"""simple docstring"""




from manim import *
class 						__A							(			A__		):






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					Rectangle(height=0.5    ,					width=0.5  )
       _lowerCAmelCase						:	Any        =					Rectangle(height=0.4_6    ,					width=0.4_6  ).set_stroke(width=0  )

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

       _lowerCAmelCase						:	str        =					[mem.copy() for i in range(1  )]
       _lowerCAmelCase						:	Union[str, Any]        =					VGroup(*lowerCamelCase__  ).arrange(lowerCamelCase__    ,					buff=0  )
       _lowerCAmelCase						:	Union[str, Any]        =					Text("""GPU"""    ,					font_size=24  )
       _lowerCAmelCase						:	Optional[int]        =					Group(lowerCamelCase__    ,					lowerCamelCase__  ).arrange(lowerCamelCase__    ,					buff=0.5    ,					aligned_edge=lowerCamelCase__  )
       gpu.align_to(lowerCamelCase__    ,					lowerCamelCase__  )
       gpu.set_x(gpu.get_x() - 1  )

       self.add(lowerCamelCase__  )

       _lowerCAmelCase						:	Dict        =					[mem.copy() for i in range(6  )]
       _lowerCAmelCase						:	Dict        =					VGroup(*lowerCamelCase__  ).arrange(lowerCamelCase__    ,					buff=0  )

       _lowerCAmelCase						:	Optional[Any]        =					Text("""Model"""    ,					font_size=24  )
       _lowerCAmelCase						:	Optional[int]        =					Group(lowerCamelCase__    ,					lowerCamelCase__  ).arrange(lowerCamelCase__    ,					buff=0.5    ,					aligned_edge=lowerCamelCase__  )
       model.move_to([3, -1.0, 0]  )

       self.play(
           Create(lowerCamelCase__    ,					run_time=1  )    ,					Create(lowerCamelCase__    ,					run_time=1  )    ,					Create(lowerCamelCase__    ,					run_time=1  )    ,					)

       _lowerCAmelCase						:	Optional[Any]        =					MarkupText(
           F"First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."    ,					font_size=24    ,					)

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

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

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

       step_a.move_to([2, 2, 0]  )
       self.play(Write(lowerCamelCase__    ,					run_time=2.5  )    ,					Write(lowerCamelCase__  )    ,					Write(lowerCamelCase__  )  )

       self.add(lowerCamelCase__  )

       _lowerCAmelCase						:	Optional[Any]        =					[]
       _lowerCAmelCase						:	int        =					[]
       _lowerCAmelCase						:	List[str]        =					[]
       for i, rect in enumerate(lowerCamelCase__  ):
         _lowerCAmelCase						:	Dict        =					Rectangle(height=0.4_6    ,					width=0.4_6  ).set_stroke(width=0.0  ).set_fill(lowerCamelCase__    ,					opacity=0.7  )
         cpu_target.move_to(lowerCamelCase__  )
         cpu_target.generate_target()
         _lowerCAmelCase						:	List[str]        =					0.4_6 / 4
         _lowerCAmelCase						:	Union[str, Any]        =					0.4_6 / 3

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

         first_animations.append(rect.animate(run_time=0.5  ).set_stroke(lowerCamelCase__  )  )
         second_animations.append(MoveToTarget(lowerCamelCase__    ,					run_time=1.5  )  )

       self.play(*lowerCamelCase__  )
       self.play(*lowerCamelCase__  )

       self.wait()





"""simple docstring"""




import argparse

from tax import checkpoints

from transformers import AutoConfig, FlaxAutoModelForSeqaSeqLM





def 							SCREAMING_SNAKE_CASE					(      _lowerCamelCase						:	List[str]      ,_lowerCamelCase						:	Any      ,_lowerCamelCase						:	Optional[Any]     )	->				str:
  _lowerCAmelCase						:	str        =					AutoConfig.from_pretrained(_lowerCamelCase     )
  _lowerCAmelCase						:	int        =					FlaxAutoModelForSeqaSeqLM.from_config(config=_lowerCamelCase     )
  _lowerCAmelCase						:	Any        =					checkpoints.load_tax_checkpoint(_lowerCamelCase     )

  _lowerCAmelCase						:	Tuple        =					"""wi_0""" in tax_model["""target"""]["""encoder"""]["""layers_0"""]["""mlp"""]

  if config.model_type == "t5":
    _lowerCAmelCase						:	Tuple        =					"""SelfAttention"""
  if config.model_type == "longt5" and config.encoder_attention_type == "local":
    _lowerCAmelCase						:	Optional[Any]        =					"""LocalSelfAttention"""
  elif config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	Union[str, Any]        =					"""TransientGlobalSelfAttention"""
  else:
    raise ValueError(
        """Given config is expected to have `model_type='t5'`, or `model_type='longt5` with `encoder_attention_type`"""
        """ attribute with a value from ['local', 'transient-global]."""     )

  # Encoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Tuple        =					f"layers_{str(_lowerCamelCase     )}"

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

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Dict        =					tax_model["""target"""]["""encoder"""][layer_name]["""attention"""]["""T5LayerNorm_0"""]["""scale"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_attention_layer_norm"""]["""scale"""]

    if split_mlp_wi:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_0"""]["""kernel"""]
      _lowerCAmelCase						:	int        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi_1"""]["""kernel"""]
    else:
      _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wi"""]["""kernel"""]

    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""encoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	Any        =					flax_model.params["""encoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	Any        =					tax_attention_key
    _lowerCAmelCase						:	str        =					tax_attention_out
    _lowerCAmelCase						:	Union[str, Any]        =					tax_attention_query
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_value

    _lowerCAmelCase						:	List[str]        =					tax_attention_layer_norm

    # Global input layer norm
    if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
      _lowerCAmelCase						:	Any        =					tax_global_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	List[Any]        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	List[str]        =					tax_mlp_wi

    _lowerCAmelCase						:	str        =					tax_mlp_wo
    _lowerCAmelCase						:	Optional[Any]        =					tax_mlp_layer_norm

    _lowerCAmelCase						:	Any        =					flax_model_encoder_layer_block

  # Only for layer 0:
  _lowerCAmelCase						:	Union[str, Any]        =					tax_model["""target"""]["""encoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Optional[Any]        =					tax_encoder_rel_embedding

  # Side/global relative position_bias + layer norm
  if config.model_type == "longt5" and config.encoder_attention_type == "transient-global":
    _lowerCAmelCase						:	List[str]        =					tax_model["""target"""]["""encoder"""]["""side_relpos_bias"""]["""rel_embedding"""].T
    _lowerCAmelCase						:	Optional[int]        =					tax_encoder_global_rel_embedding

  # Assigning
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""encoder"""]["""encoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	Any        =					tax_encoder_norm

  # Decoder
  for layer_index in range(config.num_layers     ):
    _lowerCAmelCase						:	Optional[int]        =					f"layers_{str(_lowerCamelCase     )}"

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

    # Layer Normalization
    _lowerCAmelCase						:	Optional[Any]        =					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"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""key"""]["""kernel"""]
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_module["""out"""]["""kernel"""]
    _lowerCAmelCase						:	List[str]        =					tax_enc_dec_attention_module["""query"""]["""kernel"""]
    _lowerCAmelCase						:	Dict        =					tax_enc_dec_attention_module["""value"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_cross_attention_layer_norm"""]["""scale"""]

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

    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""mlp"""]["""wo"""]["""kernel"""]

    # Layer Normalization
    _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""][layer_name]["""pre_mlp_layer_norm"""]["""scale"""]

    # Assigning
    _lowerCAmelCase						:	str        =					flax_model.params["""decoder"""]["""block"""][str(_lowerCamelCase     )]["""layer"""]
    _lowerCAmelCase						:	int        =					tax_attention_key
    _lowerCAmelCase						:	List[str]        =					tax_attention_out
    _lowerCAmelCase						:	Optional[Any]        =					tax_attention_query
    _lowerCAmelCase						:	Dict        =					tax_attention_value

    _lowerCAmelCase						:	str        =					tax_pre_attention_layer_norm

    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_key
    _lowerCAmelCase						:	List[Any]        =					tax_enc_dec_attention_out
    _lowerCAmelCase						:	Tuple        =					tax_enc_dec_attention_query
    _lowerCAmelCase						:	Any        =					tax_enc_dec_attention_value

    _lowerCAmelCase						:	Dict        =					tax_cross_layer_norm

    if split_mlp_wi:
      _lowerCAmelCase						:	Dict        =					tax_mlp_wi_a
      _lowerCAmelCase						:	int        =					tax_mlp_wi_a
    else:
      _lowerCAmelCase						:	Optional[int]        =					tax_mlp_wi

    _lowerCAmelCase						:	Dict        =					tax_mlp_wo

    _lowerCAmelCase						:	List[Any]        =					txa_mlp_layer_norm

    _lowerCAmelCase						:	Optional[Any]        =					flax_model_decoder_layer_block

  # Decoder Normalization
  _lowerCAmelCase						:	Any        =					tax_model["""target"""]["""decoder"""]["""decoder_norm"""]["""scale"""]
  _lowerCAmelCase						:	List[str]        =					txa_decoder_norm

  # Only for layer 0:
  _lowerCAmelCase						:	Optional[int]        =					tax_model["""target"""]["""decoder"""]["""relpos_bias"""]["""rel_embedding"""].T
  _lowerCAmelCase						:	Union[str, Any]        =					tax_decoder_rel_embedding

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

  # LM Head (only in v1.1 and LongT5 checkpoints)
  if "logits_dense" in tax_model["target"]["decoder"]:
    _lowerCAmelCase						:	Tuple        =					tax_model["""target"""]["""decoder"""]["""logits_dense"""]["""kernel"""]

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


if __name__ == "__main__":
     _a				:		Any	   =       argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         '--t5x_checkpoint_path', default=None, type=str, required=True, help='Path the T5X checkpoint.'
     )
     parser.add_argument('--config_name', default=None, type=str, required=True, help='Config name of LongT5/T5 model.')
     parser.add_argument(
         '--flax_dump_folder_path', default=None, type=str, required=True, help='Path to the output FLAX model.'
     )
     _a				:		List[str]	   =       parser.parse_args()
     convert_tax_checkpoint_to_flax(args.tax_checkpoint_path, args.config_name, args.flax_dump_folder_path)




