



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 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		Optional[Any] ,				SCREAMING_SNAKE_CASE__				:		int ,				SCREAMING_SNAKE_CASE__				:		Dict="attention" )     ->   Union[str, Any]:
 _snake_case			:     Any 		=    params[F'''{prefix}/layers_{i}/{layer_name}/key/kernel''']
 _snake_case			:     str 		=    params[F'''{prefix}/layers_{i}/{layer_name}/out/kernel''']
 _snake_case			:     Optional[int] 		=    params[F'''{prefix}/layers_{i}/{layer_name}/query/kernel''']
 _snake_case			:     Optional[Any] 		=    params[F'''{prefix}/layers_{i}/{layer_name}/value/kernel''']
 return k, o, q, v



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



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

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



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



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		dict ,				*, SCREAMING_SNAKE_CASE__				:		int ,				SCREAMING_SNAKE_CASE__				:		bool )     ->   Union[str, Any]:
 _snake_case			:     str 		=    traverse_util.flatten_dict(variables["""target"""] )
 _snake_case			:     Optional[int] 		=    {"""/""".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
 _snake_case			:     List[str] 		=    """encoder/layers_0/mlp/wi_0/kernel""" in old
 print("""Split MLP:""" ,				SCREAMING_SNAKE_CASE__ )

 _snake_case			:     Tuple 		=    collections.OrderedDict()

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

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

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

 _snake_case			:     Union[str, Any] 		=    old[
     """encoder/relpos_bias/rel_embedding"""
 ].T
 _snake_case			:     Union[str, 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).
   _snake_case			:     str 		=    tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__ ,				SCREAMING_SNAKE_CASE__ ,				"""decoder""" ,				"""pre_self_attention_layer_norm""" )
   _snake_case			:     Any 		=    tax_attention_lookup(SCREAMING_SNAKE_CASE__ ,				SCREAMING_SNAKE_CASE__ ,				"""decoder""" ,				"""self_attention""" )
   _snake_case			:     int 		=    layer_norm
   _snake_case			:     List[str] 		=    k.T
   _snake_case			:     Optional[int] 		=    o.T
   _snake_case			:     Tuple 		=    q.T
   _snake_case			:     Union[str, Any] 		=    v.T

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

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

  _snake_case			:     Union[str, Any] 		=    old["""decoder/decoder_norm/scale"""]
  _snake_case			:     str 		=    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:
   _snake_case			:     Union[str, Any] 		=    old["""decoder/logits_dense/kernel"""].T

 return new



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		bool )     ->   Union[str, Any]:
 _snake_case			:     Optional[Any] 		=    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[int] 		=    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			:     List[Any] 		=    state_dict["""shared.weight"""]

 return state_dict



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



def 					lowercase				(      SCREAMING_SNAKE_CASE__				:		Union[str, Any] ,				SCREAMING_SNAKE_CASE__				:		str ,				SCREAMING_SNAKE_CASE__				:		Any ,				SCREAMING_SNAKE_CASE__				:		bool = False )     ->   Tuple:
 _snake_case			:     List[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:
  _snake_case			:     Any 		=    TaEncoderModel(SCREAMING_SNAKE_CASE__ )
 else:
  _snake_case			:     str 		=    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__			=		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__			=		parser.parse_args()
  convert_tax_checkpoint_to_pytorch(
      args.tax_checkpoint_path, args.config_file, args.pytorch_dump_path, args.is_encoder_only
  )








def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int	,			__magic_name__					:  int	,			__magic_name__					:  list[int]					)		->			bool:






	"""simple docstring"""



	if graph[path[curr_ind - 1]][next_ver] == 0:
		return False

	# 2. Validate that next vertex is not already in path
	return not any(vertex == next_ver for vertex in path					)

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  list[int]	,			__magic_name__					:  int					)		->			bool:






	"""simple docstring"""



	if curr_ind == len(__magic_name__					):
		# return whether path exists between current and starting vertices
		return graph[path[curr_ind - 1]][path[0]] == 1

	# Recursive Step
	for next_ver in range(0	,			len(__magic_name__					)					):
		if valid_connection(__magic_name__	,			__magic_name__	,			__magic_name__	,			__magic_name__					):
			# Insert current vertex  into path as next transition
			UpperCamelCase     :str          =	next_ver
			# Validate created path
			if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			curr_ind + 1					):
				return True
			# Backtrack
			UpperCamelCase     :Union[str, Any]          =	-1
	return False

def    SCREAMING_SNAKE_CASE_							(							__magic_name__					:  list[list[int]]	,			__magic_name__					:  int = 0					)		->			list[int]:






	"""simple docstring"""



	UpperCamelCase     :Union[str, Any]          =	[-1] * (len(__magic_name__					) + 1)
	# initialize start and end of path with starting index
	UpperCamelCase     :Any          =	start_index
	# evaluate and if we find answer return path either return empty array
	return path if util_hamilton_cycle(__magic_name__	,			__magic_name__	,			1					) else []
