




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					:     Dict					  =  logging.get_logger(__name__)
class 				__snake_case     (_a						):
				lowerCAmelCase__			=   ["pixel_values"]



				def __init__(					self					:						Any						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Dict[str, int] = None						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Dict[str, int] = None						,		_UpperCAmelCase					:						PILImageResampling = PILImageResampling.BICUBIC						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Union[int, float] = 1 / 255						,		_UpperCAmelCase					:						bool = True						,		_UpperCAmelCase					:						Optional[Union[float, List[float]]] = None						,		_UpperCAmelCase					:						Optional[Union[float, List[float]]] = None						,		**_UpperCAmelCase					:						int						,		)     ->			None:





								'''simple docstring'''




								super().__init__(**_UpperCAmelCase					)
								_lowerCAmelCase			:			Tuple        =					crop_size if crop_size is not None else {"""height""": 256, """width""": 256}
								_lowerCAmelCase			:			List[str]        =					get_size_dict(_UpperCAmelCase						,		param_name="""crop_size"""					)
								_lowerCAmelCase			:			Any        =					size if size is not None else {"""height""": 224, """width""": 224}
								_lowerCAmelCase			:			Dict        =					get_size_dict(_UpperCAmelCase					)

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



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Dict[str, int]						,		_UpperCAmelCase					:						Optional[int] = None						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						Optional[int]						,		)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					self.size if size is None else size
								_lowerCAmelCase			:			Tuple        =					get_size_dict(_UpperCAmelCase					)
								_lowerCAmelCase			:			str        =					get_size_dict(_UpperCAmelCase						,		param_name="""crop_size"""					)

								_lowerCAmelCase    ,			_lowerCAmelCase			:			Optional[Any]        =					get_image_size(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					min(_UpperCAmelCase						,		_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[int]        =					(size["""height"""] / crop_size["""height"""]) * min_dim
								_lowerCAmelCase			:			Dict        =					(size["""width"""] / crop_size["""width"""]) * min_dim
								return center_crop(_UpperCAmelCase						,		size=(cropped_height, cropped_width)						,		data_format=_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Dict[str, int]						,		_UpperCAmelCase					:						PILImageResampling = PIL.Image.BICUBIC						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						Dict						,		)     ->			np.ndarray:





								'''simple docstring'''




								_lowerCAmelCase			:			Tuple        =					get_size_dict(_UpperCAmelCase					)
								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(
								    _UpperCAmelCase						,		size=(size["""height"""], size["""width"""])						,		resample=_UpperCAmelCase						,		data_format=_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Union[int, float]						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						str						,		)     ->			Optional[int]:





								'''simple docstring'''




								return rescale(_UpperCAmelCase						,		scale=_UpperCAmelCase						,		data_format=_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						Any						,		_UpperCAmelCase					:						np.ndarray						,		_UpperCAmelCase					:						Union[float, List[float]]						,		_UpperCAmelCase					:						Union[float, List[float]]						,		_UpperCAmelCase					:						Optional[Union[str, ChannelDimension]] = None						,		**_UpperCAmelCase					:						List[Any]						,		)     ->			np.ndarray:





								'''simple docstring'''




								return normalize(_UpperCAmelCase						,		mean=_UpperCAmelCase						,		std=_UpperCAmelCase						,		data_format=_UpperCAmelCase						,		**_UpperCAmelCase					)



				def 					SCREAMING_SNAKE_CASE				(					self					:						List[Any]						,		_UpperCAmelCase					:						ImageInput						,		_UpperCAmelCase					:						Optional[bool] = None						,		_UpperCAmelCase					:						Optional[Dict[str, int]] = None						,		_UpperCAmelCase					:						Optional[bool] = None						,		_UpperCAmelCase					:						Optional[Dict[str, int]] = None						,		_UpperCAmelCase					:						PILImageResampling = None						,		_UpperCAmelCase					:						Optional[bool] = None						,		_UpperCAmelCase					:						Optional[float] = None						,		_UpperCAmelCase					:						Optional[bool] = None						,		_UpperCAmelCase					:						Optional[Union[float, List[float]]] = None						,		_UpperCAmelCase					:						Optional[Union[float, List[float]]] = None						,		_UpperCAmelCase					:						Optional[Union[str, TensorType]] = None						,		_UpperCAmelCase					:						ChannelDimension = ChannelDimension.FIRST						,		**_UpperCAmelCase					:						int						,		)     ->			PIL.Image.Image:





								'''simple docstring'''




								_lowerCAmelCase			:			Optional[Any]        =					do_center_crop if do_center_crop is not None else self.do_center_crop
								_lowerCAmelCase			:			Any        =					crop_size if crop_size is not None else self.crop_size
								_lowerCAmelCase			:			Optional[Any]        =					get_size_dict(_UpperCAmelCase						,		param_name="""crop_size"""					)
								_lowerCAmelCase			:			Optional[int]        =					do_resize if do_resize is not None else self.do_resize
								_lowerCAmelCase			:			Optional[Any]        =					size if size is not None else self.size
								_lowerCAmelCase			:			Any        =					get_size_dict(_UpperCAmelCase					)
								_lowerCAmelCase			:			Optional[Any]        =					resample if resample is not None else self.resample
								_lowerCAmelCase			:			List[Any]        =					do_rescale if do_rescale is not None else self.do_rescale
								_lowerCAmelCase			:			List[Any]        =					rescale_factor if rescale_factor is not None else self.rescale_factor
								_lowerCAmelCase			:			Any        =					do_normalize if do_normalize is not None else self.do_normalize
								_lowerCAmelCase			:			Union[str, Any]        =					image_mean if image_mean is not None else self.image_mean
								_lowerCAmelCase			:			Any        =					image_std if image_std is not None else self.image_std

								_lowerCAmelCase			:			Optional[int]        =					make_list_of_images(_UpperCAmelCase					)

								if not valid_images(_UpperCAmelCase					):
												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			:			List[str]        =					[to_numpy_array(_UpperCAmelCase					) for image in images]

								if do_center_crop:
												_lowerCAmelCase			:			Any        =					[self.center_crop(_UpperCAmelCase						,		_UpperCAmelCase						,		size=_UpperCAmelCase					) for image in images]

								if do_resize:
												_lowerCAmelCase			:			Tuple        =					[self.resize(image=_UpperCAmelCase						,		size=_UpperCAmelCase						,		resample=_UpperCAmelCase					) for image in images]

								if do_rescale:
												_lowerCAmelCase			:			Tuple        =					[self.rescale(image=_UpperCAmelCase						,		scale=_UpperCAmelCase					) for image in images]

								if do_normalize:
												_lowerCAmelCase			:			List[Any]        =					[self.normalize(image=_UpperCAmelCase						,		mean=_UpperCAmelCase						,		std=_UpperCAmelCase					) for image in images]

								_lowerCAmelCase			:			Tuple        =					[to_channel_dimension_format(_UpperCAmelCase						,		_UpperCAmelCase					) for image in images]

								_lowerCAmelCase			:			Any        =					{"""pixel_values""": images}
								return BatchFeature(data=_UpperCAmelCase						,		tensor_type=_UpperCAmelCase					)







import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
_lowerCamelCase					:     List[str]					  =  logging.get_logger(__name__)

_lowerCamelCase					:     Tuple					  =  ["UtteranceLevel"]




@torch.no_grad()
def 							_UpperCAmelCase     (UpperCamelCase_       :       Optional[Any]					,			UpperCamelCase_       :       List[Any]					,			UpperCamelCase_       :       Tuple					,			UpperCamelCase_       :       Dict				):
				'''simple docstring'''
				_lowerCAmelCase			:			List[str]        =					torch.load(UpperCamelCase_					,			map_location="""cpu"""				)
				if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
								raise NotImplementedError(F"The supported s3prl models are {SUPPORTED_MODELS}"				)

				_lowerCAmelCase			:			List[str]        =					checkpoint["""Downstream"""]

				_lowerCAmelCase			:			Optional[int]        =					HubertConfig.from_pretrained(UpperCamelCase_				)
				_lowerCAmelCase			:			str        =					HubertForSequenceClassification.from_pretrained(UpperCamelCase_					,			config=UpperCamelCase_				)
				_lowerCAmelCase			:			Union[str, Any]        =					WavaVecaFeatureExtractor.from_pretrained(
				    UpperCamelCase_					,			return_attention_mask=UpperCamelCase_					,			do_normalize=UpperCamelCase_				)

				if hf_congfig.use_weighted_layer_sum:
								_lowerCAmelCase			:			int        =					checkpoint["""Featurizer"""]["""weights"""]

				_lowerCAmelCase			:			Optional[int]        =					downstream_dict["""projector.weight"""]
				_lowerCAmelCase			:			int        =					downstream_dict["""projector.bias"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.weight"""]
				_lowerCAmelCase			:			Dict        =					downstream_dict["""model.post_net.linear.bias"""]

				hf_feature_extractor.save_pretrained(UpperCamelCase_				)
				hf_model.save_pretrained(UpperCamelCase_				)


if __name__ == "__main__":
	_lowerCamelCase					:     Any					  =  argparse.ArgumentParser()
	parser.add_argument(
	    "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
	)
	parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
	parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
	parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
	_lowerCamelCase					:     Dict					  =  parser.parse_args()
	convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)

