




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							(	lowerCamelCase						):
					_lowerCamelCase							=				CONFIG_NAME

					@register_to_config
					def __init__(       self				, __lowercase = True				, __lowercase = 8				, __lowercase = "lanczos"				, __lowercase = True				, __lowercase = False				, ):







						'''simple docstring'''




						super().__init__()

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




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

						return pil_images

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




						if not isinstance(__lowercase				, __lowercase      ):
							lowerCamelCase_							:	List[Any]							     =							[images]
						lowerCamelCase_							:	Any							     =							[np.array(__lowercase      ).astype(np.floataa      ) / 2_55.0 for image in images]
						lowerCamelCase_							:	Optional[Any]							     =							np.stack(__lowercase				, axis=0      )

						return images

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_							:	Tuple							     =							images[..., None]

						lowerCamelCase_							:	Tuple							     =							torch.from_numpy(images.transpose(0				, 3				, 1				, 2      )      )
						return images

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							images.cpu().permute(0				, 2				, 3				, 1      ).float().numpy()
						return images

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




						return 2.0 * images - 1.0

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




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

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




						lowerCamelCase_							:	List[str]							     =							image.convert('RGB'      )
						return image

					def 			lowercase_							(       self				, __lowercase				, __lowercase = None				, __lowercase = None				, ):







						'''simple docstring'''




						if height is None:
							lowerCamelCase_							:	Union[str, Any]							     =							image.height
						if width is None:
							lowerCamelCase_							:	int							     =							image.width

						lowerCamelCase_     ,    lowerCamelCase_							:	Optional[Any]							     =							(
						    x - x % self.config.vae_scale_factor for x in (width, height)
						)  # resize to integer multiple of vae_scale_factor
						lowerCamelCase_							:	Union[str, Any]							     =							image.resize((width, height)				, resample=PIL_INTERPOLATION[self.config.resample]      )
						return image

					def 			lowercase_							(       self				, __lowercase				, __lowercase = None				, __lowercase = None				, ):







						'''simple docstring'''




						lowerCamelCase_							:	List[Any]							     =							(PIL.Image.Image, np.ndarray, torch.Tensor)
						if isinstance(__lowercase				, __lowercase      ):
							lowerCamelCase_							:	int							     =							[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:
								lowerCamelCase_							:	int							     =							[self.convert_to_rgb(__lowercase      ) for i in image]
							if self.config.do_resize:
								lowerCamelCase_							:	Optional[int]							     =							[self.resize(__lowercase				, __lowercase				, __lowercase      ) for i in image]
							lowerCamelCase_							:	Tuple							     =							self.pil_to_numpy(__lowercase      )  # to np
							lowerCamelCase_							:	int							     =							self.numpy_to_pt(__lowercase      )  # to pt

						elif isinstance(image[0]				, np.ndarray      ):
							lowerCamelCase_							:	Any							     =							np.concatenate(__lowercase				, axis=0      ) if image[0].ndim == 4 else np.stack(__lowercase				, axis=0      )
							lowerCamelCase_							:	Any							     =							self.numpy_to_pt(__lowercase      )
							lowerCamelCase_     ,    lowerCamelCase_     ,    lowerCamelCase_     ,    lowerCamelCase_							:	List[Any]							     =							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_							:	Tuple							     =							torch.cat(__lowercase				, axis=0      ) if image[0].ndim == 4 else torch.stack(__lowercase				, axis=0      )
							lowerCamelCase_     ,    lowerCamelCase_     ,    lowerCamelCase_     ,    lowerCamelCase_							:	int							     =							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_							:	Optional[int]							     =							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				, )
							lowerCamelCase_							:	Tuple							     =							False

						if do_normalize:
							lowerCamelCase_							:	Dict							     =							self.normalize(__lowercase      )

						return image





					def 			lowercase_							(       self				, __lowercase				, __lowercase = "pil"				, __lowercase = 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"]:
							lowerCamelCase_							:	Union[str, 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      )
							lowerCamelCase_							:	Any							     =							'np'

						if output_type == "latent":
							return image

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

						lowerCamelCase_							:	Optional[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

						lowerCamelCase_							:	int							     =							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							(	lowerCamelCase						):
					_lowerCamelCase							=				CONFIG_NAME

					@register_to_config
					def __init__(       self				, __lowercase = True				, __lowercase = 8				, __lowercase = "lanczos"				, __lowercase = True				, ):







						'''simple docstring'''




						super().__init__()

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




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

						return pil_images

					@staticmethod
					def 			lowercase_							(       __lowercase      ):







						'''simple docstring'''




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

					def 			lowercase_							(       self				, __lowercase      ):







						'''simple docstring'''




						if images.ndim == 3:
							lowerCamelCase_							:	List[Any]							     =							images[None, ...]
						lowerCamelCase_							:	Any							     =							images[:, :, :, 3:]
						if images.shape[-1] == 6:
							lowerCamelCase_							:	Tuple							     =							(images_depth * 255).round().astype('uint8'      )
							lowerCamelCase_							:	Optional[Any]							     =							[
							    Image.fromarray(self.rgblike_to_depthmap(__lowercase      )				, mode='I;16'      ) for image_depth in images_depth
							]
						elif images.shape[-1] == 4:
							lowerCamelCase_							:	Union[str, Any]							     =							(images_depth * 6_55_35.0).astype(np.uintaa      )
							lowerCamelCase_							:	Any							     =							[Image.fromarray(__lowercase				, mode='I;16'      ) for image_depth in images_depth]
						else:
							raise Exception('Not supported'      )

						return pil_images





					def 			lowercase_							(       self				, __lowercase				, __lowercase = "pil"				, __lowercase = 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"]:
							lowerCamelCase_							:	List[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      )
							lowerCamelCase_							:	List[str]							     =							'np'

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

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

						lowerCamelCase_							:	Union[str, Any]							     =							self.pt_to_numpy(__lowercase      )

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






# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/
