

"""simple docstring"""
import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def 				a__	(	lowerCAmelCase ,  lowerCAmelCase ,  lowerCAmelCase						)  ->   Tuple:
      UpperCAmelCase__ :       int						=						TaConfig.from_pretrained(lowerCAmelCase						)
      UpperCAmelCase__ :       Optional[int]						=						FlaxTaForConditionalGeneration(config=lowerCAmelCase						)
      UpperCAmelCase__ :       int						=						checkpoints.load_tax_checkpoint(lowerCAmelCase						)

      UpperCAmelCase__ :       Optional[Any]						=						"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

      # Encoder
      for layer_index in range(config.num_layers						):
            UpperCAmelCase__ :       Union[str, Any]						=						F"""layers_{str(lowerCAmelCase						)}"""

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

            # Layer Normalization
            UpperCAmelCase__ :       str						=						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__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
            else:
                  UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

            UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

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

            # Assigning
            UpperCAmelCase__ :       str						=						tax_attention_key
            UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_out
            UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_query
            UpperCAmelCase__ :       List[Any]						=						tax_attention_value

            UpperCAmelCase__ :       Any						=						tax_attention_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase__ :       Any						=						tax_mlp_wi_a
                  UpperCAmelCase__ :       List[Any]						=						tax_mlp_wi_a
            else:
                  UpperCAmelCase__ :       Union[str, Any]						=						tax_mlp_wi

            UpperCAmelCase__ :       int						=						tax_mlp_wo
            UpperCAmelCase__ :       List[Any]						=						tax_mlp_layer_norm

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

      # Assigning
      UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
      UpperCAmelCase__ :       Optional[Any]						=						tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_decoder_layers						):
            UpperCAmelCase__ :       str						=						F"""layers_{str(lowerCAmelCase						)}"""

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

            # Layer Normalization
            UpperCAmelCase__ :       List[str]						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
                """scale"""
            ]

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

            # Layer Normalization
            UpperCAmelCase__ :       List[str]						=						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__ :       int						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
            else:
                  UpperCAmelCase__ :       Union[str, Any]						=						tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

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

            # Layer Normalization
            UpperCAmelCase__ :       Any						=						tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

            # Assigning
            UpperCAmelCase__ :       Union[str, Any]						=						tax_attention_key
            UpperCAmelCase__ :       str						=						tax_attention_out
            UpperCAmelCase__ :       Tuple						=						tax_attention_query
            UpperCAmelCase__ :       int						=						tax_attention_value

            UpperCAmelCase__ :       List[Any]						=						tax_pre_attention_layer_norm

            UpperCAmelCase__ :       Union[str, Any]						=						tax_enc_dec_attention_key
            UpperCAmelCase__ :       Optional[int]						=						tax_enc_dec_attention_out
            UpperCAmelCase__ :       List[Any]						=						tax_enc_dec_attention_query
            UpperCAmelCase__ :       Any						=						tax_enc_dec_attention_value

            UpperCAmelCase__ :       List[Any]						=						tax_cross_layer_norm

            if split_mlp_wi:
                  UpperCAmelCase__ :       Tuple						=						tax_mlp_wi_a
                  UpperCAmelCase__ :       Dict						=						tax_mlp_wi_a
            else:
                  UpperCAmelCase__ :       str						=						tax_mlp_wi

            UpperCAmelCase__ :       Tuple						=						tax_mlp_wo

            UpperCAmelCase__ :       Any						=						txa_mlp_layer_norm

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

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

      # Token Embeddings
      UpperCAmelCase__ :       Tuple						=						tax_model["""target"""]["""token_embedder"""]["""embedding"""]
      UpperCAmelCase__ :       int						=						txa_token_embeddings

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

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


if __name__ == "__main__":
    _A						=     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."""
    )
    _A						=     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__	(	lowerCAmelCase						)  ->   set[int]:
      UpperCAmelCase__ :       List[Any]						=						{1}
      UpperCAmelCase__ :       Optional[int]						=						[1]
      for _ in range(2 ,  depth + 1						):
            UpperCAmelCase__ :       Tuple						=						[*previous_coefficients, 0]
            UpperCAmelCase__ :       List[str]						=						[0, *previous_coefficients]
            UpperCAmelCase__ :       Dict						=						[]
            for x, y in zip(lowerCAmelCase ,  lowerCAmelCase						):
                  coefficients.add(x + y						)
                  previous_coefficients.append(x + y						)
      return coefficients




def 				a__	(	lowerCAmelCase						)  ->   set[int]:
      UpperCAmelCase__ :       List[str]						=						set()
      for number in unique_coefficients:
            UpperCAmelCase__ :       int						=						2
            UpperCAmelCase__ :       List[Any]						=						number
            while divisor**2 <= copy_number:
                  UpperCAmelCase__ :       Tuple						=						0
                  while copy_number % divisor == 0:
                        copy_number //= divisor
                        multiplicity += 1
                  if multiplicity >= 2:
                        non_squarefrees.add(lowerCAmelCase						)
                        break
                  divisor += 1

      return unique_coefficients.difference(lowerCAmelCase						)




def 				a__	(	lowerCAmelCase = 51						)  ->   int:
      UpperCAmelCase__ :       Any						=						get_pascal_triangle_unique_coefficients(lowerCAmelCase						)
      UpperCAmelCase__ :       Dict						=						get_squarefrees(lowerCAmelCase						)
      return sum(lowerCAmelCase						)


if __name__ == "__main__":
    print(f'''{solution() = }''')

