"""simple docstring"""


from __future__ import annotations

from abc import abstractmethod
from collections.abc import Iterable
from typing import Generic, Protocol, TypeVar






class  a__     ( _a						):


   @abstractmethod
   def __lt__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        pass


   @abstractmethod
   def __gt__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        pass


   @abstractmethod
   def __eq__(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        pass



lowerCAmelCase_:       Optional[Any]			     =       TypeVar("T", bound=Comparable)






class  a__     ( Generic[T]						):


   def __init__(  self	):
        '''simple docstring'''



        lowercase__								=				[]
        lowercase__								=				0


   def __repr__(  self	):
        '''simple docstring'''



        return str(self.h	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if child_idx > 0:
             return (child_idx - 1) // 2
        return None


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				2 * parent_idx + 1
        if left_child_index < self.heap_size:
             return left_child_index
        return None


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				2 * parent_idx + 2
        if right_child_index < self.heap_size:
             return right_child_index
        return None


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        if index < self.heap_size:
             lowercase__								=				index
             lowercase__								=				self.left_child_idx(_UpperCAmelCase	)
             lowercase__								=				self.right_child_idx(_UpperCAmelCase	)
             # check which child is larger than its parent
             if left_child is not None and self.h[left_child] > self.h[violation]:
                  lowercase__								=				left_child
             if right_child is not None and self.h[right_child] > self.h[violation]:
                  lowercase__								=				right_child
             # if violation indeed exists
             if violation != index:
                  # swap to fix the violation
                  lowercase__			,    lowercase__								=				self.h[index], self.h[violation]
                  # fix the subsequent violation recursively if any
                  self.max_heapify(_UpperCAmelCase	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        lowercase__								=				list(_UpperCAmelCase	)
        lowercase__								=				len(self.h	)
        if self.heap_size > 1:
             # max_heapify from right to left but exclude leaves (last level)
             for i in range(self.heap_size // 2 - 1,					-1,					-1	):
                  self.max_heapify(_UpperCAmelCase	)


   def 						snake_case__						(  self	):
        '''simple docstring'''



        if self.heap_size >= 2:
             lowercase__								=				self.h[0]
             lowercase__								=				self.h.pop(-1	)
             self.heap_size -= 1
             self.max_heapify(0	)
             return me
        elif self.heap_size == 1:
             self.heap_size -= 1
             return self.h.pop(-1	)
        else:
             raise Exception("Empty heap"	)


   def 						snake_case__						(  self,					_UpperCAmelCase	):
        '''simple docstring'''



        self.h.append(_UpperCAmelCase	)
        lowercase__								=				(self.heap_size - 1) // 2
        self.heap_size += 1
        while idx >= 0:
             self.max_heapify(_UpperCAmelCase	)
             lowercase__								=				(idx - 1) // 2


   def 						snake_case__						(  self	):
        '''simple docstring'''



        lowercase__								=				self.heap_size
        for j in range(size - 1,					0,					-1	):
             lowercase__			,    lowercase__								=				self.h[j], self.h[0]
             self.heap_size -= 1
             self.max_heapify(0	)
        lowercase__								=				size


if __name__ == "__main__":
 import doctest

 # run doc test
 doctest.testmod()

 # demo
 for unsorted in [
     [0],
     [2],
     [3, 5],
     [5, 3],
     [5, 5],
     [0, 0, 0, 0],
     [1, 1, 1, 1],
     [2, 2, 3, 5],
     [0, 2, 2, 3, 5],
     [2, 5, 3, 0, 2, 3, 0, 3],
     [6, 1, 2, 7, 9, 3, 4, 5, 1_0, 8],
     [1_0_3, 9, 1, 7, 1_1, 1_5, 2_5, 2_0_1, 2_0_9, 1_0_7, 5],
     [-4_5, -2, -5],
 ]:
  print(F'unsorted array: {unsorted}')

  lowerCAmelCase_:       Heap[int]			     =       Heap()
  heap.build_max_heap(unsorted)
  print(F'after build heap: {heap}')

  print(F'max value: {heap.extract_max()}')
  print(F'after max value removed: {heap}')

  heap.insert(1_0_0)
  print(F'after new value 100 inserted: {heap}')

  heap.heap_sort()
  print(F'heap-sorted array: {heap}\n')





"""simple docstring"""


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig







def        __a (		A     ,					A     ,					A   ):






     '''simple docstring'''



     lowercase__								=				TaConfig.from_pretrained(A   )
     lowercase__								=				FlaxTaForConditionalGeneration(config=A   )
     lowercase__								=				checkpoints.load_tax_checkpoint(A   )

     lowercase__								=				"wi_0" in tax_model["target"]["encoder"]["layers_0"]["mlp"]

     # Encoder
     for layer_index in range(config.num_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_attention_layer_norm"]["scale"]

          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["encoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["encoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_attention_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo
          lowercase__								=				tax_mlp_layer_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["encoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_encoder_rel_embedding

     # Assigning
     lowercase__								=				tax_model["target"]["encoder"]["encoder_norm"]["scale"]
     lowercase__								=				tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers   ):
          lowercase__								=				f'''layers_{str(A   )}'''

          # Self-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["key"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["out"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["query"]["kernel"]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["self_attention"]["value"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_self_attention_layer_norm"][
              "scale"
          ]

          # Encoder-Decoder-Attention
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["key"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["out"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["query"][
              "kernel"
          ]
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["encoder_decoder_attention"]["value"][
              "kernel"
          ]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_cross_attention_layer_norm"]["scale"]

          # MLP
          if split_mlp_wi:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_0"]["kernel"]
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi_1"]["kernel"]
          else:
               lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wi"]["kernel"]

          lowercase__								=				tax_model["target"]["decoder"][layer_name]["mlp"]["wo"]["kernel"]

          # Layer Normalization
          lowercase__								=				tax_model["target"]["decoder"][layer_name]["pre_mlp_layer_norm"]["scale"]

          # Assigning
          lowercase__								=				tax_attention_key
          lowercase__								=				tax_attention_out
          lowercase__								=				tax_attention_query
          lowercase__								=				tax_attention_value

          lowercase__								=				tax_pre_attention_layer_norm

          lowercase__								=				tax_enc_dec_attention_key
          lowercase__								=				tax_enc_dec_attention_out
          lowercase__								=				tax_enc_dec_attention_query
          lowercase__								=				tax_enc_dec_attention_value

          lowercase__								=				tax_cross_layer_norm

          if split_mlp_wi:
               lowercase__								=				tax_mlp_wi_a
               lowercase__								=				tax_mlp_wi_a
          else:
               lowercase__								=				tax_mlp_wi

          lowercase__								=				tax_mlp_wo

          lowercase__								=				txa_mlp_layer_norm

     # Decoder Normalization
     lowercase__								=				tax_model["target"]["decoder"]["decoder_norm"]["scale"]
     lowercase__								=				txa_decoder_norm

     # Only for layer 0:
     lowercase__								=				tax_model["target"]["decoder"]["relpos_bias"]["rel_embedding"].T
     lowercase__								=				tax_decoder_rel_embedding

     # Token Embeddings
     lowercase__								=				tax_model["target"]["token_embedder"]["embedding"]
     lowercase__								=				txa_token_embeddings

     # LM Head (only in v1.1 checkpoints)
     if "logits_dense" in tax_model["target"]["decoder"]:
          lowercase__								=				tax_model["target"]["decoder"]["logits_dense"]["kernel"]

     flax_model.save_pretrained(A   )
     print("T5X Model was sucessfully converted!"   )


if __name__ == "__main__":
 lowerCAmelCase_:       List[Any]			     =       argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--t5x_checkpoint_path", default=None, type=str, required=True, help="Path the TX5 checkpoint."
 )
 parser.add_argument("--config_name", default=None, type=str, required=True, help="Config name of T5 model.")
 parser.add_argument(
     "--flax_dump_folder_path", default=None, type=str, required=True, help="Path to the output FLAX model."
 )
 lowerCAmelCase_:       Dict			     =       parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




