



'''simple docstring'''





import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def lowerCAmelCase  (__A  ,	__A  ,	__A):



     """simple docstring"""



     _a      =  AutoConfig.from_pretrained(__A)
     _a      =  FlaxAutoModelForSeqaSeqLM.from_config(config=__A)
     _a      =  checkpoints.load_tax_checkpoint(__A)

     _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(__A)}'''

          # 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(__A)]['''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(__A)}'''

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


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








'''simple docstring'''





from __future__ import annotations

from typing import TypedDict






class __A			(  A						):



 '''simple docstring'''







 __lowerCamelCase    :       str
 __lowerCamelCase    :       int





def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter s type must be str.''')

     return [s[i:] + s[:i] for i in range(len(__A))]




def lowerCAmelCase  (__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter s type must be str.''')
     if not s:
          raise ValueError('''The parameter s must not be empty.''')

     _a      =  all_rotations(__A)
     rotations.sort()  # sort the list of rotations in alphabetically order
     # make a string composed of the last char of each rotation
     _a      =  {
         "bwt_string": "".join([word[-1] for word in rotations]),
         "idx_original_string": rotations.index(__A),
     }
     return response




def lowerCAmelCase  (__A  ,	__A):



     """simple docstring"""



     if not isinstance(__A  ,	__A):
          raise TypeError('''The parameter bwt_string type must be str.''')
     if not bwt_string:
          raise ValueError('''The parameter bwt_string must not be empty.''')
     try:
          _a      =  int(__A)
     except ValueError:
          raise TypeError(
              '''The parameter idx_original_string type must be int or passive'''
              ''' of cast to int.''')
     if idx_original_string < 0:
          raise ValueError('''The parameter idx_original_string must not be lower than 0.''')
     if idx_original_string >= len(__A):
          raise ValueError(
              '''The parameter idx_original_string must be lower than''' ''' len(bwt_string).''')

     _a      =  [''''''] * len(__A)
     for _ in range(len(__A)):
          for i in range(len(__A)):
               _a      =  bwt_string[i] + ordered_rotations[i]
          ordered_rotations.sort()
     return ordered_rotations[idx_original_string]


if __name__ == "__main__":
  lowercase_		       =  "Provide a string that I will generate its BWT transform: "
  lowercase_		       =  input(entry_msg).strip()
  lowercase_		       =  bwt_transform(s)
  print(
      F"""Burrows Wheeler transform for string '{s}' results """
      F"""in '{result['bwt_string']}'"""
  )
  lowercase_		       =  reverse_bwt(result["bwt_string"], result["idx_original_string"])
  print(
      F"""Reversing Burrows Wheeler transform for entry '{result['bwt_string']}' """
      F"""we get original string '{original_string}'"""
  )



