


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      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Tuple							=		CONFIG_NAME






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






     """simple docstring"""





     super().__init__()






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





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

     return pil_images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





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

     return images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     if images.ndim == 3:
       lowerCamelCase			: Union[str, Any]   =    images[..., None]

     lowerCamelCase			: List[str]   =    torch.from_numpy(images.transpose(0						,    3						,    1						,    2			)			)
     return images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    images.cpu().permute(0						,    2						,    3						,    1			).float().numpy()
     return images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     return 2.0 * images - 1.0






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





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






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    image.convert("RGB"			)
     return image






   def _snake_case   (		self						,    __A						,    __A = None						,    __A = None						,    ):






     """simple docstring"""





     if height is None:
       lowerCamelCase			: Dict   =    image.height
     if width is None:
       lowerCamelCase			: Union[str, Any]   =    image.width

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






   def _snake_case   (		self						,    __A						,    __A = None						,    __A = None						,    ):






     """simple docstring"""





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

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

     if do_normalize:
       lowerCamelCase			: Dict   =    self.normalize(__A			)

     return image






   def _snake_case   (		self						,    __A						,    __A = "pil"						,    __A = None						,    ):






     """simple docstring"""





     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			: 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			)
       lowerCamelCase			: Any   =    "np"

     if output_type == "latent":
       return image

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

     lowerCamelCase			: str   =    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(__A			)

     if output_type == "np":
       return image

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



class      UpperCAmelCase_ ( UpperCamelCase   ):

   '''simple docstring'''




   __A			:				Dict							=		CONFIG_NAME






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






     """simple docstring"""





     super().__init__()






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





     if images.ndim == 3:
       lowerCamelCase			: List[str]   =    images[None, ...]
     lowerCamelCase			: Tuple   =    (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			: Any   =    [Image.fromarray(image[:, :, :3]			) for image in images]

     return pil_images






   @staticmethod
   def _snake_case   (		__A			):






     """simple docstring"""





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






   def _snake_case   (		self						,    __A			):






     """simple docstring"""





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

     return pil_images






   def _snake_case   (		self						,    __A						,    __A = "pil"						,    __A = None						,    ):






     """simple docstring"""





     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			: Optional[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"						,    __A						,    standard_warn=__A			)
       lowerCamelCase			: str   =    "np"

     if do_denormalize is None:
       lowerCamelCase			: Optional[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			: int   =    self.pt_to_numpy(__A			)

     if output_type == "np":
       if image.shape[-1] == 6:
         lowerCamelCase			: Optional[Any]   =    np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]						,    axis=0			)
       else:
         lowerCamelCase			: Union[str, Any]   =    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 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      UpperCAmelCase_ ( unittest.TestCase   ):

   '''simple docstring'''




   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    tempfile.mkdtemp()

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

     lowerCamelCase			: List[str]   =    GitProcessor(__A						,    __A			)

     processor.save_pretrained(self.tmpdirname			)






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).tokenizer






   def _snake_case   (		self						,    **__A			):






     """simple docstring"""





     return AutoProcessor.from_pretrained(self.tmpdirname						,    **__A			).image_processor






   def _snake_case   (		self			):






     """simple docstring"""





     shutil.rmtree(self.tmpdirname			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: str   =    [np.random.randint(255						,    size=(3, 30, 400)						,    dtype=np.uinta			)]

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

     return image_inputs






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=self.get_tokenizer()						,    image_processor=self.get_image_processor()			)
     processor.save_pretrained(self.tmpdirname			)

     lowerCamelCase			: Optional[Any]   =    self.get_tokenizer(bos_token="(BOS)"						,    eos_token="(EOS)"			)
     lowerCamelCase			: List[str]   =    self.get_image_processor(do_normalize=__A						,    padding_value=1.0			)

     lowerCamelCase			: Dict   =    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Any   =    self.get_image_processor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: Optional[Any]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: List[str]   =    self.prepare_image_inputs()

     lowerCamelCase			: str   =    image_processor(__A						,    return_tensors="np"			)
     lowerCamelCase			: Union[str, Any]   =    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: int   =    self.get_image_processor()
     lowerCamelCase			: Dict   =    self.get_tokenizer()

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: int   =    "lower newer"

     lowerCamelCase			: str   =    processor(text=__A			)

     lowerCamelCase			: Union[str, Any]   =    tokenizer(__A						,    return_token_type_ids=__A			)

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






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Dict   =    self.get_image_processor()
     lowerCamelCase			: Optional[int]   =    self.get_tokenizer()

     lowerCamelCase			: List[str]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: str   =    "lower newer"
     lowerCamelCase			: Tuple   =    self.prepare_image_inputs()

     lowerCamelCase			: str   =    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 _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: Union[str, Any]   =    self.get_image_processor()
     lowerCamelCase			: Tuple   =    self.get_tokenizer()

     lowerCamelCase			: Tuple   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

     lowerCamelCase			: str   =    processor.batch_decode(__A			)
     lowerCamelCase			: int   =    tokenizer.batch_decode(__A			)

     self.assertListEqual(__A						,    __A			)






   def _snake_case   (		self			):






     """simple docstring"""





     lowerCamelCase			: List[Any]   =    self.get_image_processor()
     lowerCamelCase			: int   =    self.get_tokenizer()

     lowerCamelCase			: Optional[int]   =    GitProcessor(tokenizer=__A						,    image_processor=__A			)

     lowerCamelCase			: Optional[Any]   =    "lower newer"
     lowerCamelCase			: List[Any]   =    self.prepare_image_inputs()

     lowerCamelCase			: Optional[int]   =    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"]			)



