


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 		a__       (							A__,	**A__							):
			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,	**A__,	)







class      __lowercase  (__SCREAMING_SNAKE_CASE   ):





							"""simple docstring"""







							@staticmethod
							def  UpperCamelCase__			(	lowerCAmelCase__    ):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= parser.add_parser('run_beam'       ,		help='Run a Beam dataset processing pipeline'    )
										run_beam_parser.add_argument('dataset'       ,		type=lowerCAmelCase__       ,		help='Name of the dataset to download'    )
										run_beam_parser.add_argument('--name'       ,		type=lowerCAmelCase__       ,		default=lowerCAmelCase__       ,		help='Dataset config name'    )
										run_beam_parser.add_argument(
										    '--cache_dir'       ,		type=lowerCAmelCase__       ,		default=lowerCAmelCase__       ,		help='Cache directory where the datasets are stored'       ,		)
										run_beam_parser.add_argument(
										    '--beam_pipeline_options'       ,		type=lowerCAmelCase__       ,		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=lowerCAmelCase__       ,		default=lowerCAmelCase__       ,		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=lowerCAmelCase__    )






							def __init__(	self       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		lowerCAmelCase__       ,		**lowerCAmelCase__       ,		):





										"""simple docstring"""




										SCREAMING_SNAKE_CASE_						:			int       						= dataset
										SCREAMING_SNAKE_CASE_						:			int       						= name
										SCREAMING_SNAKE_CASE_						:			Optional[int]       						= cache_dir
										SCREAMING_SNAKE_CASE_						:			List[Any]       						= beam_pipeline_options
										SCREAMING_SNAKE_CASE_						:			Any       						= data_dir
										SCREAMING_SNAKE_CASE_						:			Tuple       						= all_configs
										SCREAMING_SNAKE_CASE_						:			Tuple       						= save_infos
										SCREAMING_SNAKE_CASE_						:			str       						= ignore_verifications
										SCREAMING_SNAKE_CASE_						:			Any       						= force_redownload
										SCREAMING_SNAKE_CASE_						:			Tuple       						= config_kwargs






							def  UpperCamelCase__			(	self    ):





										"""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    )
										SCREAMING_SNAKE_CASE_						,	SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= self._dataset, self._name
										SCREAMING_SNAKE_CASE_						:			Tuple       						= dataset_module_factory(lowerCAmelCase__    )
										SCREAMING_SNAKE_CASE_						:			Optional[Any]       						= import_main_class(dataset_module.module_path    )
										SCREAMING_SNAKE_CASE_						:			List[DatasetBuilder]       						= []
										if self._beam_pipeline_options:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= beam.options.pipeline_options.PipelineOptions(
													    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(','    ) if opt]    )
										else:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= 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=lowerCAmelCase__       ,		cache_dir=self._cache_dir       ,		base_path=dataset_module.builder_kwargs.get('base_path'    )       ,		)    )
										else:
													builders.append(
													    builder_cls(
													        config_name=lowerCAmelCase__       ,		data_dir=self._data_dir       ,		beam_options=lowerCAmelCase__       ,		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=lowerCAmelCase__       ,		)
													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:
													SCREAMING_SNAKE_CASE_						:			List[Any]       						= os.path.join(builder_cls.get_imported_module_dir()       ,		config.DATASETDICT_INFOS_FILENAME    )

													SCREAMING_SNAKE_CASE_						:			Optional[int]       						= Path(lowerCAmelCase__    ).name + '.py'

													SCREAMING_SNAKE_CASE_						:			int       						= os.path.join(lowerCAmelCase__       ,		lowerCAmelCase__    )
													if os.path.isfile(lowerCAmelCase__    ):
																SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= os.path.dirname(lowerCAmelCase__    )
													elif os.path.isfile(lowerCAmelCase__    ):
																SCREAMING_SNAKE_CASE_						:			Union[str, Any]       						= 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
													SCREAMING_SNAKE_CASE_						:			Dict       						= os.path.join(lowerCAmelCase__       ,		config.DATASETDICT_INFOS_FILENAME    )
													copyfile(lowerCAmelCase__       ,		lowerCAmelCase__    )
													print(F'''Dataset Infos file saved at {user_dataset_infos_path}'''    )










from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__A				    =						logging.get_logger(__name__)

__A				    =						{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}






class 		lowercase						( snake_case__):






	"""simple docstring"""


	a__      :					Dict		   =							"layoutlm"




	def __init__(	self	:						List[Any]						,  __UpperCAmelCase	:						int=30_522						,  __UpperCAmelCase	:						List[Any]=768						,  __UpperCAmelCase	:						Optional[int]=12						,  __UpperCAmelCase	:						List[str]=12						,  __UpperCAmelCase	:						int=3_072						,  __UpperCAmelCase	:						str="gelu"						,  __UpperCAmelCase	:						str=0.1						,  __UpperCAmelCase	:						Any=0.1						,  __UpperCAmelCase	:						Union[str, Any]=512						,  __UpperCAmelCase	:						Any=2						,  __UpperCAmelCase	:						Any=0.02						,  __UpperCAmelCase	:						Dict=1E-12						,  __UpperCAmelCase	:						Optional[int]=0						,  __UpperCAmelCase	:						List[str]="absolute"						,  __UpperCAmelCase	:						List[Any]=True						,  __UpperCAmelCase	:						Optional[int]=1_024						,  **__UpperCAmelCase	:						int						,  )			-> Union[str, Any]:
							super().__init__(pad_token_id=__UpperCAmelCase						,  **__UpperCAmelCase     )
							UpperCAmelCase_=   vocab_size
							UpperCAmelCase_=   hidden_size
							UpperCAmelCase_=   num_hidden_layers
							UpperCAmelCase_=   num_attention_heads
							UpperCAmelCase_=   hidden_act
							UpperCAmelCase_=   intermediate_size
							UpperCAmelCase_=   hidden_dropout_prob
							UpperCAmelCase_=   attention_probs_dropout_prob
							UpperCAmelCase_=   max_position_embeddings
							UpperCAmelCase_=   type_vocab_size
							UpperCAmelCase_=   initializer_range
							UpperCAmelCase_=   layer_norm_eps
							UpperCAmelCase_=   position_embedding_type
							UpperCAmelCase_=   use_cache
							UpperCAmelCase_=   max_ad_position_embeddings






class 		lowercase						( snake_case__):






	"""simple docstring"""


	def __init__(	self	:						List[str]						,  __UpperCAmelCase	:						PretrainedConfig						,  __UpperCAmelCase	:						str = "default"						,  __UpperCAmelCase	:						List[PatchingSpec] = None						,  )			-> str:
							super().__init__(__UpperCAmelCase						,  task=__UpperCAmelCase						,  patching_specs=__UpperCAmelCase     )
							UpperCAmelCase_=   config.max_ad_position_embeddings - 1




	@property
	def   _SCREAMING_SNAKE_CASE		(	self	:						Optional[Any]     )			-> Mapping[str, Mapping[int, str]]:
							return OrderedDict(
							    [
							        ("""input_ids""", {0: """batch""", 1: """sequence"""}),
							        ("""bbox""", {0: """batch""", 1: """sequence"""}),
							        ("""attention_mask""", {0: """batch""", 1: """sequence"""}),
							        ("""token_type_ids""", {0: """batch""", 1: """sequence"""}),
							    ]     )




	def   _SCREAMING_SNAKE_CASE		(	self	:						Any						,  __UpperCAmelCase	:						PreTrainedTokenizer						,  __UpperCAmelCase	:						int = -1						,  __UpperCAmelCase	:						int = -1						,  __UpperCAmelCase	:						bool = False						,  __UpperCAmelCase	:						Optional[TensorType] = None						,  )			-> Mapping[str, Any]:
							UpperCAmelCase_=   super().generate_dummy_inputs(
							    __UpperCAmelCase						,  batch_size=__UpperCAmelCase						,  seq_length=__UpperCAmelCase						,  is_pair=__UpperCAmelCase						,  framework=__UpperCAmelCase     )

							# Generate a dummy bbox
							UpperCAmelCase_=   [48, 84, 73, 128]

							if not framework == TensorType.PYTORCH:
													raise NotImplementedError("""Exporting LayoutLM to ONNX is currently only supported for PyTorch."""     )

							if not is_torch_available():
													raise ValueError("""Cannot generate dummy inputs without PyTorch installed."""     )
							import torch

							UpperCAmelCase_,   UpperCAmelCase_=   input_dict["""input_ids"""].shape
							UpperCAmelCase_=   torch.tensor([*[box] * seq_length]     ).tile(__UpperCAmelCase						,  1						,  1     )
							return input_dict




