
'''simple docstring'''
import argparse
import collections

import torch
from flax import traverse_util
from tax import checkpoints

from transformers import TaConfig, TaEncoderModel, TaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()







def        A_     (						__SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  List[Any]="attention" )				->       int:





  """simple docstring"""

  __A		:     Dict												=		params[F"{prefix}/layers_{i}/{layer_name}/key/kernel"]
  __A		:     Optional[Any]												=		params[F"{prefix}/layers_{i}/{layer_name}/out/kernel"]
  __A		:     List[Any]												=		params[F"{prefix}/layers_{i}/{layer_name}/query/kernel"]
  __A		:     Optional[Any]												=		params[F"{prefix}/layers_{i}/{layer_name}/value/kernel"]
  return k, o, q, v







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  List[str]			,     __SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  Any=False )				->       Any:





  """simple docstring"""

  if split_mlp_wi:
    __A		:     List[str]												=		params[F"{prefix}/layers_{i}/mlp/wi_0/kernel"]
    __A		:     str												=		params[F"{prefix}/layers_{i}/mlp/wi_1/kernel"]
    __A		:     Optional[Any]												=		(wi_a, wi_a)
  else:
    __A		:     List[str]												=		params[F"{prefix}/layers_{i}/mlp/wi/kernel"]

  __A		:     List[Any]												=		params[F"{prefix}/layers_{i}/mlp/wo/kernel"]
  return wi, wo







def        A_     (						__SCREAMING_SNAKE_CASE						:  Optional[Any]			,     __SCREAMING_SNAKE_CASE						:  Tuple			,     __SCREAMING_SNAKE_CASE						:  Dict			,     __SCREAMING_SNAKE_CASE						:  List[Any] )				->       Tuple:





  """simple docstring"""

  return params[F"{prefix}/layers_{i}/{layer_name}/scale"]







def        A_     (						__SCREAMING_SNAKE_CASE						:  dict			,     *, __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  bool )				->       Optional[int]:





  """simple docstring"""

  __A		:     int												=		traverse_util.flatten_dict(variables["""target"""] )
  __A		:     Dict												=		{"""/""".join(__SCREAMING_SNAKE_CASE ): v for k, v in old.items()}

  # v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
  __A		:     List[Any]												=		"""encoder/layers_0/mlp/wi_0/kernel""" in old
  print("""Split MLP:"""			,     __SCREAMING_SNAKE_CASE )

  __A		:     int												=		collections.OrderedDict()

  # Shared embeddings.
  __A		:     List[str]												=		old["""token_embedder/embedding"""]

  # Encoder.
  for i in range(__SCREAMING_SNAKE_CASE ):
    # Block i, layer 0 (Self Attention).
    __A		:     List[str]												=		tax_layer_norm_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """encoder"""			,     """pre_attention_layer_norm""" )
    __A							,					__A							,					__A							,					__A		:     List[str]												=		tax_attention_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """encoder"""			,     """attention""" )
    __A		:     int												=		layer_norm
    __A		:     Dict												=		k.T
    __A		:     Dict												=		o.T
    __A		:     Union[str, Any]												=		q.T
    __A		:     List[str]												=		v.T

    # Block i, layer 1 (MLP).
    __A		:     str												=		tax_layer_norm_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """encoder"""			,     """pre_mlp_layer_norm""" )
    __A							,					__A		:     List[str]												=		tax_mlp_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """encoder"""			,     __SCREAMING_SNAKE_CASE )
    __A		:     Optional[int]												=		layer_norm
    if split_mlp_wi:
      __A		:     List[Any]												=		wi[0].T
      __A		:     int												=		wi[1].T
    else:
      __A		:     Optional[Any]												=		wi.T
    __A		:     int												=		wo.T

  __A		:     Dict												=		old[
      """encoder/relpos_bias/rel_embedding"""
  ].T
  __A		:     Any												=		old["""encoder/encoder_norm/scale"""]

  if not is_encoder_only:
    # Decoder.
    for i in range(__SCREAMING_SNAKE_CASE ):
      # Block i, layer 0 (Self Attention).
      __A		:     Optional[Any]												=		tax_layer_norm_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """decoder"""			,     """pre_self_attention_layer_norm""" )
      __A							,					__A							,					__A							,					__A		:     Tuple												=		tax_attention_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """decoder"""			,     """self_attention""" )
      __A		:     Tuple												=		layer_norm
      __A		:     List[Any]												=		k.T
      __A		:     List[str]												=		o.T
      __A		:     int												=		q.T
      __A		:     Optional[Any]												=		v.T

      # Block i, layer 1 (Cross Attention).
      __A		:     int												=		tax_layer_norm_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """decoder"""			,     """pre_cross_attention_layer_norm""" )
      __A							,					__A							,					__A							,					__A		:     Union[str, Any]												=		tax_attention_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """decoder"""			,     """encoder_decoder_attention""" )
      __A		:     int												=		layer_norm
      __A		:     List[Any]												=		k.T
      __A		:     str												=		o.T
      __A		:     Optional[int]												=		q.T
      __A		:     Dict												=		v.T

      # Block i, layer 2 (MLP).
      __A		:     List[str]												=		tax_layer_norm_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """decoder"""			,     """pre_mlp_layer_norm""" )
      __A							,					__A		:     str												=		tax_mlp_lookup(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     """decoder"""			,     __SCREAMING_SNAKE_CASE )
      __A		:     str												=		layer_norm
      if split_mlp_wi:
        __A		:     List[Any]												=		wi[0].T
        __A		:     Any												=		wi[1].T
      else:
        __A		:     Dict												=		wi.T
      __A		:     str												=		wo.T

    __A		:     Any												=		old["""decoder/decoder_norm/scale"""]
    __A		:     Dict												=		old[
        """decoder/relpos_bias/rel_embedding"""
    ].T

    # LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
    if "decoder/logits_dense/kernel" in old:
      __A		:     Tuple												=		old["""decoder/logits_dense/kernel"""].T

  return new







def        A_     (						__SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  bool )				->       str:





  """simple docstring"""

  __A		:     str												=		collections.OrderedDict([(k, torch.from_numpy(v.copy() )) for (k, v) in converted_params.items()] )

  # Add what is missing.
  if "encoder.embed_tokens.weight" not in state_dict:
    __A		:     Any												=		state_dict["""shared.weight"""]

  if not is_encoder_only:
    if "decoder.embed_tokens.weight" not in state_dict:
      __A		:     Optional[Any]												=		state_dict["""shared.weight"""]

    if "lm_head.weight" not in state_dict:  # For old 1.0 models.
      print("""Using shared word embeddings as lm_head.""" )
      __A		:     Union[str, Any]												=		state_dict["""shared.weight"""]

  return state_dict







def        A_     (						__SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Optional[int]			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Tuple )				->       List[str]:





  """simple docstring"""

  __A		:     str												=		checkpoints.load_tax_checkpoint(__SCREAMING_SNAKE_CASE )
  __A		:     List[Any]												=		convert_tax_to_pytorch(__SCREAMING_SNAKE_CASE			,     num_layers=config.num_layers			,     is_encoder_only=__SCREAMING_SNAKE_CASE )
  __A		:     Tuple												=		make_state_dict(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )
  model.load_state_dict(__SCREAMING_SNAKE_CASE			,     strict=__SCREAMING_SNAKE_CASE )







def        A_     (						__SCREAMING_SNAKE_CASE						:  Union[str, Any]			,     __SCREAMING_SNAKE_CASE						:  List[Any]			,     __SCREAMING_SNAKE_CASE						:  Any			,     __SCREAMING_SNAKE_CASE						:  bool = False )				->       Optional[int]:





  """simple docstring"""

  __A		:     str												=		TaConfig.from_json_file(__SCREAMING_SNAKE_CASE )
  print(F"Building PyTorch model from configuration: {config}" )
  # Non-v1.1 checkpoints could also use T5Model, but this works for all.
  # The v1.0 checkpoints will simply have an LM head that is the word embeddings.
  if is_encoder_only:
    __A		:     Optional[Any]												=		TaEncoderModel(__SCREAMING_SNAKE_CASE )
  else:
    __A		:     Union[str, Any]												=		TaForConditionalGeneration(__SCREAMING_SNAKE_CASE )

  # Load weights from tf checkpoint
  load_tax_weights_in_ta(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )

  # Save pytorch-model
  print(F"Save PyTorch model to {pytorch_dump_path}" )
  model.save_pretrained(__SCREAMING_SNAKE_CASE )

  # Verify that we can load the checkpoint.
  model.from_pretrained(__SCREAMING_SNAKE_CASE )
  print("""Done""" )


if __name__ == "__main__":
     A__   :		Tuple 		=argparse.ArgumentParser(description='Converts a native T5X checkpoint into a PyTorch checkpoint.')
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path to the T5X checkpoint.'
     )
     parser.add_argument(
         '--config_file',
         default=None,
         type=str,
         required=True,
         help='The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.',
     )
     parser.add_argument(
         '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
     )
     parser.add_argument(
         '--is_encoder_only', action='store_true', help='Check if the model is encoder-decoder model', default=False
     )
     A__   :		Optional[int] 		=parser.parse_args()
     convert_tax_checkpoint_to_pytorch(
         args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
     )





'''simple docstring'''
def        A_     (						__SCREAMING_SNAKE_CASE						:  list			,     __SCREAMING_SNAKE_CASE						:  list			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int			,     __SCREAMING_SNAKE_CASE						:  int )				->       int:





  """simple docstring"""

  if index == number_of_items:
    return 0
  __A		:     List[Any]												=		0
  __A		:     Union[str, Any]												=		0
  __A		:     List[Any]												=		knapsack(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     index + 1 )
  if weights[index] <= max_weight:
    __A		:     Tuple												=		values[index] + knapsack(
        __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     max_weight - weights[index]			,     index + 1 )
  return max(__SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
     import doctest

     doctest.testmod()



