
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def 				a						(							snake_case__:					Optional[int]						,   snake_case__:					Optional[int]						,   snake_case__:					Union[str, Any]					):


				'''simple docstring'''
				lowercase_       =					AutoConfig.from_pretrained(snake_case__					)
				lowercase_       =					FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__					)
				lowercase_       =					checkpoints.load_tax_checkpoint(snake_case__					)

				lowercase_       =					'''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

				if config.model_type == "t5":
								lowercase_       =					'''SelfAttention'''
				if config.model_type == "longt5" and config.encoder_attention_type == "local":
								lowercase_       =					'''LocalSelfAttention'''
				elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
								lowercase_       =					'''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					):
								lowercase_       =					F'''layers_{str(snake_case__					)}'''

								# Self-Attention
								lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''key''']['''kernel''']
								lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''out''']['''kernel''']
								lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''query''']['''kernel''']
								lowercase_       =					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":
												lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''attention''']['''T5LayerNorm_0''']['''scale''']

								# Layer Normalization
								lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

								if split_mlp_wi:
												lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								lowercase_       =					tax_model['''target''']['''encoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								lowercase_       =					flax_model.params['''encoder''']['''block'''][str(snake_case__					)]['''layer''']
								lowercase_       =					tax_attention_key
								lowercase_       =					tax_attention_out
								lowercase_       =					tax_attention_query
								lowercase_       =					tax_attention_value

								lowercase_       =					tax_attention_layer_norm

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

								if split_mlp_wi:
												lowercase_       =					tax_mlp_wi_a
												lowercase_       =					tax_mlp_wi_a
								else:
												lowercase_       =					tax_mlp_wi

								lowercase_       =					tax_mlp_wo
								lowercase_       =					tax_mlp_layer_norm

								lowercase_       =					flax_model_encoder_layer_block

				# Only for layer 0:
				lowercase_       =					tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
				lowercase_       =					tax_encoder_rel_embedding

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

				# Assigning
				lowercase_       =					tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
				lowercase_       =					tax_encoder_norm

				# Decoder
				for layer_index in range(config.num_layers					):
								lowercase_       =					F'''layers_{str(snake_case__					)}'''

								# Self-Attention
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

								# Layer Normalization
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''pre_self_attention_layer_norm'''][
								    '''scale'''
								]

								# Encoder-Decoder-Attention
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
								lowercase_       =					tax_enc_dec_attention_module['''key''']['''kernel''']
								lowercase_       =					tax_enc_dec_attention_module['''out''']['''kernel''']
								lowercase_       =					tax_enc_dec_attention_module['''query''']['''kernel''']
								lowercase_       =					tax_enc_dec_attention_module['''value''']['''kernel''']

								# Layer Normalization
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

								# MLP
								if split_mlp_wi:
												lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
												lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
								else:
												lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

								# Layer Normalization
								lowercase_       =					tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

								# Assigning
								lowercase_       =					flax_model.params['''decoder''']['''block'''][str(snake_case__					)]['''layer''']
								lowercase_       =					tax_attention_key
								lowercase_       =					tax_attention_out
								lowercase_       =					tax_attention_query
								lowercase_       =					tax_attention_value

								lowercase_       =					tax_pre_attention_layer_norm

								lowercase_       =					tax_enc_dec_attention_key
								lowercase_       =					tax_enc_dec_attention_out
								lowercase_       =					tax_enc_dec_attention_query
								lowercase_       =					tax_enc_dec_attention_value

								lowercase_       =					tax_cross_layer_norm

								if split_mlp_wi:
												lowercase_       =					tax_mlp_wi_a
												lowercase_       =					tax_mlp_wi_a
								else:
												lowercase_       =					tax_mlp_wi

								lowercase_       =					tax_mlp_wo

								lowercase_       =					txa_mlp_layer_norm

								lowercase_       =					flax_model_decoder_layer_block

				# Decoder Normalization
				lowercase_       =					tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
				lowercase_       =					txa_decoder_norm

				# Only for layer 0:
				lowercase_       =					tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
				lowercase_       =					tax_decoder_rel_embedding

				# Token Embeddings
				lowercase_       =					tax_model['''target''']['''token_embedder''']['''embedding''']
				lowercase_       =					txa_token_embeddings

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

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


if __name__ == "__main__":
						__a  			=					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.'
						)
						__a  			=					parser.parse_args()
						convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)











from dataclasses import dataclass, field
from typing import Tuple

from ..utils import cached_property, is_torch_available, is_torch_tpu_available, logging, requires_backends
from .benchmark_args_utils import BenchmarkArguments


if is_torch_available():
    import torch

if is_torch_tpu_available(check_device=False):
    import torch_xla.core.xla_model as xm


lowercase      :       List[str]									=  logging.get_logger(__name__)





@dataclass
class  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   __A   :				Optional[Any]         =  [
       '''no_inference''',
       '''no_cuda''',
       '''no_tpu''',
       '''no_speed''',
       '''no_memory''',
       '''no_env_print''',
       '''no_multi_process''',
   ]

   def __init__(					self					,  **lowercase)			->						Tuple:







          '''simple docstring'''

          for deprecated_arg in self.deprecated_args:
                 if deprecated_arg in kwargs:
                        a__						:	Union[str, Any] =				deprecated_arg[3:]
                        setattr(self					,  lowercase					,  not kwargs.pop(lowercase))
                        logger.warning(
                            F'{deprecated_arg} is depreciated. Please use --no_{positive_arg} or'
                            F' {positive_arg}={kwargs[positive_arg]}')

          a__						:	Union[str, Any] =				kwargs.pop('torchscript'					,  self.torchscript)
          a__						:	Tuple =				kwargs.pop('torch_xla_tpu_print_metrics'					,  self.torch_xla_tpu_print_metrics)
          a__						:	Tuple =				kwargs.pop('fp16_opt_level'					,  self.fpaa_opt_level)
          super().__init__(**lowercase)

   __A   :				bool         =  field(default=__UpperCAmelCase ,  metadata={'''help''': '''Trace the models using torchscript'''}	)
   __A   :				bool         =  field(default=__UpperCAmelCase ,  metadata={'''help''': '''Print Xla/PyTorch tpu metrics'''}	)
   __A   :				str         =  field(
       default='''O1''' ,  metadata={
           '''help''': (
               '''For fp16: Apex AMP optimization level selected in [\'O0\', \'O1\', \'O2\', and \'O3\']. '''
               '''See details at https://nvidia.github.io/apex/amp.html'''
           )
       } ,  )

   @cached_property
   def 							__lowercase		(					self)			->						Tuple["torch.device", int]:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          logger.info('PyTorch: setting up devices')
          if not self.cuda:
                 a__						:	List[str] =				torch.device('cpu')
                 a__						:	Optional[Any] =				0
          elif is_torch_tpu_available():
                 a__						:	List[str] =				xm.xla_device()
                 a__						:	Union[str, Any] =				0
          else:
                 a__						:	List[Any] =				torch.device('cuda' if torch.cuda.is_available() else 'cpu')
                 a__						:	Optional[int] =				torch.cuda.device_count()
          return device, n_gpu

   @property
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          return is_torch_tpu_available() and self.tpu

   @property
   def 							__lowercase		(					self)			->						int:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          # TODO(PVP): currently only single GPU is supported
          return torch.cuda.current_device()

   @property
   def 							__lowercase		(					self)			->						"torch.device":







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          return self._setup_devices[0]

   @property
   def 							__lowercase		(					self)			->						Dict:







          '''simple docstring'''

          requires_backends(self					,  ['torch'])
          return self._setup_devices[1]

   @property
   def 							__lowercase		(					self)			->						Optional[int]:







          '''simple docstring'''

          return self.n_gpu > 0


