






import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM

def      _lowerCamelCase	(						__A    :							Optional[int]		,		__A    :							Optional[Any]		,		__A    :							Optional[Any] )      ->					Optional[Any]:
    _UpperCAmelCase				:      str			  =				AutoConfig.from_pretrained(__A )
    _UpperCAmelCase				:      str			  =				FlaxAutoModelForSeqaSeqLM.from_config(config=__A )
    _UpperCAmelCase				:      Tuple			  =				checkpoints.load_tax_checkpoint(__A )

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

    if config.model_type == "t5":
        _UpperCAmelCase				:      Optional[int]			  =				'''SelfAttention'''
    if config.model_type == "longt5" and config.encoder_attention_type == "local":
        _UpperCAmelCase				:      Tuple			  =				'''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				:      Any			  =				f'''layers_{str(__A )}'''

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

        # Layer Normalization
        _UpperCAmelCase				:      Dict			  =				tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

        _UpperCAmelCase				:      Union[str, Any]			  =				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				:      Union[str, Any]			  =				flax_model.params['''encoder''']['''block'''][str(__A )]['''layer''']
        _UpperCAmelCase				:      int			  =				tax_attention_key
        _UpperCAmelCase				:      Optional[Any]			  =				tax_attention_out
        _UpperCAmelCase				:      Optional[Any]			  =				tax_attention_query
        _UpperCAmelCase				:      int			  =				tax_attention_value

        _UpperCAmelCase				:      Tuple			  =				tax_attention_layer_norm

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

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

        _UpperCAmelCase				:      List[str]			  =				tax_mlp_wo
        _UpperCAmelCase				:      Optional[Any]			  =				tax_mlp_layer_norm

        _UpperCAmelCase				:      Union[str, Any]			  =				flax_model_encoder_layer_block

    # Only for layer 0:
    _UpperCAmelCase				:      Any			  =				tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
    _UpperCAmelCase				:      Optional[int]			  =				tax_encoder_rel_embedding

    # Side/global relative position_bias + layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
        _UpperCAmelCase				:      Optional[int]			  =				tax_model['''target''']['''encoder''']['''side_relpos_bias''']['''rel_embedding'''].T
        _UpperCAmelCase				:      Union[str, Any]			  =				tax_encoder_global_rel_embedding

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

    # Decoder
    for layer_index in range(config.num_layers ):
        _UpperCAmelCase				:      Union[str, Any]			  =				f'''layers_{str(__A )}'''

        # Self-Attention
        _UpperCAmelCase				:      Optional[Any]			  =				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
        _UpperCAmelCase				:      Tuple			  =				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
        _UpperCAmelCase				:      str			  =				tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
        _UpperCAmelCase				:      List[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				:      Optional[Any]			  =				tax_model['''target''']['''decoder'''][layer_name]['''encoder_decoder_attention''']
        _UpperCAmelCase				:      int			  =				tax_enc_dec_attention_module['''key''']['''kernel''']
        _UpperCAmelCase				:      Optional[Any]			  =				tax_enc_dec_attention_module['''out''']['''kernel''']
        _UpperCAmelCase				:      int			  =				tax_enc_dec_attention_module['''query''']['''kernel''']
        _UpperCAmelCase				:      Dict			  =				tax_enc_dec_attention_module['''value''']['''kernel''']

        # Layer Normalization
        _UpperCAmelCase				:      Dict			  =				tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

        # MLP
        if split_mlp_wi:
            _UpperCAmelCase				:      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				:      List[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				:      int			  =				tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

        # Assigning
        _UpperCAmelCase				:      List[Any]			  =				flax_model.params['''decoder''']['''block'''][str(__A )]['''layer''']
        _UpperCAmelCase				:      str			  =				tax_attention_key
        _UpperCAmelCase				:      List[Any]			  =				tax_attention_out
        _UpperCAmelCase				:      Union[str, Any]			  =				tax_attention_query
        _UpperCAmelCase				:      Optional[Any]			  =				tax_attention_value

        _UpperCAmelCase				:      Any			  =				tax_pre_attention_layer_norm

        _UpperCAmelCase				:      Tuple			  =				tax_enc_dec_attention_key
        _UpperCAmelCase				:      Any			  =				tax_enc_dec_attention_out
        _UpperCAmelCase				:      Tuple			  =				tax_enc_dec_attention_query
        _UpperCAmelCase				:      Union[str, Any]			  =				tax_enc_dec_attention_value

        _UpperCAmelCase				:      Tuple			  =				tax_cross_layer_norm

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

        _UpperCAmelCase				:      Any			  =				tax_mlp_wo

        _UpperCAmelCase				:      Any			  =				txa_mlp_layer_norm

        _UpperCAmelCase				:      Union[str, Any]			  =				flax_model_decoder_layer_block

    # Decoder Normalization
    _UpperCAmelCase				:      Tuple			  =				tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
    _UpperCAmelCase				:      Dict			  =				txa_decoder_norm

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

    # Token Embeddings
    _UpperCAmelCase				:      Any			  =				tax_model['''target''']['''token_embedder''']['''embedding''']
    _UpperCAmelCase				:      Optional[int]			  =				txa_token_embeddings

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

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


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE 							=						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 							=						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      _lowerCamelCase	(						__A    :							int )      ->					list[int]:
    _UpperCAmelCase				:      List[str]			  =				[True] * limit
    _UpperCAmelCase				:      Optional[int]			  =				False
    _UpperCAmelCase				:      Dict			  =				False
    _UpperCAmelCase				:      List[str]			  =				True

    for i in range(3		,		int(limit**0.5 + 1 )		,		2 ):
        _UpperCAmelCase				:      List[str]			  =				i * 2
        while index < limit:
            _UpperCAmelCase				:      Union[str, Any]			  =				False
            _UpperCAmelCase				:      int			  =				index + i

    _UpperCAmelCase				:      Optional[int]			  =				[2]

    for i in range(3		,		__A		,		2 ):
        if is_prime[i]:
            primes.append(__A )

    return primes

def      _lowerCamelCase	(						__A    :							int = 1_000_000 )      ->					int:
    _UpperCAmelCase				:      Any			  =				prime_sieve(__A )
    _UpperCAmelCase				:      Optional[Any]			  =				0
    _UpperCAmelCase				:      Tuple			  =				0

    for i in range(len(__A ) ):
        for j in range(i + length		,		len(__A ) ):
            _UpperCAmelCase				:      List[Any]			  =				sum(primes[i:j] )
            if sol >= ceiling:
                break

            if sol in primes:
                _UpperCAmelCase				:      List[str]			  =				j - i
                _UpperCAmelCase				:      Optional[Any]			  =				sol

    return largest


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




