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 |
|---|---|---|---|---|
import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional
import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset
from transformers import DPRC... | 707 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils impo... | 53 | 0 |
from arguments import InitializationArguments
from transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer, HfArgumentParser
# Configuration
__UpperCamelCase : Optional[Any] = HfArgumentParser(InitializationArguments)
__UpperCamelCase : str = parse... | 708 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case ( lowerCamelCase ):
'''simple docstring'''... | 53 | 0 |
'''simple docstring'''
from __future__ import annotations
from collections.abc import Iterable, Iterator
from dataclasses import dataclass
__UpperCamelCase : List[str] = (3, 9, -11, 0, 7, 5, 1, -1)
__UpperCamelCase : int = (4, 6, 2, 0, 8, 10, 3, -2)
@dat... | 709 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_... | 53 | 0 |
import torch
from diffusers import StableDiffusionPipeline
__UpperCamelCase : Union[str, Any] = """path-to-your-trained-model"""
__UpperCamelCase : List[Any] = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.floataa).to("""cuda""")
__UpperCa... | 710 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ... | 53 | 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_p... | 711 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggi... | 53 | 0 |
class __UpperCamelCase :
def __init__( self : str , _lowerCAmelCase : int ) -> None:
"""simple docstring"""
__lowercase = size
__lowercase = [0] * size
__lowercase = [0] * size
@staticmethod
def _... | 712 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 53 | 0 |
import warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_SEQUENCE_CLASS... | 713 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All val... | 53 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_dete... | 714 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding +... | 53 | 0 |
from __future__ import annotations
import math
from collections.abc import Callable
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase = 100 , ):
'''simple docstring'''
__lowercase = x_start
__lowercase = fnc(l... | 715 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(_... | 53 | 0 |
from __future__ import annotations
def snake_case ( lowerCamelCase , lowerCamelCase = None , lowerCamelCase = None , lowerCamelCase = False , ):
'''simple docstring'''
__lowercase = cipher_alphabet or [chr(lowerCamelCase ) for i in range(97 , 123 ... | 716 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (ksize % 2) ... | 53 | 0 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowerCamelCase )
for i in range(lowerCamelCase ):
for j in range(i + 1 , lowerCamelCase ):
if numbers[j] < numbers[i]:
__lowercase , __lowercase = numbers[... | 717 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ... | 53 | 0 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All val... | 718 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 53 | 0 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ....tokenization_utils_fast import PreTrainedTokenizerFast
from ....utils import logging
from .tokenization_retribert import RetriBertTokenizer
__UpperCamelCase : Dict = logging.get_logger(__... | 719 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__lowercase = str(lowerCamelCase )
__lowercase = """""".joi... | 53 | 0 |
import inspect
import unittest
from transformers import MobileViTConfig
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 ...... | 720 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerCon... | 53 | 0 |
'''simple docstring'''
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = current_set.copy()
for row_index, row in enumerate(lowerCamelCase ):
__lowercase = row[0]
for column_index, column in enumerate(lowerCamelCase ):
... | 721 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_uti... | 53 | 0 |
'''simple docstring'''
from __future__ import annotations
def snake_case ( lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
print(F'Vertex\tShortest Distance from vertex {src}' )
for i, d in enumerate(lowerCamelCase ):
print(F'{i}\t\t{d}' )
def... | 700 |
import heapq
import sys
import numpy as np
__UpperCamelCase : List[str] = tuple[int, int]
class __UpperCamelCase :
def __init__( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = []
__lowercase ... | 53 | 0 |
import argparse
import torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
... | 701 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import log... | 53 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModelWithProjection, CLIPTokenizer
from diffusers import HeunDiscreteScheduler, PriorTransformer, ShapEPipeline
from diffusers.pipelines.shap_e import ShapERenderer
from diffusers.utils import load_numpy,... | 702 |
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
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
... | 53 | 0 |
from typing import Dict, Iterable, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension... | 703 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor... | 53 | 0 |
import json
import logging
import os
import re
import sys
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Union
import datasets
import numpy as np
import torch
import torchaudio
from packaging import version
from torch import nn
import transformers
from transformers impor... | 704 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_... | 53 | 0 |
import gc
import unittest
import numpy as np
import torch
from diffusers import DanceDiffusionPipeline, IPNDMScheduler, UNetaDModel
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps
from ..pipeline_par... | 705 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 53 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Optional[Any] = {
"""configuration_mega""": ["""MEGA_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MegaConfig""", """MegaOnnxConfig"""],... | 706 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import ... | 53 | 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 TFM... | 707 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils impo... | 53 | 0 |
import argparse
import json
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging
logging.set_verbosity_info()
__... | 708 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case ( lowerCamelCase ):
'''simple docstring'''... | 53 | 0 |
'''simple docstring'''
# 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 g... | 709 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_... | 53 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_sentencepiece_available,
is_torch_available,
)
__UpperCamelCase : Dict = {
"""configuration_speecht5""": [
"""SPEECHT5_PRETRAINED_CONFIG_ARCHIVE_MAP""",
... | 710 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ... | 53 | 0 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = len(lowerCamelCase )
__lowercase = sum(lowerCamelCase )
__lowercase = [[False for x in range(s + 1 )] for y in range(n + 1 )]
for i in range(1 , n + 1 ):
... | 711 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggi... | 53 | 0 |
import os
from bleurt import score # From: git+https://github.com/google-research/bleurt.git
import datasets
__UpperCamelCase : Optional[int] = datasets.logging.get_logger(__name__)
__UpperCamelCase : int = """\
@inproceedings{bleurt,
title={BLEURT: Lear... | 712 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 53 | 0 |
import argparse
import torch
from torch import nn
from transformers import MBartConfig, MBartForConditionalGeneration
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = [
"""encoder.version""",
"""decoder.version""",
"""model.encod... | 713 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All val... | 53 | 0 |
from dataclasses import dataclass, field
from typing import ClassVar, Dict
from ..features import Features, Sequence, Value
from .base import TaskTemplate
@dataclass(frozen=_lowerCAmelCase )
class __UpperCamelCase ( _lowerCAmelCase ):
# `task` is not a ClassVar since we want it to be part o... | 714 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding +... | 53 | 0 |
from typing import List, Optional, Union
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class __UpperCamelCase ... | 715 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(_... | 53 | 0 |
def snake_case ( lowerCamelCase = 10 ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ) or n < 0:
raise ValueError("""Invalid input""" )
__lowercase = 10**n
__lowercase = 28_433 * (pow(2 , 7_830_457 , lowerCamelC... | 716 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (ksize % 2) ... | 53 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available
__UpperCamelCase : List[Any] = {}
try:
if not is_sentencepiece_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 717 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ... | 53 | 0 |
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_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer... | 718 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 53 | 0 |
import json
import os
import unittest
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES, BioGptTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class __UpperCamelCase ( _lowerCAmelCase , unittest.Te... | 719 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__lowercase = str(lowerCamelCase )
__lowercase = """""".joi... | 53 | 0 |
import inspect
import unittest
from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_c... | 720 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerCon... | 53 | 0 |
'''simple docstring'''
import unittest
from diffusers.pipelines.pipeline_utils import is_safetensors_compatible
class __UpperCamelCase ( unittest.TestCase ):
def _a ( self : List[Any] ) -> Optional[Any]:
"""simple docstring"""
__lowercase =... | 721 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_uti... | 53 | 0 |
'''simple docstring'''
import pytest
import datasets
# Import fixture modules as plugins
__UpperCamelCase : Optional[int] = ["""tests.fixtures.files""", """tests.fixtures.hub""", """tests.fixtures.fsspec"""]
def snake_case ( lowerCamelCase , lowerCamelCase )... | 700 |
import heapq
import sys
import numpy as np
__UpperCamelCase : List[str] = tuple[int, int]
class __UpperCamelCase :
def __init__( self : Optional[int] ) -> Dict:
"""simple docstring"""
__lowercase = []
__lowercase ... | 53 | 0 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 701 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import log... | 53 | 0 |
from dataclasses import dataclass
from typing import Optional
import torch
from torch import nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .attention import BasicTransformerBlock
from .modeling_utils import ModelMixin
@dataclass
class __UpperC... | 702 |
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
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
... | 53 | 0 |
from collections import OrderedDict
from typing import Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__Up... | 703 |
from __future__ import annotations
import collections
import tempfile
import unittest
import numpy as np
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import is_tf_available, is_vision_available
from ...test_modeling_tf_common import floats_tensor, ids_tensor... | 53 | 0 |
import copy
import inspect
import unittest
from transformers import PretrainedConfig, SwiftFormerConfig
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 ...t... | 704 |
from __future__ import annotations
from scipy.special import comb # type: ignore
class __UpperCamelCase :
def __init__( self : int , _lowerCAmelCase : list[tuple[float, float]] ) -> Any:
"""simple docstring"""
__lowercase = list_of_... | 53 | 0 |
import bza
import gzip
import lzma
import os
import shutil
import struct
import tarfile
import warnings
import zipfile
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List, Optional, Type, Union
from .. import config
from .filelock import FileLock
from... | 705 |
import inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test_configuration_commo... | 53 | 0 |
import argparse
import json
import pickle
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import MaskFormerConfig, MaskFormerForInstanceSegmentation, MaskFormerImageProcessor, SwinConfig
from transformers.utils import log... | 706 |
import argparse
import json
import os
import re
import shutil
import torch
from transformers import BioGptConfig, BioGptForCausalLM
from transformers.models.biogpt.tokenization_biogpt import VOCAB_FILES_NAMES
from transformers.tokenization_utils_base import TOKENIZER_CONFIG_FILE
from transformers.utils import ... | 53 | 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_image_inputs
if is_torch_available():
impor... | 707 |
import tempfile
import unittest
import numpy as np
from diffusers import (
DDIMScheduler,
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionPipeline,
PNDMScheduler,
)
from diffusers.utils.testing_utils impo... | 53 | 0 |
import os
import pytest
import yaml
from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict
@pytest.mark.parametrize(
"""files""" , [
["""full:README.md""", """dataset_infos.json"""],
["""empty:README.md""", """dataset_infos.js... | 708 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
__lowercase = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def snake_case ( lowerCamelCase ):
'''simple docstring'''... | 53 | 0 |
'''simple docstring'''
import json
import logging
import math
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
from datasets import Dataset, load_dataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_FOR_MASKED_LM_MAPPING,
AutoCon... | 709 |
import random
import unittest
import torch
from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device
from ..pipeline_params import (
TEXT_GUIDED_IMAGE_... | 53 | 0 |
import inspect
import logging
import os
import random
import shutil
import tempfile
import unittest
import pytest
import torch
from torch import nn
from torch.utils.data import DataLoader, TensorDataset
from accelerate import Accelerator
from accelerate.test_utils import execute_subprocess_async, require_cuda
fr... | 710 |
import torch
from diffusers import UnCLIPScheduler
from .test_schedulers import SchedulerCommonTest
class __UpperCamelCase ( _lowerCAmelCase ):
__snake_case :str = (UnCLIPScheduler,)
def _a ( self : Optional[int] , **_lowerCAmelCase : Any ... | 53 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase : Union[str, ... | 711 |
import os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrainedTokenizerBase
from ...utils import loggi... | 53 | 0 |
import argparse
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed
from accelerate import Accelerator... | 712 |
import logging
import math
import os
from dataclasses import dataclass, field
from glob import glob
from typing import Optional
from torch.utils.data import ConcatDataset
import transformers
from transformers import (
CONFIG_MAPPING,
MODEL_WITH_LM_HEAD_MAPPING,
AutoConfig,
AutoModelWithLMHead,
... | 53 | 0 |
from math import sqrt
def snake_case ( lowerCamelCase ):
'''simple docstring'''
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
... | 713 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if len(lowerCamelCase ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueError("""All val... | 53 | 0 |
import unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class __UpperCamelCase ( _lowerCAmelCase , unittest.TestC... | 714 |
from __future__ import annotations
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not nums:
return 0
__lowercase = nums[0]
__lowercase = 0
for num in nums[1:]:
__lowercase , __lowercase = (
max_excluding +... | 53 | 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, DDPMScheduler, Stab... | 715 |
import argparse
import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, RobertaPreLayerNormConfig, RobertaPreLayerNormForMaskedLM
from transformers.utils import logging
logging.set_verbosity_info()
__UpperCamelCase : str = logging.get_logger(_... | 53 | 0 |
import argparse
import torch
from transformers import RemBertConfig, RemBertModel, load_tf_weights_in_rembert
from transformers.utils import logging
logging.set_verbosity_info()
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
... | 716 |
import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey
def snake_case ( lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase , lowerCamelCase ):
'''simple docstring'''
if (ksize % 2) ... | 53 | 0 |
import tempfile
import unittest
from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from transformers.testing_utils import (
is_torch_available,
require_optimum,
require_torch,
slow,
)
if is_torch_available():
import torch
@require_torch
@require_optimum
@slow
class __UpperCa... | 717 |
import argparse
import json
import os
import time
import zipfile
from get_ci_error_statistics import download_artifact, get_artifacts_links
from transformers import logging
__UpperCamelCase : Optional[Any] = logging.get_logger(__name__)
def snake_case ( lowerCamelCase ... | 53 | 0 |
from sklearn.metrics import recall_score
import datasets
__UpperCamelCase : Optional[Any] = """
Recall is the fraction of the positive examples that were correctly labeled by the model as positive. It can be computed with the equation:
Recall = TP / (TP + FN)
Where TP is the true posi... | 718 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ap... | 53 | 0 |
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 (
TFBaseModelOutputWithNoAttention,
TFBaseModelOutp... | 719 |
def snake_case ( lowerCamelCase ):
'''simple docstring'''
if not isinstance(lowerCamelCase , lowerCamelCase ):
raise ValueError("""check_bouncy() accepts only integer arguments""" )
__lowercase = str(lowerCamelCase )
__lowercase = """""".joi... | 53 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
__UpperCamelCase : Optional[int] = {
"""configuration_pegasus_x""": ["""PEGASUS_X_PRETRAINED_CONFIG_ARCHIVE_MAP""", """PegasusXConfig"""],
}
try:
if not is_t... | 720 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_torch_available,
)
__UpperCamelCase : Tuple = {
"""configuration_swiftformer""": [
"""SWIFTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""SwiftFormerCon... | 53 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__UpperCamelCase : Optional[int] = logging.get_logger(__name__)
__UpperCamelCase ... | 721 |
import gc
import unittest
from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_uti... | 53 | 0 |
def lowerCAmelCase_ ( snake_case_ ):
_A : str = set()
# edges = list of graph's edges
_A : Optional[Any] = get_edges(snake_case_ )
# While there are still elements in edges list, take an arbitrary edge
# (from_node, to_node) and add his extremity to ... | 54 |
import operator
def lowerCAmelCase_ ( snake_case_,snake_case_ = False,snake_case_ = None ):
_A : str = operator.lt if reverse else operator.gt
_A : Optional[Any] = solution or []
if not arr:
return solution
_A : Dict = [arr.po... | 54 | 1 |
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .embeddings import GaussianFourierProjection, TimestepEmbedding, Timesteps
from .modeling_utils impo... | 54 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf... | 54 | 1 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowercase ( tf.keras.layers.Layer ):
def __init__( self , _a , ... | 54 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 54 | 1 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging
_snake_case = logging.get_logger(__name__)
_snake_case = {"vocab_file": "se... | 54 |
from datetime import datetime as dt
import os
from github import Github
_snake_case = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def lowerCAmelCase_ ( ):
_A : int = Github(os.env... | 54 | 1 |
from math import pi, sqrt, tan
def lowerCAmelCase_ ( snake_case_ ):
if side_length < 0:
raise ValueError("""surface_area_cube() only accepts non-negative values""" )
return 6 * side_length**2
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_ ):
... | 54 |
from __future__ import annotations
class lowercase :
def __init__( self , _a = 0 ) -> str:
_A : Any = key
def a__ ( self , _a , _a ) -> list[str]:
assert isinstance(_a , _a ) and isinstance(_a , _a ... | 54 | 1 |
import hashlib
import unittest
from typing import Dict
import numpy as np
from transformers import (
MODEL_FOR_MASK_GENERATION_MAPPING,
TF_MODEL_FOR_MASK_GENERATION_MAPPING,
is_vision_available,
pipeline,
)
from transformers.pipelines import MaskGenerationPipeline
from transformers.testing_utils ... | 54 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_ut... | 54 | 1 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def lowerCAmelCase_ ( snake_case_ ):
if not isinstance(snake_case_,snake_case_ ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
raise ValueError("""Undefined fo... | 54 |
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : Union[str, Any] = """"""
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase_ ( snake_case_ ):
return data[1:] + data[0]
def lowerCAmelCase_ ( snake_case_,s... | 54 | 1 |
def lowerCAmelCase_ ( snake_case_ ):
_A : List[Any] = int(snake_case_ )
if n_element < 1:
_A : Any = ValueError("""a should be a positive number""" )
raise my_error
_A : int = [1]
_A , _A , _A : Opti... | 54 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import... | 54 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 54 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configu... | 54 | 1 |
_snake_case = [
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ):
# Return True if there is node that has... | 54 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( snake_case_ ):
# A local function to see if a dot lands in the circle.
def is_in_circle(snake_case_,snake_case_ ) -> bool:
_A ... | 54 | 1 |
import unittest
from pathlib import Path
from shutil import copyfile
from transformers import SPIECE_UNDERLINE, is_sentencepiece_available
from transformers.models.speech_to_text import SpeechaTextTokenizer
from transformers.models.speech_to_text.tokenization_speech_to_text import VOCAB_FILES_NAMES, save_json
fro... | 54 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_be... | 54 | 1 |
import unittest
import numpy as np
import torch
from diffusers import VersatileDiffusionImageVariationPipeline
from diffusers.utils.testing_utils import load_image, require_torch_gpu, slow, torch_device
_snake_case = False
class lowercase ( unittest.TestCase ):
pass
@s... | 54 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code ... | 54 | 1 |
from dataclasses import dataclass
from typing import Dict, 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 .attention_processor import AttentionProcessor, AttnProcessor
from .modeli... | 54 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import... | 54 | 1 |
import warnings
from ...utils import logging
from .image_processing_videomae import VideoMAEImageProcessor
_snake_case = logging.get_logger(__name__)
class lowercase ( UpperCamelCase__ ):
def __init__( self , *_a , **_a ) -> None:
warnings.warn(... | 54 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
... | 54 | 1 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
_snake_case = logging.get_logger(__name__)
_snake_case = {
"shi-labs/dinat-mini-in1k-224": "https://huggi... | 54 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow... | 54 | 1 |
def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_,snake_case_,):
_A : Dict = [redshift, radiation_density, matter_density, dark_energy]
if any(p < 0 for p in parameters ):
raise ValueError("""All input parameters must be positive""" )
if any... | 54 |
def lowerCAmelCase_ ( snake_case_ = 1000000 ):
_A : Any = limit + 1
_A : Tuple = [0] * limit
for first_term in range(1,snake_case_ ):
for n in range(snake_case_,snake_case_,snake_case_ ):
_A : Optional[int] = first_term +... | 54 | 1 |
import io
import itertools
import json
from dataclasses import dataclass
from typing import Optional
import pyarrow as pa
import pyarrow.json as paj
import datasets
from datasets.table import table_cast
from datasets.utils.file_utils import readline
_snake_case = datasets.utils.logging.get_logger(__... | 54 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowercase ( tf.keras.layers.Layer ):
def __init__( self , _a , ... | 54 | 1 |
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_flax_available():
from transformers.mod... | 54 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake... | 54 | 1 |
from collections import defaultdict
def lowerCAmelCase_ ( snake_case_ ):
_A : Optional[Any] = 1
_A : Optional[int] = True
for v in tree[start]:
if v not in visited:
ret += dfs(snake_case_ )
if ret % 2 == 0:
c... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 54 | 1 |
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,
LMSDiscreteScheduler,
PNDMScheduler,
S... | 54 |
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowercase ( UpperCamelCase__ ):
def a__ ( self ) -> Optional[int]:
return [
{"col_1": 3, "col_2": "a"},
{"... | 54 | 1 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_owlvit": [
"OWLVIT_PRETRA... | 54 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : List[str] = list(snake_case_ )
_A : List[Any] = list(snake_case_ )
_A : Tuple = 0
fo... | 54 | 1 |
import unittest
from huggingface_hub import hf_hub_download
from transformers import MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING, VideoMAEFeatureExtractor
from transformers.pipelines import VideoClassificationPipeline, pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
re... | 54 |
from __future__ import annotations
def lowerCAmelCase_ ( snake_case_,snake_case_ = None ):
_A : Tuple = word_bank or []
# create a table
_A : int = len(snake_case_ ) + 1
_A : list[list[list[str]]] = []
for _ in range(snake_case_ ):... | 54 | 1 |
import inspect
import unittest
from datasets import load_dataset
from packaging import version
from transformers import BeitConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.ut... | 54 |
import operator
def lowerCAmelCase_ ( snake_case_,snake_case_ = False,snake_case_ = None ):
_A : str = operator.lt if reverse else operator.gt
_A : Optional[Any] = solution or []
if not arr:
return solution
_A : Dict = [arr.po... | 54 | 1 |
import mpmath # for roots of unity
import numpy as np
class lowercase :
def __init__( self , _a=None , _a=None ) -> Union[str, Any]:
# Input as list
_A : Optional[int] = list(poly_a or [0] )[:]
_A : List[str] = l... | 54 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf... | 54 | 1 |
import gc
import unittest
from transformers import MODEL_FOR_MASKED_LM_MAPPING, TF_MODEL_FOR_MASKED_LM_MAPPING, FillMaskPipeline, pipeline
from transformers.pipelines import PipelineException
from transformers.testing_utils import (
is_pipeline_test,
is_torch_available,
nested_simplify,
require_tf... | 54 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 54 | 1 |
import json
import os
import unittest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@req... | 54 |
from datetime import datetime as dt
import os
from github import Github
_snake_case = [
"good first issue",
"good second issue",
"good difficult issue",
"feature request",
"new model",
"wip",
]
def lowerCAmelCase_ ( ):
_A : int = Github(os.env... | 54 | 1 |
def lowerCAmelCase_ ( snake_case_ = 50 ):
_A : str = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2,5 ):
for tile_start in range(row_length - tile_length + 1 ):
ways_number[row_length] +... | 54 |
from __future__ import annotations
class lowercase :
def __init__( self , _a = 0 ) -> str:
_A : Any = key
def a__ ( self , _a , _a ) -> list[str]:
assert isinstance(_a , _a ) and isinstance(_a , _a ... | 54 | 1 |
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_PARAMS, UNCONDITIONAL_IMAGE_G... | 54 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_ut... | 54 | 1 |
from __future__ import annotations
from collections.abc import Sequence
from typing import Literal
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : List[str] = list(snake_case_ )
_A : List[Any] = list(snake_case_ )
_A : Tuple = 0
fo... | 54 |
def lowerCAmelCase_ ( snake_case_,snake_case_ ):
_A : Union[str, Any] = """"""
for i in table:
res += inp[i - 1]
return res
def lowerCAmelCase_ ( snake_case_ ):
return data[1:] + data[0]
def lowerCAmelCase_ ( snake_case_,s... | 54 | 1 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available
_snake_case = {"tokenization_herbert": ["HerbertTokenizer"]}
try:
if not is_tokenizers_available():
raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvai... | 54 |
import unittest
import numpy as np
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_torch_available():
import... | 54 | 1 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from datasets import load_dataset
from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_ut... | 54 |
from typing import Any, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from torch.utils.data import DistributedSampler, RandomSampler
from transformers import PreTrainedModel, Trainer, logging
from transformers.integrations import is_fairscale_available
from transformers.models.fsmt.configu... | 54 | 1 |
import argparse
import re
import numpy as np
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SamConfig,
SamImageProcessor,
SamModel,
SamProcessor,
SamVisionConfig,
)
_snake_case = {
"iou_prediction_head... | 54 |
from collections.abc import Callable
from math import pi, sqrt
from random import uniform
from statistics import mean
def lowerCAmelCase_ ( snake_case_ ):
# A local function to see if a dot lands in the circle.
def is_in_circle(snake_case_,snake_case_ ) -> bool:
_A ... | 54 | 1 |
import builtins
import sys
from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP
_snake_case = False
try:
_snake_case = _is_package... | 54 |
import json
import os
import shutil
import tempfile
from unittest import TestCase
from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_be... | 54 | 1 |
from ... import PretrainedConfig
_snake_case = {
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowercase ( UpperCamelCase__ ):
_a = NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP
_a = "nezha"
... | 54 |
import os
import re
import shutil
import sys
import tempfile
import unittest
import black
_snake_case = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, "utils"))
import check_copies # noqa: E402
# This is the reference code ... | 54 | 1 |
import sys
def lowerCAmelCase_ ( snake_case_ ):
_A : Dict = len(snake_case_ )
_A : int = [[0 for x in range(snake_case_ )] for x in range(snake_case_ )]
_A : Union[str, Any] = [[0 for x in range(snake_case_ )] for x in range(snake_case_ )]
... | 54 |
import itertools
import os
import random
import tempfile
import unittest
import numpy as np
from transformers import TvltFeatureExtractor, is_datasets_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import... | 54 | 1 |
import json
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer
_snake_case = logging.get_logger(__name__)
_snake_case ... | 54 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_snake_case = {
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"TimeSeriesTransformerConfig",
],
}
try:
... | 54 | 1 |
import unittest
from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake_case = "▁"
_snake_case... | 54 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from diffusers import (
DDIMScheduler,
KandinskyVaaImgaImgPipeline,
KandinskyVaaPriorPipeline,
UNetaDConditionModel,
VQModel,
)
from diffusers.utils import floats_tensor, load_image, load_numpy, slow... | 54 | 1 |
import random
import unittest
import numpy as np
from diffusers import (
DPMSolverMultistepScheduler,
EulerAncestralDiscreteScheduler,
EulerDiscreteScheduler,
LMSDiscreteScheduler,
OnnxStableDiffusionImgaImgPipeline,
PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffuser... | 54 |
def lowerCAmelCase_ ( snake_case_ = 1000000 ):
_A : Any = limit + 1
_A : Tuple = [0] * limit
for first_term in range(1,snake_case_ ):
for n in range(snake_case_,snake_case_,snake_case_ ):
_A : Optional[int] = first_term +... | 54 | 1 |
import logging
import os
import threading
import time
try:
import warnings
except ImportError:
_snake_case = None
try:
import msvcrt
except ImportError:
_snake_case = None
try:
import fcntl
except ImportError:
_snake_case = None
# Backward compatibility
# --------... | 54 |
import os
from typing import Dict, List, Union
import tensorflow as tf
from keras_nlp.tokenizers import BytePairTokenizer
from tensorflow_text import pad_model_inputs
from .tokenization_gpta import GPTaTokenizer
class lowercase ( tf.keras.layers.Layer ):
def __init__( self , _a , ... | 54 | 1 |
def lowerCAmelCase_ ( snake_case_ = 1000000 ):
_A : Any = limit + 1
_A : Tuple = [0] * limit
for first_term in range(1,snake_case_ ):
for n in range(snake_case_,snake_case_,snake_case_ ):
_A : Optional[int] = first_term +... | 54 |
import unittest
from transformers import PegasusTokenizer, PegasusTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property
from ...test_tokenization_common import TokenizerTesterMixin
_snake... | 54 | 1 |
from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def lowerCAmelCase_ ( ):
_A : Optional[Any] = HfArgumentParser(snake_case_ )
_A : Optional[int] = parser.parse_args_into_dataclasses()[0]
_A : Optional[Any] ... | 54 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_torch_available,
is_vision_available,
)
_snake_case = {
"configuration_efficientformer": [
"EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 54 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.