


import math
import time
from typing import Dict, List, Optional

from torch.utils.data import Dataset

from transformers import SeqaSeqTrainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput, speed_metrics


if is_torch_tpu_available(check_device=False):
	import torch_xla.core.xla_model as xm
	import torch_xla.debug.metrics as met
class 	lowerCamelCase_       (				lowerCAmelCase__				):



					'''simple docstring'''







					def __init__(				self ,       *snake_case_ ,       snake_case_=None ,       snake_case_=None ,       **snake_case_						)   ->			List[str]:







								'''simple docstring'''

								super().__init__(*snake_case_ ,       **snake_case_						)
								__lowercase 					=   eval_examples
								__lowercase 					=   post_process_function






					def A      (				self ,       snake_case_ = None ,       snake_case_=None ,       snake_case_ = None ,       snake_case_ = "eval" ,       **snake_case_ ,       )   ->			Dict[str, float]:







								'''simple docstring'''

								__lowercase 					=   gen_kwargs.copy()
								__lowercase 					=   (
								    gen_kwargs['''max_length'''] if gen_kwargs.get('''max_length'''						) is not None else self.args.generation_max_length
								)
								__lowercase 					=   (
								    gen_kwargs['''num_beams'''] if gen_kwargs.get('''num_beams'''						) is not None else self.args.generation_num_beams
								)
								__lowercase 					=   gen_kwargs

								__lowercase 					=   self.eval_dataset if eval_dataset is None else eval_dataset
								__lowercase 					=   self.get_eval_dataloader(snake_case_						)
								__lowercase 					=   self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								__lowercase 					=   self.compute_metrics
								__lowercase 					=   None
								__lowercase 					=   time.time()
								__lowercase 					=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
											__lowercase 					=   eval_loop(
											    snake_case_ ,       description='''Evaluation''' ,       prediction_loss_only=True if compute_metrics is None else None ,       ignore_keys=snake_case_ ,       metric_key_prefix=snake_case_ ,       )
								finally:
											__lowercase 					=   compute_metrics
								__lowercase 					=   self.args.eval_batch_size * self.args.world_size
								if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
											start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
								output.metrics.update(
								    speed_metrics(
								        snake_case_ ,       snake_case_ ,       num_samples=output.num_samples ,       num_steps=math.ceil(output.num_samples / total_batch_size						) ,       )						)

								if self.post_process_function is not None and self.compute_metrics is not None and self.args.should_save:
											# Only the main node write the results by default
											__lowercase 					=   self.post_process_function(snake_case_ ,       snake_case_ ,       snake_case_						)
											__lowercase 					=   self.compute_metrics(snake_case_						)

											# Prefix all keys with metric_key_prefix + '_'
											for key in list(metrics.keys()						):
														if not key.startswith(F'{metric_key_prefix}_'						):
																	__lowercase 					=   metrics.pop(snake_case_						)

											metrics.update(output.metrics						)
								else:
											__lowercase 					=   output.metrics

								if self.args.should_log:
											# Only the main node log the results by default
											self.log(snake_case_						)

								if self.args.tpu_metrics_debug or self.args.debug:
											# tpu-comment: Logging debug metrics for PyTorch/XLA (compile, execute times, ops, etc.)
											xm.master_print(met.metrics_report()						)

								__lowercase 					=   self.callback_handler.on_evaluate(self.args ,       self.state ,       self.control ,       snake_case_						)
								return metrics






					def A      (				self ,       snake_case_ ,       snake_case_ ,       snake_case_=None ,       snake_case_ = "test" ,       **snake_case_						)   ->			Optional[Any]:







								'''simple docstring'''

								__lowercase 					=   gen_kwargs.copy()

								__lowercase 					=   self.get_test_dataloader(snake_case_						)

								# Temporarily disable metric computation, we will do it in the loop here.
								__lowercase 					=   self.compute_metrics
								__lowercase 					=   None
								__lowercase 					=   time.time()
								__lowercase 					=   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
											__lowercase 					=   eval_loop(
											    snake_case_ ,       description='''Prediction''' ,       prediction_loss_only=True if compute_metrics is None else None ,       ignore_keys=snake_case_ ,       metric_key_prefix=snake_case_ ,       )
								finally:
											__lowercase 					=   compute_metrics

								__lowercase 					=   self.args.eval_batch_size * self.args.world_size
								if F'{metric_key_prefix}_jit_compilation_time' in output.metrics:
											start_time += output.metrics[F'{metric_key_prefix}_jit_compilation_time']
								output.metrics.update(
								    speed_metrics(
								        snake_case_ ,       snake_case_ ,       num_samples=output.num_samples ,       num_steps=math.ceil(output.num_samples / total_batch_size						) ,       )						)
								if self.post_process_function is None or self.compute_metrics is None:
											return output

								__lowercase 					=   self.post_process_function(snake_case_ ,       snake_case_ ,       snake_case_ ,       '''predict'''						)
								__lowercase 					=   self.compute_metrics(snake_case_						)

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()						):
											if not key.startswith(F'{metric_key_prefix}_'						):
														__lowercase 					=   metrics.pop(snake_case_						)
								metrics.update(output.metrics						)
								return PredictionOutput(predictions=predictions.predictions ,       label_ids=predictions.label_ids ,       metrics=snake_case_						)






import argparse

from transformers import TaConfig, TaForConditionalGeneration, load_tf_weights_in_ta
from transformers.utils import logging


logging.set_verbosity_info()


def 		lowercase_  (     _UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  ):


			'''simple docstring'''







			__lowercase 					=   TaConfig.from_json_file(_UpperCamelCase  )
			print(F'Building PyTorch model from configuration: {config}'  )
			__lowercase 					=   TaForConditionalGeneration(_UpperCamelCase  )

			# Load weights from tf checkpoint
			load_tf_weights_in_ta(_UpperCamelCase       ,			_UpperCamelCase       ,			_UpperCamelCase  )

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


if __name__ == "__main__":
	a : Optional[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(
	    '''--config_file''',
	    default=None,
	    type=str,
	    required=True,
	    help=(
	        '''The config json file corresponding to the pre-trained T5 model. \nThis specifies the model architecture.'''
	    ),
	)
	parser.add_argument(
	    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
	)
	a : str		=				parser.parse_args()
	convert_tf_checkpoint_to_pytorch(args.tf_checkpoint_path, args.config_file, args.pytorch_dump_path)


