


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile
from typing import List

from datasets import config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_config import DownloadConfig
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.info_utils import VerificationMode


def 					__lowerCAmelCase    (    a__		,					**a__						) ->						Union[str, Any]:
				return RunBeamCommand(
				    args.dataset		,					args.name		,					args.cache_dir		,					args.beam_pipeline_options		,					args.data_dir		,					args.all_configs		,					args.save_info or args.save_infos		,					args.ignore_verifications		,					args.force_redownload		,					**__lowerCAmelCase		,					)







class     __A(				A__    ):




				@staticmethod
				def        SCREAMING_SNAKE_CASE_	(  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					parser.add_parser('''run_beam'''	,  help='''Run a Beam dataset processing pipeline'''       )
								run_beam_parser.add_argument('''dataset'''	,  type=_snake_case	,  help='''Name of the dataset to download'''       )
								run_beam_parser.add_argument('''--name'''	,  type=_snake_case	,  default=_snake_case	,  help='''Dataset config name'''       )
								run_beam_parser.add_argument(
								    '''--cache_dir'''	,  type=_snake_case	,  default=_snake_case	,  help='''Cache directory where the datasets are stored'''	,  )
								run_beam_parser.add_argument(
								    '''--beam_pipeline_options'''	,  type=_snake_case	,  default=''''''	,  help='''Beam pipeline options, separated by commas. Example:: `--beam_pipeline_options=job_name=my-job,project=my-project`'''	,  )
								run_beam_parser.add_argument(
								    '''--data_dir'''	,  type=_snake_case	,  default=_snake_case	,  help='''Can be used to specify a manual directory to get the files from'''	,  )
								run_beam_parser.add_argument('''--all_configs'''	,  action='''store_true'''	,  help='''Test all dataset configurations'''       )
								run_beam_parser.add_argument('''--save_info'''	,  action='''store_true'''	,  help='''Save the dataset infos file'''       )
								run_beam_parser.add_argument(
								    '''--ignore_verifications'''	,  action='''store_true'''	,  help='''Run the test without checksums and splits checks'''       )
								run_beam_parser.add_argument('''--force_redownload'''	,  action='''store_true'''	,  help='''Force dataset redownload'''       )
								# aliases
								run_beam_parser.add_argument('''--save_infos'''	,  action='''store_true'''	,  help='''alias for save_info'''       )
								run_beam_parser.set_defaults(func=_snake_case       )




				def __init__(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case	,  **_snake_case	,  )					->   Optional[Any]:


								'''simple docstring'''




								__a    							=					dataset
								__a    							=					name
								__a    							=					cache_dir
								__a    							=					beam_pipeline_options
								__a    							=					data_dir
								__a    							=					all_configs
								__a    							=					save_infos
								__a    							=					ignore_verifications
								__a    							=					force_redownload
								__a    							=					config_kwargs




				def        SCREAMING_SNAKE_CASE_	(  self       )					->   Optional[int]:


								'''simple docstring'''




								import apache_beam as beam

								if self._name is not None and self._all_configs:
												print('''Both parameters `name` and `all_configs` can\'t be used at once.'''       )
												exit(1       )
								__a    							=					self._dataset, self._name
								__a    							=					dataset_module_factory(_snake_case       )
								__a    							=					import_main_class(dataset_module.module_path       )
								__a    							=					[]
								if self._beam_pipeline_options:
												__a    							=					beam.options.pipeline_options.PipelineOptions(
												    flags=[F"""--{opt.strip()}""" for opt in self._beam_pipeline_options.split(''','''       ) if opt]       )
								else:
												__a    							=					None
								if self._all_configs and len(builder_cls.BUILDER_CONFIGS       ) > 0:
												for builder_config in builder_cls.BUILDER_CONFIGS:
																builders.append(
																    builder_cls(
																        config_name=builder_config.name	,  data_dir=self._data_dir	,  hash=dataset_module.hash	,  beam_options=_snake_case	,  cache_dir=self._cache_dir	,  base_path=dataset_module.builder_kwargs.get('''base_path'''       )	,  )       )
								else:
												builders.append(
												    builder_cls(
												        config_name=_snake_case	,  data_dir=self._data_dir	,  beam_options=_snake_case	,  cache_dir=self._cache_dir	,  base_path=dataset_module.builder_kwargs.get('''base_path'''       )	,  **self._config_kwargs	,  )       )

								for builder in builders:
												builder.download_and_prepare(
												    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
												    if not self._force_redownload
												    else DownloadMode.FORCE_REDOWNLOAD	,  download_config=DownloadConfig(cache_dir=config.DOWNLOADED_DATASETS_PATH       )	,  verification_mode=VerificationMode.NO_CHECKS
												    if self._ignore_verifications
												    else VerificationMode.ALL_CHECKS	,  try_from_hf_gcs=_snake_case	,  )
												if self._save_infos:
																builder._save_infos()

								print('''Apache beam run successful.'''       )

								# If save_infos=True, the dataset infos file is created next to the loaded module file.
								# Let's move it to the original directory of the dataset script, to allow the user to
								# upload them on S3 at the same time afterwards.
								if self._save_infos:
												__a    							=					os.path.join(builder_cls.get_imported_module_dir()	,  config.DATASETDICT_INFOS_FILENAME       )

												__a    							=					Path(_snake_case       ).name + ".py"

												__a    							=					os.path.join(_snake_case	,  _snake_case       )
												if os.path.isfile(_snake_case       ):
																__a    							=					os.path.dirname(_snake_case       )
												elif os.path.isfile(_snake_case       ):
																__a    							=					path
												else:  # in case of a remote dataset
																print(F"""Dataset Infos file saved at {dataset_infos_path}"""       )
																exit(1       )

												# Move datasetinfo back to the user
												__a    							=					os.path.join(_snake_case	,  config.DATASETDICT_INFOS_FILENAME       )
												copyfile(_snake_case	,  _snake_case       )
												print(F"""Dataset Infos file saved at {user_dataset_infos_path}"""       )

import math
from collections import OrderedDict

import torch
from packaging import version
from torch import Tensor, nn

from .utils import logging


A  :			List[str]     				=				logging.get_logger(__name__)







class     __A(				nn.Module    ):




				def __init__(  self       )					->   Union[str, Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.12.0'''       ):
												raise ImportError(
												    F"""You are using torch=={torch.__version__}, but torch>=1.12.0 is required to use """
												    '''PytorchGELUTanh. Please upgrade torch.'''       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.gelu(_snake_case	,  approximate='''tanh'''       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi       ) * (input + 0.04_4715 * torch.pow(_snake_case	,  3.0       ))       ))







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case = False       )					->   Tuple:


								'''simple docstring'''




								super().__init__()
								if use_gelu_python:
												__a    							=					self._gelu_python
								else:
												__a    							=					nn.functional.gelu




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * 0.5 * (1.0 + torch.erf(input / math.sqrt(2.0       )       ))




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1.0 + torch.tanh(input * 0.79_7884_5608 * (1.0 + 0.04_4715 * input * input)       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.sigmoid(1.702 * input       )







class     __A(				nn.Module    ):




				def __init__(  self	,  _snake_case	,  _snake_case       )					->   Optional[int]:


								'''simple docstring'''




								if min > max:
												raise ValueError(F"""min should be < max (got min: {min}, max: {max})"""       )

								super().__init__()
								__a    							=					min
								__a    							=					max




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return torch.clip(gelu(_snake_case       )	,  self.min	,  self.max       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   str:


								'''simple docstring'''




								super().__init__()
								__a    							=					math.sqrt(2 / math.pi       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return 0.5 * input * (1 + torch.tanh(self.precomputed_constant * (input + 0.04_4715 * torch.pow(_snake_case	,  3       ))       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return nn.functional.silu(_snake_case       )







class     __A(				nn.Module    ):




				def __init__(  self       )					->   List[Any]:


								'''simple docstring'''




								super().__init__()
								if version.parse(torch.__version__       ) < version.parse('''1.9.0'''       ):
												__a    							=					self._mish_python
								else:
												__a    							=					nn.functional.mish




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input * torch.tanh(nn.functional.softplus(_snake_case       )       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return self.act(_snake_case       )







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Tensor:


								'''simple docstring'''




								return input







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case=0.70_7107	,  _snake_case=0.28_2095       )					->   int:


								'''simple docstring'''




								__a    							=					(input - mu).div(sigma * math.sqrt(2.0       )       )
								return 0.5 * (1.0 + torch.erf(_snake_case       ))







class     __A(				nn.Module    ):




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case       )					->   Any:


								'''simple docstring'''




								__a    							=					nn.functional.relu(_snake_case       )
								__a    							=					torch.square(_snake_case       )
								return squared







class     __A(				a    ):




				def __getitem__(  self	,  _snake_case       )					->   Dict:


								'''simple docstring'''




								__a    							=					super().__getitem__(_snake_case       )
								__a							,  __a    							=					content if isinstance(_snake_case	,  _snake_case       ) else (content, {})
								return cls(**_snake_case       )



A  :			str     				=				{
    'gelu': GELUActivation,
    'gelu_10': (ClippedGELUActivation, {'min': -1_0, 'max': 1_0}),
    'gelu_fast': FastGELUActivation,
    'gelu_new': NewGELUActivation,
    'gelu_python': (GELUActivation, {'use_gelu_python': True}),
    'gelu_pytorch_tanh': PytorchGELUTanh,
    'gelu_accurate': AccurateGELUActivation,
    'laplace': LaplaceActivation,
    'linear': LinearActivation,
    'mish': MishActivation,
    'quick_gelu': QuickGELUActivation,
    'relu': nn.ReLU,
    'relu2': ReLUSquaredActivation,
    'relu6': nn.ReLUa,
    'sigmoid': nn.Sigmoid,
    'silu': SiLUActivation,
    'swish': SiLUActivation,
    'tanh': nn.Tanh,
}
A  :			Any     				=				ClassInstantier(ACTaCLS)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				if activation_string in ACTaFN:
								return ACTaFN[activation_string]
				else:
								raise KeyError(F"""function {activation_string} not found in ACT2FN mapping {list(ACTaFN.keys()						)}"""						)


# For backwards compatibility with: from activations import gelu_python
A  :			Optional[Any]     				=				get_activation('gelu_python')
A  :			str     				=				get_activation('gelu_new')
A  :			Any     				=				get_activation('gelu')
A  :			int     				=				get_activation('gelu_fast')
A  :			Any     				=				get_activation('quick_gelu')
A  :			int     				=				get_activation('silu')
A  :			List[str]     				=				get_activation('mish')






A  :			Dict     				=				get_activation('linear')