code stringlengths 82 54.1k | code_codestyle int64 0 699 | style_context stringlengths 111 35.6k | style_context_codestyle int64 0 699 | label int64 0 1 |
|---|---|---|---|---|
"""simple docstring"""
def __snake_case ( __A = 1000000 ) -> int:
lowercase : Optional[int] = [i - 1 for i in range(limit + 1 )]
for i in range(2 ,limit + 1 ):
if phi[i] == i - 1:
for j in range(2 * i ,limit + 1 ,__A ):
... | 607 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 607 | 1 |
"""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 ... | 607 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: int ... | 607 | 1 |
"""simple docstring"""
from typing import Optional, Tuple
import jax
import jax.numpy as jnp
from flax import linen as nn
from flax.core.frozen_dict import FrozenDict
from transformers import CLIPConfig, FlaxPreTrainedModel
from transformers.models.clip.modeling_flax_clip import FlaxCLIPVisionModul... | 607 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase: List[str] =parse(importlib.metadata.version("torch"))
def __snake_case ( __A ,__A ... | 607 | 1 |
"""simple docstring"""
import jax.numpy as jnp
from ...utils import logging
from ..ta.modeling_flax_ta import FlaxTaEncoderModel, FlaxTaForConditionalGeneration, FlaxTaModel
from .configuration_mta import MTaConfig
lowerCAmelCase: Optional[int] =logging.get_logger(__name__)
lowerCAm... | 607 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 | 1 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 |
"""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 tra... | 607 | 1 |
"""simple docstring"""
import time
from contextlib import contextmanager
from pathlib import Path
import pytest
import requests
from huggingface_hub.hf_api import HfApi, HfFolder
lowerCAmelCase: List[Any] ="__DUMMY_TRANSFORMERS_USER__"
lowerCAmelCase: List[str] ="Dummy Us... | 607 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config i... | 607 | 1 |
"""simple docstring"""
import argparse
import fairseq
import torch
from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging
logging.set_verbosity_info()
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: Tuple ... | 607 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def __snake_case ( __A ,__A ) -> Any:
lowercase : Any = int(__A )
assert noofclusters < len(__A )
# Find out the dimensionality
lower... | 607 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Tuple
import numpy as np
import torch
@dataclass
class lowerCamelCase__ :
__UpperCAmelCase = 42 # [batch_size x 3]
__UpperCAmelCase = 42 # [batch_size x 3]
__UpperCAmelCase ... | 607 |
"""simple docstring"""
import qiskit
def __snake_case ( __A ,__A ) -> qiskit.result.counts.Counts:
lowercase : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
lowercase : int = qiskit.QuantumCircuit(4 ,2 )
# encod... | 607 | 1 |
"""simple docstring"""
import sys
import webbrowser
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
if __name__ == "__main__":
print("Googling.....")
lowerCAmelCase: int ="https://www.google.com/search?q=" + " ".join(sys.argv[1:])
lowerCAme... | 607 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 607 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional, Tuple, Union
import numpy as np
import torch
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin
@dataclas... | 607 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __snake_case ( __A ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the C... | 607 | 1 |
"""simple docstring"""
from scipy.stats import pearsonr
import datasets
lowerCAmelCase: Optional[int] ="\nPearson correlation coefficient and p-value for testing non-correlation.\nThe Pearson correlation coefficient measures the linear relationship between two datasets. The calculation o... | 607 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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 .... | 607 | 1 |
"""simple docstring"""
def __snake_case ( __A ,__A ,__A ,__A ) -> List[str]:
# Return True if there is node that has not iterated.
lowercase : str = [False] * len(__A )
lowercase : Tuple = []
queue.append(__A )
lowercase ... | 607 |
"""simple docstring"""
import math
import qiskit
def __snake_case ( __A = 1 ,__A = 1 ,__A = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__A ,__A )
or isinstance(__A ,__A )
or isinstance(__A ,__A )
):
raise ... | 607 | 1 |
"""simple docstring"""
import requests
from bsa import BeautifulSoup
def __snake_case ( __A = "AAPL" ) -> str:
lowercase : str = F'''https://in.finance.yahoo.com/quote/{symbol}?s={symbol}'''
lowercase : Optional[int] = BeautifulSoup(reques... | 607 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_convbert": ["CONVBERT_PRETRAINE... | 607 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, LEDTokenizer, LEDTokenizerFast
from transformers.models.led.tokenization_led import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils... | 607 |
"""simple docstring"""
import re
def __snake_case ( __A ) -> str:
if len(re.findall("""[ATCG]""" ,__A ) ) != len(__A ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" ,"""TAGC""" ) )
if _... | 607 | 1 |
"""simple docstring"""
import unittest
from transformers import load_tool
from .test_tools_common import ToolTesterMixin
lowerCAmelCase: List[Any] ="\nHugging Face was founded in 2016 by French entrepreneurs Clément Delangue, Julien Chaumond, and Thomas Wolf originally as a company th... | 607 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __snake_case ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" ... | 607 | 1 |
"""simple docstring"""
import torch
from transformers import PreTrainedModel, XLMRobertaConfig, XLMRobertaModel
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = """M-CLIP"""
def __init__( self , snake_case=1_0_2_4 , snake_case=7_6_8 , **snak... | 607 |
"""simple docstring"""
from ... import PretrainedConfig
lowerCAmelCase: str ={
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = NEZHA_PRETRAIN... | 607 | 1 |
"""simple docstring"""
import torch
from transformers import CamembertForMaskedLM, CamembertTokenizer
def __snake_case ( __A ,__A ,__A ,__A=5 ) -> Dict:
# Adapted from https://github.com/pytorch/fairseq/blob/master/fairseq/models/roberta/hub_interface.py
assert... | 607 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class lowerCamelCase__ ( tf.keras.layers.Layer ):
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False , **snake_case ) -> ... | 607 | 1 |
"""simple docstring"""
import inspect
import os
import unittest
from pathlib import Path
import torch
import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command
class lowerCamelCase__ ( unittest.TestCase ):
__Up... | 607 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __snake_case ( __A ,__A = "cpu" ,__A = None ) -> None:
lowercase : Optional[int] = torch.load(__A ,map_location=__A )
for k, v in tqdm(state_... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( __A ,__A ,__A ,__A ) -> None:
if (direction == 1 and array[indexa] > array[indexa]) or (
direction == 0 and array[indexa] < array[indexa]
):
lowercase , lowercase ... | 607 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __snake_case ( __A ) -> Any:
lowercase : List[str] = os.path.join(args.tf_model_dir ,"""para... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
import random
# Maximum size of the population. Bigger could be faster but is more memory expensive.
lowerCAmelCase: str =200
# Number of elements selected in every generation of evolution. The selection takes
# place from best ... | 607 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_... | 607 | 1 |
"""simple docstring"""
from .glue import GlueDataset, GlueDataTrainingArguments
from .language_modeling import (
LineByLineTextDataset,
LineByLineWithRefDataset,
LineByLineWithSOPTextDataset,
TextDataset,
TextDatasetForNextSentencePrediction,
)
from .squad import SquadDataset, S... | 607 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( __A ) -> Any:
def wrapper(*__A ,**__A ):
lowercase : Tuple ... | 607 | 1 |
"""simple docstring"""
import os
import unittest
from transformers.models.phobert.tokenization_phobert import VOCAB_FILES_NAMES, PhobertTokenizer
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase__ ( __UpperCamelCase , unittest.TestCase ):
__UpperCAme... | 607 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
ge... | 607 | 1 |
"""simple docstring"""
import json
import os
import tempfile
import datasets
from utils import generate_example_dataset, get_duration
lowerCAmelCase: Union[str, Any] =50000
lowerCAmelCase: List[str] =5000
lowerCAmelCase , lowerCAmelCase: Union[str, Any] ... | 607 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 607 | 1 |
"""simple docstring"""
import json
import os
from collections import Counter
import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset
lowerCAmelCase: List[str] ={1: (1, 1), 2: (2, 1)... | 607 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: int ... | 607 | 1 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config i... | 607 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase: List[str] =parse(importlib.metadata.version("torch"))
def __snake_case ( __A ,__A ... | 607 | 1 |
"""simple docstring"""
import numpy as np
from nltk.translate import meteor_score
import datasets
from datasets.config import importlib_metadata, version
lowerCAmelCase: List[str] =version.parse(importlib_metadata.version("nltk"))
if NLTK_VERSION >= version.Version("3.6.4"):
from ... | 607 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import _LazyModule
lowerCAmelCase: int ={"tokenization_wav2vec2_phoneme": ["Wav2Vec2PhonemeCTCTokenizer"]}
if TYPE_CHECKING:
from .tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizer
else:
im... | 607 |
"""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 tra... | 607 | 1 |
"""simple docstring"""
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(l... | 607 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config i... | 607 | 1 |
"""simple docstring"""
import inspect
import jax
import jax.lax as lax
import jax.numpy as jnp
from ..utils import add_start_docstrings
from ..utils.logging import get_logger
lowerCAmelCase: Tuple =get_logger(__name__)
lowerCAmelCase: Optional[int] =R"\n Args:\n ... | 607 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def __snake_case ( __A ,__A ) -> Any:
lowercase : Any = int(__A )
assert noofclusters < len(__A )
# Find out the dimensionality
lower... | 607 | 1 |
"""simple docstring"""
from PIL import Image
def __snake_case ( __A ,__A ) -> Image:
lowercase : str = (259 * (level + 255)) / (255 * (259 - level))
def contrast(__A ) -> int:
return int(128 + factor * (c - 128) )
return img.point... | 607 |
"""simple docstring"""
import qiskit
def __snake_case ( __A ,__A ) -> qiskit.result.counts.Counts:
lowercase : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
lowercase : int = qiskit.QuantumCircuit(4 ,2 )
# encod... | 607 | 1 |
"""simple docstring"""
def __snake_case ( __A ,__A = False ) -> bool:
if n == 2:
return True
if not n % 2 or n < 2:
return False
if n > 5 and n % 10 not in (1, 3, 7, 9): # can quickly check last digit
return False
if n > 3317044064679887385961981 ... | 607 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 607 | 1 |
"""simple docstring"""
import os
def __snake_case ( ) -> Tuple:
with open(os.path.dirname(__A ) + """/p022_names.txt""" ) as file:
lowercase : Dict = str(file.readlines()[0] )
lowercase : Union[str, Any] = names.replace(""... | 607 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __snake_case ( __A ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the C... | 607 | 1 |
"""simple docstring"""
import unittest
from transformers import MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING, AutoTokenizer, is_vision_available
from transformers.pipelines import pipeline
from transformers.pipelines.document_question_answering import apply_tesseract
from transformers.testing_utils ... | 607 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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 .... | 607 | 1 |
"""simple docstring"""
from PIL import Image
def __snake_case ( __A ,__A ) -> Image:
def brightness(__A ) -> float:
return 128 + level + (c - 128)
if not -2_55.0 <= level <= 2_55.0:
raise ValueError("""level must be between -255.0 (black) and 255... | 607 |
"""simple docstring"""
import math
import qiskit
def __snake_case ( __A = 1 ,__A = 1 ,__A = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__A ,__A )
or isinstance(__A ,__A )
or isinstance(__A ,__A )
):
raise ... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import choice
def __snake_case ( __A ) -> Any:
return choice(__A )
def __snake_case ( __A ,__A ) -> int:
lowercase : List[str] = random_pivot(__A ... | 607 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_convbert": ["CONVBERT_PRETRAINE... | 607 | 1 |
"""simple docstring"""
import math
from typing import Dict, Iterable, List, Optional, Tuple, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from .... | 607 |
"""simple docstring"""
import re
def __snake_case ( __A ) -> str:
if len(re.findall("""[ATCG]""" ,__A ) ) != len(__A ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" ,"""TAGC""" ) )
if _... | 607 | 1 |
"""simple docstring"""
import argparse
import torch
from transformers import OpenAIGPTConfig, OpenAIGPTModel, load_tf_weights_in_openai_gpt
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging
logging.set_verbosity_info()
def __snake_case ( __A ,__A ,__A ... | 607 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __snake_case ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" ... | 607 | 1 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase: List[str] =parse(importlib.metadata.version("torch"))
def __snake_case ( __A ,__A ... | 607 |
"""simple docstring"""
from ... import PretrainedConfig
lowerCAmelCase: str ={
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = NEZHA_PRETRAIN... | 607 | 1 |
"""simple docstring"""
# 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/LI... | 607 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class lowerCamelCase__ ( tf.keras.layers.Layer ):
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False , **snake_case ) -> ... | 607 | 1 |
"""simple docstring"""
import unittest
import numpy as np
from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
expand_dims,
flatten_dict,
is_flax_available,
is_tf_available,
is_torch_available,
reshape,
squ... | 607 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __snake_case ( __A ,__A = "cpu" ,__A = None ) -> None:
lowercase : Optional[int] = torch.load(__A ,map_location=__A )
for k, v in tqdm(state_... | 607 | 1 |
"""simple docstring"""
import argparse
import numpy as np
import torch
from transformers import SpeechTaHifiGan, SpeechTaHifiGanConfig, logging
logging.set_verbosity_info()
lowerCAmelCase: List[Any] =logging.get_logger("transformers.models.speecht5")
def __snake_case ... | 607 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __snake_case ( __A ) -> Any:
lowercase : List[str] = os.path.join(args.tf_model_dir ,"""para... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
from PIL import Image
# Define glider example
lowerCAmelCase: Optional[Any] =[
[0, 1, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0],
[1, 1, 1, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0, 0, 0... | 607 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_... | 607 | 1 |
"""simple docstring"""
from argparse import ArgumentParser
from ..pipelines import Pipeline, PipelineDataFormat, get_supported_tasks, pipeline
from ..utils import logging
from . import BaseTransformersCLICommand
lowerCAmelCase: Optional[int] =logging.get_logger(__name__) # pylint: dis... | 607 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( __A ) -> Any:
def wrapper(*__A ,**__A ):
lowercase : Tuple ... | 607 | 1 |
"""simple docstring"""
import copy
from typing import Any, Dict, List, Optional, Union
import numpy as np
from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import Batch... | 607 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
ge... | 607 | 1 |
"""simple docstring"""
import unittest
import numpy as np
import torch
from diffusers import KarrasVePipeline, KarrasVeScheduler, UNetaDModel
from diffusers.utils.testing_utils import enable_full_determinism, require_torch, slow, torch_device
enable_full_determinism()
class lowerCamelCase__ (... | 607 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 607 | 1 |
"""simple docstring"""
# Note: if you intend to run this script make sure you look under scripts/fsmt/
# to locate the appropriate script to do the work correctly. There is a set of scripts to:
# - download and prepare data and run the conversion script
# - perform eval to get the best hparam into the c... | 607 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: int ... | 607 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_flax_available,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_whisper... | 607 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase: List[str] =parse(importlib.metadata.version("torch"))
def __snake_case ( __A ,__A ... | 607 | 1 |
"""simple docstring"""
import argparse
import json
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import SegformerImageProcessor, SwinConfig, UperNetConfig, UperNetForSemanticSegmentation
def __snake_case ( __A ... | 607 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 | 1 |
"""simple docstring"""
class lowerCamelCase__ :
def __init__( self , snake_case ) -> None:
"""simple docstring"""
lowercase : Union[str, Any] = set_counts
lowercase : List[str] = max(snake_case )
lower... | 607 |
"""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 tra... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
def __snake_case ( __A ,__A ,__A ,) -> tuple:
if (electron_conc, hole_conc, intrinsic_conc).count(0 ) != 1:
raise ValueError("""You cannot supply more or less than 2 values""" )
elif electron_con... | 607 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config i... | 607 | 1 |
"""simple docstring"""
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer
import datasets
from datasets import logging
lowerCAmelCase: Tuple ="\\n\n"
lowerCAmelCase: Tuple ="\nPerplexity (PP... | 607 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def __snake_case ( __A ,__A ) -> Any:
lowercase : Any = int(__A )
assert noofclusters < len(__A )
# Find out the dimensionality
lower... | 607 | 1 |
"""simple docstring"""
import copy
from ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase: int =logging.get_logger(__name__)
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = """encoder-decoder"""
__U... | 607 |
"""simple docstring"""
import qiskit
def __snake_case ( __A ,__A ) -> qiskit.result.counts.Counts:
lowercase : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
lowercase : int = qiskit.QuantumCircuit(4 ,2 )
# encod... | 607 | 1 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __snake_case ( __A ,__A = "cpu" ,__A = None ) -> None:
lowercase : Optional[int] = torch.load(__A ,map_location=__A )
for k, v in tqdm(state_... | 607 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 607 | 1 |
"""simple docstring"""
import json
import os
import re
import sys
import urllib.request
import requests
from bsa import BeautifulSoup
lowerCAmelCase: int ={
"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36"
" (KHTML, like Gecko) Chrome/70.0.3538.10... | 607 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __snake_case ( __A ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the C... | 607 | 1 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 607 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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 .... | 607 | 1 |
"""simple docstring"""
import copy
from typing import Dict, List, Optional
from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto import CONFIG_MAPPING
lowerCAmelCase: Dict ={
"facebook/mask2former-swin-small-coco-instance": (
"https... | 607 |
"""simple docstring"""
import math
import qiskit
def __snake_case ( __A = 1 ,__A = 1 ,__A = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__A ,__A )
or isinstance(__A ,__A )
or isinstance(__A ,__A )
):
raise ... | 607 | 1 |
"""simple docstring"""
lowerCAmelCase: Tuple =65521
def __snake_case ( __A ) -> int:
lowercase : int = 1
lowercase : Union[str, Any] = 0
for plain_chr in plain_text:
lowercase : Union[str, Any] = (a + or... | 607 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_convbert": ["CONVBERT_PRETRAINE... | 607 | 1 |
"""simple docstring"""
import sacrebleu as scb
from packaging import version
from sacrebleu import CHRF
import datasets
lowerCAmelCase: Union[str, Any] ="\\n@inproceedings{popovic-2015-chrf,\n title = \"chr{F}: character n-gram {F}-score for automatic {MT} evaluation\",\n author ... | 607 |
"""simple docstring"""
import re
def __snake_case ( __A ) -> str:
if len(re.findall("""[ATCG]""" ,__A ) ) != len(__A ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" ,"""TAGC""" ) )
if _... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
from random import random
class lowerCamelCase__ :
def __init__( self , snake_case = None ) -> Dict:
"""simple docstring"""
lowercase : Tuple = value
lowercase : ... | 607 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __snake_case ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" ... | 607 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 607 |
"""simple docstring"""
from ... import PretrainedConfig
lowerCAmelCase: str ={
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = NEZHA_PRETRAIN... | 607 | 1 |
"""simple docstring"""
from dataclasses import dataclass
from typing import Optional
import numpy as np
import torch
import torch.nn as nn
from ..utils import BaseOutput, is_torch_version, randn_tensor
from .attention_processor import SpatialNorm
from .unet_ad_blocks import UNetMidBlockaD, get_d... | 607 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class lowerCamelCase__ ( tf.keras.layers.Layer ):
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False , **snake_case ) -> ... | 607 | 1 |
"""simple docstring"""
from unittest import TestCase
from datasets import Sequence, Value
from datasets.arrow_dataset import Dataset
class lowerCamelCase__ ( __UpperCamelCase ):
def _UpperCAmelCase ( self ) -> List[str]:
"""simple docstring... | 607 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __snake_case ( __A ,__A = "cpu" ,__A = None ) -> None:
lowercase : Optional[int] = torch.load(__A ,map_location=__A )
for k, v in tqdm(state_... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
import queue
class lowerCamelCase__ :
def __init__( self , snake_case ) -> Tuple:
"""simple docstring"""
lowercase : str = data
lowercase : Tuple = Non... | 607 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __snake_case ( __A ) -> Any:
lowercase : List[str] = os.path.join(args.tf_model_dir ,"""para... | 607 | 1 |
"""simple docstring"""
# this script reports modified .py files under the desired list of top-level sub-dirs passed as a list of arguments, e.g.:
# python ./utils/get_modified_files.py utils src tests examples
#
# it uses git to find the forking point and which files were modified - i.e. files not und... | 607 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_... | 607 | 1 |
"""simple docstring"""
from ... import PretrainedConfig
lowerCAmelCase: str ={
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = NEZHA_PRETRAIN... | 607 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( __A ) -> Any:
def wrapper(*__A ,**__A ):
lowercase : Tuple ... | 607 | 1 |
"""simple docstring"""
import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple
import sentencepiece as spm
from ...tokenization_utils import AddedToken, BatchEncoding, PreTrainedTokenizer
from ...utils import logging
lowerCAmelCase: Optional[Any] =... | 607 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
ge... | 607 | 1 |
"""simple docstring"""
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 no... | 607 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
import csv
import requests
from bsa import BeautifulSoup
def __snake_case ( __A = "" ) -> dict[str, float]:
lowercase : Dict = url or """https://www.imdb.com/chart/top/?ref_=nv_mv_250"""
low... | 607 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: int ... | 607 | 1 |
"""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 AutoImageProcessor, ViTImageProcessor
from transformers.te... | 607 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase: List[str] =parse(importlib.metadata.version("torch"))
def __snake_case ( __A ,__A ... | 607 | 1 |
"""simple docstring"""
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_c... | 607 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 | 1 |
"""simple docstring"""
import json
import os
import unittest
from transformers.models.xlm.tokenization_xlm import VOCAB_FILES_NAMES, XLMTokenizer
from transformers.testing_utils import slow
from ...test_tokenization_common import TokenizerTesterMixin
class lowerCamelCase__ ( __UpperCamelCase ... | 607 |
"""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 tra... | 607 | 1 |
"""simple docstring"""
import warnings
from ...utils import logging
from .image_processing_yolos import YolosImageProcessor
lowerCAmelCase: Optional[Any] =logging.get_logger(__name__)
class lowerCamelCase__ ( __UpperCamelCase ):
def __init__( self , *snake_cas... | 607 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config i... | 607 | 1 |
"""simple docstring"""
import inspect
import unittest
import numpy as np
from tests.test_modeling_common import floats_tensor
from transformers import DetrConfig, MaskFormerConfig, SwinConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_tor... | 607 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def __snake_case ( __A ,__A ) -> Any:
lowercase : Any = int(__A )
assert noofclusters < len(__A )
# Find out the dimensionality
lower... | 607 | 1 |
"""simple docstring"""
from math import factorial
def __snake_case ( __A = 100 ) -> int:
return sum(int(__A ) for x in str(factorial(__A ) ) )
if __name__ == "__main__":
print(solution(int(input("Enter the Number: ").strip())))
| 607 |
"""simple docstring"""
import qiskit
def __snake_case ( __A ,__A ) -> qiskit.result.counts.Counts:
lowercase : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
lowercase : int = qiskit.QuantumCircuit(4 ,2 )
# encod... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
import time
import numpy as np
lowerCAmelCase: int =[8, 5, 9, 7]
lowerCAmelCase: Union[str, Any] =[
[2, 0, 1, 1],
[0, 1, 2, 1],
[4, 0, 0, 3],
[0, 2, 1, 0],
[1, 0, 3, 0],
]
lowerCAmelCase: ... | 607 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 607 | 1 |
"""simple docstring"""
from typing import List, Optional, Union
import numpy as np
import PIL.Image
from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
ChannelDimension... | 607 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __snake_case ( __A ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the C... | 607 | 1 |
"""simple docstring"""
# 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/LI... | 607 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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 .... | 607 | 1 |
"""simple docstring"""
# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licens... | 607 |
"""simple docstring"""
import math
import qiskit
def __snake_case ( __A = 1 ,__A = 1 ,__A = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__A ,__A )
or isinstance(__A ,__A )
or isinstance(__A ,__A )
):
raise ... | 607 | 1 |
"""simple docstring"""
from typing import Callable, Optional
from .. import Features
from ..packaged_modules.generator.generator import Generator
from .abc import AbstractDatasetInputStream
class lowerCamelCase__ ( __UpperCamelCase ):
def __init__( self , snake_case , snak... | 607 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_convbert": ["CONVBERT_PRETRAINE... | 607 | 1 |
"""simple docstring"""
import collections
import os
import re
from pathlib import Path
lowerCAmelCase: int ="src/transformers"
# Matches is_xxx_available()
lowerCAmelCase: List[str] =re.compile(R"is\_([a-z_]*)_available()")
# Catches a one-line _import_struct = {xxx}
l... | 607 |
"""simple docstring"""
import re
def __snake_case ( __A ) -> str:
if len(re.findall("""[ATCG]""" ,__A ) ) != len(__A ):
raise ValueError("""Invalid Strand""" )
return dna.translate(dna.maketrans("""ATCG""" ,"""TAGC""" ) )
if _... | 607 | 1 |
"""simple docstring"""
import math
def __snake_case ( __A ) -> list:
lowercase : Optional[int] = [True] * n
lowercase : Union[str, Any] = False
lowercase : List[Any] = False
lowercase : List[Any] = True... | 607 |
"""simple docstring"""
import os
import random
import sys
from . import cryptomath_module as cryptoMath # noqa: N812
from . import rabin_miller as rabinMiller # noqa: N812
def __snake_case ( ) -> None:
print("""Making key files...""" )
make_key_files("""rsa""" ... | 607 | 1 |
"""simple docstring"""
lowerCAmelCase: Optional[int] ="\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transfor... | 607 |
"""simple docstring"""
from ... import PretrainedConfig
lowerCAmelCase: str ={
"sijunhe/nezha-cn-base": "https://huggingface.co/sijunhe/nezha-cn-base/resolve/main/config.json",
}
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = NEZHA_PRETRAIN... | 607 | 1 |
"""simple docstring"""
from typing import List, Optional, Tuple, Union
import PIL
import torch
from torchvision import transforms
from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor
... | 607 |
"""simple docstring"""
import tensorflow as tf
from ...tf_utils import shape_list
class lowerCamelCase__ ( tf.keras.layers.Layer ):
def __init__( self , snake_case , snake_case , snake_case , snake_case , snake_case=1 , snake_case=False , **snake_case ) -> ... | 607 | 1 |
"""simple docstring"""
import argparse
import torch
from safetensors.torch import load_file
from diffusers import StableDiffusionPipeline
def __snake_case ( __A ,__A ,__A ,__A ,__A ) -> int:
# load base model
lowercase : int = StableDiffusi... | 607 |
"""simple docstring"""
from typing import Union
import fire
import torch
from tqdm import tqdm
def __snake_case ( __A ,__A = "cpu" ,__A = None ) -> None:
lowercase : Optional[int] = torch.load(__A ,map_location=__A )
for k, v in tqdm(state_... | 607 | 1 |
"""simple docstring"""
import unittest
from transformers import BertGenerationConfig, 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 ..... | 607 |
"""simple docstring"""
import argparse
import json
import os
from collections import OrderedDict
import numpy as np
import tensorflow as tf
import torch
def __snake_case ( __A ) -> Any:
lowercase : List[str] = os.path.join(args.tf_model_dir ,"""para... | 607 | 1 |
"""simple docstring"""
def __snake_case ( __A ,__A = 0 ) -> list:
lowercase : int = length or len(__A )
lowercase : List[Any] = False
for i in range(length - 1 ):
if list_data[i] > list_data[i + 1]:
lowercase , l... | 607 |
"""simple docstring"""
import unittest
import numpy as np
from transformers import RobertaConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
if is_... | 607 | 1 |
"""simple docstring"""
from typing import List, Union
import numpy as np
from ..utils import add_end_docstrings, 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 ..im... | 607 |
"""simple docstring"""
import timeit
import numpy as np
import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD
def __snake_case ( __A ) -> Any:
def wrapper(*__A ,**__A ):
lowercase : Tuple ... | 607 | 1 |
"""simple docstring"""
import math
def __snake_case ( __A ,__A ) -> int:
lowercase : Optional[int] = len(__A )
lowercase : Optional[int] = int(math.floor(math.sqrt(__A ) ) )
lowercase : Optional[int] = 0... | 607 |
"""simple docstring"""
import unittest
from pathlib import Path
from tempfile import NamedTemporaryFile, TemporaryDirectory
from transformers import BertConfig, BertTokenizerFast, FeatureExtractionPipeline
from transformers.convert_graph_to_onnx import (
convert,
ensure_valid_input,
ge... | 607 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase: Any ={
"configuration_clap": [
"CLAP_PRETRAINED_MODEL_ARCHIVE_LIST",
"ClapAudioConfig",
"ClapConfig... | 607 |
"""simple docstring"""
import uuid
from typing import Any, Dict, List, Optional, Union
from ..utils import add_end_docstrings, is_tf_available, is_torch_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline
if is_tf_available():
import tensorflow as tf
if is_torch_available():
... | 607 | 1 |
"""simple docstring"""
from __future__ import annotations
from collections.abc import Generator
def __snake_case ( ) -> Generator[int, None, None]:
lowercase : dict[int, int] = {}
lowercase : Union[str, Any] = 2
while True:
lowerc... | 607 |
"""simple docstring"""
import argparse
import torch
from transformers import BlenderbotConfig, BlenderbotForConditionalGeneration
from transformers.utils import logging
logging.set_verbosity_info()
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: int ... | 607 | 1 |
"""simple docstring"""
def __snake_case ( __A ,__A ) -> str:
if number < 0 or shift_amount < 0:
raise ValueError("""both inputs must be positive integers""" )
lowercase : Optional[int] = str(bin(__A ) )
binary_number += "0" * shift_amoun... | 607 |
"""simple docstring"""
import importlib.metadata
from typing import Union
from packaging.version import Version, parse
from .constants import STR_OPERATION_TO_FUNC
lowerCAmelCase: List[str] =parse(importlib.metadata.version("torch"))
def __snake_case ( __A ,__A ... | 607 | 1 |
"""simple docstring"""
import itertools
import json
import os
import unittest
from transformers import AddedToken, LongformerTokenizer, LongformerTokenizerFast
from transformers.models.longformer.tokenization_longformer import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokeniz... | 607 |
"""simple docstring"""
def __snake_case ( ) -> Union[str, Any]:
lowercase : str = 0
for i in range(1 ,1001 ):
total += i**i
return str(__A )[-10:]
if __name__ == "__main__":
print(solution())
| 607 | 1 |
"""simple docstring"""
def __snake_case ( __A = 50 ) -> int:
lowercase : Union[str, Any] = [1] * (length + 1)
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_length + 1 ... | 607 |
"""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 tra... | 607 | 1 |
"""simple docstring"""
def __snake_case ( __A ) -> bool:
lowercase : Union[str, Any] = [int(__A ) for i in ip_va_address.split(""".""" ) if i.isdigit()]
return len(__A ) == 4 and all(0 <= int(__A ) <= 254 for octet in octets )
if __name__ ... | 607 |
"""simple docstring"""
from collections import OrderedDict
from typing import TYPE_CHECKING, Any, List, Mapping, Optional, Union
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import TensorType, logging
if TYPE_CHECKING:
from ...onnx.config i... | 607 | 1 |
"""simple docstring"""
import os
import tempfile
import unittest
import numpy as np
from diffusers.utils import is_flax_available
from diffusers.utils.testing_utils import require_flax, slow
if is_flax_available():
import jax
import jax.numpy as jnp
from flax.jax_utils import repl... | 607 |
"""simple docstring"""
from random import shuffle
import tensorflow as tf
from numpy import array
def __snake_case ( __A ,__A ) -> Any:
lowercase : Any = int(__A )
assert noofclusters < len(__A )
# Find out the dimensionality
lower... | 607 | 1 |
"""simple docstring"""
from collections import Counter
from timeit import timeit
def __snake_case ( __A = "" ,) -> bool:
return sum(c % 2 for c in Counter(input_str.replace(""" """ ,"""""" ).lower() ).values() ) < 2
def __snake_case ( __A = ... | 607 |
"""simple docstring"""
import qiskit
def __snake_case ( __A ,__A ) -> qiskit.result.counts.Counts:
lowercase : List[Any] = qiskit.Aer.get_backend("""aer_simulator""" )
lowercase : int = qiskit.QuantumCircuit(4 ,2 )
# encod... | 607 | 1 |
"""simple docstring"""
def __snake_case ( __A ) -> List[str]:
lowercase : List[str] = 1
lowercase : Dict = 2
while i * i <= n:
lowercase : Union[str, Any] = 0
while n % i == 0:
n //= i
multiplicit... | 607 |
"""simple docstring"""
import json
import os
import unittest
from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transforme... | 607 | 1 |
"""simple docstring"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase: int ={
"configuration_chinese_clip": [
"CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP",
"C... | 607 |
"""simple docstring"""
import argparse
import json
from typing import List
from ltp import LTP
from transformers.models.bert.tokenization_bert import BertTokenizer
def __snake_case ( __A ) -> Union[str, Any]:
# This defines a "chinese character" as anything in the C... | 607 | 1 |
"""simple docstring"""
import string
from math import logaa
def __snake_case ( __A ,__A ) -> int:
lowercase : Dict = document.translate(
str.maketrans("""""" ,"""""" ,string.punctuation ) ).replace("""\n""" ,"""""" )
lowercase... | 607 |
"""simple docstring"""
import gc
import unittest
from transformers import CTRLConfig, 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 .... | 607 | 1 |
"""simple docstring"""
import argparse
import json
import os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_wa... | 607 |
"""simple docstring"""
import math
import qiskit
def __snake_case ( __A = 1 ,__A = 1 ,__A = 1 ) -> qiskit.result.counts.Counts:
if (
isinstance(__A ,__A )
or isinstance(__A ,__A )
or isinstance(__A ,__A )
):
raise ... | 607 | 1 |
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