





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def lowerCamelCase__    (							snake_case_	:  Dict       ,      snake_case_	:  List[str]       ,      snake_case_	:  Any       )      ->				Optional[int]:
   __snake_case							       =   AutoConfig.from_pretrained(snake_case_       )
   __snake_case							       =   FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case_       )
   __snake_case							       =   checkpoints.load_tax_checkpoint(snake_case_       )

   __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(snake_case_       )}"""

      # 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(snake_case_       )]['''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(snake_case_       )}"""

      # 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(snake_case_       )]['''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(snake_case_       )
   print('''T5X Model was sucessfully converted!'''       )


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








from __future__ import annotations






def lowerCamelCase__    (							snake_case_	:  list[int]       )      ->				list[int]:  # This function is recursive
   __snake_case							       =   len(snake_case_       )
   # If the array contains only one element, we return it (it's the stop condition of
   # recursion)
   if array_length <= 1:
      return array
      # Else
   __snake_case							       =   array[0]
   __snake_case							       =   False
   __snake_case							       =   1
   __snake_case							       =   []
   while not is_found and i < array_length:
      if array[i] < pivot:
         __snake_case							       =   True
         __snake_case							       =   [element for element in array[i:] if element >= array[i]]
         __snake_case							       =   longest_subsequence(snake_case_       )
         if len(snake_case_       ) > len(snake_case_       ):
            __snake_case							       =   temp_array
      else:
         i += 1

   __snake_case							       =   [element for element in array[1:] if element >= pivot]
   __snake_case							       =   [pivot, *longest_subsequence(snake_case_       )]
   if len(snake_case_       ) > len(snake_case_       ):
      return temp_array
   else:
      return longest_subseq


if __name__ == "__main__":
    import doctest

    doctest.testmod()

