
'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase__							:							List[Any]									=			logging.get_logger(__name__)

lowercase__							:							List[Any]									=			{
    "post_extract_proj": "feature_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.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}




def 	A_ (							snake_case							:   Any ,      snake_case							:   int ,      snake_case							:   Dict ,      snake_case							:   Tuple ,      snake_case							:   Dict )      ->    Any:



						'''simple docstring'''

						for attribute in key.split('''.''' ):
												__UpperCamelCase       =			getattr(snake_case ,      snake_case )

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

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




def 	A_ (							snake_case							:   Tuple ,      snake_case							:   Optional[int] ,      snake_case							:   List[Any] )      ->    Dict:



						'''simple docstring'''

						__UpperCamelCase       =			[]
						__UpperCamelCase       =			fairseq_model.state_dict()

						__UpperCamelCase       =			hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												__UpperCamelCase       =			False
												if "conv_layers" in name:
																		load_conv_layer(
																		    snake_case ,      snake_case ,      snake_case ,      snake_case ,      hf_model.config.feat_extract_norm == '''group''' ,      )
																		__UpperCamelCase       =			True
												else:
																		for key, mapped_key in MAPPING.items():
																								__UpperCamelCase       =			'''sew.''' + 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]:
																														__UpperCamelCase       =			True
																														if "*" in mapped_key:
																																				__UpperCamelCase       =			name.split(snake_case )[0].split('''.''' )[-2]
																																				__UpperCamelCase       =			mapped_key.replace('''*''' ,      snake_case )
																														if "weight_g" in name:
																																				__UpperCamelCase       =			'''weight_g'''
																														elif "weight_v" in name:
																																				__UpperCamelCase       =			'''weight_v'''
																														elif "weight" in name:
																																				__UpperCamelCase       =			'''weight'''
																														elif "bias" in name:
																																				__UpperCamelCase       =			'''bias'''
																														else:
																																				__UpperCamelCase       =			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 	A_ (							snake_case							:   List[str] ,      snake_case							:   Optional[int] ,      snake_case							:   List[str] ,      snake_case							:   List[Any] ,      snake_case							:   Any )      ->    Optional[Any]:



						'''simple docstring'''

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




def 	A_ (							snake_case							:   Dict ,      snake_case							:   int )      ->    Tuple:



						'''simple docstring'''

						__UpperCamelCase       =			SEWConfig()
						if is_finetuned:
												__UpperCamelCase       =			model.wav_encoder.wav_model.cfg
						else:
												__UpperCamelCase       =			model.cfg

						__UpperCamelCase       =			fs_config.conv_bias
						__UpperCamelCase       =			eval(fs_config.conv_feature_layers )
						__UpperCamelCase       =			[x[0] for x in conv_layers]
						__UpperCamelCase       =			[x[1] for x in conv_layers]
						__UpperCamelCase       =			[x[2] for x in conv_layers]
						__UpperCamelCase       =			'''gelu'''
						__UpperCamelCase       =			'''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
						__UpperCamelCase       =			0.0
						__UpperCamelCase       =			fs_config.activation_fn.name
						__UpperCamelCase       =			fs_config.encoder_embed_dim
						__UpperCamelCase       =			0.02
						__UpperCamelCase       =			fs_config.encoder_ffn_embed_dim
						__UpperCamelCase       =			1e-5
						__UpperCamelCase       =			fs_config.encoder_layerdrop
						__UpperCamelCase       =			fs_config.encoder_attention_heads
						__UpperCamelCase       =			fs_config.conv_pos_groups
						__UpperCamelCase       =			fs_config.conv_pos
						__UpperCamelCase       =			len(snake_case )
						__UpperCamelCase       =			fs_config.encoder_layers
						__UpperCamelCase       =			fs_config.squeeze_factor

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												__UpperCamelCase       =			model.cfg
												__UpperCamelCase       =			fs_config.final_dropout
												__UpperCamelCase       =			fs_config.layerdrop
						__UpperCamelCase       =			fs_config.activation_dropout
						__UpperCamelCase       =			fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						__UpperCamelCase       =			fs_config.attention_dropout
						__UpperCamelCase       =			fs_config.dropout_input
						__UpperCamelCase       =			fs_config.dropout
						__UpperCamelCase       =			fs_config.mask_channel_length
						__UpperCamelCase       =			fs_config.mask_channel_prob
						__UpperCamelCase       =			fs_config.mask_length
						__UpperCamelCase       =			fs_config.mask_prob

						__UpperCamelCase       =			'''Wav2Vec2FeatureExtractor'''
						__UpperCamelCase       =			'''Wav2Vec2CTCTokenizer'''

						return config




@torch.no_grad()
def 	A_ (							snake_case							:   List[Any] ,      snake_case							:   List[Any] ,      snake_case							:   List[str]=None ,      snake_case							:   Tuple=None ,      snake_case							:   Optional[int]=True )      ->    str:



						'''simple docstring'''

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

						if config_path is not None:
												__UpperCamelCase       =			SEWConfig.from_pretrained(snake_case )
						else:
												__UpperCamelCase       =			convert_config(model[0] ,      snake_case )
						__UpperCamelCase       =			model[0].eval()

						__UpperCamelCase       =			True if config.feat_extract_norm == '''layer''' else False
						__UpperCamelCase       =			WavaVecaFeatureExtractor(
						    feature_size=1 ,      sampling_rate=16000 ,      padding_value=0 ,      do_normalize=snake_case ,      return_attention_mask=snake_case ,      )

						if is_finetuned:
												if dict_path:
																		__UpperCamelCase       =			Dictionary.load(snake_case )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		__UpperCamelCase       =			target_dict.pad_index
																		__UpperCamelCase       =			target_dict.bos_index
																		__UpperCamelCase       =			target_dict.pad_index
																		__UpperCamelCase       =			target_dict.bos_index
																		__UpperCamelCase       =			target_dict.eos_index
																		__UpperCamelCase       =			len(target_dict.symbols )
																		__UpperCamelCase       =			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 )
																		__UpperCamelCase       =			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 ,      )
																		__UpperCamelCase       =			WavaVecaProcessor(feature_extractor=snake_case ,      tokenizer=snake_case )
																		processor.save_pretrained(snake_case )

												__UpperCamelCase       =			SEWForCTC(snake_case )
						else:
												__UpperCamelCase       =			SEWModel(snake_case )
												feature_extractor.save_pretrained(snake_case )

						recursively_load_weights(snake_case ,      snake_case ,      snake_case )

						hf_model.save_pretrained(snake_case )


if __name__ == "__main__":
				lowercase__							:							List[str]									=			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(
				    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				lowercase__							:							str									=			parser.parse_args()
				convert_sew_checkpoint(
				    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_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        SCREAMING_SNAKE_CASE__    (							SCREAMING_SNAKE_CASE_   ,		SCREAMING_SNAKE_CASE_    ):

							"""simple docstring"""






							_snake_case     		=  1



							@register_to_config
							def __init__(  self     ,	SCREAMING_SNAKE_CASE_ = 1000     ,	SCREAMING_SNAKE_CASE_ = None			)->   Tuple:





													'''simple docstring'''






													self.set_timesteps(SCREAMING_SNAKE_CASE_			)

													# standard deviation of the initial noise distribution
													__UpperCamelCase       =			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.
													__UpperCamelCase       =			4

													# running values
													__UpperCamelCase       =			[]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = None			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			num_inference_steps
													__UpperCamelCase       =			torch.linspace(1     ,	0     ,	num_inference_steps + 1			)[:-1]
													__UpperCamelCase       =			torch.cat([steps, torch.tensor([0.0]			)]			)

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

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

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

													__UpperCamelCase       =			[]



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_ = True     ,	)->   Union[SchedulerOutput, Tuple]:





													'''simple docstring'''






													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'''			)

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

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

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

													__UpperCamelCase       =			self._get_prev_sample(SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)

													if not return_dict:
																			return (prev_sample,)

													return SchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_			)



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	*SCREAMING_SNAKE_CASE_     ,	**SCREAMING_SNAKE_CASE_			)->   torch.FloatTensor:





													'''simple docstring'''






													return sample



							def     A__		(  self     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_     ,	SCREAMING_SNAKE_CASE_			)->   Optional[int]:





													'''simple docstring'''






													__UpperCamelCase       =			self.alphas[timestep_index]
													__UpperCamelCase       =			self.betas[timestep_index]

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

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

													return prev_sample



							def __len__(  self			)->   Optional[int]:





													'''simple docstring'''






													return self.config.num_train_timesteps




