code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
import os
from collections import namedtuple
import pytest
from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict
__a = namedtuple(
"_TestCommandArgs",
[
... | 357 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 0 |
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.utils import is_xformers_availa... | 358 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 | 0 |
'''simple docstring'''
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__a = Lock()
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ... | 359 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 | 0 |
'''simple docstring'''
import json
import os
from pathlib import Path
import pytest
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadManager
from datasets.utils.file_utils import hash_url_to_filename
__a = "http://www.mocksite.com... | 360 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 43 | 0 |
'''simple docstring'''
import numpy as np
import torch
from torch.utils.data import Dataset
from utils import logger
class UpperCAmelCase_ ( _a ):
"""simple docstring"""
def __init__( self : Any , snake_case_ : Any , snake_case_ : List[str] ):
... | 361 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 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_rembert im... | 362 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 | 0 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 363 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 0 |
'''simple docstring'''
from typing import Optional, Tuple, Union
import tensorflow as tf
from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
TFBaseModelOutputWithNoAtten... | 364 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[... | 43 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> list[list[float]]:
snake_case__ : list[list[float]] = []
for data in source_data:
for i, el in enumerate(_lowerCAmelCase ):
if len(_lowerCAmelCase ) < i + 1:
... | 365 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils im... | 43 | 0 |
'''simple docstring'''
import argparse
import math
import traceback
import dateutil.parser as date_parser
import requests
def __snake_case( _lowerCAmelCase ) -> Optional[Any]:
snake_case__ : Optional[Any] = {}
snake_case__ : Union[str, Any] = job["""s... | 366 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.uti... | 43 | 0 |
'''simple docstring'''
__a = {
"A": ["B", "C", "E"],
"B": ["A", "D", "E"],
"C": ["A", "F", "G"],
"D": ["B"],
"E": ["A", "B", "D"],
"F": ["C"],
"G": ["C"],
}
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase ) -> list[... | 367 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1_000 ) -> int:
snake_case__ : Any = 1
snake_case__ : Union[str, Any] = 0
for divide_by_number in range(_lowerCAmelCase , digit + 1 ):
... | 368 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case( _lowerCAmelCase ) -> list[int]:
return [ord(_lowerCAmelCase ) - 96 for elem in plain]
def __snake_case( _lowerCAmelCase ) -> str:
return "".join(chr(elem + 96 ) for elem in encoded )
... | 369 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 0 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH... | 370 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__nam... | 43 | 0 |
'''simple docstring'''
import os
try:
from .build_directory_md import good_file_paths
except ImportError:
from build_directory_md import good_file_paths # type: ignore
a_ = list(good_file_paths())
assert filepaths, "good_file_paths() failed!"
a_ = [file for file in filepaths i... | 371 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 0 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteSchedule... | 350 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https:... | 43 | 0 |
'''simple docstring'''
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class UpperCAmelCase_ ( _a , unittest.TestCase ):
"""simple docstring"""
... | 351 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 | 0 |
'''simple docstring'''
import argparse
import gc
import json
import os
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_wa... | 352 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 | 0 |
'''simple docstring'''
__a = [
(1000, "M"),
(900, "CM"),
(500, "D"),
(400, "CD"),
(100, "C"),
(90, "XC"),
(50, "L"),
(40, "XL"),
(10, "X"),
(9, "IX"),
(5, "V"),
(4, "IV"),
(1, "I"),
]
def __snake_case( _lowerCAmelCase ) -> int:
... | 353 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(_lowerCAmelCase )[0] == np.shape(_lowerCAmelCase )[1]... | 43 | 0 |
from __future__ import annotations
import unittest
from transformers import MobileBertConfig, is_tf_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_tf, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFMode... | 354 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_low... | 43 | 0 |
'''simple docstring'''
from collections import defaultdict
from graphs.minimum_spanning_tree_prims import prisms_algorithm as mst
def __snake_case( ) -> Optional[int]:
snake_case__ : Dict = 9, 14 # noqa: F841
snake_case__ : List[Any] = [
[0, ... | 355 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from tran... | 43 | 0 |
'''simple docstring'''
__a = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def __snake_case( _lowerCAmelCase ) -> int:
snake_case__ : str = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
... | 356 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict ):
snake_case__ : List[str] = {}
def lowerCame... | 43 | 0 |
'''simple docstring'''
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : int , snake_case_ : str = "" , snake_case_ : bool = False ):
# Mapping from the first character of the prefix of the node
snake_case__ : dict[str, R... | 357 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 0 |
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 UpperCAmelCase_ ( unittest.TestCase ):
"""simple d... | 358 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 | 0 |
'''simple docstring'''
from __future__ import annotations
from sys import maxsize
from typing import Generic, TypeVar
__a = TypeVar("""T""")
def __snake_case( _lowerCAmelCase ) -> int:
return (position - 1) // 2
def __snake_case( _lowerCAmelCase ) -> int:
... | 359 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 | 0 |
'''simple docstring'''
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"facebook/wav2vec2-base-960h": "https://huggingface.co/facebook/wav2vec2-base-960h/resolve/main/config.js... | 360 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 43 | 0 |
'''simple docstring'''
import argparse
import json
import requests
import timm
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import AutoImageProcessor, SwinConfig, SwinForImageClassification
def __snake_case( _lowerCAmelCase ) -> Tuple:
... | 361 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 0 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import GenerationTe... | 362 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 | 0 |
'''simple docstring'''
import argparse
import os
import shutil
from pathlib import Path
import onnx
import torch
from packaging import version
from torch.onnx import export
from diffusers import OnnxRuntimeModel, OnnxStableDiffusionPipeline, StableDiffusionPipeline
__a = version.parse(version.parse... | 363 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_... | 364 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[... | 43 | 0 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"google/realm-cc-news-pretrained-embedder": (
"https://huggingface.co/google/realm-cc-news-pretrained-embedder/resolve/main/config.js... | 365 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils im... | 43 | 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,
)
__a = {"configuration_xglm": ["XGLM_P... | 366 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.uti... | 43 | 0 |
'''simple docstring'''
import logging
import os
import sys
from dataclasses import dataclass, field
from importlib import import_module
from typing import Dict, List, Optional, Tuple
import numpy as np
from seqeval.metrics import accuracy_score, fa_score, precision_score, recall_score
from torch import nn
fro... | 367 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> Optional[int]:
snake_case__ : Any = min(_lowerCAmelCase ) # min() finds the minimum value
snake_case__ : str = max(_lowerCAmelCase ) # max() finds the maximum value
snake_case__ : ... | 368 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 0 |
'''simple docstring'''
__a = {
"joule": 1.0,
"kilojoule": 1000,
"megajoule": 100_0000,
"gigajoule": 10_0000_0000,
"wattsecond": 1.0,
"watthour": 3600,
"kilowatthour": 360_0000,
"newtonmeter": 1.0,
"calorie_nutr": 4186.8,
"kilocalorie_nutr": 418_6800.00,
"elec... | 369 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 0 |
'''simple docstring'''
from __future__ import annotations
def __snake_case( _lowerCAmelCase ) -> int:
if not nums:
return 0
snake_case__ : List[str] = nums[0]
snake_case__ : Tuple = 0
for num in nums[1:]:
snake_cas... | 370 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__nam... | 43 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
a_ = {
"configuration_mobilenet_v2": [
"MOBILENET_V2_PRETRAINED_CONFIG_ARCHIVE_MAP",
"MobileNetV2Config",
"Mob... | 371 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 0 |
'''simple docstring'''
import sys
import turtle
def __snake_case( _lowerCAmelCase , _lowerCAmelCase ) -> tuple[float, float]:
return (pa[0] + pa[0]) / 2, (pa[1] + pa[1]) / 2
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase... | 350 |
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
__a = logging.get_logger(__name__)
__a = {
"microsoft/markuplm-base": "https://huggingface.co/microsoft/markuplm-base/resolve/main/config.json",
"microsoft/markuplm-large": "https:... | 43 | 0 |
'''simple docstring'''
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... | 351 |
'''simple docstring'''
import re
from filelock import FileLock
try:
import nltk
__a = True
except (ImportError, ModuleNotFoundError):
__a = False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.download("punkt", quiet=True)
def _... | 43 | 0 |
'''simple docstring'''
from typing import Any
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict , snake_case_ : Any ):
snake_case__ : List[Any] = data
snake_case__ : Dict ... | 352 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 43 | 0 |
'''simple docstring'''
import warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class UpperCAmelCase_... | 353 |
'''simple docstring'''
import numpy as np
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase = 1e-12 , _lowerCAmelCase = 100 , ) -> tuple[float, np.ndarray]:
assert np.shape(_lowerCAmelCase )[0] == np.shape(_lowerCAmelCase )[1]... | 43 | 0 |
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
__a = 5_0000
__a = 5000
__a , __a = os.path.split(__file__)
__a = os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))
@get_dura... | 354 |
'''simple docstring'''
import math
import qiskit
def __snake_case( _lowerCAmelCase = 1 , _lowerCAmelCase = 1 , _lowerCAmelCase = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(_lowerCAmelCase , _lowerCAmelCase )
or isinstance(_low... | 43 | 0 |
'''simple docstring'''
from math import ceil
from typing import List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging
... | 355 |
'''simple docstring'''
import shutil
import tempfile
import unittest
from unittest.mock import patch
from transformers import (
DefaultFlowCallback,
IntervalStrategy,
PrinterCallback,
ProgressCallback,
Trainer,
TrainerCallback,
TrainingArguments,
is_torch_available,
)
from tran... | 43 | 0 |
'''simple docstring'''
import math
import os
from copy import deepcopy
import datasets
import evaluate
import torch
import transformers
from datasets import load_dataset
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer
from accelerate import Acc... | 356 |
'''simple docstring'''
from collections import deque
from math import floor
from random import random
from time import time
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Dict ):
snake_case__ : List[str] = {}
def lowerCame... | 43 | 0 |
'''simple docstring'''
from __future__ import annotations
from scipy.special import comb # type: ignore
class UpperCAmelCase_ :
"""simple docstring"""
def __init__( self : Tuple , snake_case_ : list[tuple[float, float]] ):
snake_case__ : str = l... | 357 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise ValueError("""multiplicative_persistence() only accepts integral values""" )
if num < 0:
raise ValueError("""multiplicati... | 43 | 0 |
import argparse
import torch
from transformers import (
EncodecConfig,
EncodecFeatureExtractor,
EncodecModel,
logging,
)
# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_... | 358 |
'''simple docstring'''
import unittest
from transformers import DonutProcessor
__a = "naver-clova-ix/donut-base"
class UpperCAmelCase_ ( unittest.TestCase ):
"""simple docstring"""
def lowerCamelCase ( self : List[str] ):
snake_case__ : Optional[A... | 43 | 0 |
'''simple docstring'''
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
__a = logging.get_logger(__name__)
__a =... | 359 |
'''simple docstring'''
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch... | 43 | 0 |
'''simple docstring'''
import os
import tempfile
import unittest
from transformers import FlaubertConfig, is_torch_available
from transformers.testing_utils import require_torch, require_torch_gpu, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ... | 360 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 43 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
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... | 361 |
'''simple docstring'''
import logging
import os
from typing import List, Tuple
import numpy as np
import psutil
import torch
import torch.distributed as dist
from transformers import RagRetriever
__a = logging.getLogger(__name__)
class UpperCAmelCase_ ( _a ):
"""simple docstring""... | 43 | 0 |
from __future__ import annotations
from collections.abc import Callable
from typing import Any, Generic, TypeVar
__a = TypeVar("T")
class UpperCAmelCase_ ( Generic[T] ):
"""simple docstring"""
def __init__( self : List[Any] , snake_case_ : list[T] , snake... | 362 |
'''simple docstring'''
import math
import os
import re
import sys
import unittest
from pathlib import Path
from typing import Tuple
from unittest.mock import patch
from parameterized import parameterized
from transformers.testing_utils import (
CaptureStderr,
ExtendSysPath,
TestCasePlus,
exec... | 43 | 0 |
'''simple docstring'''
import argparse
import hashlib
import os
import urllib
import warnings
import torch
from torch import nn
from tqdm import tqdm
from transformers import WhisperConfig, WhisperForConditionalGeneration
__a = {
"tiny.en": "https://openaipublic.azureedge.net/main/whisper/model... | 363 |
'''simple docstring'''
import gc
import random
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, CycleDiffusionPipeline, DDIMScheduler, UNetaDConditionModel
from diffusers.utils import floats_tensor, load... | 43 | 0 |
'''simple docstring'''
import argparse
from pathlib import Path
import torch
from packaging import version
from torch.onnx import export
from diffusers import AutoencoderKL
__a = version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")
def __snake_case( _lo... | 364 |
'''simple docstring'''
def __snake_case( ) -> list[list[int]]:
return [list(range(1_000 - i , -1_000 - i , -1 ) ) for i in range(1_000 )]
__a = generate_large_matrix()
__a = (
[[4, 3, 2, -1], [3, 2, 1, -1], [1, 1, -1, -2], [-1, -1, -2, -3]],
[[... | 43 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , _lowerCAmelCase , ) -> float:
snake_case__ : str = [redshift, radiation_density, matter_density, dark_energy]
... | 365 |
'''simple docstring'''
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils im... | 43 | 0 |
'''simple docstring'''
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Input value must be an 'int' type""" )
snake_case__ : List[str] = 0
while number:
positi... | 366 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
from diffusers.uti... | 43 | 0 |
'''simple docstring'''
from typing import Any, Callable, Dict, List, Optional, Union
import torch
from transformers import CLIPImageProcessor, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DiffusionPipeline,
LMSDiscreteScheduler,
PNDMScheduler,
Sta... | 367 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import ... | 43 | 0 |
'''simple docstring'''
from typing import Dict, Iterable, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image
from ...image_utils import... | 368 |
'''simple docstring'''
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@requir... | 43 | 0 |
'''simple docstring'''
from math import factorial
__a = {str(digit): factorial(digit) for digit in range(10)}
def __snake_case( _lowerCAmelCase ) -> int:
if not isinstance(_lowerCAmelCase , _lowerCAmelCase ):
raise TypeError("""Parameter number must be in... | 369 |
'''simple docstring'''
from queue import Queue
from typing import TYPE_CHECKING, Optional
if TYPE_CHECKING:
from ..models.auto import AutoTokenizer
class UpperCAmelCase_ :
"""simple docstring"""
def lowerCamelCase ( self : Optional[Any] , snake_case_ : Optional... | 43 | 0 |
'''simple docstring'''
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils impo... | 370 |
'''simple docstring'''
import collections
from typing import List, Optional, Union
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert import BertTokenizer
__a = logging.get_logger(__nam... | 43 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
a_ ... | 371 |
'''simple docstring'''
import unittest
import numpy as np
import torch
from diffusers import DDIMPipeline, DDIMScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, slow, torch_device
from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PA... | 43 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from transformers import (
CLIPImageProcessor,
CLIPTextConfig,
CLIPTextModel,
CLIPTokenizer,
CLIPVisionConfig,
CLIPVisionModelWithProjection,
)
from diffusers import AutoencoderKL, DDIMScheduler, DDP... | 44 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 1 |
from scipy.stats import pearsonr
import datasets
A__ = '''
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 assumption that each dat... | 44 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 1 |
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 ):
def __lowerCamelCase ( self :List[str] ):
snake_case__ : Any ... | 44 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 1 |
import os
import pytest
from datasets import (
get_dataset_config_info,
get_dataset_config_names,
get_dataset_infos,
get_dataset_split_names,
inspect_dataset,
inspect_metric,
)
A__ = pytest.mark.integration
@pytest.mark.parametrize('''path''' , ['''... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
import copy
import tempfile
import unittest
from transformers import MaMaaaConfig, is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device
from transformers.utils import cached_property
from ...generation.test_utils import ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if len(__lowerCAmelCase ) <= 1:
return [tuple(__lowerCAmelCase )]
snake_case__ : List[Any] = []
def generate(__lowerCAmelCase , __lowerCAmelCase ):
... | 44 |
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,
... | 44 | 1 |
from __future__ import annotations
from math import pow, sqrt
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> dict[str, float]:
"""simple docstring"""
if (resistance, reactance, impedance).count(0 ) != 1:
raise ValueError... | 44 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 1 |
import re
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : int = re.compile(r'''^(\+91[\-\s]?)?[0]?(91)?[789]\d{9}$''' )
if match := re.search(__lowerCAmelCase , __lowerCAmelCase ):
return match.s... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 1 |
# 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 ... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
A__ = logging.get_logger(__name__)
A__ = {
'''google/bit-50''': '''https://huggingface.co/go... | 44 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 1 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {
'''vinvino02/glpn-kitti''': '''https://huggingface.co/vinvino02/glpn-kitti/resolve/main/config.json''',
# See all GLPN models at https://huggingf... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 1 |
A__ = {0: [2, 3], 1: [0], 2: [1], 3: [4], 4: []}
A__ = {0: [1, 2, 3], 1: [2], 2: [0], 3: [4], 4: [5], 5: [3]}
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> list[int]:
"""simple docstring"""
snake_case__ ... | 44 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 1 |
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 _lowerCAmelCase ( ) ... | 44 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 1 |
# 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 requi... | 44 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 1 |
A__ = {
'''Pillow''': '''Pillow''',
'''accelerate''': '''accelerate>=0.11.0''',
'''compel''': '''compel==0.1.8''',
'''black''': '''black~=23.1''',
'''datasets''': '''datasets''',
'''filelock''': '''filelock''',
'''flax''': '''flax>=0.4.1''',
'''hf-doc-builder'... | 44 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 | 1 |
import copy
from typing import Dict, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
from ..detr import DetrConfig
from ..swin import SwinConfig
A__ = {
'''facebook/maskformer-swin-base-ade''': (
'... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 1 |
import argparse
import logging
import pickle
import random
import time
import numpy as np
from transformers import BertTokenizer, GPTaTokenizer, RobertaTokenizer
logging.basicConfig(
format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s''', datefmt='''%m/%d/%Y %H:%M:%S''', level=log... | 44 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 1 |
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():
from PIL import Image
from... | 44 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 1 |
# Algorithm for the pigeonhole sorting
def _lowerCAmelCase ( __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : str = min(__lowerCAmelCase ) # min() finds the minimum value
snake_case__ : Optional[int] = ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Optional[int] = len(__lowerCAmelCase ) + 1
snake_case__ : Tuple = len(__lowerCAmelCase ) + 1
# dp is a 2d matrix where d... | 44 | 1 |
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class a ( __lowerCamelCase ):
__lowerCAmelCase : Tuple = ["""image_processor""", """tokenizer"""]
__lowerCAmelCase : Optional[Any] ... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase = 50 ) -> int:
"""simple docstring"""
snake_case__ : Optional[int] = [1] * (length + 1)
for row_length in range(3 , length + 1 ):
for block_length in range(3 , row_length + 1 ):
for bloc... | 44 | 1 |
import math
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> int:
"""simple docstring"""
if 0 not in (x, y):
# We use the relation x^y = y*log10(x), where 10 is the base.
return y * math.logaa(__lowerCAmelCase )
else:
if x ... | 44 |
import gc
import tempfile
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionPipeline
from diffusers.utils.testing_utils import load_image, nightly, require_torch_gpu, torch_device
A__ = False
class a ( unittest.TestCase ):
... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Dict = [int(__lowerCAmelCase ) for i in ip_va_address.split('''.''' ) if i.isdigit()]
return len(__lowerCAmelCase ) == 4 and all(0 <= int(__lowerCAmelCase ) <=... | 44 |
from typing import TYPE_CHECKING
from ...file_utils import _LazyModule, is_tokenizers_available, is_torch_available, is_vision_available
from ...utils import OptionalDependencyNotAvailable
A__ = {'''configuration_dpt''': ['''DPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DPTConfig''']}
try:
... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase = 100 ) -> int:
"""simple docstring"""
snake_case__ : Union[str, Any] = set()
snake_case__ : Optional[Any] = 0
snake_case__ : Optional[int] = n + 1 # maximum limit
for a... | 44 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> str:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
while cur > 1:
# Find the maximum number in arr
snake_case__ : str = arr.index(max(arr[0:cur] ) )
... | 44 |
A__ = 256
# Modulus to hash a string
A__ = 100_0003
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : str = len(__lowerCAmelCase )
snake_case__ : Optional[in... | 44 | 1 |
import argparse
import os
import torch
from transformers import (
XLNetConfig,
XLNetForQuestionAnswering,
XLNetForSequenceClassification,
XLNetLMHeadModel,
load_tf_weights_in_xlnet,
)
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
A__ = {
... | 44 |
import os
import unittest
from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class a ( __lowerCamelCase , unittest.TestCase ):
__lowerCAmelCase : Dict ... | 44 | 1 |
from __future__ import annotations
import unittest
from transformers import XGLMConfig, XGLMTokenizer, 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... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
if not all(x.isalpha() for x in string ):
raise ValueError('''String must only contain alphabetic characters.''' )
snake_case__ : str = sorted(string.lower() )
r... | 44 | 1 |
from datetime import datetime
import requests
def _lowerCAmelCase ( __lowerCAmelCase ) -> bytes:
"""simple docstring"""
snake_case__ : str = '''https://downloadgram.net/wp-json/wppress/video-downloader/video?url='''
snake_case__ : Union[str, ... | 44 |
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,
... | 44 | 1 |
import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple
import regex as re
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
A__ = logging.get_logger(__name__)
A__ = {'''vocab_file''':... | 44 |
A__ = 0 # The first color of the flag.
A__ = 1 # The second color of the flag.
A__ = 2 # The third color of the flag.
A__ = (red, white, blue)
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
if n... | 44 | 1 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
if divisor % 5 == 0 or divisor % 2 == 0:
return 0
snake_case__ : List[str] = 1
snake_case__ : int = 1
while repunit:
snake_case__ : Dic... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 |
import argparse
from collections import defaultdict
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> Optional[Any]:
"""simple docstring"""
snake_case__ : Dict = f"""{file}_{clas... | 44 | 1 |
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> float:
"""simple docstring"""
return round(float(moles / volume ) * nfactor )
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase , __lowerCAmelCase ) -> flo... | 44 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import C... | 44 | 1 |
import numpy as np
def _lowerCAmelCase ( __lowerCAmelCase ) -> np.array:
"""simple docstring"""
return 1 / (1 + np.exp(-vector ))
if __name__ == "__main__":
import doctest
doctest.testmod()
| 44 |
import math
import tensorflow as tf
from packaging import version
def _lowerCAmelCase ( __lowerCAmelCase ) -> Tuple:
"""simple docstring"""
snake_case__ : List[str] = tf.convert_to_tensor(__lowerCAmelCase )
snake_case__ : Dict = ... | 44 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( __lowerCAmelCase , __lowerCAmelCase ) -> list[list[int]]:
"""simple docstring"""
snake_case__ : list[list[int]] = []
create_all_state(1 , __lowerCAmelCase , __lowerCAmelCase , [] , __lo... | 44 |
def _lowerCAmelCase ( __lowerCAmelCase ) -> list:
"""simple docstring"""
for i in range(len(__lowerCAmelCase ) - 1 , 0 , -1 ):
snake_case__ : List[Any] = False
for j in range(__lowerCAmelCase , 0 , -1 ):
if unsorte... | 44 | 1 |
import warnings
from diffusers import StableDiffusionInpaintPipeline as StableDiffusionInpaintPipeline # noqa F401
warnings.warn(
'''The `inpainting.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionInpaintPipeline` instead.'''
)
| 44 |
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 PoolFormerConfig, PoolFormerForImageClassification, PoolFormerImageProcessor
from transfor... | 44 | 1 |
from __future__ import annotations
def _lowerCAmelCase ( __lowerCAmelCase ) -> bool:
"""simple docstring"""
snake_case__ : Any = str(__lowerCAmelCase )
return n == n[::-1]
def _lowerCAmelCase ( __lowerCAmelCase = 1000000 ) -> L... | 44 |
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
A__ = 200
# Number of elements selected in every generation of evolution. The selection takes
# place from best to worst of that generation and must be sm... | 44 | 1 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
... | 44 |
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
... | 44 | 1 |
import gc
import random
import tempfile
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import AutoencoderKL, DDIMScheduler, LMSDiscreteScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.stable_... | 44 |
import collections
import os
from typing import List, Optional, Tuple
from transformers.utils import is_jieba_available, requires_backends
if is_jieba_available():
import jieba
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
A__ = logging.ge... | 44 | 1 |
# XXX: we want transformers master here - in the absense of conftest manipulating sys.path:
# hack it in for now:
import sys
from pathlib import Path
A__ = Path(__file__).resolve().parents[3] / '''src'''
sys.path.insert(1, str(git_repo_path))
import dataclasses # noqa
import io # noqa... | 44 |
from sklearn.metrics import mean_squared_error
import datasets
A__ = '''\
@article{scikit-learn,
title={Scikit-learn: Machine Learning in {P}ython},
author={Pedregosa, F. and Varoquaux, G. and Gramfort, A. and Michel, V.
and Thirion, B. and Grisel, O. and Blondel, M. and Prettenho... | 44 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
A__ = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOn... | 44 |
from manim import *
class a ( __lowerCamelCase ):
def __lowerCamelCase ( self :Union[str, Any] ):
snake_case__ : Optional[Any] = Rectangle(height=0.5 ,width=0.5 )
snake_case__ : Optional[int] = Rectangle(he... | 44 | 1 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def _lowerCAmelCase ( __lowerCAmelCase ) -> int:
"""simple docstring"""
snake_case__ : Optional[Any] = [
'''encoder.version... | 44 |
import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_... | 44 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.