

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
      import torch

if is_vision_available():
      from PIL import Image

      from transformers import DeiTImageProcessor






class     __magic_name__     (							unittest.TestCase     ):


 def __init__(    self		:			Any      ,    lowerCAmelCase__		:			int      ,    lowerCAmelCase__		:			Tuple=7      ,    lowerCAmelCase__		:			int=3      ,    lowerCAmelCase__		:			Dict=1_8      ,    lowerCAmelCase__		:			int=3_0      ,    lowerCAmelCase__		:			Optional[Any]=4_0_0      ,    lowerCAmelCase__		:			List[Any]=True      ,    lowerCAmelCase__		:			Any=None      ,    lowerCAmelCase__		:			Optional[Any]=True      ,    lowerCAmelCase__		:			Tuple=None      ,    lowerCAmelCase__		:			Optional[int]=True      ,    lowerCAmelCase__		:			Dict=[0.5, 0.5, 0.5]      ,    lowerCAmelCase__		:			Tuple=[0.5, 0.5, 0.5]      ,    )     ->   Union[str, Any]:
   UpperCAmelCase          =	size if size is not None else {"height": 2_0, "width": 2_0}
   UpperCAmelCase          =	crop_size if crop_size is not None else {"height": 1_8, "width": 1_8}

   UpperCAmelCase          =	parent
   UpperCAmelCase          =	batch_size
   UpperCAmelCase          =	num_channels
   UpperCAmelCase          =	image_size
   UpperCAmelCase          =	min_resolution
   UpperCAmelCase          =	max_resolution
   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	do_center_crop
   UpperCAmelCase          =	crop_size
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	image_mean
   UpperCAmelCase          =	image_std


 def _UpperCamelCase						(    self		:			str     )     ->   Union[str, Any]:
   return {
       "do_resize": self.do_resize,
       "size": self.size,
       "do_center_crop": self.do_center_crop,
       "crop_size": self.crop_size,
       "do_normalize": self.do_normalize,
       "image_mean": self.image_mean,
       "image_std": self.image_std,
   }






@require_torch
@require_vision
class     __magic_name__     (							_snake_case						,    unittest.TestCase     ):
 UpperCAmelCase				     =    DeiTImageProcessor if is_vision_available() else None
 UpperCAmelCase				     =    True


 def _UpperCamelCase						(    self		:			Union[str, Any]     )     ->   str:
   UpperCAmelCase          =	DeiTImageProcessingTester(self     )


 @property
 def _UpperCamelCase						(    self		:			List[str]     )     ->   str:
   return self.image_processor_tester.prepare_image_processor_dict()


 def _UpperCamelCase						(    self		:			Optional[Any]     )     ->   str:
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "do_resize"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "size"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "do_center_crop"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "center_crop"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "do_normalize"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "image_mean"     )     )
   self.assertTrue(hasattr(lowerCAmelCase__      ,    "image_std"     )     )


 def _UpperCamelCase						(    self		:			str     )     ->   int:
   UpperCAmelCase          =	self.image_processing_class.from_dict(self.image_processor_dict     )
   self.assertEqual(image_processor.size      ,    {"height": 2_0, "width": 2_0}     )
   self.assertEqual(image_processor.crop_size      ,    {"height": 1_8, "width": 1_8}     )

   UpperCAmelCase          =	self.image_processing_class.from_dict(self.image_processor_dict      ,    size=4_2      ,    crop_size=8_4     )
   self.assertEqual(image_processor.size      ,    {"height": 4_2, "width": 4_2}     )
   self.assertEqual(image_processor.crop_size      ,    {"height": 8_4, "width": 8_4}     )


 def _UpperCamelCase						(    self		:			Any     )     ->   str:
   pass


 def _UpperCamelCase						(    self		:			List[str]     )     ->   Dict:
   # Initialize image_processing
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random PIL images
   UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester      ,    equal_resolution=lowerCAmelCase__     )
   for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__      ,    Image.Image     )

   # Test not batched input
   UpperCAmelCase          =	image_processing(image_inputs[0]      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	image_processing(lowerCAmelCase__      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   Optional[int]:
   # Initialize image_processing
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random numpy tensors
   UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester      ,    equal_resolution=lowerCAmelCase__      ,    numpify=lowerCAmelCase__     )
   for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__      ,    np.ndarray     )

   # Test not batched input
   UpperCAmelCase          =	image_processing(image_inputs[0]      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	image_processing(lowerCAmelCase__      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )


 def _UpperCamelCase						(    self		:			Optional[int]     )     ->   int:
   # Initialize image_processing
   UpperCAmelCase          =	self.image_processing_class(**self.image_processor_dict     )
   # create random PyTorch tensors
   UpperCAmelCase          =	prepare_image_inputs(self.image_processor_tester      ,    equal_resolution=lowerCAmelCase__      ,    torchify=lowerCAmelCase__     )
   for image in image_inputs:
     self.assertIsInstance(lowerCAmelCase__      ,    torch.Tensor     )

   # Test not batched input
   UpperCAmelCase          =	image_processing(image_inputs[0]      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           1,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )

   # Test batched
   UpperCAmelCase          =	image_processing(lowerCAmelCase__      ,    return_tensors="pt"     ).pixel_values
   self.assertEqual(
       encoded_images.shape      ,    (
           self.image_processor_tester.batch_size,
           self.image_processor_tester.num_channels,
           self.image_processor_tester.crop_size["height"],
           self.image_processor_tester.crop_size["width"],
       )      ,    )






from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCAmelCase__							=			logging.get_logger(__name__)


if is_vision_available():
      import PIL






class     __magic_name__     (							_snake_case     ):
 UpperCAmelCase				     =    ["""pixel_values"""]


 def __init__(    self		:			str      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Union[int, float] = 1 / 2_5_5      ,    lowerCAmelCase__		:			bool = True      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = True      ,    **lowerCAmelCase__		:			str      ,    )     ->   None:
   super().__init__(**lowerCAmelCase__     )
   UpperCAmelCase          =	size if size is not None else {"shortest_edge": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )

   UpperCAmelCase          =	do_resize
   UpperCAmelCase          =	size
   UpperCAmelCase          =	resample
   UpperCAmelCase          =	do_center_crop
   UpperCAmelCase          =	crop_size
   UpperCAmelCase          =	do_rescale
   UpperCAmelCase          =	rescale_factor
   UpperCAmelCase          =	do_normalize
   UpperCAmelCase          =	image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   UpperCAmelCase          =	image_std if image_std is not None else OPENAI_CLIP_STD
   UpperCAmelCase          =	do_convert_rgb


 def _UpperCamelCase						(    self		:			Optional[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			PILImageResampling = PILImageResampling.BICUBIC      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			Optional[int]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	get_resize_output_image_size(
       lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    default_to_square=lowerCAmelCase__     )
   return resize(lowerCAmelCase__      ,    size=lowerCAmelCase__      ,    resample=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Dict[str, int]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   np.ndarray:
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   return center_crop(lowerCAmelCase__      ,    size=(size["height"], size["width"])      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Any      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[int, float]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[Any]      ,    )     ->   Optional[int]:
   return rescale(lowerCAmelCase__      ,    scale=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			Union[str, Any]      ,    lowerCAmelCase__		:			np.ndarray      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Union[float, List[float]]      ,    lowerCAmelCase__		:			Optional[Union[str, ChannelDimension]] = None      ,    **lowerCAmelCase__		:			List[str]      ,    )     ->   np.ndarray:
   return normalize(lowerCAmelCase__      ,    mean=lowerCAmelCase__      ,    std=lowerCAmelCase__      ,    data_format=lowerCAmelCase__      ,    **lowerCAmelCase__     )


 def _UpperCamelCase						(    self		:			List[str]      ,    lowerCAmelCase__		:			ImageInput      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Dict[str, int] = None      ,    lowerCAmelCase__		:			PILImageResampling = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			int = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			float = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			Optional[Union[float, List[float]]] = None      ,    lowerCAmelCase__		:			bool = None      ,    lowerCAmelCase__		:			Optional[Union[str, TensorType]] = None      ,    lowerCAmelCase__		:			Optional[ChannelDimension] = ChannelDimension.FIRST      ,    **lowerCAmelCase__		:			int      ,    )     ->   PIL.Image.Image:
   UpperCAmelCase          =	do_resize if do_resize is not None else self.do_resize
   UpperCAmelCase          =	size if size is not None else self.size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__      ,    default_to_square=lowerCAmelCase__     )
   UpperCAmelCase          =	resample if resample is not None else self.resample
   UpperCAmelCase          =	do_center_crop if do_center_crop is not None else self.do_center_crop
   UpperCAmelCase          =	crop_size if crop_size is not None else self.crop_size
   UpperCAmelCase          =	get_size_dict(lowerCAmelCase__     )
   UpperCAmelCase          =	do_rescale if do_rescale is not None else self.do_rescale
   UpperCAmelCase          =	rescale_factor if rescale_factor is not None else self.rescale_factor
   UpperCAmelCase          =	do_normalize if do_normalize is not None else self.do_normalize
   UpperCAmelCase          =	image_mean if image_mean is not None else self.image_mean
   UpperCAmelCase          =	image_std if image_std is not None else self.image_std
   UpperCAmelCase          =	do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   UpperCAmelCase          =	make_list_of_images(lowerCAmelCase__     )

   if not valid_images(lowerCAmelCase__     ):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray."     )

   if do_resize and size is None:
     raise ValueError("Size must be specified if do_resize is True."     )

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True."     )

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True."     )

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True."     )

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     UpperCAmelCase          =	[convert_to_rgb(lowerCAmelCase__     ) for image in images]

   # All transformations expect numpy arrays.
   UpperCAmelCase          =	[to_numpy_array(lowerCAmelCase__     ) for image in images]

   if do_resize:
     UpperCAmelCase          =	[self.resize(image=lowerCAmelCase__      ,    size=lowerCAmelCase__      ,    resample=lowerCAmelCase__     ) for image in images]

   if do_center_crop:
     UpperCAmelCase          =	[self.center_crop(image=lowerCAmelCase__      ,    size=lowerCAmelCase__     ) for image in images]

   if do_rescale:
     UpperCAmelCase          =	[self.rescale(image=lowerCAmelCase__      ,    scale=lowerCAmelCase__     ) for image in images]

   if do_normalize:
     UpperCAmelCase          =	[self.normalize(image=lowerCAmelCase__      ,    mean=lowerCAmelCase__      ,    std=lowerCAmelCase__     ) for image in images]

   UpperCAmelCase          =	[to_channel_dimension_format(lowerCAmelCase__      ,    lowerCAmelCase__     ) for image in images]

   UpperCAmelCase          =	{"pixel_values": images}
   return BatchFeature(data=lowerCAmelCase__      ,    tensor_type=lowerCAmelCase__     )



