




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(    __A					):
       snake_case_			:					Tuple   = CONFIG_NAME

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

           """simple docstring"""



           super().__init__()

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> PIL.Image.Image:

           """simple docstring"""



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

           return pil_images

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> np.ndarray:

           """simple docstring"""



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

           return images

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> torch.FloatTensor:

           """simple docstring"""



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

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

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> np.ndarray:

           """simple docstring"""



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

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> List[Any]:

           """simple docstring"""



           return 2.0 * images - 1.0

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> List[str]:

           """simple docstring"""



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

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> PIL.Image.Image:

           """simple docstring"""



           __lowercase          =						image.convert("""RGB"""					)
           return image

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ = None ,		lowerCamelCase__ = None ,		)   -> PIL.Image.Image:

           """simple docstring"""



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

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

       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ = None ,		lowerCamelCase__ = None ,		)   -> torch.Tensor:

           """simple docstring"""



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

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

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

           if do_normalize:
               __lowercase          =						self.normalize(lowerCamelCase__					)

           return image




       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ = "pil" ,		lowerCamelCase__ = None ,		)   -> int:

           """simple docstring"""



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

           if output_type == "latent":
               return image

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

           __lowercase          =						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

           __lowercase          =						self.pt_to_numpy(lowerCamelCase__					)

           if output_type == "np":
               return image

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






class 	SCREAMING_SNAKE_CASE(    __A					):
       snake_case_			:					List[str]   = CONFIG_NAME

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

           """simple docstring"""



           super().__init__()

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> Tuple:

           """simple docstring"""



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

           return pil_images

       @staticmethod
       def    snake_case__      (   lowerCamelCase__					)   -> Optional[int]:

           """simple docstring"""



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

       def    snake_case__      (   self ,		lowerCamelCase__					)   -> Union[str, Any]:

           """simple docstring"""



           if images.ndim == 3:
               __lowercase          =						images[None, ...]
           __lowercase          =						images[:, :, :, 3:]
           if images.shape[-1] == 6:
               __lowercase          =						(images_depth * 255).round().astype("""uint8"""					)
               __lowercase          =						[
                   Image.fromarray(self.rgblike_to_depthmap(lowerCamelCase__					) ,		mode="""I;16"""					) for image_depth in images_depth
               ]
           elif images.shape[-1] == 4:
               __lowercase          =						(images_depth * 6_5535.0).astype(np.uintaa					)
               __lowercase          =						[Image.fromarray(lowerCamelCase__ ,		mode="""I;16"""					) for image_depth in images_depth]
           else:
               raise Exception("""Not supported"""					)

           return pil_images




       def    snake_case__      (   self ,		lowerCamelCase__ ,		lowerCamelCase__ = "pil" ,		lowerCamelCase__ = None ,		)   -> Tuple:

           """simple docstring"""



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

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

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

           __lowercase          =						self.pt_to_numpy(lowerCamelCase__					)

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

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










'''simple docstring'''
import unittest
import warnings
from dataclasses import dataclass

from transformers.convert_slow_tokenizer import SpmConverter
from transformers.testing_utils import get_tests_dir






@dataclass
class 	SCREAMING_SNAKE_CASE:
       snake_case_			:					str






class 	SCREAMING_SNAKE_CASE(    unittest.TestCase					):

       def    snake_case__      (   self					)   -> Optional[int]:

           """simple docstring"""



           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece.model"""					)
           __lowercase          =						get_tests_dir("""fixtures/test_sentencepiece_with_bytefallback.model"""					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		0					)

           __lowercase          =						FakeOriginalTokenizer(vocab_file=lowerCamelCase__					)

           with warnings.catch_warnings(record=lowerCamelCase__					) as w:
               __lowercase          =						SpmConverter(lowerCamelCase__					)
           self.assertEqual(len(lowerCamelCase__					) ,		1					)

           self.assertIn(
               """The sentencepiece tokenizer that you are converting to a fast tokenizer uses the byte fallback option"""
               """ which is not implemented in the fast tokenizers.""" ,		str(w[0].message					) ,		)




