'''simple docstring'''


import inspect
from typing import List, Optional, Union

import torch
from torch import nn
from torch.nn import functional as F
from torchvision import transforms
from transformers import CLIPImageProcessor, CLIPModel, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DiffusionPipeline,
    DPMSolverMultistepScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion import StableDiffusionPipelineOutput






class      __SCREAMING_SNAKE_CASE						(       nn.Module       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		snake_case_=1.0						):




       '''simple docstring'''

       super().__init__()

       UpperCAmelCase_							:   int   =      cut_size
       UpperCAmelCase_							:   Union[str, Any]   =      cut_power






  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_						):




       '''simple docstring'''

       UpperCAmelCase_   ,			UpperCAmelCase_							:   Tuple   =      pixel_values.shape[2:4]
       UpperCAmelCase_							:   Tuple   =      min(snake_case_      ,		snake_case_						)
       UpperCAmelCase_							:   List[Any]   =      min(snake_case_      ,		snake_case_      ,		self.cut_size						)
       UpperCAmelCase_							:   List[str]   =      []
       for _ in range(snake_case_						):
            UpperCAmelCase_							:   Dict   =      int(torch.rand([]						) ** self.cut_power * (max_size - min_size) + min_size						)
            UpperCAmelCase_							:   str   =      torch.randint(0      ,		sideX - size + 1      ,		()						)
            UpperCAmelCase_							:   Dict   =      torch.randint(0      ,		sideY - size + 1      ,		()						)
            UpperCAmelCase_							:   Optional[int]   =      pixel_values[:, :, offsety : offsety + size, offsetx : offsetx + size]
            cutouts.append(F.adaptive_avg_poolad(snake_case_      ,		self.cut_size						)						)
       return torch.cat(snake_case_						)







def 					_lowerCamelCase    (      lowerCamelCase_					:							Any	,     lowerCamelCase_					:							Optional[int]   ):



     """simple docstring"""





     UpperCAmelCase_							:   Dict   =      F.normalize(lowerCamelCase_	,     dim=-1   )
     UpperCAmelCase_							:   Union[str, Any]   =      F.normalize(lowerCamelCase_	,     dim=-1   )
     return (x - y).norm(dim=-1   ).div(2   ).arcsin().pow(2   ).mul(2   )


def 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     for param in model.parameters():
          UpperCAmelCase_							:   List[Any]   =      value






class      __SCREAMING_SNAKE_CASE						(       lowerCamelCase_       ):







  '''simple docstring'''






  def __init__( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		):




       '''simple docstring'''

       super().__init__()
       self.register_modules(
           vae=snake_case_      ,		text_encoder=snake_case_      ,		clip_model=snake_case_      ,		tokenizer=snake_case_      ,		unet=snake_case_      ,		scheduler=snake_case_      ,		feature_extractor=snake_case_      ,		)

       UpperCAmelCase_							:   List[Any]   =      transforms.Normalize(mean=feature_extractor.image_mean      ,		std=feature_extractor.image_std						)
       UpperCAmelCase_							:   Dict   =      (
           feature_extractor.size
           if isinstance(feature_extractor.size      ,		snake_case_						)
           else feature_extractor.size['shortest_edge']
       )
       UpperCAmelCase_							:   str   =      MakeCutouts(self.cut_out_size						)

       set_requires_grad(self.text_encoder      ,		snake_case_						)
       set_requires_grad(self.clip_model      ,		snake_case_						)


  def _UpperCamelCase     ( self      ,		snake_case_ = "auto"						):




       '''simple docstring'''

       if slice_size == "auto":
            # half the attention head size is usually a good trade-off between
            # speed and memory
            UpperCAmelCase_							:   Union[str, Any]   =      self.unet.config.attention_head_dim // 2
       self.unet.set_attention_slice(snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       self.enable_attention_slicing(snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       set_requires_grad(self.vae      ,		snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       set_requires_grad(self.vae      ,		snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       set_requires_grad(self.unet      ,		snake_case_						)


  def _UpperCamelCase     ( self						):




       '''simple docstring'''

       set_requires_grad(self.unet      ,		snake_case_						)


  @torch.enable_grad()
  def _UpperCamelCase     ( self      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_=True      ,		):




       '''simple docstring'''

       UpperCAmelCase_							:   str   =      latents.detach().requires_grad_()

       UpperCAmelCase_							:   Any   =      self.scheduler.scale_model_input(snake_case_      ,		snake_case_						)

       # predict the noise residual
       UpperCAmelCase_							:   int   =      self.unet(snake_case_      ,		snake_case_      ,		encoder_hidden_states=snake_case_						).sample

       if isinstance(self.scheduler      ,		(PNDMScheduler, DDIMScheduler, DPMSolverMultistepScheduler)						):
            UpperCAmelCase_							:   Optional[Any]   =      self.scheduler.alphas_cumprod[timestep]
            UpperCAmelCase_							:   List[str]   =      1 - alpha_prod_t
            # compute predicted original sample from predicted noise also called
            # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
            UpperCAmelCase_							:   Optional[int]   =      (latents - beta_prod_t ** 0.5 * noise_pred) / alpha_prod_t ** 0.5

            UpperCAmelCase_							:   List[str]   =      torch.sqrt(snake_case_						)
            UpperCAmelCase_							:   List[str]   =      pred_original_sample * (fac) + latents * (1 - fac)
       elif isinstance(self.scheduler      ,		snake_case_						):
            UpperCAmelCase_							:   Optional[Any]   =      self.scheduler.sigmas[index]
            UpperCAmelCase_							:   Any   =      latents - sigma * noise_pred
       else:
            raise ValueError(F'''scheduler type {type(self.scheduler						)} not supported'''						)

       UpperCAmelCase_							:   int   =      1 / self.vae.config.scaling_factor * sample
       UpperCAmelCase_							:   List[Any]   =      self.vae.decode(snake_case_						).sample
       UpperCAmelCase_							:   Union[str, Any]   =      (image / 2 + 0.5).clamp(0      ,		1						)

       if use_cutouts:
            UpperCAmelCase_							:   Optional[Any]   =      self.make_cutouts(snake_case_      ,		snake_case_						)
       else:
            UpperCAmelCase_							:   Any   =      transforms.Resize(self.cut_out_size						)(snake_case_						)
       UpperCAmelCase_							:   Optional[int]   =      self.normalize(snake_case_						).to(latents.dtype						)

       UpperCAmelCase_							:   List[str]   =      self.clip_model.get_image_features(snake_case_						)
       UpperCAmelCase_							:   Tuple   =      image_embeddings_clip / image_embeddings_clip.norm(p=2      ,		dim=-1      ,		keepdim=snake_case_						)

       if use_cutouts:
            UpperCAmelCase_							:   Tuple   =      spherical_dist_loss(snake_case_      ,		snake_case_						)
            UpperCAmelCase_							:   Dict   =      dists.view([num_cutouts, sample.shape[0], -1]						)
            UpperCAmelCase_							:   str   =      dists.sum(2						).mean(0						).sum() * clip_guidance_scale
       else:
            UpperCAmelCase_							:   List[str]   =      spherical_dist_loss(snake_case_      ,		snake_case_						).mean() * clip_guidance_scale

       UpperCAmelCase_							:   Dict   =      -torch.autograd.grad(snake_case_      ,		snake_case_						)[0]

       if isinstance(self.scheduler      ,		snake_case_						):
            UpperCAmelCase_							:   Tuple   =      latents.detach() + grads * (sigma**2)
            UpperCAmelCase_							:   Optional[int]   =      noise_pred_original
       else:
            UpperCAmelCase_							:   Any   =      noise_pred_original - torch.sqrt(snake_case_						) * grads
       return noise_pred, latents






  @torch.no_grad()
  def __call__( self      ,		snake_case_      ,		snake_case_ = 5_1_2      ,		snake_case_ = 5_1_2      ,		snake_case_ = 5_0      ,		snake_case_ = 7.5      ,		snake_case_ = 1      ,		snake_case_ = 0.0      ,		snake_case_ = 1_0_0      ,		snake_case_ = None      ,		snake_case_ = 4      ,		snake_case_ = True      ,		snake_case_ = None      ,		snake_case_ = None      ,		snake_case_ = "pil"      ,		snake_case_ = True      ,		):




       '''simple docstring'''

       if isinstance(snake_case_      ,		snake_case_						):
            UpperCAmelCase_							:   Optional[Any]   =      1
       elif isinstance(snake_case_      ,		snake_case_						):
            UpperCAmelCase_							:   List[Any]   =      len(snake_case_						)
       else:
            raise ValueError(F'''`prompt` has to be of type `str` or `list` but is {type(snake_case_						)}'''						)

       if height % 8 != 0 or width % 8 != 0:
            raise ValueError(F'''`height` and `width` have to be divisible by 8 but are {height} and {width}.'''						)

       # get prompt text embeddings
       UpperCAmelCase_							:   Union[str, Any]   =      self.tokenizer(
           snake_case_      ,		padding='max_length'      ,		max_length=self.tokenizer.model_max_length      ,		truncation=snake_case_      ,		return_tensors='pt'      ,		)
       UpperCAmelCase_							:   Tuple   =      self.text_encoder(text_input.input_ids.to(self.device						)						)[0]
       # duplicate text embeddings for each generation per prompt
       UpperCAmelCase_							:   List[str]   =      text_embeddings.repeat_interleave(snake_case_      ,		dim=0						)

       if clip_guidance_scale > 0:
            if clip_prompt is not None:
                 UpperCAmelCase_							:   Tuple   =      self.tokenizer(
                     snake_case_      ,		padding='max_length'      ,		max_length=self.tokenizer.model_max_length      ,		truncation=snake_case_      ,		return_tensors='pt'      ,		).input_ids.to(self.device						)
            else:
                 UpperCAmelCase_							:   str   =      text_input.input_ids.to(self.device						)
            UpperCAmelCase_							:   List[str]   =      self.clip_model.get_text_features(snake_case_						)
            UpperCAmelCase_							:   Tuple   =      text_embeddings_clip / text_embeddings_clip.norm(p=2      ,		dim=-1      ,		keepdim=snake_case_						)
            # duplicate text embeddings clip for each generation per prompt
            UpperCAmelCase_							:   List[str]   =      text_embeddings_clip.repeat_interleave(snake_case_      ,		dim=0						)

       # here `guidance_scale` is defined analog to the guidance weight `w` of equation (2)
       # of the Imagen paper: https://arxiv.org/pdf/2205.11487.pdf . `guidance_scale = 1`
       # corresponds to doing no classifier free guidance.
       UpperCAmelCase_							:   List[str]   =      guidance_scale > 1.0
       # get unconditional embeddings for classifier free guidance
       if do_classifier_free_guidance:
            UpperCAmelCase_							:   int   =      text_input.input_ids.shape[-1]
            UpperCAmelCase_							:   Optional[int]   =      self.tokenizer(['']      ,		padding='max_length'      ,		max_length=snake_case_      ,		return_tensors='pt'						)
            UpperCAmelCase_							:   Tuple   =      self.text_encoder(uncond_input.input_ids.to(self.device						)						)[0]
            # duplicate unconditional embeddings for each generation per prompt
            UpperCAmelCase_							:   Optional[Any]   =      uncond_embeddings.repeat_interleave(snake_case_      ,		dim=0						)

            # For classifier free guidance, we need to do two forward passes.
            # Here we concatenate the unconditional and text embeddings into a single batch
            # to avoid doing two forward passes
            UpperCAmelCase_							:   List[Any]   =      torch.cat([uncond_embeddings, text_embeddings]						)

       # get the initial random noise unless the user supplied it

       # Unlike in other pipelines, latents need to be generated in the target device
       # for 1-to-1 results reproducibility with the CompVis implementation.
       # However this currently doesn't work in `mps`.
       UpperCAmelCase_							:   Optional[int]   =      (batch_size * num_images_per_prompt, self.unet.config.in_channels, height // 8, width // 8)
       UpperCAmelCase_							:   Dict   =      text_embeddings.dtype
       if latents is None:
            if self.device.type == "mps":
                 # randn does not work reproducibly on mps
                 UpperCAmelCase_							:   List[str]   =      torch.randn(snake_case_      ,		generator=snake_case_      ,		device='cpu'      ,		dtype=snake_case_						).to(
                     self.device						)
            else:
                 UpperCAmelCase_							:   Optional[Any]   =      torch.randn(snake_case_      ,		generator=snake_case_      ,		device=self.device      ,		dtype=snake_case_						)
       else:
            if latents.shape != latents_shape:
                 raise ValueError(F'''Unexpected latents shape, got {latents.shape}, expected {latents_shape}'''						)
            UpperCAmelCase_							:   Optional[int]   =      latents.to(self.device						)

       # set timesteps
       UpperCAmelCase_							:   Any   =      'offset' in set(inspect.signature(self.scheduler.set_timesteps						).parameters.keys()						)
       UpperCAmelCase_							:   int   =      {}
       if accepts_offset:
            UpperCAmelCase_							:   List[Any]   =      1

       self.scheduler.set_timesteps(snake_case_      ,		**snake_case_						)

       # Some schedulers like PNDM have timesteps as arrays
       # It's more optimized to move all timesteps to correct device beforehand
       UpperCAmelCase_							:   List[str]   =      self.scheduler.timesteps.to(self.device						)

       # scale the initial noise by the standard deviation required by the scheduler
       UpperCAmelCase_							:   Dict   =      latents * self.scheduler.init_noise_sigma

       # prepare extra kwargs for the scheduler step, since not all schedulers have the same signature
       # eta (η) is only used with the DDIMScheduler, it will be ignored for other schedulers.
       # eta corresponds to η in DDIM paper: https://arxiv.org/abs/2010.02502
       # and should be between [0, 1]
       UpperCAmelCase_							:   Dict   =      'eta' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
       UpperCAmelCase_							:   str   =      {}
       if accepts_eta:
            UpperCAmelCase_							:   Union[str, Any]   =      eta

       # check if the scheduler accepts generator
       UpperCAmelCase_							:   Union[str, Any]   =      'generator' in set(inspect.signature(self.scheduler.step						).parameters.keys()						)
       if accepts_generator:
            UpperCAmelCase_							:   Dict   =      generator

       for i, t in enumerate(self.progress_bar(snake_case_						)						):
            # expand the latents if we are doing classifier free guidance
            UpperCAmelCase_							:   Any   =      torch.cat([latents] * 2						) if do_classifier_free_guidance else latents
            UpperCAmelCase_							:   Union[str, Any]   =      self.scheduler.scale_model_input(snake_case_      ,		snake_case_						)

            # predict the noise residual
            UpperCAmelCase_							:   Optional[Any]   =      self.unet(snake_case_      ,		snake_case_      ,		encoder_hidden_states=snake_case_						).sample

            # perform classifier free guidance
            if do_classifier_free_guidance:
                 UpperCAmelCase_   ,			UpperCAmelCase_							:   Tuple   =      noise_pred.chunk(2						)
                 UpperCAmelCase_							:   Any   =      noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)

            # perform clip guidance
            if clip_guidance_scale > 0:
                 UpperCAmelCase_							:   List[str]   =      (
                     text_embeddings.chunk(2						)[1] if do_classifier_free_guidance else text_embeddings
                 )
                 UpperCAmelCase_   ,			UpperCAmelCase_							:   int   =      self.cond_fn(
                     snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		snake_case_      ,		)

            # compute the previous noisy sample x_t -> x_t-1
            UpperCAmelCase_							:   Any   =      self.scheduler.step(snake_case_      ,		snake_case_      ,		snake_case_      ,		**snake_case_						).prev_sample

       # scale and decode the image latents with vae
       UpperCAmelCase_							:   str   =      1 / self.vae.config.scaling_factor * latents
       UpperCAmelCase_							:   List[str]   =      self.vae.decode(snake_case_						).sample

       UpperCAmelCase_							:   Tuple   =      (image / 2 + 0.5).clamp(0      ,		1						)
       UpperCAmelCase_							:   Optional[int]   =      image.cpu().permute(0      ,		2      ,		3      ,		1						).numpy()

       if output_type == "pil":
            UpperCAmelCase_							:   List[Any]   =      self.numpy_to_pil(snake_case_						)

       if not return_dict:
            return (image, None)

       return StableDiffusionPipelineOutput(images=snake_case_      ,		nsfw_content_detected=snake_case_						)







'''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()
snake_case__     :			Tuple      			=  logging.get_logger(__name__)

snake_case__     :			Tuple      			=  {
    '''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 					_lowerCamelCase    (      lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							Union[str, Any]	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							Any	,     lowerCamelCase_					:							Any   ):



     """simple docstring"""





     for attribute in key.split('.'   ):
          UpperCAmelCase_							:   List[Any]   =      getattr(lowerCamelCase_	,     lowerCamelCase_   )

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

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


def 					_lowerCamelCase    (      lowerCamelCase_					:							Any	,     lowerCamelCase_					:							str	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      []
     UpperCAmelCase_							:   Union[str, Any]   =      fairseq_model.state_dict()

     UpperCAmelCase_							:   Optional[Any]   =      hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          UpperCAmelCase_							:   Optional[Any]   =      False
          if "conv_layers" in name:
               load_conv_layer(
                   lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_	,     hf_model.config.feat_extract_norm == 'group'	,     )
               UpperCAmelCase_							:   Any   =      True
          else:
               for key, mapped_key in MAPPING.items():
                    UpperCAmelCase_							:   Optional[Any]   =      '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]:
                         UpperCAmelCase_							:   int   =      True
                         if "*" in mapped_key:
                              UpperCAmelCase_							:   str   =      name.split(lowerCamelCase_   )[0].split('.'   )[-2]
                              if not layer_index.isnumeric():
                                   continue
                              UpperCAmelCase_							:   str   =      mapped_key.replace('*'	,     lowerCamelCase_   )
                         if "weight_g" in name:
                              UpperCAmelCase_							:   Dict   =      'weight_g'
                         elif "weight_v" in name:
                              UpperCAmelCase_							:   Optional[int]   =      'weight_v'
                         elif "weight" in name:
                              UpperCAmelCase_							:   Optional[int]   =      'weight'
                         elif "bias" in name:
                              UpperCAmelCase_							:   List[Any]   =      'bias'
                         else:
                              UpperCAmelCase_							:   Any   =      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 					_lowerCamelCase    (      lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							Optional[int]	,     lowerCamelCase_					:							int   ):



     """simple docstring"""





     UpperCAmelCase_							:   int   =      full_name.split('conv_layers.'   )[-1]
     UpperCAmelCase_							:   int   =      name.split('.'   )
     UpperCAmelCase_							:   int   =      int(items[0]   )
     UpperCAmelCase_							:   str   =      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_							:   Optional[Any]   =      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_							:   Optional[Any]   =      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_							:   Any   =      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_							:   List[Any]   =      value
               logger.info(F'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''   )
     else:
          unused_weights.append(lowerCamelCase_   )


def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							str   ):



     """simple docstring"""





     UpperCAmelCase_							:   str   =      SEWDConfig()
     if is_finetuned:
          UpperCAmelCase_							:   List[Any]   =      model.wav_encoder.wav_model.cfg
     else:
          UpperCAmelCase_							:   str   =      model.cfg

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

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          UpperCAmelCase_							:   str   =      model.cfg
          UpperCAmelCase_							:   Tuple   =      fs_config.final_dropout
          UpperCAmelCase_							:   str   =      fs_config.layerdrop
     UpperCAmelCase_							:   List[Any]   =      fs_config.activation_dropout
     UpperCAmelCase_							:   Dict   =      fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     UpperCAmelCase_							:   Any   =      fs_config.attention_dropout
     UpperCAmelCase_							:   Optional[Any]   =      fs_config.dropout_input
     UpperCAmelCase_							:   Optional[Any]   =      fs_config.dropout
     UpperCAmelCase_							:   int   =      fs_config.mask_channel_length
     UpperCAmelCase_							:   str   =      fs_config.mask_channel_prob
     UpperCAmelCase_							:   int   =      fs_config.mask_length
     UpperCAmelCase_							:   Union[str, Any]   =      fs_config.mask_prob

     UpperCAmelCase_							:   Any   =      'Wav2Vec2FeatureExtractor'
     UpperCAmelCase_							:   Union[str, Any]   =      'Wav2Vec2CTCTokenizer'

     return config


@torch.no_grad()
def 					_lowerCamelCase    (      lowerCamelCase_					:							Dict	,     lowerCamelCase_					:							Tuple	,     lowerCamelCase_					:							int=None	,     lowerCamelCase_					:							int=None	,     lowerCamelCase_					:							Dict=True   ):



     """simple docstring"""





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

     if config_path is not None:
          UpperCAmelCase_							:   str   =      SEWDConfig.from_pretrained(lowerCamelCase_   )
     else:
          UpperCAmelCase_							:   Optional[Any]   =      convert_config(model[0]	,     lowerCamelCase_   )
     UpperCAmelCase_							:   List[Any]   =      model[0].eval()

     UpperCAmelCase_							:   Optional[int]   =      True if config.feat_extract_norm == 'layer' else False
     UpperCAmelCase_							:   Dict   =      WavaVecaFeatureExtractor(
         feature_size=1	,     sampling_rate=16000	,     padding_value=0	,     do_normalize=lowerCamelCase_	,     return_attention_mask=lowerCamelCase_	,     )

     if is_finetuned:
          if dict_path:
               UpperCAmelCase_							:   Union[str, Any]   =      Dictionary.load(lowerCamelCase_   )

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

          UpperCAmelCase_							:   Optional[int]   =      SEWDForCTC(lowerCamelCase_   )
     else:
          UpperCAmelCase_							:   List[Any]   =      SEWDModel(lowerCamelCase_   )
          feature_extractor.save_pretrained(lowerCamelCase_   )

     recursively_load_weights(lowerCamelCase_	,     lowerCamelCase_	,     lowerCamelCase_   )

     hf_model.save_pretrained(lowerCamelCase_   )


if __name__ == "__main__":
 snake_case__     :			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'''
 )
 snake_case__     :			Any      			=  parser.parse_args()
 convert_sew_checkpoint(
     args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
 )






