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 				_snake_case     (       lowerCAmelCase							:       Tuple       ,			**lowerCAmelCase							:       Any     ):
		"""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       ,			**lowerCAmelCase       ,			)







class 		a__						( A__ ):

					@staticmethod
					def     __UpperCamelCase       (   _A				:	ArgumentParser   ):







							"""simple docstring"""






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

					def __init__(   self				:	Optional[Any],_A				:	str,_A				:	str,_A				:	str,_A				:	str,_A				:	str,_A				:	bool,_A				:	bool,_A				:	bool,_A				:	bool,**_A				:	Dict,):







							"""simple docstring"""






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






					def     __UpperCamelCase       (   self				:	Tuple   ):







							"""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_  :		str    				=   self._dataset, self._name
							SCREAMING_SNAKE_CASE_  :		Optional[int]    				=   dataset_module_factory(_A   )
							SCREAMING_SNAKE_CASE_  :		List[str]    				=   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_  :		Union[str, 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=_A,cache_dir=self._cache_dir,base_path=dataset_module.builder_kwargs.get("base_path"   ),)   )
							else:
									builders.append(
									    builder_cls(
									        config_name=_A,data_dir=self._data_dir,beam_options=_A,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=_A,)
									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_  :		str    				=   os.path.join(builder_cls.get_imported_module_dir(),config.DATASETDICT_INFOS_FILENAME   )

									SCREAMING_SNAKE_CASE_  :		Optional[Any]    				=   Path(_A   ).name + ".py"

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

import argparse

from transformers import ConvBertConfig, ConvBertModel, TFConvBertModel, load_tf_weights_in_convbert
from transformers.utils import logging


logging.set_verbosity_info()




def 				_snake_case     (       lowerCAmelCase							:       Dict       ,			lowerCAmelCase							:       int       ,			lowerCAmelCase							:       Union[str, Any]     ):
		"""simple docstring"""







		SCREAMING_SNAKE_CASE_  :		List[Any]    				=   ConvBertConfig.from_json_file(lowerCAmelCase     )
		SCREAMING_SNAKE_CASE_  :		Tuple    				=   ConvBertModel(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Dict    				=   load_tf_weights_in_convbert(lowerCAmelCase       ,			lowerCAmelCase       ,			lowerCAmelCase     )
		model.save_pretrained(lowerCAmelCase     )

		SCREAMING_SNAKE_CASE_  :		Union[str, Any]    				=   TFConvBertModel.from_pretrained(lowerCAmelCase       ,			from_pt=lowerCAmelCase     )
		tf_model.save_pretrained(lowerCAmelCase     )


if __name__ == "__main__":
		__lowerCamelCase :     Optional[Any]          =  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--tf_checkpoint_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
		)
		parser.add_argument(
		    '''--convbert_config_file''',
		    default=None,
		    type=str,
		    required=True,
		    help=(
		        '''The config json file corresponding to the pre-trained ConvBERT model. \n'''
		        '''This specifies the model architecture.'''
		    ),
		)
		parser.add_argument(
		    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
		)
		__lowerCamelCase :     Optional[int]          =  parser.parse_args()
		convert_orig_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.convbert_config_file, args.pytorch_dump_path)
