformers.utils import logging
... | 721 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
... | 49 | 0 |
def _lowerCamelCase ( a_ : float , a_ : float):
if density <= 0:
raise ValueError('''Impossible fluid density''')
if bulk_modulus <= 0:
raise ValueError('''Impossible bulk modulus''')
return (bulk_modulus / density) ** 0.5
if __name__ == "__main__":
import doct... | 700 | import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase... | 49 | 0 |
import pickle
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_tokeniza... | 701 | 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 accelerate.utils import wr... | 49 | 0 |
import unittest
from transformers import is_torch_available
from transformers.testing_utils import require_torch
if is_torch_available():
import torch
from transformers.activations import gelu_new, gelu_python, get_activation
@require_torch
class _lowerCAmelCase ( unittest.TestCase ):
... | 702 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 49 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from ...utils import logging, randn_tensor
from ..pipeline_utils import AudioPipelineOutput, DiffusionPipeline
A__ = logging.get_logger(__name__) # pylint: disable=invalid-name
class _lowerCAmelCase ( __SCREAMING_SNAKE_CAS... | 703 | import os
from math import logaa
def _lowerCamelCase ( a_ : str = "base_exp.txt"):
lowerCamelCase :float = 0
lowerCamelCase :Optional[int] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a_) , a_))):
lowerCamelCase , lowe... | 49 | 0 |
'''simple docstring'''
# Imports
import numpy as np
class _lowerCAmelCase :
def __init__( self : List[Any] , __snake_case : List[Any]=None , __snake_case : Optional[int]=None , __snake_case : Optional[Any]=None , __snake_case : ... | 704 | def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | 0 |
import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 705 | import math
def _lowerCamelCase ( a_ : int):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number... | 49 | 0 |
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 accelerate.utils import wr... | 706 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 49 | 0 |
def _lowerCamelCase ( a_ : int = 50_00_00_00):
lowerCamelCase :int = set()
lowerCamelCase :Union[str, Any] = int((limit - 24) ** (1 / 2))
lowerCamelCase :Optional[Any] = set(range(3 , prime_square_limit + 1 , 2))
primes.add(2)
... | 707 | from maths.prime_factors import prime_factors
def _lowerCamelCase ( a_ : int):
if not isinstance(a_ , a_):
lowerCamelCase :Tuple = F"Input value of [number={number}] must be an integer"
raise TypeError(a_)
if number < 1:
raise ValueError('''Input ... | 49 | 0 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ['image_processor', 'tokenizer']
_UpperCAmelCase = 'CLIPImageProcessor'
_Upper... | 708 | 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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 49 | 0 |
from __future__ import annotations
def _lowerCamelCase ( a_ : list[int] , a_ : int):
if len(a_) == 0:
return False
lowerCamelCase :Optional[int] = len(a_) // 2
if a_list[midpoint] == item:
return True
if item < a_list[midpoint]:
return ... | 709 | def _lowerCamelCase ( a_ : int = 4_00_00_00):
lowerCamelCase :Dict = [0, 1]
lowerCamelCase :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
lowerCamelCase :Dict = ... | 49 | 0 |
import unittest
import torch
from torch import nn
from diffusers.models.activations import get_activation
class _lowerCAmelCase ( unittest.TestCase ):
def snake_case ( self : Union[str, Any] ):
lowerCamelCase :int = get_activation('''swish''' )
... | 710 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 49 | 0 |
def _lowerCamelCase ( ):
return [list(range(10_00 - i , -10_00 - i , -1)) for i in range(10_00)]
A__ = generate_large_matrix()
A__ = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[3, 2], [1, 0]],
[[7, 7, 6]],
[[7, ... | 711 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 0 |
import unittest
import numpy as np
import timeout_decorator # noqa
from transformers import BlenderbotSmallConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...generation.test_flax_utils import FlaxGenerationTesterMixin
from ...test_modeling_flax_common import FlaxModelTesterM... | 712 | def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {"""processing_layoutxlm""": ["""LayoutXLMProcessor"""]}
tr... | 713 | import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 49 | 0 |
import warnings
from transformers import AutoTokenizer
from transformers.utils import is_torch_available
from transformers.utils.generic import ExplicitEnum
from ...processing_utils import ProcessorMixin
if is_torch_available():
import torch
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
... | 714 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import p... | 49 | 0 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use... | 715 | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ''
_UpperCAmelCase = (
None # p... | 49 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""google/pegasus-large""": """https://huggingface.co/google/pegasus-large/resolve/main/config.json""",
# See all PEGASUS models at https://huggi... | 716 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 49 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""naver-clova-ix/donut-base""": """https://huggingface.co/naver-clova-ix/donut-base/resolve/main/config.json""",
# See all Donut models at https... | 717 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Layo... | 49 | 0 |
import unittest
import numpy as np
def _lowerCamelCase ( a_ : np.ndarray , a_ : np.ndarray , a_ : np.ndarray , a_ : np.ndarray | None = None , ):
lowerCamelCase :List[str] = np.shape(a_)
lowerCamelCase :str = np.shape(a_)
lowe... | 718 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 0 |
def _lowerCamelCase ( a_ : int):
if not isinstance(a_ , a_):
raise ValueError('''multiplicative_persistence() only accepts integral values''')
if num < 0:
raise ValueError('''multiplicative_persistence() does not accept negative values''')
lowerCamelCase :Optional[Any... | 719 | import operator as op
def _lowerCamelCase ( a_ : Tuple):
lowerCamelCase :int = []
lowerCamelCase :List[str] = lambda a_ , a_: int(x / y) # noqa: E731 integer division operation
lowerCamelCase :Optional[int] = {
'''^''': op.... | 49 | 0 |
def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 720 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fai... | 49 | 0 |
from .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBackend,
default_hp_space_optuna,
... | 721 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
... | 49 | 0 |
def _lowerCamelCase ( a_ : int , a_ : int):
return base * power(a_ , (exponent - 1)) if exponent else 1
if __name__ == "__main__":
print("""Raise base to the power of exponent using recursion...""")
A__ = int(input("""Enter the base: """).strip())
A__ ... | 700 | import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase... | 49 | 0 |
import unittest
import numpy as np
from transformers import is_flax_available
from transformers.testing_utils import require_flax
from ..test_modeling_flax_common import ids_tensor
if is_flax_available():
import jax
import jax.numpy as jnp
from transformers.generation import (
FlaxForcedBOSTokenLo... | 701 | 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 accelerate.utils import wr... | 49 | 0 |
import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 702 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 49 | 0 |
import json
import os
import subprocess
import unittest
from ast import literal_eval
import pytest
from parameterized import parameterized_class
from . import is_sagemaker_available
if is_sagemaker_available():
from sagemaker import Session, TrainingJobAnalytics
from sagemaker.huggingface import... | 703 | import os
from math import logaa
def _lowerCamelCase ( a_ : str = "base_exp.txt"):
lowerCamelCase :float = 0
lowerCamelCase :Optional[int] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a_) , a_))):
lowerCamelCase , lowe... | 49 | 0 |
'''simple docstring'''
def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :A... | 704 | def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | 0 |
from __future__ import annotations
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : int ):
lowerCamelCase :Tuple = order
# a_{0} ... a_{k}
lowerCamelCase :Dict = [1.0] + [0.0] * order
... | 705 | import math
def _lowerCamelCase ( a_ : int):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number... | 49 | 0 |
import warnings
from ...utils import logging
from .image_processing_beit import BeitImageProcessor
A__ = logging.get_logger(__name__)
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
def __init__( self : List[Any] , *__snake_case : Optional... | 706 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 49 | 0 |
from collections import deque
class _lowerCAmelCase :
def __init__( self : Union[str, Any] , __snake_case : str , __snake_case : int , __snake_case : int ):
lowerCamelCase :List[Any] = process_name # process name
... | 707 | from maths.prime_factors import prime_factors
def _lowerCamelCase ( a_ : int):
if not isinstance(a_ , a_):
lowerCamelCase :Tuple = F"Input value of [number={number}] must be an integer"
raise TypeError(a_)
if number < 1:
raise ValueError('''Input ... | 49 | 0 |
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():
import torch
i... | 708 | 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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 49 | 0 |
from __future__ import annotations
class _lowerCAmelCase :
def __init__( self : Union[str, Any] , __snake_case : List[str]=None ):
lowerCamelCase :List[str] = data
lowerCamelCase :str = None
def _... | 709 | def _lowerCamelCase ( a_ : int = 4_00_00_00):
lowerCamelCase :Dict = [0, 1]
lowerCamelCase :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
lowerCamelCase :Dict = ... | 49 | 0 |
class _lowerCAmelCase :
def __init__( self : int , __snake_case : int , __snake_case : List[Any]=None , __snake_case : List[str]=None ):
lowerCamelCase :Union[str, Any] = data
lowerCamelCase :str = previous
... | 710 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 49 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""facebook/data2vec-text-base""": """https://huggi... | 711 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 0 |
from __future__ import annotations
import math
def _lowerCamelCase ( a_ : int):
if num <= 0:
lowerCamelCase :Union[str, Any] = F"{num}: Invalid input, please enter a positive integer."
raise ValueError(a_)
lowerCamelCase :Any = [True] * (num + 1)
lo... | 712 | def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""sayakpaul/vit-msn-base""": """https://huggingface.co/sayakpaul/vit-msn-base/resolve/main/config.json""",
# See all ViT MSN models at https://h... | 713 | import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 49 | 0 |
import argparse
from pathlib import Path
import requests
import torch
from PIL import Image
from transformers import (
RobertaTokenizer,
TrOCRConfig,
TrOCRForCausalLM,
TrOCRProcessor,
VisionEncoderDecoderModel,
ViTConfig,
ViTImageProcessor,
ViTModel,
)
from transformer... | 714 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import p... | 49 | 0 |
import numpy as np
from numpy import ndarray
from scipy.optimize import Bounds, LinearConstraint, minimize
def _lowerCamelCase ( a_ : ndarray):
return np.dot(a_ , a_)
class _lowerCAmelCase :
def __init__( self : Any , *,
... | 715 | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ''
_UpperCAmelCase = (
None # p... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {"""configuration_opt""": ["""OPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OPTC... | 716 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 49 | 0 |
import os
from typing import Dict, List, Tuple, TypeVar, Union
A__ = TypeVar("""T""")
A__ = Union[List[T], Tuple[T, ...]]
A__ = Union[T, List[T], Dict[str, T]]
A__ = Union[str, bytes, os.PathLike]
| 717 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Layo... | 49 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
"""https://... | 718 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 0 |
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.text_encoder ... | 719 | import operator as op
def _lowerCamelCase ( a_ : Tuple):
lowerCamelCase :int = []
lowerCamelCase :List[str] = lambda a_ , a_: int(x / y) # noqa: E731 integer division operation
lowerCamelCase :Optional[int] = {
'''^''': op.... | 49 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils import AddedToken
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_alber... | 720 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fai... | 49 | 0 |
def _lowerCamelCase ( a_ : list):
lowerCamelCase :Union[str, Any] = 0
while len(a_) > 1:
lowerCamelCase :List[Any] = 0
# Consider two files with minimum cost to be merged
for _ in range(2):
lowerCamelCase :int = files.index... | 721 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
... | 49 | 0 |
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate im... | 700 | import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase... | 49 | 0 |
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ConvNextConfig, SegformerImageProcessor, UperNetConfig, UperNetForSemanticSegmentation
def _lowerCamelCase ( a_ : Union[str, Any]):
lowerCamelCase ... | 701 | 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 accelerate.utils import wr... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 702 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 49 | 0 |
import heapq
import sys
import numpy as np
A__ = tuple[int, int]
class _lowerCAmelCase :
def __init__( self : str ):
lowerCamelCase :int = []
lowerCamelCase :List[str] = set()
def ... | 703 | import os
from math import logaa
def _lowerCamelCase ( a_ : str = "base_exp.txt"):
lowerCamelCase :float = 0
lowerCamelCase :Optional[int] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a_) , a_))):
lowerCamelCase , lowe... | 49 | 0 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class _lowerCAmelCase :
def __init__( self : Any ):
lowerCamelCase :Union[str, Any] = {}
def ... | 704 | def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""kssteven/ibert-roberta-base""": """https://huggi... | 705 | import math
def _lowerCamelCase ( a_ : int):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number... | 49 | 0 |
import json
import os
import shutil
import tempfile
import unittest
from multiprocessing import get_context
from pathlib import Path
import datasets
import numpy as np
from datasets import load_dataset
from parameterized import parameterized
from transformers import AutoProcessor
from transformers.model... | 706 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 49 | 0 |
import argparse
import random
import joblib
import numpy as np
import torch
from igf.igf import (
SecondaryLearner,
collect_objective_set,
compute_perplexity,
generate_datasets,
load_gpta,
recopy_gpta,
set_seed,
train_secondary_learner,
)
from torch.utils.data import Dat... | 707 | from maths.prime_factors import prime_factors
def _lowerCamelCase ( a_ : int):
if not isinstance(a_ , a_):
lowerCamelCase :Tuple = F"Input value of [number={number}] must be an integer"
raise TypeError(a_)
if number < 1:
raise ValueError('''Input ... | 49 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.text_encoder import MCL... | 708 | 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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 49 | 0 |
import gc
import unittest
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DDPMScheduler,
PriorTransformer,
StableUnCLIPPipeline,
UNetaDConditionModel,
)
from diff... | 709 | def _lowerCamelCase ( a_ : int = 4_00_00_00):
lowerCamelCase :Dict = [0, 1]
lowerCamelCase :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
lowerCamelCase :Dict = ... | 49 | 0 |
def _lowerCamelCase ( a_ : str):
assert column_title.isupper()
lowerCamelCase :List[Any] = 0
lowerCamelCase :int = len(a_) - 1
lowerCamelCase :Dict = 0
while index >= 0:
lowerCamelCase :List[Any] = (ord(column_title[index]) ... | 710 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import _LazyModule
A__ = {"""tokenization_bertweet""": ["""BertweetTokenizer"""]}
if TYPE_CHECKING:
from .tokenization_bertweet import BertweetTokenizer
else:
import sys
A__ = _LazyModule(__name__, globals()["""... | 711 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 0 |
import argparse
import os
from io import BytesIO
from pathlib import Path
import requests
from clip_retrieval.clip_client import ClipClient
from PIL import Image
from tqdm import tqdm
def _lowerCamelCase ( a_ : List[Any] , a_ : List[str] , a_ : Optional[Any]):
lowerCamelCase ... | 712 | def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | 0 |
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
Pipeline,
ZeroShotClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slo... | 713 | import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 49 | 0 |
def _lowerCAmelCase ( a_ : int = 4_00_00_00):
lowerCamelCase :Dict = [0, 1]
lowerCamelCase :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
lowerCamelCase :Dict = ... | 714 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import p... | 49 | 0 |
from typing import Callable, Dict, Optional, Tuple
import torch
from torch import nn
from torch.distributions import (
AffineTransform,
Distribution,
Independent,
NegativeBinomial,
Normal,
StudentT,
TransformedDistribution,
)
class _lowerCAmelCase ( __SCREA... | 715 | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ''
_UpperCAmelCase = (
None # p... | 49 | 0 |
from __future__ import annotations
A__ = 10
def _lowerCamelCase ( a_ : list[int]):
lowerCamelCase :Any = 1
lowerCamelCase :List[str] = max(a_)
while placement <= max_digit:
# declare and initialize empty buckets
lowerCamel... | 716 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
A__ = {
"""configuration_mvp""": ["""MVP_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MvpConfig""", """MvpOnnxConfig"""],
"""tokenization_mvp""": ["""M... | 717 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Layo... | 49 | 0 |
import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase ( _... | 718 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_tokenizers_available,
is_torch_available,
)
A__ = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAv... | 719 | import operator as op
def _lowerCamelCase ( a_ : Tuple):
lowerCamelCase :int = []
lowerCamelCase :List[str] = lambda a_ , a_: int(x / y) # noqa: E731 integer division operation
lowerCamelCase :Optional[int] = {
'''^''': op.... | 49 | 0 |
from __future__ import absolute_import, division, print_function, unicode_literals
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss
from transformers import RobertaConfig
from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.... | 720 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fai... | 49 | 0 |
import shutil
import tempfile
import unittest
from transformers import SPIECE_UNDERLINE, BatchEncoding, MBartaaTokenizer, MBartaaTokenizerFast, is_torch_available
from transformers.testing_utils import (
get_tests_dir,
nested_simplify,
require_sentencepiece,
require_tokenizers,
require_to... | 721 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
... | 49 | 0 |
import re
import time
from typing import Optional
import IPython.display as disp
from ..trainer_callback import TrainerCallback
from ..trainer_utils import IntervalStrategy, has_length
def _lowerCamelCase ( a_ : Any):
lowerCamelCase :Any = int(a_)
lowerCamelCase ... | 700 | import json
import os
import unittest
from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class _lowerCAmelCase... | 49 | 0 |
from __future__ import annotations
def _lowerCamelCase ( a_ : int):
lowerCamelCase :Union[str, Any] = [True] * limit
lowerCamelCase :Tuple = False
lowerCamelCase :List[str] = False
lowerCamelCase :List[Any] = True
for i in ra... | 701 | 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 accelerate.utils import wr... | 49 | 0 |
import json
from typing import TYPE_CHECKING, List, Optional, Tuple
from tokenizers import pre_tokenizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
if TYPE_CHECKING:
from transformers.pipelines.conversational import Conversation
A__ = ... | 702 | import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_... | 49 | 0 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .vae import Decoder, DecoderOutput, E... | 703 | import os
from math import logaa
def _lowerCamelCase ( a_ : str = "base_exp.txt"):
lowerCamelCase :float = 0
lowerCamelCase :Optional[int] = 0
for i, line in enumerate(open(os.path.join(os.path.dirname(a_) , a_))):
lowerCamelCase , lowe... | 49 | 0 |
'''simple docstring'''
from __future__ import annotations
def _lowerCamelCase ( a_ : list[int]): # This function is recursive
lowerCamelCase :Union[str, Any] = len(a_)
# If the array contains only one element, we return it (it's the stop condition of
# recurs... | 704 | def _lowerCamelCase ( a_ : list):
if not grid or not grid[0]:
raise TypeError('''The grid does not contain the appropriate information''')
for cell_n in range(1 , len(grid[0])):
grid[0][cell_n] += grid[0][cell_n - 1]
lowerCamelCase :Any = grid[0]
for ... | 49 | 0 |
import copy
import inspect
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils i... | 705 | import math
def _lowerCamelCase ( a_ : int):
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multiples of 3 are not primes
return False
# All primes number... | 49 | 0 |
import torch
from diffusers import EulerDiscreteScheduler
from diffusers.utils import torch_device
from .test_schedulers import SchedulerCommonTest
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = (EulerDiscreteScheduler,)
_UpperCAmelCase = ... | 706 | import unittest
from queue import Empty
from threading import Thread
from transformers import AutoTokenizer, TextIteratorStreamer, TextStreamer, is_torch_available
from transformers.testing_utils import CaptureStdout, require_torch, torch_device
from ..test_modeling_common import ids_tensor
if is_torch_av... | 49 | 0 |
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def _lowerCamelCase ( a_ : Dict):
lowerCamelCase :Dict = {}
lowerCamelCase :Dict = job['''started_at''']
lowerCamelCase :List[str] = ... | 707 | from maths.prime_factors import prime_factors
def _lowerCamelCase ( a_ : int):
if not isinstance(a_ , a_):
lowerCamelCase :Tuple = F"Input value of [number={number}] must be an integer"
raise TypeError(a_)
if number < 1:
raise ValueError('''Input ... | 49 | 0 |
def _lowerCamelCase ( a_ : Optional[Any] , a_ : int):
print('''\nThe shortest path matrix using Floyd Warshall algorithm\n''')
for i in range(a_):
for j in range(a_):
if dist[i][j] != float('''inf'''):
print(int(dist[i][j]) , end='''\t''')
else:
... | 708 | 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 timm.data import resolve_data_config
from timm.data.transforms_factory import create_transform
from transformers import (
BitConfig,
... | 49 | 0 |
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required... | 709 | def _lowerCamelCase ( a_ : int = 4_00_00_00):
lowerCamelCase :Dict = [0, 1]
lowerCamelCase :Optional[Any] = 0
while fib[i] <= n:
fib.append(fib[i] + fib[i + 1])
if fib[i + 2] > n:
break
i += 1
lowerCamelCase :Dict = ... | 49 | 0 |
import warnings
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""xlnet-base-cased""": """https://huggingface.co/xlnet-base-cased/resolve/main/config.json""",
"""xlnet-large-cased""": """https://hugg... | 710 | from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_nllb_moe""": [
"""NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""NllbMoeConfig""",
]
}
try:
if not is_torch_available(... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {
"""configuration_table_transformer""": [
"""TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""TableTransformerConfig""",
"""TableTran... | 711 | import numpy
class _lowerCAmelCase :
def __init__( self : Dict , __snake_case : numpy.ndarray , __snake_case : numpy.ndarray ):
lowerCamelCase :Dict = input_array
# Random initial weights are assigned where first argument... | 49 | 0 |
def _lowerCamelCase ( a_ : str):
lowerCamelCase :Optional[int] = hex_num.strip()
if not hex_num:
raise ValueError('''No value was passed to the function''')
lowerCamelCase :str = hex_num[0] == '''-'''
if is_negative:
lowerCamelCase :int = ... | 712 | def _lowerCamelCase ( a_ : str , a_ : str):
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :List[str] = len(a_)
lowerCamelCase :int = [[False for _ in range(m + 1)] for _ in range(n + 1)]
lowerCamelCase :Optional[Any] = ... | 49 | 0 |
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union
import sentencepiece
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ ... | 713 | import inspect
import unittest
from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
from ...tes... | 49 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ = {"""configuration_sew""": ["""SEW_PRETRAINED_CONFIG_ARCHIVE_MAP""", """SEWConfig"""]}
try:
if not is_torch_available():
raise OptionalDependencyNotAvailable()... | 714 | import inspect
import os
import unittest
import torch
import accelerate
from accelerate import debug_launcher
from accelerate.test_utils import (
execute_subprocess_async,
require_cpu,
require_huggingface_suite,
require_multi_gpu,
require_single_gpu,
)
from accelerate.utils import p... | 49 | 0 |
import argparse
import os
import shutil
import torch
from emmental.modules import MagnitudeBinarizer, ThresholdBinarizer, TopKBinarizer
def _lowerCamelCase ( a_ : List[str]):
lowerCamelCase :List[str] = args.pruning_method
lowerCamelCase :Tuple ... | 715 | import os
from typing import Optional
import fsspec
from fsspec.archive import AbstractArchiveFileSystem
from fsspec.utils import DEFAULT_BLOCK_SIZE
class _lowerCAmelCase ( __SCREAMING_SNAKE_CASE ):
_UpperCAmelCase = ''
_UpperCAmelCase = (
None # p... | 49 | 0 |
import json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from torch.utils.data import IterableDatase... | 716 | import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property
from ...t... | 49 | 0 |
import os
import unittest
from transformers import MobileBertTokenizer, MobileBertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_u... | 717 | from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
A__ = {
"""configuration_layoutlmv2""": ["""LAYOUTLMV2_PRETRAINED_CONFIG_ARCHIVE_MAP""", """Layo... | 49 | 0 |
from math import ceil
def _lowerCamelCase ( a_ : int = 10_01):
lowerCamelCase :Union[str, Any] = 1
for i in range(1 , int(ceil(n / 2.0))):
lowerCamelCase :Any = 2 * i + 1
lowerCamelCase :str = 2 * i
lowerCamelCase :Tuple... | 718 | import unittest
from transformers import is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
slow,
)
from .test_pipelines_common import ANY
if is... | 49 | 0 |
from __future__ import annotations
def _lowerCamelCase ( a_ : int | float | str , a_ : int | float | str):
if nth_term == "":
return [""]
lowerCamelCase :List[str] = int(a_)
lowerCamelCase :List[Any] = int(a_)
lowerCamelCase :lis... | 719 | import operator as op
def _lowerCamelCase ( a_ : Tuple):
lowerCamelCase :int = []
lowerCamelCase :List[str] = lambda a_ , a_: int(x / y) # noqa: E731 integer division operation
lowerCamelCase :Optional[int] = {
'''^''': op.... | 49 | 0 |
from math import sqrt
def _lowerCamelCase ( a_ : int):
lowerCamelCase :List[Any] = 0
for i in range(1 , int(sqrt(a_) + 1)):
if n % i == 0 and i != sqrt(a_):
total += i + n // i
elif i == sqrt(a_):
total += i
return total - n
... | 720 | import argparse
from pathlib import Path
import fairseq
import torch
from fairseq.models.xmod import XMODModel as FairseqXmodModel
from packaging import version
from transformers import XmodConfig, XmodForMaskedLM, XmodForSequenceClassification
from transformers.utils import logging
if version.parse(fai... | 49 | 0 |
def _lowerCamelCase ( a_ : dict):
lowerCamelCase :set[int] = set()
# To detect a back edge, keep track of vertices currently in the recursion stack
lowerCamelCase :set[int] = set()
return any(
node not in visited and depth_first_search(a_ , a_ ... | 721 | from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
"""andreasmadsen/efficient_mlm_m0.40""": (
... | 49 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
_lowercase : List[Any] = {
"""configuration_blenderbot""": [
... | 50 |
'''simple docstring'''
import inspect
import unittest
from transformers import RegNetConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from transformers.utils import cached_property, is_vision_available
from ...test_configuration_common import ConfigTester
from ...test_modeling... | 50 | 1 |
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