


"""simple docstring"""


from manim import *

class 				_SCREAMING_SNAKE_CASE				(							__UpperCAmelCase  ):
   """simple docstring"""



   def 					UpperCAmelCase__(						self						)      ->							Dict:
    lowercase__       :       Optional[int]				=						Rectangle(height=0.5 ,     width=0.5						)
    lowercase__       :       List[Any]				=						Rectangle(height=0.25 ,     width=0.25						)
    lowercase__       :       int				=						Rectangle(height=0.46 ,     width=0.46						).set_stroke(width=0						)

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

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

    lowercase__       :       Union[str, Any]				=						[mem.copy() for i in range(6						)]
    lowercase__       :       Tuple				=						VGroup(*lowerCamelCase__						).arrange(lowerCamelCase__ ,     buff=0						)

    lowercase__       :       str				=						Text("""Model""" ,     font_size=24						)
    lowercase__       :       str				=						Group(lowerCamelCase__ ,     lowerCamelCase__						).arrange(lowerCamelCase__ ,     buff=0.5 ,     aligned_edge=lowerCamelCase__						)
    model.move_to([3, -1.0, 0]						)
    self.add(lowerCamelCase__						)

    lowercase__       :       str				=						[]
    lowercase__       :       Optional[int]				=						[]
    lowercase__       :       Any				=						[]

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

     lowercase__       :       List[Any]				=						Rectangle(height=0.46 / 4 ,     width=0.46 / 3						).set_stroke(width=0.0						).set_fill(lowerCamelCase__ ,     opacity=0.7						)

     if i == 0:
      cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT						) ,     buff=0.02 ,     direction=lowerCamelCase__						)
      cpu_target.set_x(cpu_target.get_x() + 0.1						)
     elif i == 3:
      cpu_target.next_to(model_cpu_arr[0] ,     direction=lowerCamelCase__ ,     buff=0.0						)
     else:
      cpu_target.next_to(model_cpu_arr[i - 1] ,     direction=lowerCamelCase__ ,     buff=0.0						)
     self.add(lowerCamelCase__						)
     model_cpu_arr.append(lowerCamelCase__						)

    self.add(*lowerCamelCase__ ,     *lowerCamelCase__ ,     *lowerCamelCase__						)

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

    lowercase__       :       Optional[int]				=						Text("""Loaded Checkpoint""" ,     font_size=24						)
    lowercase__       :       Any				=						Group(lowerCamelCase__ ,     lowerCamelCase__						).arrange(lowerCamelCase__ ,     buff=0.5 ,     aligned_edge=lowerCamelCase__						)
    checkpoint.move_to([3, 0.5, 0]						)

    self.add(lowerCamelCase__						)

    lowercase__       :       List[Any]				=						[]
    lowercase__       :       Optional[int]				=						[]

    for i, rect in enumerate(lowerCamelCase__						):
     lowercase__       :       int				=						fill.copy().set_fill(lowerCamelCase__ ,     opacity=0.7						)
     target.move_to(lowerCamelCase__						)
     ckpt_arr.append(lowerCamelCase__						)

     lowercase__       :       Union[str, Any]				=						target.copy()
     if i < 5:
      cpu_target.move_to(cpu_left_col_base[i + 1]						)
     else:
      cpu_target.move_to(cpu_right_col_base[i - 5]						)
     ckpt_cpu_arr.append(lowerCamelCase__						)
    self.add(*lowerCamelCase__ ,     *lowerCamelCase__						)

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

    lowercase__       :       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(lowerCamelCase__ ,     lowerCamelCase__						)

    lowercase__       :       Optional[int]				=						MarkupText(
        F'''<span fgcolor=\'{BLUE}\'>●</span> Checkpoint''' ,     font_size=18 ,     )

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

    lowercase__       :       Tuple				=						MarkupText(
        F'''Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.''' ,     font_size=24 ,     )
    step_a.move_to([2, 2, 0]						)

    lowercase__       :       Dict				=						[meta_mem.copy() for i in range(6						)]
    lowercase__       :       Union[str, Any]				=						[meta_mem.copy() for i in range(6						)]
    lowercase__       :       Optional[Any]				=						VGroup(*lowerCamelCase__						).arrange(lowerCamelCase__ ,     buff=0						)
    lowercase__       :       Optional[int]				=						VGroup(*lowerCamelCase__						).arrange(lowerCamelCase__ ,     buff=0						)
    lowercase__       :       Optional[Any]				=						VGroup(lowerCamelCase__ ,     lowerCamelCase__						).arrange(lowerCamelCase__ ,     buff=0						)
    lowercase__       :       Optional[int]				=						Text("""Disk""" ,     font_size=24						)
    lowercase__       :       List[str]				=						Group(lowerCamelCase__ ,     lowerCamelCase__						).arrange(lowerCamelCase__ ,     buff=0.5 ,     aligned_edge=lowerCamelCase__						)
    disk.move_to([-4.0, -1.25, 0]						)
    self.play(Write(lowerCamelCase__ ,     run_time=3						) ,     Write(lowerCamelCase__ ,     run_time=1						) ,     Create(lowerCamelCase__ ,     run_time=1						)						)

    lowercase__       :       Any				=						[]
    for i, rect in enumerate(lowerCamelCase__						):
     lowercase__       :       Any				=						rect.copy()
     target.generate_target()
     target.target.move_to(disk_left_col_base[i]						).scale(0.5						)
     animations.append(MoveToTarget(lowerCamelCase__ ,     run_time=1.5						)						)
    self.play(*lowerCamelCase__						)

    self.play(FadeOut(lowerCamelCase__						)						)

    lowercase__       :       Optional[Any]				=						MarkupText(F'''Then, the checkpoint is removed from memory\nthrough garbage collection.''' ,     font_size=24						)
    step_a.move_to([2, 2, 0]						)

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

    self.play(
        FadeOut(lowerCamelCase__ ,     lowerCamelCase__ ,     *lowerCamelCase__ ,     *lowerCamelCase__						) ,     )

    self.wait()



"""simple docstring"""


import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration


def    _lowerCamelCase   (     lowerCamelCase__	:							List[Any]       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							str       ,		lowerCamelCase__	:							Path       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		lowerCamelCase__	:							str = None       ,		):
 if config_name_or_path is None:
  lowercase__       :       Tuple				=						"""facebook/rag-token-base""" if model_type == """rag_token""" else """facebook/rag-sequence-base"""

 if generator_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  lowercase__       :       List[Any]				=						question_encoder_name_or_path

 lowercase__       :       Any				=						RagTokenForGeneration if model_type == """rag_token""" else RagSequenceForGeneration

 # Save model.
 lowercase__       :       Union[str, Any]				=						RagConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       List[Any]				=						AutoConfig.from_pretrained(lowerCamelCase__  )
 lowercase__       :       Dict				=						AutoConfig.from_pretrained(lowerCamelCase__  )

 lowercase__       :       Union[str, Any]				=						gen_config
 lowercase__       :       Any				=						question_encoder_config

 lowercase__       :       str				=						model_class.from_pretrained_question_encoder_generator(
     lowerCamelCase__       ,		lowerCamelCase__       ,		config=lowerCamelCase__  )
 rag_model.save_pretrained(lowerCamelCase__  )

 # Sanity check.
 model_class.from_pretrained(lowerCamelCase__  )

 # Save tokenizers.
 lowercase__       :       Optional[Any]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 gen_tokenizer.save_pretrained(dest_dir / """generator_tokenizer/"""  )
 lowercase__       :       Optional[int]				=						AutoTokenizer.from_pretrained(lowerCamelCase__  )
 question_encoder_tokenizer.save_pretrained(dest_dir / """question_encoder_tokenizer/"""  )


if __name__ == "__main__":
   __snake_case    					=					argparse.ArgumentParser()
   parser.add_argument(
       '--model_type',
       choices=['rag_sequence', 'rag_token'],
       required=True,
       type=str,
       help='RAG model type: rag_sequence, rag_token',
   )
   parser.add_argument('--dest', type=str, required=True, help='Path to the output checkpoint directory.')
   parser.add_argument('--generator_name_or_path', type=str, required=True, help='Generator model identifier')
   parser.add_argument(
       '--question_encoder_name_or_path', type=str, required=True, help='Question encoder model identifier'
   )

   parser.add_argument(
       '--generator_tokenizer_name_or_path',
       type=str,
       help='Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``',
   )
   parser.add_argument(
       '--question_encoder_tokenizer_name_or_path',
       type=str,
       help='Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``',
   )
   parser.add_argument(
       '--config_name_or_path',
       type=str,
       help=(
           'Identifier of the model config to use, if not provided, resolves to a base config for a given'
           ' ``model_type``'
       ),
   )

   __snake_case    					=					parser.parse_args()

   __snake_case    					=					Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )