
'''simple docstring'''





import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE   :		Any					 =       logging.get_logger(__name__)

SCREAMING_SNAKE_CASE   :		int					 =       {
    "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        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):





				for attribute in key.split('''.'''     ):
								A_       	=  getattr(__a ,						__a     )

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

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  []
				A_       	=  fairseq_model.state_dict()

				A_       	=  hf_model.feature_extractor

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

																if key in name:
																				A_       	=  True
																				if "*" in mapped_key:
																								A_       	=  name.split(__a     )[0].split('''.'''     )[-2]
																								A_       	=  mapped_key.replace('''*''' ,						__a     )
																				if "weight_g" in name:
																								A_       	=  '''weight_g'''
																				elif "weight_v" in name:
																								A_       	=  '''weight_v'''
																				elif "weight" in name:
																								A_       	=  '''weight'''
																				elif "bias" in name:
																								A_       	=  '''bias'''
																				else:
																								A_       	=  None
																				set_recursively(__a ,						__a ,						__a ,						__a ,						__a     )
																continue
								if not is_used:
												unused_weights.append(__a     )

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



def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				A_       	=  full_name.split('''conv_layers.'''     )[-1]
				A_       	=  name.split('''.'''     )
				A_       	=  int(items[0]     )
				A_       	=  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."
												)
												A_       	=  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."
												)
												A_       	=  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."
												)
												A_       	=  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."
												)
												A_       	=  value
												logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."     )
				else:
								unused_weights.append(__a     )



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  HubertConfig()
				A_       	=  model.config

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

				return config



@torch.no_grad()
def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase=None     ):
				A_       	=  distilhubert().model.model

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

				A_       	=  WavaVecaFeatureExtractor(
				    feature_size=1 ,						sampling_rate=1_60_00 ,						padding_value=0 ,						do_normalize=__a ,						return_attention_mask=__a ,						)
				A_       	=  HubertModel(__a     )

				recursively_load_weights(__a ,						__a     )

				feature_extractor.save_pretrained(__a     )
				hf_model.save_pretrained(__a     )


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

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import center_crop, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
						import PIL


SCREAMING_SNAKE_CASE   :		Optional[int]					 =       logging.get_logger(__name__)






class 					__lowercase		(					A       ):
					__magic_name__						:							int     	=	['''pixel_values''']


					def __init__(	self       ,       a__ = True       ,       a__ = None       ,       a__ = True       ,       a__ = None       ,       a__ = PILImageResampling.BICUBIC       ,       a__ = True       ,       a__ = 1 / 2_5_5       ,       a__ = True       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							None:





									'''simple docstring'''
									super().__init__(**a__						)
									A_       	=  crop_size if crop_size is not None else {'''height''': 2_5_6, '''width''': 2_5_6}
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  size if size is not None else {'''height''': 2_2_4, '''width''': 2_2_4}
									A_       	=  get_size_dict(a__						)

									A_       	=  do_center_crop
									A_       	=  crop_size
									A_       	=  do_resize
									A_       	=  size
									A_       	=  resample
									A_       	=  do_rescale
									A_       	=  rescale_factor
									A_       	=  do_normalize
									A_       	=  image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
									A_       	=  image_std if image_std is not None else IMAGENET_DEFAULT_STD


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  self.size if size is None else size
									A_       	=  get_size_dict(a__						)
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)

									A_	,					A_       	=  get_image_size(a__						)
									A_       	=  min(a__       ,       a__						)
									A_       	=  (size['''height'''] / crop_size['''height''']) * min_dim
									A_       	=  (size['''width'''] / crop_size['''width''']) * min_dim
									return center_crop(a__       ,       size=(cropped_height, cropped_width)       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = PIL.Image.BICUBIC       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									A_       	=  get_size_dict(a__						)
									if "height" not in size or "width" not in size:
													raise ValueError(F"The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"						)
									return resize(
									    a__       ,       size=(size['''height'''], size['''width'''])       ,       resample=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							Union[str, Any]:





									'''simple docstring'''
									return rescale(a__       ,       scale=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__       ,       a__       ,       a__ = None       ,       **a__       ,       )		->							np.ndarray:





									'''simple docstring'''
									return normalize(a__       ,       mean=a__       ,       std=a__       ,       data_format=a__       ,       **a__						)


					def        lowerCAmelCase_    (	self       ,       a__       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = None       ,       a__ = ChannelDimension.FIRST       ,       **a__       ,       )		->							PIL.Image.Image:





									'''simple docstring'''
									A_       	=  do_center_crop if do_center_crop is not None else self.do_center_crop
									A_       	=  crop_size if crop_size is not None else self.crop_size
									A_       	=  get_size_dict(a__       ,       param_name='''crop_size'''						)
									A_       	=  do_resize if do_resize is not None else self.do_resize
									A_       	=  size if size is not None else self.size
									A_       	=  get_size_dict(a__						)
									A_       	=  resample if resample is not None else self.resample
									A_       	=  do_rescale if do_rescale is not None else self.do_rescale
									A_       	=  rescale_factor if rescale_factor is not None else self.rescale_factor
									A_       	=  do_normalize if do_normalize is not None else self.do_normalize
									A_       	=  image_mean if image_mean is not None else self.image_mean
									A_       	=  image_std if image_std is not None else self.image_std

									A_       	=  make_list_of_images(a__						)

									if not valid_images(a__						):
													raise ValueError(
													    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
													    '''torch.Tensor, tf.Tensor or jax.ndarray.'''						)

									if do_center_crop and crop_size is None:
													raise ValueError('''If `do_center_crop` is set to `True`, `crop_size` must be provided.'''						)

									if do_resize and size is None:
													raise ValueError('''Size must be specified if do_resize is True.'''						)

									if do_rescale and rescale_factor is None:
													raise ValueError('''Rescale factor must be specified if do_rescale is True.'''						)

									if do_normalize and (image_mean is None or image_std is None):
													raise ValueError('''Image mean and image standard deviation must be specified if do_normalize is True.'''						)

									# All transformations expect numpy arrays.
									A_       	=  [to_numpy_array(a__						) for image in images]

									if do_center_crop:
													A_       	=  [self.center_crop(a__       ,       a__       ,       size=a__						) for image in images]

									if do_resize:
													A_       	=  [self.resize(image=a__       ,       size=a__       ,       resample=a__						) for image in images]

									if do_rescale:
													A_       	=  [self.rescale(image=a__       ,       scale=a__						) for image in images]

									if do_normalize:
													A_       	=  [self.normalize(image=a__       ,       mean=a__       ,       std=a__						) for image in images]

									A_       	=  [to_channel_dimension_format(a__       ,       a__						) for image in images]

									A_       	=  {'''pixel_values''': images}
									return BatchFeature(data=a__       ,       tensor_type=a__						)