


import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
snake_case							: Any      =   logging.get_logger(__name__)

snake_case							: 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''',
    '''encoder.layer_norm_for_extract''': '''layer_norm_for_extract''',
    '''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''',
    '''label_embs_concat''': '''label_embeddings_concat''',
    '''mask_emb''': '''masked_spec_embed''',
    '''spk_proj''': '''speaker_proj''',
}
snake_case							: Any      =   [
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]






def 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       List[str]      ,					__lowerCAmelCase							:       Optional[Any]     ):
							for attribute in key.split('.'     ):
														a__    =  getattr(__lowerCAmelCase      ,					__lowerCAmelCase     )

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

							if hf_shape != value.shape:
														raise ValueError(
														    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 							__lowercase     (				__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       Any     ):
							a__    =  []
							a__    =  fairseq_model.state_dict()

							a__    =  hf_model.unispeech_sat.feature_extractor

							for name, value in fairseq_dict.items():
														a__    =  False
														if "conv_layers" in name:
																					load_conv_layer(
																					    __lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					__lowerCAmelCase      ,					hf_model.config.feat_extract_norm == 'group'      ,					)
																					a__    =  True
														else:
																					for key, mapped_key in MAPPING.items():
																												a__    =  'unispeech_sat.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split('w2v_model.'     )[-1] == name.split('.'     )[0]:
																																			if "layer_norm_for_extract" in name and (".".join(name.split('.'     )[:-1]     ) != key):
																																										# special case since naming is very similar
																																										continue
																																			a__    =  True
																																			if "*" in mapped_key:
																																										a__    =  name.split(__lowerCAmelCase     )[0].split('.'     )[-2]
																																										a__    =  mapped_key.replace('*'      ,					__lowerCAmelCase     )
																																			if "weight_g" in name:
																																										a__    =  'weight_g'
																																			elif "weight_v" in name:
																																										a__    =  'weight_v'
																																			elif "bias" in name:
																																										a__    =  'bias'
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										a__    =  'weight'
																																			else:
																																										a__    =  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 							__lowercase     (				__lowerCAmelCase							:       Union[str, Any]      ,					__lowerCAmelCase							:       Tuple      ,					__lowerCAmelCase							:       int      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       int     ):
							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:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    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:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    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:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F'{full_name} has size {value.shape}, but'
																												    F' {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:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    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(__lowerCAmelCase     )






@torch.no_grad()
def 							__lowercase     (				__lowerCAmelCase							:       Any      ,					__lowerCAmelCase							:       str      ,					__lowerCAmelCase							:       Dict=None      ,					__lowerCAmelCase							:       Optional[int]=None      ,					__lowerCAmelCase							:       Union[str, Any]=True     ):
							if config_path is not None:
														a__    =  UniSpeechSatConfig.from_pretrained(__lowerCAmelCase     )
							else:
														a__    =  UniSpeechSatConfig()

							a__    =  ''

							if is_finetuned:
														a__    =  UniSpeechSatForCTC(__lowerCAmelCase     )
							else:
														a__    =  UniSpeechSatForPreTraining(__lowerCAmelCase     )

							a__   ,      a__   ,      a__    =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
							    [checkpoint_path]      ,					arg_overrides={'data': '/'.join(dict_path.split('/'     )[:-1]     )}     )
							a__    =  model[0].eval()

							recursively_load_weights(__lowerCAmelCase      ,					__lowerCAmelCase     )

							hf_wavavec.save_pretrained(__lowerCAmelCase     )


if __name__ == "__main__":
						snake_case							: List[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'''
						)
						snake_case							: Optional[int]      =   parser.parse_args()
						convert_unispeech_sat_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)









import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class  snake_case_     (lowerCamelCase_			,		lowerCamelCase_   ):
					UpperCAmelCase__      :					Optional[Any]					   =    1
					@register_to_config
					def __init__(   self     :Optional[int]    ,__snake_case     :int = 10_00    ,__snake_case     :Optional[Union[np.ndarray, List[float]]] = None   )      ->					int:
												# set `betas`, `alphas`, `timesteps`
												self.set_timesteps(__snake_case   )

												# standard deviation of the initial noise distribution
												a__    =  1.0

												# For now we only support F-PNDM, i.e. the runge-kutta method
												# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
												# mainly at formula (9), (12), (13) and the Algorithm 2.
												a__    =  4

												# running values
												a__    =  []
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :int    ,__snake_case     :Union[str, torch.device] = None   )      ->					Union[str, Any]:
												a__    =  num_inference_steps
												a__    =  torch.linspace(1    ,0    ,num_inference_steps + 1   )[:-1]
												a__    =  torch.cat([steps, torch.tensor([0.0]   )]   )

												if self.config.trained_betas is not None:
																			a__    =  torch.tensor(self.config.trained_betas    ,dtype=torch.floataa   )
												else:
																			a__    =  torch.sin(steps * math.pi / 2   ) ** 2

												a__    =  (1.0 - self.betas**2) ** 0.5

												a__    =  (torch.atana(self.betas    ,self.alphas   ) / math.pi * 2)[:-1]
												a__    =  timesteps.to(__snake_case   )

												a__    =  []
					def 				lowerCamelCase__(   self     :Any    ,__snake_case     :torch.FloatTensor    ,__snake_case     :int    ,__snake_case     :torch.FloatTensor    ,__snake_case     :bool = True    ,)      ->					Union[SchedulerOutput, Tuple]:
												if self.num_inference_steps is None:
																			raise ValueError(
																			    'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

												a__    =  (self.timesteps == timestep).nonzero().item()
												a__    =  timestep_index + 1

												a__    =  sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
												self.ets.append(__snake_case   )

												if len(self.ets   ) == 1:
																			a__    =  self.ets[-1]
												elif len(self.ets   ) == 2:
																			a__    =  (3 * self.ets[-1] - self.ets[-2]) / 2
												elif len(self.ets   ) == 3:
																			a__    =  (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
												else:
																			a__    =  (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

												a__    =  self._get_prev_sample(__snake_case    ,__snake_case    ,__snake_case    ,__snake_case   )

												if not return_dict:
																			return (prev_sample,)

												return SchedulerOutput(prev_sample=__snake_case   )
					def 				lowerCamelCase__(   self     :Union[str, Any]    ,__snake_case     :torch.FloatTensor    ,*__snake_case     :int    ,**__snake_case     :Optional[int]   )      ->					torch.FloatTensor:
												return sample
					def 				lowerCamelCase__(   self     :Optional[Any]    ,__snake_case     :List[Any]    ,__snake_case     :Optional[int]    ,__snake_case     :Dict    ,__snake_case     :Any   )      ->					Optional[Any]:
												a__    =  self.alphas[timestep_index]
												a__    =  self.betas[timestep_index]

												a__    =  self.alphas[prev_timestep_index]
												a__    =  self.betas[prev_timestep_index]

												a__    =  (sample - sigma * ets) / max(__snake_case    ,1E-8   )
												a__    =  next_alpha * pred + ets * next_sigma

												return prev_sample



					def __len__(   self     :Any   )      ->					Union[str, Any]:
												return self.config.num_train_timesteps




