


import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM




def    a__    (		snake_case__					:							Any						,   snake_case__					:							Union[str, Any]						,   snake_case__					:							Tuple     ):
  _UpperCAmelCase		:							List[Any]							=     AutoConfig.from_pretrained(snake_case__     )
  _UpperCAmelCase		:							Union[str, Any]							=     FlaxAutoModelForSeqaSeqLM.from_config(config=snake_case__     )
  _UpperCAmelCase		:							Tuple							=     checkpoints.load_tax_checkpoint(snake_case__     )

  _UpperCAmelCase		:							int							=     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  if config.model_type == "t5":
    _UpperCAmelCase		:							str							=     """SelfAttention"""
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _UpperCAmelCase		:							Optional[Any]							=     """LocalSelfAttention"""
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _UpperCAmelCase		:							Tuple							=     """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		:							Dict							=     f'''layers_{str(snake_case__     )}'''

    # Self-Attention
    _UpperCAmelCase		:							Dict							=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
    _UpperCAmelCase		:							List[Any]							=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
    _UpperCAmelCase		:							int							=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
    _UpperCAmelCase		:							List[str]							=     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		:							Dict							=     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

    # Layer Normalization
    _UpperCAmelCase		:							Optional[int]							=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

    if split_mlp_wi:
      _UpperCAmelCase		:							int							=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _UpperCAmelCase		:							int							=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _UpperCAmelCase		:							List[Any]							=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _UpperCAmelCase		:							int							=     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _UpperCAmelCase		:							str							=     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _UpperCAmelCase		:							Dict							=     flax_model.params["""encoder"""]["""block"""][str(snake_case__     )]["""layer"""]
    _UpperCAmelCase		:							Dict							=     tax_attention_key
    _UpperCAmelCase		:							str							=     tax_attention_out
    _UpperCAmelCase		:							List[Any]							=     tax_attention_query
    _UpperCAmelCase		:							List[str]							=     tax_attention_value

    _UpperCAmelCase		:							Dict							=     tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _UpperCAmelCase		:							List[str]							=     tax_global_layer_norm

    if split_mlp_wi:
      _UpperCAmelCase		:							Dict							=     tax_mlp_wi_a
      _UpperCAmelCase		:							Optional[Any]							=     tax_mlp_wi_a
    else:
      _UpperCAmelCase		:							Tuple							=     tax_mlp_wi

    _UpperCAmelCase		:							Tuple							=     tax_mlp_wo
    _UpperCAmelCase		:							int							=     tax_mlp_layer_norm

    _UpperCAmelCase		:							List[str]							=     flax_model_encoder_layer_block

  # Only for layer 0:
  _UpperCAmelCase		:							List[str]							=     tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _UpperCAmelCase		:							List[str]							=     tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _UpperCAmelCase		:							List[Any]							=     tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
    _UpperCAmelCase		:							List[str]							=     tax_encoder_global_rel_embedding

  # Assigning
  _UpperCAmelCase		:							int							=     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  _UpperCAmelCase		:							Dict							=     tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers     ):
    _UpperCAmelCase		:							Dict							=     f'''layers_{str(snake_case__     )}'''

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

    # Layer Normalization
    _UpperCAmelCase		:							int							=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

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

    # Layer Normalization
    _UpperCAmelCase		:							int							=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

    # MLP
    if split_mlp_wi:
      _UpperCAmelCase		:							Union[str, Any]							=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _UpperCAmelCase		:							List[str]							=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _UpperCAmelCase		:							Any							=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _UpperCAmelCase		:							str							=     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _UpperCAmelCase		:							Dict							=     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _UpperCAmelCase		:							str							=     flax_model.params["""decoder"""]["""block"""][str(snake_case__     )]["""layer"""]
    _UpperCAmelCase		:							List[str]							=     tax_attention_key
    _UpperCAmelCase		:							int							=     tax_attention_out
    _UpperCAmelCase		:							List[str]							=     tax_attention_query
    _UpperCAmelCase		:							Any							=     tax_attention_value

    _UpperCAmelCase		:							Tuple							=     tax_pre_attention_layer_norm

    _UpperCAmelCase		:							List[Any]							=     tax_enc_dec_attention_key
    _UpperCAmelCase		:							List[Any]							=     tax_enc_dec_attention_out
    _UpperCAmelCase		:							List[str]							=     tax_enc_dec_attention_query
    _UpperCAmelCase		:							Optional[Any]							=     tax_enc_dec_attention_value

    _UpperCAmelCase		:							int							=     tax_cross_layer_norm

    if split_mlp_wi:
      _UpperCAmelCase		:							Tuple							=     tax_mlp_wi_a
      _UpperCAmelCase		:							int							=     tax_mlp_wi_a
    else:
      _UpperCAmelCase		:							List[Any]							=     tax_mlp_wi

    _UpperCAmelCase		:							int							=     tax_mlp_wo

    _UpperCAmelCase		:							Optional[Any]							=     txa_mlp_layer_norm

    _UpperCAmelCase		:							Dict							=     flax_model_decoder_layer_block

  # Decoder Normalization
  _UpperCAmelCase		:							Tuple							=     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  _UpperCAmelCase		:							List[Any]							=     txa_decoder_norm

  # Only for layer 0:
  _UpperCAmelCase		:							List[str]							=     tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _UpperCAmelCase		:							Optional[Any]							=     tax_decoder_rel_embedding

  # Token Embeddings
  _UpperCAmelCase		:							Optional[Any]							=     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  _UpperCAmelCase		:							Dict							=     txa_token_embeddings

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

  flax_model.save_pretrained(snake_case__     )
  print("""T5X Model was sucessfully converted!"""     )


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE__					:     List[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.'
 )
 SCREAMING_SNAKE_CASE__					:     Tuple       =     parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









def    a__    (		snake_case__					:							Tuple     ):  # noqa: E741
  _UpperCAmelCase		:							Dict							=     len(snake_case__     )
  _UpperCAmelCase		:							Tuple							=     0
  _UpperCAmelCase		:							Union[str, Any]							=     [0] * n
  _UpperCAmelCase		:							Union[str, Any]							=     [False] * n
  _UpperCAmelCase		:							Tuple							=     [False] * n

  def dfs(snake_case__					:							List[str]						,   snake_case__					:							int						,   snake_case__					:							str						,   snake_case__					:							int     ):
    if parent == root:
      out_edge_count += 1
    _UpperCAmelCase		:							List[Any]							=     True
    _UpperCAmelCase		:							List[str]							=     at

    for to in l[at]:
      if to == parent:
        pass
      elif not visited[to]:
        _UpperCAmelCase		:							Optional[Any]							=     dfs(snake_case__						,   snake_case__						,   snake_case__						,   snake_case__     )
        _UpperCAmelCase		:							Optional[Any]							=     min(low[at]						,   low[to]     )

        # AP found via bridge
        if at < low[to]:
          _UpperCAmelCase		:							List[str]							=     True
        # AP found via cycle
        if at == low[to]:
          _UpperCAmelCase		:							Optional[Any]							=     True
      else:
        _UpperCAmelCase		:							str							=     min(low[at]						,   snake_case__     )
    return out_edge_count

  for i in range(snake_case__     ):
    if not visited[i]:
      _UpperCAmelCase		:							List[Any]							=     0
      _UpperCAmelCase		:							List[str]							=     dfs(snake_case__						,   snake_case__						,   -1						,   snake_case__     )
      _UpperCAmelCase		:							Optional[Any]							=     out_edge_count > 1

  for x in range(len(snake_case__     )     ):
    if is_art[x] is True:
      print(snake_case__     )


# Adjacency list of graph
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     {
    0: [1, 2],
    1: [0, 2],
    2: [0, 1, 3, 5],
    3: [2, 4],
    4: [3],
    5: [2, 6, 8],
    6: [5, 7],
    7: [6, 8],
    8: [5, 7],
}





compute_ap(data)





