

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM


def   __SCREAMING_SNAKE_CASE					(SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__  ,		SCREAMING_SNAKE_CASE__       ):
						snake_case_   =	AutoConfig.from_pretrained(__A       )
						snake_case_   =	FlaxAutoModelForSeqaSeqLM.from_config(config=__A       )
						snake_case_   =	checkpoints.load_tax_checkpoint(__A       )

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

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

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

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

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

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

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

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

												snake_case_   =	tax_attention_layer_norm

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

												if split_mlp_wi:
																		snake_case_   =	tax_mlp_wi_a
																		snake_case_   =	tax_mlp_wi_a
												else:
																		snake_case_   =	tax_mlp_wi

												snake_case_   =	tax_mlp_wo
												snake_case_   =	tax_mlp_layer_norm

												snake_case_   =	flax_model_encoder_layer_block

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

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

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

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

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

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

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

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

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

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

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

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

												snake_case_   =	tax_pre_attention_layer_norm

												snake_case_   =	tax_enc_dec_attention_key
												snake_case_   =	tax_enc_dec_attention_out
												snake_case_   =	tax_enc_dec_attention_query
												snake_case_   =	tax_enc_dec_attention_value

												snake_case_   =	tax_cross_layer_norm

												if split_mlp_wi:
																		snake_case_   =	tax_mlp_wi_a
																		snake_case_   =	tax_mlp_wi_a
												else:
																		snake_case_   =	tax_mlp_wi

												snake_case_   =	tax_mlp_wo

												snake_case_   =	txa_mlp_layer_norm

												snake_case_   =	flax_model_decoder_layer_block

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

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

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

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

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


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




'''simple docstring'''





import warnings
from contextlib import contextmanager

from ....processing_utils import ProcessorMixin






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       Optional[Any]          =       'MCTCTFeatureExtractor'
 __lowerCamelCase    :       Optional[Any]          =       'AutoTokenizer'



 def __init__(self ,   A ,   A       )    ->     Dict:


      """simple docstring"""



      super().__init__(A ,   A       )
      _a      =  self.feature_extractor
      _a      =  False



 def __call__(self ,   *A ,   **A       )    ->     Optional[int]:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor(*A ,   **A       )

      if "raw_speech" in kwargs:
           warnings.warn('''Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead.'''       )
           _a      =  kwargs.pop('''raw_speech'''       )
      else:
           _a      =  kwargs.pop('''audio''' ,   A       )
      _a      =  kwargs.pop('''sampling_rate''' ,   A       )
      _a      =  kwargs.pop('''text''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if audio is None and text is None:
           raise ValueError('''You need to specify either an `audio` or `text` input to process.'''       )

      if audio is not None:
           _a      =  self.feature_extractor(A ,   *A ,   sampling_rate=A ,   **A       )
      if text is not None:
           _a      =  self.tokenizer(A ,   **A       )

      if text is None:
           return inputs
      elif audio is None:
           return encodings
      else:
           _a      =  encodings['''input_ids''']
           return inputs



 def 					a__  (self ,   *A ,   **A       )    ->     int:


      """simple docstring"""



      return self.tokenizer.batch_decode(*A ,   **A       )



 def 					a__  (self ,   *A ,   **A       )    ->     Tuple:


      """simple docstring"""



      if self._in_target_context_manager:
           return self.current_processor.pad(*A ,   **A       )

      _a      =  kwargs.pop('''input_features''' ,   A       )
      _a      =  kwargs.pop('''labels''' ,   A       )
      if len(A       ) > 0:
           _a      =  args[0]
           _a      =  args[1:]

      if input_features is not None:
           _a      =  self.feature_extractor.pad(A ,   *A ,   **A       )
      if labels is not None:
           _a      =  self.tokenizer.pad(A ,   **A       )

      if labels is None:
           return input_features
      elif input_features is None:
           return labels
      else:
           _a      =  labels['''input_ids''']
           return input_features



 def 					a__  (self ,   *A ,   **A       )    ->     List[str]:


      """simple docstring"""



      return self.tokenizer.decode(*A ,   **A       )



 @contextmanager
 def 					a__  (self       )    ->     Dict:


      """simple docstring"""



      warnings.warn(
          '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
          '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
          '''your audio inputs, or in a separate call.'''       )
      _a      =  True
      _a      =  self.tokenizer
      yield
      _a      =  self.feature_extractor
      _a      =  False



