
"""simple docstring"""




import warnings
from pathlib import Path
from typing import List, Tuple, Union

import fire
from torch import nn

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer, PreTrainedModel
from transformers.utils import logging


A__      :	Tuple          =    logging.get_logger(__name__)



def 							a__ (				lowerCAmelCase	:       nn.ModuleList       ,    lowerCAmelCase	:       nn.ModuleList       ,    lowerCAmelCase	:       List[int]		):






					'''simple docstring'''
					UpperCAmelCase__							: Optional[Any] 							=						nn.ModuleList([src_layers[i] for i in layers_to_copy]		)
					assert len(lowerCAmelCase		) == len(lowerCAmelCase		), F"{len(lowerCAmelCase		)} != {len(lowerCAmelCase		)}"
					dest_layers.load_state_dict(layers_to_copy.state_dict()		)


A__      :	List[Any]          =    {
    # maps  num layers in teacher -> num_layers in student -> which teacher layers to copy.
    # 12: bart, 16: pegasus, 6: marian/Helsinki-NLP
    12: {
        1: [0],  # This says that if the teacher has 12 layers and the student has 1, copy layer 0 of the teacher
        2: [0, 6],
        3: [0, 6, 11],
        4: [0, 4, 8, 11],
        6: [0, 2, 4, 7, 9, 11],
        9: [0, 1, 2, 4, 5, 7, 9, 10, 11],
        12: list(range(12)),
    },
    16: {  # maps  num layers in student -> which teacher layers to copy
        1: [0],
        2: [0, 15],
        3: [0, 8, 15],
        4: [0, 5, 10, 15],
        6: [0, 3, 6, 9, 12, 15],
        8: [0, 2, 4, 6, 8, 10, 12, 15],
        9: [0, 1, 3, 5, 7, 9, 11, 13, 15],
        12: [0, 1, 2, 3, 4, 5, 6, 7, 9, 11, 13, 15],
        16: list(range(16)),
    },
    6: {1: [0], 2: [0, 5], 3: [0, 2, 5], 4: [0, 1, 3, 5], 6: list(range(6))},
}
A__      :	List[Any]          =    {
    # maps  num layers in student -> which teacher layers to copy.
    6: {1: [5], 2: [3, 5], 3: [1, 4, 5], 4: [1, 2, 4, 5]},
    12: {1: [11], 2: [5, 11], 3: [3, 7, 11], 6: [1, 3, 5, 8, 10, 11]},
    16: {1: [15], 4: [4, 9, 12, 15], 8: [1, 3, 5, 7, 9, 11, 13, 15]},
}



def 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Dict		):






					'''simple docstring'''
					try:
										UpperCAmelCase__							: Tuple 							=						LAYERS_TO_COPY[n_teacher][n_student]
										return val
					except KeyError:
										if n_student != n_teacher:
															warnings.warn(
															    F"no hardcoded layers to copy for teacher {n_teacher} -> student {n_student}, defaulting to first"
															    F" {n_student}"		)
										return list(range(lowerCAmelCase		)		)



def 							a__ (				lowerCAmelCase	:       int       ,    lowerCAmelCase	:       Tuple		):






					'''simple docstring'''
					if n_student > n_teacher:
										raise ValueError(F"Cannot perform intermediate supervision for student {n_student} > teacher {n_teacher}"		)
					elif n_teacher == n_student:
										return list(range(lowerCAmelCase		)		)
					elif n_student == 1:
										return [n_teacher - 1]
					else:
										return LAYERS_TO_SUPERVISE[n_teacher][n_student]



def 							a__ (				lowerCAmelCase	:       Union[str, PreTrainedModel]       ,    lowerCAmelCase	:       Union[str, Path] = "student"       ,    lowerCAmelCase	:       Union[int, None] = None       ,    lowerCAmelCase	:       Union[int, None] = None       ,    lowerCAmelCase	:       List[str]=False       ,    lowerCAmelCase	:       List[str]=None       ,    lowerCAmelCase	:       List[str]=None       ,    **lowerCAmelCase	:       List[str]       ,    ):






					'''simple docstring'''
					UpperCAmelCase__							: List[str] 							=						"encoder_layers and decoder_layers cannot be both None-- you would just have an identical teacher."
					assert (e is not None) or (d is not None), _msg
					if isinstance(lowerCAmelCase       ,    lowerCAmelCase		):
										AutoTokenizer.from_pretrained(lowerCAmelCase		).save_pretrained(lowerCAmelCase		)  # purely for convenience
										UpperCAmelCase__							: List[str] 							=						AutoModelForSeqaSeqLM.from_pretrained(lowerCAmelCase		).eval()
					else:
										assert isinstance(lowerCAmelCase       ,    lowerCAmelCase		), F"teacher must be a model or string got type {type(lowerCAmelCase		)}"
					UpperCAmelCase__							: int 							=						teacher.config.to_diff_dict()

					try:
										UpperCAmelCase__							: Optional[Any] 							=						teacher.config.encoder_layers, teacher.config.decoder_layers
										if e is None:
															UpperCAmelCase__							: Tuple 							=						teacher_e
										if d is None:
															UpperCAmelCase__							: str 							=						teacher_d
										init_kwargs.update({"encoder_layers": e, "decoder_layers": d}		)
					except AttributeError:  # T5
										if hasattr(teacher.config       ,    "num_encoder_layers"		):
															UpperCAmelCase__							: Union[str, Any] 							=						teacher.config.num_encoder_layers, teacher.config.num_decoder_layers
										else:
															UpperCAmelCase__							: Union[str, Any] 							=						teacher.config.num_layers, teacher.config.num_decoder_layers
										if e is None:
															UpperCAmelCase__							: Optional[Any] 							=						teacher_e
										if d is None:
															UpperCAmelCase__							: Optional[Any] 							=						teacher_d
										if hasattr(teacher.config       ,    "num_encoder_layers"		):
															init_kwargs.update({"num_encoder_layers": e, "num_decoder_layers": d}		)
										else:
															init_kwargs.update({"num_layers": e, "num_decoder_layers": d}		)

    # Kwargs to instantiate student: teacher kwargs with updated layer numbers + **extra_config_kwargs
					init_kwargs.update(lowerCAmelCase		)

					# Copy weights
					UpperCAmelCase__							: Tuple 							=						teacher.config_class(**lowerCAmelCase		)
					UpperCAmelCase__							: List[str] 							=						AutoModelForSeqaSeqLM.from_config(lowerCAmelCase		)
					# Start by copying the full teacher state dict this will copy the first N teacher layers to the student.
					UpperCAmelCase__							: Optional[int] 							=						student.load_state_dict(teacher.state_dict()       ,    strict=lowerCAmelCase		)
					assert info.missing_keys == [], info.missing_keys  # every student key should have a teacher keys.

					if copy_first_teacher_layers:  # Our copying is done. We just log and save
										UpperCAmelCase__							: int 							=						list(range(lowerCAmelCase		)		), list(range(lowerCAmelCase		)		)
										logger.info(
										    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to"
										    F" {save_path}"		)
										student.save_pretrained(lowerCAmelCase		)
										return student, e_layers_to_copy, d_layers_to_copy

					# Decide which layers of the teacher to copy. Not exactly alternating -- we try to keep first and last layer.
					if e_layers_to_copy is None:
										UpperCAmelCase__							: List[int] 							=						pick_layers_to_copy(lowerCAmelCase       ,    lowerCAmelCase		)
					if d_layers_to_copy is None:
										UpperCAmelCase__							: List[int] 							=						pick_layers_to_copy(lowerCAmelCase       ,    lowerCAmelCase		)

					try:
										if hasattr(
										    lowerCAmelCase       ,    "prophetnet"		):  # For ProphetNet, student.model.encoder.layers is called student.prophetnet.encoder.layers
															copy_layers(teacher.prophetnet.encoder.layers       ,    student.prophetnet.encoder.layers       ,    lowerCAmelCase		)
															copy_layers(teacher.prophetnet.decoder.layers       ,    student.prophetnet.decoder.layers       ,    lowerCAmelCase		)
										else:
															copy_layers(teacher.model.encoder.layers       ,    student.model.encoder.layers       ,    lowerCAmelCase		)
															copy_layers(teacher.model.decoder.layers       ,    student.model.decoder.layers       ,    lowerCAmelCase		)
					except AttributeError:  # For t5, student.model.encoder.layers is called student.encoder.block
										copy_layers(teacher.encoder.block       ,    student.encoder.block       ,    lowerCAmelCase		)
										copy_layers(teacher.decoder.block       ,    student.decoder.block       ,    lowerCAmelCase		)
					logger.info(
					    F"Copied encoder layers {e_layers_to_copy} and decoder layers {d_layers_to_copy}. Saving them to {save_path}"		)
					UpperCAmelCase__							: int 							=						{
					    "teacher_type": teacher.config.model_type,
					    "copied_encoder_layers": e_layers_to_copy,
					    "copied_decoder_layers": d_layers_to_copy,
					}
					student.save_pretrained(lowerCAmelCase		)
					# Save information about copying for easier reproducibility

					return student, e_layers_to_copy, d_layers_to_copy


if __name__ == "__main__":
				fire.Fire(create_student_by_copying_alternating_layers)





"""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()



