






"""simple docstring"""





from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


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

__lowerCamelCase							=  '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'



def   a    (     __UpperCAmelCase      :    Dict      ,   __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    List[Any]=8  )    ->							Any:
						__magic_name__:	List[str]							=       height // scale_factor**2
						if height % scale_factor**2 != 0:
												new_height += 1
						__magic_name__:	Any							=       width // scale_factor**2
						if width % scale_factor**2 != 0:
												new_width += 1
						return new_height * scale_factor, new_width * scale_factor



def   a    (     __UpperCAmelCase      :    List[Any]      ,   __UpperCAmelCase      :    Any=5_1_2      ,   __UpperCAmelCase      :    str=5_1_2  )    ->							int:
						__magic_name__:	int							=       pil_image.resize((w, h)      ,   resample=Image.BICUBIC      ,   reducing_gap=1  )
						__magic_name__:	str							=       np.array(pil_image.convert("""RGB"""  )  )
						__magic_name__:	str							=       arr.astype(np.floataa  ) / 1_27.5 - 1
						__magic_name__:	Dict							=       np.transpose(__UpperCAmelCase      ,   [2, 0, 1]  )
						__magic_name__:	Union[str, Any]							=       torch.from_numpy(__UpperCAmelCase  ).unsqueeze(0  )
						return image



class       __A		(   SCREAMING_SNAKE_CASE_			):

					def __init__(			self  :      List[str]     ,		__snake_case  :      UNetaDConditionModel     ,		__snake_case  :      DDPMScheduler     ,		__snake_case  :      VQModel     ,		)			->							List[str]:
											super().__init__()

											self.register_modules(
											    unet=__snake_case     ,		scheduler=__snake_case     ,		movq=__snake_case     ,		)
											__magic_name__:	Tuple							=       2 ** (len(self.movq.config.block_out_channels					) - 1)

					def   lowerCamelCase__   (			self  :      str     ,		__snake_case  :      List[Any]     ,		__snake_case  :      str     ,		__snake_case  :      Any					)			->							Optional[Any]:
											# get the original timestep using init_timestep
											__magic_name__:	str							=       min(int(num_inference_steps * strength					)     ,		__snake_case					)

											__magic_name__:	Dict							=       max(num_inference_steps - init_timestep     ,		0					)
											__magic_name__:	Tuple							=       self.scheduler.timesteps[t_start:]

											return timesteps, num_inference_steps - t_start

					def   lowerCamelCase__   (			self  :      Dict     ,		__snake_case  :      List[Any]     ,		__snake_case  :      List[str]     ,		__snake_case  :      Tuple     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Union[str, Any]     ,		__snake_case  :      Optional[Any]     ,		__snake_case  :      Optional[Any]=None					)			->							Optional[int]:
											if not isinstance(__snake_case     ,		(torch.Tensor, PIL.Image.Image, list)					):
																	raise ValueError(
																	    F'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(__snake_case					)}'					)

											__magic_name__:	Union[str, Any]							=       image.to(device=__snake_case     ,		dtype=__snake_case					)

											__magic_name__:	List[str]							=       batch_size * num_images_per_prompt

											if image.shape[1] == 4:
																	__magic_name__:	Dict							=       image

											else:
																	if isinstance(__snake_case     ,		__snake_case					) and len(__snake_case					) != batch_size:
																							raise ValueError(
																							    F'You have passed a list of generators of length {len(__snake_case					)}, but requested an effective batch'
																							    F' size of {batch_size}. Make sure the batch size matches the length of the generators.'					)

																	elif isinstance(__snake_case     ,		__snake_case					):
																							__magic_name__:	str							=       [
																							    self.movq.encode(image[i : i + 1]					).latent_dist.sample(generator[i]					) for i in range(__snake_case					)
																							]
																							__magic_name__:	List[Any]							=       torch.cat(__snake_case     ,		dim=0					)
																	else:
																							__magic_name__:	Any							=       self.movq.encode(__snake_case					).latent_dist.sample(__snake_case					)

																	__magic_name__:	str							=       self.movq.config.scaling_factor * init_latents

											__magic_name__:	Optional[Any]							=       torch.cat([init_latents]     ,		dim=0					)

											__magic_name__:	Optional[Any]							=       init_latents.shape
											__magic_name__:	Dict							=       randn_tensor(__snake_case     ,		generator=__snake_case     ,		device=__snake_case     ,		dtype=__snake_case					)

											# get latents
											__magic_name__:	Optional[Any]							=       self.scheduler.add_noise(__snake_case     ,		__snake_case     ,		__snake_case					)

											__magic_name__:	Optional[Any]							=       init_latents

											return latents

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Tuple=0					)			->							Tuple:
											if is_accelerate_available():
																	from accelerate import cpu_offload
											else:
																	raise ImportError("""Please install accelerate via `pip install accelerate`"""					)

											__magic_name__:	str							=       torch.device(F'cuda:{gpu_id}'					)

											__magic_name__:	List[str]							=       [
											    self.unet,
											    self.movq,
											]
											for cpu_offloaded_model in models:
																	if cpu_offloaded_model is not None:
																							cpu_offload(__snake_case     ,		__snake_case					)

					def   lowerCamelCase__   (			self  :      List[str]     ,		__snake_case  :      Optional[Any]=0					)			->							Any:
											if is_accelerate_available() and is_accelerate_version(""">="""     ,		"""0.17.0.dev0"""					):
																	from accelerate import cpu_offload_with_hook
											else:
																	raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""					)

											__magic_name__:	Any							=       torch.device(F'cuda:{gpu_id}'					)

											if self.device.type != "cpu":
																	self.to("""cpu"""     ,		silence_dtype_warnings=__snake_case					)
																	torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

											__magic_name__:	Tuple							=       None
											for cpu_offloaded_model in [self.unet, self.movq]:
																	__magic_name__,			__magic_name__:	Any							=       cpu_offload_with_hook(__snake_case     ,		__snake_case     ,		prev_module_hook=__snake_case					)

											# We'll offload the last model manually.
											__magic_name__:	List[Any]							=       hook

					@property
					# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
					def   lowerCamelCase__   (			self  :      Optional[Any]					)			->							int:
											if not hasattr(self.unet     ,		"""_hf_hook"""					):
																	return self.device
											for module in self.unet.modules():
																	if (
																	    hasattr(__snake_case     ,		"""_hf_hook"""					)
																	    and hasattr(module._hf_hook     ,		"""execution_device"""					)
																	    and module._hf_hook.execution_device is not None
																	):
																							return torch.device(module._hf_hook.execution_device					)
											return self.device






					@torch.no_grad()
					@replace_example_docstring(__snake_case					)
					def __call__(			self  :      List[Any]     ,		__snake_case  :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__snake_case  :      Union[torch.FloatTensor, PIL.Image.Image, List[torch.FloatTensor], List[PIL.Image.Image]]     ,		__snake_case  :      Union[torch.FloatTensor, List[torch.FloatTensor]]     ,		__snake_case  :      int = 5_1_2     ,		__snake_case  :      int = 5_1_2     ,		__snake_case  :      int = 1_0_0     ,		__snake_case  :      float = 4.0     ,		__snake_case  :      float = 0.3     ,		__snake_case  :      int = 1     ,		__snake_case  :      Optional[Union[torch.Generator, List[torch.Generator]]] = None     ,		__snake_case  :      Optional[str] = "pil"     ,		__snake_case  :      bool = True     ,		)			->							List[Any]:
											__magic_name__:	Tuple							=       self._execution_device

											__magic_name__:	str							=       guidance_scale > 1.0

											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Optional[int]							=       torch.cat(__snake_case     ,		dim=0					)
											__magic_name__:	Any							=       image_embeds.shape[0]
											if isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	str							=       torch.cat(__snake_case     ,		dim=0					)

											if do_classifier_free_guidance:
																	__magic_name__:	List[str]							=       image_embeds.repeat_interleave(__snake_case     ,		dim=0					)
																	__magic_name__:	Optional[int]							=       negative_image_embeds.repeat_interleave(__snake_case     ,		dim=0					)

											__magic_name__:	str							=       torch.cat([negative_image_embeds, image_embeds]     ,		dim=0					).to(dtype=self.unet.dtype     ,		device=__snake_case					)

											if not isinstance(__snake_case     ,		__snake_case					):
																	__magic_name__:	Dict							=       [image]
											if not all(isinstance(__snake_case     ,		(PIL.Image.Image, torch.Tensor)					) for i in image					):
																	raise ValueError(
																	    F'Input is in incorrect format: {[type(__snake_case					) for i in image]}. Currently, we only support  PIL image and pytorch tensor'					)

											__magic_name__:	List[Any]							=       torch.cat([prepare_image(__snake_case     ,		__snake_case     ,		__snake_case					) for i in image]     ,		dim=0					)
											__magic_name__:	Optional[Any]							=       image.to(dtype=image_embeds.dtype     ,		device=__snake_case					)

											__magic_name__:	Tuple							=       self.movq.encode(__snake_case					)["""latents"""]
											__magic_name__:	str							=       latents.repeat_interleave(__snake_case     ,		dim=0					)
											self.scheduler.set_timesteps(__snake_case     ,		device=__snake_case					)
											__magic_name__,			__magic_name__:	Tuple							=       self.get_timesteps(__snake_case     ,		__snake_case     ,		__snake_case					)
											__magic_name__:	Tuple							=       timesteps[:1].repeat(batch_size * num_images_per_prompt					)
											__magic_name__,			__magic_name__:	str							=       downscale_height_and_width(__snake_case     ,		__snake_case     ,		self.movq_scale_factor					)
											__magic_name__:	List[Any]							=       self.prepare_latents(
											    __snake_case     ,		__snake_case     ,		__snake_case     ,		__snake_case     ,		image_embeds.dtype     ,		__snake_case     ,		__snake_case					)
											for i, t in enumerate(self.progress_bar(__snake_case					)					):
																	# expand the latents if we are doing classifier free guidance
																	__magic_name__:	Union[str, Any]							=       torch.cat([latents] * 2					) if do_classifier_free_guidance else latents

																	__magic_name__:	Optional[Any]							=       {"""image_embeds""": image_embeds}
																	__magic_name__:	str							=       self.unet(
																	    sample=__snake_case     ,		timestep=__snake_case     ,		encoder_hidden_states=__snake_case     ,		added_cond_kwargs=__snake_case     ,		return_dict=__snake_case     ,		)[0]

																	if do_classifier_free_guidance:
																							__magic_name__,			__magic_name__:	List[Any]							=       noise_pred.split(latents.shape[1]     ,		dim=1					)
																							__magic_name__,			__magic_name__:	Optional[int]							=       noise_pred.chunk(2					)
																							__magic_name__,			__magic_name__:	str							=       variance_pred.chunk(2					)
																							__magic_name__:	int							=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
																							__magic_name__:	Optional[Any]							=       torch.cat([noise_pred, variance_pred_text]     ,		dim=1					)

																	if not (
																	    hasattr(self.scheduler.config     ,		"""variance_type"""					)
																	    and self.scheduler.config.variance_type in ["learned", "learned_range"]
																	):
																							__magic_name__,			__magic_name__:	str							=       noise_pred.split(latents.shape[1]     ,		dim=1					)

																	# compute the previous noisy sample x_t -> x_t-1
																	__magic_name__:	List[str]							=       self.scheduler.step(
																	    __snake_case     ,		__snake_case     ,		__snake_case     ,		generator=__snake_case     ,		)[0]

											# post-processing
											__magic_name__:	Any							=       self.movq.decode(__snake_case     ,		force_not_quantize=__snake_case					)["""sample"""]

											if output_type not in ["pt", "np", "pil"]:
																	raise ValueError(F'Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}'					)

											if output_type in ["np", "pil"]:
																	__magic_name__:	Any							=       image * 0.5 + 0.5
																	__magic_name__:	int							=       image.clamp(0     ,		1					)
																	__magic_name__:	Union[str, Any]							=       image.cpu().permute(0     ,		2     ,		3     ,		1					).float().numpy()

											if output_type == "pil":
																	__magic_name__:	Dict							=       self.numpy_to_pil(__snake_case					)

											if not return_dict:
																	return (image,)

											return ImagePipelineOutput(images=__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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__snake_case						  =		{
    """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_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Any:
  for attribute in key.split("""."""						):
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  if weight_type is not None:
    lowercase_     =       getattr(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						).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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  lowercase_     =       []
  lowercase_     =       fairseq_model.state_dict()

  lowercase_     =       hf_model.sew.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(
          SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	hf_model.config.feat_extract_norm == """group""" ,	)
      lowercase_     =       True
    else:
      for key, mapped_key in MAPPING.items():
        lowercase_     =       """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]:
          lowercase_     =       True
          if "*" in mapped_key:
            lowercase_     =       name.split(SCREAMING_SNAKE_CASE_						)[0].split("""."""						)[-2]
            lowercase_     =       mapped_key.replace("""*""" ,	SCREAMING_SNAKE_CASE_						)
          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(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)
        continue
    if not is_used:
      unused_weights.append(SCREAMING_SNAKE_CASE_						)

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

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->Union[str, Any]:
  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(SCREAMING_SNAKE_CASE_						)

def   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)			->List[str]:
  lowercase_     =       SEWConfig()
  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(SCREAMING_SNAKE_CASE_						)
  lowercase_     =       fs_config.encoder_layers
  lowercase_     =       fs_config.squeeze_factor

  # 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   A_      (       SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=None ,	SCREAMING_SNAKE_CASE_=True						)			->Optional[Any]:

  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_     =       SEWConfig.from_pretrained(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       convert_config(model[0] ,	SCREAMING_SNAKE_CASE_						)
  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=SCREAMING_SNAKE_CASE_ ,	return_attention_mask=SCREAMING_SNAKE_CASE_ ,	)

  if is_finetuned:
    if dict_path:
      lowercase_     =       Dictionary.load(SCREAMING_SNAKE_CASE_						)

      # 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(SCREAMING_SNAKE_CASE_ ,	"""vocab.json"""						)
      if not os.path.isdir(SCREAMING_SNAKE_CASE_						):
        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(SCREAMING_SNAKE_CASE_						)						)
        return
      os.makedirs(SCREAMING_SNAKE_CASE_ ,	exist_ok=SCREAMING_SNAKE_CASE_						)
      with open(SCREAMING_SNAKE_CASE_ ,	"""w""" ,	encoding="""utf-8"""						) as vocab_handle:
        json.dump(target_dict.indices ,	SCREAMING_SNAKE_CASE_						)
      lowercase_     =       WavaVecaCTCTokenizer(
          SCREAMING_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=SCREAMING_SNAKE_CASE_ ,	)
      lowercase_     =       WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE_ ,	tokenizer=SCREAMING_SNAKE_CASE_						)
      processor.save_pretrained(SCREAMING_SNAKE_CASE_						)

    lowercase_     =       SEWForCTC(SCREAMING_SNAKE_CASE_						)
  else:
    lowercase_     =       SEWModel(SCREAMING_SNAKE_CASE_						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_						)

  recursively_load_weights(SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_ ,	SCREAMING_SNAKE_CASE_						)

  hf_model.save_pretrained(SCREAMING_SNAKE_CASE_						)


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

