


'''simple docstring'''


import argparse

import torch

from transformers import RoFormerConfig, RoFormerForMaskedLM, load_tf_weights_in_roformer
from transformers.utils import logging


logging.set_verbosity_info()






def 			__lowerCAmelCase			(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase ):
		# Initialise PyTorch model
		_UpperCAmelCase		:							Tuple             =   RoFormerConfig.from_json_file(__lowerCAmelCase )
		print(F"""Building PyTorch model from configuration: {config}""" )
		_UpperCAmelCase		:							List[str]             =   RoFormerForMaskedLM(__lowerCAmelCase )

		# Load weights from tf checkpoint
		load_tf_weights_in_roformer(__lowerCAmelCase   ,			__lowerCAmelCase   ,			__lowerCAmelCase )

		# Save pytorch-model
		print(F"""Save PyTorch model to {pytorch_dump_path}""" )
		torch.save(model.state_dict()   ,			__lowerCAmelCase   ,			_use_new_zipfile_serialization=__lowerCAmelCase )


if __name__ == "__main__":
						lowerCamelCase__     						=  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(
						    '--bert_config_file',
						    default=None,
						    type=str,
						    required=True,
						    help=(
						        'The config json file corresponding to the pre-trained BERT 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__     						=  parser.parse_args()
						convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)





import itertools
from dataclasses import dataclass
from io import StringIO
from typing import Optional

import pyarrow as pa

import datasets
from datasets.features.features import require_storage_cast
from datasets.table import table_cast


SCREAMING_SNAKE_CASE__				=							datasets.utils.logging.get_logger(__name__)
@dataclass
class       A__  (		datasets.BuilderConfig							):
							lowerCAmelCase__						:		Optional[datasets.Features]            =				None
							lowerCAmelCase__						:		str            =				"utf-8"
							lowerCAmelCase__						:		Optional[str]            =				None
							lowerCAmelCase__						:		int            =				10 << 20  # 10MB
							lowerCAmelCase__						:		bool            =				False
							lowerCAmelCase__						:		str            =				"line"






class       A__  (		datasets.ArrowBasedBuilder							):
							lowerCAmelCase__						:		Optional[int]            =				TextConfig





							def      a__					(					self    :  List[str]							)		->						Any:



											"""simple docstring"""


											return datasets.DatasetInfo(features=self.config.features							)





							def      a__					(					self    :  Tuple	,					_UpperCAmelCase    :  str							)		->						List[str]:



											"""simple docstring"""


											if not self.config.data_files:
															raise ValueError(f"""At least one data file must be specified, but got data_files={self.config.data_files}"""							)
											__lowercase								=   dl_manager.download_and_extract(self.config.data_files							)
											if isinstance(_UpperCAmelCase	,					(str, list, tuple)							):
															__lowercase								=   data_files
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															return [datasets.SplitGenerator(name=datasets.Split.TRAIN	,					gen_kwargs={'files': files}							)]
											__lowercase								=   []
											for split_name, files in data_files.items():
															if isinstance(_UpperCAmelCase	,					_UpperCAmelCase							):
																			__lowercase								=   [files]
															__lowercase								=   [dl_manager.iter_files(_UpperCAmelCase							) for file in files]
															splits.append(datasets.SplitGenerator(name=_UpperCAmelCase	,					gen_kwargs={'files': files}							)							)
											return splits





							def      a__					(					self    :  int	,					_UpperCAmelCase    :  pa.Table							)		->						pa.Table:



											"""simple docstring"""


											if self.config.features is not None:
															__lowercase								=   self.config.features.arrow_schema
															if all(not require_storage_cast(_UpperCAmelCase							) for feature in self.config.features.values()							):
																			# cheaper cast
																			__lowercase								=   pa_table.cast(_UpperCAmelCase							)
															else:
																			# more expensive cast; allows str <-> int/float or str to Audio for example
																			__lowercase								=   table_cast(_UpperCAmelCase	,					_UpperCAmelCase							)
															return pa_table
											else:
															return pa_table.cast(pa.schema({'text': pa.string()}							)							)





							def      a__					(					self    :  Union[str, Any]	,					_UpperCAmelCase    :  Optional[Any]							)		->						List[Any]:



											"""simple docstring"""


											__lowercase								=   list(self.config.features							) if self.config.features is not None else ['text']
											for file_idx, file in enumerate(itertools.chain.from_iterable(_UpperCAmelCase							)							):
															# open in text mode, by default translates universal newlines ("\n", "\r\n" and "\r") into "\n"
															with open(_UpperCAmelCase	,					encoding=self.config.encoding	,					errors=self.config.errors							) as f:
																			if self.config.sample_by == "line":
																							__lowercase								=   0
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not batch:
																															break
																											batch += f.readline()  # finish current line
																											# StringIO.readlines, by default splits only on "\n" (and keeps line breaks)
																											__lowercase								=   StringIO(_UpperCAmelCase							).readlines()
																											if not self.config.keep_linebreaks:
																															__lowercase								=   [line.rstrip('\n'							) for line in batch]
																											__lowercase								=   pa.Table.from_arrays([pa.array(_UpperCAmelCase							)]	,					names=_UpperCAmelCase							)
																											# Uncomment for debugging (will print the Arrow table size and elements)
																											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																											batch_idx += 1
																			elif self.config.sample_by == "paragraph":
																							__lowercase								=   0
																							__lowercase								=   ''
																							while True:
																											__lowercase								=   f.read(self.config.chunksize							)
																											if not new_batch:
																															break
																											batch += new_batch
																											batch += f.readline()  # finish current line
																											__lowercase								=   batch.split('\n\n'							)
																											__lowercase								=   pa.Table.from_arrays(
																											    [pa.array([example for example in batch[:-1] if example]							)]	,					names=_UpperCAmelCase							)
																											# Uncomment for debugging (will print the Arrow table size and elements)
																											# logger.warning(f"pa_table: {pa_table} num rows: {pa_table.num_rows}")
																											# logger.warning('\n'.join(str(pa_table.slice(i, 1).to_pydict()) for i in range(pa_table.num_rows)))
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																											batch_idx += 1
																											__lowercase								=   batch[-1]
																							if batch:
																											__lowercase								=   pa.Table.from_arrays([pa.array([batch]							)]	,					names=_UpperCAmelCase							)
																											yield (file_idx, batch_idx), self._cast_table(_UpperCAmelCase							)
																			elif self.config.sample_by == "document":
																							__lowercase								=   f.read()
																							__lowercase								=   pa.Table.from_arrays([pa.array([text]							)]	,					names=_UpperCAmelCase							)
																							yield file_idx, self._cast_table(_UpperCAmelCase							)





