



import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= AutoConfig.from_pretrained(__UpperCAmelCase       )
 a__												= FlaxAutoModelForSeqaSeqLM.from_config(config=__UpperCAmelCase       )
 a__												= checkpoints.load_tax_checkpoint(__UpperCAmelCase       )

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

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

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

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

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

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

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

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

  a__												= tax_attention_layer_norm

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

  if split_mlp_wi:
   a__												= tax_mlp_wi_a
   a__												= tax_mlp_wi_a
  else:
   a__												= tax_mlp_wi

  a__												= tax_mlp_wo
  a__												= tax_mlp_layer_norm

  a__												= flax_model_encoder_layer_block

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

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

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

 # Decoder
 for layer_index in range(config.num_layers       ):
  a__												= f"layers_{str(__UpperCAmelCase       )}"

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

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

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

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

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

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

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

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

  a__												= tax_pre_attention_layer_norm

  a__												= tax_enc_dec_attention_key
  a__												= tax_enc_dec_attention_out
  a__												= tax_enc_dec_attention_query
  a__												= tax_enc_dec_attention_value

  a__												= tax_cross_layer_norm

  if split_mlp_wi:
   a__												= tax_mlp_wi_a
   a__												= tax_mlp_wi_a
  else:
   a__												= tax_mlp_wi

  a__												= tax_mlp_wo

  a__												= txa_mlp_layer_norm

  a__												= flax_model_decoder_layer_block

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

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

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

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

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


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










import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM






def     __a					(		__UpperCAmelCase       ):
 if "model" in orig_key:
  a__												= orig_key.replace('''model.'''							,  ''''''       )
 if "norm1" in orig_key:
  a__												= orig_key.replace('''norm1'''							,  '''attention.output.LayerNorm'''       )
 if "norm2" in orig_key:
  a__												= orig_key.replace('''norm2'''							,  '''output.LayerNorm'''       )
 if "norm" in orig_key:
  a__												= orig_key.replace('''norm'''							,  '''LayerNorm'''       )
 if "transformer" in orig_key:
  a__												= orig_key.split('''.'''       )[0].split('''_'''       )[-1]
  a__												= orig_key.replace(f"transformer_{layer_num}"							,  f"encoder.layer.{layer_num}"       )
 if "mha.attn" in orig_key:
  a__												= orig_key.replace('''mha.attn'''							,  '''attention.self'''       )
 if "mha" in orig_key:
  a__												= orig_key.replace('''mha'''							,  '''attention'''       )
 if "W_q" in orig_key:
  a__												= orig_key.replace('''W_q'''							,  '''self.query'''       )
 if "W_k" in orig_key:
  a__												= orig_key.replace('''W_k'''							,  '''self.key'''       )
 if "W_v" in orig_key:
  a__												= orig_key.replace('''W_v'''							,  '''self.value'''       )
 if "ff1" in orig_key:
  a__												= orig_key.replace('''ff1'''							,  '''intermediate.dense'''       )
 if "ff2" in orig_key:
  a__												= orig_key.replace('''ff2'''							,  '''output.dense'''       )
 if "ff" in orig_key:
  a__												= orig_key.replace('''ff'''							,  '''output.dense'''       )
 if "mlm_class" in orig_key:
  a__												= orig_key.replace('''mlm.mlm_class'''							,  '''cls.predictions.decoder'''       )
 if "mlm" in orig_key:
  a__												= orig_key.replace('''mlm'''							,  '''cls.predictions.transform'''       )
 if "cls" not in orig_key:
  a__												= '''yoso.''' + orig_key

 return orig_key






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase       ):
 for key in orig_state_dict.copy().keys():
  a__												= orig_state_dict.pop(__UpperCAmelCase       )

  if ("pooler" in key) or ("sen_class" in key):
   continue
  else:
   a__												= val

 a__												= orig_state_dict['''cls.predictions.decoder.bias''']
 a__												= torch.arange(__UpperCAmelCase       ).expand((1, -1)       ) + 2

 return orig_state_dict






def     __a					(		__UpperCAmelCase							,  __UpperCAmelCase							,  __UpperCAmelCase       ):
 a__												= torch.load(__UpperCAmelCase							,  map_location='''cpu'''       )['''model_state_dict''']
 a__												= YosoConfig.from_json_file(__UpperCAmelCase       )
 a__												= YosoForMaskedLM(__UpperCAmelCase       )

 a__												= convert_checkpoint_helper(config.max_position_embeddings							,  __UpperCAmelCase       )

 print(model.load_state_dict(__UpperCAmelCase       )       )
 model.eval()
 model.save_pretrained(__UpperCAmelCase       )

 print(f"Checkpoint successfuly converted. Model saved at {pytorch_dump_path}"       )


if __name__ == "__main__":
     a_      :      int    =     argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The json file for YOSO model config.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     a_      :      Any    =     parser.parse_args()
     convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)





