



import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		Optional[int] ,				SCREAMING_SNAKE_CASE__				:		List[Any] )     ->   Any:
 return params[F'''{prefix}/{prefix}/relpos_bias/rel_embedding'''][:, i, :]



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		List[str] ,				SCREAMING_SNAKE_CASE__				:		List[str] ,				SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		List[str]="attention" )     ->   List[str]:
 _snake_case			:     List[Any] 		=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/key/kernel'''][:, i, :, :] )
 _snake_case			:     List[str] 		=    k_tmp.reshape(k_tmp.shape[0] ,				k_tmp.shape[1] * k_tmp.shape[2] )
 _snake_case			:     List[str] 		=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/out/kernel'''][:, i, :, :] )
 _snake_case			:     Tuple 		=    o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1] ,				o_tmp.shape[2] )
 _snake_case			:     List[str] 		=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/query/kernel'''][:, i, :, :] )
 _snake_case			:     List[Any] 		=    q_tmp.reshape(q_tmp.shape[0] ,				q_tmp.shape[1] * q_tmp.shape[2] )
 _snake_case			:     int 		=    np.ascontiguousarray(params[F'''{prefix}/{prefix}/{layer_name}/value/kernel'''][:, i, :, :] )
 _snake_case			:     Any 		=    v_tmp.reshape(v_tmp.shape[0] ,				v_tmp.shape[1] * v_tmp.shape[2] )
 return k, o, q, v



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Tuple ,				SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		Tuple ,				SCREAMING_SNAKE_CASE__				:		Optional[int]=False )     ->   str:


 if split_mlp_wi:
  _snake_case			:     Tuple 		=    params[F'''{prefix}/{prefix}/mlp/wi_0/kernel'''][:, i, :]
  _snake_case			:     List[Any] 		=    params[F'''{prefix}/{prefix}/mlp/wi_1/kernel'''][:, i, :]
  _snake_case			:     int 		=    (wi_a, wi_a)
 else:
  _snake_case			:     Optional[Any] 		=    params[F'''{prefix}/{prefix}/mlp/wi/kernel'''][:, i, :]

 _snake_case			:     Tuple 		=    params[F'''{prefix}/{prefix}/mlp/wo/kernel'''][:, i, :]
 return wi, wo



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Union[str, Any] ,				SCREAMING_SNAKE_CASE__				:		Optional[int] ,				SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any] )     ->   str:
 return params[F'''{prefix}/{prefix}/{layer_name}/scale'''][:, i]



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Tuple ,				*, SCREAMING_SNAKE_CASE__				:		List[str] ,				SCREAMING_SNAKE_CASE__				:		Optional[Any] ,				SCREAMING_SNAKE_CASE__				:		Union[str, Any] = False )     ->   Dict:
 _snake_case			:     Dict 		=    traverse_util.flatten_dict(variables["""target"""] )
 _snake_case			:     Optional[Any] 		=    {"""/""".join(a__ ): v for k, v in old.items()}

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

 _snake_case			:     List[Any] 		=    collections.OrderedDict()

 # Shared embeddings.
 _snake_case			:     Dict 		=    old["""token_embedder/embedding"""]

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

  # Block i, layer 1 (MLP).
  _snake_case			:     List[Any] 		=    tax_layer_norm_lookup(a__ ,				a__ ,				"""encoder""" ,				"""pre_mlp_layer_norm""" )
  _snake_case		,			_snake_case			:     List[Any] 		=    tax_mlp_lookup(a__ ,				a__ ,				"""encoder""" ,				a__ )
  _snake_case			:     Optional[Any] 		=    layer_norm
  if split_mlp_wi:
   _snake_case			:     Tuple 		=    wi[0].T
   _snake_case			:     List[str] 		=    wi[1].T
  else:
   _snake_case			:     Any 		=    wi.T
  _snake_case			:     List[Any] 		=    wo.T
  if scalable_attention:
   # convert the rel_embedding of each layer
   _snake_case			:     List[Any] 		=    tax_relpos_bias_lookup(
       a__ ,				a__ ,				"""encoder""" ).T

 _snake_case			:     Any 		=    old["""encoder/encoder_norm/scale"""]

 if not scalable_attention:
  _snake_case			:     Optional[Any] 		=    tax_relpos_bias_lookup(
      a__ ,				0 ,				"""encoder""" ).T
  _snake_case			:     str 		=    tax_relpos_bias_lookup(
      a__ ,				0 ,				"""decoder""" ).T

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

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

   # Block i, layer 2 (MLP).
   _snake_case			:     Optional[Any] 		=    tax_layer_norm_lookup(a__ ,				a__ ,				"""decoder""" ,				"""pre_mlp_layer_norm""" )
   _snake_case		,			_snake_case			:     Dict 		=    tax_mlp_lookup(a__ ,				a__ ,				"""decoder""" ,				a__ )
   _snake_case			:     Optional[int] 		=    layer_norm
   if split_mlp_wi:
    _snake_case			:     Tuple 		=    wi[0].T
    _snake_case			:     Any 		=    wi[1].T
   else:
    _snake_case			:     Tuple 		=    wi.T
   _snake_case			:     str 		=    wo.T

   if scalable_attention:
    # convert the rel_embedding of each layer
    _snake_case			:     int 		=    tax_relpos_bias_lookup(a__ ,				a__ ,				"""decoder""" ).T

  _snake_case			:     List[str] 		=    old["""decoder/decoder_norm/scale"""]

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

 return new



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Tuple ,				SCREAMING_SNAKE_CASE__				:		Tuple )     ->   Dict:
 _snake_case			:     Tuple 		=    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:
  _snake_case			:     Optional[Any] 		=    state_dict["""shared.weight"""]

 if not is_encoder_only:
  if "decoder.embed_tokens.weight" not in state_dict:
   _snake_case			:     Dict 		=    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.""" )
   _snake_case			:     Any 		=    state_dict["""shared.weight"""]

 return state_dict



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Union[str, Any] ,				SCREAMING_SNAKE_CASE__				:		Dict ,				SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		int ,				SCREAMING_SNAKE_CASE__				:		Dict )     ->   Tuple:
 _snake_case			:     Optional[Any] 		=    checkpoints.load_tax_checkpoint(a__ )
 _snake_case			:     List[Any] 		=    convert_tax_to_pytorch(
     a__ ,				num_layers=config.num_layers ,				is_encoder_only=a__ ,				scalable_attention=a__ )
 _snake_case			:     int 		=    make_state_dict(a__ ,				a__ )
 model.load_state_dict(a__ ,				strict=a__ )



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		List[str] ,				SCREAMING_SNAKE_CASE__				:		Tuple ,				SCREAMING_SNAKE_CASE__				:		Optional[int] = False ,				SCREAMING_SNAKE_CASE__				:		Any = False ,				)     ->   Union[str, Any]:
 _snake_case			:     Optional[int] 		=    MTaConfig.from_json_file(a__ )
 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:
  _snake_case			:     Tuple 		=    UMTaEncoderModel(a__ )
 else:
  _snake_case			:     List[str] 		=    UMTaForConditionalGeneration(a__ )

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

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

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


if __name__ == "__main__":
  a__			=		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
  )
  parser.add_argument(
      """--scalable_attention""",
      action="""store_true""",
      help="""Whether the model uses scaled attention (umt5 model)""",
      default=False,
  )
  a__			=		parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path,
      args.config_file,
      args.pytorch_dump_path,
      args.is_encoder_only,
      args.scalable_attention,
  )



'''simple docstring'''



class 						lowerCAmelCase__   :



 """simple docstring"""



 def __init__(		self					:       List[Any]					,							__SCREAMING_SNAKE_CASE					:       int					,							__SCREAMING_SNAKE_CASE					:       Union[str, Any]					,							__SCREAMING_SNAKE_CASE					:       Any			)  -> Tuple:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE     =      name
      __SCREAMING_SNAKE_CASE     =      value
      __SCREAMING_SNAKE_CASE     =      weight
 def __repr__(		self					:       str			)  -> Union[str, Any]:


      """simple docstring"""




      return f'{self.__class__.__name__}({self.name}, {self.value}, {self.weight})'
 def       UpperCAmelCase__		(		self					:       List[Any]			)  -> List[Any]:


      """simple docstring"""




      return self.value
 def       UpperCAmelCase__		(		self					:       Any			)  -> str:


      """simple docstring"""




      return self.name
 def       UpperCAmelCase__		(		self					:       Union[str, Any]			)  -> Union[str, Any]:


      """simple docstring"""




      return self.weight


 def       UpperCAmelCase__		(		self					:       int			)  -> Tuple:


      """simple docstring"""




      return self.value / self.weight







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      []
     for i in range(len(a__						)						):
          menu.append(Things(name[i]      ,    value[i]      ,    weight[i]						)						)
     return menu







def    a__       (  a__      ,    a__      ,    a__						):




     """simple docstring"""


     __SCREAMING_SNAKE_CASE     =      sorted(a__      ,    key=a__      ,    reverse=a__						)
     __SCREAMING_SNAKE_CASE     =      []
     __SCREAMING_SNAKE_CASE   ,			__SCREAMING_SNAKE_CASE     =      0.0, 0.0
     for i in range(len(a__						)						):
          if (total_cost + items_copy[i].get_weight()) <= max_cost:
               result.append(items_copy[i]						)
               total_cost += items_copy[i].get_weight()
               total_value += items_copy[i].get_value()
     return (result, total_value)







def    a__       (  ):




     """simple docstring"""




if __name__ == "__main__":
  import doctest

  doctest.testmod()






