


"""simple docstring"""


import argparse

import torch

from transformers import BertForMaskedLM


if __name__ == "__main__":
	A  :     List[str]    					=    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  :     int    					=    parser.parse_args()

	if args.model_type == "bert":
		A  :     int    					=    BertForMaskedLM.from_pretrained(args.model_name)
		A  :     List[str]    					=    "bert"
	else:
		raise ValueError("args.model_type should be \"bert\".")

	A  :     int    					=    model.state_dict()
	A  :     Optional[int]    					=    {}

	for w in ["word_embeddings", "position_embeddings"]:
		A  :     Dict    					=    state_dict[f'''{prefix}.embeddings.{w}.weight''']
	for w in ["weight", "bias"]:
		A  :     Any    					=    state_dict[f'''{prefix}.embeddings.LayerNorm.{w}''']

	A  :     Optional[Any]    					=    0
	for teacher_idx in [0, 2, 4, 7, 9, 1_1]:
		for w in ["weight", "bias"]:
			A  :     Any    					=    state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.query.{w}'''
			]
			A  :     Any    					=    state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.self.key.{w}'''
			]
			A  :     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  :     str    					=    state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.attention.output.LayerNorm.{w}'''
			]

			A  :     List[Any]    					=    state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.intermediate.dense.{w}'''
			]
			A  :     Optional[int]    					=    state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.output.dense.{w}'''
			]
			A  :     Optional[Any]    					=    state_dict[
			    f'''{prefix}.encoder.layer.{teacher_idx}.output.LayerNorm.{w}'''
			]
		std_idx += 1

	A  :     Optional[Any]    					=    state_dict["cls.predictions.decoder.weight"]
	A  :     Optional[int]    					=    state_dict["cls.predictions.bias"]
	if args.vocab_transform:
		for w in ["weight", "bias"]:
			A  :     List[str]    					=    state_dict[f'''cls.predictions.transform.dense.{w}''']
			A  :     Optional[int]    					=    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 						_UpperCamelCase						(     lowerCAmelCase__	):
							'''simple docstring'''







							def     snake_case       (  self			):
										__lowerCAmelCase       					=    Rectangle(height=0.5					,     width=0.5			)
										__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0			)

										__lowerCAmelCase       					=    Rectangle(height=0.2_5					,     width=0.2_5			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(__a					,     __a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("CPU"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										cpu.move_to([-2.5, -0.5, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(4			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("GPU"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										gpu.move_to([-1, -1, 0]			)
										self.add(__a			)

										__lowerCAmelCase       					=    [mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)

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

										__lowerCAmelCase       					=    []
										__lowerCAmelCase       					=    []

										for i, rect in enumerate(__a			):
													__lowerCAmelCase       					=    fill.copy().set_fill(__a					,     opacity=0.8			)
													target.move_to(__a			)
													model_arr.append(__a			)

													__lowerCAmelCase       					=    Rectangle(height=0.4_6					,     width=0.4_6			).set_stroke(width=0.0			).set_fill(__a					,     opacity=0.8			)
													cpu_target.move_to(cpu_left_col_base[i]			)
													model_cpu_arr.append(__a			)

										self.add(*__a					,     *__a			)

										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    [meta_mem.copy() for i in range(6			)]
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(*__a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    VGroup(__a					,     __a			).arrange(__a					,     buff=0			)
										__lowerCAmelCase       					=    Text("Disk"					,     font_size=24			)
										__lowerCAmelCase       					=    Group(__a					,     __a			).arrange(__a					,     buff=0.5					,     aligned_edge=__a			)
										disk.move_to([-4, -1.2_5, 0]			)
										self.add(__a					,     __a			)

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

										__lowerCAmelCase       					=    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			)

										__lowerCAmelCase       					=    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			)

										__lowerCAmelCase       					=    MarkupText(
										    f"Now watch as an input is passed through the model\nand how the memory is utilized and handled."					,     font_size=24					,     )
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a			)			)

										__lowerCAmelCase       					=    Square(0.3			)
										input.set_fill(__a					,     opacity=1.0			)
										input.set_stroke(width=0.0			)
										input.next_to(model_base[0]					,     __a					,     buff=0.5			)

										self.play(Write(__a			)			)

										input.generate_target()
										input.target.next_to(model_arr[0]					,     direction=__a					,     buff=0.0_2			)
										self.play(MoveToTarget(__a			)			)

										self.play(FadeOut(__a			)			)

										__lowerCAmelCase       					=    Arrow(start=__a					,     end=__a					,     color=__a					,     buff=0.5			)
										a.next_to(model_arr[0].get_left()					,     __a					,     buff=0.2			)

										model_cpu_arr[0].generate_target()
										model_cpu_arr[0].target.move_to(gpu_rect[0]			)

										__lowerCAmelCase       					=    MarkupText(
										    f"As the input reaches a layer, the hook triggers\nand weights are moved from the CPU\nto the GPU and back."					,     font_size=24					,     )
										step_a.move_to([2, 2, 0]			)

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

										__lowerCAmelCase       					=    {"run_time": 1, "fade_in": True, "fade_out": True, "buff": 0.0_2}

										self.play(
										    Write(__a			)					,     Circumscribe(model_arr[0]					,     color=__a					,     **__a			)					,     Circumscribe(model_cpu_arr[0]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     )
										self.play(MoveToTarget(model_cpu_arr[0]			)			)

										__lowerCAmelCase       					=    a.copy()
										for i in range(6			):
													a_c.next_to(model_arr[i].get_right() + 0.0_2					,     __a					,     buff=0.2			)

													input.generate_target()
													input.target.move_to(model_arr[i].get_right() + 0.0_2			)

													__lowerCAmelCase       					=    AnimationGroup(
													    FadeOut(__a					,     run_time=0.5			)					,     MoveToTarget(__a					,     run_time=0.5			)					,     FadeIn(__a					,     run_time=0.5			)					,     lag_ratio=0.2			)

													self.play(__a			)

													model_cpu_arr[i].generate_target()
													model_cpu_arr[i].target.move_to(cpu_left_col_base[i]			)

													if i < 5:
																model_cpu_arr[i + 1].generate_target()
																model_cpu_arr[i + 1].target.move_to(gpu_rect[0]			)
																if i >= 1:
																			__lowerCAmelCase       					=    0.7

																self.play(
																    Circumscribe(model_arr[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i]					,     **__a			)					,     Circumscribe(cpu_left_col_base[i + 1]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     Circumscribe(model_arr[i + 1]					,     color=__a					,     **__a			)					,     )
																if i < 1:
																			self.play(
																			    MoveToTarget(model_cpu_arr[i]			)					,     MoveToTarget(model_cpu_arr[i + 1]			)					,     )
																else:
																			self.play(
																			    MoveToTarget(model_cpu_arr[i]					,     run_time=0.7			)					,     MoveToTarget(model_cpu_arr[i + 1]					,     run_time=0.7			)					,     )
													else:
																model_cpu_arr[i].generate_target()
																model_cpu_arr[i].target.move_to(cpu_left_col_base[-1]			)
																input.generate_target()
																input.target.next_to(model_arr[-1].get_right()					,     RIGHT + 0.0_2					,     buff=0.2			)

																self.play(
																    Circumscribe(model_arr[-1]					,     color=__a					,     **__a			)					,     Circumscribe(cpu_left_col_base[-1]					,     color=__a					,     **__a			)					,     Circumscribe(gpu_rect[0]					,     color=__a					,     **__a			)					,     )

																self.play(MoveToTarget(model_cpu_arr[i]			)			)

													__lowerCAmelCase       					=    a_c
													__lowerCAmelCase       					=    a_c.copy()

										input.generate_target()
										input.target.next_to(model_base[-1]					,     RIGHT + 0.0_2					,     buff=0.5			)
										self.play(
										    FadeOut(__a			)					,     FadeOut(__a					,     run_time=0.5			)					,     )

										__lowerCAmelCase       					=    MarkupText(f"Inference on a model too large for GPU memory\nis successfully completed."					,     font_size=24			)
										step_a.move_to([2, 2, 0]			)

										self.play(Write(__a					,     run_time=3			)					,     MoveToTarget(__a			)			)

										self.wait()




