

"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig

def 				A_					(							a   ,				a   ,				a					):

      """simple docstring"""



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

      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       'wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

      # Encoder
      for layer_index in range(config.num_layers					):
            SCREAMING_SNAKE_CASE_			:					List[str]          =       f"layers_{str(a					)}"

            # Self-Attention
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
            SCREAMING_SNAKE_CASE_			:					str          =       tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
            SCREAMING_SNAKE_CASE_			:					Dict          =       tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_			:					int          =       tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
            else:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

            SCREAMING_SNAKE_CASE_			:					Any          =       tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_attention_key
            SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_attention_out
            SCREAMING_SNAKE_CASE_			:					str          =       tax_attention_query
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_attention_value

            SCREAMING_SNAKE_CASE_			:					List[Any]          =       tax_attention_layer_norm

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

            SCREAMING_SNAKE_CASE_			:					int          =       tax_mlp_wo
            SCREAMING_SNAKE_CASE_			:					str          =       tax_mlp_layer_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
      SCREAMING_SNAKE_CASE_			:					Any          =       tax_encoder_rel_embedding

      # Assigning
      SCREAMING_SNAKE_CASE_			:					Dict          =       tax_model['target']['encoder']['encoder_norm']['scale']
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_encoder_norm

      # Decoder
      for layer_index in range(config.num_decoder_layers					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       f"layers_{str(a					)}"

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
                'scale'
            ]

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

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

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

            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

            # Layer Normalization
            SCREAMING_SNAKE_CASE_			:					str          =       tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

            # Assigning
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_attention_key
            SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_attention_out
            SCREAMING_SNAKE_CASE_			:					Dict          =       tax_attention_query
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_attention_value

            SCREAMING_SNAKE_CASE_			:					str          =       tax_pre_attention_layer_norm

            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_enc_dec_attention_key
            SCREAMING_SNAKE_CASE_			:					Dict          =       tax_enc_dec_attention_out
            SCREAMING_SNAKE_CASE_			:					str          =       tax_enc_dec_attention_query
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_enc_dec_attention_value

            SCREAMING_SNAKE_CASE_			:					Tuple          =       tax_cross_layer_norm

            if split_mlp_wi:
                  SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_mlp_wi_a
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_mlp_wi_a
            else:
                  SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_mlp_wi

            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       tax_mlp_wo

            SCREAMING_SNAKE_CASE_			:					List[Any]          =       txa_mlp_layer_norm

      # Decoder Normalization
      SCREAMING_SNAKE_CASE_			:					Dict          =       tax_model['target']['decoder']['decoder_norm']['scale']
      SCREAMING_SNAKE_CASE_			:					List[Any]          =       txa_decoder_norm

      # Only for layer 0:
      SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
      SCREAMING_SNAKE_CASE_			:					Any          =       tax_decoder_rel_embedding

      # Token Embeddings
      SCREAMING_SNAKE_CASE_			:					List[str]          =       tax_model['target']['token_embedder']['embedding']
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       txa_token_embeddings

      # LM Head (only in v1.1 checkpoints)
      if "logits_dense" in tax_model["target"]["decoder"]:
            SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       tax_model['target']['decoder']['logits_dense']['kernel']

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


if __name__ == "__main__":
    lowerCAmelCase       :				int         =					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       :				Tuple         =					parser.parse_args()
    convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)









from math import sqrt

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					List[str]          =       [1]
      for i in range(2   ,				int(sqrt(a					)					) + 1   ,				1					):
            if number % i == 0:
                  values.append(a					)
                  if int(number // i					) != i:
                        values.append(int(number // i					)					)
      return sorted(a					)

def 				A_					(							a					):

      """simple docstring"""



      return sum(factors(a					)					) > n

def 				A_					(							a					):

      """simple docstring"""



      SCREAMING_SNAKE_CASE_			:					Union[str, Any]          =       factors(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       len(a					)
      SCREAMING_SNAKE_CASE_			:					Optional[int]          =       [[0 for i in range(number + 1					)] for j in range(r + 1					)]
      for i in range(r + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[int]          =       True

      for i in range(1   ,				number + 1					):
            SCREAMING_SNAKE_CASE_			:					Optional[Any]          =       False

      for i in range(1   ,				r + 1					):
            for j in range(1   ,				number + 1					):
                  if j < values[i - 1]:
                        SCREAMING_SNAKE_CASE_			:					Dict          =       subset[i - 1][j]
                  else:
                        SCREAMING_SNAKE_CASE_			:					List[str]          =       subset[i - 1][j] or subset[i - 1][j - values[i - 1]]

      return subset[r][number] != 0

def 				A_					(							a					):

      """simple docstring"""



      return abundant(a					) and not semi_perfect(a					)


if __name__ == "__main__":
    import doctest

    doctest.testmod(verbose=True)
    for number in (69, 70, 71):
        print(F'{number} is {"" if weird(number) else "not "}weird.')






