
"""simple docstring"""

import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM






def 							A		(       snake_case   :List[Any]       ,      snake_case   :List[str]       ,      snake_case   :Optional[int]	)   ->			str:
 __UpperCamelCase       						= AutoConfig.from_pretrained(snake_case	)
 __UpperCamelCase       						= FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case	)
 __UpperCamelCase       						= checkpoints.load_tax_checkpoint(snake_case	)

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

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

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

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

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

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

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

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

  __UpperCamelCase       						= tax_attention_layer_norm

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

  if split_mlp_wi:
   __UpperCamelCase       						= tax_mlp_wi_a
   __UpperCamelCase       						= tax_mlp_wi_a
  else:
   __UpperCamelCase       						= tax_mlp_wi

  __UpperCamelCase       						= tax_mlp_wo
  __UpperCamelCase       						= tax_mlp_layer_norm

  __UpperCamelCase       						= flax_model_encoder_layer_block

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

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

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

 # Decoder
 for layer_index in range(config.num_layers	):
  __UpperCamelCase       						= f'layers_{str(snake_case	)}'

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

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

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

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

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

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

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

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

  __UpperCamelCase       						= tax_pre_attention_layer_norm

  __UpperCamelCase       						= tax_enc_dec_attention_key
  __UpperCamelCase       						= tax_enc_dec_attention_out
  __UpperCamelCase       						= tax_enc_dec_attention_query
  __UpperCamelCase       						= tax_enc_dec_attention_value

  __UpperCamelCase       						= tax_cross_layer_norm

  if split_mlp_wi:
   __UpperCamelCase       						= tax_mlp_wi_a
   __UpperCamelCase       						= tax_mlp_wi_a
  else:
   __UpperCamelCase       						= tax_mlp_wi

  __UpperCamelCase       						= tax_mlp_wo

  __UpperCamelCase       						= txa_mlp_layer_norm

  __UpperCamelCase       						= flax_model_decoder_layer_block

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

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

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

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

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


if __name__ == "__main__":
      UpperCamelCase			:       Union[str, 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."
      )
      UpperCamelCase			:       str									=				parser.parse_args()
      convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)





"""simple docstring"""

def 							A		(       snake_case   :int       ,      snake_case   :int	)   ->			int:
 return int(input_a == input_a == 0	)






def 							A		(       )   ->			None:
 print('Truth Table of NOR Gate:'	)
 print('|   Input 1   |  Input 2  | Output |'	)
 print(f'|      0      |    0      |  {nor_gate(0       ,      0	)}     |'	)
 print(f'|      0      |    1      |  {nor_gate(0       ,      1	)}     |'	)
 print(f'|      1      |    0      |  {nor_gate(1       ,      0	)}     |'	)
 print(f'|      1      |    1      |  {nor_gate(1       ,      1	)}     |'	)


if __name__ == "__main__":
      import doctest

      doctest.testmod()
      main()



