




'''simple docstring'''






import argparse

from tax import checkpoints

from transformers import FlaxTaForConditionalGeneration, TaConfig




def 		a_(			lowerCamelCase ,					lowerCamelCase ,					lowerCamelCase   )					->List[str]:






       '''simple docstring'''




       lowercase	:		Tuple    =   TaConfig.from_pretrained(lowerCamelCase   )
       lowercase	:		Union[str, Any]    =   FlaxTaForConditionalGeneration(config=lowerCamelCase   )
       lowercase	:		Optional[Any]    =   checkpoints.load_tax_checkpoint(lowerCamelCase   )

       lowercase	:		Optional[int]    =   '''wi_0''' in tax_model['''target''']['''encoder''']['''layers_0''']['''mlp''']

       # Encoder
       for layer_index in range(config.num_layers   ):
              lowercase	:		Any    =   f"""layers_{str(lowerCamelCase   )}"""

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

              # Layer Normalization
              lowercase	:		List[str]    =   tax_model['''target''']['''encoder'''][layer_name]['''pre_attention_layer_norm''']['''scale''']

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

              lowercase	:		Any    =   tax_model['''target''']['''encoder'''][layer_name]['''mlp''']['''wo''']['''kernel''']

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

              # Assigning
              lowercase	:		List[str]    =   tax_attention_key
              lowercase	:		Dict    =   tax_attention_out
              lowercase	:		Tuple    =   tax_attention_query
              lowercase	:		Union[str, Any]    =   tax_attention_value

              lowercase	:		Any    =   tax_attention_layer_norm

              if split_mlp_wi:
                     lowercase	:		Dict    =   tax_mlp_wi_a
                     lowercase	:		Tuple    =   tax_mlp_wi_a
              else:
                     lowercase	:		Dict    =   tax_mlp_wi

              lowercase	:		Union[str, Any]    =   tax_mlp_wo
              lowercase	:		List[Any]    =   tax_mlp_layer_norm

       # Only for layer 0:
       lowercase	:		Any    =   tax_model['''target''']['''encoder''']['''relpos_bias''']['''rel_embedding'''].T
       lowercase	:		str    =   tax_encoder_rel_embedding

       # Assigning
       lowercase	:		Tuple    =   tax_model['''target''']['''encoder''']['''encoder_norm''']['''scale''']
       lowercase	:		Optional[Any]    =   tax_encoder_norm

       # Decoder
       for layer_index in range(config.num_decoder_layers   ):
              lowercase	:		str    =   f"""layers_{str(lowerCamelCase   )}"""

              # Self-Attention
              lowercase	:		Tuple    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''key''']['''kernel''']
              lowercase	:		Optional[int]    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''out''']['''kernel''']
              lowercase	:		Tuple    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''query''']['''kernel''']
              lowercase	:		str    =   tax_model['''target''']['''decoder'''][layer_name]['''self_attention''']['''value''']['''kernel''']

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

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

              # Layer Normalization
              lowercase	:		str    =   tax_model['''target''']['''decoder'''][layer_name]['''pre_cross_attention_layer_norm''']['''scale''']

              # MLP
              if split_mlp_wi:
                     lowercase	:		Any    =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_0''']['''kernel''']
                     lowercase	:		str    =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi_1''']['''kernel''']
              else:
                     lowercase	:		Any    =   tax_model['''target''']['''decoder'''][layer_name]['''mlp''']['''wi''']['''kernel''']

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

              # Layer Normalization
              lowercase	:		Optional[Any]    =   tax_model['''target''']['''decoder'''][layer_name]['''pre_mlp_layer_norm''']['''scale''']

              # Assigning
              lowercase	:		Tuple    =   tax_attention_key
              lowercase	:		Union[str, Any]    =   tax_attention_out
              lowercase	:		Any    =   tax_attention_query
              lowercase	:		Optional[Any]    =   tax_attention_value

              lowercase	:		Optional[int]    =   tax_pre_attention_layer_norm

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

              lowercase	:		Union[str, Any]    =   tax_cross_layer_norm

              if split_mlp_wi:
                     lowercase	:		Dict    =   tax_mlp_wi_a
                     lowercase	:		Optional[Any]    =   tax_mlp_wi_a
              else:
                     lowercase	:		Dict    =   tax_mlp_wi

              lowercase	:		Dict    =   tax_mlp_wo

              lowercase	:		str    =   txa_mlp_layer_norm

       # Decoder Normalization
       lowercase	:		List[Any]    =   tax_model['''target''']['''decoder''']['''decoder_norm''']['''scale''']
       lowercase	:		int    =   txa_decoder_norm

       # Only for layer 0:
       lowercase	:		Dict    =   tax_model['''target''']['''decoder''']['''relpos_bias''']['''rel_embedding'''].T
       lowercase	:		str    =   tax_decoder_rel_embedding

       # Token Embeddings
       lowercase	:		Union[str, Any]    =   tax_model['''target''']['''token_embedder''']['''embedding''']
       lowercase	:		Optional[int]    =   txa_token_embeddings

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

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


if __name__ == "__main__":
      lowercase						:   str      						=							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.'''
      )
      lowercase						:   int      						=							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 	SCREAMING_SNAKE_CASE_		(							lowerCamelCase_					):



 def  SCREAMING_SNAKE_CASE(	self			):






        '''simple docstring'''




        lowercase	:		str    =   Rectangle(height=0.5 ,		width=0.5			)
        lowercase	:		Optional[Any]    =   Rectangle(height=0.4_6 ,		width=0.4_6			).set_stroke(width=0			)
        lowercase	:		str    =   Rectangle(height=0.2_5 ,		width=0.2_5			)

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

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

        lowercase	:		str    =   [mem.copy() for i in range(6			)]
        lowercase	:		int    =   VGroup(*A			).arrange(A ,		buff=0			)

        lowercase	:		int    =   Text('''Model''' ,		font_size=24			)
        lowercase	:		Tuple    =   Group(A ,		A			).arrange(A ,		buff=0.5 ,		aligned_edge=A			)
        model.move_to([3, -1.0, 0]			)
        self.add(A			)

        lowercase	:		str    =   []
        lowercase	:		int    =   []

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

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

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

        self.add(*A ,		*A			)

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

        lowercase	:		List[str]    =   []

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

        self.add(*A			)

        lowercase	:		Any    =   Square(side_length=2.2			)
        key.move_to([-5, 2, 0]			)

        lowercase	:		Tuple    =   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(A ,		A			)

        lowercase	:		List[Any]    =   MarkupText(
            f"""<span fgcolor='{BLUE}'>●</span> Checkpoint""" ,		font_size=18 ,		)

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

        lowercase	:		Optional[Any]    =   MarkupText(f"""The offloaded weights are all sent to the CPU.""" ,		font_size=24			)
        step_a.move_to([2, 2, 0]			)

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

        for i in range(6			):
               lowercase	:		List[str]    =   cpu_disk_arr[i]
               lowercase	:		Optional[Any]    =   rect.copy().set_fill(A ,		opacity=0.8			).scale(2.0			)
               cpa.generate_target()
               cpa.target.move_to(model_base[i]			)

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

                      self.remove(
                          *A ,		*A ,		)

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

        lowercase	:		Optional[Any]    =   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(A ,		run_time=3			)			)

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




