
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      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      CONFIG_NAME



	@register_to_config
	def __init__(						self			:					str    ,						lowercase			:					bool = True    ,						lowercase			:					int = 8    ,						lowercase			:					str = "lanczos"    ,						lowercase			:					bool = True    ,						lowercase			:					bool = False    ,						):


				'''simple docstring'''


				super().__init__()



	@staticmethod
	def 				A							(						lowercase			:					np.ndarray			):


				'''simple docstring'''


				if images.ndim == 3:
							_snake_case						=    images[None, ...]
				_snake_case						=    (images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							_snake_case						=    [Image.fromarray(image.squeeze()    ,						mode='L'			) for image in images]
				else:
							_snake_case						=    [Image.fromarray(lowercase			) for image in images]

				return pil_images



	@staticmethod
	def 				A							(						lowercase			:					Union[List[PIL.Image.Image], PIL.Image.Image]			):


				'''simple docstring'''


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

				return images



	@staticmethod
	def 				A							(						lowercase			:					np.ndarray			):


				'''simple docstring'''


				if images.ndim == 3:
							_snake_case						=    images[..., None]

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



	@staticmethod
	def 				A							(						lowercase			:					torch.FloatTensor			):


				'''simple docstring'''


				_snake_case						=    images.cpu().permute(0    ,						2    ,						3    ,						1			).float().numpy()
				return images



	@staticmethod
	def 				A							(						lowercase			:					Tuple			):


				'''simple docstring'''


				return 2.0 * images - 1.0



	@staticmethod
	def 				A							(						lowercase			:					Optional[Any]			):


				'''simple docstring'''


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



	@staticmethod
	def 				A							(						lowercase			:					PIL.Image.Image			):


				'''simple docstring'''


				_snake_case						=    image.convert('RGB'			)
				return image



	def 				A							(						self			:					List[Any]    ,						lowercase			:					PIL.Image.Image    ,						lowercase			:					Optional[int] = None    ,						lowercase			:					Optional[int] = None    ,						):


				'''simple docstring'''


				if height is None:
							_snake_case						=    image.height
				if width is None:
							_snake_case						=    image.width

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



	def 				A							(						self			:					Optional[Any]    ,						lowercase			:					Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]    ,						lowercase			:					Optional[int] = None    ,						lowercase			:					Optional[int] = None    ,						):


				'''simple docstring'''


				_snake_case						=    (PIL.Image.Image, np.ndarray, torch.Tensor)
				if isinstance(lowercase    ,						lowercase			):
							_snake_case						=    [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:
										_snake_case						=    [self.convert_to_rgb(lowercase			) for i in image]
							if self.config.do_resize:
										_snake_case						=    [self.resize(lowercase    ,						lowercase    ,						lowercase			) for i in image]
							_snake_case						=    self.pil_to_numpy(lowercase			)  # to np
							_snake_case						=    self.numpy_to_pt(lowercase			)  # to pt

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

				if do_normalize:
							_snake_case						=    self.normalize(lowercase			)

				return image





	def 				A							(						self			:					Dict    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					str = "pil"    ,						lowercase			:					Optional[List[bool]] = None    ,						):


				'''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"]:
							_snake_case						=    (
							    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			)
							_snake_case						=    'np'

				if output_type == "latent":
							return image

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

				_snake_case						=    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

				_snake_case						=    self.pt_to_numpy(lowercase			)

				if output_type == "np":
							return image

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




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Dict					     =      CONFIG_NAME



	@register_to_config
	def __init__(						self			:					Dict    ,						lowercase			:					bool = True    ,						lowercase			:					int = 8    ,						lowercase			:					str = "lanczos"    ,						lowercase			:					bool = True    ,						):


				'''simple docstring'''


				super().__init__()



	@staticmethod
	def 				A							(						lowercase			:					Optional[int]			):


				'''simple docstring'''


				if images.ndim == 3:
							_snake_case						=    images[None, ...]
				_snake_case						=    (images * 255).round().astype('uint8'			)
				if images.shape[-1] == 1:
							# special case for grayscale (single channel) images
							_snake_case						=    [Image.fromarray(image.squeeze()    ,						mode='L'			) for image in images]
				else:
							_snake_case						=    [Image.fromarray(image[:, :, :3]			) for image in images]

				return pil_images



	@staticmethod
	def 				A							(						lowercase			:					Union[str, Any]			):


				'''simple docstring'''


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



	def 				A							(						self			:					Any    ,						lowercase			:					Dict			):


				'''simple docstring'''


				if images.ndim == 3:
							_snake_case						=    images[None, ...]
				_snake_case						=    images[:, :, :, 3:]
				if images.shape[-1] == 6:
							_snake_case						=    (images_depth * 255).round().astype('uint8'			)
							_snake_case						=    [
							    Image.fromarray(self.rgblike_to_depthmap(lowercase			)    ,						mode='I;16'			) for image_depth in images_depth
							]
				elif images.shape[-1] == 4:
							_snake_case						=    (images_depth * 65535.0).astype(np.uintaa			)
							_snake_case						=    [Image.fromarray(lowercase    ,						mode='I;16'			) for image_depth in images_depth]
				else:
							raise Exception('Not supported'			)

				return pil_images





	def 				A							(						self			:					List[str]    ,						lowercase			:					torch.FloatTensor    ,						lowercase			:					str = "pil"    ,						lowercase			:					Optional[List[bool]] = None    ,						):


				'''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"]:
							_snake_case						=    (
							    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			)
							_snake_case						=    'np'

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

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

				_snake_case						=    self.pt_to_numpy(lowercase			)

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

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	int					     =      ["image_processor", "tokenizer"]
	_UpperCAmelCase :	Tuple					     =      "EfficientNetImageProcessor"
	_UpperCAmelCase :	Optional[Any]					     =      ("BertTokenizer", "BertTokenizerFast")



	def __init__(						self			:					Tuple    ,						lowercase			:					List[Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				super().__init__(lowercase    ,						lowercase			)



	def __call__(						self			:					Union[str, Any]    ,						lowercase			:					Dict=None    ,						lowercase			:					Optional[int]=None    ,						lowercase			:					List[Any]="max_length"    ,						lowercase			:					Optional[int]=64    ,						lowercase			:					int=None    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				if text is None and images is None:
							raise ValueError('You have to specify either text or images. Both cannot be none.'			)

				if text is not None:
							_snake_case						=    self.tokenizer(
							    lowercase    ,						padding=lowercase    ,						max_length=lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if images is not None:
							_snake_case						=    self.image_processor(lowercase    ,						return_tensors=lowercase    ,						**lowercase			)

				if text is not None and images is not None:
							_snake_case						=    image_features.pixel_values
							return encoding
				elif text is not None:
							return encoding
				else:
							return BatchEncoding(data=dict(**lowercase			)    ,						tensor_type=lowercase			)



	def 				A							(						self			:					Any    ,						*lowercase			:					List[str]    ,						**lowercase			:					Optional[Any]			):


				'''simple docstring'''


				return self.tokenizer.batch_decode(*lowercase    ,						**lowercase			)



	def 				A							(						self			:					List[Any]    ,						*lowercase			:					Union[str, Any]    ,						**lowercase			:					Tuple			):


				'''simple docstring'''


				return self.tokenizer.decode(*lowercase    ,						**lowercase			)





	@property
	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				_snake_case						=    self.tokenizer.model_input_names
				_snake_case						=    self.image_processor.model_input_names
				return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names			)			)