


"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def      a_				(   SCREAMING_SNAKE_CASE__ :				Optional[Any]		,      SCREAMING_SNAKE_CASE__ :				List[Any]		,      SCREAMING_SNAKE_CASE__ :				Dict					):
       '''simple docstring'''





       _lowerCamelCase	:		int          =TaConfig.from_pretrained(_lowerCamelCase					)
       _lowerCamelCase	:		str          =FlaxTaForConditionalGeneration(config=_lowerCamelCase					)
       _lowerCamelCase	:		Union[str, Any]          =checkpoints.load_tax_checkpoint(_lowerCamelCase					)

       _lowerCamelCase	:		List[str]          ='wi_0' in tax_model['target']['encoder']['layers_0']['mlp']

       # Encoder
       for layer_index in range(config.num_layers					):
              _lowerCamelCase	:		Optional[int]          =F'''layers_{str(_lowerCamelCase					)}'''

              # Self-Attention
              _lowerCamelCase	:		List[Any]          =tax_model['target']['encoder'][layer_name]['attention']['key']['kernel']
              _lowerCamelCase	:		Tuple          =tax_model['target']['encoder'][layer_name]['attention']['out']['kernel']
              _lowerCamelCase	:		Optional[Any]          =tax_model['target']['encoder'][layer_name]['attention']['query']['kernel']
              _lowerCamelCase	:		str          =tax_model['target']['encoder'][layer_name]['attention']['value']['kernel']

              # Layer Normalization
              _lowerCamelCase	:		Any          =tax_model['target']['encoder'][layer_name]['pre_attention_layer_norm']['scale']

              if split_mlp_wi:
                     _lowerCamelCase	:		int          =tax_model['target']['encoder'][layer_name]['mlp']['wi_0']['kernel']
                     _lowerCamelCase	:		Optional[Any]          =tax_model['target']['encoder'][layer_name]['mlp']['wi_1']['kernel']
              else:
                     _lowerCamelCase	:		List[str]          =tax_model['target']['encoder'][layer_name]['mlp']['wi']['kernel']

              _lowerCamelCase	:		str          =tax_model['target']['encoder'][layer_name]['mlp']['wo']['kernel']

              # Layer Normalization
              _lowerCamelCase	:		Union[str, Any]          =tax_model['target']['encoder'][layer_name]['pre_mlp_layer_norm']['scale']

              # Assigning
              _lowerCamelCase	:		Optional[int]          =tax_attention_key
              _lowerCamelCase	:		Tuple          =tax_attention_out
              _lowerCamelCase	:		Optional[Any]          =tax_attention_query
              _lowerCamelCase	:		Optional[int]          =tax_attention_value

              _lowerCamelCase	:		int          =tax_attention_layer_norm

              if split_mlp_wi:
                     _lowerCamelCase	:		Union[str, Any]          =tax_mlp_wi_a
                     _lowerCamelCase	:		List[Any]          =tax_mlp_wi_a
              else:
                     _lowerCamelCase	:		str          =tax_mlp_wi

              _lowerCamelCase	:		Dict          =tax_mlp_wo
              _lowerCamelCase	:		Optional[Any]          =tax_mlp_layer_norm

       # Only for layer 0:
       _lowerCamelCase	:		Tuple          =tax_model['target']['encoder']['relpos_bias']['rel_embedding'].T
       _lowerCamelCase	:		int          =tax_encoder_rel_embedding

       # Assigning
       _lowerCamelCase	:		int          =tax_model['target']['encoder']['encoder_norm']['scale']
       _lowerCamelCase	:		Any          =tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_decoder_layers					):
              _lowerCamelCase	:		Union[str, Any]          =F'''layers_{str(_lowerCamelCase					)}'''

              # Self-Attention
              _lowerCamelCase	:		List[str]          =tax_model['target']['decoder'][layer_name]['self_attention']['key']['kernel']
              _lowerCamelCase	:		Union[str, Any]          =tax_model['target']['decoder'][layer_name]['self_attention']['out']['kernel']
              _lowerCamelCase	:		int          =tax_model['target']['decoder'][layer_name]['self_attention']['query']['kernel']
              _lowerCamelCase	:		List[str]          =tax_model['target']['decoder'][layer_name]['self_attention']['value']['kernel']

              # Layer Normalization
              _lowerCamelCase	:		List[str]          =tax_model['target']['decoder'][layer_name]['pre_self_attention_layer_norm'][
                  'scale'
              ]

              # Encoder-Decoder-Attention
              _lowerCamelCase	:		List[Any]          =tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['key'][
                  'kernel'
              ]
              _lowerCamelCase	:		Union[str, Any]          =tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['out'][
                  'kernel'
              ]
              _lowerCamelCase	:		Dict          =tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['query'][
                  'kernel'
              ]
              _lowerCamelCase	:		Optional[int]          =tax_model['target']['decoder'][layer_name]['encoder_decoder_attention']['value'][
                  'kernel'
              ]

              # Layer Normalization
              _lowerCamelCase	:		int          =tax_model['target']['decoder'][layer_name]['pre_cross_attention_layer_norm']['scale']

              # MLP
              if split_mlp_wi:
                     _lowerCamelCase	:		Optional[Any]          =tax_model['target']['decoder'][layer_name]['mlp']['wi_0']['kernel']
                     _lowerCamelCase	:		Union[str, Any]          =tax_model['target']['decoder'][layer_name]['mlp']['wi_1']['kernel']
              else:
                     _lowerCamelCase	:		Optional[int]          =tax_model['target']['decoder'][layer_name]['mlp']['wi']['kernel']

              _lowerCamelCase	:		List[Any]          =tax_model['target']['decoder'][layer_name]['mlp']['wo']['kernel']

              # Layer Normalization
              _lowerCamelCase	:		Union[str, Any]          =tax_model['target']['decoder'][layer_name]['pre_mlp_layer_norm']['scale']

              # Assigning
              _lowerCamelCase	:		int          =tax_attention_key
              _lowerCamelCase	:		Optional[int]          =tax_attention_out
              _lowerCamelCase	:		str          =tax_attention_query
              _lowerCamelCase	:		Tuple          =tax_attention_value

              _lowerCamelCase	:		str          =tax_pre_attention_layer_norm

              _lowerCamelCase	:		Any          =tax_enc_dec_attention_key
              _lowerCamelCase	:		str          =tax_enc_dec_attention_out
              _lowerCamelCase	:		List[Any]          =tax_enc_dec_attention_query
              _lowerCamelCase	:		List[Any]          =tax_enc_dec_attention_value

              _lowerCamelCase	:		int          =tax_cross_layer_norm

              if split_mlp_wi:
                     _lowerCamelCase	:		Optional[Any]          =tax_mlp_wi_a
                     _lowerCamelCase	:		int          =tax_mlp_wi_a
              else:
                     _lowerCamelCase	:		Any          =tax_mlp_wi

              _lowerCamelCase	:		int          =tax_mlp_wo

              _lowerCamelCase	:		Tuple          =txa_mlp_layer_norm

       # Decoder Normalization
       _lowerCamelCase	:		Dict          =tax_model['target']['decoder']['decoder_norm']['scale']
       _lowerCamelCase	:		Optional[Any]          =txa_decoder_norm

       # Only for layer 0:
       _lowerCamelCase	:		Tuple          =tax_model['target']['decoder']['relpos_bias']['rel_embedding'].T
       _lowerCamelCase	:		Tuple          =tax_decoder_rel_embedding

       # Token Embeddings
       _lowerCamelCase	:		List[Any]          =tax_model['target']['token_embedder']['embedding']
       _lowerCamelCase	:		List[str]          =txa_token_embeddings

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

       flax_model.save_pretrained(_lowerCamelCase					)
       print('T5X Model was sucessfully converted!'					)


if __name__ == "__main__":
 lowerCamelCase						 =	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						 =	parser.parse_args()
 convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)






from __future__ import annotations

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




class A  (       UpperCamelCase_					):






      @abstractmethod
      def __lt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __gt__( self						:					T      ,       lowercase_						:					T			) ->						bool:
             """simple docstring"""




             pass






      @abstractmethod
      def __eq__( self						:					T      ,       lowercase_						:					object			) ->						bool:
             """simple docstring"""




             pass




lowerCamelCase						 =	TypeVar('T', bound=Comparable)




class A  (       Generic[T]					):






      def __init__( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		list[T]          =[]
             _lowerCamelCase	:		int          =0






      def __repr__( self						:					int			) ->						str:
             """simple docstring"""




             return str(self.h			)






      def 	lowerCamelCase	( self						:					str      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					Union[str, Any]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					List[str]      ,       lowercase_						:					int			) ->						int | None:
             """simple docstring"""




             _lowerCamelCase	:		Optional[int]          =2 * parent_idx + 2
             if right_child_index < self.heap_size:
                    return right_child_index
             return None






      def 	lowerCamelCase	( self						:					Any      ,       lowercase_						:					int			) ->						None:
             """simple docstring"""




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






      def 	lowerCamelCase	( self						:					int      ,       lowercase_						:					Iterable[T]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		List[Any]          =list(lowercase_			)
             _lowerCamelCase	:		Dict          =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(lowercase_			)






      def 	lowerCamelCase	( self						:					Dict			) ->						T:
             """simple docstring"""




             if self.heap_size >= 2:
                    _lowerCamelCase	:		Optional[int]          =self.h[0]
                    _lowerCamelCase	:		Optional[int]          =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 	lowerCamelCase	( self						:					Tuple      ,       lowercase_						:					T			) ->						None:
             """simple docstring"""




             self.h.append(lowercase_			)
             _lowerCamelCase	:		List[Any]          =(self.heap_size - 1) // 2
             self.heap_size += 1
             while idx >= 0:
                    self.max_heapify(lowercase_			)
                    _lowerCamelCase	:		Tuple          =(idx - 1) // 2






      def 	lowerCamelCase	( self						:					Optional[Any]			) ->						None:
             """simple docstring"""




             _lowerCamelCase	:		Union[str, Any]          =self.heap_size
             for j in range(size - 1      ,       0      ,       -1			):
                    _lowerCamelCase     ,					_lowerCamelCase	:		Optional[Any]          =self.h[j], self.h[0]
                    self.heap_size -= 1
                    self.max_heapify(0			)
             _lowerCamelCase	:		Union[str, Any]          =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, 10, 8],
     [1_03, 9, 1, 7, 11, 15, 25, 2_01, 2_09, 1_07, 5],
     [-45, -2, -5],
 ]:
  print(F"""unsorted array: {unsorted}""")

  lowerCamelCase						 =	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_00)
  print(F"""after new value 100 inserted: {heap}""")

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


