code stringlengths 86 54.5k | code_codestyle int64 0 371 | style_context stringlengths 87 49.2k | style_context_codestyle int64 0 349 | label int64 0 1 |
|---|---|---|---|---|
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ : Optional[Any] = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if... | 361 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 0 |
import argparse
import json
import numpy
import torch
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''si... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 0 |
import os
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch
import numpy as np
from datasets import Dataset
from transformers.models.realm.configuration_realm import RealmConfig
from transformers.models.realm.retrieval_realm import _REALM_BLOCK_RECORDS_FILENAME, RealmRetrie... | 363 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 0 |
import collections
import json
import math
import os
import re
import time
from fnmatch import fnmatch
from typing import Dict
import requests
from slack_sdk import WebClient
lowerCamelCase_ = WebClient(token=os.environ['''CI_SLACK_BOT_TOKEN'''])
def UpperCamelCase( lowercase_ ) -> ... | 364 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 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
lowerCame... | 365 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import ... | 366 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-128... | 367 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 0 |
import json
import os
import unittest
from transformers.models.ctrl.tokenization_ctrl import VOCAB_FILES_NAMES, CTRLTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class __lowerCamelCase ( __snake_case , unittest.TestCase ):
lowerCamelCase_ : Any ... | 368 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 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 __lowerCamelCase ( ... | 369 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 0 |
from pathlib import PurePosixPath
from typing import Optional
import fsspec
from fsspec import AbstractFileSystem
from huggingface_hub.hf_api import DatasetInfo
from ..utils.file_utils import get_authentication_headers_for_url
from ..utils.hub import hf_hub_url
class __lowerCamelCase ( __snake_case... | 370 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 0 |
import unittest
from pathlib import Path
from tempfile import TemporaryDirectory
from transformers import AutoConfig, TFAutoModel, is_tensorflow_text_available, is_tf_available
from transformers.models.bert.tokenization_bert import BertTokenizer
from transformers.testing_utils import require_tensorflow_text, require... | 371 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 0 |
import math
from typing import Optional
import numpy as np
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/encodec_24khz''': '''https://huggingface.co/facebook/encodec_24khz/resol... | 350 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/convnextv2-tiny-1k-224''': '''htt... | 351 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 0 |
import os
from shutil import copyfile
from typing import List, Optional, Tuple
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging
if is_sentencepiece_available():
from .tokenization_pegasus import PegasusTokenizer
else:
lowerCamelCase_ ... | 352 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 0 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
snake_case_ = len(lowercase_ ) + 1
snake_case_ = len(lowercase_ ) + 1
# dp is a 2d matrix where dp[i][j] denotes whether prefix string of
# length i of input_string... | 353 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 0 |
from PIL import Image
def UpperCamelCase( lowercase_ , lowercase_ ) -> Image:
'''simple docstring'''
snake_case_ = (259 * (level + 255)) / (255 * (259 - level))
def contrast(lowercase_ ) -> int:
return int(128 + factor * (c - 128) )
return img.p... | 354 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''tiiuae/falcon-40b''': '''https://huggingface.co/tiiuae/falcon-40b/resolve/main/config.json''',
'''tiiuae/falcon-7b''': '''https://huggingf... | 355 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 0 |
# 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 applica... | 356 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 0 |
from queue import PriorityQueue
from typing import Any
import numpy as np
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , lowercase_ , ) -> float | int:
'''simple docstring... | 357 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple d... | 358 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_distilbert''': [
'''DISTILBERT_PRETRAINED_CONFIG_... | 359 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 0 |
class __lowerCamelCase :
def __init__( self ) -> List[str]:
snake_case_ = """"""
snake_case_ = """"""
snake_case_ = []
def lowerCAmelCase_ ( self , lowerCamelCase , lowerCamelCase ) ->... | 360 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 0 |
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "https://www.worldometers.info/coronavirus" ) -> dict:
'''simple docstring'''
snake_case_ = BeautifulSoup(requests.get(lowercase_ ).text , """html.parser""" )
snake_case_ ... | 361 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 0 |
import json
import pathlib
import unittest
import numpy as np
from transformers.testing_utils import require_torch, require_vision, slow
from transformers.utils import is_torch_available, is_vision_available
from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs
if is_to... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 0 |
from ..utils import DummyObject, requires_backends
class __lowerCamelCase ( metaclass=__snake_case ):
lowerCamelCase_ : Any = ['flax', 'transformers']
def __init__( self , *lowerCamelCase , **lowerCamelCase ) -> int:
requires_b... | 363 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 0 |
from typing import List
from .keymap import KEYMAP, get_character
def UpperCamelCase( lowercase_ ) -> str:
'''simple docstring'''
def decorator(lowercase_ ):
snake_case_ = getattr(lowercase_ , """handle_key""" , [] )
handle += [key]
seta... | 364 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 0 |
import gc
import unittest
from diffusers import FlaxStableDiffusionInpaintPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import replicate
f... | 365 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
import inspect
import unittest
from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from ...te... | 366 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 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 UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
... | 367 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 0 |
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
from torch.nn import CrossEntropyLoss
from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_t... | 368 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 0 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
... | 369 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 0 |
import logging
from transformers import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
lowerCamelCase_ = {
'''bertabs-finetuned-cnndm''': '''https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json''',
}
class ... | 370 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 0 |
import qiskit
def UpperCamelCase( lowercase_ , lowercase_ ) -> qiskit.result.counts.Counts:
'''simple docstring'''
snake_case_ = qiskit.Aer.get_backend("""aer_simulator""" )
# Create a Quantum Circuit acting on the q register
snake_case_ = qiskit.... | 371 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 0 |
import math
def UpperCamelCase( lowercase_ ) -> bool:
'''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 F... | 350 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 0 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if resi... | 351 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_torch_available,
is_vision_available,
)
lowerCamelCase_ = {'''configuration_beit''': ['''BEIT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BeitConfig''', '''BeitOnnxConfig... | 352 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 0 |
import unittest
from transformers import MPNetConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor, random_attention_mask
from ...test_pipeline_mixin ... | 353 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_albert''': ['''ALBERT_PRE... | 354 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 0 |
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_ = {
'''facebook/data2vec-text-base''': '''https://hugg... | 355 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 0 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 356 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_funnel''': ['''FUNNEL_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FunnelConfig'''],
... | 357 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
import warnings
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = ['image_processor', 'tokenizer']
lowerCamelCase_ ... | 358 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 0 |
from transformers import DistilBertTokenizer, DistilBertTokenizerFast
from transformers.testing_utils import require_tokenizers, slow
from ..bert.test_tokenization_bert import BertTokenizationTest
@require_tokenizers
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Dict ... | 359 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 0 |
def UpperCamelCase( lowercase_ , lowercase_ = False ) -> str:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
snake_case_ = f'''Expected string as input, found {type(lowercase_ )}'''
raise ValueError(lowercase_ )
if n... | 360 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 0 |
import gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
StableDiffusionSAGPipeline,
UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.util... | 361 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 0 |
from datetime import datetime
import requests
from bsa import BeautifulSoup
if __name__ == "__main__":
lowerCamelCase_ = input('''Enter image url: ''').strip()
print(f"""Downloading image from {url} ...""")
lowerCamelCase_ = BeautifulSoup(requests.get(url).content, '''html.parser''')
... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 0 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 363 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 0 |
import math
def UpperCamelCase( lowercase_ , lowercase_ ) -> float:
'''simple docstring'''
return math.pow(lowercase_ , 2 ) - a
def UpperCamelCase( lowercase_ ) -> float:
'''simple docstring'''
return 2 * x
def UpperCa... | 364 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 0 |
lowerCamelCase_ = {
'''a''': '''AAAAA''',
'''b''': '''AAAAB''',
'''c''': '''AAABA''',
'''d''': '''AAABB''',
'''e''': '''AABAA''',
'''f''': '''AABAB''',
'''g''': '''AABBA''',
'''h''': '''AABBB''',
'''i''': '''ABAAA''',
'''j''': '''BBBAA''',
'''k''': '''ABAAB''',
... | 365 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ ) -> list[str]:
'''simple docstring'''
if nth_term == "":
return [""]
snake_case_ = int(lowercase_ )
snake_case_ = int(lowercase_ )
... | 366 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 0 |
import random
from .binary_exp_mod import bin_exp_mod
def UpperCamelCase( lowercase_ , lowercase_=1000 ) -> Tuple:
'''simple docstring'''
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
snake_case_ = n - 1
snake_case_ ... | 367 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 0 |
import json
import os
import shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import CLIPTokenizer, CLIPTokenizerFast
from transformers.models.clip.tokenization_clip import VOCAB_FILES_NAMES
from transformers.testing_utils import require_vision
from transformers.utils import... | 368 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 0 |
import math
import time
from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics
if is_torch_tpu_available(check_device=False):
import torch_xla.core.xla_model as xm
import torch_xla.debug.metrics as met
class __lowerCamelCas... | 369 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 0 |
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 __lowerCamelCase ( ... | 370 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 0 |
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ , lowercase_ ) -> bool:
'''simple docstring'''
if graph[path[curr_ind - 1]][next_ver] == 0:
return False
# 2. Validate that next vertex is not already in path
return not any(vertex == next_ver for vertex... | 371 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 0 |
# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py
import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile
def UpperCamelCase( lowercase_ , lowercase_ , lowe... | 350 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 0 |
from collections import OrderedDict
from typing import Any, Mapping, Optional
from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils ... | 351 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 0 |
from __future__ import annotations
def UpperCamelCase( lowercase_ ) -> bool:
'''simple docstring'''
if len(lowercase_ ) < 2:
raise ValueError("""Monogons and Digons are not polygons in the Euclidean space""" )
if any(i <= 0 for i in nums ):
raise ValueE... | 352 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 0 |
def UpperCamelCase( lowercase_ ) -> Dict:
'''simple docstring'''
stooge(lowercase_ , 0 , len(lowercase_ ) - 1 )
return arr
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
... | 353 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 0 |
def UpperCamelCase( lowercase_ ) -> str:
'''simple docstring'''
snake_case_ = """"""
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 UpperCamelCase( lowercase_ ) -> dict[str,... | 354 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 0 |
import numpy as np
from cva import destroyAllWindows, imread, imshow, waitKey
class __lowerCamelCase :
def __init__( self , lowerCamelCase , lowerCamelCase , lowerCamelCase ) -> List[str]:
if dst_width < 0 or dst_height < 0:
raise ValueEr... | 355 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 0 |
def UpperCamelCase( lowercase_ ) -> Dict:
'''simple docstring'''
snake_case_ = [0] * len(lowercase_ )
snake_case_ = []
snake_case_ = [1] * len(lowercase_ )
for values in graph.values():
for i in values:
indegree[i] += 1
for i... | 356 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 0 |
import argparse
from transformers import (
TapasConfig,
TapasForMaskedLM,
TapasForQuestionAnswering,
TapasForSequenceClassification,
TapasModel,
TapasTokenizer,
load_tf_weights_in_tapas,
)
from transformers.utils import logging
logging.set_verbosity_info()
def UpperCamelCase( ... | 357 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
from numpy import exp, pi, sqrt
def UpperCamelCase( lowercase_ , lowercase_ = 0.0 , lowercase_ = 1.0 ) -> int:
'''simple docstring'''
return 1 / sqrt(2 * pi * sigma**2 ) * exp(-((x - mu) ** 2) / (2 * sigma**2) )
if __name__ == "__... | 358 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 0 |
from decimal import Decimal, getcontext
from math import ceil, factorial
def UpperCamelCase( lowercase_ ) -> str:
'''simple docstring'''
if not isinstance(lowercase_ , lowercase_ ):
raise TypeError("""Undefined for non-integers""" )
elif precision < 1:
r... | 359 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 0 |
import os
def UpperCamelCase( ) -> Dict:
'''simple docstring'''
snake_case_ = os.path.join(os.path.dirname(lowercase_ ) , """num.txt""" )
with open(lowercase_ ) as file_hand:
return str(sum(int(lowercase_ ) for line in file_hand ) ... | 360 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 0 |
from __future__ import annotations
from collections.abc import MutableSequence
class __lowerCamelCase :
def __init__( self , lowerCamelCase , lowerCamelCase ) -> None:
if len(lowerCamelCase ) != degree + 1:
raise ValueError(
... | 361 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''transfo-xl-wt103''': '''https://huggingface.co/transfo-xl-wt103/resolve/main/config.json''',
}
class __lowerCamelCase ( __sna... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 0 |
from typing import List, Optional, TypeVar
from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _inter... | 363 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 0 |
def UpperCamelCase( lowercase_ = 100 ) -> int:
'''simple docstring'''
snake_case_ = set()
snake_case_ = 0
snake_case_ = n + 1 # maximum limit
for a in range(2 , lowercase_ ):
for b in range(2 , lowercase_ ):
snake_c... | 364 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 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 import (
... | 365 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
def UpperCamelCase( lowercase_ ) -> bool:
'''simple docstring'''
snake_case_ = (1 + 24 * n) ** 0.5
return ((1 + root) / 6) % 1 == 0
def UpperCamelCase( lowercase_ = 5000 ) -> int:
'''simple docstring'''
snake_... | 366 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 0 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCamel... | 367 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 0 |
import copy
import json
import os
import tempfile
from transformers import is_torch_available
from .test_configuration_utils import config_common_kwargs
class __lowerCamelCase ( __snake_case ):
def __init__( self , lowerCamelCase , lowerCamelCase=None , low... | 368 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_sentencepiece_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_xlm_roberta''': [
... | 369 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 0 |
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCamelCase_ = {
'''configuration_roformer''': ['''ROFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''... | 370 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 0 |
import argparse
import logging
import sys
from unittest.mock import patch
import run_glue_deebert
from transformers.testing_utils import TestCasePlus, get_gpu_count, require_torch_non_multi_gpu, slow
logging.basicConfig(level=logging.DEBUG)
lowerCamelCase_ = logging.getLogger()
def UpperCamelC... | 371 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 0 |
import os
from distutils.util import strtobool
def UpperCamelCase( lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
for e in env_keys:
snake_case_ = int(os.environ.get(lowercase_ , -1 ) )
if val >= 0:
return val
return default... | 350 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> str:
'''simple docstring'''
return "\n".join(
f'''{number} * {i} = {number * i}''' for i in range(1 , number_of_terms + 1 ) )
if __name__ == "__main__":
print(multiplication_table(number=5, number_of_t... | 34 | 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,
DDIMScheduler,
... | 351 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available
lowerCamelCase_ = {
'''configuration_groupvit''': [
'''GROUPVIT_PRETRAINED_CONFIG_ARCHIVE_MAP''',
'''GroupViTConfig''',
'''GroupViTOnnxConfig''... | 34 | 0 |
from __future__ import annotations
import unittest
from transformers import AutoTokenizer, MBartConfig, 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... | 352 |
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def UpperCamelCase( lowercase_ = "" ) -> dict[str, float]:
'''simple docstring'''
snake_case_ = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
snake_case_ = ... | 34 | 0 |
import argparse
import logging
import os
from pathlib import Path
from typing import Any, Dict
import pytorch_lightning as pl
from pytorch_lightning.utilities import rank_zero_info
from transformers import (
AdamW,
AutoConfig,
AutoModel,
AutoModelForPreTraining,
AutoModelForQuestionAnswering,
... | 353 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''facebook/levit-1... | 34 | 0 |
def UpperCamelCase( lowercase_ ) -> List[str]:
'''simple docstring'''
snake_case_ = []
snake_case_ = set({"""(""", """[""", """{"""} )
snake_case_ = set({""")""", """]""", """}"""} )
snake_case_ = {"""{""": """}""", """[""": """]"... | 354 |
import logging
from transformers.configuration_utils import PretrainedConfig
lowerCamelCase_ = logging.getLogger(__name__)
class __lowerCamelCase ( __snake_case ):
lowerCamelCase_ : Optional[int] = 'masked_bert'
def __init__( self , lowerCam... | 34 | 0 |
from math import acos, sin
from typing import List, Tuple, Union
import numpy as np
import torch
from PIL import Image
from ...models import AutoencoderKL, UNetaDConditionModel
from ...schedulers import DDIMScheduler, DDPMScheduler
from ...utils import randn_tensor
from ..pipeline_utils import AudioPipelineOutput, ... | 355 |
from unittest import TestCase
from datasets import Dataset
from minhash_deduplication import deduplicate_dataset, make_duplicate_clusters
def UpperCamelCase( ) -> Optional[Any]:
'''simple docstring'''
snake_case_ = {
"""repo_name""": ["""test_repo1""", """test_repo2""", ... | 34 | 0 |
def UpperCamelCase( lowercase_ ) -> bool:
'''simple docstring'''
snake_case_ = 0
for ch in input_str:
snake_case_ = ord(lowercase_ )
snake_case_ = pow(2 , lowercase_ )
# If we already turned on bit for current character's uni... | 356 |
import unittest
from transformers import GPTNeoXJapaneseConfig, is_torch_available
from transformers.models.gpt_neox_japanese.tokenization_gpt_neox_japanese import GPTNeoXJapaneseTokenizer
from transformers.testing_utils import require_torch, slow, torch_device
from ...test_configuration_common import ConfigTester
... | 34 | 0 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod()
| 357 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
import argparse
import json
from pathlib import Path
import torch
import torchaudio
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from transformers import ASTConfig, ASTFeatureExtractor, ASTForAudioClassification
from transformers.utils import logging
log... | 358 |
# HF Trainer benchmarking tool
#
# This tool can be used to run and compare multiple dimensions of the HF Trainers args.
#
# It then prints a report once in github format with all the information that needs to be shared
# with others and second time in a console-friendly format, so it's easier to use for tuning thing... | 34 | 0 |
from collections import OrderedDict
from typing import Any, List, Mapping, Optional
from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging
lowerCamelCase_ = lo... | 359 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ ... | 34 | 0 |
def UpperCamelCase( lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
while a != 0:
snake_case_ , snake_case_ = b % a, a
return b
def UpperCamelCase( lowercase_ , lowercase_ ) -> int:
'''simple docstring'''
... | 360 |
from argparse import ArgumentParser
from datasets.commands.convert import ConvertCommand
from datasets.commands.dummy_data import DummyDataCommand
from datasets.commands.env import EnvironmentCommand
from datasets.commands.run_beam import RunBeamCommand
from datasets.commands.test import TestCommand
from datasets.ut... | 34 | 0 |
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not under git won't be considered
# since th... | 361 |
import numpy as np
def UpperCamelCase( lowercase_ ) -> np.array:
'''simple docstring'''
return (2 / (1 + np.exp(-2 * vector ))) - 1
if __name__ == "__main__":
import doctest
doctest.testmod() | 34 | 0 |
import argparse
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, DistributedTy... | 362 |
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCamelCase_ = {
'''configuration_graphormer''': ['''GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''GraphormerConfig'''],
}
try:
if not is_torch_avail... | 34 | 0 |
from typing import Dict, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
resize,
to_channel_dimension_format,... | 363 |
from typing import List, Optional, Union
import torch
from transformers import (
XLMRobertaTokenizer,
)
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDIMScheduler, DDPMSchedule... | 34 | 0 |
import argparse
import intel_extension_for_pytorch as ipex
import torch
from diffusers import DPMSolverMultistepScheduler, StableDiffusionPipeline
lowerCamelCase_ = argparse.ArgumentParser('''Stable Diffusion script with intel optimization''', add_help=False)
parser.add_argument('''--dpm''', action='''st... | 364 |
import enum
import os
from hashlib import shaaaa
from typing import Optional
from .. import config
from .logging import get_logger
lowerCamelCase_ = get_logger(__name__)
class __lowerCamelCase ( enum.Enum ):
lowerCamelCase_ : Dict = 'all_checks'
lowerCamelCas... | 34 | 0 |
import doctest
from collections import deque
import numpy as np
class __lowerCamelCase :
def __init__( self ) -> None:
snake_case_ = [2, 1, 2, -1]
snake_case_ = [1, 2, 3, 4]
def lowerCAmelCase_ ( self ) -> l... | 365 |
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
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''google/mobilenet... | 34 | 0 |
"""simple docstring"""
from collections import defaultdict
from math import ceil, sqrt
def UpperCamelCase( lowercase_ = 1000000 , lowercase_ = 10 ) -> int:
'''simple docstring'''
snake_case_ = defaultdict(lowercase_ )
for outer_width in range(3 , (t... | 366 |
import argparse
import json
import os
import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image
from transformers import (
Efficie... | 34 | 0 |
import warnings
from diffusers import StableDiffusionImgaImgPipeline # noqa F401
warnings.warn(
'''The `image_to_image.py` script is outdated. Please use directly `from diffusers import'''
''' StableDiffusionImg2ImgPipeline` instead.'''
) | 367 |
import numpy as np
from matplotlib import pyplot as plt
from sklearn.datasets import load_iris
from sklearn.metrics import ConfusionMatrixDisplay
from sklearn.model_selection import train_test_split
from xgboost import XGBClassifier
def UpperCamelCase( lowercase_ ) -> tuple:
'''simple do... | 34 | 0 |
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCamelCase_ = logging.get_logger(__name__)
lowerCamelCase_ = {
'''shi-labs/nat-mini-in1k-224''': '''https://... | 368 |
import argparse
from tax import checkpoints
from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> Any:
'''simple docstring'''
snake_case_ = AutoConfig.from_pretrained(lowercase_ )
s... | 34 | 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():
import torc... | 369 |
import argparse
import collections
import json
import os
import re
import string
import sys
import numpy as np
lowerCamelCase_ = re.compile(R'''\b(a|an|the)\b''', re.UNICODE)
lowerCamelCase_ = None
def UpperCamelCase( ) -> List[Any]:
'''simple docstring'''
snake_ca... | 34 | 0 |
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
if TYPE_CHECKING:
from ... import FeatureExtractionMixin, TensorType
lowerCamelCase_ = logging.g... | 370 |
from torch import nn
def UpperCamelCase( lowercase_ ) -> Tuple:
'''simple docstring'''
if act_fn in ["swish", "silu"]:
return nn.SiLU()
elif act_fn == "mish":
return nn.Mish()
elif act_fn == "gelu":
return nn.GELU()
else:
raise ValueError(f'''Unsupported acti... | 34 | 0 |
def UpperCamelCase( lowercase_ ) -> list:
'''simple docstring'''
def merge(lowercase_ , lowercase_ ) -> list:
def _merge():
while left and right:
yield (left if left[0] <= right[0] else right).pop(0 )
yield from left
yield from right
... | 371 |
from __future__ import annotations
def UpperCamelCase( lowercase_ , lowercase_ , lowercase_ ) -> dict[str, float]:
'''simple docstring'''
if (voltage, current, resistance).count(0 ) != 1:
raise ValueError("""One and only one argument must be 0""" )
if r... | 34 | 0 |
import sys
from .dependency_versions_table import deps
from .utils.versions import require_version, require_version_core
# define which module versions we always want to check at run time
# (usually the ones defined in `install_requires` in setup.py)
#
# order specific notes:
# - tqdm must be checked befor... | 35 | import warnings
from typing import List, Optional, Tuple, Union
import numpy as np
import PIL
import torch
from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineO... | 35 | 1 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.