






'''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


UpperCamelCase    :      List[str]  			=		logging.get_logger(__name__)
def 						A__			(				__lowerCAmelCase		:			List[str]   ,    __lowerCAmelCase		:			Union[str, Any]						):
						lowerCamelCase__         =  b.T
						lowerCamelCase__         =  np.sum(np.square(__lowerCAmelCase						)   ,    axis=1						)
						lowerCamelCase__         =  np.sum(np.square(__lowerCAmelCase						)   ,    axis=0						)
						lowerCamelCase__         =  np.matmul(__lowerCAmelCase   ,    __lowerCAmelCase						)
						lowerCamelCase__         =  aa[:, None] - 2 * ab + ba[None, :]
						return d
def 						A__			(				__lowerCAmelCase		:			List[Any]   ,    __lowerCAmelCase		:			str						):
						lowerCamelCase__         =  x.reshape(-1   ,    3						)
						lowerCamelCase__         =  squared_euclidean_distance(__lowerCAmelCase   ,    __lowerCAmelCase						)
						return np.argmin(__lowerCAmelCase   ,    axis=1						)





class 		UpperCamelCase__    (a			):




		'''simple docstring'''





		_UpperCamelCase				  =					['pixel_values']






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






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase   ,_lowerCAmelCase = PILImageResampling.BILINEAR   ,_lowerCAmelCase = None   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase       )
								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(
								    _lowerCAmelCase   ,size=(size["""height"""], size["""width"""])   ,resample=_lowerCAmelCase   ,data_format=_lowerCAmelCase   ,**_lowerCAmelCase       )






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,):
								lowerCamelCase__         =  rescale(image=_lowerCAmelCase   ,scale=1 / 127.5   ,data_format=_lowerCAmelCase       )
								lowerCamelCase__         =  image - 1
								return image






		def 			UpperCamelCase_					(      self   ,_lowerCAmelCase   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = None   ,_lowerCAmelCase = ChannelDimension.FIRST   ,**_lowerCAmelCase   ,):
								lowerCamelCase__         =  do_resize if do_resize is not None else self.do_resize
								lowerCamelCase__         =  size if size is not None else self.size
								lowerCamelCase__         =  get_size_dict(_lowerCAmelCase       )
								lowerCamelCase__         =  resample if resample is not None else self.resample
								lowerCamelCase__         =  do_normalize if do_normalize is not None else self.do_normalize
								lowerCamelCase__         =  do_color_quantize if do_color_quantize is not None else self.do_color_quantize
								lowerCamelCase__         =  clusters if clusters is not None else self.clusters
								lowerCamelCase__         =  np.array(_lowerCAmelCase       )

								lowerCamelCase__         =  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 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.
								lowerCamelCase__         =  [to_numpy_array(_lowerCAmelCase       ) for image in images]

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

								if do_normalize:
														lowerCamelCase__         =  [self.normalize(image=_lowerCAmelCase       ) for image in images]

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

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

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

								lowerCamelCase__         =  {"""input_ids""": images}
								return BatchFeature(data=_lowerCAmelCase   ,tensor_type=_lowerCAmelCase       )









'''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 LevitImageProcessor




class 		UpperCamelCase__    (unittest.TestCase			):




		'''simple docstring'''





		def __init__(      self   ,_lowerCAmelCase   ,_lowerCAmelCase=7   ,_lowerCAmelCase=3   ,_lowerCAmelCase=18   ,_lowerCAmelCase=30   ,_lowerCAmelCase=4_00   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=None   ,_lowerCAmelCase=True   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,_lowerCAmelCase=[0.5, 0.5, 0.5]   ,):
								lowerCamelCase__         =  size if size is not None else {"""shortest_edge""": 18}
								lowerCamelCase__         =  crop_size if crop_size is not None else {"""height""": 18, """width""": 18}
								lowerCamelCase__         =  parent
								lowerCamelCase__         =  batch_size
								lowerCamelCase__         =  num_channels
								lowerCamelCase__         =  image_size
								lowerCamelCase__         =  min_resolution
								lowerCamelCase__         =  max_resolution
								lowerCamelCase__         =  do_resize
								lowerCamelCase__         =  size
								lowerCamelCase__         =  do_center_crop
								lowerCamelCase__         =  crop_size
								lowerCamelCase__         =  do_normalize
								lowerCamelCase__         =  image_mean
								lowerCamelCase__         =  image_std






		def 			UpperCamelCase_					(      self       ):
								return {
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_normalize": self.do_normalize,
								    "do_resize": self.do_resize,
								    "do_center_crop": self.do_center_crop,
								    "size": self.size,
								    "crop_size": self.crop_size,
								}




@require_torch
@require_vision
class 		UpperCamelCase__    (a   ,unittest.TestCase			):




		'''simple docstring'''





		_UpperCamelCase				  =					LevitImageProcessor if is_vision_available() else None






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  LevitImageProcessingTester(self       )






		@property
		def 			UpperCamelCase_					(      self       ):
								return self.image_processor_tester.prepare_image_processor_dict()






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_mean"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""image_std"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_normalize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_resize"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""do_center_crop"""       )       )
								self.assertTrue(hasattr(_lowerCAmelCase   ,"""size"""       )       )






		def 			UpperCamelCase_					(      self       ):
								lowerCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict       )
								self.assertEqual(image_processor.size   ,{"""shortest_edge""": 18}       )
								self.assertEqual(image_processor.crop_size   ,{"""height""": 18, """width""": 18}       )

								lowerCamelCase__         =  self.image_processing_class.from_dict(self.image_processor_dict   ,size=42   ,crop_size=84       )
								self.assertEqual(image_processor.size   ,{"""shortest_edge""": 42}       )
								self.assertEqual(image_processor.crop_size   ,{"""height""": 84, """width""": 84}       )






		def 			UpperCamelCase_					(      self       ):
								pass






		def 			UpperCamelCase_					(      self       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PIL images
								lowerCamelCase__         =  prepare_image_inputs(self.image_processor_tester   ,equal_resolution=_lowerCAmelCase       )
								for image in image_inputs:
														self.assertIsInstance(_lowerCAmelCase   ,Image.Image       )

								# Test not batched input
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random numpy tensors
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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       ):
								# Initialize image_processing
								lowerCamelCase__         =  self.image_processing_class(**self.image_processor_dict       )
								# create random PyTorch tensors
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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
								lowerCamelCase__         =  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"""],
								    )   ,)

