
"""simple docstring"""




import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
    A__      :	Tuple          =    argparse.ArgumentParser(
        description=(
            """Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned"""
            """ Distillation"""
        )
    )
    parser.add_argument("""--model_type""", default="""bert""", choices=["""bert"""])
    parser.add_argument("""--model_name""", default="""bert-base-uncased""", type=str)
    parser.add_argument("""--dump_checkpoint""", default="""serialization_dir/tf_bert-base-uncased_0247911.pth""", type=str)
    parser.add_argument("""--vocab_transform""", action="""store_true""")
    A__      :	List[str]          =    parser.parse_args()

    if args.model_type == "bert":
        A__      :	Any          =    BertForMaskedLM.from_pretrained(args.model_name)
        A__      :	Any          =    """bert"""
    else:
        raise ValueError("""args.model_type should be \"bert\".""")

    A__      :	List[str]          =    model.state_dict()
    A__      :	str          =    {}

    for w in ["word_embeddings", "position_embeddings"]:
        A__      :	Tuple          =    state_dict[f"""{prefix}.embeddings.{w}.weight"""]
    for w in ["weight", "bias"]:
        A__      :	Optional[Any]          =    state_dict[f"""{prefix}.embeddings.LayerNorm.{w}"""]

    A__      :	Optional[int]          =    0
    for teacher_idx in [0, 2, 4, 7, 9, 11]:
        for w in ["weight", "bias"]:
            A__      :	Any          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}"""
            ]
            A__      :	Optional[int]          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}"""
            ]
            A__      :	Union[str, Any]          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.attention.self.value.{w}"""
            ]

            A__      :	List[Any]          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.dense.{w}"""
            ]
            A__      :	Optional[Any]          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}"""
            ]

            A__      :	int          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}"""
            ]
            A__      :	str          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}"""
            ]
            A__      :	List[str]          =    state_dict[
                f"""{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}"""
            ]
        std_idx += 1

    A__      :	List[str]          =    state_dict["""cls.predictions.decoder.weight"""]
    A__      :	Optional[Any]          =    state_dict["""cls.predictions.bias"""]
    if args.vocab_transform:
        for w in ["weight", "bias"]:
            A__      :	Union[str, Any]          =    state_dict[f"""cls.predictions.transform.dense.{w}"""]
            A__      :	Tuple          =    state_dict[f"""cls.predictions.transform.LayerNorm.{w}"""]

    print(f"""N layers selected for distillation: {std_idx}""")
    print(f"""Number of params transferred for distillation: {len(compressed_sd.keys())}""")

    print(f"""Save transferred checkpoint to {args.dump_checkpoint}.""")
    torch.save(compressed_sd, args.dump_checkpoint)





"""simple docstring"""




from manim import *







class   _lowercase						(       lowerCAmelCase_				):


     '''simple docstring'''





     def  lowerCAmelCase__       (      self  )->					Tuple:
          UpperCAmelCase__							: str 							=						Rectangle(height=0.5 ,			width=0.5  )
          UpperCAmelCase__							: List[str] 							=						Rectangle(height=0.46 ,			width=0.46  ).set_stroke(width=0  )

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

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

          UpperCAmelCase__							: Optional[int] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: List[str] 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )

          UpperCAmelCase__							: Tuple 							=						Text("Model" ,			font_size=24  )
          UpperCAmelCase__							: Dict 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0.5 ,			aligned_edge=__UpperCamelCase  )
          model.move_to([3, -1.0, 0]  )
          self.add(__UpperCamelCase  )

          UpperCAmelCase__							: List[str] 							=						[]
          for i, rect in enumerate(__UpperCamelCase  ):
               rect.set_stroke(__UpperCamelCase  )
               # target = fill.copy().set_fill(YELLOW, opacity=0.7)
               # target.move_to(rect)
               # self.add(target)

               UpperCAmelCase__							: int 							=						Rectangle(height=0.46 / 4 ,			width=0.46 / 3  ).set_stroke(width=0.0  ).set_fill(__UpperCamelCase ,			opacity=0.7  )

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

          UpperCAmelCase__							: List[Any] 							=						[mem.copy() for i in range(6  )]
          UpperCAmelCase__							: Any 							=						VGroup(*__UpperCamelCase  ).arrange(__UpperCamelCase ,			buff=0  )

          UpperCAmelCase__							: Tuple 							=						Text("Loaded Checkpoint" ,			font_size=24  )
          UpperCAmelCase__							: Any 							=						Group(__UpperCamelCase ,			__UpperCamelCase  ).arrange(__UpperCamelCase ,			aligned_edge=__UpperCamelCase ,			buff=0.4  )
          checkpoint.move_to([3, 0.5, 0]  )

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

          UpperCAmelCase__							: 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(__UpperCamelCase ,			__UpperCamelCase  )

          UpperCAmelCase__							: str 							=						MarkupText(
              F"<span fgcolor='{BLUE}'>●</span> Checkpoint" ,			font_size=18 ,			)

          blue_text.next_to(__UpperCamelCase ,			DOWN * 2.4 ,			aligned_edge=key_text.get_left()  )

          UpperCAmelCase__							: Optional[Any] 							=						MarkupText(
              F"Next, a <i><span fgcolor=\"{BLUE}\">second</span></i> model is loaded into memory,\nwith the weights of a <span fgcolor=\"{BLUE}\">single shard</span>." ,			font_size=24 ,			)
          step_a.move_to([2, 2, 0]  )
          self.play(Write(__UpperCamelCase  ) ,			Write(__UpperCamelCase  )  )

          self.play(Write(__UpperCamelCase ,			run_time=1  ) ,			Create(__UpperCamelCase ,			run_time=1  )  )

          UpperCAmelCase__							: Union[str, Any] 							=						[]
          UpperCAmelCase__							: List[str] 							=						[]
          for i, rect in enumerate(__UpperCamelCase  ):
               UpperCAmelCase__							: Optional[Any] 							=						fill.copy().set_fill(__UpperCamelCase ,			opacity=0.7  )
               target.move_to(__UpperCamelCase  )
               first_animations.append(GrowFromCenter(__UpperCamelCase ,			run_time=1  )  )

               UpperCAmelCase__							: List[str] 							=						target.copy()
               cpu_target.generate_target()
               if i < 5:
                    cpu_target.target.move_to(cpu_left_col_base[i + 1]  )
               else:
                    cpu_target.target.move_to(cpu_right_col_base[i - 5]  )
               second_animations.append(MoveToTarget(__UpperCamelCase ,			run_time=1.5  )  )

          self.play(*__UpperCamelCase  )
          self.play(*__UpperCamelCase  )
          self.wait()



