






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def   __lowercase       (			snake_case,	snake_case,	snake_case			):







				"""simple docstring"""






				__magic_name__		:str   =   AutoConfig.from_pretrained(snake_case			)
				__magic_name__		:Dict   =   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case			)
				__magic_name__		:Any   =   checkpoints.load_tax_checkpoint(snake_case			)

				__magic_name__		:List[str]   =   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

				if config.model_type == "t5":
								__magic_name__		:Tuple   =   '''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								__magic_name__		:Optional[int]   =   '''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__magic_name__		:Any   =   '''TransientGlobalSelfAttention'''
				else:
								raise ValueError(
								    '''Given config is expected to have `model_type=\'t5\'`, or `model_type=\'longt5` with `encoder_attention_type`'''
								    ''' attribute with a value from [\'local\', \'transient-global].'''			)

				# Encoder
				for layer_index in range(config.num_layers			):
								__magic_name__		:Union[str, Any]   =   f'''layers_{str(snake_case			)}'''

								# Self-Attention
								__magic_name__		:List[str]   =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
								__magic_name__		:str   =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
								__magic_name__		:str   =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
								__magic_name__		:Tuple   =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''value''']['''kernel''']

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__magic_name__		:List[str]   =   tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

								# Layer Normalization
								__magic_name__		:Any   =   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

								if split_mlp_wi:
												__magic_name__		:Any   =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												__magic_name__		:Tuple   =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												__magic_name__		:List[str]   =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								__magic_name__		:List[str]   =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								__magic_name__		:Union[str, Any]   =   tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								__magic_name__		:Optional[int]   =   flax_model.params['''encoder''']['''block'''][str(snake_case			)]['''layer''']
								__magic_name__		:List[Any]   =   tax_attention_key
								__magic_name__		:List[str]   =   tax_attention_out
								__magic_name__		:Optional[int]   =   tax_attention_query
								__magic_name__		:str   =   tax_attention_value

								__magic_name__		:Dict   =   tax_attention_layer_norm

								# Global input layer norm
								if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
												__magic_name__		:Any   =   tax_global_layer_norm

								if split_mlp_wi:
												__magic_name__		:str   =   tax_mlp_wi_a
												__magic_name__		:Dict   =   tax_mlp_wi_a
								else:
												__magic_name__		:Tuple   =   tax_mlp_wi

								__magic_name__		:Optional[int]   =   tax_mlp_wo
								__magic_name__		:Optional[int]   =   tax_mlp_layer_norm

								__magic_name__		:Any   =   flax_model_encoder_layer_block

				# Only for layer 0:
				__magic_name__		:Dict   =   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
				__magic_name__		:List[Any]   =   tax_encoder_rel_embedding

				# Side/global relative position_bias + layer norm
				if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								__magic_name__		:Any   =   tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
								__magic_name__		:Dict   =   tax_encoder_global_rel_embedding

				# Assigning
				__magic_name__		:Union[str, Any]   =   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
				__magic_name__		:List[str]   =   tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers			):
								__magic_name__		:List[Any]   =   f'''layers_{str(snake_case			)}'''

								# Self-Attention
								__magic_name__		:Union[str, Any]   =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
								__magic_name__		:str   =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
								__magic_name__		:Union[str, Any]   =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
								__magic_name__		:Optional[int]   =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

								# Layer Normalization
								__magic_name__		:Optional[int]   =   tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
								    '''scale'''
								]

								# Encoder-Decoder-Attention
								__magic_name__		:Union[str, Any]   =   tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
								__magic_name__		:Tuple   =   tax_enc_dec_attention_module['''key''']['''kernel''']
								__magic_name__		:Optional[int]   =   tax_enc_dec_attention_module['''out''']['''kernel''']
								__magic_name__		:List[str]   =   tax_enc_dec_attention_module['''query''']['''kernel''']
								__magic_name__		:Tuple   =   tax_enc_dec_attention_module['''value''']['''kernel''']

								# Layer Normalization
								__magic_name__		:int   =   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

								# MLP
								if split_mlp_wi:
												__magic_name__		:Optional[Any]   =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												__magic_name__		:Dict   =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												__magic_name__		:int   =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								__magic_name__		:Optional[Any]   =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								__magic_name__		:Dict   =   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								__magic_name__		:List[str]   =   flax_model.params['''decoder''']['''block'''][str(snake_case			)]['''layer''']
								__magic_name__		:Any   =   tax_attention_key
								__magic_name__		:List[str]   =   tax_attention_out
								__magic_name__		:Tuple   =   tax_attention_query
								__magic_name__		:Tuple   =   tax_attention_value

								__magic_name__		:Tuple   =   tax_pre_attention_layer_norm

								__magic_name__		:Optional[Any]   =   tax_enc_dec_attention_key
								__magic_name__		:str   =   tax_enc_dec_attention_out
								__magic_name__		:Union[str, Any]   =   tax_enc_dec_attention_query
								__magic_name__		:Any   =   tax_enc_dec_attention_value

								__magic_name__		:Tuple   =   tax_cross_layer_norm

								if split_mlp_wi:
												__magic_name__		:Optional[int]   =   tax_mlp_wi_a
												__magic_name__		:Union[str, Any]   =   tax_mlp_wi_a
								else:
												__magic_name__		:Optional[int]   =   tax_mlp_wi

								__magic_name__		:List[str]   =   tax_mlp_wo

								__magic_name__		:int   =   txa_mlp_layer_norm

								__magic_name__		:str   =   flax_model_decoder_layer_block

				# Decoder Normalization
				__magic_name__		:Optional[Any]   =   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
				__magic_name__		:Tuple   =   txa_decoder_norm

				# Only for layer 0:
				__magic_name__		:Optional[Any]   =   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
				__magic_name__		:str   =   tax_decoder_rel_embedding

				# Token Embeddings
				__magic_name__		:List[Any]   =   tax_model['''target''']['''token_embedder''']['''embedding''']
				__magic_name__		:Any   =   txa_token_embeddings

				# LM Head (only in v1.1 and LongT5 checkpoints)
				if "logits_dense" in tax_model["target"]["decoder"]:
								__magic_name__		:int   =   tax_model['''target''']['''decoder''']['''logits_dense''']['''kernel''']

				flax_model.save_pretrained(snake_case			)
				print('''T5X Model was sucessfully converted!'''			)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__   :      Union[str, Any]				=		argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    """--t5x_checkpoint_path""", default=None, type=str, required=True, help="""Path the T5X checkpoint."""
					)
					parser.add_argument("""--config_name""", default=None, type=str, required=True, help="""Config name of LongT5/T5 model.""")
					parser.add_argument(
					    """--flax_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output FLAX model."""
					)
					SCREAMING_SNAKE_CASE__   :      Optional[int]				=		parser.parse_args()
					convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)














import logging
import os

import quant_trainer
import torch
from torch.utils.data import DataLoader

from transformers import Trainer, is_torch_tpu_available
from transformers.trainer_utils import PredictionOutput


SCREAMING_SNAKE_CASE__   :      List[str]				=		logging.getLogger(__name__)

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			):







				def __init__(    self							,       *__lowerCAmelCase							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       **__lowerCAmelCase  ):
								"""simple docstring"""
								super().__init__(*__lowerCAmelCase							,       **__lowerCAmelCase  )
								__magic_name__		:Any   =   eval_examples
								__magic_name__		:str   =   post_process_function
								__magic_name__		:int   =   quant_trainer_args
								__magic_name__		:List[str]   =   1_2_8  # default number of calibration samples







				def       A						(    self							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								if calib_dataset is None and self.calib_dataset is None:
												raise ValueError('''Trainer: calibration requires an calib_dataset.'''  )
								__magic_name__		:Optional[Any]   =   calib_dataset if calib_dataset is not None else self.calib_dataset

								__magic_name__		:Optional[int]   =   self._remove_unused_columns(__lowerCAmelCase							,       description='''Calibration'''  )

								return DataLoader(
								    __lowerCAmelCase							,       batch_size=self.args.eval_batch_size							,       collate_fn=self.data_collator							,       drop_last=self.args.dataloader_drop_last							,       num_workers=self.args.dataloader_num_workers							,       pin_memory=self.args.dataloader_pin_memory							,       shuffle=__lowerCAmelCase							,       )







				def       A						(    self							,       __lowerCAmelCase=None  ):
								"""simple docstring"""
								__magic_name__		:Dict   =   self.train_dataset if calib_dataset is None else calib_dataset
								__magic_name__		:Any   =   self.get_calib_dataloader(__lowerCAmelCase  )

								__magic_name__		:List[str]   =   self.model
								quant_trainer.configure_model(__lowerCAmelCase							,       self.quant_trainer_args							,       calib=__lowerCAmelCase  )
								model.eval()
								quant_trainer.enable_calibration(__lowerCAmelCase  )

								logger.info('''***** Running calibration *****'''  )
								logger.info(F'''  Num examples = {self.calib_num}'''  )
								logger.info(F'''  Batch size = {calib_dataloader.batch_size}'''  )

								for step, inputs in enumerate(__lowerCAmelCase  ):
												# Prediction step
												__magic_name__  ,					__magic_name__  ,					__magic_name__		:str   =   self.prediction_step(__lowerCAmelCase							,       __lowerCAmelCase							,       prediction_loss_only=__lowerCAmelCase  )
												if (step + 1) * calib_dataloader.batch_size >= self.calib_num:
																break

								quant_trainer.finish_calibration(__lowerCAmelCase							,       self.quant_trainer_args  )
								__magic_name__		:Any   =   model







				def       A						(    self							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase=None							,       __lowerCAmelCase = "eval"  ):
								"""simple docstring"""
								__magic_name__		:Tuple   =   self.eval_dataset if eval_dataset is None else eval_dataset
								__magic_name__		:Optional[Any]   =   self.get_eval_dataloader(__lowerCAmelCase  )
								__magic_name__		:str   =   self.eval_examples if eval_examples is None else eval_examples

								# Temporarily disable metric computation, we will do it in the loop here.
								__magic_name__		:Any   =   self.compute_metrics
								__magic_name__		:List[Any]   =   None
								__magic_name__		:List[str]   =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
												__magic_name__		:Optional[Any]   =   eval_loop(
												    __lowerCAmelCase							,       description='''Evaluation'''							,       prediction_loss_only=True if compute_metrics is None else None							,       ignore_keys=__lowerCAmelCase							,       )
								finally:
												__magic_name__		:Union[str, Any]   =   compute_metrics

								if self.post_process_function is not None and self.compute_metrics is not None:
												__magic_name__		:Union[str, Any]   =   self.post_process_function(__lowerCAmelCase							,       __lowerCAmelCase							,       output.predictions  )
												__magic_name__		:int   =   self.compute_metrics(__lowerCAmelCase  )

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

												self.log(__lowerCAmelCase  )
								else:
												__magic_name__		:List[str]   =   {}

								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()  )

								__magic_name__		:Optional[Any]   =   self.callback_handler.on_evaluate(self.args							,       self.state							,       self.control							,       __lowerCAmelCase  )
								return metrics







				def       A						(    self							,       __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase=None							,       __lowerCAmelCase = "test"  ):
								"""simple docstring"""
								__magic_name__		:int   =   self.get_test_dataloader(__lowerCAmelCase  )

								# Temporarily disable metric computation, we will do it in the loop here.
								__magic_name__		:Dict   =   self.compute_metrics
								__magic_name__		:str   =   None
								__magic_name__		:Optional[int]   =   self.prediction_loop if self.args.use_legacy_prediction_loop else self.evaluation_loop
								try:
												__magic_name__		:int   =   eval_loop(
												    __lowerCAmelCase							,       description='''Prediction'''							,       prediction_loss_only=True if compute_metrics is None else None							,       ignore_keys=__lowerCAmelCase							,       )
								finally:
												__magic_name__		:List[Any]   =   compute_metrics

								if self.post_process_function is None or self.compute_metrics is None:
												return output

								__magic_name__		:Optional[Any]   =   self.post_process_function(__lowerCAmelCase							,       __lowerCAmelCase							,       output.predictions							,       '''predict'''  )
								__magic_name__		:Dict   =   self.compute_metrics(__lowerCAmelCase  )

								# Prefix all keys with metric_key_prefix + '_'
								for key in list(metrics.keys()  ):
												if not key.startswith(F'''{metric_key_prefix}_'''  ):
																__magic_name__		:List[str]   =   metrics.pop(__lowerCAmelCase  )

								return PredictionOutput(predictions=predictions.predictions							,       label_ids=predictions.label_ids							,       metrics=__lowerCAmelCase  )







				def       A						(    self							,       __lowerCAmelCase="./"  ):
								"""simple docstring"""
								__magic_name__		:List[Any]   =   self.eval_dataset
								__magic_name__		:Any   =   self.get_eval_dataloader(__lowerCAmelCase  )

								__magic_name__		:int   =   next(iter(__lowerCAmelCase  )  )

								# saving device - to make it consistent
								__magic_name__		:str   =   torch.device('''cuda''' if torch.cuda.is_available() else '''cpu'''  )

								# convert to tuple
								__magic_name__		:int   =   tuple(v.to(__lowerCAmelCase  ) for k, v in batch.items()  )

								logger.info('''Converting model to be onnx compatible'''  )
								from pytorch_quantization.nn import TensorQuantizer

								__magic_name__		:Any   =   True

								__magic_name__		:Optional[int]   =   self.model.to(__lowerCAmelCase  )

								model.eval()
								model.float()

								__magic_name__		:Any   =   model.module if hasattr(__lowerCAmelCase							,       '''module'''  ) else model
								quant_trainer.configure_model(__lowerCAmelCase							,       self.quant_trainer_args  )

								__magic_name__		:int   =   os.path.join(__lowerCAmelCase							,       '''model.onnx'''  )
								logger.info(F'''exporting model to {output_model_file}'''  )

								__magic_name__		:Dict   =   {0: '''batch_size''', 1: '''seq_len'''}

								torch.onnx.export(
								    __lowerCAmelCase							,       __lowerCAmelCase							,       __lowerCAmelCase							,       export_params=__lowerCAmelCase							,       opset_version=1_3							,       do_constant_folding=__lowerCAmelCase							,       input_names=['''input_ids''', '''attention_mask''', '''token_type_ids''']							,       output_names=['''output_start_logits''', '''output_end_logits''']							,       dynamic_axes={
								        '''input_ids''': axes,
								        '''attention_mask''': axes,
								        '''token_type_ids''': axes,
								        '''output_start_logits''': axes,
								        '''output_end_logits''': axes,
								    }							,       verbose=__lowerCAmelCase							,       )
								logger.info('''onnx export finished'''  )






