




'''simple docstring'''


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

import numpy as np
import torch
from scipy import integrate

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






@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->LMSDiscrete
class 						lowerCAmelCase_	(       UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      torch.FloatTensor
  UpperCamelCase_			:      Optional[torch.FloatTensor]										=				None






def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase=0.9_9_9		,				__lowercase="cosine"		,				)			-> List[Any]:
     if alpha_transform_type == "cosine":

          def alpha_bar_fn(__lowercase	):
               return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2	) ** 2

     elif alpha_transform_type == "exp":

          def alpha_bar_fn(__lowercase	):
               return math.exp(t * -1_2.0	)

     else:
          raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""	)

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






class 						lowerCAmelCase_	(       UpperCAmelCase_       ,					UpperCAmelCase_							):







  '''simple docstring'''

  UpperCamelCase_			:      Optional[Any]										=				[e.name for e in KarrasDiffusionSchedulers]
  UpperCamelCase_			:      Dict										=				1



  @register_to_config
  def __init__( self				:							Any					,  SCREAMING_SNAKE_CASE_				:							int = 10_00					,  SCREAMING_SNAKE_CASE_				:							float = 0.0001					,  SCREAMING_SNAKE_CASE_				:							float = 0.02					,  SCREAMING_SNAKE_CASE_				:							str = "linear"					,  SCREAMING_SNAKE_CASE_				:							Optional[Union[np.ndarray, List[float]]] = None					,  SCREAMING_SNAKE_CASE_				:							Optional[bool] = False					,  SCREAMING_SNAKE_CASE_				:							str = "epsilon"					,  SCREAMING_SNAKE_CASE_				:							str = "linspace"					,  SCREAMING_SNAKE_CASE_				:							int = 0					,  )			->		List[str]:







       '''simple docstring'''



       if trained_betas is not None:
            A:		Optional[int]          =		torch.tensor(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       elif beta_schedule == "linear":
            A:		Dict          =		torch.linspace(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       elif beta_schedule == "scaled_linear":
            # this schedule is very specific to the latent diffusion model.
            A:		int          =		(
                torch.linspace(beta_start**0.5					,  beta_end**0.5					,  SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	) ** 2
            )
       elif beta_schedule == "squaredcos_cap_v2":
            # Glide cosine schedule
            A:		str          =		betas_for_alpha_bar(SCREAMING_SNAKE_CASE_	)
       else:
            raise NotImplementedError(f"""{beta_schedule} does is not implemented for {self.__class__}"""	)

       A:		int          =		1.0 - self.betas
       A:		Any          =		torch.cumprod(self.alphas					,  dim=0	)

       A:		Union[str, Any]          =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
       A:		Tuple          =		np.concatenate([sigmas[::-1], [0.0]]	).astype(np.floataa	)
       A:		Union[str, Any]          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	)

       # setable values
       A:		List[Any]          =		None
       A:		List[str]          =		use_karras_sigmas
       self.set_timesteps(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	)
       A:		int          =		[]
       A:		List[Any]          =		False



  @property
  def 					_snake_case     ( self				:							str	)			->		Any:







       '''simple docstring'''



       if self.config.timestep_spacing in ["linspace", "trailing"]:
            return self.sigmas.max()

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



  def 					_snake_case     ( self				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							Union[float, torch.FloatTensor]	)			->		torch.FloatTensor:







       '''simple docstring'''



       if isinstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor	):
            A:		Any          =		timestep.to(self.timesteps.device	)
       A:		List[str]          =		(self.timesteps == timestep).nonzero().item()
       A:		str          =		self.sigmas[step_index]
       A:		List[str]          =		sample / ((sigma**2 + 1) ** 0.5)
       A:		Union[str, Any]          =		True
       return sample



  def 					_snake_case     ( self				:							Tuple					,  SCREAMING_SNAKE_CASE_				:							Dict					,  SCREAMING_SNAKE_CASE_				:							Optional[Any]					,  SCREAMING_SNAKE_CASE_				:							str	)			->		str:







       '''simple docstring'''



       def lms_derivative(SCREAMING_SNAKE_CASE_				:							Dict	):
            A:		int          =		1.0
            for k in range(SCREAMING_SNAKE_CASE_	):
                 if current_order == k:
                      continue
                 prod *= (tau - self.sigmas[t - k]) / (self.sigmas[t - current_order] - self.sigmas[t - k])
            return prod

       A:		str          =		integrate.quad(SCREAMING_SNAKE_CASE_					,  self.sigmas[t]					,  self.sigmas[t + 1]					,  epsrel=1E-4	)[0]

       return integrated_coeff



  def 					_snake_case     ( self				:							str					,  SCREAMING_SNAKE_CASE_				:							int					,  SCREAMING_SNAKE_CASE_				:							Union[str, torch.device] = None	)			->		List[str]:







       '''simple docstring'''



       A:		Union[str, Any]          =		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":
            A:		Any          =		np.linspace(0					,  self.config.num_train_timesteps - 1					,  SCREAMING_SNAKE_CASE_					,  dtype=SCREAMING_SNAKE_CASE_	)[
                ::-1
            ].copy()
       elif self.config.timestep_spacing == "leading":
            A:		Dict          =		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
            A:		Optional[int]          =		(np.arange(0					,  SCREAMING_SNAKE_CASE_	) * step_ratio).round()[::-1].copy().astype(SCREAMING_SNAKE_CASE_	)
            timesteps += self.config.steps_offset
       elif self.config.timestep_spacing == "trailing":
            A:		List[str]          =		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
            A:		Optional[int]          =		(np.arange(self.config.num_train_timesteps					,  0					,  -step_ratio	)).round().copy().astype(SCREAMING_SNAKE_CASE_	)
            timesteps -= 1
       else:
            raise ValueError(
                f"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""	)

       A:		Optional[int]          =		np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5	)
       A:		List[Any]          =		np.log(SCREAMING_SNAKE_CASE_	)
       A:		Tuple          =		np.interp(SCREAMING_SNAKE_CASE_					,  np.arange(0					,  len(SCREAMING_SNAKE_CASE_	)	)					,  SCREAMING_SNAKE_CASE_	)

       if self.use_karras_sigmas:
            A:		int          =		self._convert_to_karras(in_sigmas=SCREAMING_SNAKE_CASE_	)
            A:		Tuple          =		np.array([self._sigma_to_t(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	) for sigma in sigmas]	)

       A:		str          =		np.concatenate([sigmas, [0.0]]	).astype(np.floataa	)

       A:		Optional[Any]          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_	)
       if str(SCREAMING_SNAKE_CASE_	).startswith('''mps'''	):
            # mps does not support float64
            A:		Optional[int]          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(SCREAMING_SNAKE_CASE_					,  dtype=torch.floataa	)
       else:
            A:		Dict          =		torch.from_numpy(SCREAMING_SNAKE_CASE_	).to(device=SCREAMING_SNAKE_CASE_	)

       A:		int          =		[]



  def 					_snake_case     ( self				:							Optional[int]					,  SCREAMING_SNAKE_CASE_				:							List[Any]					,  SCREAMING_SNAKE_CASE_				:							Dict	)			->		List[Any]:







       '''simple docstring'''



       A:		Optional[Any]          =		np.log(SCREAMING_SNAKE_CASE_	)

       # get distribution
       A:		Optional[Any]          =		log_sigma - log_sigmas[:, np.newaxis]

       # get sigmas range
       A:		List[str]          =		np.cumsum((dists >= 0)					,  axis=0	).argmax(axis=0	).clip(max=log_sigmas.shape[0] - 2	)
       A:		Optional[Any]          =		low_idx + 1

       A:		List[Any]          =		log_sigmas[low_idx]
       A:		int          =		log_sigmas[high_idx]

       # interpolate sigmas
       A:		Optional[int]          =		(low - log_sigma) / (low - high)
       A:		Optional[Any]          =		np.clip(SCREAMING_SNAKE_CASE_					,  0					,  1	)

       # transform interpolation to time range
       A:		Tuple          =		(1 - w) * low_idx + w * high_idx
       A:		Optional[Any]          =		t.reshape(sigma.shape	)
       return t



  def 					_snake_case     ( self				:							Union[str, Any]					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor	)			->		torch.FloatTensor:







       '''simple docstring'''



       A:		float          =		in_sigmas[-1].item()
       A:		float          =		in_sigmas[0].item()

       A:		Any          =		7.0  # 7.0 is the value used in the paper
       A:		Tuple          =		np.linspace(0					,  1					,  self.num_inference_steps	)
       A:		Optional[Any]          =		sigma_min ** (1 / rho)
       A:		int          =		sigma_max ** (1 / rho)
       A:		Union[str, Any]          =		(max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
       return sigmas



  def 					_snake_case     ( self				:							str					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							Union[float, torch.FloatTensor]					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							int = 4					,  SCREAMING_SNAKE_CASE_				:							bool = True					,  )			->		Union[LMSDiscreteSchedulerOutput, Tuple]:







       '''simple docstring'''



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

       if isinstance(SCREAMING_SNAKE_CASE_					,  torch.Tensor	):
            A:		str          =		timestep.to(self.timesteps.device	)
       A:		int          =		(self.timesteps == timestep).nonzero().item()
       A:		Any          =		self.sigmas[step_index]

       # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
       if self.config.prediction_type == "epsilon":
            A:		Optional[Any]          =		sample - sigma * model_output
       elif self.config.prediction_type == "v_prediction":
            # * c_out + input * c_skip
            A:		List[Any]          =		model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
       elif self.config.prediction_type == "sample":
            A:		List[str]          =		model_output
       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
       A:		Union[str, Any]          =		(sample - pred_original_sample) / sigma
       self.derivatives.append(SCREAMING_SNAKE_CASE_	)
       if len(self.derivatives	) > order:
            self.derivatives.pop(0	)

       # 3. Compute linear multistep coefficients
       A:		Union[str, Any]          =		min(step_index + 1					,  SCREAMING_SNAKE_CASE_	)
       A:		int          =		[self.get_lms_coefficient(SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_					,  SCREAMING_SNAKE_CASE_	) for curr_order in range(SCREAMING_SNAKE_CASE_	)]

       # 4. Compute previous sample based on the derivatives path
       A:		Optional[int]          =		sample + sum(
           coeff * derivative for coeff, derivative in zip(SCREAMING_SNAKE_CASE_					,  reversed(self.derivatives	)	)	)

       if not return_dict:
            return (prev_sample,)

       return LMSDiscreteSchedulerOutput(prev_sample=SCREAMING_SNAKE_CASE_					,  pred_original_sample=SCREAMING_SNAKE_CASE_	)



  def 					_snake_case     ( self				:							Dict					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  SCREAMING_SNAKE_CASE_				:							torch.FloatTensor					,  )			->		torch.FloatTensor:







       '''simple docstring'''



       A:		Optional[int]          =		self.sigmas.to(device=original_samples.device					,  dtype=original_samples.dtype	)
       if original_samples.device.type == "mps" and torch.is_floating_point(SCREAMING_SNAKE_CASE_	):
            # mps does not support float64
            A:		Dict          =		self.timesteps.to(original_samples.device					,  dtype=torch.floataa	)
            A:		Union[str, Any]          =		timesteps.to(original_samples.device					,  dtype=torch.floataa	)
       else:
            A:		Dict          =		self.timesteps.to(original_samples.device	)
            A:		Union[str, Any]          =		timesteps.to(original_samples.device	)

       A:		Union[str, Any]          =		[(schedule_timesteps == t).nonzero().item() for t in timesteps]

       A:		List[str]          =		sigmas[step_indices].flatten()
       while len(sigma.shape	) < len(original_samples.shape	):
            A:		List[Any]          =		sigma.unsqueeze(-1	)

       A:		Optional[Any]          =		original_samples + noise * sigma
       return noisy_samples




  def __len__( self				:							Dict	)			->		Any:







       '''simple docstring'''



       return self.config.num_train_timesteps












'''simple docstring'''


import argparse
import os
from functools import reduce

import fairseq
import torch
from datasets import load_dataset

from transformers import WavaVecaProcessor, logging
from transformers.models.dataavec.configuration_dataavec_audio import DataaVecAudioConfig

# Copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_audio.py
from transformers.models.dataavec.dataavec_audio import DataaVecAudioModel as Dummy  # noqa: F401
from transformers.models.dataavec.modeling_dataavec_audio import DataaVecAudioForCTC, DataaVecAudioModel


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

UpperCamelCase			     =					{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''models.0.layer_norm''': '''feature_projection.layer_norm''',
    '''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''',
}
UpperCamelCase			     =					[
    '''lm_head''',
]
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Any:
     for attribute in key.split('''.'''	):
          A:		Union[str, Any]          =		getattr(__lowercase		,				__lowercase	)

     if weight_type is not None:
          A:		Tuple          =		getattr(__lowercase		,				__lowercase	).shape
     else:
          A:		Optional[int]          =		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:		int          =		value
     elif weight_type == "weight_g":
          A:		Any          =		value
     elif weight_type == "weight_v":
          A:		str          =		value
     elif weight_type == "bias":
          A:		List[str]          =		value
     else:
          A:		str          =		value

     logger.info(F"""{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase	)			-> Dict:
     A:		Optional[int]          =		[]
     A:		Tuple          =		fairseq_model.state_dict()

     if not is_headless:
          A:		int          =		hf_model.dataavec_audio.feature_extractor
          A:		Dict          =		hf_model.dataavec_audio.encoder.pos_conv_embed

     else:
          A:		str          =		hf_model.feature_extractor
          A:		Tuple          =		hf_model.encoder.pos_conv_embed

     for name, value in fairseq_dict.items():
          A:		int          =		False
          if "conv_layers" in name:
               load_conv_layer(
                   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				)
               A:		List[Any]          =		True
          elif "pos_conv" in name:
               load_pos_conv_layer(
                   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				)
               A:		Union[str, Any]          =		True
          else:
               for key, mapped_key in MAPPING.items():
                    if not is_headless:
                         A:		str          =		'''data2vec_audio.''' + 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]:
                         A:		Optional[int]          =		True
                         if "*" in mapped_key:
                              A:		Any          =		name.split(__lowercase	)[0].split('''.'''	)[-2]
                              A:		Any          =		mapped_key.replace('''*'''		,				__lowercase	)
                         if "weight_g" in name:
                              A:		List[Any]          =		'''weight_g'''
                         elif "weight_v" in name:
                              A:		List[Any]          =		'''weight_v'''
                         elif "bias" in name:
                              A:		int          =		'''bias'''
                         elif "weight" in name:
                              # TODO: don't match quantizer.weight_proj
                              A:		int          =		'''weight'''
                         else:
                              A:		List[Any]          =		None
                         set_recursively(__lowercase		,				__lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)
                    continue
          if not is_used:
               unused_weights.append(__lowercase	)

     logger.warning(F"""Unused weights: {unused_weights}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase	)			-> str:
     A:		int          =		path.split('''.'''	)
     return reduce(__lowercase		,				__lowercase		,				__lowercase	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Tuple          =		access_by_string(__lowercase		,				__lowercase	)
     A:		Tuple          =		hf_weight.data

     if fsq_value.shape != hf_value.shape:
          raise ValueError(F"""{full_name} has size {fsq_value.shape}, but {hf_value.shape} was found."""	)
     A:		List[Any]          =		fsq_value
     logger.info(F"""{full_name} was correctly initialized from {hf_weight_path}."""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> List[Any]:
     A:		List[Any]          =		full_name.split('''conv_layers.'''	)[-1]
     A:		List[str]          =		name.split('''.'''	)
     A:		Tuple          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		List[str]          =		name.split('''.'''	)[-1]
     if type_id == 0:
          A:		Optional[Any]          =		'''conv'''
     elif type_id == 2:
          A:		List[str]          =		'''layer_norm'''
     else:
          unused_weights.append(__lowercase	)
          return

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""conv_layers.{layer_id}.{layer_type}.{weight_type}"""	)
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase		,				__lowercase	)			-> Optional[Any]:
     A:		Any          =		full_name.split('''pos_conv.'''	)[-1]
     A:		Any          =		name.split('''.'''	)
     A:		int          =		int(items[0]	)
     A:		Optional[int]          =		int(items[1]	)

     A:		Optional[Any]          =		name.split('''.'''	)[-1]
     if type_id != 0:
          unused_weights.append(__lowercase	)
          return
     else:
          A:		List[str]          =		'''conv'''

     set_weights(__lowercase		,				__lowercase		,				__lowercase		,				F"""layers.{layer_id}.{layer_type}.{weight_type}"""	)
@torch.no_grad()
def 					SCREAMING_SNAKE_CASE(   __lowercase		,				__lowercase		,				__lowercase=None		,				__lowercase=None		,				__lowercase=True	)			-> Optional[Any]:
     if config_path is not None:
          A:		Tuple          =		DataaVecAudioConfig.from_pretrained(__lowercase	)
     else:
          A:		Optional[Any]          =		DataaVecAudioConfig()

     if not is_finetuned:
          # Modify final_proj layer name
          A:		Union[str, Any]          =		DataaVecAudioModel(__lowercase	)
          A:		List[Any]          =		os.path.dirname(__lowercase	)

          A:		Union[str, Any]          =		torch.load(__lowercase	)
          A:		int          =		state_dict['''model'''].pop('''final_proj.0.weight'''	)
          A:		str          =		state_dict['''model'''].pop('''final_proj.0.bias'''	)
          A:		Any          =		os.path.join(__lowercase		,				'''converted.pt'''	)
          torch.save(__lowercase		,				__lowercase	)
     else:
          A:		Tuple          =		DataaVecAudioForCTC(__lowercase	)
          A:		Optional[int]          =		checkpoint_path

     def load_dataavec(__lowercase	):
          A     ,    A     ,    A:		List[Any]          =		fairseq.checkpoint_utils.load_model_ensemble_and_task([path]	)
          return model[0].eval()

     A:		Any          =		load_dataavec(__lowercase	)

     recursively_load_weights(__lowercase		,				__lowercase		,				not is_finetuned	)

     A:		Optional[int]          =		WavaVecaProcessor.from_pretrained('''facebook/wav2vec2-large-lv60'''	)

     A:		Optional[Any]          =		load_dataset('''patrickvonplaten/librispeech_asr_dummy'''		,				'''clean'''		,				split='''validation'''	)
     A:		Optional[int]          =		[x['''array'''] for x in ds[:4]['''audio''']]

     A:		Dict          =		processor(__lowercase		,				return_tensors='''pt'''		,				padding=__lowercase	)

     A:		str          =		inputs.input_values
     A:		str          =		inputs.attention_mask
     #    input_values = inputs.input_values[:, :-1]
     #    attention_mask = inputs.attention_mask[:, :-1]

     hf_wavavec.eval()
     model.eval()
     if is_finetuned:
          A:		Any          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''encoder_out'''
          ].transpose(0		,				1	)
          A:		int          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''logits''']

          A:		str          =		torch.argmax(__lowercase		,				dim=-1	)
          A:		Any          =		processor.batch_decode(__lowercase	)

          print(F"""Expected Output: {ds[:4]['text']}, Pred: {output_string}"""	)
     else:
          A:		int          =		model(source=__lowercase		,				padding_mask=(1 - attention_mask)		,				mask=__lowercase		,				features_only=__lowercase	)[
              '''layer_results'''
          ][-1][0].transpose(0		,				1	)
          A:		List[Any]          =		hf_wavavec(__lowercase		,				attention_mask=__lowercase	)['''last_hidden_state''']

     print(our_output.shape		,				their_output.shape	)
     A:		List[Any]          =		torch.max(torch.abs(our_output - their_output	)	).item()
     print(F"""max_absolute_diff = {max_absolute_diff}"""	)  # ~ 1e-7
     A:		Optional[int]          =		torch.allclose(__lowercase		,				__lowercase		,				atol=1E-3	)
     print('''Do both models output the same tensors?'''		,				'''🔥''' if success else '''💩'''	)
     if not success:
          raise Exception('''Something went wRoNg'''	)

     hf_wavavec.save_pretrained(__lowercase	)

     if is_finetuned:
          processor.save_pretrained(__lowercase	)
     else:
          processor.feature_extractor.save_pretrained(__lowercase	)


if __name__ == "__main__":
     UpperCamelCase			     =					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'''
     )
     UpperCamelCase			     =					parser.parse_args()
     convert_wavaveca_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )






