"""simple docstring"""
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   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     lowercase = False       ,     )      ->     Any:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	List[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	Tuple     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	str     				=      [Image.fromarray(lowercase) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        if not isinstance(lowercase       ,     lowercase):
          a__:	int     				=      [images]
        a__:	Optional[Any]     				=      [np.array(lowercase).astype(np.floataa) / 255.0 for image in images]
        a__:	int     				=      np.stack(lowercase       ,     axis=0)

        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     torch.FloatTensor:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Any     				=      images[..., None]

        a__:	Any     				=      torch.from_numpy(images.transpose(0       ,     3       ,     1       ,     2))
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     np.ndarray:





        '''simple docstring'''





        a__:	Tuple     				=      images.cpu().permute(0       ,     2       ,     3       ,     1).float().numpy()
        return images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Any:





        '''simple docstring'''





        return 2.0 * images - 1.0



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return (images / 2 + 0.5).clamp(0       ,     1)



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     PIL.Image.Image:





        '''simple docstring'''





        a__:	List[Any]     				=      image.convert('RGB')
        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     PIL.Image.Image:





        '''simple docstring'''





        if height is None:
          a__:	Union[str, Any]     				=      image.height
        if width is None:
          a__:	Optional[Any]     				=      image.width

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



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = None       ,     lowercase = None       ,     )      ->     torch.Tensor:





        '''simple docstring'''





        a__:	Union[str, Any]     				=      (PIL.Image.Image, np.ndarray, torch.Tensor)
        if isinstance(lowercase       ,     lowercase):
          a__:	str     				=      [image]
        elif not (isinstance(lowercase       ,     lowercase) and all(isinstance(lowercase       ,     lowercase) for i in image)):
          raise ValueError(
              f'Input is in incorrect format: {[type(lowercase) for i in image]}. Currently, we only support {", ".join(lowercase)}')

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

        elif isinstance(image[0]       ,     np.ndarray):
          a__:	Any     				=      np.concatenate(lowercase       ,     axis=0) if image[0].ndim == 4 else np.stack(lowercase       ,     axis=0)
          a__:	List[Any]     				=      self.numpy_to_pt(lowercase)
          a__				,       a__				,       a__				,       a__:	Dict     				=      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):
          a__:	int     				=      torch.cat(lowercase       ,     axis=0) if image[0].ndim == 4 else torch.stack(lowercase       ,     axis=0)
          a__				,       a__				,       a__				,       a__:	Tuple     				=      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]
        a__:	Optional[Any]     				=      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()}]'       ,     lowercase       ,     )
          a__:	Union[str, Any]     				=      False

        if do_normalize:
          a__:	Optional[Any]     				=      self.normalize(lowercase)

        return image



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     Dict:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	str     				=      (
              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'       ,     lowercase       ,     standard_warn=lowercase)
          a__:	int     				=      'np'

        if output_type == "latent":
          return image

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

        a__:	int     				=      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

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

        if output_type == "np":
          return image

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







class   __snake_case			(	__lowerCAmelCase							):
      a__								=						CONFIG_NAME



      @register_to_config
      def __init__(		self       ,     lowercase = True       ,     lowercase = 8       ,     lowercase = "lanczos"       ,     lowercase = True       ,     )      ->     Union[str, Any]:





        '''simple docstring'''





        super().__init__()



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     Union[str, Any]:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Tuple     				=      images[None, ...]
        a__:	Optional[Any]     				=      (images * 2_55).round().astype('uint8')
        if images.shape[-1] == 1:
          # special case for grayscale (single channel) images
          a__:	int     				=      [Image.fromarray(image.squeeze()       ,     mode='L') for image in images]
        else:
          a__:	Optional[Any]     				=      [Image.fromarray(image[:, :, :3]) for image in images]

        return pil_images



      @staticmethod
      def     lowerCamelCase_  (		lowercase)      ->     List[str]:





        '''simple docstring'''





        return image[:, :, 1] * 2**8 + image[:, :, 2]



      def     lowerCamelCase_  (		self       ,     lowercase)      ->     Dict:





        '''simple docstring'''





        if images.ndim == 3:
          a__:	Optional[Any]     				=      images[None, ...]
        a__:	List[Any]     				=      images[:, :, :, 3:]
        if images.shape[-1] == 6:
          a__:	List[str]     				=      (images_depth * 2_55).round().astype('uint8')
          a__:	Any     				=      [
              Image.fromarray(self.rgblike_to_depthmap(lowercase)       ,     mode='I;16') for image_depth in images_depth
          ]
        elif images.shape[-1] == 4:
          a__:	Dict     				=      (images_depth * 65535.0).astype(np.uintaa)
          a__:	Optional[Any]     				=      [Image.fromarray(lowercase       ,     mode='I;16') for image_depth in images_depth]
        else:
          raise Exception('Not supported')

        return pil_images



      def     lowerCamelCase_  (		self       ,     lowercase       ,     lowercase = "pil"       ,     lowercase = None       ,     )      ->     List[str]:





        '''simple docstring'''





        if not isinstance(lowercase       ,     torch.Tensor):
          raise ValueError(
              f'Input for postprocessing is in incorrect format: {type(lowercase)}. We only support pytorch tensor')
        if output_type not in ["latent", "pt", "np", "pil"]:
          a__:	Optional[Any]     				=      (
              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'       ,     lowercase       ,     standard_warn=lowercase)
          a__:	List[str]     				=      'np'

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

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

        a__:	Optional[Any]     				=      self.pt_to_numpy(lowercase)

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

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









"""simple docstring"""
from typing import List, Optional, Union

import numpy as np

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


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






class    _UpperCAmelCase  (      lowercase_					):
					UpperCamelCase						=				['''input_values''', '''attention_mask''']


					def __init__(   self     :str	,			__UpperCamelCase     :List[str]=1	,			__UpperCamelCase     :Tuple=1_60_00	,			__UpperCamelCase     :Optional[int]=0.0	,			__UpperCamelCase     :Optional[int]=False	,			__UpperCamelCase     :Optional[Any]=True	,			**__UpperCamelCase     :Optional[Any]	,			):
												super().__init__(feature_size=__UpperCamelCase	,			sampling_rate=__UpperCamelCase	,			padding_value=__UpperCamelCase	,			**__UpperCamelCase						)
												A						=    return_attention_mask
												A						=    do_normalize


					@staticmethod
					def       lowerCamelCase			(   __UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :List[np.ndarray]	,			__UpperCamelCase     :float = 0.0						):
												if attention_mask is not None:
																			A						=    np.array(__UpperCamelCase	,			np.intaa						)
																			A						=    []

																			for vector, length in zip(__UpperCamelCase	,			attention_mask.sum(-1						)						):
																										A						=    (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7						)
																										if length < normed_slice.shape[0]:
																																	A						=    padding_value

																										normed_input_values.append(__UpperCamelCase						)
												else:
																			A						=    [(x - x.mean()) / np.sqrt(x.var() + 1e-7						) for x in input_values]

												return normed_input_values


					def __call__(   self     :Tuple	,			__UpperCamelCase     :Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]	,			__UpperCamelCase     :Union[bool, str, PaddingStrategy] = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :bool = False	,			__UpperCamelCase     :Optional[int] = None	,			__UpperCamelCase     :Optional[bool] = None	,			__UpperCamelCase     :Optional[Union[str, TensorType]] = None	,			__UpperCamelCase     :Optional[int] = None	,			**__UpperCamelCase     :List[str]	,			):

												if sampling_rate is not None:
																			if sampling_rate != self.sampling_rate:
																										raise ValueError(
																										    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
																										    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
																										    f" {self.sampling_rate} and not {sampling_rate}."						)
												else:
																			logger.warning(
																			    "It is strongly recommended to pass the ``sampling_rate`` argument to this function. "
																			    "Failing to do so can result in silent errors that might be hard to debug."						)

												A						=    isinstance(__UpperCamelCase	,			np.ndarray						) and len(raw_speech.shape						) > 1
												if is_batched_numpy and len(raw_speech.shape						) > 2:
																			raise ValueError(f"Only mono-channel audio is supported for input to {self}"						)
												A						=    is_batched_numpy or (
												    isinstance(__UpperCamelCase	,			(list, tuple)						) and (isinstance(raw_speech[0]	,			(np.ndarray, tuple, list)						))
												)

												# always return batch
												if not is_batched:
																			A						=    [raw_speech]

												# convert into correct format for padding
												A						=    BatchFeature({"input_values": raw_speech}						)

												A						=    self.pad(
												    __UpperCamelCase	,			padding=__UpperCamelCase	,			max_length=__UpperCamelCase	,			truncation=__UpperCamelCase	,			pad_to_multiple_of=__UpperCamelCase	,			return_attention_mask=__UpperCamelCase	,			)

												# convert input values to correct format
												A						=    padded_inputs["input_values"]
												if not isinstance(input_values[0]	,			np.ndarray						):
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.floataa						) for array in input_values]
												elif (
												    not isinstance(__UpperCamelCase	,			np.ndarray						)
												    and isinstance(input_values[0]	,			np.ndarray						)
												    and input_values[0].dtype is np.dtype(np.floataa						)
												):
																			A						=    [array.astype(np.floataa						) for array in input_values]
												elif isinstance(__UpperCamelCase	,			np.ndarray						) and input_values.dtype is np.dtype(np.floataa						):
																			A						=    input_values.astype(np.floataa						)

												# convert attention_mask to correct format
												A						=    padded_inputs.get("attention_mask"						)
												if attention_mask is not None:
																			A						=    [np.asarray(__UpperCamelCase	,			dtype=np.intaa						) for array in attention_mask]

												# zero-mean and unit-variance normalization
												if self.do_normalize:
																			A						=    (
																			    attention_mask
																			    if self._get_padding_strategies(__UpperCamelCase	,			max_length=__UpperCamelCase						) is not PaddingStrategy.DO_NOT_PAD
																			    else None
																			)
																			A						=    self.zero_mean_unit_var_norm(
																			    padded_inputs["input_values"]	,			attention_mask=__UpperCamelCase	,			padding_value=self.padding_value						)

												if return_tensors is not None:
																			A						=    padded_inputs.convert_to_tensors(__UpperCamelCase						)

												return padded_inputs


