

'''simple docstring'''





import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor


class  UpperCAmelCase (							unittest.TestCase    ):

		'''simple docstring'''






		def __init__(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = None   ,			SCREAMING_SNAKE_CASE_ = 32   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = 1 / 255   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_ = [0.48_145_466, 0.4_578_275, 0.40_821_073]   ,			SCREAMING_SNAKE_CASE_ = [0.26_862_954, 0.26_130_258, 0.27_577_711]   ,			SCREAMING_SNAKE_CASE_ = True   ,			SCREAMING_SNAKE_CASE_=7   ,			SCREAMING_SNAKE_CASE_=30   ,			SCREAMING_SNAKE_CASE_=400   ,			SCREAMING_SNAKE_CASE_=3   ,			)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      parent
							lowerCamelCase_  				=      do_resize
							lowerCamelCase_  				=      size if size is not None else {'shortest_edge': 288}
							lowerCamelCase_  				=      size_divisor
							lowerCamelCase_  				=      do_rescale
							lowerCamelCase_  				=      rescale_factor
							lowerCamelCase_  				=      do_normalize
							lowerCamelCase_  				=      do_center_crop
							lowerCamelCase_  				=      image_mean
							lowerCamelCase_  				=      image_std
							lowerCamelCase_  				=      do_pad
							lowerCamelCase_  				=      batch_size
							lowerCamelCase_  				=      num_channels
							lowerCamelCase_  				=      min_resolution
							lowerCamelCase_  				=      max_resolution
		def 			UpperCamelCase(					self		)  ->     Dict:






							'''simple docstring'''



							return {
							    "image_mean": self.image_mean,
							    "image_std": self.image_std,
							    "do_normalize": self.do_normalize,
							    "do_resize": self.do_resize,
							    "size": self.size,
							    "size_divisor": self.size_divisor,
							}






		def 			UpperCamelCase(					self   ,			SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_=False		)  ->     List[str]:






							'''simple docstring'''



							if not batched:
												lowerCamelCase_  				=      self.size['shortest_edge']
												lowerCamelCase_  				=      image_inputs[0]
												if isinstance(SCREAMING_SNAKE_CASE_   ,			Image.Image		):
																	lowerCamelCase_				,lowerCamelCase_  				=      image.size
												else:
																	lowerCamelCase_				,lowerCamelCase_  				=      image.shape[1], image.shape[2]
												lowerCamelCase_  				=      size / min(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
												if h < w:
																	lowerCamelCase_				,lowerCamelCase_  				=      size, scale * w
												else:
																	lowerCamelCase_				,lowerCamelCase_  				=      scale * h, size

												lowerCamelCase_  				=      int((1333 / 800) * size		)
												if max(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		) > max_size:
																	lowerCamelCase_  				=      max_size / max(SCREAMING_SNAKE_CASE_   ,			SCREAMING_SNAKE_CASE_		)
																	lowerCamelCase_  				=      newh * scale
																	lowerCamelCase_  				=      neww * scale

												lowerCamelCase_				,lowerCamelCase_  				=      int(newh + 0.5		), int(neww + 0.5		)
												lowerCamelCase_				,lowerCamelCase_  				=      (
												    newh // self.size_divisor * self.size_divisor,
												    neww // self.size_divisor * self.size_divisor,
												)

							else:
												lowerCamelCase_  				=      []
												for image in image_inputs:
																	lowerCamelCase_				,lowerCamelCase_  				=      self.get_expected_values([image]		)
																	expected_values.append((expected_height, expected_width)		)
												lowerCamelCase_  				=      max(SCREAMING_SNAKE_CASE_   ,			key=lambda SCREAMING_SNAKE_CASE_		: item[0]		)[0]
												lowerCamelCase_  				=      max(SCREAMING_SNAKE_CASE_   ,			key=lambda SCREAMING_SNAKE_CASE_		: item[1]		)[1]

							return expected_height, expected_width


@require_torch
@require_vision
class  UpperCAmelCase (							UpperCAmelCase__  , unittest.TestCase    ):

		'''simple docstring'''






		SCREAMING_SNAKE_CASE_           =					BridgeTowerImageProcessor if is_vision_available() else None
		def 			UpperCamelCase(					self		)  ->     int:






							'''simple docstring'''



							lowerCamelCase_  				=      BridgeTowerImageProcessingTester(self		)
		@property
		def 			UpperCamelCase(					self		)  ->     List[Any]:






							'''simple docstring'''



							return self.image_processor_tester.prepare_image_processor_dict()
		def 			UpperCamelCase(					self		)  ->     str:






							'''simple docstring'''



							lowerCamelCase_  				=      self.image_processing_class(**self.image_processor_dict		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'image_mean'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'image_std'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'do_normalize'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'do_resize'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'size'		)		)
							self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_   ,			'size_divisor'		)		)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							pass
		def 			UpperCamelCase(					self		)  ->     Optional[int]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.image_processing_class(**self.image_processor_dict		)
							# create random PIL images
							lowerCamelCase_  				=      prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=SCREAMING_SNAKE_CASE_		)
							for image in image_inputs:
												self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,			Image.Image		)

							# Test not batched input
							lowerCamelCase_  				=      image_processing(image_inputs[0]   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,			)

							# Test batched
							lowerCamelCase_  				=      image_processing(SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,			batched=SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   ,			)
		def 			UpperCamelCase(					self		)  ->     Any:






							'''simple docstring'''



							lowerCamelCase_  				=      self.image_processing_class(**self.image_processor_dict		)
							# create random numpy tensors
							lowerCamelCase_  				=      prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=SCREAMING_SNAKE_CASE_   ,			numpify=SCREAMING_SNAKE_CASE_		)
							for image in image_inputs:
												self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,			np.ndarray		)

							# Test not batched input
							lowerCamelCase_  				=      image_processing(image_inputs[0]   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,			)

							# Test batched
							lowerCamelCase_  				=      image_processing(SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,			batched=SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_width,
							    )   ,			)






		def 			UpperCamelCase(					self		)  ->     Union[str, Any]:






							'''simple docstring'''



							lowerCamelCase_  				=      self.image_processing_class(**self.image_processor_dict		)
							# create random PyTorch tensors
							lowerCamelCase_  				=      prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=SCREAMING_SNAKE_CASE_   ,			torchify=SCREAMING_SNAKE_CASE_		)
							for image in image_inputs:
												self.assertIsInstance(SCREAMING_SNAKE_CASE_   ,			torch.Tensor		)

							# Test not batched input
							lowerCamelCase_  				=      image_processing(image_inputs[0]   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(1, self.image_processor_tester.num_channels, expected_height, expected_width)   ,			)

							# Test batched
							lowerCamelCase_  				=      image_processing(SCREAMING_SNAKE_CASE_   ,			return_tensors='pt'		).pixel_values

							lowerCamelCase_				,lowerCamelCase_  				=      self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE_   ,			batched=SCREAMING_SNAKE_CASE_		)
							self.assertEqual(
							    encoded_images.shape   ,			(
							        self.image_processor_tester.batch_size,
							        self.image_processor_tester.num_channels,
							        expected_height,
							        expected_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 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


_snake_case      = logging.get_logger(__name__)

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      Optional[Any]:
     UpperCamelCase						    =					b.T
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=1			)
     UpperCamelCase						    =					np.sum(np.square(_lowercase			)						,     axis=0			)
     UpperCamelCase						    =					np.matmul(_lowercase						,     _lowercase			)
     UpperCamelCase						    =					aa[:, None] - 2 * ab + ba[None, :]
     return d

def  __lowerCamelCase					( _lowercase						,     _lowercase			)       ->      List[Any]:
     UpperCamelCase						    =					x.reshape(-1						,     3			)
     UpperCamelCase						    =					squared_euclidean_distance(_lowercase						,     _lowercase			)
     return np.argmin(_lowercase						,     axis=1			)







class       _lowerCAmelCase  (		__magic_name__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Optional[Any]       						=["pixel_values"]

 def __init__(						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			bool = True					, SCREAMING_SNAKE_CASE__			:			bool = True					, **SCREAMING_SNAKE_CASE__			:			Optional[int]					, ):






      """simple docstring"""



      super().__init__(**SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					size if size is not None else {'height': 2_56, 'width': 2_56}
      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   ) if clusters is not None else None
      UpperCamelCase						    =					do_resize
      UpperCamelCase						    =					size
      UpperCamelCase						    =					resample
      UpperCamelCase						    =					do_normalize
      UpperCamelCase						    =					do_color_quantize

 def      __lowerCAmelCase  (						self			:			int					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Dict[str, int]					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = PILImageResampling.BILINEAR					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, **SCREAMING_SNAKE_CASE__			:			List[str]					, ):






      """simple docstring"""



      UpperCamelCase						    =					get_size_dict(SCREAMING_SNAKE_CASE__   )
      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(
          SCREAMING_SNAKE_CASE__					, size=(size['height'], size['width'])					, resample=SCREAMING_SNAKE_CASE__					, data_format=SCREAMING_SNAKE_CASE__					, **SCREAMING_SNAKE_CASE__   )

 def      __lowerCAmelCase  (						self			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			np.ndarray					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = None					, ):






      """simple docstring"""



      UpperCamelCase						    =					rescale(image=SCREAMING_SNAKE_CASE__					, scale=1 / 127.5					, data_format=SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					image - 1
      return image






 def      __lowerCAmelCase  (						self			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			ImageInput					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Dict[str, int] = None					, SCREAMING_SNAKE_CASE__			:			PILImageResampling = None					, SCREAMING_SNAKE_CASE__			:			bool = None					, SCREAMING_SNAKE_CASE__			:			Optional[bool] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[List[List[int]], np.ndarray]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, TensorType]] = None					, SCREAMING_SNAKE_CASE__			:			Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					, **SCREAMING_SNAKE_CASE__			:			str					, ):






      """simple docstring"""



      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(SCREAMING_SNAKE_CASE__   )
      UpperCamelCase						    =					resample if resample is not None else self.resample
      UpperCamelCase						    =					do_normalize if do_normalize is not None else self.do_normalize
      UpperCamelCase						    =					do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCamelCase						    =					clusters if clusters is not None else self.clusters
      UpperCamelCase						    =					np.array(SCREAMING_SNAKE_CASE__   )

      UpperCamelCase						    =					make_list_of_images(SCREAMING_SNAKE_CASE__   )

      if not valid_images(SCREAMING_SNAKE_CASE__   ):
           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.
      UpperCamelCase						    =					[to_numpy_array(SCREAMING_SNAKE_CASE__   ) for image in images]

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

      if do_normalize:
           UpperCamelCase						    =					[self.normalize(image=SCREAMING_SNAKE_CASE__   ) for image in images]

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

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

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

      UpperCamelCase						    =					{'input_ids': images}
      return BatchFeature(data=SCREAMING_SNAKE_CASE__					, tensor_type=SCREAMING_SNAKE_CASE__   )


