






import argparse

import torch

from transformers import YosoConfig, YosoForMaskedLM







def      snake_case		(							UpperCAmelCase	:       List[Any]   ):
				if "model" in orig_key:
								A	 =      orig_key.replace('model.',						''   )
				if "norm1" in orig_key:
								A	 =      orig_key.replace('norm1',						'attention.output.LayerNorm'   )
				if "norm2" in orig_key:
								A	 =      orig_key.replace('norm2',						'output.LayerNorm'   )
				if "norm" in orig_key:
								A	 =      orig_key.replace('norm',						'LayerNorm'   )
				if "transformer" in orig_key:
								A	 =      orig_key.split('.'   )[0].split('_'   )[-1]
								A	 =      orig_key.replace(f'transformer_{layer_num}',						f'encoder.layer.{layer_num}'   )
				if "mha.attn" in orig_key:
								A	 =      orig_key.replace('mha.attn',						'attention.self'   )
				if "mha" in orig_key:
								A	 =      orig_key.replace('mha',						'attention'   )
				if "W_q" in orig_key:
								A	 =      orig_key.replace('W_q',						'self.query'   )
				if "W_k" in orig_key:
								A	 =      orig_key.replace('W_k',						'self.key'   )
				if "W_v" in orig_key:
								A	 =      orig_key.replace('W_v',						'self.value'   )
				if "ff1" in orig_key:
								A	 =      orig_key.replace('ff1',						'intermediate.dense'   )
				if "ff2" in orig_key:
								A	 =      orig_key.replace('ff2',						'output.dense'   )
				if "ff" in orig_key:
								A	 =      orig_key.replace('ff',						'output.dense'   )
				if "mlm_class" in orig_key:
								A	 =      orig_key.replace('mlm.mlm_class',						'cls.predictions.decoder'   )
				if "mlm" in orig_key:
								A	 =      orig_key.replace('mlm',						'cls.predictions.transform'   )
				if "cls" not in orig_key:
								A	 =      'yoso.' + orig_key

				return orig_key







def      snake_case		(							UpperCAmelCase	:       Tuple,						UpperCAmelCase	:       str   ):
				for key in orig_state_dict.copy().keys():
								A	 =      orig_state_dict.pop(UpperCAmelCase   )

								if ("pooler" in key) or ("sen_class" in key):
												continue
								else:
												A	 =      val

				A	 =      orig_state_dict['cls.predictions.decoder.bias']
				A	 =      torch.arange(UpperCAmelCase   ).expand((1, -1)   ) + 2

				return orig_state_dict







def      snake_case		(							UpperCAmelCase	:       int,						UpperCAmelCase	:       Union[str, Any],						UpperCAmelCase	:       str   ):
				A	 =      torch.load(UpperCAmelCase,						map_location='cpu'   )['model_state_dict']
				A	 =      YosoConfig.from_json_file(UpperCAmelCase   )
				A	 =      YosoForMaskedLM(UpperCAmelCase   )

				A	 =      convert_checkpoint_helper(config.max_position_embeddings,						UpperCAmelCase   )

				print(model.load_state_dict(UpperCAmelCase   )   )
				model.eval()
				model.save_pretrained(UpperCAmelCase   )

				print(f'Checkpoint successfuly converted. Model saved at {pytorch_dump_path}'   )


if __name__ == "__main__":
					lowerCAmelCase_           = argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    '--pytorch_model_path', default=None, type=str, required=True, help='Path to YOSO pytorch checkpoint.'
					)
					parser.add_argument(
					    '--config_file',
					    default=None,
					    type=str,
					    required=True,
					    help='The json file for YOSO model config.',
					)
					parser.add_argument(
					    '--pytorch_dump_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
					)
					lowerCAmelCase_           = parser.parse_args()
					convert_yoso_checkpoint(args.pytorch_model_path, args.config_file, args.pytorch_dump_path)













from manim import *
class      UpperCamelCase (				snake_case__ ):







		"""simple docstring"""



		def       A(  self		:       Dict		)    ->   Tuple:


						'''simple docstring'''
						A	 =      Rectangle(height=0.5						,width=0.5		)
						A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0		)

						A	 =      [mem.copy() for i in range(6		)]
						A	 =      [mem.copy() for i in range(6		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      VGroup(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('CPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						cpu.move_to([-2.5, -0.5, 0]		)
						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      [mem.copy() for i in range(1		)]
						A	 =      VGroup(*_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0		)
						A	 =      Text('GPU'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						gpu.align_to(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		)
						gpu.set_x(gpu.get_x() - 1		)

						self.add(_SCREAMING_SNAKE_CASE		)

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

						A	 =      Text('Model'						,font_size=2_4		)
						A	 =      Group(_SCREAMING_SNAKE_CASE						,_SCREAMING_SNAKE_CASE		).arrange(_SCREAMING_SNAKE_CASE						,buff=0.5						,aligned_edge=_SCREAMING_SNAKE_CASE		)
						model.move_to([3, -1.0, 0]		)

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

						A	 =      MarkupText(
						    f'First, an empty model skeleton is loaded\ninto <span fgcolor=\'{YELLOW}\'>memory</span> without using much RAM.'						,font_size=2_4						,)

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

						A	 =      MarkupText(
						    f'<b>Key:</b>\n\n<span fgcolor=\'{YELLOW}\'>●</span> Empty Model'						,font_size=1_8						,)

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

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

						self.add(_SCREAMING_SNAKE_CASE		)

						A	 =      []
						A	 =      []
						A	 =      []
						for i, rect in enumerate(_SCREAMING_SNAKE_CASE		):
										A	 =      Rectangle(height=0.46						,width=0.46		).set_stroke(width=0.0		).set_fill(_SCREAMING_SNAKE_CASE						,opacity=0.7		)
										cpu_target.move_to(_SCREAMING_SNAKE_CASE		)
										cpu_target.generate_target()
										A	 =      0.46 / 4
										A	 =      0.46 / 3

										if i == 0:
														cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT		)						,buff=0.02						,direction=_SCREAMING_SNAKE_CASE		)
														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=_SCREAMING_SNAKE_CASE						,buff=0.0		)
										else:
														cpu_target.target.next_to(cpu_targs[i - 1].target						,direction=_SCREAMING_SNAKE_CASE						,buff=0.0		)
										cpu_targs.append(_SCREAMING_SNAKE_CASE		)

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

						self.play(*_SCREAMING_SNAKE_CASE		)
						self.play(*_SCREAMING_SNAKE_CASE		)

						self.wait()





