



'''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 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__="attention"    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  params[F"""{prefix}/layers_{i}/{layer_name}/key/kernel"""]
   UpperCamelCase					=  params[F"""{prefix}/layers_{i}/{layer_name}/out/kernel"""]
   UpperCamelCase					=  params[F"""{prefix}/layers_{i}/{layer_name}/query/kernel"""]
   UpperCamelCase					=  params[F"""{prefix}/layers_{i}/{layer_name}/value/kernel"""]
   return k, o, q, v







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__=False    )       ->	Optional[int]:






   """simple docstring"""
   if split_mlp_wi:
      UpperCamelCase					=  params[F"""{prefix}/layers_{i}/mlp/wi_0/kernel"""]
      UpperCamelCase					=  params[F"""{prefix}/layers_{i}/mlp/wi_1/kernel"""]
      UpperCamelCase					=  (wi_a, wi_a)
   else:
      UpperCamelCase					=  params[F"""{prefix}/layers_{i}/mlp/wi/kernel"""]

   UpperCamelCase					=  params[F"""{prefix}/layers_{i}/mlp/wo/kernel"""]
   return wi, wo







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	Tuple:






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







def 				__lowerCamelCase				(   A__    ,						*, A__    ,						A__    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  traverse_util.flatten_dict(variables['target']    )
   UpperCamelCase					=  {'/'.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
   UpperCamelCase					=  'encoder/layers_0/mlp/wi_0/kernel' in old
   print('Split MLP:'    ,						A__    )

   UpperCamelCase					=  collections.OrderedDict()

   # Shared embeddings.
   UpperCamelCase					=  old['token_embedder/embedding']

   # Encoder.
   for i in range(A__    ):
      # Block i, layer 0 (Self Attention).
      UpperCamelCase					=  tax_layer_norm_lookup(A__    ,						A__    ,						'encoder'    ,						'pre_attention_layer_norm'    )
      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  tax_attention_lookup(A__    ,						A__    ,						'encoder'    ,						'attention'    )
      UpperCamelCase					=  layer_norm
      UpperCamelCase					=  k.T
      UpperCamelCase					=  o.T
      UpperCamelCase					=  q.T
      UpperCamelCase					=  v.T

      # Block i, layer 1 (MLP).
      UpperCamelCase					=  tax_layer_norm_lookup(A__    ,						A__    ,						'encoder'    ,						'pre_mlp_layer_norm'    )
      UpperCamelCase				,      UpperCamelCase					=  tax_mlp_lookup(A__    ,						A__    ,						'encoder'    ,						A__    )
      UpperCamelCase					=  layer_norm
      if split_mlp_wi:
         UpperCamelCase					=  wi[0].T
         UpperCamelCase					=  wi[1].T
      else:
         UpperCamelCase					=  wi.T
      UpperCamelCase					=  wo.T

   UpperCamelCase					=  old[
       'encoder/relpos_bias/rel_embedding'
   ].T
   UpperCamelCase					=  old['encoder/encoder_norm/scale']

   if not is_encoder_only:
      # Decoder.
      for i in range(A__    ):
         # Block i, layer 0 (Self Attention).
         UpperCamelCase					=  tax_layer_norm_lookup(A__    ,						A__    ,						'decoder'    ,						'pre_self_attention_layer_norm'    )
         UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  tax_attention_lookup(A__    ,						A__    ,						'decoder'    ,						'self_attention'    )
         UpperCamelCase					=  layer_norm
         UpperCamelCase					=  k.T
         UpperCamelCase					=  o.T
         UpperCamelCase					=  q.T
         UpperCamelCase					=  v.T

         # Block i, layer 1 (Cross Attention).
         UpperCamelCase					=  tax_layer_norm_lookup(A__    ,						A__    ,						'decoder'    ,						'pre_cross_attention_layer_norm'    )
         UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase				,      UpperCamelCase					=  tax_attention_lookup(A__    ,						A__    ,						'decoder'    ,						'encoder_decoder_attention'    )
         UpperCamelCase					=  layer_norm
         UpperCamelCase					=  k.T
         UpperCamelCase					=  o.T
         UpperCamelCase					=  q.T
         UpperCamelCase					=  v.T

         # Block i, layer 2 (MLP).
         UpperCamelCase					=  tax_layer_norm_lookup(A__    ,						A__    ,						'decoder'    ,						'pre_mlp_layer_norm'    )
         UpperCamelCase				,      UpperCamelCase					=  tax_mlp_lookup(A__    ,						A__    ,						'decoder'    ,						A__    )
         UpperCamelCase					=  layer_norm
         if split_mlp_wi:
            UpperCamelCase					=  wi[0].T
            UpperCamelCase					=  wi[1].T
         else:
            UpperCamelCase					=  wi.T
         UpperCamelCase					=  wo.T

      UpperCamelCase					=  old['decoder/decoder_norm/scale']
      UpperCamelCase					=  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:
         UpperCamelCase					=  old['decoder/logits_dense/kernel'].T

   return new







def 				__lowerCamelCase				(   A__    ,						A__    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  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:
      UpperCamelCase					=  state_dict['shared.weight']

   if not is_encoder_only:
      if "decoder.embed_tokens.weight" not in state_dict:
         UpperCamelCase					=  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.'    )
         UpperCamelCase					=  state_dict['shared.weight']

   return state_dict







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__    )       ->	str:






   """simple docstring"""
   UpperCamelCase					=  checkpoints.load_tax_checkpoint(A__    )
   UpperCamelCase					=  convert_tax_to_pytorch(A__    ,						num_layers=config.num_layers    ,						is_encoder_only=A__    )
   UpperCamelCase					=  make_state_dict(A__    ,						A__    )
   model.load_state_dict(A__    ,						strict=A__    )







def 				__lowerCamelCase				(   A__    ,						A__    ,						A__    ,						A__ = False    )       ->	Tuple:






   """simple docstring"""
   UpperCamelCase					=  TaConfig.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:
      UpperCamelCase					=  TaEncoderModel(A__    )
   else:
      UpperCamelCase					=  TaForConditionalGeneration(A__    )

   # Load weights from tf checkpoint
   load_tax_weights_in_ta(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__":
   _lowerCamelCase	:       int      	=	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
   )
   _lowerCamelCase	:       Union[str, Any]      	=	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'''


import math

class  SCREAMING_SNAKE_CASE :






    """simple docstring"""





    def __init__(  self      :					Union[str, Any]  ,						UpperCamelCase__      :					Optional[Any]=0 ):  # a graph with Node 0,1,...,N-1


       """simple docstring"""







       UpperCamelCase					=  n
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # adjacency matrix for weight
       UpperCamelCase					=  [
           [math.inf for j in range(0  ,						UpperCamelCase__ )] for i in range(0  ,						UpperCamelCase__ )
       ]  # dp[i][j] stores minimum distance from i to j






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Union[str, Any]  ,						UpperCamelCase__      :					Tuple ):


       """simple docstring"""







       UpperCamelCase					=  w






    def  A       (  self      :					str ):


       """simple docstring"""







       for k in range(0  ,						self.n ):
          for i in range(0  ,						self.n ):
             for j in range(0  ,						self.n ):
                UpperCamelCase					=  min(self.dp[i][j]  ,						self.dp[i][k] + self.dp[k][j] )






    def  A       (  self      :					Optional[Any]  ,						UpperCamelCase__      :					str  ,						UpperCamelCase__      :					List[Any] ):


       """simple docstring"""







       return self.dp[u][v]


if __name__ == "__main__":
   _lowerCamelCase	:       List[str]      	=	Graph(5)
   graph.add_edge(0, 2, 9)
   graph.add_edge(0, 4, 10)
   graph.add_edge(1, 3, 5)
   graph.add_edge(2, 3, 7)
   graph.add_edge(3, 0, 10)
   graph.add_edge(3, 1, 2)
   graph.add_edge(3, 2, 1)
   graph.add_edge(3, 4, 6)
   graph.add_edge(4, 1, 3)
   graph.add_edge(4, 2, 4)
   graph.add_edge(4, 3, 9)
   graph.floyd_warshall()
   graph.show_min(1, 4)
   graph.show_min(0, 3)

