
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 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = CONFIG_NAME


  @register_to_config
  def __init__(					self	,		a__ = True	,		a__ = 8	,		a__ = "lanczos"	,		a__ = True	,		a__ = False	,		):
    super().__init__()


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

    return pil_images


  @staticmethod
  def     _UpperCAmelCase  (					a__		):
    if not isinstance(a__	,		a__		):
      _lowerCamelCase      =						[images]
    _lowerCamelCase      =						[np.array(a__		).astype(np.floataa		) / 255.0 for image in images]
    _lowerCamelCase      =						np.stack(a__	,		axis=0		)

    return images


  @staticmethod
  def     _UpperCAmelCase  (					a__		):
    if images.ndim == 3:
      _lowerCamelCase      =						images[..., None]

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


  @staticmethod
  def     _UpperCAmelCase  (					a__		):
    _lowerCamelCase      =						images.cpu().permute(0	,		2	,		3	,		1		).float().numpy()
    return images


  @staticmethod
  def     _UpperCAmelCase  (					a__		):
    return 2.0 * images - 1.0


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


  @staticmethod
  def     _UpperCAmelCase  (					a__		):
    _lowerCamelCase      =						image.convert('''RGB'''		)
    return image


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = None	,		a__ = None	,		):
    if height is None:
      _lowerCamelCase      =						image.height
    if width is None:
      _lowerCamelCase      =						image.width

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


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = None	,		a__ = None	,		):
    _lowerCamelCase      =						(PIL.Image.Image, np.ndarray, torch.Tensor)
    if isinstance(a__	,		a__		):
      _lowerCamelCase      =						[image]
    elif not (isinstance(a__	,		a__		) and all(isinstance(a__	,		a__		) for i in image		)):
      raise ValueError(
          f'''Input is in incorrect format: {[type(a__		) for i in image]}. Currently, we only support {', '.join(a__		)}'''		)

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

    elif isinstance(image[0]	,		np.ndarray		):
      _lowerCamelCase      =						np.concatenate(a__	,		axis=0		) if image[0].ndim == 4 else np.stack(a__	,		axis=0		)
      _lowerCamelCase      =						self.numpy_to_pt(a__		)
      _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase      =						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		):
      _lowerCamelCase      =						torch.cat(a__	,		axis=0		) if image[0].ndim == 4 else torch.stack(a__	,		axis=0		)
      _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase    ,    _lowerCamelCase      =						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]
    _lowerCamelCase      =						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()}]'''	,		a__	,		)
      _lowerCamelCase      =						False

    if do_normalize:
      _lowerCamelCase      =						self.normalize(a__		)

    return image


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = "pil"	,		a__ = None	,		):
    if not isinstance(a__	,		torch.Tensor		):
      raise ValueError(
          f'''Input for postprocessing is in incorrect format: {type(a__		)}. We only support pytorch tensor'''		)
    if output_type not in ["latent", "pt", "np", "pil"]:
      _lowerCamelCase      =						(
          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'''	,		a__	,		standard_warn=a__		)
      _lowerCamelCase      =						'''np'''

    if output_type == "latent":
      return image

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

    _lowerCamelCase      =						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

    _lowerCamelCase      =						self.pt_to_numpy(a__		)

    if output_type == "np":
      return image

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







class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = CONFIG_NAME


  @register_to_config
  def __init__(					self	,		a__ = True	,		a__ = 8	,		a__ = "lanczos"	,		a__ = True	,		):
    super().__init__()


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

    return pil_images


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


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

    return pil_images


  def     _UpperCAmelCase  (					self	,		a__	,		a__ = "pil"	,		a__ = None	,		):
    if not isinstance(a__	,		torch.Tensor		):
      raise ValueError(
          f'''Input for postprocessing is in incorrect format: {type(a__		)}. We only support pytorch tensor'''		)
    if output_type not in ["latent", "pt", "np", "pil"]:
      _lowerCamelCase      =						(
          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'''	,		a__	,		standard_warn=a__		)
      _lowerCamelCase      =						'''np'''

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

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

    _lowerCamelCase      =						self.pt_to_numpy(a__		)

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

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






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin

class 		__magic_name__				( lowercase_					):


  """simple docstring"""





  _UpperCamelCase						 = ["image_processor", "tokenizer"]
  _UpperCamelCase						 = "AutoImageProcessor"
  _UpperCamelCase						 = "AutoTokenizer"


  def __init__(					self	,		a__=None	,		a__=None	,		**a__		):
    _lowerCamelCase      =						None
    if "feature_extractor" in kwargs:
      warnings.warn(
          '''The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'''
          ''' instead.'''	,		a__	,		)
      _lowerCamelCase      =						kwargs.pop('''feature_extractor'''		)

    _lowerCamelCase      =						image_processor if image_processor is not None else feature_extractor
    if image_processor is None:
      raise ValueError('''You need to specify an `image_processor`.'''		)
    if tokenizer is None:
      raise ValueError('''You need to specify a `tokenizer`.'''		)

    super().__init__(a__	,		a__		)
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  def __call__(					self	,		*a__	,		**a__		):
    # For backward compatibility
    if self._in_target_context_manager:
      return self.current_processor(*a__	,		**a__		)

    _lowerCamelCase      =						kwargs.pop('''images'''	,		a__		)
    _lowerCamelCase      =						kwargs.pop('''text'''	,		a__		)
    if len(a__		) > 0:
      _lowerCamelCase      =						args[0]
      _lowerCamelCase      =						args[1:]

    if images is None and text is None:
      raise ValueError('''You need to specify either an `images` or `text` input to process.'''		)

    if images is not None:
      _lowerCamelCase      =						self.image_processor(a__	,		*a__	,		**a__		)
    if text is not None:
      _lowerCamelCase      =						self.tokenizer(a__	,		**a__		)

    if text is None:
      return inputs
    elif images is None:
      return encodings
    else:
      _lowerCamelCase      =						encodings['''input_ids''']
      return inputs


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.batch_decode(*a__	,		**a__		)


  def     _UpperCAmelCase  (					self	,		*a__	,		**a__		):
    return self.tokenizer.decode(*a__	,		**a__		)


  @contextmanager
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '''
        '''labels by using the argument `text` of the regular `__call__` method (either in the same call as '''
        '''your images inputs, or in a separate call.'''		)
    _lowerCamelCase      =						True
    _lowerCamelCase      =						self.tokenizer
    yield
    _lowerCamelCase      =						self.image_processor
    _lowerCamelCase      =						False


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'''	,		a__	,		)
    return self.image_processor_class


  @property
  def     _UpperCAmelCase  (					self		):
    warnings.warn(
        '''`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'''	,		a__	,		)
    return self.image_processor




