



import argparse

import fairseq
import torch
from torch import nn

from transformers import (
    MBartaaTokenizer,
    MBartConfig,
    MBartForCausalLM,
    SpeechEncoderDecoderConfig,
    SpeechEncoderDecoderModel,
    WavaVecaConfig,
    WavaVecaFeatureExtractor,
    WavaVecaModel,
    logging,
)


logging.set_verbosity_info()
lowercase_													=      logging.get_logger(__name__)

lowercase_													=      {
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.k_proj''': '''encoder.layers.*.attention.k_proj''',
    '''self_attn.v_proj''': '''encoder.layers.*.attention.v_proj''',
    '''self_attn.q_proj''': '''encoder.layers.*.attention.q_proj''',
    '''self_attn.out_proj''': '''encoder.layers.*.attention.out_proj''',
    '''self_attn_layer_norm''': '''encoder.layers.*.layer_norm''',
    '''fc1''': '''encoder.layers.*.feed_forward.intermediate_dense''',
    '''fc2''': '''encoder.layers.*.feed_forward.output_dense''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
lowercase_													=      [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def        __lowerCAmelCase		(	__lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     str						)	->    Union[str, Any]:
					for attribute in key.split("""."""						):
										__lowerCAmelCase						 =getattr(__lowerCamelCase					, __lowerCamelCase						)

					if weight_type is not None:
										__lowerCAmelCase						 =getattr(__lowerCamelCase					, __lowerCamelCase						).shape
					else:
										__lowerCAmelCase						 =hf_pointer.shape

					assert hf_shape == value.shape, (
					    f"""Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"""
					    f""" {value.shape} for {full_name}"""
					)

					if weight_type == "weight":
										__lowerCAmelCase						 =value
					elif weight_type == "weight_g":
										__lowerCAmelCase						 =value
					elif weight_type == "weight_v":
										__lowerCAmelCase						 =value
					elif weight_type == "bias":
										__lowerCAmelCase						 =value
					else:
										__lowerCAmelCase						 =value

					logger.info(f"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Dict						)	->    int:
					__lowerCAmelCase						 =[]
					__lowerCAmelCase						 =fairseq_model.state_dict()

					__lowerCAmelCase						 =hf_model.feature_extractor
					__lowerCAmelCase						 =hf_model.adapter

					for name, value in fairseq_dict.items():
										__lowerCAmelCase						 =False
										if "conv_layers" in name:
															load_conv_layer(
															    __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, hf_model.config.feat_extract_norm == """group"""					, )
															__lowerCAmelCase						 =True
										elif any(x in name for x in ["""adaptor""", """w2v_encoder.proj.""", """w2v_proj_ln."""]						):
															load_adapter(__lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase						)
															__lowerCAmelCase						 =True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split("""w2v_model."""						)[-1] == name.split("""."""						)[0]:
																									__lowerCAmelCase						 =True
																									if "*" in mapped_key:
																														__lowerCAmelCase						 =name.split(__lowerCamelCase						)[0].split("""."""						)[-2]
																														__lowerCAmelCase						 =mapped_key.replace("""*"""					, __lowerCamelCase						)
																									if "weight_g" in name:
																														__lowerCAmelCase						 ="""weight_g"""
																									elif "weight_v" in name:
																														__lowerCAmelCase						 ="""weight_v"""
																									elif "bias" in name:
																														__lowerCAmelCase						 ="""bias"""
																									elif "weight" in name:
																														__lowerCAmelCase						 ="""weight"""
																									else:
																														__lowerCAmelCase						 =None
																									set_recursively(__lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase					, __lowerCamelCase						)
																				continue
										if not is_used:
															unused_weights.append(__lowerCamelCase						)

					logger.warning(f"""Unused weights: {unused_weights}"""						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     int					, __lowerCamelCase						:     int					, __lowerCamelCase						:     Optional[int]					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Dict						)	->    int:
					__lowerCAmelCase						 =full_name.split("""conv_layers."""						)[-1]
					__lowerCAmelCase						 =name.split("""."""						)
					__lowerCAmelCase						 =int(items[0]						)
					__lowerCAmelCase						 =int(items[1]						)

					if type_id == 0:
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""
															)
															__lowerCAmelCase						 =value
															logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""
															)
															__lowerCAmelCase						 =value
															logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""						)
					elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
										if "bias" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
															    f"""{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"""
															    " found."
															)
															__lowerCAmelCase						 =value
															logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
										elif "weight" in name:
															assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
															    f"""{full_name} has size {value.shape}, but"""
															    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""
															)
															__lowerCAmelCase						 =value
															logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""						)
					else:
										unused_weights.append(__lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     str					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Union[str, Any]						)	->    Dict:
					__lowerCAmelCase						 =full_name.split("""adaptor."""						)[-1]
					__lowerCAmelCase						 =name.split("""."""						)

					if items[1].isdigit():
										__lowerCAmelCase						 =int(items[1]						)
					else:
										__lowerCAmelCase						 =None

					if "adaptor" not in full_name:
										if "proj_ln" in full_name:
															# has to be layer norm
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.bias.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.bias.data.shape} was found."""
																				__lowerCAmelCase						 =value
																				logger.info(f"""Adapter proj layer norm bias was initialized from {full_name}."""						)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj_layer_norm.weight.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj_layer_norm.weight.data.shape} was found."""
																				__lowerCAmelCase						 =value
										else:
															# has to be projection layer
															if "bias" in name:
																				assert (
																				    value.shape == adapter.proj.bias.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj.bias.data.shape} was found."""
																				__lowerCAmelCase						 =value
																				logger.info(f"""Adapter proj layer bias was initialized from {full_name}."""						)
															if "weight" in name:
																				assert (
																				    value.shape == adapter.proj.weight.data.shape
																				), f"""{full_name} has size {value.shape}, but {adapter.proj.weight.data.shape} was found."""
																				__lowerCAmelCase						 =value
																				logger.info(f"""Adapter proj layer weight was initialized from {full_name}."""						)
					elif isinstance(__lowerCamelCase					, __lowerCamelCase						):
										if "bias" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.bias.data.shape
															), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.bias.data.shape} was found."""
															__lowerCAmelCase						 =value
															logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
										elif "weight" in name:
															assert (
															    value.shape == adapter.layers[layer_id].conv.weight.data.shape
															), f"""{full_name} has size {value.shape}, but {adapter.layers[layer_id].conv.weight.data.shape} was found."""
															__lowerCAmelCase						 =value
															logger.info(f"""Adapter layer {layer_id} bias was initialized from {full_name}."""						)
					else:
										unused_weights.append(__lowerCamelCase						)






def        __lowerCAmelCase		(	__lowerCamelCase						:     str						)	->    List[str]:
					__lowerCAmelCase ,						__lowerCAmelCase						 =emb.weight.shape
					__lowerCAmelCase						 =nn.Linear(__lowerCamelCase					, __lowerCamelCase					, bias=__lowerCamelCase						)
					__lowerCAmelCase						 =emb.weight.data
					return lin_layer






@torch.no_grad()
def        __lowerCAmelCase		(	__lowerCamelCase						:     List[str]					, __lowerCamelCase						:     int					, __lowerCamelCase						:     List[Any]					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     Any					, __lowerCamelCase						:     Dict					, __lowerCamelCase						:     Optional[Any]					, __lowerCamelCase						:     Tuple					, __lowerCamelCase						:     str					, __lowerCamelCase						:     Dict					, __lowerCamelCase						:     Optional[Any]					, )	->    Dict:
					__lowerCAmelCase						 =WavaVecaConfig.from_pretrained(
					    __lowerCamelCase					, add_adapter=__lowerCamelCase					, adapter_stride=__lowerCamelCase					, adapter_kernel_size=__lowerCamelCase					, use_auth_token=__lowerCamelCase					, output_hidden_size=__lowerCamelCase					, )
					__lowerCAmelCase						 =MBartConfig.from_pretrained(__lowerCamelCase						)

					# load model
					__lowerCAmelCase ,						__lowerCAmelCase ,						__lowerCAmelCase						 =fairseq.checkpoint_utils.load_model_ensemble_and_task(
					    [checkpoint_path]					, arg_overrides={
					        """config_yaml""": config_yaml_path,
					        """data""": """/""".join(dict_path.split("""/"""						)[:-1]						),
					        """w2v_path""": checkpoint_path,
					        """load_pretrained_decoder_from""": None,
					    }					, )
					__lowerCAmelCase						 =model[0].eval()

					# load feature extractor
					__lowerCAmelCase						 =WavaVecaFeatureExtractor.from_pretrained(__lowerCamelCase					, use_auth_token=__lowerCamelCase						)

					# set weights for wav2vec2 encoder
					__lowerCAmelCase						 =WavaVecaModel(__lowerCamelCase						)

					recursively_load_weights_wavaveca(model.encoder					, __lowerCamelCase						)

					# load decoder weights
					__lowerCAmelCase						 =MBartForCausalLM(__lowerCamelCase						)
					__lowerCAmelCase ,						__lowerCAmelCase						 =hf_decoder.model.decoder.load_state_dict(model.decoder.state_dict()					, strict=__lowerCamelCase						)
					logger.warning(f"""The following keys are missing when loading the decoder weights: {missing_keys}"""						)
					logger.warning(f"""The following keys are unexpected when loading the decoder weights: {unexpected_keys}"""						)

					__lowerCAmelCase						 =SpeechEncoderDecoderModel(encoder=__lowerCamelCase					, decoder=__lowerCamelCase						)
					__lowerCAmelCase						 =False

					__lowerCAmelCase						 =MBartaaTokenizer(__lowerCamelCase						)
					tokenizer.save_pretrained(__lowerCamelCase						)

					__lowerCAmelCase						 =hf_wavavec.config.to_dict()
					__lowerCAmelCase						 =tokenizer.pad_token_id
					__lowerCAmelCase						 =tokenizer.bos_token_id
					__lowerCAmelCase						 =tokenizer.eos_token_id
					__lowerCAmelCase						 ="""mbart50"""
					__lowerCAmelCase						 ="""wav2vec2"""

					__lowerCAmelCase						 =tokenizer.eos_token_id
					__lowerCAmelCase						 =250004
					__lowerCAmelCase						 =tokenizer.eos_token_id

					__lowerCAmelCase						 =SpeechEncoderDecoderConfig.from_dict(__lowerCamelCase						)

					hf_wavavec.save_pretrained(__lowerCamelCase						)
					feature_extractor.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
				lowercase_													=      argparse.ArgumentParser()
				parser.add_argument('''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model.''')
				parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to fairseq checkpoint''')
				parser.add_argument('''--dict_path''', default=None, type=str, help='''Path to dict of fine-tuned model''')
				parser.add_argument('''--config_yaml_path''', default=None, type=str, help='''Path to yaml file of fine-tuned model''')
				parser.add_argument(
				    '''--encoder_config_path''',
				    default='''facebook/wav2vec2-xls-r-1b''',
				    type=str,
				    help='''Path to hf encoder wav2vec2 checkpoint config''',
				)
				parser.add_argument(
				    '''--decoder_config_path''',
				    default='''facebook/mbart-large-50-one-to-many-mmt''',
				    type=str,
				    help='''Path to hf decoder checkpoint config''',
				)
				parser.add_argument('''--add_adapter''', default=True, type=bool, help='''whethere to add model adapter layers''')
				parser.add_argument('''--adapter_stride''', default=2, type=int, help='''stride of adapter layers''')
				parser.add_argument('''--adapter_kernel_size''', default=3, type=int, help='''kernel size of adapter layers''')
				parser.add_argument('''--encoder_output_dim''', default=10_24, type=int, help='''encoder output dim''')
				parser.add_argument('''--start_token_id''', default=25_00_04, type=int, help='''`decoder_start_token_id` of model config''')

				lowercase_													=      parser.parse_args()
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.dict_path,
				    args.config_yaml_path,
				    encoder_config_path=args.encoder_config_path,
				    decoder_config_path=args.decoder_config_path,
				    add_adapter=args.add_adapter,
				    adapter_kernel_size=args.adapter_kernel_size,
				    adapter_stride=args.adapter_stride,
				    decoder_start_token_id=args.start_token_id,
				    encoder_output_dim=args.encoder_output_dim,
				)









from manim import *






class     __a			(  SCREAMING_SNAKE_CASE					):



	def 		UpperCamelCase				(    self				:     Tuple)->				Dict:
						__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(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =VGroup(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =Text("""CPU"""					,						font_size=24)
						__lowerCAmelCase						 =Group(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0.5					,						aligned_edge=snake_case_)
						cpu.move_to([-2.5, -0.5, 0])
						self.add(snake_case_)

						__lowerCAmelCase						 =[mem.copy() for i in range(1)]
						__lowerCAmelCase						 =VGroup(*snake_case_).arrange(snake_case_					,						buff=0)
						__lowerCAmelCase						 =Text("""GPU"""					,						font_size=24)
						__lowerCAmelCase						 =Group(snake_case_					,						snake_case_).arrange(snake_case_					,						buff=0.5					,						aligned_edge=snake_case_)
						gpu.align_to(snake_case_					,						snake_case_)
						gpu.set_x(gpu.get_x() - 1)

						self.add(snake_case_)

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

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

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

						__lowerCAmelCase						 =MarkupText(
						    F"""First, an empty model skeleton is loaded\ninto <span fgcolor='{YELLOW}'>memory</span> without using much RAM."""					,						font_size=24					,						)

						__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])

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

						self.add(snake_case_)

						__lowerCAmelCase						 =[]
						__lowerCAmelCase						 =[]
						__lowerCAmelCase						 =[]
						for i, rect in enumerate(snake_case_):
											__lowerCAmelCase						 =Rectangle(height=0.4_6					,						width=0.4_6).set_stroke(width=0.0).set_fill(snake_case_					,						opacity=0.7)
											cpu_target.move_to(snake_case_)
											cpu_target.generate_target()
											__lowerCAmelCase						 =0.4_6 / 4
											__lowerCAmelCase						 =0.4_6 / 3

											if i == 0:
																cpu_target.target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT)					,						buff=0.0_2					,						direction=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=snake_case_					,						buff=0.0)
											else:
																cpu_target.target.next_to(cpu_targs[i - 1].target					,						direction=snake_case_					,						buff=0.0)
											cpu_targs.append(snake_case_)

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

						self.play(*snake_case_)
						self.play(*snake_case_)

						self.wait()




