code stringlengths 87 55.2k | code_codestyle int64 0 349 | style_context stringlengths 135 49.1k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
'''simple docstring'''
from ...configuration_utils import PretrainedConfig
from ...utils import logging
_A : Dict =logging.get_logger(__name__)
_A : List[Any] ={
'''google/vivit-b-16x2-kinetics400''': (
'''https://huggingface.co/google/vivit-b-16x2... | 41 |
"""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
... | 291 | 0 |
'''simple docstring'''
from __future__ import annotations
class __UpperCAmelCase :
def __init__( self , lowerCAmelCase_ ):
"""simple docstring"""
_snake_case = TypeError(
'Matrices must be formed from a list of zero or more lists ... | 42 |
"""simple docstring"""
from math import ceil
def a__ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCamelCase = list(range(0 , snake_case__ ) )
lowerCamelCase = [item for sublist in list(device_map.values() ) for item in sublist]... | 291 | 0 |
__lowercase = [sum(int(c, 10) ** 2 for c in i.__str__()) for i in range(10_0000)]
def lowerCamelCase ( SCREAMING_SNAKE_CASE ):
'''simple docstring'''
__UpperCamelCase :Tuple = 0
while number:
# Increased Speed Slightly by checking every 5 digits together.
sum... | 43 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 291 | 0 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import torch
from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
class __... | 44 |
"""simple docstring"""
from typing import Any
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> list:
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ... | 291 | 0 |
"""simple docstring"""
import tempfile
import numpy as np
import torch
from transformers import AutoTokenizer, TaEncoderModel
from diffusers import DDPMScheduler, UNetaDConditionModel
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.pipelines.deepfloyd_if imp... | 45 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) ... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
SCREAMING_SNAKE_CASE__ = {
"configuration_x_clip": [
"XCLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XCLIPConfig",
"XCLIPTextConfig",
... | 46 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 291 | 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 A__ ( A__... | 47 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ) -> Union[str, Any]:
lowerCamelCase = ArgumentParser(
description=(
... | 291 | 0 |
from __future__ import annotations
from math import pi, sqrt
def A ( _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ) -> tuple:
if inductance <= 0:
raise ValueError("Inductance cannot be 0 or negative" )
elif capacitance <= 0:
raise Va... | 48 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : List[str] = {
"""asapp/sew-d-tiny-100k""": """h... | 291 | 0 |
import os
def __snake_case ( ):
__a = os.path.dirname(os.path.realpath(_UpperCAmelCase ) )
__a = os.path.join(_UpperCAmelCase , '''triangle.txt''' )
with open(_UpperCAmelCase ) as f:
__a = f.readlines()
__a ... | 49 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase : 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 ... | 291 | 0 |
from __future__ import annotations
import math
def SCREAMING_SNAKE_CASE ( _UpperCAmelCase ) -> bool:
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or number % 2 == 0 or number % 3 == 0:
# Negatives, 0, 1, all even numbers, all multi... | 50 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , _a , _a , _a ):
... | 291 | 0 |
import inspect
from typing import Optional, Union
import numpy as np
import PIL
import torch
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPFeatureExtractor, CLIPModel, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
... | 51 |
"""simple docstring"""
def a__ ( snake_case__ ) -> bool:
lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( snake_case__ = 50_00 ) -> int:
lowerCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , sna... | 291 | 0 |
import argparse
import requests
import torch
from PIL import Image
from transformers import ViTMAEConfig, ViTMAEForPreTraining, ViTMAEImageProcessor
def A_ ( _lowerCAmelCase ) -> Optional[int]:
if "cls_token" in name:
UpperCamelCase : Union[str, Any] = name.replace("cls_toke... | 52 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 0 |
'''simple docstring'''
def lowercase__ ( __lowercase : str , __lowercase : int ) -> list:
"""simple docstring"""
__UpperCamelCase = word.split()
def justify(__lowercase : list , __lowercase : int , __lowercase : int ... | 53 |
"""simple docstring"""
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():
fro... | 291 | 0 |
"""simple docstring"""
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
a__ : Tuple = False
class UpperCamelCase_ ( unittest.Test... | 54 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.uti... | 291 | 0 |
'''simple docstring'''
def __snake_case ( UpperCAmelCase_ : int = 1000 ):
return sum(2 * a * ((a - 1) // 2) for a in range(3 , n + 1 ) )
if __name__ == "__main__":
print(solution())
| 55 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ = False ) -> str:
if not isinstance(snake_case__ , snake_case__ ):
lowerCamelCase = F'Expected string as input, found {type(snake_case__ )}'
raise ValueError(snake_case__ )
if not... | 291 | 0 |
'''simple docstring'''
from collections import UserDict
from typing import Union
import numpy as np
import requests
from ..utils import (
add_end_docstrings,
logging,
)
from .audio_classification import ffmpeg_read
from .base import PIPELINE_INIT_ARGS, Pipeline
a : Union[str, Any] = loggi... | 56 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 291 | 0 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING, is_vision_available
from transformers.pipelines import pipeline
from transformers.testing_utils import (
is_pipeline_test,
nested_simplify,
require_tf,
require_torch,
require_vision,
... | 57 |
"""simple docstring"""
import operator as op
lowerCAmelCase : Dict = """scaler.pt"""
lowerCAmelCase : Tuple = """pytorch_model"""
lowerCAmelCase : Union[str, Any] = """random_states"""
lowerCAmelCase : Union[str, Any] ... | 291 | 0 |
'''simple docstring'''
import argparse
import os
import torch
from diffusers import (
CMStochasticIterativeScheduler,
ConsistencyModelPipeline,
UNetaDModel,
)
lowercase_ = {
"""sample_size""": 32,
"""in_channels""": 3,
"""out_channels""": 3,
"""layers_per_block""": 2,
"""n... | 58 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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... | 291 | 0 |
import os
from glob import glob
import imageio
import torch
import torchvision
import wandb
from img_processing import custom_to_pil, loop_post_process, preprocess, preprocess_vqgan
from loaders import load_vqgan
from PIL import Image
from torch import nn
from transformers import CLIPModel, CLIPTokenize... | 59 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 291 | 0 |
"""simple docstring"""
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
snake_case__ : int = logging.get_logger(__name__)
snake_case__ :... | 60 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a... | 291 | 0 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
TextClassificationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, s... | 61 |
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if len(snake_case__ ) < 2:
return collection
def circle_sort_util(snake_case__ , snake_case__ , snake_case__ ) -> bool:
lowerCamelCase = False
if low == high:
return swa... | 291 | 0 |
from typing import Optional
import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import (
BackboneOutput,
BaseModelOutputWithNoAttention,
BaseModelOutputWithPo... | 62 |
"""simple docstring"""
from collections.abc import Generator
def a__ ( ) -> Generator[int, None, None]:
lowerCamelCase , lowerCamelCase = 0, 1
while True:
lowerCamelCase , lowerCamelCase = b, a + b
yield b
def a__ ( snake_case... | 291 | 0 |
'''simple docstring'''
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase_ : Union[str, Any] = logging.get_logger(__name__)
lowerCAmelCase_ : ... | 63 |
"""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
... | 291 | 0 |
"""simple docstring"""
from ...configuration_utils import PretrainedConfig
class lowercase( __a ):
'''simple docstring'''
lowercase__ = "bert-generation"
def __init__( self: str, a_: str=50_358, a_: List[Any]=1_024, a_:... | 64 |
"""simple docstring"""
from math import ceil
def a__ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCamelCase = list(range(0 , snake_case__ ) )
lowerCamelCase = [item for sublist in list(device_map.values() ) for item in sublist]... | 291 | 0 |
def lowerCAmelCase_ ( __A ) -> str:
'''simple docstring'''
UpperCAmelCase__ = ""
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 lowerCAme... | 65 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 291 | 0 |
"""simple docstring"""
__a = {}
def A_ ( _lowercase, _lowercase, _lowercase ):
'''simple docstring'''
if late == 3 or absent == 2:
return 0
# if we have no days left, and have not failed any other rules,
# we have a prize string
if days == 0:... | 66 |
"""simple docstring"""
from typing import Any
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> list:
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ... | 291 | 0 |
'''simple docstring'''
import itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
from torch.utils.data i... | 67 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) ... | 291 | 0 |
import inspect
import unittest
from transformers import ConvNextConfig
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_backbone_common import BackboneTesterMixin
from ...test... | 68 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 291 | 0 |
"""simple docstring"""
def UpperCAmelCase ( UpperCAmelCase , UpperCAmelCase ) -> str:
if not (isinstance(UpperCAmelCase , UpperCAmelCase ) and isinstance(UpperCAmelCase , UpperCAmelCase )):
raise ValueError('longest_common_substring() takes two s... | 69 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ) -> Union[str, Any]:
lowerCamelCase = ArgumentParser(
description=(
... | 291 | 0 |
'''simple docstring'''
import os
import string
import sys
A__ : str =1 << 8
A__ : Optional[int] ={
'''tab''': ord('''\t'''),
'''newline''': ord('''\r'''),
'''esc''': 27,
'''up''': 65 + ARROW_KEY_FLAG,
'''down''': 66 + ARROW_KEY_FLAG,
'... | 70 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : List[str] = {
"""asapp/sew-d-tiny-100k""": """h... | 291 | 0 |
import json
import os
import unittest
from transformers import OpenAIGPTTokenizer, OpenAIGPTTokenizerFast
from transformers.models.openai.tokenization_openai import VOCAB_FILES_NAMES
from transformers.testing_utils import require_ftfy, require_spacy, require_tokenizers
from ...test_tokenization_... | 71 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase : 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 ... | 291 | 0 |
"""simple docstring"""
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase__ = logging.get_logger(__name__)
lowerCAmelCase__ = {
'''roberta-base... | 72 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , _a , _a , _a ):
... | 291 | 0 |
from __future__ import annotations
from random import random
class A_ :
def __init__( self : Any ,SCREAMING_SNAKE_CASE__ : int | None = None):
__lowerCamelCase : Union[str, Any] = value
__lowerCamelCase : int = random()
... | 73 |
"""simple docstring"""
def a__ ( snake_case__ ) -> bool:
lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( snake_case__ = 50_00 ) -> int:
lowerCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , sna... | 291 | 0 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase = {
'''configuration_nllb_moe''': [
'''NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''NllbMoeConfig''',
]
}
try:
if not is_t... | 74 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 0 |
'''simple docstring'''
import argparse
import json
import os
import sys
import tempfile
import unittest
from argparse import Namespace
from dataclasses import dataclass, field
from enum import Enum
from pathlib import Path
from typing import List, Literal, Optional
import yaml
from transformers import HfArgu... | 75 |
"""simple docstring"""
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():
fro... | 291 | 0 |
from collections.abc import Callable
import numpy as np
def lowerCamelCase__ ( _a , _a , _a , _a , _a):
SCREAMING_SNAKE_CASE : Dict = int(np.ceil((x_end - xa) / step_size))
SCREAMING_SNAKE_CASE : Tuple = np.zeros((n + 1,))
SCREAMING_SNAKE_CASE... | 76 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.uti... | 291 | 0 |
"""simple docstring"""
from typing import Optional, Union
import torch
from torch import nn
from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin
class UpperCAmelCase_ ( _a , _a):
@register_to_config
def __init__( self ... | 77 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ = False ) -> str:
if not isinstance(snake_case__ , snake_case__ ):
lowerCamelCase = F'Expected string as input, found {type(snake_case__ )}'
raise ValueError(snake_case__ )
if not... | 291 | 0 |
"""simple docstring"""
import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple
import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetP... | 78 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 291 | 0 |
'''simple docstring'''
from __future__ import annotations
from typing import Any
def __lowercase ( __lowercase ) -> int:
'''simple docstring'''
if not postfix_notation:
return 0
_A = {"+", "-", "*", "/"}
_A = []
for t... | 79 |
"""simple docstring"""
import operator as op
lowerCAmelCase : Dict = """scaler.pt"""
lowerCAmelCase : Tuple = """pytorch_model"""
lowerCAmelCase : Union[str, Any] = """random_states"""
lowerCAmelCase : Union[str, Any] ... | 291 | 0 |
'''simple docstring'''
import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union
import numpy as np
import pyarrow as pa
from .. import config
from ..download.streaming_download_manager im... | 80 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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... | 291 | 0 |
"""simple docstring"""
import random
import timeit
from functools import wraps
from typing import Callable, Optional
from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_tf_auto import TF_MODEL_MAPPING, TF_MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_a... | 81 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 291 | 0 |
import os
import unittest
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import (
VOCAB_FILES_NAMES,
BasicTokenizer,
BertTokenizer,
WordpieceTokenizer,
_is_control,
_is_punctuation,
_is_whitespace,
)
from transformers.testing_utils import requ... | 82 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a... | 291 | 0 |
'''simple docstring'''
from typing import List, Union
import numpy as np
from ..tokenization_utils import TruncationStrategy
from ..utils import add_end_docstrings, logging
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, ChunkPipeline
snake_case_ : int = logging.get_logger(__name__)
class ... | 83 |
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if len(snake_case__ ) < 2:
return collection
def circle_sort_util(snake_case__ , snake_case__ , snake_case__ ) -> bool:
lowerCamelCase = False
if low == high:
return swa... | 291 | 0 |
"""simple docstring"""
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, lo... | 84 |
"""simple docstring"""
from collections.abc import Generator
def a__ ( ) -> Generator[int, None, None]:
lowerCamelCase , lowerCamelCase = 0, 1
while True:
lowerCamelCase , lowerCamelCase = b, a + b
yield b
def a__ ( snake_case... | 291 | 0 |
'''simple docstring'''
import os
from pathlib import Path
def UpperCamelCase_( ):
'''simple docstring'''
from torch.utils.cpp_extension import load
snake_case_ = Path(snake_case ).resolve().parent.parent.parent / "kernels" / "deformable_detr"
snake_case... | 85 |
"""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
... | 291 | 0 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
lowerCamelCase__ = [8, 5, 9, 7]
lowerCamelCase__ = [
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCamelCase__ = [
[3, 2, 1, 4],
[0, 2, 5... | 86 |
"""simple docstring"""
from math import ceil
def a__ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCamelCase = list(range(0 , snake_case__ ) )
lowerCamelCase = [item for sublist in list(device_map.values() ) for item in sublist]... | 291 | 0 |
from __future__ import annotations
def lowercase_ ( _lowerCamelCase : list[int]):
return len(set(_lowerCamelCase)) == len(_lowerCamelCase)
if __name__ == "__main__":
import doctest
doctest.testmod()
| 87 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 291 | 0 |
from __future__ import annotations
import string
from itertools import cycle, product
from pathlib import Path
__lowerCAmelCase : str = (
string.ascii_letters + string.digits + string.punctuation + string.whitespace
)
__lowerCAmelCase : list[int] = [ord(letter) for l... | 88 |
"""simple docstring"""
from typing import Any
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> list:
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ... | 291 | 0 |
'''simple docstring'''
import logging
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union
from filelock import FileLock
from transformers import PreTrainedTokenizer, is_tf_available, is_torch_available
__lowerCAmelCase = logging.getLogger(__name_... | 89 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) ... | 291 | 0 |
import gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyInpaintPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffusers.pipelines.kandinsky.tex... | 90 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 291 | 0 |
"""simple docstring"""
import datasets
import faiss
import numpy as np
import streamlit as st
import torch
from elasticsearch import Elasticsearch
from elia_utils import (
embed_questions_for_retrieval,
make_qa_sas_model,
qa_sas_generate,
query_es_index,
query_qa_dense_index,
)
import ... | 91 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ) -> Union[str, Any]:
lowerCamelCase = ArgumentParser(
description=(
... | 291 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_torch_available,
is_vision_available,
)
UpperCamelCase__ = {"""configuration_vit""": ["""VIT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ViTConfig"... | 92 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : List[str] = {
"""asapp/sew-d-tiny-100k""": """h... | 291 | 0 |
'''simple docstring'''
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
_lowercase : str = {
"configuration_xlm_roberta_xl": [
"XLM_ROBERTA_XL_PRETRAINED_CONFIG_ARCHIVE_MAP",
"XLMRoberta... | 93 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase : 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 ... | 291 | 0 |
from math import pow
def __lowerCamelCase ( UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , UpperCAmelCase_ : int , ):
"""simple docstring"""
if current_sum == needed_sum:
# If the sum of the ... | 94 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , _a , _a , _a ):
... | 291 | 0 |
from collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
UpperCAmelCase : List[str] = {
"""albert-base-v1""": """https://huggingface.co/albert-base-v1/resolve/main/config.json""",
"""albe... | 95 |
"""simple docstring"""
def a__ ( snake_case__ ) -> bool:
lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( snake_case__ = 50_00 ) -> int:
lowerCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , sna... | 291 | 0 |
"""simple docstring"""
import logging
import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader
from transformers import Aut... | 96 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 0 |
'''simple docstring'''
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_... | 97 |
"""simple docstring"""
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():
fro... | 291 | 0 |
"""simple docstring"""
import os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCAmelCase__ : List[Any]... | 98 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.uti... | 291 | 0 |
import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List
from ..utils import logging
from . import BaseTransformersCLICommand
try:
from cookiecutter.main import cookiecutter
lowercase : Union[str, Any... | 99 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ = False ) -> str:
if not isinstance(snake_case__ , snake_case__ ):
lowerCamelCase = F'Expected string as input, found {type(snake_case__ )}'
raise ValueError(snake_case__ )
if not... | 291 | 0 |
"""simple docstring"""
from dataclasses import dataclass, field
from typing import Tuple
from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments
if is_torch_available():
import torch
if is_torch_tpu... | 100 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 291 | 0 |
from __future__ import annotations
from itertools import permutations
from random import randint
from timeit import repeat
def UpperCamelCase ( ):
'''simple docstring'''
lowercase = [randint(-1000 , 1000 ) for i in range(10 )]
lowercase = randint(-5000 , 5... | 101 |
"""simple docstring"""
import operator as op
lowerCAmelCase : Dict = """scaler.pt"""
lowerCAmelCase : Tuple = """pytorch_model"""
lowerCAmelCase : Union[str, Any] = """random_states"""
lowerCAmelCase : Union[str, Any] ... | 291 | 0 |
"""simple docstring"""
from statistics import mean
import numpy as np
def lowercase ( _snake_case : list , _snake_case : list , _snake_case : list , _snake_case : int ) ->list:
"""simple docstring"""
__snake_case : List[... | 102 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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... | 291 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
A__ : List[Any] = {
'''configuration_bigbird_pegasus''': [
'''BIGBIRD_PEGASUS_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''BigBirdPegasusConfig''',
'''BigBirdPega... | 103 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 291 | 0 |
'''simple docstring'''
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _A ( A__ ... | 104 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a... | 291 | 0 |
"""simple docstring"""
import logging
from transformers import PretrainedConfig
a : Tuple = logging.getLogger(__name__)
a : Dict = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/r... | 105 |
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if len(snake_case__ ) < 2:
return collection
def circle_sort_util(snake_case__ , snake_case__ , snake_case__ ) -> bool:
lowerCamelCase = False
if low == high:
return swa... | 291 | 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 SCREAMING_SNAKE_... | 106 |
"""simple docstring"""
from collections.abc import Generator
def a__ ( ) -> Generator[int, None, None]:
lowerCamelCase , lowerCamelCase = 0, 1
while True:
lowerCamelCase , lowerCamelCase = b, a + b
yield b
def a__ ( snake_case... | 291 | 0 |
from multiprocessing import Lock, Pipe, Process
# lock used to ensure that two processes do not access a pipe at the same time
__lowerCAmelCase : str = Lock()
def __magic_name__ ( A : Optional[Any], A : Any, A : Union[str, Any], A : List[str], A : int, A : ... | 107 |
"""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
... | 291 | 0 |
"""simple docstring"""
import sys
import tempfile
import unittest
import unittest.mock as mock
from pathlib import Path
from huggingface_hub import HfFolder, delete_repo
from requests.exceptions import HTTPError
from transformers import AutoFeatureExtractor, WavaVecaFeatureExtractor
from transformers.testin... | 108 |
"""simple docstring"""
from math import ceil
def a__ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCamelCase = list(range(0 , snake_case__ ) )
lowerCamelCase = [item for sublist in list(device_map.values() ) for item in sublist]... | 291 | 0 |
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
A: int = logging.get_logger(__name__)
A: Union[str, Any] = {
"post_extract_proj": "feature... | 109 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 291 | 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 lowerCAmelCase_ ... | 158 |
"""simple docstring"""
from typing import Any
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> list:
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ... | 291 | 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 ..auto import CONFIG_MAPPING
_a = logging.get_logger(__name__)
_a = {
"""... | 209 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) ... | 291 | 0 |
import unittest
from transformers import AutoTokenizer, FalconConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelT... | 222 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 291 | 0 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_layoutlmva import LayoutLMvaImageProcessor
lowercase_ = logging.get_logger(__name__)
class snake_case ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self : str, ... | 266 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ) -> Union[str, Any]:
lowerCamelCase = ArgumentParser(
description=(
... | 291 | 0 |
import qiskit
def A ( _UpperCAmelCase : Union[str, Any] = 2 ) -> qiskit.result.counts.Counts:
'''simple docstring'''
_UpperCAmelCase = qubits
# Using Aer's simulator
_UpperCAmelCase = qiskit.Aer.get_backend('aer_simulator' )
# Creating a Quan... | 339 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : List[str] = {
"""asapp/sew-d-tiny-100k""": """h... | 291 | 0 |
def _a ( SCREAMING_SNAKE_CASE , SCREAMING_SNAKE_CASE = False ):
"""simple docstring"""
if not isinstance(snake_case__ , snake_case__ ):
lowercase__ = f'Expected string as input, found {type(snake_case__ )}'
raise ValueError(snake_case... | 110 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase : 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 ... | 291 | 0 |
class lowerCamelCase__:
def __init__( self: int , UpperCamelCase_: str , UpperCamelCase_: List[str] , UpperCamelCase_: Tuple ):
__lowerCamelCase = None
__lowerCamelCase = None
__lowerCamelCase = graph
... | 12 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , _a , _a , _a ):
... | 291 | 0 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
__a : List[str] = {
"""configuration_mask2former""": [
"""MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
"""Mask2FormerConfig""",
],
}
try:
... | 210 |
"""simple docstring"""
def a__ ( snake_case__ ) -> bool:
lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( snake_case__ = 50_00 ) -> int:
lowerCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , sna... | 291 | 0 |
import numpy as np
import torch
import torch.nn as nn
from transformers import CLIPConfig, CLIPVisionModelWithProjection, PreTrainedModel
from ...utils import logging
a__ = logging.get_logger(__name__)
class snake_case ( UpperCAmelCase__ ):
'''simple docstring'... | 317 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 0 |
import logging
from pathlib import Path
import numpy as np
import pytorch_lightning as pl
import torch
from pytorch_lightning.callbacks import EarlyStopping, ModelCheckpoint
from pytorch_lightning.utilities import rank_zero_only
from utils_rag import save_json
def _snake_case( SCREAMING_SNAKE_CAS... | 7 |
"""simple docstring"""
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():
fro... | 291 | 0 |
'''simple docstring'''
import flax.linen as nn
import jax.numpy as jnp
from .attention_flax import FlaxTransformeraDModel
from .resnet_flax import FlaxDownsampleaD, FlaxResnetBlockaD, FlaxUpsampleaD
class __lowerCAmelCase ( nn.Module ):
"""simple docstring"""
_snake_case : Opt... | 324 |
"""simple docstring"""
import unittest
from transformers import (
MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
TextaTextGenerationPipeline,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, require_tf, require_torch
from transformers.uti... | 291 | 0 |
'''simple docstring'''
from typing import Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
convert_to_rgb,
get_resize_output_image_size,
normalize,
rescale,
... | 158 |
"""simple docstring"""
def a__ ( snake_case__ , snake_case__ = False ) -> str:
if not isinstance(snake_case__ , snake_case__ ):
lowerCamelCase = F'Expected string as input, found {type(snake_case__ )}'
raise ValueError(snake_case__ )
if not... | 291 | 0 |
from __future__ import annotations
def lowerCAmelCase__(__snake_case ) -> bool:
'''simple docstring'''
lowerCamelCase__ = len(snake_case__ )
# We need to create solution object to save path.
lowerCamelCase__ = [[0 for _ in range(snake_case__ )] for ... | 209 |
"""simple docstring"""
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
... | 291 | 0 |
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import PreTrainedTokenizer
from ...utils import logging
_UpperCAmelCase : Tuple = logging.get_logger(__name__)
_UpperCAmelCase : Optional[int] = ... | 222 |
"""simple docstring"""
import operator as op
lowerCAmelCase : Dict = """scaler.pt"""
lowerCAmelCase : Tuple = """pytorch_model"""
lowerCAmelCase : Union[str, Any] = """random_states"""
lowerCAmelCase : Union[str, Any] ... | 291 | 0 |
"""simple docstring"""
import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union
import flax
import jax.numpy as jnp
from ..utils import BaseOutput
lowercase_ = """scheduler_config.json"""
class sna... | 266 |
"""simple docstring"""
import inspect
import unittest
from transformers import ViTMSNConfig
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... | 291 | 0 |
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
UpperCAmelCase__ = logging.get_logger(__name__)
def A ( _UpperCAmelCase : Union[... | 339 |
"""simple docstring"""
import argparse
import collections
import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints
from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
def ... | 291 | 0 |
import itertools
import random
import unittest
import numpy as np
from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio
from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin
if is_speech_available(... | 110 |
"""simple docstring"""
from __future__ import annotations
def a__ ( snake_case__ , snake_case__ ) -> bool:
if len(snake_case__ ) == 0:
return False
lowerCamelCase = len(snake_case__ ) // 2
if a_list[midpoint] == item:
return True
if item < a... | 291 | 0 |
import unittest
import numpy as np
from transformers.testing_utils import is_flaky, 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():
... | 12 |
"""simple docstring"""
def a__ ( snake_case__ ) -> list:
if len(snake_case__ ) < 2:
return collection
def circle_sort_util(snake_case__ , snake_case__ , snake_case__ ) -> bool:
lowerCamelCase = False
if low == high:
return swa... | 291 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, PegasusConfig, is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from transformers.utils import cached_property
from ...test_configuration_common import ConfigTe... | 210 |
"""simple docstring"""
from collections.abc import Generator
def a__ ( ) -> Generator[int, None, None]:
lowerCamelCase , lowerCamelCase = 0, 1
while True:
lowerCamelCase , lowerCamelCase = b, a + b
yield b
def a__ ( snake_case... | 291 | 0 |
import argparse
import os
import gluonnlp as nlp
import mxnet as mx
import numpy as np
import torch
from gluonnlp.base import get_home_dir
from gluonnlp.model.bert import BERTEncoder
from gluonnlp.model.utils import _load_vocab
from gluonnlp.vocab import Vocab
from packaging import version
from tor... | 317 |
"""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
... | 291 | 0 |
def _snake_case( SCREAMING_SNAKE_CASE__ : Tuple ) -> bool:
'''simple docstring'''
return number & 1 == 0
if __name__ == "__main__":
import doctest
doctest.testmod()
| 7 |
"""simple docstring"""
from math import ceil
def a__ ( snake_case__ , snake_case__ ) -> Optional[int]:
lowerCamelCase = list(range(0 , snake_case__ ) )
lowerCamelCase = [item for sublist in list(device_map.values() ) for item in sublist]... | 291 | 0 |
'''simple docstring'''
import inspect
import unittest
import torch
import torch.nn as nn
from accelerate.hooks import (
AlignDevicesHook,
ModelHook,
SequentialHook,
add_hook_to_module,
attach_align_device_hook,
remove_hook_from_module,
remove_hook_from_submodules,
)
from accelerate.... | 324 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available():
impor... | 291 | 0 |
'''simple docstring'''
from collections.abc import Generator
def __a():
'''simple docstring'''
_lowerCAmelCase , _lowerCAmelCase = 0, 1
while True:
_lowerCAmelCase , _lowerCAmelCase = b, a + b
yield b
def __a(SCREAMING_SNAKE_CASE_ : int = 1000 ):
... | 158 |
"""simple docstring"""
from typing import Any
def a__ ( snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ , ) -> list:
_validation(
snake_case__ , snake_case__ , snake_case__ , snake_case__ , snake_case__ ... | 291 | 0 |
from PIL import Image
def lowerCAmelCase__(__snake_case ,__snake_case ) -> Image:
'''simple docstring'''
lowerCamelCase__ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__snake_case ) -> int:
return int(128 + factor * (c - 128) )
ret... | 209 |
"""simple docstring"""
import argparse
from pathlib import Path
import torch
from transformers import OPTConfig, OPTModel
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase : Dict = logging.get_logger(__name__)
def a__ ( snake_case__ ) ... | 291 | 0 |
import numpy as np
import torch
from imwatermark import WatermarkEncoder
# Copied from https://github.com/Stability-AI/generative-models/blob/613af104c6b85184091d42d374fef420eddb356d/scripts/demo/streamlit_helpers.py#L66
_UpperCAmelCase : List[Any] = 0b101_100_111_110_110_010_010_000_011_110_111_011_0... | 222 |
"""simple docstring"""
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
from transformers import BertTokenizerFast
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES, BertTokenizer
from transformers.testing_utils import require_tokenizers, require_v... | 291 | 0 |
"""simple docstring"""
from collections import UserDict
from typing import List, Union
from ..utils import (
add_end_docstrings,
is_tf_available,
is_torch_available,
is_vision_available,
logging,
requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_vision_availab... | 266 |
"""simple docstring"""
import importlib
import sys
from argparse import REMAINDER, ArgumentParser
from pathlib import Path
import torch_xla.distributed.xla_multiprocessing as xmp
def a__ ( ) -> Union[str, Any]:
lowerCamelCase = ArgumentParser(
description=(
... | 291 | 0 |
from __future__ import annotations
import inspect
import unittest
from math import floor
import numpy as np
from transformers import CvtConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_available
from ...test... | 339 |
"""simple docstring"""
import functools
import operator
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase : int = logging.get_logger(__name__)
lowerCAmelCase : List[str] = {
"""asapp/sew-d-tiny-100k""": """h... | 291 | 0 |
import doctest
import glob
import importlib
import inspect
import os
import re
from contextlib import contextmanager
from functools import wraps
from unittest.mock import patch
import numpy as np
import pytest
from absl.testing import parameterized
import datasets
from datasets import load_metric... | 110 |
"""simple docstring"""
from sklearn.metrics import recall_score
import datasets
lowerCAmelCase : 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 ... | 291 | 0 |
import unittest
import numpy as np
import torch
from .utils_summarization import build_mask, compute_token_type_ids, process_story, truncate_or_pad
class lowerCamelCase__( unittest.TestCase):
def lowerCAmelCase__ ( self: List[str] ):
__lowerCamelCase = 10
... | 12 |
"""simple docstring"""
import numpy as np
import torch
from torch.utils.data import Dataset, IterableDataset
from ..utils.generic import ModelOutput
class __magic_name__ ( UpperCAmelCase__ ):
'''simple docstring'''
def __init__( self , _a , _a , _a ):
... | 291 | 0 |
from collections import OrderedDict
from typing import List, Mapping
from packaging import version
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
__a : str = logging.get_logger(__name__)
__a : List[str] = {
"""google/effic... | 210 |
"""simple docstring"""
def a__ ( snake_case__ ) -> bool:
lowerCamelCase = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def a__ ( snake_case__ = 50_00 ) -> int:
lowerCamelCase = [(i * (3 * i - 1)) // 2 for i in range(1 , sna... | 291 | 0 |
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 ..i... | 317 |
"""simple docstring"""
from pickle import UnpicklingError
import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict
from ..utils import logging
lowerCAmelCase : Tuple = logging.get_logger(__name__)
... | 291 | 0 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.