


import argparse

from . import (
    BertConfig,
    BertGenerationConfig,
    BertGenerationDecoder,
    BertGenerationEncoder,
    load_tf_weights_in_bert_generation,
    logging,
)


logging.set_verbosity_info()
def 							UpperCamelCase_  (				__snake_case			:						Optional[Any]	,			__snake_case			:						Tuple	,			__snake_case			:						List[str]	,			__snake_case			:						List[Any]	,			__snake_case			:						List[Any]     )->      Optional[int]:




						'''simple docstring'''
						# Initialise PyTorch model
						__magic_name__ 		=			BertConfig.from_pretrained(
						    'bert-large-cased'	,			vocab_size=__snake_case	,			max_position_embeddings=5_12	,			is_decoder=__snake_case	,			add_cross_attention=__snake_case	,			)
						__magic_name__ 		=			bert_config.to_dict()
						del bert_config_dict["type_vocab_size"]
						__magic_name__ 		=			BertGenerationConfig(**__snake_case     )
						if is_encoder:
												__magic_name__ 		=			BertGenerationEncoder(__snake_case     )
						else:
												__magic_name__ 		=			BertGenerationDecoder(__snake_case     )
						print(F'''Building PyTorch model from configuration: {config}'''     )

						# Load weights from tf checkpoint
						load_tf_weights_in_bert_generation(
						    __snake_case	,			__snake_case	,			model_class='bert'	,			is_encoder_named_decoder=__snake_case	,			is_encoder=__snake_case	,			)

						# Save pytorch-model
						print(F'''Save PyTorch model and config to {pytorch_dump_path}'''     )
						model.save_pretrained(__snake_case     )


if __name__ == "__main__":
					UpperCamelCase :				Optional[int]						    =       argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '''--tf_hub_path''', default=None, type=str, required=True, help='''Path to the TensorFlow checkpoint path.'''
					)
					parser.add_argument(
					    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
					)
					parser.add_argument(
					    '''--is_encoder_named_decoder''',
					    action='''store_true''',
					    help='''If decoder has to be renamed to encoder in PyTorch model.''',
					)
					parser.add_argument('''--is_encoder''', action='''store_true''', help='''If model is an encoder.''')
					parser.add_argument('''--vocab_size''', default=50_358, type=int, help='''Vocab size of model''')
					UpperCamelCase :				Optional[Any]						    =       parser.parse_args()
					convert_tf_checkpoint_to_pytorch(
					    args.tf_hub_path,
					    args.pytorch_dump_path,
					    args.is_encoder_named_decoder,
					    args.vocab_size,
					    is_encoder=args.is_encoder,
					)





import csv
import dataclasses
import json
from dataclasses import dataclass
from typing import List, Optional, Union

from ...utils import is_tf_available, is_torch_available, logging


UpperCamelCase :				Optional[Any]						    =       logging.get_logger(__name__)

@dataclass
class     __snake_case							:
					SCREAMING_SNAKE_CASE_		:					str
					SCREAMING_SNAKE_CASE_		:					str
					SCREAMING_SNAKE_CASE_		:					Optional[str] 				= None
					SCREAMING_SNAKE_CASE_		:					Optional[str] 				= None
					def   a (	self					:	Tuple     )       ->				Dict:



											'''simple docstring'''





											return json.dumps(dataclasses.asdict(self     )  ,			indent=2     ) + "\n"







@dataclass(frozen=_UpperCAmelCase)
class     __snake_case							:
					SCREAMING_SNAKE_CASE_		:					List[int]
					SCREAMING_SNAKE_CASE_		:					Optional[List[int]] 				= None
					SCREAMING_SNAKE_CASE_		:					Optional[List[int]] 				= None
					SCREAMING_SNAKE_CASE_		:					Optional[Union[int, float]] 				= None
					def   a (	self					:	List[str]     )       ->				str:



											'''simple docstring'''





											return json.dumps(dataclasses.asdict(self     )     ) + "\n"







class     __snake_case							:
					def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	List[str]     )       ->				List[Any]:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	Optional[Any]  ,			_lowerCAmelCase					:	str     )       ->				List[Any]:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	List[str]  ,			_lowerCAmelCase					:	Optional[Any]     )       ->				Optional[Any]:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	Optional[int]  ,			_lowerCAmelCase					:	Union[str, Any]     )       ->				int:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	Any     )       ->				int:



											'''simple docstring'''





											raise NotImplementedError()
					def   a (	self					:	int  ,			_lowerCAmelCase					:	Optional[int]     )       ->				Dict:



											'''simple docstring'''





											if len(self.get_labels()     ) > 1:
																	__magic_name__ 		=			self.get_labels()[int(example.label     )]
											return example







					@classmethod
					def   a (	cls					:	Dict  ,			_lowerCAmelCase					:	int  ,			_lowerCAmelCase					:	Optional[Any]=None     )       ->				int:



											'''simple docstring'''





											with open(_lowerCAmelCase  ,			'r'  ,			encoding='utf-8-sig'     ) as f:
																	return list(csv.reader(_lowerCAmelCase  ,			delimiter='\t'  ,			quotechar=_lowerCAmelCase     )     )







class     __snake_case							(      _UpperCAmelCase):
					def __init__(	self					:	List[Any]  ,			_lowerCAmelCase					:	str=None  ,			_lowerCAmelCase					:	Dict=None  ,			_lowerCAmelCase					:	List[Any]="classification"  ,			_lowerCAmelCase					:	List[Any]=False     )       ->				Any:



											'''simple docstring'''





											__magic_name__ 		=			[] if labels is None else labels
											__magic_name__ 		=			[] if examples is None else examples
											__magic_name__ 		=			mode
											__magic_name__ 		=			verbose
					def __len__(	self					:	Any     )       ->				Union[str, Any]:



											'''simple docstring'''





											return len(self.examples     )
					def __getitem__(	self					:	Tuple  ,			_lowerCAmelCase					:	Dict     )       ->				Any:



											'''simple docstring'''





											if isinstance(_lowerCAmelCase  ,			_lowerCAmelCase     ):
																	return SingleSentenceClassificationProcessor(labels=self.labels  ,			examples=self.examples[idx]     )
											return self.examples[idx]
					@classmethod
					def   a (	cls					:	List[Any]  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	Union[str, Any]=""  ,			_lowerCAmelCase					:	Optional[Any]=0  ,			_lowerCAmelCase					:	Optional[int]=1  ,			_lowerCAmelCase					:	List[str]=None  ,			_lowerCAmelCase					:	str=False  ,			**_lowerCAmelCase					:	Dict     )       ->				Union[str, Any]:



											'''simple docstring'''





											__magic_name__ 		=			cls(**_lowerCAmelCase     )
											processor.add_examples_from_csv(
											    _lowerCAmelCase  ,			split_name=_lowerCAmelCase  ,			column_label=_lowerCAmelCase  ,			column_text=_lowerCAmelCase  ,			column_id=_lowerCAmelCase  ,			skip_first_row=_lowerCAmelCase  ,			overwrite_labels=_lowerCAmelCase  ,			overwrite_examples=_lowerCAmelCase  ,			)
											return processor
					@classmethod
					def   a (	cls					:	Optional[Any]  ,			_lowerCAmelCase					:	List[Any]  ,			_lowerCAmelCase					:	Optional[Any]=None  ,			**_lowerCAmelCase					:	Union[str, Any]     )       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			cls(**_lowerCAmelCase     )
											processor.add_examples(_lowerCAmelCase  ,			labels=_lowerCAmelCase     )
											return processor
					def   a (	self					:	Union[str, Any]  ,			_lowerCAmelCase					:	str  ,			_lowerCAmelCase					:	Optional[Any]=""  ,			_lowerCAmelCase					:	Optional[Any]=0  ,			_lowerCAmelCase					:	str=1  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	List[str]=False  ,			_lowerCAmelCase					:	Dict=False  ,			_lowerCAmelCase					:	Any=False  ,			)       ->				Tuple:



											'''simple docstring'''





											__magic_name__ 		=			self._read_tsv(_lowerCAmelCase     )
											if skip_first_row:
																	__magic_name__ 		=			lines[1:]
											__magic_name__ 		=			[]
											__magic_name__ 		=			[]
											__magic_name__ 		=			[]
											for i, line in enumerate(_lowerCAmelCase     ):
																	texts.append(line[column_text]     )
																	labels.append(line[column_label]     )
																	if column_id is not None:
																							ids.append(line[column_id]     )
																	else:
																							__magic_name__ 		=			F'''{split_name}-{i}''' if split_name else str(_lowerCAmelCase     )
																							ids.append(_lowerCAmelCase     )

											return self.add_examples(
											    _lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase  ,			overwrite_labels=_lowerCAmelCase  ,			overwrite_examples=_lowerCAmelCase     )
					def   a (	self					:	Any  ,			_lowerCAmelCase					:	Optional[int]  ,			_lowerCAmelCase					:	Optional[Any]=None  ,			_lowerCAmelCase					:	Any=None  ,			_lowerCAmelCase					:	Optional[int]=False  ,			_lowerCAmelCase					:	List[str]=False     )       ->				Dict:



											'''simple docstring'''





											if labels is not None and len(_lowerCAmelCase     ) != len(_lowerCAmelCase     ):
																	raise ValueError(
																	    F'''Text and labels have mismatched lengths {len(_lowerCAmelCase     )} and {len(_lowerCAmelCase     )}'''     )
											if ids is not None and len(_lowerCAmelCase     ) != len(_lowerCAmelCase     ):
																	raise ValueError(F'''Text and ids have mismatched lengths {len(_lowerCAmelCase     )} and {len(_lowerCAmelCase     )}'''     )
											if ids is None:
																	__magic_name__ 		=			[None] * len(_lowerCAmelCase     )
											if labels is None:
																	__magic_name__ 		=			[None] * len(_lowerCAmelCase     )
											__magic_name__ 		=			[]
											__magic_name__ 		=			set()
											for text_or_text_and_label, label, guid in zip(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     ):
																	if isinstance(_lowerCAmelCase  ,			(tuple, list)     ) and label is None:
																							__magic_name__		,			__magic_name__ 		=			text_or_text_and_label
																	else:
																							__magic_name__ 		=			text_or_text_and_label
																	added_labels.add(_lowerCAmelCase     )
																	examples.append(InputExample(guid=_lowerCAmelCase  ,			text_a=_lowerCAmelCase  ,			text_b=_lowerCAmelCase  ,			label=_lowerCAmelCase     )     )

											# Update examples
											if overwrite_examples:
																	__magic_name__ 		=			examples
											else:
																	self.examples.extend(_lowerCAmelCase     )

											# Update labels
											if overwrite_labels:
																	__magic_name__ 		=			list(_lowerCAmelCase     )
											else:
																	__magic_name__ 		=			list(set(self.labels     ).union(_lowerCAmelCase     )     )

											return self.examples







					def   a (	self					:	Optional[int]  ,			_lowerCAmelCase					:	Tuple  ,			_lowerCAmelCase					:	Optional[int]=None  ,			_lowerCAmelCase					:	Dict=False  ,			_lowerCAmelCase					:	Tuple=0  ,			_lowerCAmelCase					:	List[str]=True  ,			_lowerCAmelCase					:	List[str]=None  ,			)       ->				Dict:



											'''simple docstring'''





											if max_length is None:
																	__magic_name__ 		=			tokenizer.max_len

											__magic_name__ 		=			{label: i for i, label in enumerate(self.labels     )}

											__magic_name__ 		=			[]
											for ex_index, example in enumerate(self.examples     ):
																	if ex_index % 1_0_0_0_0 == 0:
																							logger.info(F'''Tokenizing example {ex_index}'''     )

																	__magic_name__ 		=			tokenizer.encode(
																	    example.text_a  ,			add_special_tokens=_lowerCAmelCase  ,			max_length=min(_lowerCAmelCase  ,			tokenizer.max_len     )  ,			)
																	all_input_ids.append(_lowerCAmelCase     )

											__magic_name__ 		=			max(len(_lowerCAmelCase     ) for input_ids in all_input_ids     )

											__magic_name__ 		=			[]
											for ex_index, (input_ids, example) in enumerate(zip(_lowerCAmelCase  ,			self.examples     )     ):
																	if ex_index % 1_0_0_0_0 == 0:
																							logger.info(F'''Writing example {ex_index}/{len(self.examples     )}'''     )
																	# The mask has 1 for real tokens and 0 for padding tokens. Only real
																	# tokens are attended to.
																	__magic_name__ 		=			[1 if mask_padding_with_zero else 0] * len(_lowerCAmelCase     )

																	# Zero-pad up to the sequence length.
																	__magic_name__ 		=			batch_length - len(_lowerCAmelCase     )
																	if pad_on_left:
																							__magic_name__ 		=			([pad_token] * padding_length) + input_ids
																							__magic_name__ 		=			([0 if mask_padding_with_zero else 1] * padding_length) + attention_mask
																	else:
																							__magic_name__ 		=			input_ids + ([pad_token] * padding_length)
																							__magic_name__ 		=			attention_mask + ([0 if mask_padding_with_zero else 1] * padding_length)

																	if len(_lowerCAmelCase     ) != batch_length:
																							raise ValueError(F'''Error with input length {len(_lowerCAmelCase     )} vs {batch_length}'''     )
																	if len(_lowerCAmelCase     ) != batch_length:
																							raise ValueError(F'''Error with input length {len(_lowerCAmelCase     )} vs {batch_length}'''     )

																	if self.mode == "classification":
																							__magic_name__ 		=			label_map[example.label]
																	elif self.mode == "regression":
																							__magic_name__ 		=			float(example.label     )
																	else:
																							raise ValueError(self.mode     )

																	if ex_index < 5 and self.verbose:
																							logger.info('*** Example ***'     )
																							logger.info(F'''guid: {example.guid}'''     )
																							logger.info(F'''input_ids: {" ".join([str(_lowerCAmelCase     ) for x in input_ids]     )}'''     )
																							logger.info(F'''attention_mask: {" ".join([str(_lowerCAmelCase     ) for x in attention_mask]     )}'''     )
																							logger.info(F'''label: {example.label} (id = {label})'''     )

																	features.append(InputFeatures(input_ids=_lowerCAmelCase  ,			attention_mask=_lowerCAmelCase  ,			label=_lowerCAmelCase     )     )

											if return_tensors is None:
																	return features
											elif return_tensors == "tf":
																	if not is_tf_available():
																							raise RuntimeError('return_tensors set to \'tf\' but TensorFlow 2.0 can\'t be imported'     )
																	import tensorflow as tf

																	def gen():
																							for ex in features:
																													yield ({"input_ids": ex.input_ids, "attention_mask": ex.attention_mask}, ex.label)

																	__magic_name__ 		=			tf.data.Dataset.from_generator(
																	    _lowerCAmelCase  ,			({'input_ids': tf.intaa, 'attention_mask': tf.intaa}, tf.intaa)  ,			({'input_ids': tf.TensorShape([None]     ), 'attention_mask': tf.TensorShape([None]     )}, tf.TensorShape([]     ))  ,			)
																	return dataset
											elif return_tensors == "pt":
																	if not is_torch_available():
																							raise RuntimeError('return_tensors set to \'pt\' but PyTorch can\'t be imported'     )
																	import torch
																	from torch.utils.data import TensorDataset

																	__magic_name__ 		=			torch.tensor([f.input_ids for f in features]  ,			dtype=torch.long     )
																	__magic_name__ 		=			torch.tensor([f.attention_mask for f in features]  ,			dtype=torch.long     )
																	if self.mode == "classification":
																							__magic_name__ 		=			torch.tensor([f.label for f in features]  ,			dtype=torch.long     )
																	elif self.mode == "regression":
																							__magic_name__ 		=			torch.tensor([f.label for f in features]  ,			dtype=torch.float     )

																	__magic_name__ 		=			TensorDataset(_lowerCAmelCase  ,			_lowerCAmelCase  ,			_lowerCAmelCase     )
																	return dataset
											else:
																	raise ValueError('return_tensors should be one of \'tf\' or \'pt\''     )

