






"""simple docstring"""





import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig






def     A(  snake_case_						,       snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__:    List[Any]        =      TaConfig.from_pretrained(snake_case_		)
       lowercase__:    Tuple        =      FlaxTaForConditionalGeneration(config=snake_case_		)
       lowercase__:    str        =      checkpoints.load_tax_checkpoint(snake_case_		)

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

       # Encoder
       for layer_index in range(config.num_layers		):
              lowercase__:    Optional[int]        =      F"""layers_{str(snake_case_		)}"""

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

              # Layer Normalization
              lowercase__:    Tuple        =      tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

              if split_mlp_wi:
                     lowercase__:    Optional[int]        =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
                     lowercase__:    int        =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
              else:
                     lowercase__:    int        =      tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

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

              # Layer Normalization
              lowercase__:    Optional[int]        =      tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

              # Assigning
              lowercase__:    str        =      tax_attention_key
              lowercase__:    Dict        =      tax_attention_out
              lowercase__:    Optional[Any]        =      tax_attention_query
              lowercase__:    Optional[int]        =      tax_attention_value

              lowercase__:    Tuple        =      tax_attention_layer_norm

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

              lowercase__:    Optional[Any]        =      tax_mlp_wo
              lowercase__:    int        =      tax_mlp_layer_norm

       # Only for layer 0:
       lowercase__:    Optional[Any]        =      tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
       lowercase__:    Union[str, Any]        =      tax_encoder_rel_embedding

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

       # Decoder
       for layer_index in range(config.num_decoder_layers		):
              lowercase__:    Dict        =      F"""layers_{str(snake_case_		)}"""

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

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

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

              # Layer Normalization
              lowercase__:    Any        =      tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

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

              lowercase__:    Dict        =      tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

              # Layer Normalization
              lowercase__:    Tuple        =      tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

              # Assigning
              lowercase__:    int        =      tax_attention_key
              lowercase__:    Optional[Any]        =      tax_attention_out
              lowercase__:    List[str]        =      tax_attention_query
              lowercase__:    List[str]        =      tax_attention_value

              lowercase__:    str        =      tax_pre_attention_layer_norm

              lowercase__:    Any        =      tax_enc_dec_attention_key
              lowercase__:    Tuple        =      tax_enc_dec_attention_out
              lowercase__:    int        =      tax_enc_dec_attention_query
              lowercase__:    Optional[int]        =      tax_enc_dec_attention_value

              lowercase__:    List[Any]        =      tax_cross_layer_norm

              if split_mlp_wi:
                     lowercase__:    int        =      tax_mlp_wi_a
                     lowercase__:    List[Any]        =      tax_mlp_wi_a
              else:
                     lowercase__:    Optional[int]        =      tax_mlp_wi

              lowercase__:    Any        =      tax_mlp_wo

              lowercase__:    Optional[int]        =      txa_mlp_layer_norm

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

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

       # Token Embeddings
       lowercase__:    Optional[Any]        =      tax_model["target"]["token_embedder"]["embedding"]
       lowercase__:    str        =      txa_token_embeddings

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

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


if __name__ == "__main__":
  UpperCamelCase			=      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."""
  )
  UpperCamelCase			=      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






def     A(  snake_case_						,       snake_case_		):
       """simple docstring"""





       lowercase__  ,     lowercase__:    Optional[int]        =      0, len(snake_case_		) - 1
       while start_ind <= end_ind:
              if array[start_ind] == search_item:
                     return start_ind
              elif array[end_ind] == search_item:
                     return end_ind
              else:
                     start_ind += 1
                     end_ind -= 1
    # returns -1 if search_item is not found in array
       return -1


if __name__ == "__main__":
  print(double_linear_search(list(range(1_00)), 40))






