





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig





def 		UpperCAmelCase__						(	_UpperCAmelCase					,   _UpperCAmelCase					,   _UpperCAmelCase   ):






  """simple docstring"""





  A_    :			Tuple									=    TaConfig.from_pretrained(__A   )
  A_    :			List[Any]									=    FlaxTaForConditionalGeneration(config=__A   )
  A_    :			Tuple									=    checkpoints.load_tax_checkpoint(__A   )

  A_    :			Union[str, Any]									=    """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  # Encoder
  for layer_index in range(config.num_layers   ):
    A_    :			Optional[int]									=    f"""layers_{str(__A   )}"""

    # Self-Attention
    A_    :			str									=    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
    A_    :			int									=    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
    A_    :			Optional[Any]									=    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
    A_    :			int									=    tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

    # Layer Normalization
    A_    :			str									=    tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

    if split_mlp_wi:
      A_    :			Tuple									=    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      A_    :			Union[str, Any]									=    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      A_    :			str									=    tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    A_    :			Tuple									=    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_    :			int									=    tax_attention_key
    A_    :			Tuple									=    tax_attention_out
    A_    :			List[str]									=    tax_attention_query
    A_    :			Tuple									=    tax_attention_value

    A_    :			Dict									=    tax_attention_layer_norm

    if split_mlp_wi:
      A_    :			Dict									=    tax_mlp_wi_a
      A_    :			Optional[int]									=    tax_mlp_wi_a
    else:
      A_    :			List[str]									=    tax_mlp_wi

    A_    :			Tuple									=    tax_mlp_wo
    A_    :			Optional[Any]									=    tax_mlp_layer_norm

  # Only for layer 0:
  A_    :			Optional[int]									=    tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  A_    :			List[Any]									=    tax_encoder_rel_embedding

  # Assigning
  A_    :			Union[str, Any]									=    tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  A_    :			int									=    tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_decoder_layers   ):
    A_    :			Union[str, Any]									=    f"""layers_{str(__A   )}"""

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

    # Layer Normalization
    A_    :			List[Any]									=    tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
        """scale"""
    ]

    # Encoder-Decoder-Attention
    A_    :			Union[str, Any]									=    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
        """kernel"""
    ]
    A_    :			Optional[int]									=    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
        """kernel"""
    ]
    A_    :			Dict									=    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
        """kernel"""
    ]
    A_    :			Optional[Any]									=    tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
        """kernel"""
    ]

    # Layer Normalization
    A_    :			Tuple									=    tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

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

    A_    :			Optional[int]									=    tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    A_    :			Optional[int]									=    tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    A_    :			Optional[int]									=    tax_attention_key
    A_    :			Tuple									=    tax_attention_out
    A_    :			Optional[int]									=    tax_attention_query
    A_    :			Optional[int]									=    tax_attention_value

    A_    :			Optional[Any]									=    tax_pre_attention_layer_norm

    A_    :			str									=    tax_enc_dec_attention_key
    A_    :			str									=    tax_enc_dec_attention_out
    A_    :			Tuple									=    tax_enc_dec_attention_query
    A_    :			Dict									=    tax_enc_dec_attention_value

    A_    :			List[Any]									=    tax_cross_layer_norm

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

    A_    :			Optional[int]									=    tax_mlp_wo

    A_    :			List[str]									=    txa_mlp_layer_norm

  # Decoder Normalization
  A_    :			Tuple									=    tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  A_    :			Union[str, Any]									=    txa_decoder_norm

  # Only for layer 0:
  A_    :			Tuple									=    tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  A_    :			Tuple									=    tax_decoder_rel_embedding

  # Token Embeddings
  A_    :			List[str]									=    tax_model["""target"""]["""token_embedder"""]["""embedding"""]
  A_    :			List[str]									=    txa_token_embeddings

  # LM Head (only in v1.1 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    A_    :			List[Any]									=    tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


if __name__ == "__main__":
   lowerCamelCase_	:		Any         =   argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the TX5 checkpoint.'
   )
   parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of T5 model.')
   parser.add_argument(
       '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
   )
   lowerCamelCase_	:		Any         =   parser.parse_args()
   convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)
from __future__ import annotations







def     A__	(       __A ,			__A						):







    '''simple docstring'''
    _lowerCamelCase  :		List[str]			    =    [None] * (len(__A						) + len(__A						))

    _lowerCamelCase  :		Optional[int]			    =    0  # pointer to current index for left Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the right Half
    _lowerCamelCase  :		List[str]			    =    0  # pointer to current index for the sorted array Half

    while pointera < len(__A						) and pointera < len(__A						):
        if left_half[pointera] < right_half[pointera]:
            _lowerCamelCase  :		Optional[Any]			    =    left_half[pointera]
            pointera += 1
            index += 1
        else:
            _lowerCamelCase  :		List[Any]			    =    right_half[pointera]
            pointera += 1
            index += 1
    while pointera < len(__A						):
        _lowerCamelCase  :		int			    =    left_half[pointera]
        pointera += 1
        index += 1

    while pointera < len(__A						):
        _lowerCamelCase  :		Optional[Any]			    =    right_half[pointera]
        pointera += 1
        index += 1

    return sorted_array







def     A__	(       __A						):







    '''simple docstring'''
    if len(__A						) <= 1:
        return array
    # the actual formula to calculate the middle element = left + (right - left) // 2
    # this avoids integer overflow in case of large N
    _lowerCamelCase  :		int			    =    0 + (len(__A						) - 0) // 2

    # Split the array into halves till the array length becomes equal to One
    # merge the arrays of single length returned by mergeSort function and
    # pass them into the merge arrays function which merges the array
    _lowerCamelCase  :		List[str]			    =    array[:middle]
    _lowerCamelCase  :		Optional[int]			    =    array[middle:]

    return merge(merge_sort(__A						) ,			merge_sort(__A						)						)


if __name__ == "__main__":
    import doctest

    doctest.testmod()




