

'''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


lowercase__         =      logging.get_logger(__name__)




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     Optional[Any]						 =			b.T
 UpperCAmelCase	:     Optional[int]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=1     )
 UpperCAmelCase	:     List[Any]						 =			np.sum(np.square(UpperCAmelCase_     )  ,	axis=0     )
 UpperCAmelCase	:     List[str]						 =			np.matmul(UpperCAmelCase_  ,	UpperCAmelCase_     )
 UpperCAmelCase	:     Union[str, Any]						 =			aa[:, None] - 2 * ab + ba[None, :]
 return d




def 				UpperCamelCase(					UpperCAmelCase_  ,	UpperCAmelCase_     ):
 UpperCAmelCase	:     int						 =			x.reshape(-1  ,	3     )
 UpperCAmelCase	:     Optional[int]						 =			squared_euclidean_distance(UpperCAmelCase_  ,	UpperCAmelCase_     )
 return np.argmin(UpperCAmelCase_  ,	axis=1     )




class 	A_			(    _snake_case							):






     '''simple docstring'''



     UpperCAmelCase_						:     List[Any]  		=  ["""pixel_values"""]


     def __init__(		self			:					List[Any]					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					bool = True					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					bool = True					,      lowercase_			:					bool = True					,      **lowercase_			:					Optional[Any]					,      )						->     None:
      super().__init__(**lowercase_	)
      UpperCAmelCase	:     Any						 =			size if size is not None else {'height': 256, 'width': 256}
      UpperCAmelCase	:     List[Any]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     str						 =			np.array(lowercase_	) if clusters is not None else None
      UpperCAmelCase	:     Any						 =			do_resize
      UpperCAmelCase	:     List[Any]						 =			size
      UpperCAmelCase	:     Any						 =			resample
      UpperCAmelCase	:     Dict						 =			do_normalize
      UpperCAmelCase	:     List[Any]						 =			do_color_quantize


     def        UpperCAmelCase_	(		self			:					int					,      lowercase_			:					np.ndarray					,      lowercase_			:					Dict[str, int]					,      lowercase_			:					PILImageResampling = PILImageResampling.BILINEAR					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      **lowercase_			:					Any					,      )						->     np.ndarray:
      UpperCAmelCase	:     Dict						 =			get_size_dict(lowercase_	)
      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(
          lowercase_					,      size=(size['height'], size['width'])					,      resample=lowercase_					,      data_format=lowercase_					,      **lowercase_	)


     def        UpperCAmelCase_	(		self			:					Optional[int]					,      lowercase_			:					np.ndarray					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = None					,      )						->     np.ndarray:
      UpperCAmelCase	:     int						 =			rescale(image=lowercase_					,      scale=1 / 127.5					,      data_format=lowercase_	)
      UpperCAmelCase	:     Dict						 =			image - 1
      return image




     def        UpperCAmelCase_	(		self			:					str					,      lowercase_			:					ImageInput					,      lowercase_			:					bool = None					,      lowercase_			:					Dict[str, int] = None					,      lowercase_			:					PILImageResampling = None					,      lowercase_			:					bool = None					,      lowercase_			:					Optional[bool] = None					,      lowercase_			:					Optional[Union[List[List[int]], np.ndarray]] = None					,      lowercase_			:					Optional[Union[str, TensorType]] = None					,      lowercase_			:					Optional[Union[str, ChannelDimension]] = ChannelDimension.FIRST					,      **lowercase_			:					List[str]					,      )						->     PIL.Image.Image:
      UpperCAmelCase	:     Optional[int]						 =			do_resize if do_resize is not None else self.do_resize
      UpperCAmelCase	:     Optional[Any]						 =			size if size is not None else self.size
      UpperCAmelCase	:     Optional[int]						 =			get_size_dict(lowercase_	)
      UpperCAmelCase	:     Any						 =			resample if resample is not None else self.resample
      UpperCAmelCase	:     Optional[Any]						 =			do_normalize if do_normalize is not None else self.do_normalize
      UpperCAmelCase	:     str						 =			do_color_quantize if do_color_quantize is not None else self.do_color_quantize
      UpperCAmelCase	:     Optional[int]						 =			clusters if clusters is not None else self.clusters
      UpperCAmelCase	:     List[str]						 =			np.array(lowercase_	)

      UpperCAmelCase	:     int						 =			make_list_of_images(lowercase_	)

      if not valid_images(lowercase_	):
       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	:     Dict						 =			[to_numpy_array(lowercase_	) for image in images]

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

      if do_normalize:
       UpperCAmelCase	:     Tuple						 =			[self.normalize(image=lowercase_	) for image in images]

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

       # flatten to (batch_size, height*width)
       UpperCAmelCase	:     Optional[int]						 =			images.shape[0]
       UpperCAmelCase	:     Union[str, Any]						 =			images.reshape(lowercase_					,      -1	)

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

      UpperCAmelCase	:     Any						 =			{'input_ids': images}
      return BatchFeature(data=lowercase_					,      tensor_type=lowercase_	)









'''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 PoolFormerImageProcessor




class 	A_			(    unittest.TestCase							):






     '''simple docstring'''



     def __init__(		self			:					Any					,      lowercase_			:					str					,      lowercase_			:					Union[str, Any]=7					,      lowercase_			:					Union[str, Any]=3					,      lowercase_			:					int=30					,      lowercase_			:					Tuple=400					,      lowercase_			:					Tuple=True					,      lowercase_			:					Optional[int]=None					,      lowercase_			:					List[str]=0.9					,      lowercase_			:					Tuple=None					,      lowercase_			:					Union[str, Any]=True					,      lowercase_			:					int=[0.5, 0.5, 0.5]					,      lowercase_			:					List[str]=[0.5, 0.5, 0.5]					,      )						->     Tuple:
      UpperCAmelCase	:     Optional[int]						 =			size if size is not None else {'shortest_edge': 30}
      UpperCAmelCase	:     int						 =			crop_size if crop_size is not None else {'height': 30, 'width': 30}
      UpperCAmelCase	:     Tuple						 =			parent
      UpperCAmelCase	:     Optional[Any]						 =			batch_size
      UpperCAmelCase	:     int						 =			num_channels
      UpperCAmelCase	:     int						 =			min_resolution
      UpperCAmelCase	:     Optional[int]						 =			max_resolution
      UpperCAmelCase	:     str						 =			do_resize_and_center_crop
      UpperCAmelCase	:     int						 =			size
      UpperCAmelCase	:     Dict						 =			crop_pct
      UpperCAmelCase	:     Union[str, Any]						 =			crop_size
      UpperCAmelCase	:     Optional[int]						 =			do_normalize
      UpperCAmelCase	:     Optional[Any]						 =			image_mean
      UpperCAmelCase	:     Optional[Any]						 =			image_std




     def        UpperCAmelCase_	(		self			:					str	)						->     int:
      return {
          "size": self.size,
          "do_resize_and_center_crop": self.do_resize_and_center_crop,
          "crop_pct": self.crop_pct,
          "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 	A_			(    _snake_case	,       unittest.TestCase							):






     '''simple docstring'''



     UpperCAmelCase_						:     Tuple  		=  PoolFormerImageProcessor if is_vision_available() else None


     def        UpperCAmelCase_	(		self			:					Dict	)						->     str:
      UpperCAmelCase	:     Any						 =			PoolFormerImageProcessingTester(self	)


     @property
     def        UpperCAmelCase_	(		self			:					List[Any]	)						->     Union[str, Any]:
      return self.image_processor_tester.prepare_image_processor_dict()


     def        UpperCAmelCase_	(		self			:					Tuple	)						->     str:
      UpperCAmelCase	:     Any						 =			self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(lowercase_					,      'do_resize_and_center_crop'	)	)
      self.assertTrue(hasattr(lowercase_					,      'size'	)	)
      self.assertTrue(hasattr(lowercase_					,      'crop_pct'	)	)
      self.assertTrue(hasattr(lowercase_					,      'do_normalize'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_mean'	)	)
      self.assertTrue(hasattr(lowercase_					,      'image_std'	)	)


     def        UpperCAmelCase_	(		self			:					Optional[Any]	)						->     Tuple:
      UpperCAmelCase	:     List[str]						 =			self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size					,      {'shortest_edge': 30}	)
      self.assertEqual(image_processor.crop_size					,      {'height': 30, 'width': 30}	)

      UpperCAmelCase	:     Any						 =			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			:					Union[str, Any]	)						->     str:
      pass


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

      # Test not batched input
      UpperCAmelCase	:     Dict						 =			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	:     str						 =			image_processing(lowercase_					,      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			:					List[Any]	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     int						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      UpperCAmelCase	:     Dict						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      numpify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      np.ndarray	)

      # Test not batched input
      UpperCAmelCase	:     Optional[Any]						 =			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	:     Optional[Any]						 =			image_processing(lowercase_					,      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			:					str	)						->     Dict:
      # Initialize image_processing
      UpperCAmelCase	:     Optional[Any]						 =			self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      UpperCAmelCase	:     Tuple						 =			prepare_image_inputs(self.image_processor_tester					,      equal_resolution=lowercase_					,      torchify=lowercase_	)
      for image in image_inputs:
       self.assertIsInstance(lowercase_					,      torch.Tensor	)

      # Test not batched input
      UpperCAmelCase	:     int						 =			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	:     Optional[int]						 =			image_processing(lowercase_					,      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'],
          )					,      )




