"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  a__:					int      							=     AutoConfig.from_pretrained(_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     FlaxAutoModelForSeqaSeqLM.from_config(config=_SCREAMING_SNAKE_CASE       )
  a__:					List[str]      							=     checkpoints.load_tax_checkpoint(_SCREAMING_SNAKE_CASE       )

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

  if config.model_type == "t5":
    a__:					List[str]      							=     'SelfAttention'
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    a__:					Tuple      							=     'LocalSelfAttention'
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    a__:					List[Any]      							=     '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__:					str      							=     F'layers_{str(_SCREAMING_SNAKE_CASE       )}'

    # Self-Attention
    a__:					Optional[Any]      							=     tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
    a__:					Optional[Any]      							=     tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
    a__:					str      							=     tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
    a__:					Dict      							=     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__:					Tuple      							=     tax_model['target']['encoder'][layer_name]['attention']['T5LayerNorm_0']['scale']

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

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

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

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

    # Assigning
    a__:					Union[str, Any]      							=     flax_model.params['encoder']['block'][str(_SCREAMING_SNAKE_CASE       )]['layer']
    a__:					Dict      							=     tax_attention_key
    a__:					Optional[Any]      							=     tax_attention_out
    a__:					List[Any]      							=     tax_attention_query
    a__:					Optional[int]      							=     tax_attention_value

    a__:					Union[str, Any]      							=     tax_attention_layer_norm

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

    if split_mlp_wi:
      a__:					Dict      							=     tax_mlp_wi_a
      a__:					Tuple      							=     tax_mlp_wi_a
    else:
      a__:					Union[str, Any]      							=     tax_mlp_wi

    a__:					Dict      							=     tax_mlp_wo
    a__:					Optional[Any]      							=     tax_mlp_layer_norm

    a__:					int      							=     flax_model_encoder_layer_block

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

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

  # Assigning
  a__:					List[Any]      							=     tax_model['target']['encoder']['encoder_norm']['scale']
  a__:					List[str]      							=     tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers       ):
    a__:					Any      							=     F'layers_{str(_SCREAMING_SNAKE_CASE       )}'

    # Self-Attention
    a__:					List[str]      							=     tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
    a__:					List[Any]      							=     tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
    a__:					Dict      							=     tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
    a__:					Any      							=     tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

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

    # Encoder-Decoder-Attention
    a__:					str      							=     tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']
    a__:					Tuple      							=     tax_enc_dec_attention_module['key']['kernel']
    a__:					List[str]      							=     tax_enc_dec_attention_module['out']['kernel']
    a__:					Any      							=     tax_enc_dec_attention_module['query']['kernel']
    a__:					int      							=     tax_enc_dec_attention_module['value']['kernel']

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

    # MLP
    if split_mlp_wi:
      a__:					Optional[int]      							=     tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
      a__:					int      							=     tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
    else:
      a__:					Union[str, Any]      							=     tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

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

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

    # Assigning
    a__:					List[Any]      							=     flax_model.params['decoder']['block'][str(_SCREAMING_SNAKE_CASE       )]['layer']
    a__:					List[str]      							=     tax_attention_key
    a__:					List[Any]      							=     tax_attention_out
    a__:					Optional[int]      							=     tax_attention_query
    a__:					Dict      							=     tax_attention_value

    a__:					Any      							=     tax_pre_attention_layer_norm

    a__:					Any      							=     tax_enc_dec_attention_key
    a__:					str      							=     tax_enc_dec_attention_out
    a__:					Dict      							=     tax_enc_dec_attention_query
    a__:					List[str]      							=     tax_enc_dec_attention_value

    a__:					Any      							=     tax_cross_layer_norm

    if split_mlp_wi:
      a__:					str      							=     tax_mlp_wi_a
      a__:					Optional[int]      							=     tax_mlp_wi_a
    else:
      a__:					int      							=     tax_mlp_wi

    a__:					Any      							=     tax_mlp_wo

    a__:					List[str]      							=     txa_mlp_layer_norm

    a__:					Union[str, Any]      							=     flax_model_decoder_layer_block

  # Decoder Normalization
  a__:					Optional[int]      							=     tax_model['target']['decoder']['decoder_norm']['scale']
  a__:					Dict      							=     txa_decoder_norm

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

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

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

  flax_model.save_pretrained(_SCREAMING_SNAKE_CASE       )
  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"""
def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Tuple:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  if n == 0:
    return 0
  a__:					List[Any]      							=     float('-inf'       )
  for i in range(1		,					n + 1       ):
    a__:					Optional[Any]      							=     max(
        _SCREAMING_SNAKE_CASE		,					prices[i - 1] + naive_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE       )       )

  return max_revue







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->str:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  return _top_down_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Optional[Any]:
  if max_rev[n] >= 0:
    return max_rev[n]
  elif n == 0:
    return 0
  else:
    a__:					Dict      							=     float('-inf'       )
    for i in range(1		,					n + 1       ):
      a__:					Optional[Any]      							=     max(
          _SCREAMING_SNAKE_CASE		,					prices[i - 1] + _top_down_cut_rod_recursive(n - i		,					_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )		,					)

    a__:					Optional[int]      							=     max_revenue

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Dict:
  _enforce_args(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
  # length 0.
  a__:					str      							=     [float('-inf'       ) for _ in range(n + 1       )]
  a__:					Tuple      							=     0

  for i in range(1		,					n + 1       ):
    a__:					List[str]      							=     max_rev[i]
    for j in range(1		,					i + 1       ):
      a__:					Tuple      							=     max(_SCREAMING_SNAKE_CASE		,					prices[j - 1] + max_rev[i - j]       )

    a__:					Union[str, Any]      							=     max_revenue_i

  return max_rev[n]







def 						__a       (     _SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )     ->Any:
  if n < 0:
    a__:					Optional[int]      							=     F'n must be greater than or equal to 0. Got n = {n}'
    raise ValueError(_SCREAMING_SNAKE_CASE       )

  if n > len(_SCREAMING_SNAKE_CASE       ):
    a__:					List[str]      							=     (
        'Each integral piece of rod must have a corresponding price. '
        F'Got n = {n} but length of prices = {len(_SCREAMING_SNAKE_CASE       )}'
    )
    raise ValueError(_SCREAMING_SNAKE_CASE       )







def 						__a       (     )     ->str:
  a__:					int      							=     [6, 10, 12, 15, 20, 23]
  a__:					Union[str, Any]      							=     len(_SCREAMING_SNAKE_CASE       )

  # the best revenue comes from cutting the rod into 6 pieces, each
  # of length 1 resulting in a revenue of 6 * 6 = 36.
  a__:					Any      							=     36

  a__:					Optional[int]      							=     top_down_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					List[Any]      							=     bottom_up_cut_rod(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )
  a__:					int      							=     naive_cut_rod_recursive(_SCREAMING_SNAKE_CASE		,					_SCREAMING_SNAKE_CASE       )

  assert expected_max_revenue == max_rev_top_down
  assert max_rev_top_down == max_rev_bottom_up
  assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
 main()


