


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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]				):
     return params[F'{prefix}/{prefix}/relpos_bias/rel_embedding'][:, i, :]

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Union[str, Any]			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]			,     SCREAMING_SNAKE_CASE__			:					List[Any]=False				):
     if split_mlp_wi:
          __UpperCamelCase    							=params[F'{prefix}/{prefix}/mlp/wi_0/kernel'][:, i, :]
          __UpperCamelCase    							=params[F'{prefix}/{prefix}/mlp/wi_1/kernel'][:, i, :]
          __UpperCamelCase    							=(wi_a, wi_a)
     else:
          __UpperCamelCase    							=params[F'{prefix}/{prefix}/mlp/wi/kernel'][:, i, :]

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

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					str			,     SCREAMING_SNAKE_CASE__			:					Dict			,     SCREAMING_SNAKE_CASE__			:					Optional[Any]				):
     return params[F'{prefix}/{prefix}/{layer_name}/scale'][:, i]

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					dict			,     *, SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					bool			,     SCREAMING_SNAKE_CASE__			:					bool = False				):
     __UpperCamelCase    							=traverse_util.flatten_dict(variables['target']				)
     __UpperCamelCase    							={'/'.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
     __UpperCamelCase    							='encoder/encoder/mlp/wi_0/kernel' in old
     print('Split MLP:'			,     SCREAMING_SNAKE_CASE__				)

     __UpperCamelCase    							=collections.OrderedDict()

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

     # Encoder.
     for i in range(SCREAMING_SNAKE_CASE__				):
          # Block i, layer 0 (Self Attention).
          __UpperCamelCase    							=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'			,     'pre_attention_layer_norm'				)
          __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=tax_attention_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     '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(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'			,     'pre_mlp_layer_norm'				)
          __UpperCamelCase							,      __UpperCamelCase    							=tax_mlp_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'			,     SCREAMING_SNAKE_CASE__				)
          __UpperCamelCase    							=layer_norm
          if split_mlp_wi:
               __UpperCamelCase    							=wi[0].T
               __UpperCamelCase    							=wi[1].T
          else:
               __UpperCamelCase    							=wi.T
          __UpperCamelCase    							=wo.T
          if scalable_attention:
               # convert the rel_embedding of each layer
               __UpperCamelCase    							=tax_relpos_bias_lookup(
                   SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'encoder'				).T

     __UpperCamelCase    							=old['encoder/encoder_norm/scale']

     if not scalable_attention:
          __UpperCamelCase    							=tax_relpos_bias_lookup(
              SCREAMING_SNAKE_CASE__			,     0			,     'encoder'				).T
          __UpperCamelCase    							=tax_relpos_bias_lookup(
              SCREAMING_SNAKE_CASE__			,     0			,     'decoder'				).T

     if not is_encoder_only:
          # Decoder.
          for i in range(SCREAMING_SNAKE_CASE__				):
               # Block i, layer 0 (Self Attention).
               __UpperCamelCase    							=tax_layer_norm_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'pre_self_attention_layer_norm'				)
               __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=tax_attention_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     '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(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'pre_cross_attention_layer_norm'				)
               __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase							,      __UpperCamelCase    							=tax_attention_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     '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(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     'pre_mlp_layer_norm'				)
               __UpperCamelCase							,      __UpperCamelCase    							=tax_mlp_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'			,     SCREAMING_SNAKE_CASE__				)
               __UpperCamelCase    							=layer_norm
               if split_mlp_wi:
                    __UpperCamelCase    							=wi[0].T
                    __UpperCamelCase    							=wi[1].T
               else:
                    __UpperCamelCase    							=wi.T
               __UpperCamelCase    							=wo.T

               if scalable_attention:
                    # convert the rel_embedding of each layer
                    __UpperCamelCase    							=tax_relpos_bias_lookup(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__			,     'decoder'				).T

          __UpperCamelCase    							=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:
               __UpperCamelCase    							=old['decoder/logits_dense/kernel'].T

     return new

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					bool				):
     __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 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					List[str]			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					List[str]				):
     __UpperCamelCase    							=checkpoints.load_tax_checkpoint(SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=convert_tax_to_pytorch(
         SCREAMING_SNAKE_CASE__			,     num_layers=config.num_layers			,     is_encoder_only=SCREAMING_SNAKE_CASE__			,     scalable_attention=SCREAMING_SNAKE_CASE__				)
     __UpperCamelCase    							=make_state_dict(SCREAMING_SNAKE_CASE__			,     SCREAMING_SNAKE_CASE__				)
     model.load_state_dict(SCREAMING_SNAKE_CASE__			,     strict=SCREAMING_SNAKE_CASE__				)

def 						_UpperCAmelCase				(			SCREAMING_SNAKE_CASE__			:					Tuple			,     SCREAMING_SNAKE_CASE__			:					List[Any]			,     SCREAMING_SNAKE_CASE__			:					int			,     SCREAMING_SNAKE_CASE__			:					bool = False			,     SCREAMING_SNAKE_CASE__			:					bool = False			,     ):
     __UpperCamelCase    							=MTaConfig.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:
          __UpperCamelCase    							=UMTaEncoderModel(SCREAMING_SNAKE_CASE__				)
     else:
          __UpperCamelCase    							=UMTaForConditionalGeneration(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__			,     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
   )
   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,
   )












class     lowercase__       :

   '''simple docstring'''


   def __init__(    self,		__magic_name__ = "",		__magic_name__ = False							) ->      None:






       """simple docstring"""
       # Mapping from the first character of the prefix of the node
       UpperCamelCase__							:				dict[str, RadixNode]          =      {}

       # A node will be a leaf if the tree contains its word
       UpperCamelCase__							:				Optional[Any]          =      is_leaf

       UpperCamelCase__							:				List[str]          =      prefix




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      tuple[str, str, str]:






       """simple docstring"""
       UpperCamelCase__							:				Dict          =      0
       for q, w in zip(self.prefix,		__magic_name__							):
           if q != w:
               break

           x += 1

       return self.prefix[:x], self.prefix[x:], word[x:]




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       for word in words:
           self.insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      None:






       """simple docstring"""
       # Case 1: If the word is the prefix of the node
       # Solution: We set the current node as leaf
       if self.prefix == word:
           UpperCamelCase__							:				Union[str, Any]          =      True

       # Case 2: The node has no edges that have a prefix to the word
       # Solution: We create an edge from the current node to a new one
       # containing the word
       elif word[0] not in self.nodes:
           UpperCamelCase__							:				Tuple          =      RadixNode(prefix=__magic_name__,		is_leaf=__magic_name__							)

       else:
           UpperCamelCase__							:				Any          =      self.nodes[word[0]]
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)

           # Case 3: The node prefix is equal to the matching
           # Solution: We insert remaining word on the next node
           if remaining_prefix == "":
               self.nodes[matching_string[0]].insert(__magic_name__							)

           # Case 4: The word is greater equal to the matching
           # Solution: Create a node in between both nodes, change
           # prefixes and add the new node for the remaining word
           else:
               UpperCamelCase__							:				Tuple          =      remaining_prefix

               UpperCamelCase__							:				Tuple          =      self.nodes[matching_string[0]]
               UpperCamelCase__							:				List[Any]          =      RadixNode(__magic_name__,		__magic_name__							)
               UpperCamelCase__							:				str          =      aux_node

               if remaining_word == "":
                   UpperCamelCase__							:				Any          =      True
               else:
                   self.nodes[matching_string[0]].insert(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Dict          =      incoming_node.match(
               __magic_name__							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
               return False
           # This applies when the word and the prefix are equal
           elif remaining_word == "":
               return incoming_node.is_leaf
           # We have word remaining so we check the next node
           else:
               return incoming_node.find(__magic_name__							)




   def       UpperCamelCase__  (    self,		__magic_name__							) ->      bool:






       """simple docstring"""
       UpperCamelCase__							:				Optional[Any]          =      self.nodes.get(word[0],		__magic_name__							)
       if not incoming_node:
           return False
       else:
           UpperCamelCase__   ,UpperCamelCase__   ,UpperCamelCase__							:				Union[str, Any]          =      incoming_node.match(
               __magic_name__							)
           # If there is remaining prefix, the word can't be on the tree
           if remaining_prefix != "":
               return False
           # We have word remaining so we check the next node
           elif remaining_word != "":
               return incoming_node.delete(__magic_name__							)
           else:
               # If it is not a leaf, we don't have to delete
               if not incoming_node.is_leaf:
                   return False
               else:
                   # We delete the nodes if no edges go from it
                   if len(incoming_node.nodes							) == 0:
                       del self.nodes[word[0]]
                       # We merge the current node with its only child
                       if len(self.nodes							) == 1 and not self.is_leaf:
                           UpperCamelCase__							:				Optional[Any]          =      list(self.nodes.values()							)[0]
                           UpperCamelCase__							:				Union[str, Any]          =      merging_node.is_leaf
                           self.prefix += merging_node.prefix
                           UpperCamelCase__							:				int          =      merging_node.nodes
                    # If there is more than 1 edge, we just mark it as non-leaf
                   elif len(incoming_node.nodes							) > 1:
                       UpperCamelCase__							:				Any          =      False
                   # If there is 1 edge, we merge it with its child
                   else:
                       UpperCamelCase__							:				Union[str, Any]          =      list(incoming_node.nodes.values()							)[0]
                       UpperCamelCase__							:				List[str]          =      merging_node.is_leaf
                       incoming_node.prefix += merging_node.prefix
                       UpperCamelCase__							:				int          =      merging_node.nodes

                   return True




   def       UpperCamelCase__  (    self,		__magic_name__ = 0							) ->      None:






       """simple docstring"""
       if self.prefix != "":
           print('''-''' * height,		self.prefix,		'''  (leaf)''' if self.is_leaf else ''''''							)

       for value in self.nodes.values():
           value.print_tree(height + 1							)




def 		lowerCAmelCase_    (  )      ->	bool:
    UpperCamelCase__							:				Optional[int]          =      '''banana bananas bandana band apple all beast'''.split()
    UpperCamelCase__							:				Optional[int]          =      RadixNode()
    root.insert_many(__UpperCAmelCase      )

    assert all(root.find(__UpperCAmelCase      ) for word in words      )
    assert not root.find('''bandanas'''      )
    assert not root.find('''apps'''      )
    root.delete('''all'''      )
    assert not root.find('''all'''      )
    root.delete('''banana'''      )
    assert not root.find('''banana'''      )
    assert root.find('''bananas'''      )

    return True




def 		lowerCAmelCase_    (  )      ->	None:
    assert test_trie()




def 		lowerCAmelCase_    (  )      ->	None:
    UpperCamelCase__							:				int          =      RadixNode()
    UpperCamelCase__							:				Any          =      '''banana bananas bandanas bandana band apple all beast'''.split()
    root.insert_many(__UpperCAmelCase      )

    print('''Words:'''		,   __UpperCAmelCase      )
    print('''Tree:'''      )
    root.print_tree()


if __name__ == "__main__":
  main()

