

"""simple docstring"""

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

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, logging, randn_tensor
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin


a__			:						Any							=			logging.get_logger(__name__)  # pylint: disable=invalid-name


@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class       _snake_case  (    UpperCamelCase							):
						__snake_case			      =  42
						__snake_case			      =  None





def 					__UpperCamelCase (     a__:			List[str]			,						a__:			int=0.9_99			,						a__:			str="cosine"			,						)					->      Dict:




					"""simple docstring"""



					if alpha_transform_type == "cosine":

										def alpha_bar_fn(a__:			Optional[Any]      ):
															return math.cos((t + 0.0_08) / 1.0_08 * math.pi / 2      ) ** 2

					elif alpha_transform_type == "exp":

										def alpha_bar_fn(a__:			int      ):
															return math.exp(t * -12.0      )

					else:
										raise ValueError(f'''Unsupported alpha_tranform_type: {alpha_transform_type}'''      )

					__lowercase        =      []
					for i in range(a__      ):
										__lowercase        =      i / num_diffusion_timesteps
										__lowercase        =      (i + 1) / num_diffusion_timesteps
										betas.append(min(1 - alpha_bar_fn(a__      ) / alpha_bar_fn(a__      )			,						a__      )      )
					return torch.tensor(a__			,						dtype=torch.floataa      )


class       _snake_case  (    UpperCamelCase    ,      UpperCamelCase							):
						__snake_case			      =  [e.name for e in KarrasDiffusionSchedulers]
						__snake_case			      =  1







						@register_to_config
						def __init__(self  ,				UpperCAmelCase_ = 1_0_0_0  ,				UpperCAmelCase_ = 0.0_0_0_1  ,				UpperCAmelCase_ = 0.0_2  ,				UpperCAmelCase_ = "linear"  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = "epsilon"  ,				UpperCAmelCase_ = "linear"  ,				UpperCAmelCase_ = False  ,				UpperCAmelCase_ = "linspace"  ,				UpperCAmelCase_ = 0  ,				)      ->    Union[str, Any]:
											if trained_betas is not None:
																__lowercase        =      torch.tensor(UpperCAmelCase_  ,				dtype=torch.floataa				)
											elif beta_schedule == "linear":
																__lowercase        =      torch.linspace(UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				dtype=torch.floataa				)
											elif beta_schedule == "scaled_linear":
																# this schedule is very specific to the latent diffusion model.
																__lowercase        =      (
																    torch.linspace(beta_start**0.5  ,				beta_end**0.5  ,				UpperCAmelCase_  ,				dtype=torch.floataa				) ** 2
																)
											elif beta_schedule == "squaredcos_cap_v2":
																# Glide cosine schedule
																__lowercase        =      betas_for_alpha_bar(UpperCAmelCase_				)
											else:
																raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}'''				)

											__lowercase        =      1.0 - self.betas
											__lowercase        =      torch.cumprod(self.alphas  ,				dim=0				)

											__lowercase        =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
											__lowercase        =      np.concatenate([sigmas[::-1], [0.0]]				).astype(np.floataa				)
											__lowercase        =      torch.from_numpy(UpperCAmelCase_				)

											# setable values
											__lowercase        =      None
											__lowercase        =      np.linspace(0  ,				num_train_timesteps - 1  ,				UpperCAmelCase_  ,				dtype=UpperCAmelCase_				)[::-1].copy()
											__lowercase        =      torch.from_numpy(UpperCAmelCase_				)
											__lowercase        =      False
											__lowercase        =      use_karras_sigmas







						@property
						def 							_UpperCamelCase     (self				)      ->    Any:
											# standard deviation of the initial noise distribution
											if self.config.timestep_spacing in ["linspace", "trailing"]:
																return self.sigmas.max()

											return (self.sigmas.max() ** 2 + 1) ** 0.5







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    torch.FloatTensor:
											if isinstance(UpperCAmelCase_  ,				torch.Tensor				):
																__lowercase        =      timestep.to(self.timesteps.device				)
											__lowercase        =      (self.timesteps == timestep).nonzero().item()
											__lowercase        =      self.sigmas[step_index]

											__lowercase        =      sample / ((sigma**2 + 1) ** 0.5)

											__lowercase        =      True
											return sample







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = None				)      ->    Tuple:
											__lowercase        =      num_inference_steps

											# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
											if self.config.timestep_spacing == "linspace":
																__lowercase        =      np.linspace(0  ,				self.config.num_train_timesteps - 1  ,				UpperCAmelCase_  ,				dtype=UpperCAmelCase_				)[
																    ::-1
																].copy()
											elif self.config.timestep_spacing == "leading":
																__lowercase        =      self.config.num_train_timesteps // self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																__lowercase        =      (np.arange(0  ,				UpperCAmelCase_				) * step_ratio).round()[::-1].copy().astype(UpperCAmelCase_				)
																timesteps += self.config.steps_offset
											elif self.config.timestep_spacing == "trailing":
																__lowercase        =      self.config.num_train_timesteps / self.num_inference_steps
																# creates integer timesteps by multiplying by ratio
																# casting to int to avoid issues when num_inference_step is power of 3
																__lowercase        =      (np.arange(self.config.num_train_timesteps  ,				0  ,				-step_ratio				)).round().copy().astype(UpperCAmelCase_				)
																timesteps -= 1
											else:
																raise ValueError(
																    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.'''				)

											__lowercase        =      np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
											__lowercase        =      np.log(UpperCAmelCase_				)

											if self.config.interpolation_type == "linear":
																__lowercase        =      np.interp(UpperCAmelCase_  ,				np.arange(0  ,				len(UpperCAmelCase_				)				)  ,				UpperCAmelCase_				)
											elif self.config.interpolation_type == "log_linear":
																__lowercase        =      torch.linspace(np.log(sigmas[-1]				)  ,				np.log(sigmas[0]				)  ,				num_inference_steps + 1				).exp()
											else:
																raise ValueError(
																    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
																    ''' \'linear\' or \'log_linear\''''				)

											if self.use_karras_sigmas:
																__lowercase        =      self._convert_to_karras(in_sigmas=UpperCAmelCase_  ,				num_inference_steps=self.num_inference_steps				)
																__lowercase        =      np.array([self._sigma_to_t(UpperCAmelCase_  ,				UpperCAmelCase_				) for sigma in sigmas]				)

											__lowercase        =      np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
											__lowercase        =      torch.from_numpy(UpperCAmelCase_				).to(device=UpperCAmelCase_				)
											if str(UpperCAmelCase_				).startswith('''mps'''				):
																# mps does not support float64
																__lowercase        =      torch.from_numpy(UpperCAmelCase_				).to(UpperCAmelCase_  ,				dtype=torch.floataa				)
											else:
																__lowercase        =      torch.from_numpy(UpperCAmelCase_				).to(device=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    int:
											# get log sigma
											__lowercase        =      np.log(UpperCAmelCase_				)

											# get distribution
											__lowercase        =      log_sigma - log_sigmas[:, np.newaxis]

											# get sigmas range
											__lowercase        =      np.cumsum((dists >= 0)  ,				axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
											__lowercase        =      low_idx + 1

											__lowercase        =      log_sigmas[low_idx]
											__lowercase        =      log_sigmas[high_idx]

											# interpolate sigmas
											__lowercase        =      (low - log_sigma) / (low - high)
											__lowercase        =      np.clip(UpperCAmelCase_  ,				0  ,				1				)

											# transform interpolation to time range
											__lowercase        =      (1 - w) * low_idx + w * high_idx
											__lowercase        =      t.reshape(sigma.shape				)
											return t







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_				)      ->    torch.FloatTensor:
											__lowercase        =      in_sigmas[-1].item()
											__lowercase        =      in_sigmas[0].item()

											__lowercase        =      7.0  # 7.0 is the value used in the paper
											__lowercase        =      np.linspace(0  ,				1  ,				UpperCAmelCase_				)
											__lowercase        =      sigma_min ** (1 / rho)
											__lowercase        =      sigma_max ** (1 / rho)
											__lowercase        =      (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
											return sigmas







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_ = 0.0  ,				UpperCAmelCase_ = 0.0  ,				UpperCAmelCase_ = float('''inf'''				)  ,				UpperCAmelCase_ = 1.0  ,				UpperCAmelCase_ = None  ,				UpperCAmelCase_ = True  ,				)      ->    Union[EulerDiscreteSchedulerOutput, Tuple]:

											if (
											    isinstance(UpperCAmelCase_  ,				UpperCAmelCase_				)
											    or isinstance(UpperCAmelCase_  ,				torch.IntTensor				)
											    or isinstance(UpperCAmelCase_  ,				torch.LongTensor				)
											):
																raise ValueError(
																    (
																        '''Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to'''
																        ''' `EulerDiscreteScheduler.step()` is not supported. Make sure to pass'''
																        ''' one of the `scheduler.timesteps` as a timestep.'''
																    )  ,				)

											if not self.is_scale_input_called:
																logger.warning(
																    '''The `scale_model_input` function should be called before `step` to ensure correct denoising. '''
																    '''See `StableDiffusionPipeline` for a usage example.'''				)

											if isinstance(UpperCAmelCase_  ,				torch.Tensor				):
																__lowercase        =      timestep.to(self.timesteps.device				)

											__lowercase        =      (self.timesteps == timestep).nonzero().item()
											__lowercase        =      self.sigmas[step_index]

											__lowercase        =      min(s_churn / (len(self.sigmas				) - 1)  ,				2**0.5 - 1				) if s_tmin <= sigma <= s_tmax else 0.0

											__lowercase        =      randn_tensor(
											    model_output.shape  ,				dtype=model_output.dtype  ,				device=model_output.device  ,				generator=UpperCAmelCase_				)

											__lowercase        =      noise * s_noise
											__lowercase        =      sigma * (gamma + 1)

											if gamma > 0:
																__lowercase        =      sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

											# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
											# NOTE: "original_sample" should not be an expected prediction_type but is left in for
											# backwards compatibility
											if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
																__lowercase        =      model_output
											elif self.config.prediction_type == "epsilon":
																__lowercase        =      sample - sigma_hat * model_output
											elif self.config.prediction_type == "v_prediction":
																# * c_out + input * c_skip
																__lowercase        =      model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
											else:
																raise ValueError(
																    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''				)

											# 2. Convert to an ODE derivative
											__lowercase        =      (sample - pred_original_sample) / sigma_hat

											__lowercase        =      self.sigmas[step_index + 1] - sigma_hat

											__lowercase        =      sample + derivative * dt

											if not return_dict:
																return (prev_sample,)

											return EulerDiscreteSchedulerOutput(prev_sample=UpperCAmelCase_  ,				pred_original_sample=UpperCAmelCase_				)







						def 							_UpperCamelCase     (self  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				UpperCAmelCase_  ,				)      ->    torch.FloatTensor:
											# Make sure sigmas and timesteps have the same device and dtype as original_samples
											__lowercase        =      self.sigmas.to(device=original_samples.device  ,				dtype=original_samples.dtype				)
											if original_samples.device.type == "mps" and torch.is_floating_point(UpperCAmelCase_				):
																# mps does not support float64
																__lowercase        =      self.timesteps.to(original_samples.device  ,				dtype=torch.floataa				)
																__lowercase        =      timesteps.to(original_samples.device  ,				dtype=torch.floataa				)
											else:
																__lowercase        =      self.timesteps.to(original_samples.device				)
																__lowercase        =      timesteps.to(original_samples.device				)

											__lowercase        =      [(schedule_timesteps == t).nonzero().item() for t in timesteps]

											__lowercase        =      sigmas[step_indices].flatten()
											while len(sigma.shape				) < len(original_samples.shape				):
																__lowercase        =      sigma.unsqueeze(-1				)

											__lowercase        =      original_samples + noise * sigma
											return noisy_samples







						def __len__(self				)      ->    Tuple:
											return self.config.num_train_timesteps





"""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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

a__			:						Union[str, Any]							=			{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """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 					__UpperCamelCase (     a__:			str			,						a__:			Any			,						a__:			Dict			,						a__:			List[str]			,						a__:			str      )					->      str:




					"""simple docstring"""



					for attribute in key.split('''.'''      ):
										__lowercase        =      getattr(a__			,						a__      )

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

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





def 					__UpperCamelCase (     a__:			Union[str, Any]			,						a__:			Optional[int]			,						a__:			Optional[Any]      )					->      Any:




					"""simple docstring"""



					__lowercase        =      []
					__lowercase        =      fairseq_model.state_dict()

					__lowercase        =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__lowercase        =      False
										if "conv_layers" in name:
															load_conv_layer(
															    a__			,						a__			,						a__			,						a__			,						hf_model.config.feat_extract_norm == '''group'''			,						)
															__lowercase        =      True
										else:
															for key, mapped_key in MAPPING.items():
																				__lowercase        =      '''sew_d.''' + 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]:
																									__lowercase        =      True
																									if "*" in mapped_key:
																														__lowercase        =      name.split(a__      )[0].split('''.'''      )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														__lowercase        =      mapped_key.replace('''*'''			,						a__      )
																									if "weight_g" in name:
																														__lowercase        =      '''weight_g'''
																									elif "weight_v" in name:
																														__lowercase        =      '''weight_v'''
																									elif "weight" in name:
																														__lowercase        =      '''weight'''
																									elif "bias" in name:
																														__lowercase        =      '''bias'''
																									else:
																														__lowercase        =      None
																									set_recursively(a__			,						a__			,						a__			,						a__			,						a__      )
																				continue
										if not is_used:
															unused_weights.append(a__      )

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





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			int			,						a__:			List[Any]			,						a__:			int			,						a__:			List[str]      )					->      Optional[Any]:




					"""simple docstring"""



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





def 					__UpperCamelCase (     a__:			Optional[Any]			,						a__:			List[str]      )					->      List[str]:




					"""simple docstring"""



					__lowercase        =      SEWDConfig()
					if is_finetuned:
										__lowercase        =      model.wav_encoder.wav_model.cfg
					else:
										__lowercase        =      model.cfg

					__lowercase        =      fs_config.conv_bias
					__lowercase        =      eval(fs_config.conv_feature_layers      )
					__lowercase        =      [x[0] for x in conv_layers]
					__lowercase        =      [x[1] for x in conv_layers]
					__lowercase        =      [x[2] for x in conv_layers]
					__lowercase        =      '''gelu'''
					__lowercase        =      '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
					__lowercase        =      0.0
					__lowercase        =      fs_config.activation_fn.name
					__lowercase        =      fs_config.encoder_embed_dim
					__lowercase        =      0.02
					__lowercase        =      fs_config.encoder_ffn_embed_dim
					__lowercase        =      1e-5
					__lowercase        =      fs_config.encoder_layerdrop
					__lowercase        =      fs_config.encoder_attention_heads
					__lowercase        =      fs_config.conv_pos_groups
					__lowercase        =      fs_config.conv_pos
					__lowercase        =      len(a__      )
					__lowercase        =      fs_config.encoder_layers
					__lowercase        =      fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					__lowercase        =      fs_config.max_position_embeddings
					__lowercase        =      fs_config.position_buckets
					__lowercase        =      fs_config.share_att_key
					__lowercase        =      fs_config.relative_attention
					__lowercase        =      fs_config.position_biased_input
					__lowercase        =      tuple(fs_config.pos_att_type.split('''|'''      )      )
					__lowercase        =      fs_config.norm_rel_ebd

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

					__lowercase        =      '''Wav2Vec2FeatureExtractor'''
					__lowercase        =      '''Wav2Vec2CTCTokenizer'''

					return config





@torch.no_grad()
def 					__UpperCamelCase (     a__:			str			,						a__:			str			,						a__:			Optional[int]=None			,						a__:			str=None			,						a__:			Union[str, Any]=True      )					->      List[Any]:




					"""simple docstring"""



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

					if config_path is not None:
										__lowercase        =      SEWDConfig.from_pretrained(a__      )
					else:
										__lowercase        =      convert_config(model[0]			,						a__      )
					__lowercase        =      model[0].eval()

					__lowercase        =      True if config.feat_extract_norm == '''layer''' else False
					__lowercase        =      WavaVecaFeatureExtractor(
					    feature_size=1			,						sampling_rate=1_60_00			,						padding_value=0			,						do_normalize=a__			,						return_attention_mask=a__			,						)

					if is_finetuned:
										if dict_path:
															__lowercase        =      Dictionary.load(a__      )

															# important change bos & pad token id since CTC symbol is <pad> and
															# not <s> as in fairseq
															__lowercase        =      target_dict.pad_index
															__lowercase        =      target_dict.bos_index
															__lowercase        =      target_dict.pad_index
															__lowercase        =      target_dict.bos_index
															__lowercase        =      target_dict.eos_index
															__lowercase        =      len(target_dict.symbols      )
															__lowercase        =      os.path.join(a__			,						'''vocab.json'''      )
															if not os.path.isdir(a__      ):
																				logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(a__      )      )
																				return
															os.makedirs(a__			,						exist_ok=a__      )
															with open(a__			,						'''w'''			,						encoding='''utf-8'''      ) as vocab_handle:
																				json.dump(target_dict.indices			,						a__      )
															__lowercase        =      WavaVecaCTCTokenizer(
															    a__			,						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=a__			,						)
															__lowercase        =      WavaVecaProcessor(feature_extractor=a__			,						tokenizer=a__      )
															processor.save_pretrained(a__      )

										__lowercase        =      SEWDForCTC(a__      )
					else:
										__lowercase        =      SEWDModel(a__      )
										feature_extractor.save_pretrained(a__      )

					recursively_load_weights(a__			,						a__			,						a__      )

					hf_model.save_pretrained(a__      )


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


