'''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      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      __UpperCamelCase:  Union[str, Any]     =						CONFIG_NAME




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




      @staticmethod
      def 					_A     (	A						:    np.ndarray	):
             if images.ndim == 3:
                    _UpperCAmelCase					:  str											=   images[None, ...]
             _UpperCAmelCase					:  Optional[int]											=   (images * 255).round().astype("uint8"	)
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    _UpperCAmelCase					:  Tuple											=   [Image.fromarray(image.squeeze()						, mode="L"	) for image in images]
             else:
                    _UpperCAmelCase					:  Union[str, Any]											=   [Image.fromarray(A	) for image in images]

             return pil_images




      @staticmethod
      def 					_A     (	A						:    Union[List[PIL.Image.Image], PIL.Image.Image]	):
             if not isinstance(A						, A	):
                    _UpperCAmelCase					:  Tuple											=   [images]
             _UpperCAmelCase					:  Optional[Any]											=   [np.array(A	).astype(np.floataa	) / 255.0 for image in images]
             _UpperCAmelCase					:  Optional[int]											=   np.stack(A						, axis=0	)

             return images




      @staticmethod
      def 					_A     (	A						:    np.ndarray	):
             if images.ndim == 3:
                    _UpperCAmelCase					:  Optional[int]											=   images[..., None]

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




      @staticmethod
      def 					_A     (	A						:    torch.FloatTensor	):
             _UpperCAmelCase					:  Union[str, Any]											=   images.cpu().permute(0						, 2						, 3						, 1	).float().numpy()
             return images




      @staticmethod
      def 					_A     (	A						:    str	):
             return 2.0 * images - 1.0




      @staticmethod
      def 					_A     (	A						:    Any	):
             return (images / 2 + 0.5).clamp(0						, 1	)




      @staticmethod
      def 					_A     (	A						:    PIL.Image.Image	):
             _UpperCAmelCase					:  List[str]											=   image.convert("RGB"	)
             return image




      def 					_A     (	self						:    Optional[Any]						, A						:    PIL.Image.Image						, A						:    Optional[int] = None						, A						:    Optional[int] = None						, ):
             if height is None:
                    _UpperCAmelCase					:  Optional[int]											=   image.height
             if width is None:
                    _UpperCAmelCase					:  str											=   image.width

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




      def 					_A     (	self						:    Optional[Any]						, A						:    Union[torch.FloatTensor, PIL.Image.Image, np.ndarray]						, A						:    Optional[int] = None						, A						:    Optional[int] = None						, ):
             _UpperCAmelCase					:  Optional[int]											=   (PIL.Image.Image, np.ndarray, torch.Tensor)
             if isinstance(A						, A	):
                    _UpperCAmelCase					:  List[Any]											=   [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:
                           _UpperCAmelCase					:  Union[str, Any]											=   [self.convert_to_rgb(A	) for i in image]
                    if self.config.do_resize:
                           _UpperCAmelCase					:  Optional[int]											=   [self.resize(A						, A						, A	) for i in image]
                    _UpperCAmelCase					:  List[Any]											=   self.pil_to_numpy(A	)  # to np
                    _UpperCAmelCase					:  Optional[Any]											=   self.numpy_to_pt(A	)  # to pt

             elif isinstance(image[0]						, np.ndarray	):
                    _UpperCAmelCase					:  List[str]											=   np.concatenate(A						, axis=0	) if image[0].ndim == 4 else np.stack(A						, axis=0	)
                    _UpperCAmelCase					:  Dict											=   self.numpy_to_pt(A	)
                    _UpperCAmelCase					:  int											=   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	):
                    _UpperCAmelCase					:  int											=   torch.cat(A						, axis=0	) if image[0].ndim == 4 else torch.stack(A						, axis=0	)
                    _UpperCAmelCase					:  List[str]											=   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]
             _UpperCAmelCase					:  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()}]"""						, A						, )
                    _UpperCAmelCase					:  Any											=   False

             if do_normalize:
                    _UpperCAmelCase					:  List[Any]											=   self.normalize(A	)

             return image





      def 					_A     (	self						:    Any						, A						:    torch.FloatTensor						, A						:    str = "pil"						, A						:    Optional[List[bool]] = 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"]:
                    _UpperCAmelCase					:  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"						, A						, standard_warn=A	)
                    _UpperCAmelCase					:  Dict											=   "np"

             if output_type == "latent":
                    return image

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

             _UpperCAmelCase					:  Dict											=   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

             _UpperCAmelCase					:  Dict											=   self.pt_to_numpy(A	)

             if output_type == "np":
                    return image

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

class      lowerCamelCase_     (snake_case__   ):
      '''simple docstring'''

      __UpperCamelCase:  str     =						CONFIG_NAME




      @register_to_config
      def __init__(	self						:    Any						, A						:    bool = True						, A						:    int = 8						, A						:    str = "lanczos"						, A						:    bool = True						, ):
             super().__init__()




      @staticmethod
      def 					_A     (	A						:    int	):
             if images.ndim == 3:
                    _UpperCAmelCase					:  str											=   images[None, ...]
             _UpperCAmelCase					:  List[Any]											=   (images * 255).round().astype("uint8"	)
             if images.shape[-1] == 1:
                    # special case for grayscale (single channel) images
                    _UpperCAmelCase					:  List[Any]											=   [Image.fromarray(image.squeeze()						, mode="L"	) for image in images]
             else:
                    _UpperCAmelCase					:  Any											=   [Image.fromarray(image[:, :, :3]	) for image in images]

             return pil_images




      @staticmethod
      def 					_A     (	A						:    Union[str, Any]	):
             return image[:, :, 1] * 2**8 + image[:, :, 2]




      def 					_A     (	self						:    Optional[Any]						, A						:    List[str]	):
             if images.ndim == 3:
                    _UpperCAmelCase					:  List[Any]											=   images[None, ...]
             _UpperCAmelCase					:  int											=   images[:, :, :, 3:]
             if images.shape[-1] == 6:
                    _UpperCAmelCase					:  str											=   (images_depth * 255).round().astype("uint8"	)
                    _UpperCAmelCase					:  Dict											=   [
                        Image.fromarray(self.rgblike_to_depthmap(A	)						, mode="I;16"	) for image_depth in images_depth
                    ]
             elif images.shape[-1] == 4:
                    _UpperCAmelCase					:  Dict											=   (images_depth * 65535.0).astype(np.uintaa	)
                    _UpperCAmelCase					:  List[Any]											=   [Image.fromarray(A						, mode="I;16"	) for image_depth in images_depth]
             else:
                    raise Exception("Not supported"	)

             return pil_images





      def 					_A     (	self						:    Optional[int]						, A						:    torch.FloatTensor						, A						:    str = "pil"						, A						:    Optional[List[bool]] = 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"]:
                    _UpperCAmelCase					:  Tuple											=   (
                        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	)
                    _UpperCAmelCase					:  Tuple											=   "np"

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

             _UpperCAmelCase					:  Union[str, Any]											=   torch.stack(
                 [self.denormalize(image[i]	) if do_denormalize[i] else image[i] for i in range(image.shape[0]	)]	)

             _UpperCAmelCase					:  Dict											=   self.pt_to_numpy(A	)

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


'''simple docstring'''




import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
  from PIL import Image

  from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast

@require_vision
class      lowerCamelCase_     (unittest.TestCase   ):
      '''simple docstring'''

      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   tempfile.mkdtemp()

             _UpperCAmelCase					:  Any											=   CLIPImageProcessor()
             _UpperCAmelCase					:  Optional[int]											=   BertTokenizer.from_pretrained(
                 "hf-internal-testing/tiny-random-BertModel"						, model_input_names=["input_ids", "attention_mask"]	)

             _UpperCAmelCase					:  Dict											=   GitProcessor(A						, A	)

             processor.save_pretrained(self.tmpdirname	)




      def 					_A     (	self						:    Dict						, **A						:    Tuple	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).tokenizer




      def 					_A     (	self						:    List[str]						, **A						:    str	):
             return AutoProcessor.from_pretrained(self.tmpdirname						, **A	).image_processor




      def 					_A     (	self						:    Optional[int]	):
             shutil.rmtree(self.tmpdirname	)




      def 					_A     (	self						:    Tuple	):
             _UpperCAmelCase					:  Dict											=   [np.random.randint(255						, size=(3, 30, 400)						, dtype=np.uinta	)]

             _UpperCAmelCase					:  Optional[Any]											=   [Image.fromarray(np.moveaxis(A						, 0						, -1	)	) for x in image_inputs]

             return image_inputs




      def 					_A     (	self						:    Optional[int]	):
             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=self.get_tokenizer()						, image_processor=self.get_image_processor()	)
             processor.save_pretrained(self.tmpdirname	)

             _UpperCAmelCase					:  Dict											=   self.get_tokenizer(bos_token="(BOS)"						, eos_token="(EOS)"	)
             _UpperCAmelCase					:  Optional[Any]											=   self.get_image_processor(do_normalize=A						, padding_value=1.0	)

             _UpperCAmelCase					:  Union[str, Any]											=   GitProcessor.from_pretrained(
                 self.tmpdirname						, bos_token="(BOS)"						, eos_token="(EOS)"						, do_normalize=A						, padding_value=1.0	)

             self.assertEqual(processor.tokenizer.get_vocab()						, tokenizer_add_kwargs.get_vocab()	)
             self.assertIsInstance(processor.tokenizer						, A	)

             self.assertEqual(processor.image_processor.to_json_string()						, image_processor_add_kwargs.to_json_string()	)
             self.assertIsInstance(processor.image_processor						, A	)




      def 					_A     (	self						:    Union[str, Any]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  int											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[Any]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Optional[Any]											=   image_processor(A						, return_tensors="np"	)
             _UpperCAmelCase					:  int											=   processor(images=A						, return_tensors="np"	)

             for key in input_feat_extract.keys():
                    self.assertAlmostEqual(input_feat_extract[key].sum()						, input_processor[key].sum()						, delta=1E-2	)




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  str											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[Any]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   "lower newer"

             _UpperCAmelCase					:  Optional[int]											=   processor(text=A	)

             _UpperCAmelCase					:  Tuple											=   tokenizer(A						, return_token_type_ids=A	)

             for key in encoded_tok.keys():
                    self.assertListEqual(encoded_tok[key]						, encoded_processor[key]	)




      def 					_A     (	self						:    List[str]	):
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Dict											=   self.get_tokenizer()

             _UpperCAmelCase					:  Any											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Optional[Any]											=   "lower newer"
             _UpperCAmelCase					:  Optional[int]											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Any											=   processor(text=A						, images=A	)

             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

             # test if it raises when no input is passed
             with pytest.raises(A	):
                    processor()




      def 					_A     (	self						:    Optional[Any]	):
             _UpperCAmelCase					:  List[str]											=   self.get_image_processor()
             _UpperCAmelCase					:  List[Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  List[str]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  List[str]											=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

             _UpperCAmelCase					:  Dict											=   processor.batch_decode(A	)
             _UpperCAmelCase					:  Dict											=   tokenizer.batch_decode(A	)

             self.assertListEqual(A						, A	)





      def 					_A     (	self						:    Any	):
             _UpperCAmelCase					:  List[Any]											=   self.get_image_processor()
             _UpperCAmelCase					:  Union[str, Any]											=   self.get_tokenizer()

             _UpperCAmelCase					:  Optional[int]											=   GitProcessor(tokenizer=A						, image_processor=A	)

             _UpperCAmelCase					:  Dict											=   "lower newer"
             _UpperCAmelCase					:  Tuple											=   self.prepare_image_inputs()

             _UpperCAmelCase					:  Dict											=   processor(text=A						, images=A	)

             # For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
             self.assertListEqual(list(inputs.keys()	)						, ["input_ids", "attention_mask", "pixel_values"]	)

