


import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig



def 			_a      (    __UpperCamelCase	,	__UpperCamelCase	,	__UpperCamelCase			):
     a_     :  Union[str, Any]				      =     TaConfig.from_pretrained(__UpperCamelCase			)
     a_     :  Dict				      =     FlaxTaForConditionalGeneration(config=__UpperCamelCase			)
     a_     :  Tuple				      =     checkpoints.load_tax_checkpoint(__UpperCamelCase			)

     a_     :  List[str]				      =     """wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

     # Encoder
     for layer_index in range(config.num_layers			):
          a_     :  Dict				      =     F'''layers_{str(__UpperCamelCase			)}'''

          # Self-Attention
          a_     :  List[Any]				      =     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""key"""]["""kernel"""]
          a_     :  Dict				      =     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""out"""]["""kernel"""]
          a_     :  List[str]				      =     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""query"""]["""kernel"""]
          a_     :  str				      =     tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""value"""]["""kernel"""]

          # Layer Normalization
          a_     :  List[str]				      =     tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

          if split_mlp_wi:
               a_     :  Union[str, Any]				      =     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_     :  int				      =     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_     :  Dict				      =     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_     :  Optional[Any]				      =     tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_     :  Dict				      =     tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_     :  List[str]				      =     tax_attention_key
          a_     :  str				      =     tax_attention_out
          a_     :  List[Any]				      =     tax_attention_query
          a_     :  Union[str, Any]				      =     tax_attention_value

          a_     :  List[str]				      =     tax_attention_layer_norm

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

          a_     :  Union[str, Any]				      =     tax_mlp_wo
          a_     :  Tuple				      =     tax_mlp_layer_norm

     # Only for layer 0:
     a_     :  Dict				      =     tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
     a_     :  Optional[int]				      =     tax_encoder_rel_embedding

     # Assigning
     a_     :  Optional[int]				      =     tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
     a_     :  Tuple				      =     tax_encoder_norm

     # Decoder
     for layer_index in range(config.num_decoder_layers			):
          a_     :  List[Any]				      =     F'''layers_{str(__UpperCamelCase			)}'''

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

          # Layer Normalization
          a_     :  List[str]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""pre_self_attention_layer_norm"""][
              """scale"""
          ]

          # Encoder-Decoder-Attention
          a_     :  List[Any]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""key"""][
              """kernel"""
          ]
          a_     :  int				      =     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""out"""][
              """kernel"""
          ]
          a_     :  Any				      =     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""query"""][
              """kernel"""
          ]
          a_     :  List[Any]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""encoder_decoder_attention"""]["""value"""][
              """kernel"""
          ]

          # Layer Normalization
          a_     :  int				      =     tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

          # MLP
          if split_mlp_wi:
               a_     :  Optional[int]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
               a_     :  List[Any]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
          else:
               a_     :  Optional[int]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

          a_     :  Optional[Any]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

          # Layer Normalization
          a_     :  List[str]				      =     tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

          # Assigning
          a_     :  str				      =     tax_attention_key
          a_     :  Any				      =     tax_attention_out
          a_     :  Dict				      =     tax_attention_query
          a_     :  Any				      =     tax_attention_value

          a_     :  Tuple				      =     tax_pre_attention_layer_norm

          a_     :  int				      =     tax_enc_dec_attention_key
          a_     :  Tuple				      =     tax_enc_dec_attention_out
          a_     :  Optional[Any]				      =     tax_enc_dec_attention_query
          a_     :  Tuple				      =     tax_enc_dec_attention_value

          a_     :  int				      =     tax_cross_layer_norm

          if split_mlp_wi:
               a_     :  Optional[int]				      =     tax_mlp_wi_a
               a_     :  Any				      =     tax_mlp_wi_a
          else:
               a_     :  List[str]				      =     tax_mlp_wi

          a_     :  Union[str, Any]				      =     tax_mlp_wo

          a_     :  Tuple				      =     txa_mlp_layer_norm

     # Decoder Normalization
     a_     :  Union[str, Any]				      =     tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
     a_     :  str				      =     txa_decoder_norm

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

     # Token Embeddings
     a_     :  str				      =     tax_model["""target"""]["""token_embedder"""]["""embedding"""]
     a_     :  Optional[int]				      =     txa_token_embeddings

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

     flax_model.save_pretrained(__UpperCamelCase			)
     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)










'''simple docstring'''







from manim import *


class        __a   (lowerCamelCase  ):

							def      UpperCAmelCase__						(				self				:  str )       ->							Any:







													"""simple docstring"""
													UpperCAmelCase_      :   List[str]      =	Rectangle(height=0.5							,  width=0.5 )
													UpperCAmelCase_      :   int      =	Rectangle(height=0.4_6							,  width=0.4_6 ).set_stroke(width=0 )
													UpperCAmelCase_      :   str      =	Rectangle(height=0.2_5							,  width=0.2_5 )

													UpperCAmelCase_      :   Optional[Any]      =	[mem.copy() for i in range(6 )]
													UpperCAmelCase_      :   int      =	[mem.copy() for i in range(6 )]
													UpperCAmelCase_      :   List[str]      =	VGroup(*__magic_name__ ).arrange(__magic_name__							,  buff=0 )
													UpperCAmelCase_      :   Optional[Any]      =	VGroup(*__magic_name__ ).arrange(__magic_name__							,  buff=0 )
													UpperCAmelCase_      :   List[Any]      =	VGroup(__magic_name__							,  __magic_name__ ).arrange(__magic_name__							,  buff=0 )
													UpperCAmelCase_      :   int      =	Text('''CPU'''							,  font_size=24 )
													UpperCAmelCase_      :   Optional[Any]      =	Group(__magic_name__							,  __magic_name__ ).arrange(__magic_name__							,  buff=0.5							,  aligned_edge=__magic_name__ )
													cpu.move_to([-2.5, -0.5, 0] )
													self.add(__magic_name__ )

													UpperCAmelCase_      :   List[Any]      =	[mem.copy() for i in range(4 )]
													UpperCAmelCase_      :   List[str]      =	VGroup(*__magic_name__ ).arrange(__magic_name__							,  buff=0 )
													UpperCAmelCase_      :   str      =	Text('''GPU'''							,  font_size=24 )
													UpperCAmelCase_      :   Union[str, Any]      =	Group(__magic_name__							,  __magic_name__ ).arrange(__magic_name__							,  buff=0.5							,  aligned_edge=__magic_name__ )
													gpu.move_to([-1, -1, 0] )
													self.add(__magic_name__ )

													UpperCAmelCase_      :   Dict      =	[mem.copy() for i in range(6 )]
													UpperCAmelCase_      :   Optional[Any]      =	VGroup(*__magic_name__ ).arrange(__magic_name__							,  buff=0 )

													UpperCAmelCase_      :   Dict      =	Text('''Model'''							,  font_size=24 )
													UpperCAmelCase_      :   str      =	Group(__magic_name__							,  __magic_name__ ).arrange(__magic_name__							,  buff=0.5							,  aligned_edge=__magic_name__ )
													model.move_to([3, -1.0, 0] )
													self.add(__magic_name__ )

													UpperCAmelCase_      :   List[str]      =	[]
													UpperCAmelCase_      :   int      =	[]

													for i, rect in enumerate(__magic_name__ ):
																			rect.set_stroke(__magic_name__ )

																			UpperCAmelCase_      :   List[Any]      =	Rectangle(height=0.4_6 / 4							,  width=0.4_6 / 3 ).set_stroke(width=0.0 ).set_fill(__magic_name__							,  opacity=0.7 )

																			if i == 0:
																									cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT )							,  buff=0.0_2							,  direction=__magic_name__ )
																									cpu_target.set_x(cpu_target.get_x() + 0.1 )
																			elif i == 3:
																									cpu_target.next_to(model_cpu_arr[0]							,  direction=__magic_name__							,  buff=0.0 )
																			else:
																									cpu_target.next_to(model_cpu_arr[i - 1]							,  direction=__magic_name__							,  buff=0.0 )
																			self.add(__magic_name__ )
																			model_cpu_arr.append(__magic_name__ )

													self.add(*__magic_name__							,  *__magic_name__ )

													UpperCAmelCase_      :   Any      =	[meta_mem.copy() for i in range(6 )]
													UpperCAmelCase_      :   int      =	[meta_mem.copy() for i in range(6 )]
													UpperCAmelCase_      :   Optional[Any]      =	VGroup(*__magic_name__ ).arrange(__magic_name__							,  buff=0 )
													UpperCAmelCase_      :   Optional[int]      =	VGroup(*__magic_name__ ).arrange(__magic_name__							,  buff=0 )
													UpperCAmelCase_      :   Dict      =	VGroup(__magic_name__							,  __magic_name__ ).arrange(__magic_name__							,  buff=0 )
													UpperCAmelCase_      :   Optional[Any]      =	Text('''Disk'''							,  font_size=24 )
													UpperCAmelCase_      :   Any      =	Group(__magic_name__							,  __magic_name__ ).arrange(__magic_name__							,  buff=0.5							,  aligned_edge=__magic_name__ )
													disk.move_to([-4.0, -1.2_5, 0] )
													self.add(__magic_name__							,  __magic_name__ )

													UpperCAmelCase_      :   Optional[Any]      =	[]

													for i in range(6 ):
																			UpperCAmelCase_      :   Tuple      =	fill.copy().set_fill(__magic_name__							,  opacity=0.8 )
																			target.move_to(disk_left_col_base[i] ).scale(0.5 )
																			cpu_disk_arr.append(__magic_name__ )

													self.add(*__magic_name__ )

													UpperCAmelCase_      :   List[str]      =	Square(side_length=2.2 )
													key.move_to([-5, 2, 0] )

													UpperCAmelCase_      :   Union[str, Any]      =	MarkupText(
													    F"""<b>Key:</b>\n\n<span fgcolor='{YELLOW}'>●</span> Empty Model"""							,  font_size=18							,  )

													key_text.move_to([-5, 2.4, 0] )

													self.add(__magic_name__							,  __magic_name__ )

													UpperCAmelCase_      :   Union[str, Any]      =	MarkupText(
													    F"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""							,  font_size=18							,  )

													blue_text.next_to(__magic_name__							,  DOWN * 2.4							,  aligned_edge=key_text.get_left() )
													self.add(__magic_name__ )

													UpperCAmelCase_      :   Dict      =	MarkupText(F"""The offloaded weights are all sent to the CPU."""							,  font_size=24 )
													step_a.move_to([2, 2, 0] )

													self.play(Write(__magic_name__							,  run_time=3 ) )

													for i in range(6 ):
																			UpperCAmelCase_      :   Dict      =	cpu_disk_arr[i]
																			UpperCAmelCase_      :   int      =	rect.copy().set_fill(__magic_name__							,  opacity=0.8 ).scale(2.0 )
																			cpa.generate_target()
																			cpa.target.move_to(model_base[i] )

																			if i == 0:
																									rect.set_fill(__magic_name__							,  opacity=0.8 )
																									rect.generate_target()
																									rect.target.move_to(cpu_left_col_base[0] ).scale(2.0 )

																									self.remove(
																									    *__magic_name__							,  *__magic_name__							,  )

																			else:
																									rect.generate_target()
																									rect.target.move_to(cpu_left_col_base[i] ).scale(2.0 )
																			self.play(MoveToTarget(__magic_name__ )							,  MoveToTarget(__magic_name__ )							,  model_base[i].animate.set_stroke(__magic_name__ ) )
													self.play(FadeOut(__magic_name__ ) )

													UpperCAmelCase_      :   int      =	MarkupText(
													    F"""Finally, hooks are added to each weight in the model\nto transfer the weights from CPU to GPU\n\t\tand back when needed."""							,  font_size=24							,  )
													step_a.move_to([2, 2, 0] )

													self.play(Write(__magic_name__							,  run_time=3 ) )

													UpperCAmelCase_      :   Union[str, Any]      =	[]
													UpperCAmelCase_      :   Tuple      =	[]
													for i in range(6 ):
																			UpperCAmelCase_      :   List[Any]      =	Arrow(start=__magic_name__							,  end=__magic_name__							,  color=__magic_name__							,  buff=0.5 )
																			a.next_to(model_base[i].get_left()							,  __magic_name__							,  buff=0.2 )
																			arrows.append(__magic_name__ )
																			animations.append(Write(__magic_name__ ) )
													self.play(*__magic_name__ )
													self.wait()





