





from manim import *

class       __A     (						a    ):


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

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

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

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

								lowerCamelCase	 =[]
								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)

												lowerCamelCase	 =Rectangle(height=0.4_6 / 4   , width=0.4_6 / 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=0.0_2   , 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_							)

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

								lowerCamelCase	 =Text("""Loaded Checkpoint"""   , font_size=24							)
								lowerCamelCase	 =Group(UpperCAmelCase_   , UpperCAmelCase_							).arrange(UpperCAmelCase_   , aligned_edge=UpperCAmelCase_   , buff=0.4							)
								checkpoint.move_to([3, 0.5, 0]							)

								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(UpperCAmelCase_   , UpperCAmelCase_							)

								lowerCamelCase	 =MarkupText(
								    f"""<span fgcolor='{BLUE}'>●</span> Checkpoint"""   , font_size=18   , )

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

								lowerCamelCase	 =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							)							)

								lowerCamelCase	 =[]
								lowerCamelCase	 =[]
								for i, rect in enumerate(UpperCAmelCase_							):
												lowerCamelCase	 =fill.copy().set_fill(UpperCAmelCase_   , opacity=0.7							)
												target.move_to(UpperCAmelCase_							)
												first_animations.append(GrowFromCenter(UpperCAmelCase_   , run_time=1							)							)

												lowerCamelCase	 =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()












import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
UpperCAmelCase__						:			int									=logging.get_logger(__name__)





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Any:
				lowerCamelCase	 =UniSpeechSatForSequenceClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""projector.weight"""]
				lowerCamelCase	 =downstream_dict["""projector.bias"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.post_net.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  int:
				lowerCamelCase	 =UniSpeechSatForAudioFrameClassification.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""model.linear.weight"""]
				lowerCamelCase	 =downstream_dict["""model.linear.bias"""]
				return model





def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  Dict:
				lowerCamelCase	 =UniSpeechSatForXVector.from_pretrained(_UpperCAmelCase			,      config=_UpperCAmelCase				)
				lowerCamelCase	 =downstream_dict["""connector.weight"""]
				lowerCamelCase	 =downstream_dict["""connector.bias"""]
				for i, kernel_size in enumerate(hf_config.tdnn_kernel				):
								lowerCamelCase	 =downstream_dict[
								    F"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
								]
								lowerCamelCase	 =downstream_dict[F"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear1.bias"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.weight"""]
				lowerCamelCase	 =downstream_dict["""model.utterancelevel_feature_extractor.linear2.bias"""]
				lowerCamelCase	 =downstream_dict["""objective.W"""]
				return model





@torch.no_grad()
def      _lowercase					(						_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)						->  str:
				lowerCamelCase	 =torch.load(_UpperCAmelCase			,      map_location="""cpu"""				)

				lowerCamelCase	 =checkpoint["""Downstream"""]

				lowerCamelCase	 =UniSpeechSatConfig.from_pretrained(_UpperCAmelCase				)
				lowerCamelCase	 =WavaVecaFeatureExtractor.from_pretrained(
				    _UpperCAmelCase			,      return_attention_mask=_UpperCAmelCase			,      do_normalize=_UpperCAmelCase				)

				lowerCamelCase	 =hf_config.architectures[0]
				if arch.endswith("""ForSequenceClassification"""				):
								lowerCamelCase	 =convert_classification(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForAudioFrameClassification"""				):
								lowerCamelCase	 =convert_diarization(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				elif arch.endswith("""ForXVector"""				):
								lowerCamelCase	 =convert_xvector(_UpperCAmelCase			,      _UpperCAmelCase			,      _UpperCAmelCase				)
				else:
								raise NotImplementedError(F"""S3PRL weights conversion is not supported for {arch}"""				)

				if hf_config.use_weighted_layer_sum:
								lowerCamelCase	 =checkpoint["""Featurizer"""]["""weights"""]

				hf_feature_extractor.save_pretrained(_UpperCAmelCase				)
				hf_model.save_pretrained(_UpperCAmelCase				)


if __name__ == "__main__":
							UpperCAmelCase__						:			Dict									=argparse.ArgumentParser()
							parser.add_argument(
							    '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
							)
							parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
							parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
							parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
							UpperCAmelCase__						:			List[Any]									=parser.parse_args()
							convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)





