
"""simple docstring"""







import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


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

lowerCamelCase__     =						{
    """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('.'						):
		__lowerCAmelCase     :		str		     = getattr(_UpperCamelCase			,    _UpperCamelCase						)

	if weight_type is not None:
		__lowerCAmelCase     :		Any		     = getattr(_UpperCamelCase			,    _UpperCamelCase						).shape
	else:
		__lowerCAmelCase     :		Optional[Any]		     = 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":
		__lowerCAmelCase     :		List[str]		     = value
	elif weight_type == "weight_g":
		__lowerCAmelCase     :		Dict		     = value
	elif weight_type == "weight_v":
		__lowerCAmelCase     :		Tuple		     = value
	elif weight_type == "bias":
		__lowerCAmelCase     :		Optional[Any]		     = value
	else:
		__lowerCAmelCase     :		Union[str, Any]		     = value

	logger.info(F"{key + '.' + weight_type if weight_type is not None else ''} was initialized from {full_name}."						)
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Any		     = []
	__lowerCAmelCase     :		Union[str, Any]		     = fairseq_model.state_dict()

	__lowerCAmelCase     :		Optional[Any]		     = hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		__lowerCAmelCase     :		List[Any]		     = False
		if "conv_layers" in name:
			load_conv_layer(
			    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    hf_model.config.feat_extract_norm == 'group'			,    )
			__lowerCAmelCase     :		Union[str, Any]		     = True
		else:
			for key, mapped_key in MAPPING.items():
				__lowerCAmelCase     :		Any		     = mapped_key

				if key in name:
					__lowerCAmelCase     :		int		     = True
					if "*" in mapped_key:
						__lowerCAmelCase     :		Optional[Any]		     = name.split(_UpperCamelCase						)[0].split('.'						)[-2]
						__lowerCAmelCase     :		List[str]		     = mapped_key.replace('*'			,    _UpperCamelCase						)
					if "weight_g" in name:
						__lowerCAmelCase     :		str		     = 'weight_g'
					elif "weight_v" in name:
						__lowerCAmelCase     :		int		     = 'weight_v'
					elif "weight" in name:
						__lowerCAmelCase     :		Dict		     = 'weight'
					elif "bias" in name:
						__lowerCAmelCase     :		Dict		     = 'bias'
					else:
						__lowerCAmelCase     :		Union[str, Any]		     = None
					set_recursively(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						)
				continue
		if not is_used:
			unused_weights.append(_UpperCamelCase						)

	logger.warning(F"Unused weights: {unused_weights}"						)
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase			,    _UpperCamelCase						):
	__lowerCAmelCase     :		Optional[Any]		     = full_name.split('conv_layers.'						)[-1]
	__lowerCAmelCase     :		Dict		     = name.split('.'						)
	__lowerCAmelCase     :		Dict		     = int(items[0]						)
	__lowerCAmelCase     :		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."
			)
			__lowerCAmelCase     :		Tuple		     = 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."
			)
			__lowerCAmelCase     :		Tuple		     = 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."
			)
			__lowerCAmelCase     :		Union[str, 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."
			)
			__lowerCAmelCase     :		Optional[Any]		     = value
			logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
	else:
		unused_weights.append(_UpperCamelCase						)
def        __lowerCAmelCase							(_UpperCamelCase						):
	__lowerCAmelCase     :		Tuple		     = HubertConfig()
	__lowerCAmelCase     :		Any		     = model.config

	__lowerCAmelCase     :		List[str]		     = fs_config.activation_dropout
	__lowerCAmelCase     :		List[Any]		     = False
	__lowerCAmelCase     :		Optional[int]		     = fs_config.attention_dropout
	__lowerCAmelCase     :		List[str]		     = False
	__lowerCAmelCase     :		List[str]		     = eval(fs_config.extractor_conv_feature_layers						)
	__lowerCAmelCase     :		List[Any]		     = [x[0] for x in conv_layers]
	__lowerCAmelCase     :		Tuple		     = [x[1] for x in conv_layers]
	__lowerCAmelCase     :		Union[str, Any]		     = [x[2] for x in conv_layers]
	__lowerCAmelCase     :		int		     = 'gelu'
	__lowerCAmelCase     :		Dict		     = 'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	__lowerCAmelCase     :		Optional[Any]		     = False
	__lowerCAmelCase     :		List[Any]		     = 0.0
	__lowerCAmelCase     :		Dict		     = 0.0
	__lowerCAmelCase     :		List[str]		     = fs_config.activation_fn
	__lowerCAmelCase     :		Optional[Any]		     = fs_config.dropout
	__lowerCAmelCase     :		Optional[Any]		     = fs_config.encoder_embed_dim
	__lowerCAmelCase     :		Dict		     = 0.02
	__lowerCAmelCase     :		Optional[int]		     = fs_config.encoder_ffn_embed_dim
	__lowerCAmelCase     :		Optional[Any]		     = 1e-5
	__lowerCAmelCase     :		int		     = 0.0
	__lowerCAmelCase     :		Union[str, Any]		     = fs_config.encoder_attention_heads
	__lowerCAmelCase     :		Any		     = fs_config.conv_pos_groups
	__lowerCAmelCase     :		Optional[Any]		     = fs_config.conv_pos
	__lowerCAmelCase     :		List[str]		     = len(_UpperCamelCase						)
	__lowerCAmelCase     :		Optional[int]		     = fs_config.encoder_layers

	return config
@torch.no_grad()
def        __lowerCAmelCase							(_UpperCamelCase			,    _UpperCamelCase=None						):
	__lowerCAmelCase     :		Optional[int]		     = distilhubert().model.model

	if config_path is not None:
		__lowerCAmelCase     :		Optional[int]		     = HubertConfig.from_pretrained(_UpperCamelCase						)
	else:
		__lowerCAmelCase     :		List[str]		     = convert_config(_UpperCamelCase						)
	__lowerCAmelCase     :		Union[str, Any]		     = model.eval()

	__lowerCAmelCase     :		str		     = WavaVecaFeatureExtractor(
	    feature_size=1			,    sampling_rate=1_6000			,    padding_value=0			,    do_normalize=_UpperCamelCase			,    return_attention_mask=_UpperCamelCase			,    )
	__lowerCAmelCase     :		Optional[Any]		     = HubertModel(_UpperCamelCase						)

	recursively_load_weights(_UpperCamelCase			,    _UpperCamelCase						)

	feature_extractor.save_pretrained(_UpperCamelCase						)
	hf_model.save_pretrained(_UpperCamelCase						)


if __name__ == "__main__":
							lowerCamelCase__     =						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""")
							lowerCamelCase__     =						parser.parse_args()
							convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)

"""simple docstring"""







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


lowerCamelCase__     =						logging.get_logger(__name__)




class 					A__			(      _lowerCamelCase):
		A_		:			List[str]							  =      ['pixel_values']

		def __init__( self			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = PILImageResampling.BICUBIC			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = 1 / 2_55			,						_SCREAMING_SNAKE_CASE = True			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			super().__init__(**_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		List[str]		     = crop_size if crop_size is not None else {'height': 2_56, 'width': 2_56}
			__lowerCAmelCase     :		int		     = get_size_dict(_SCREAMING_SNAKE_CASE			,						param_name='crop_size'					)
			__lowerCAmelCase     :		List[str]		     = size if size is not None else {'height': 2_24, 'width': 2_24}
			__lowerCAmelCase     :		Optional[int]		     = get_size_dict(_SCREAMING_SNAKE_CASE					)

			__lowerCAmelCase     :		str		     = do_center_crop
			__lowerCAmelCase     :		Optional[int]		     = crop_size
			__lowerCAmelCase     :		Optional[Any]		     = do_resize
			__lowerCAmelCase     :		Optional[int]		     = size
			__lowerCAmelCase     :		List[Any]		     = resample
			__lowerCAmelCase     :		str		     = do_rescale
			__lowerCAmelCase     :		str		     = rescale_factor
			__lowerCAmelCase     :		Union[str, Any]		     = do_normalize
			__lowerCAmelCase     :		str		     = image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
			__lowerCAmelCase     :		List[Any]		     = image_std if image_std is not None else IMAGENET_DEFAULT_STD

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		List[str]		     = self.size if size is None else size
			__lowerCAmelCase     :		List[str]		     = get_size_dict(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[Any]		     = get_size_dict(_SCREAMING_SNAKE_CASE			,						param_name='crop_size'					)

			__lowerCAmelCase     ,  __lowerCAmelCase     :		Optional[int]		     = get_image_size(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Tuple		     = min(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Optional[Any]		     = (size['height'] / crop_size['height']) * min_dim
			__lowerCAmelCase     :		Tuple		     = (size['width'] / crop_size['width']) * min_dim
			return center_crop(_SCREAMING_SNAKE_CASE			,						size=(cropped_height, cropped_width)			,						data_format=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = PIL.Image.BICUBIC			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Dict		     = get_size_dict(_SCREAMING_SNAKE_CASE					)
			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(
			    _SCREAMING_SNAKE_CASE			,						size=(size['height'], size['width'])			,						resample=_SCREAMING_SNAKE_CASE			,						data_format=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			return rescale(_SCREAMING_SNAKE_CASE			,						scale=_SCREAMING_SNAKE_CASE			,						data_format=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)

		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						**_SCREAMING_SNAKE_CASE			,						):
			return normalize(_SCREAMING_SNAKE_CASE			,						mean=_SCREAMING_SNAKE_CASE			,						std=_SCREAMING_SNAKE_CASE			,						data_format=_SCREAMING_SNAKE_CASE			,						**_SCREAMING_SNAKE_CASE					)







		def 				__lowerCamelCase				( self			,						_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = None			,						_SCREAMING_SNAKE_CASE = ChannelDimension.FIRST			,						**_SCREAMING_SNAKE_CASE			,						):
			__lowerCAmelCase     :		Tuple		     = do_center_crop if do_center_crop is not None else self.do_center_crop
			__lowerCAmelCase     :		Optional[int]		     = crop_size if crop_size is not None else self.crop_size
			__lowerCAmelCase     :		int		     = get_size_dict(_SCREAMING_SNAKE_CASE			,						param_name='crop_size'					)
			__lowerCAmelCase     :		List[str]		     = do_resize if do_resize is not None else self.do_resize
			__lowerCAmelCase     :		Any		     = size if size is not None else self.size
			__lowerCAmelCase     :		Any		     = get_size_dict(_SCREAMING_SNAKE_CASE					)
			__lowerCAmelCase     :		Any		     = resample if resample is not None else self.resample
			__lowerCAmelCase     :		Union[str, Any]		     = do_rescale if do_rescale is not None else self.do_rescale
			__lowerCAmelCase     :		int		     = rescale_factor if rescale_factor is not None else self.rescale_factor
			__lowerCAmelCase     :		List[Any]		     = do_normalize if do_normalize is not None else self.do_normalize
			__lowerCAmelCase     :		Tuple		     = image_mean if image_mean is not None else self.image_mean
			__lowerCAmelCase     :		Union[str, Any]		     = image_std if image_std is not None else self.image_std

			__lowerCAmelCase     :		Tuple		     = make_list_of_images(_SCREAMING_SNAKE_CASE					)

			if not valid_images(_SCREAMING_SNAKE_CASE					):
				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.
			__lowerCAmelCase     :		int		     = [to_numpy_array(_SCREAMING_SNAKE_CASE					) for image in images]

			if do_center_crop:
				__lowerCAmelCase     :		int		     = [self.center_crop(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE			,						size=_SCREAMING_SNAKE_CASE					) for image in images]

			if do_resize:
				__lowerCAmelCase     :		Optional[Any]		     = [self.resize(image=_SCREAMING_SNAKE_CASE			,						size=_SCREAMING_SNAKE_CASE			,						resample=_SCREAMING_SNAKE_CASE					) for image in images]

			if do_rescale:
				__lowerCAmelCase     :		str		     = [self.rescale(image=_SCREAMING_SNAKE_CASE			,						scale=_SCREAMING_SNAKE_CASE					) for image in images]

			if do_normalize:
				__lowerCAmelCase     :		Optional[int]		     = [self.normalize(image=_SCREAMING_SNAKE_CASE			,						mean=_SCREAMING_SNAKE_CASE			,						std=_SCREAMING_SNAKE_CASE					) for image in images]

			__lowerCAmelCase     :		Tuple		     = [to_channel_dimension_format(_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE					) for image in images]

			__lowerCAmelCase     :		str		     = {'pixel_values': images}
			return BatchFeature(data=_SCREAMING_SNAKE_CASE			,						tensor_type=_SCREAMING_SNAKE_CASE					)