

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_lowerCAmelCase					:			Union[str, Any]  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			int  				=			{
    "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",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}






def        UpperCamelCase_(							_snake_case   :  List[Any]       ,     _snake_case   :  int       ,     _snake_case   :  Tuple       ,     _snake_case   :  Optional[int]       ,     _snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			for attribute in key.split('.'	):
						__a        =getattr(_snake_case       ,     _snake_case	)

			if weight_type is not None:
						__a        =getattr(_snake_case       ,     _snake_case	).shape
			else:
						__a        =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":
						__a        =value
			elif weight_type == "weight_g":
						__a        =value
			elif weight_type == "weight_v":
						__a        =value
			elif weight_type == "bias":
						__a        =value
			else:
						__a        =value

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






def        UpperCamelCase_(							_snake_case   :  Tuple       ,     _snake_case   :  Optional[Any]       ,     _snake_case   :  List[Any]	):




			"""simple docstring"""






			__a        =[]
			__a        =fairseq_model.state_dict()

			__a        =hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						__a        =False
						if "conv_layers" in name:
									load_conv_layer(
									    _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     hf_model.config.feat_extract_norm == 'group'       ,     )
									__a        =True
						else:
									for key, mapped_key in MAPPING.items():
												__a        ='hubert.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

												if key in name or (key.split('w2v_model.'	)[-1] == name.split('.'	)[0] and not is_finetuned):
															__a        =True
															if "*" in mapped_key:
																		__a        =name.split(_snake_case	)[0].split('.'	)[-2]
																		__a        =mapped_key.replace('*'       ,     _snake_case	)
															if "weight_g" in name:
																		__a        ='weight_g'
															elif "weight_v" in name:
																		__a        ='weight_v'
															elif "weight" in name:
																		__a        ='weight'
															elif "bias" in name:
																		__a        ='bias'
															else:
																		__a        =None
															set_recursively(_snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case       ,     _snake_case	)
												continue
						if not is_used:
									unused_weights.append(_snake_case	)

			logger.warning(F'Unused weights: {unused_weights}'	)






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]       ,     _snake_case   :  str       ,     _snake_case   :  Any       ,     _snake_case   :  int       ,     _snake_case   :  int	):




			"""simple docstring"""






			__a        =full_name.split('conv_layers.'	)[-1]
			__a        =name.split('.'	)
			__a        =int(items[0]	)
			__a        =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.'
									)
									__a        =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.'
									)
									__a        =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."
									)
									__a        =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.'
									)
									__a        =value
									logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
			else:
						unused_weights.append(_snake_case	)






@torch.no_grad()
def        UpperCamelCase_(							_snake_case   :  int       ,     _snake_case   :  Any       ,     _snake_case   :  str=None       ,     _snake_case   :  List[str]=None       ,     _snake_case   :  List[str]=True	):




			"""simple docstring"""






			if config_path is not None:
						__a        =HubertConfig.from_pretrained(_snake_case	)
			else:
						__a        =HubertConfig()

			if is_finetuned:
						if dict_path:
									__a        =Dictionary.load(_snake_case	)

									# important change bos & pad token id since CTC symbol is <pad> and
									# not <s> as in fairseq
									__a        =target_dict.pad_index
									__a        =target_dict.bos_index
									__a        =target_dict.eos_index
									__a        =len(target_dict.symbols	)
									__a        =os.path.join(_snake_case       ,     'vocab.json'	)
									if not os.path.isdir(_snake_case	):
												logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(_snake_case	)	)
												return
									os.makedirs(_snake_case       ,     exist_ok=_snake_case	)
									with open(_snake_case       ,     'w'       ,     encoding='utf-8'	) as vocab_handle:
												json.dump(target_dict.indices       ,     _snake_case	)
									__a        =WavaVecaCTCTokenizer(
									    _snake_case       ,     unk_token=target_dict.unk_word       ,     pad_token=target_dict.pad_word       ,     bos_token=target_dict.bos_word       ,     eos_token=target_dict.eos_word       ,     word_delimiter_token='|'       ,     do_lower_case=_snake_case       ,     )
									__a        =True if config.feat_extract_norm == 'layer' else False
									__a        =WavaVecaFeatureExtractor(
									    feature_size=1       ,     sampling_rate=16000       ,     padding_value=0       ,     do_normalize=_snake_case       ,     return_attention_mask=_snake_case       ,     )
									__a        =WavaVecaProcessor(feature_extractor=_snake_case       ,     tokenizer=_snake_case	)
									processor.save_pretrained(_snake_case	)

						__a        =HubertForCTC(_snake_case	)
			else:
						__a        =HubertModel(_snake_case	)

			if is_finetuned:
						__a      ,    __a      ,    __a        =fairseq.checkpoint_utils.load_model_ensemble_and_task(
						    [checkpoint_path]       ,     arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
			else:
						__a      ,    __a      ,    __a        =fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

			__a        =model[0].eval()

			recursively_load_weights(_snake_case       ,     _snake_case       ,     _snake_case	)

			hf_wavavec.save_pretrained(_snake_case	)


if __name__ == "__main__":
						_lowerCAmelCase					:			Union[str, Any]  				=			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_path", default=None, type=str, help="Path to hf config.json of model to convert")
						parser.add_argument(
						    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
						)
						_lowerCAmelCase					:			Dict  				=			parser.parse_args()
						convert_hubert_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)









from manim import *






class 	__magic_name__    (			lowerCAmelCase_      ):





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =Rectangle(height=0.5					,  width=0.5					)
						__a        =Rectangle(height=0.25					,  width=0.25					)
						__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(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('CPU'					,  font_size=24					)
						__a        =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					)

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

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

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

						__a        =[]
						__a        =[]
						__a        =[]

						for i, rect in enumerate(__snake_case					):
									rect.set_stroke(__snake_case					)

									__a        =Rectangle(height=0.46 / 4					,  width=0.46 / 3					).set_stroke(width=0.0					).set_fill(__snake_case					,  opacity=0.7					)

									if i == 0:
												cpu_target.next_to(cpu_left_col_base[0].get_corner(DOWN + LEFT					)					,  buff=0.02					,  direction=__snake_case					)
												cpu_target.set_x(cpu_target.get_x() + 0.1					)
									elif i == 3:
												cpu_target.next_to(model_cpu_arr[0]					,  direction=__snake_case					,  buff=0.0					)
									else:
												cpu_target.next_to(model_cpu_arr[i - 1]					,  direction=__snake_case					,  buff=0.0					)
									self.add(__snake_case					)
									model_cpu_arr.append(__snake_case					)

						self.add(*__snake_case					,  *__snake_case					,  *__snake_case					)

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

						__a        =Text('Loaded Checkpoint'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						checkpoint.move_to([3, 0.5, 0]					)

						self.add(__snake_case					)

						__a        =[]
						__a        =[]

						for i, rect in enumerate(__snake_case					):
									__a        =fill.copy().set_fill(__snake_case					,  opacity=0.7					)
									target.move_to(__snake_case					)
									ckpt_arr.append(__snake_case					)

									__a        =target.copy()
									if i < 5:
												cpu_target.move_to(cpu_left_col_base[i + 1]					)
									else:
												cpu_target.move_to(cpu_right_col_base[i - 5]					)
									ckpt_cpu_arr.append(__snake_case					)
						self.add(*__snake_case					,  *__snake_case					)

						__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=18					,  )

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

						self.add(__snake_case					,  __snake_case					)

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

						blue_text.next_to(__snake_case					,  DOWN * 2.4					,  aligned_edge=key_text.get_left()					)
						self.add(__snake_case					)

						__a        =MarkupText(
						    f'Based on the passed in configuration, weights are stored in\na variety of np.memmaps on disk or to a particular device.'					,  font_size=24					,  )
						step_a.move_to([2, 2, 0]					)

						__a        =[meta_mem.copy() for i in range(6					)]
						__a        =[meta_mem.copy() for i in range(6					)]
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(*__snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =VGroup(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0					)
						__a        =Text('Disk'					,  font_size=24					)
						__a        =Group(__snake_case					,  __snake_case					).arrange(__snake_case					,  buff=0.5					,  aligned_edge=__snake_case					)
						disk.move_to([-4.0, -1.25, 0]					)
						self.play(Write(__snake_case					,  run_time=3					)					,  Write(__snake_case					,  run_time=1					)					,  Create(__snake_case					,  run_time=1					)					)

						__a        =[]
						for i, rect in enumerate(__snake_case					):
									__a        =rect.copy()
									target.generate_target()
									target.target.move_to(disk_left_col_base[i]					).scale(0.5					)
									animations.append(MoveToTarget(__snake_case					,  run_time=1.5					)					)
						self.play(*__snake_case					)

						self.play(FadeOut(__snake_case					)					)

						__a        =MarkupText(f'Then, the checkpoint is removed from memory\nthrough garbage collection.'					,  font_size=24					)
						step_a.move_to([2, 2, 0]					)

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

						self.play(
						    FadeOut(__snake_case					,  __snake_case					,  *__snake_case					,  *__snake_case					)					,  )

						self.wait()






