




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 		lowercase__ (		__A:						Union[str, Any]				,**__A:						Tuple					):





					'''simple docstring'''






					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  lowerCamelCase     (		_lowerCamelCase    ):






			'''simple docstring'''





			@staticmethod
			def 				UpperCAmelCase__							(			lowerCamelCase_      : ArgumentParser	)    -> Any:
								__magic_name__				:						List[Any]     				=				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      : Tuple							,    lowerCamelCase_      : str							,    lowerCamelCase_      : str							,    lowerCamelCase_      : str							,    lowerCamelCase_      : str							,    lowerCamelCase_      : str							,    lowerCamelCase_      : bool							,    lowerCamelCase_      : bool							,    lowerCamelCase_      : bool							,    lowerCamelCase_      : bool							,    **lowerCamelCase_      : List[str]							,    )    -> Any:
								__magic_name__				:						Union[str, Any]     				=				dataset
								__magic_name__				:						Optional[int]     				=				name
								__magic_name__				:						Optional[int]     				=				cache_dir
								__magic_name__				:						Dict     				=				beam_pipeline_options
								__magic_name__				:						Optional[Any]     				=				data_dir
								__magic_name__				:						int     				=				all_configs
								__magic_name__				:						Union[str, Any]     				=				save_infos
								__magic_name__				:						Any     				=				ignore_verifications
								__magic_name__				:						Any     				=				force_redownload
								__magic_name__				:						List[str]     				=				config_kwargs


			def 				UpperCAmelCase__							(			self      : Dict	)    -> List[str]:
								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	)
								__magic_name__				,     __magic_name__				:						Dict     				=				self._dataset, self._name
								__magic_name__				:						Optional[Any]     				=				dataset_module_factory(lowerCamelCase_	)
								__magic_name__				:						Any     				=				import_main_class(dataset_module.module_path	)
								__magic_name__				:						List[str]     				=				[]
								if self._beam_pipeline_options:
													__magic_name__				:						Optional[Any]     				=				beam.options.pipeline_options.PipelineOptions(
													    flags=[F'''--{opt.strip()}''' for opt in self._beam_pipeline_options.split(''','''	) if opt]	)
								else:
													__magic_name__				:						Tuple     				=				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:
													__magic_name__				:						List[str]     				=				os.path.join(builder_cls.get_imported_module_dir()							,    config.DATASETDICT_INFOS_FILENAME	)

													__magic_name__				:						Union[str, Any]     				=				Path(lowerCamelCase_	).name + '''.py'''

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









import argparse

import torch

from transformers import NystromformerConfig, NystromformerForMaskedLM
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			if "model" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""model."""       ,						"""""" )
			if "norm1" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm1"""       ,						"""attention.output.LayerNorm""" )
			if "norm2" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm2"""       ,						"""output.LayerNorm""" )
			if "norm" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""norm"""       ,						"""LayerNorm""" )
			if "transformer" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.split(""".""" )[0].split("""_""" )[-1]
						SCREAMING_SNAKE_CASE     =       orig_key.replace(f"""transformer_{layer_num}"""       ,						f"""encoder.layer.{layer_num}""" )
			if "mha.attn" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mha.attn"""       ,						"""attention.self""" )
			if "mha" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mha"""       ,						"""attention""" )
			if "W_q" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_q"""       ,						"""self.query""" )
			if "W_k" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_k"""       ,						"""self.key""" )
			if "W_v" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""W_v"""       ,						"""self.value""" )
			if "ff1" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff1"""       ,						"""intermediate.dense""" )
			if "ff2" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff2"""       ,						"""output.dense""" )
			if "ff" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""ff"""       ,						"""output.dense""" )
			if "mlm_class" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mlm.mlm_class"""       ,						"""cls.predictions.decoder""" )
			if "mlm" in orig_key:
						SCREAMING_SNAKE_CASE     =       orig_key.replace("""mlm"""       ,						"""cls.predictions.transform""" )
			if "cls" not in orig_key:
						SCREAMING_SNAKE_CASE     =       """nystromformer.""" + orig_key

			return orig_key
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						SCREAMING_SNAKE_CASE     =       orig_state_dict.pop(SCREAMING_SNAKE_CASE )

						if ("pooler" in key) or ("sen_class" in key) or ("conv.bias" in key):
									continue
						else:
									SCREAMING_SNAKE_CASE     =       val

			SCREAMING_SNAKE_CASE     =       orig_state_dict["""cls.predictions.decoder.bias"""]
			SCREAMING_SNAKE_CASE     =       (
			    torch.arange(config.max_position_embeddings ).expand((1, -1) ) + 2
			)

			return orig_state_dict
def        lowerCamelCase_     (     SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE ):
			'''simple docstring'''
			SCREAMING_SNAKE_CASE     =       torch.load(SCREAMING_SNAKE_CASE       ,						map_location="""cpu""" )["""model_state_dict"""]
			SCREAMING_SNAKE_CASE     =       NystromformerConfig.from_json_file(SCREAMING_SNAKE_CASE )
			SCREAMING_SNAKE_CASE     =       NystromformerForMaskedLM(SCREAMING_SNAKE_CASE )

			SCREAMING_SNAKE_CASE     =       convert_checkpoint_helper(SCREAMING_SNAKE_CASE       ,						SCREAMING_SNAKE_CASE )

			model.load_state_dict(SCREAMING_SNAKE_CASE )
			model.eval()
			model.save_pretrained(SCREAMING_SNAKE_CASE )

			print(f"""Checkpoint successfuly converted. Model saved at {pytorch_dump_path}""" )


if __name__ == "__main__":
		__A    :				List[str]    						=	argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--pytorch_model_path""", default=None, type=str, required=True, help="""Path to Nystromformer pytorch checkpoint."""
		)
		parser.add_argument(
		    """--config_file""",
		    default=None,
		    type=str,
		    required=True,
		    help="""The json file for Nystromformer model config.""",
		)
		parser.add_argument(
		    """--pytorch_dump_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
		)
		__A    :				Union[str, Any]    						=	parser.parse_args()
		convert_nystromformer_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)






