"""simple docstring"""




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 ViTImageProcessor
class 			UpperCamelCase__(	unittest.TestCase       ):





						def __init__(				self						,__UpperCAmelCase						,__UpperCAmelCase=13						,__UpperCAmelCase=3						,__UpperCAmelCase=2_24						,__UpperCAmelCase=30						,__UpperCAmelCase=4_00						,__UpperCAmelCase=True						,__UpperCAmelCase=None						,__UpperCAmelCase=True						,__UpperCAmelCase=[0.5, 0.5, 0.5]						,__UpperCAmelCase=[0.5, 0.5, 0.5]						,)    -> int:
													A__		   =							size if size is not None else {'height': 18, 'width': 18}
													A__		   =							parent
													A__		   =							batch_size
													A__		   =							num_channels
													A__		   =							image_size
													A__		   =							min_resolution
													A__		   =							max_resolution
													A__		   =							do_resize
													A__		   =							size
													A__		   =							do_normalize
													A__		   =							image_mean
													A__		   =							image_std





						def  snake_case__		(				self   )    -> List[str]:
													return {
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_normalize": self.do_normalize,
													    "do_resize": self.do_resize,
													    "size": self.size,
													}






@require_torch
@require_vision
class 			UpperCamelCase__(	__A						,  unittest.TestCase       ):
						lowerCAmelCase__     :  Dict  							=						ViTImageProcessor if is_vision_available() else None





						def  snake_case__		(				self   )    -> Optional[Any]:
													A__		   =							EfficientFormerImageProcessorTester(self   )





						@property
						def  snake_case__		(				self   )    -> Tuple:
													return self.image_proc_tester.prepare_image_processor_dict()





						def  snake_case__		(				self   )    -> Any:
													A__		   =							self.image_processing_class(**self.image_processor_dict   )
													self.assertTrue(hasattr(__UpperCAmelCase						,'image_mean'   )   )
													self.assertTrue(hasattr(__UpperCAmelCase						,'image_std'   )   )
													self.assertTrue(hasattr(__UpperCAmelCase						,'do_normalize'   )   )
													self.assertTrue(hasattr(__UpperCAmelCase						,'do_resize'   )   )
													self.assertTrue(hasattr(__UpperCAmelCase						,'size'   )   )





						def  snake_case__		(				self   )    -> Dict:
													pass





						def  snake_case__		(				self   )    -> List[str]:
													# Initialize image_processor
													A__		   =							self.image_processing_class(**self.image_processor_dict   )
													# create random PIL images
													A__		   =							prepare_image_inputs(self.image_proc_tester						,equal_resolution=__UpperCAmelCase   )
													for image in image_inputs:
																				self.assertIsInstance(__UpperCAmelCase						,Image.Image   )

													# Test not batched input
													A__		   =							image_processor(image_inputs[0]						,return_tensors='pt'   ).pixel_values
													self.assertEqual(
													    encoded_images.shape						,(
													        1,
													        self.image_proc_tester.num_channels,
													        self.image_proc_tester.size['height'],
													        self.image_proc_tester.size['width'],
													    )						,)

													# Test batched
													A__		   =							image_processor(__UpperCAmelCase						,return_tensors='pt'   ).pixel_values
													self.assertEqual(
													    encoded_images.shape						,(
													        self.image_proc_tester.batch_size,
													        self.image_proc_tester.num_channels,
													        self.image_proc_tester.size['height'],
													        self.image_proc_tester.size['width'],
													    )						,)





						def  snake_case__		(				self   )    -> Dict:
													# Initialize image_processor
													A__		   =							self.image_processing_class(**self.image_processor_dict   )
													# create random numpy tensors
													A__		   =							prepare_image_inputs(self.image_proc_tester						,equal_resolution=__UpperCAmelCase						,numpify=__UpperCAmelCase   )
													for image in image_inputs:
																				self.assertIsInstance(__UpperCAmelCase						,np.ndarray   )

													# Test not batched input
													A__		   =							image_processor(image_inputs[0]						,return_tensors='pt'   ).pixel_values
													self.assertEqual(
													    encoded_images.shape						,(
													        1,
													        self.image_proc_tester.num_channels,
													        self.image_proc_tester.size['height'],
													        self.image_proc_tester.size['width'],
													    )						,)

													# Test batched
													A__		   =							image_processor(__UpperCAmelCase						,return_tensors='pt'   ).pixel_values
													self.assertEqual(
													    encoded_images.shape						,(
													        self.image_proc_tester.batch_size,
													        self.image_proc_tester.num_channels,
													        self.image_proc_tester.size['height'],
													        self.image_proc_tester.size['width'],
													    )						,)





						def  snake_case__		(				self   )    -> int:
													# Initialize image_processor
													A__		   =							self.image_processing_class(**self.image_processor_dict   )
													# create random PyTorch tensors
													A__		   =							prepare_image_inputs(self.image_proc_tester						,equal_resolution=__UpperCAmelCase						,torchify=__UpperCAmelCase   )
													for image in image_inputs:
																				self.assertIsInstance(__UpperCAmelCase						,torch.Tensor   )

													# Test not batched input
													A__		   =							image_processor(image_inputs[0]						,return_tensors='pt'   ).pixel_values
													self.assertEqual(
													    encoded_images.shape						,(
													        1,
													        self.image_proc_tester.num_channels,
													        self.image_proc_tester.size['height'],
													        self.image_proc_tester.size['width'],
													    )						,)

													# Test batched
													A__		   =							image_processor(__UpperCAmelCase						,return_tensors='pt'   ).pixel_values
													self.assertEqual(
													    encoded_images.shape						,(
													        self.image_proc_tester.batch_size,
													        self.image_proc_tester.num_channels,
													        self.image_proc_tester.size['height'],
													        self.image_proc_tester.size['width'],
													    )						,)






"""simple docstring"""




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 rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
				import PIL


__lowerCamelCase							 =     logging.get_logger(__name__)






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							b.T
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=1 )
							A__		   =							np.sum(np.square(UpperCamelCase__ )				,			axis=0 )
							A__		   =							np.matmul(UpperCamelCase__				,			UpperCamelCase__ )
							A__		   =							aa[:, None] - 2 * ab + ba[None, :]
							return d






def      UpperCAmelCase    (		UpperCamelCase__				,			UpperCamelCase__ ):






							"""simple docstring"""





							A__		   =							x.reshape(-1				,			3 )
							A__		   =							squared_euclidean_distance(UpperCamelCase__				,			UpperCamelCase__ )
							return np.argmin(UpperCamelCase__				,			axis=1 )


class 			UpperCamelCase__(	__A       ):
						lowerCAmelCase__     :  Tuple  							=						['pixel_values']





						def __init__(				self						,__UpperCAmelCase = None						,__UpperCAmelCase = True						,__UpperCAmelCase = None						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = True						,__UpperCAmelCase = True						,**__UpperCAmelCase						,)    -> None:
													super().__init__(**__UpperCAmelCase   )
													A__		   =							size if size is not None else {'height': 2_56, 'width': 2_56}
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							np.array(__UpperCAmelCase   ) if clusters is not None else None
													A__		   =							do_resize
													A__		   =							size
													A__		   =							resample
													A__		   =							do_normalize
													A__		   =							do_color_quantize





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase						,__UpperCAmelCase = PILImageResampling.BILINEAR						,__UpperCAmelCase = None						,**__UpperCAmelCase						,)    -> np.ndarray:
													A__		   =							get_size_dict(__UpperCAmelCase   )
													if "height" not in size or "width" not in size:
																				raise ValueError(f'''Size dictionary must contain both height and width keys. Got {size.keys()}'''   )
													return resize(
													    __UpperCAmelCase						,size=(size['height'], size['width'])						,resample=__UpperCAmelCase						,data_format=__UpperCAmelCase						,**__UpperCAmelCase   )





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,)    -> np.ndarray:
													A__		   =							rescale(image=__UpperCAmelCase						,scale=1 / 1_2_7.5						,data_format=__UpperCAmelCase   )
													A__		   =							image - 1
													return image





						def  snake_case__		(				self						,__UpperCAmelCase						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = None						,__UpperCAmelCase = ChannelDimension.FIRST						,**__UpperCAmelCase						,)    -> PIL.Image.Image:
													A__		   =							do_resize if do_resize is not None else self.do_resize
													A__		   =							size if size is not None else self.size
													A__		   =							get_size_dict(__UpperCAmelCase   )
													A__		   =							resample if resample is not None else self.resample
													A__		   =							do_normalize if do_normalize is not None else self.do_normalize
													A__		   =							do_color_quantize if do_color_quantize is not None else self.do_color_quantize
													A__		   =							clusters if clusters is not None else self.clusters
													A__		   =							np.array(__UpperCAmelCase   )

													A__		   =							make_list_of_images(__UpperCAmelCase   )

													if not valid_images(__UpperCAmelCase   ):
																				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 or resample is None:
																				raise ValueError('Size and resample must be specified if do_resize is True.'   )

													if do_color_quantize and clusters is None:
																				raise ValueError('Clusters must be specified if do_color_quantize is True.'   )

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

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

													if do_normalize:
																				A__		   =							[self.normalize(image=__UpperCAmelCase   ) for image in images]

													if do_color_quantize:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,ChannelDimension.LAST   ) for image in images]
																				# color quantize from (batch_size, height, width, 3) to (batch_size, height, width)
																				A__		   =							np.array(__UpperCAmelCase   )
																				A__		   =							color_quantize(__UpperCAmelCase						,__UpperCAmelCase   ).reshape(images.shape[:-1]   )

																				# flatten to (batch_size, height*width)
																				A__		   =							images.shape[0]
																				A__		   =							images.reshape(__UpperCAmelCase						,-1   )

																				# We need to convert back to a list of images to keep consistent behaviour across processors.
																				A__		   =							list(__UpperCAmelCase   )
													else:
																				A__		   =							[to_channel_dimension_format(__UpperCAmelCase						,__UpperCAmelCase   ) for image in images]

													A__		   =							{'input_ids': images}
													return BatchFeature(data=__UpperCAmelCase						,tensor_type=__UpperCAmelCase   )





