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"""
import argparse
import os
from transformers.utils import direct_transformers_import
# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_task_guides.py
lowerCAmelCase: List[Any] ="src/tran... | 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
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List
import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor
from ... | 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: Any =[
[0, 16, 13, 0, 0, 0],
[0, 0, 10, 12, 0, 0],
[0, 4, 0, 0, 14, 0],
[0, 0, 9, 0, 0, 20],
[0, 0, 0, 7, 0, 4],
[0, 0, 0, 0, 0, 0],
]
def __snake_case ( __A ,__A ,__A ,__A ) -> Optional[Any]... | 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"""
# tests directory-specific settings - this file is run automatically
# by pytest before any tests are run
import sys
import warnings
from os.path import abspath, dirname, join
# allow having multiple repository checkouts and not needing to remember to rerun
# 'pip install... | 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 ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class lowerCamelCase__ ( __UpperCamelCase ):
__UpperCAmelCase = """ClapFeatureExtractor"""
__UpperCAmelCase = ("""RobertaTokenizer""", "... | 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"""
from __future__ import annotations
import json
import requests
from bsa import BeautifulSoup
from fake_useragent import UserAgent
lowerCAmelCase: Optional[int] ={"UserAgent": UserAgent().random}
def __snake_case ( __A ) -> dict:
... | 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 os
import time
import warnings
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Optional, Union
import torch
from filelock import FileLock
from torch.utils.data import Dataset
from ...tokenization_utils_base import PreTrained... | 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 __future__ import annotations
class lowerCamelCase__ :
def __init__( self , snake_case ) -> None:
"""simple docstring"""
lowercase : Union[str, Any] = data
lowercase : Node | None = No... | 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 statistics import mean
import numpy as np
def __snake_case ( __A ,__A ,__A ,__A ) -> list:
lowercase : Any = 0
# Number of processes finished
lowercase : Optional[int] = 0
# Displays the finished... | 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"""
import argparse
import hashlib # hashlib is only used inside the Test class
import struct
class lowerCamelCase__ :
def __init__( self , snake_case ) -> Dict:
"""simple docstring"""
lowercase : Optional[Any] = ... | 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"""
def __snake_case ( __A ,__A ) -> int:
while b:
lowercase , lowercase : Optional[Any] = b, a % b
return a
def __snake_case ( __A ,__A ) -> int:
return a if b == 0 else euclidean_gcd_recursive(... | 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 unittest
from datasets import load_dataset
from transformers import BloomTokenizerFast
from transformers.testing_utils import require_tokenizers
from ...test_tokenization_common import TokenizerTesterMixin
@require_tokenizers
class lowerCamelCase__ ( __UpperCam... | 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 itertools
import json
import linecache
import os
import pickle
import re
import socket
import string
from collections import Counter
from logging import getLogger
from pathlib import Path
from typing import Callable, Dict, Iterable, List
import git
import torch
... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase: Optional[int] ={
"configuration_conditional_detr": [
"CONDITIONAL_DETR_PRETRAINED_CONFIG_ARCHIVE... | 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_torch_available
lowerCAmelCase: Union[str, Any] ={
"configuration_time_series_transformer": [
"TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP"... | 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 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 |
"""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 OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase: Union[str, Any] ={
"configuration_table_transformer": [
"TABLE_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
"... | 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 collections import OrderedDict
from typing import Mapping
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
lowerCAmelCase: Optional[Any] =logging.get_logger(__name__)
lowerCAmelCase: Opt... | 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"""
from manim import *
class lowerCamelCase__ ( __UpperCamelCase ):
def _UpperCAmelCase ( self ) -> List[Any]:
"""simple docstring"""
lowercase : Optional[Any] = Rectangle(height=0.5 , width=0.5 ... | 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 typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available
lowerCAmelCase: Union[str, Any] ={
"configuration_nezha": ["NEZHA_PRETRAINED_CONFIG_ARCHIVE_MAP", "NezhaConfig"],
}
... | 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 Dict, List, Optional, Union
import numpy as np
from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
center_crop,
get_resize_output_image_size,
normalize,
rescale,
res... | 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 ast
import os
import re
import shutil
import tempfile
import unittest
from unittest import mock
import torch
from accelerate.test_utils.examples import compare_against_test
from accelerate.test_utils.testing import TempDirTestCase, require_trackers, run_command, s... | 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 json
import multiprocessing
import os
import re
from collections import defaultdict
import torch
from accelerate import Accelerator
from accelerate.utils import set_seed
from arguments import HumanEvalArguments
from datasets import load_dataset, load_metric
from to... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices
lowerCAmelCase: Optional[int] =logging.get_logger(__name__)
lowerCAmelCase: L... | 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 inspect
import unittest
from typing import List
import numpy as np
from transformers import EfficientFormerConfig
from transformers.testing_utils import require_tf, require_vision, slow
from transformers.utils import cached_property, is_tf_available, is_vision_availa... | 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
from typing import List, Optional, Tuple
from tokenizers import normalizers
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_distilbert import DistilBertTokenizer
lowerCAmelCase: str =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 collections.abc
from typing import Optional, Tuple, Union
import torch
import torch.utils.checkpoint
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOut... | 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 logging
import os
from .state import PartialState
class lowerCamelCase__ ( logging.LoggerAdapter ):
@staticmethod
def _UpperCAmelCase ( snake_case ) -> List[str]:
"""simple docstring"""
lowercase ... | 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 __future__ import annotations
import unittest
from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow
from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_com... | 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 os
import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_see... | 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 transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments
def __snake_case ( ) -> List[str]:
lowercase : List[Any] = HfArgumentParser(__A )
lowercase : int = parser.parse_args_into_d... | 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"""
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
DPMSolverMultistepScheduler,
TextToVideoSDPipeline,
UNetaDConditionModel,
)
f... | 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"""
def __snake_case ( __A ) -> int:
if not isinstance(__A ,__A ):
raise ValueError("""Input must be an integer""" )
if input_num <= 0:
raise ValueError("""Input must be positive""" )
return sum(
divisor for divisor in r... | 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 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 |
"""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 argparse
import json
from collections import OrderedDict
from pathlib import Path
import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from transformers import (
SegformerConfig,
SegformerForImageClassification,
... | 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 itertools
import random
import unittest
import numpy as np
from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available
from ...test_sequence_fea... | 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 ) -> str:
lowercase : List[str] = """"""
for ch in key:
if ch == " " or ch not in key_no_dups and ch.isalpha():
key_no_dups += ch
return key_no_dups
def __snake_case ( __A )... | 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 math
import os
import time
import traceback
import zipfile
from collections import Counter
import requests
def __snake_case ( __A ,__A=None ) -> List[str]:
lowercase : str = None
if tok... | 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 re
from filelock import FileLock
try:
import nltk
lowerCAmelCase: Tuple =True
except (ImportError, ModuleNotFoundError):
lowerCAmelCase: Optional[Any] =False
if NLTK_AVAILABLE:
with FileLock(".lock") as lock:
nltk.down... | 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 unittest
from transformers import TrOCRConfig
from transformers.testing_utils import is_torch_available, require_torch, torch_device
from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_... | 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"""
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: Dict ... | 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 __future__ import annotations
def __snake_case ( __A ,__A ) -> list[str]:
if nth_term == "":
return [""]
lowercase : str = int(__A )
lowercase : List[Any] = int(__A )
lowercase : ... | 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 TYPE_CHECKING
from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
lowerCAmelCase: Tuple ={"configuration_van": ["VAN_PRETRAINED_CONFIG_ARCHIVE_MAP", "VanConfig"]}
try:
if not is_torc... | 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"""
class lowerCamelCase__ :
def __init__( self , snake_case ) -> None:
"""simple docstring"""
lowercase : Optional[int] = size
lowercase : Any = [0] * size
lowercase : Union[str... | 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 logging
from dataclasses import dataclass, field
from typing import Optional
from seqaseq_trainer import arg_to_scheduler
from transformers import TrainingArguments
lowerCAmelCase: Optional[Any] =logging.getLogger(__name__)
@dataclass
class lowerCamel... | 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 torch
from transformers import LxmertConfig, LxmertForPreTraining, load_tf_weights_in_lxmert
from transformers.utils import logging
logging.set_verbosity_info()
def __snake_case ( __A ,__A ,__A ) -> Any:
# Initia... | 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
import os
import tempfile
import unittest
import numpy as np
from huggingface_hub import hf_hub_download
from transformers import is_tensorflow_text_available, is_tf_available
from transformers.testing_utils import require_tensorflow_te... | 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"""
from typing import TYPE_CHECKING
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available
lowerCAmelCase: Dict ={"configuration_vit_msn": ["VIT_MSN_PRETRAINED_CONFIG_ARCHIVE_MAP", "ViTMSNConfig"]}
try:
if not is_torch_available():... | 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 ) -> bool:
assert isinstance(__A ,__A ) and (
number >= 0
), "'number' must been an int and positive"
if 1 < number < 4:
# 2 and 3 are primes
return True
elif number < 2 or n... | 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"""
from ...utils import (
OptionalDependencyNotAvailable,
is_torch_available,
is_transformers_available,
is_transformers_version,
)
try:
if not (is_transformers_available() and is_torch_available()):
raise OptionalDependencyNotAvailable()
except Opt... | 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"""
# We ignore warnings about stepping the scheduler since we step it ourselves during gradient accumulation
import warnings
from .state import AcceleratorState, GradientState
warnings.filterwarnings("ignore", category=UserWarning, module="torch.optim.lr_scheduler")
class lo... | 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 gc
import unittest
import numpy as np
import torch
import torch.nn.functional as F
from transformers import (
ClapTextConfig,
ClapTextModelWithProjection,
RobertaTokenizer,
SpeechTaHifiGan,
SpeechTaHifiGanConfig,
)
from diffusers import (
... | 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 cva
import numpy as np
class lowerCamelCase__ :
def __init__( self , snake_case , snake_case ) -> Optional[Any]:
"""simple docstring"""
if k in (0.04, 0.06):
lowercase : Tuple = k
... | 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"""
import unittest
import numpy as np
from transformers import RoFormerConfig, is_flax_available
from transformers.testing_utils import require_flax, slow
from ...test_modeling_flax_common import FlaxModelTesterMixin, ids_tensor, random_attention_mask
if is_flax_available... | 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"""
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 |
"""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 datetime import datetime as dt
from github import Github
lowerCAmelCase: Union[str, Any] =[
"good first issue",
"good second issue",
"good difficult issue",
"enhancement",
"new pipeline/model",
"new scheduler",
"wi... | 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, is_vision_available
lowerCAmelCase: Union[str, Any] ={
"configuration_mask2former": [
"MASK2FORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
... | 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
def __snake_case ( __A ) -> list[int]:
lowercase : List[Any] = [True] * limit
lowercase : Optional[Any] = False
lowercase : Tuple = False
lowercase : ... | 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 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 |
"""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
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 |
"""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"""
# Copyright 2023 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 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 math
import random
def __snake_case ( __A ,__A = False ) -> float:
if deriv:
return value * (1 - value)
return 1 / (1 + math.exp(-value ))
# Initial Value
lowerCAmelCase: Union[str, Any] =0.0_2
def ... | 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"""
lowerCAmelCase: int ={
0: "0",
1: "1",
2: "2",
3: "3",
4: "4",
5: "5",
6: "6",
7: "7",
8: "8",
9: "9",
10: "a",
11: "b",
12: "c",
13: "d",
14: "e",
15: "f",
}
def __snake_case ... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase: Tuple =logging.get_logger(__name__)
lowerCAmelCase: Optional[int] ={
"uw-madison/mra-base-512-4": "https://huggingface.co/uw-madison/mra-base-512-4/res... | 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 ) -> Tuple:
lowercase : List[str] = [0] * len(__A )
lowercase : str = []
lowercase : Optional[int] = []
lowercase : List[str] = 0
for values in graph.value... | 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 warnings
from typing import Dict
import numpy as np
from ..utils import ExplicitEnum, add_end_docstrings, is_tf_available, is_torch_available
from .base import PIPELINE_INIT_ARGS, GenericTensor, Pipeline
if is_tf_available():
from ..models.auto.modeling_tf_aut... | 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 ...configuration_utils import PretrainedConfig
from ...utils import logging
lowerCAmelCase: str =logging.get_logger(__name__)
lowerCAmelCase: List[Any] ={
"google/switch-base-8": "https://huggingface.co/google/switch-base-8/blob/main/config.js... | 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 random
from .binary_exp_mod import bin_exp_mod
def __snake_case ( __A ,__A=1000 ) -> Tuple:
if n < 2:
return False
if n % 2 == 0:
return n == 2
# this means n is odd
lowercase : List[str] = n - ... | 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 collections.abc import Generator
import requests
from bsa import BeautifulSoup
lowerCAmelCase: Optional[Any] ="https://www.indeed.co.in/jobs?q=mobile+app+development&l="
def __snake_case ( __A = "mumbai"... | 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"""
from typing import List, Optional, Union
import torch
from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
... | 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 typing import Dict, List
from nltk.translate import gleu_score
import datasets
from datasets import MetricInfo
lowerCAmelCase: Optional[Any] ="\\n@misc{wu2016googles,\n title={Google's Neural Machine Translation System: Bridging the Gap between Human ... | 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"""
from __future__ import annotations
lowerCAmelCase: Any =list[tuple[int, int]]
lowerCAmelCase: Any =[
[0, 0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 0, 0, 0], # 0 are free path whereas 1's are obstacles
[0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0]... | 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 TYPE_CHECKING
from ...utils import (
OptionalDependencyNotAvailable,
_LazyModule,
is_tf_available,
is_tokenizers_available,
is_torch_available,
)
lowerCAmelCase: str ={
"configuration_convbert": ["CONVBERT_PRETRAINE... | 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 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 |
"""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 math
from collections.abc import Callable
def __snake_case ( __A ,__A ,__A ,__A = 100 ,) -> float:
lowercase : int = x_start
lowercase : Any = fnc(__A )
lowerc... | 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 tempfile import TemporaryDirectory
from unittest import TestCase
from unittest.mock import MagicMock, patch
from transformers import AutoModel, TFAutoModel
from transformers.onnx import FeaturesManager
from transformers.testing_utils import SMALL_MODEL_IDENTIFIER, require... | 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"""
import tempfile
import unittest
import numpy as np
import transformers
from transformers import GPTaTokenizer, GPTJConfig, is_flax_available, is_torch_available
from transformers.testing_utils import is_pt_flax_cross_test, require_flax, tooslow
from ...generation.test_fla... | 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"""
def __snake_case ( __A ,__A ) -> bool:
lowercase : Dict = len(__A )
lowercase : int = len(__A )
lowercase : Optional[Any] = [[False for _ in range(m + 1 )] for _ in range(n + 1 )]
lower... | 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 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 |
"""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 ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=__UpperCamelCase ):
__UpperCAmelCase = ["""flax"""]
def __init__( self , *snake_case , **snake_case ) -> Union[str, Any]:
... | 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 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 |
"""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 warnings
from typing import List, Optional, Union
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class lowerCamelCase_... | 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"""
# Lint as: python3
import dataclasses
import re
from dataclasses import dataclass
from functools import total_ordering
from typing import Optional, Union
lowerCAmelCase: Optional[Any] =re.compile(R"^(?P<major>\d+)" R"\.(?P<minor>\d+)" R"\.(?P<patch>\d+)$")
@tota... | 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 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 |
"""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"""
from ..utils import DummyObject, requires_backends
class lowerCamelCase__ ( metaclass=__UpperCamelCase ):
__UpperCAmelCase = ["""torch""", """torchsde"""]
def __init__( self , *snake_case , **snake_case ) -> List[str]:
... | 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 numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image
from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing imp... | 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 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 |
"""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 .integrations import (
is_optuna_available,
is_ray_available,
is_sigopt_available,
is_wandb_available,
run_hp_search_optuna,
run_hp_search_ray,
run_hp_search_sigopt,
run_hp_search_wandb,
)
from .trainer_utils import (
HPSearchBacken... | 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 os
from typing import Any, Callable, Dict, List, Optional, Tuple, Union
import torch
from torch import nn
from ...models.controlnet import ControlNetModel, ControlNetOutput
from ...models.modeling_utils import ModelMixin
from ...utils import logging
lowerCAmelCa... | 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 inspect
import unittest
import warnings
from transformers import DeiTConfig
from transformers.models.auto import get_values
from transformers.testing_utils import (
require_accelerate,
require_torch,
require_torch_gpu,
require_vision,
slow,
... | 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 gc
import unittest
import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer
from diffusers import (
AutoencoderKL,
DDIMScheduler,
EulerAncestralDiscreteScheduler,
LMSDiscreteScheduler,
PNDMScheduler... | 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 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 |
"""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"""
# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LI... | 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"""
def __snake_case ( __A = 50 ) -> int:
lowercase : Dict = [[0] * 3 for _ in range(length + 1 )]
for row_length in range(length + 1 ):
for tile_length in range(2 ,5 ):
for tile_start in range(row_length - tile_... | 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 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
... | 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 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 |
"""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 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 |
"""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 dataclasses import dataclass
from typing import Optional, Tuple, Union
import torch
import torch.nn as nn
from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, apply_forward_hook
from .modeling_utils import ModelMixin
from .... | 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 .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... | 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 gc
import random
import unittest
import numpy as np
import torch
from PIL import Image
from transformers import XLMRobertaTokenizerFast
from diffusers import DDIMScheduler, KandinskyImgaImgPipeline, KandinskyPriorPipeline, UNetaDConditionModel, VQModel
from diffuse... | 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 |
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