




import warnings
from typing import List, Optional, Union

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

from .configuration_utils import ConfigMixin, register_to_config
from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate

class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Tuple     =       CONFIG_NAME







      @register_to_config
      def __init__(  self       :			int			,							snake_case       :			bool = True			,							snake_case       :			int = 8			,							snake_case       :			str = "lanczos"			,							snake_case       :			bool = True			,							snake_case       :			bool = False			,							):
          super().__init__()







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    (images * 255).round().astype('''uint8'''					)
          if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              __UpperCamelCase			=    [Image.fromarray(image.squeeze()			,							mode='''L'''					) for image in images]
          else:
              __UpperCamelCase			=    [Image.fromarray(snake_case					) for image in images]

          return pil_images







      @staticmethod
      def    snake_case						(  snake_case       :			Union[List[PIL.Image.Image], PIL.Image.Image]					):
          if not isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    [images]
          __UpperCamelCase			=    [np.array(snake_case					).astype(np.floataa					) / 2_55.0 for image in images]
          __UpperCamelCase			=    np.stack(snake_case			,							axis=0					)

          return images







      @staticmethod
      def    snake_case						(  snake_case       :			np.ndarray					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[..., None]

          __UpperCamelCase			=    torch.from_numpy(images.transpose(0			,							3			,							1			,							2					)					)
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			torch.FloatTensor					):
          __UpperCamelCase			=    images.cpu().permute(0			,							2			,							3			,							1					).float().numpy()
          return images







      @staticmethod
      def    snake_case						(  snake_case       :			List[Any]					):
          return 2.0 * images - 1.0







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          return (images / 2 + 0.5).clamp(0			,							1					)







      @staticmethod
      def    snake_case						(  snake_case       :			PIL.Image.Image					):
          __UpperCamelCase			=    image.convert('''RGB'''					)
          return image







      def    snake_case						(  self       :			Optional[Any]			,							snake_case       :			PIL.Image.Image			,							snake_case       :			Optional[int] = None			,							snake_case       :			Optional[int] = None			,							):
          if height is None:
              __UpperCamelCase			=    image.height
          if width is None:
              __UpperCamelCase			=    image.width

          __UpperCamelCase		,   __UpperCamelCase			=    (
              x - x % self.config.vae_scale_factor for x in (width, height)
          )  # resize to integer multiple of vae_scale_factor
          __UpperCamelCase			=    image.resize((width, height)			,							resample=PIL_INTERPOLATION[self.config.resample]					)
          return image







      def    snake_case						(  self       :			Optional[int]			,							snake_case       :			Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]			,							snake_case       :			Optional[int] = None			,							snake_case       :			Optional[int] = None			,							):
          __UpperCamelCase			=    (PIL.Image.Image, np.ndarray, torch.Tensor)
          if isinstance(snake_case			,							snake_case					):
              __UpperCamelCase			=    [image]
          elif not (isinstance(snake_case			,							snake_case					) and all(isinstance(snake_case			,							snake_case					) for i in image					)):
              raise ValueError(
                  F"Input is in incorrect format: {[type(snake_case					) for i in image]}. Currently, we only support {', '.join(snake_case					)}"					)

          if isinstance(image[0]			,							PIL.Image.Image					):
              if self.config.do_convert_rgb:
                  __UpperCamelCase			=    [self.convert_to_rgb(snake_case					) for i in image]
              if self.config.do_resize:
                  __UpperCamelCase			=    [self.resize(snake_case			,							snake_case			,							snake_case					) for i in image]
              __UpperCamelCase			=    self.pil_to_numpy(snake_case					)  # to np
              __UpperCamelCase			=    self.numpy_to_pt(snake_case					)  # to pt

          elif isinstance(image[0]			,							np.ndarray					):
              __UpperCamelCase			=    np.concatenate(snake_case			,							axis=0					) if image[0].ndim == 4 else np.stack(snake_case			,							axis=0					)
              __UpperCamelCase			=    self.numpy_to_pt(snake_case					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    image.shape
              if self.config.do_resize and (
                  height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
              ):
                  raise ValueError(
                      F"Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}"
                      F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"					)

          elif isinstance(image[0]			,							torch.Tensor					):
              __UpperCamelCase			=    torch.cat(snake_case			,							axis=0					) if image[0].ndim == 4 else torch.stack(snake_case			,							axis=0					)
              __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase		,   __UpperCamelCase			=    image.shape

              # don't need any preprocess if the image is latents
              if channel == 4:
                  return image

              if self.config.do_resize and (
                  height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
              ):
                  raise ValueError(
                      F"Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}"
                      F"currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor"					)

        # expected range [0,1], normalize to [-1,1]
          __UpperCamelCase			=    self.config.do_normalize
          if image.min() < 0:
              warnings.warn(
                  '''Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '''
                  F"when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]"			,							snake_case			,							)
              __UpperCamelCase			=    False

          if do_normalize:
              __UpperCamelCase			=    self.normalize(snake_case					)

          return image







      def    snake_case						(  self       :			Union[str, Any]			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. We only support pytorch tensor"					)
          if output_type not in ["latent", "pt", "np", "pil"]:
              __UpperCamelCase			=    (
                  F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                  '''`pil`, `np`, `pt`, `latent`'''
              )
              deprecate('''Unsupported output_type'''			,							'''1.0.0'''			,							snake_case			,							standard_warn=snake_case					)
              __UpperCamelCase			=    '''np'''

          if output_type == "latent":
              return image

          if do_denormalize is None:
              __UpperCamelCase			=    [self.config.do_normalize] * image.shape[0]

          __UpperCamelCase			=    torch.stack(
              [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

          if output_type == "pt":
              return image

          __UpperCamelCase			=    self.pt_to_numpy(snake_case					)

          if output_type == "np":
              return image

          if output_type == "pil":
              return self.numpy_to_pil(snake_case					)







class 			_lowerCamelCase    (      _SCREAMING_SNAKE_CASE ):

      """simple docstring"""

      lowerCAmelCase__      :			Optional[Any]     =       CONFIG_NAME







      @register_to_config
      def __init__(  self       :			Any			,							snake_case       :			bool = True			,							snake_case       :			int = 8			,							snake_case       :			str = "lanczos"			,							snake_case       :			bool = True			,							):
          super().__init__()







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    (images * 255).round().astype('''uint8'''					)
          if images.shape[-1] == 1:
              # special case for grayscale (single channel) images
              __UpperCamelCase			=    [Image.fromarray(image.squeeze()			,							mode='''L'''					) for image in images]
          else:
              __UpperCamelCase			=    [Image.fromarray(image[:, :, :3]					) for image in images]

          return pil_images







      @staticmethod
      def    snake_case						(  snake_case       :			str					):
          return image[:, :, 1] * 2**8 + image[:, :, 2]







      def    snake_case						(  self       :			Dict			,							snake_case       :			List[str]					):
          if images.ndim == 3:
              __UpperCamelCase			=    images[None, ...]
          __UpperCamelCase			=    images[:, :, :, 3:]
          if images.shape[-1] == 6:
              __UpperCamelCase			=    (images_depth * 255).round().astype('''uint8'''					)
              __UpperCamelCase			=    [
                  Image.fromarray(self.rgblike_to_depthmap(snake_case					)			,							mode='''I;16'''					) for image_depth in images_depth
              ]
          elif images.shape[-1] == 4:
              __UpperCamelCase			=    (images_depth * 6_55_35.0).astype(np.uintaa					)
              __UpperCamelCase			=    [Image.fromarray(snake_case			,							mode='''I;16'''					) for image_depth in images_depth]
          else:
              raise Exception('''Not supported'''					)

          return pil_images







      def    snake_case						(  self       :			Dict			,							snake_case       :			torch.FloatTensor			,							snake_case       :			str = "pil"			,							snake_case       :			Optional[List[bool]] = None			,							):
          if not isinstance(snake_case			,							torch.Tensor					):
              raise ValueError(
                  F"Input for postprocessing is in incorrect format: {type(snake_case					)}. We only support pytorch tensor"					)
          if output_type not in ["latent", "pt", "np", "pil"]:
              __UpperCamelCase			=    (
                  F"the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: "
                  '''`pil`, `np`, `pt`, `latent`'''
              )
              deprecate('''Unsupported output_type'''			,							'''1.0.0'''			,							snake_case			,							standard_warn=snake_case					)
              __UpperCamelCase			=    '''np'''

          if do_denormalize is None:
              __UpperCamelCase			=    [self.config.do_normalize] * image.shape[0]

          __UpperCamelCase			=    torch.stack(
              [self.denormalize(image[i]					) if do_denormalize[i] else image[i] for i in range(image.shape[0]					)]					)

          __UpperCamelCase			=    self.pt_to_numpy(snake_case					)

          if output_type == "np":
              if image.shape[-1] == 6:
                  __UpperCamelCase			=    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]					) for im in image]			,							axis=0					)
              else:
                  __UpperCamelCase			=    image[:, :, :, 3:]
              return image[:, :, :, :3], image_depth

          if output_type == "pil":
              return self.numpy_to_pil(snake_case					), self.numpy_to_depth(snake_case					)
          else:
              raise Exception(F"This type {output_type} is not supported"					)












import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

a_ 		=  {
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "mask_emb": "masked_spec_embed",
}




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Any:

    """simple docstring"""


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

    if weight_type is not None:
        __UpperCamelCase			=    getattr(lowercase_       ,  lowercase_   ).shape
    else:
        __UpperCamelCase			=    hf_pointer.shape

    assert hf_shape == value.shape, (
        F"Shape of hf {key + '.' + weight_type if weight_type is not None else ''} is {hf_shape}, but should be"
        F" {value.shape} for {full_name}"
    )

    if weight_type == "weight":
        __UpperCamelCase			=    value
    elif weight_type == "weight_g":
        __UpperCamelCase			=    value
    elif weight_type == "weight_v":
        __UpperCamelCase			=    value
    elif weight_type == "bias":
        __UpperCamelCase			=    value
    else:
        __UpperCamelCase			=    value

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




def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_   )							->						int:

    """simple docstring"""


    __UpperCamelCase			=    []
    __UpperCamelCase			=    fairseq_model.state_dict()

    __UpperCamelCase			=    hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        __UpperCamelCase			=    False
        if "conv_layers" in name:
            load_conv_layer(
                lowercase_       ,  lowercase_       ,  lowercase_       ,  lowercase_       ,  hf_model.config.feat_extract_norm == '''group'''       ,  )
            __UpperCamelCase			=    True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCamelCase			=    mapped_key

                if key in name:
                    __UpperCamelCase			=    True
                    if "*" in mapped_key:
                        __UpperCamelCase			=    name.split(lowercase_   )[0].split('''.'''   )[-2]
                        __UpperCamelCase			=    mapped_key.replace('''*'''       ,  lowercase_   )
                    if "weight_g" in name:
                        __UpperCamelCase			=    '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCamelCase			=    '''weight_v'''
                    elif "weight" in name:
                        __UpperCamelCase			=    '''weight'''
                    elif "bias" in name:
                        __UpperCamelCase			=    '''bias'''
                    else:
                        __UpperCamelCase			=    None
                    set_recursively(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_       ,  lowercase_       ,  lowercase_       ,  lowercase_   )							->						Optional[Any]:

    """simple docstring"""


    __UpperCamelCase			=    full_name.split('''conv_layers.'''   )[-1]
    __UpperCamelCase			=    name.split('''.'''   )
    __UpperCamelCase			=    int(items[0]   )
    __UpperCamelCase			=    int(items[1]   )

    if type_id == 0:
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."   )
    elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
        if "bias" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
                F"{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was"
                " found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
        elif "weight" in name:
            assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
                F"{full_name} has size {value.shape}, but"
                F" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."
            )
            __UpperCamelCase			=    value
            logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
    else:
        unused_weights.append(lowercase_   )




def        __SCREAMING_SNAKE_CASE (	lowercase_   )							->						List[Any]:

    """simple docstring"""


    __UpperCamelCase			=    HubertConfig()
    __UpperCamelCase			=    model.config

    __UpperCamelCase			=    fs_config.activation_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    fs_config.attention_dropout
    __UpperCamelCase			=    False
    __UpperCamelCase			=    eval(fs_config.extractor_conv_feature_layers   )
    __UpperCamelCase			=    [x[0] for x in conv_layers]
    __UpperCamelCase			=    [x[1] for x in conv_layers]
    __UpperCamelCase			=    [x[2] for x in conv_layers]
    __UpperCamelCase			=    '''gelu'''
    __UpperCamelCase			=    '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    __UpperCamelCase			=    False
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.activation_fn
    __UpperCamelCase			=    fs_config.dropout
    __UpperCamelCase			=    fs_config.encoder_embed_dim
    __UpperCamelCase			=    0.02
    __UpperCamelCase			=    fs_config.encoder_ffn_embed_dim
    __UpperCamelCase			=    1E-5
    __UpperCamelCase			=    0.0
    __UpperCamelCase			=    fs_config.encoder_attention_heads
    __UpperCamelCase			=    fs_config.conv_pos_groups
    __UpperCamelCase			=    fs_config.conv_pos
    __UpperCamelCase			=    len(lowercase_   )
    __UpperCamelCase			=    fs_config.encoder_layers

    return config




@torch.no_grad()
def        __SCREAMING_SNAKE_CASE (	lowercase_       ,  lowercase_=None   )							->						Dict:

    """simple docstring"""


    __UpperCamelCase			=    distilhubert().model.model

    if config_path is not None:
        __UpperCamelCase			=    HubertConfig.from_pretrained(lowercase_   )
    else:
        __UpperCamelCase			=    convert_config(lowercase_   )
    __UpperCamelCase			=    model.eval()

    __UpperCamelCase			=    WavaVecaFeatureExtractor(
        feature_size=1       ,  sampling_rate=1_60_00       ,  padding_value=0       ,  do_normalize=lowercase_       ,  return_attention_mask=lowercase_       ,  )
    __UpperCamelCase			=    HubertModel(lowercase_   )

    recursively_load_weights(lowercase_       ,  lowercase_   )

    feature_extractor.save_pretrained(lowercase_   )
    hf_model.save_pretrained(lowercase_   )


if __name__ == "__main__":
 a_ 		=  argparse.ArgumentParser()
 parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
 parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
 a_ 		=  parser.parse_args()
 convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)






