
'''simple docstring'''
from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
						import datasets


class 			SCREAMING_SNAKE_CASE__      (     UpperCAmelCase__):







				@slow
				@require_torch
				def 						UpperCAmelCase_    (       self     )->		List[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     EncoderDecoderModel.from_encoder_decoder_pretrained('prajjwal1/bert-tiny'						,				'prajjwal1/bert-tiny'     )
											UpperCamelCase   			=     BertTokenizer.from_pretrained('bert-base-uncased'     )

											UpperCamelCase   			=     bertabert.config.encoder.vocab_size
											UpperCamelCase   			=     tokenizer.sep_token_id
											UpperCamelCase   			=     tokenizer.cls_token_id
											UpperCamelCase   			=     128

											UpperCamelCase   			=     datasets.load_dataset('cnn_dailymail'						,				'3.0.0'						,				split='train[:1%]'     )
											UpperCamelCase   			=     datasets.load_dataset('cnn_dailymail'						,				'3.0.0'						,				split='validation[:1%]'     )

											UpperCamelCase   			=     train_dataset.select(range(32     )     )
											UpperCamelCase   			=     val_dataset.select(range(16     )     )

											UpperCamelCase   			=     4

											def _map_to_encoder_decoder_inputs(A_     ):
																		# Tokenizer will automatically set [BOS] <text> [EOS]
																		UpperCamelCase   			=     tokenizer(batch['article']						,				padding='max_length'						,				truncation=_UpperCAmelCase						,				max_length=512     )
																		UpperCamelCase   			=     tokenizer(batch['highlights']						,				padding='max_length'						,				truncation=_UpperCAmelCase						,				max_length=128     )
																		UpperCamelCase   			=     inputs.input_ids
																		UpperCamelCase   			=     inputs.attention_mask

																		UpperCamelCase   			=     outputs.input_ids
																		UpperCamelCase   			=     outputs.input_ids.copy()
																		UpperCamelCase   			=     [
																		    [-100 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['labels']
																		]
																		UpperCamelCase   			=     outputs.attention_mask

																		assert all(len(_UpperCAmelCase     ) == 512 for x in inputs.input_ids     )
																		assert all(len(_UpperCAmelCase     ) == 128 for x in outputs.input_ids     )

																		return batch

											def _compute_metrics(A_     ):
																		UpperCamelCase   			=     pred.label_ids
																		UpperCamelCase   			=     pred.predictions

																		# all unnecessary tokens are removed
																		UpperCamelCase   			=     tokenizer.batch_decode(_UpperCAmelCase						,				skip_special_tokens=_UpperCAmelCase     )
																		UpperCamelCase   			=     tokenizer.batch_decode(_UpperCAmelCase						,				skip_special_tokens=_UpperCAmelCase     )

																		UpperCamelCase   			=     sum([int(pred_str[i] == label_str[i]     ) for i in range(len(_UpperCAmelCase     )     )]     ) / len(_UpperCAmelCase     )

																		return {"accuracy": accuracy}

											# map train dataset
											UpperCamelCase   			=     train_dataset.map(
											    _map_to_encoder_decoder_inputs						,				batched=_UpperCAmelCase						,				batch_size=_UpperCAmelCase						,				remove_columns=['article', 'highlights']						,				)
											train_dataset.set_format(
											    type='torch'						,				columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,				)

											# same for validation dataset
											UpperCamelCase   			=     val_dataset.map(
											    _map_to_encoder_decoder_inputs						,				batched=_UpperCAmelCase						,				batch_size=_UpperCAmelCase						,				remove_columns=['article', 'highlights']						,				)
											val_dataset.set_format(
											    type='torch'						,				columns=['input_ids', 'attention_mask', 'decoder_input_ids', 'decoder_attention_mask', 'labels']						,				)

											UpperCamelCase   			=     self.get_auto_remove_tmp_dir()

											UpperCamelCase   			=     SeqaSeqTrainingArguments(
											    output_dir=_UpperCAmelCase						,				per_device_train_batch_size=_UpperCAmelCase						,				per_device_eval_batch_size=_UpperCAmelCase						,				predict_with_generate=_UpperCAmelCase						,				evaluation_strategy='steps'						,				do_train=_UpperCAmelCase						,				do_eval=_UpperCAmelCase						,				warmup_steps=0						,				eval_steps=2						,				logging_steps=2						,				)

											# instantiate trainer
											UpperCamelCase   			=     SeqaSeqTrainer(
											    model=_UpperCAmelCase						,				args=_UpperCAmelCase						,				compute_metrics=_compute_metrics						,				train_dataset=_UpperCAmelCase						,				eval_dataset=_UpperCAmelCase						,				tokenizer=_UpperCAmelCase						,				)

											# start training
											trainer.train()












import argparse

import torch

from transformers import MobileBertConfig, MobileBertForPreTraining, load_tf_weights_in_mobilebert
from transformers.utils import logging


logging.set_verbosity_info()






def      _snake_case     (__lowercase     ,   __lowercase     ,   __lowercase):
   # Initialise PyTorch model
   UpperCamelCase_						=  MobileBertConfig.from_json_file(__lowercase)
   print(f"""Building PyTorch model from configuration: {config}""")
   UpperCamelCase_						=  MobileBertForPreTraining(__lowercase)
   # Load weights from tf checkpoint
   UpperCamelCase_						=  load_tf_weights_in_mobilebert(__lowercase     ,   __lowercase     ,   __lowercase)
   # Save pytorch-model
   print(f"""Save PyTorch model to {pytorch_dump_path}""")
   torch.save(model.state_dict()     ,   __lowercase)


if __name__ == "__main__":
 snake_case__   :						int													=   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(
     """--mobilebert_config_file""",
     default=None,
     type=str,
     required=True,
     help=(
         """The config json file corresponding to the pre-trained MobileBERT 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."""
 )
 snake_case__   :						Optional[Any]													=   parser.parse_args()
 convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.mobilebert_config_file, args.pytorch_dump_path)





