

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						):

  def __init__(		self    ,_A    ,_A = True    ,_A = None    ,_A = 32    ,_A = True    ,_A = 1 / 255    ,_A = True    ,_A = True    ,_A = [0.4_8_1_4_5_4_6_6, 0.4_5_7_8_2_7_5, 0.4_0_8_2_1_0_7_3]    ,_A = [0.2_6_8_6_2_9_5_4, 0.2_6_1_3_0_2_5_8, 0.2_7_5_7_7_7_1_1]    ,_A = True    ,_A=7    ,_A=30    ,_A=400    ,_A=3    ,):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[Any]  =    parent
      _lowerCAmelCase       :   List[Any]  =    do_resize
      _lowerCAmelCase       :   Dict  =    size if size is not None else {'shortest_edge': 288}
      _lowerCAmelCase       :   str  =    size_divisor
      _lowerCAmelCase       :   List[str]  =    do_rescale
      _lowerCAmelCase       :   int  =    rescale_factor
      _lowerCAmelCase       :   Dict  =    do_normalize
      _lowerCAmelCase       :   str  =    do_center_crop
      _lowerCAmelCase       :   Optional[int]  =    image_mean
      _lowerCAmelCase       :   Dict  =    image_std
      _lowerCAmelCase       :   int  =    do_pad
      _lowerCAmelCase       :   Union[str, Any]  =    batch_size
      _lowerCAmelCase       :   Tuple  =    num_channels
      _lowerCAmelCase       :   List[str]  =    min_resolution
      _lowerCAmelCase       :   Dict  =    max_resolution

  def __lowerCamelCase		(		self      ):
      '''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 __lowerCamelCase		(		self    ,_A    ,_A=False      ):
      '''simple docstring'''

      if not batched:
          _lowerCAmelCase       :   Optional[Any]  =    self.size['shortest_edge']
          _lowerCAmelCase       :   Tuple  =    image_inputs[0]
          if isinstance(_A    ,Image.Image      ):
              _lowerCAmelCase       :   Optional[Any]  =    image.size
          else:
              _lowerCAmelCase       :   Union[str, Any]  =    image.shape[1], image.shape[2]
          _lowerCAmelCase       :   Union[str, Any]  =    size / min(_A    ,_A      )
          if h < w:
              _lowerCAmelCase       :   List[str]  =    size, scale * w
          else:
              _lowerCAmelCase       :   Tuple  =    scale * h, size

          _lowerCAmelCase       :   Optional[int]  =    int((1333 / 800) * size      )
          if max(_A    ,_A      ) > max_size:
              _lowerCAmelCase       :   Union[str, Any]  =    max_size / max(_A    ,_A      )
              _lowerCAmelCase       :   Optional[Any]  =    newh * scale
              _lowerCAmelCase       :   Any  =    neww * scale

          _lowerCAmelCase       :   Union[str, Any]  =    int(newh + 0.5      ), int(neww + 0.5      )
          _lowerCAmelCase       :   Optional[Any]  =    (
              newh // self.size_divisor * self.size_divisor,
              neww // self.size_divisor * self.size_divisor,
          )

      else:
          _lowerCAmelCase       :   List[Any]  =    []
          for image in image_inputs:
              _lowerCAmelCase       :   int  =    self.get_expected_values([image]      )
              expected_values.append((expected_height, expected_width)      )
          _lowerCAmelCase       :   Union[str, Any]  =    max(_A    ,key=lambda _A      : item[0]      )[0]
          _lowerCAmelCase       :   List[str]  =    max(_A    ,key=lambda _A      : item[1]      )[1]

      return expected_height, expected_width







@require_torch
@require_vision
class 	__UpperCamelCase    (    a__							,							unittest.TestCase						):
  _UpperCAmelCase          =		BridgeTowerImageProcessor if is_vision_available() else None

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    BridgeTowerImageProcessingTester(self      )

  @property
  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      return self.image_processor_tester.prepare_image_processor_dict()

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.image_processing_class(**self.image_processor_dict      )
      self.assertTrue(hasattr(_A    ,'image_mean'      )      )
      self.assertTrue(hasattr(_A    ,'image_std'      )      )
      self.assertTrue(hasattr(_A    ,'do_normalize'      )      )
      self.assertTrue(hasattr(_A    ,'do_resize'      )      )
      self.assertTrue(hasattr(_A    ,'size'      )      )
      self.assertTrue(hasattr(_A    ,'size_divisor'      )      )

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      pass

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Optional[int]  =    self.image_processing_class(**self.image_processor_dict      )
      # create random PIL images
      _lowerCAmelCase       :   List[Any]  =    prepare_image_inputs(self.image_processor_tester    ,equal_resolution=_A      )
      for image in image_inputs:
          self.assertIsInstance(_A    ,Image.Image      )

      # Test not batched input
      _lowerCAmelCase       :   Optional[Any]  =    image_processing(image_inputs[0]    ,return_tensors='pt'      ).pixel_values

      _lowerCAmelCase       :   List[Any]  =    self.image_processor_tester.get_expected_values(_A      )
      self.assertEqual(
          encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

      # Test batched
      _lowerCAmelCase       :   Optional[int]  =    image_processing(_A    ,return_tensors='pt'      ).pixel_values

      _lowerCAmelCase       :   Union[str, Any]  =    self.image_processor_tester.get_expected_values(_A    ,batched=_A      )
      self.assertEqual(
          encoded_images.shape    ,(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,)

  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   Union[str, Any]  =    self.image_processing_class(**self.image_processor_dict      )
      # create random numpy tensors
      _lowerCAmelCase       :   Dict  =    prepare_image_inputs(self.image_processor_tester    ,equal_resolution=_A    ,numpify=_A      )
      for image in image_inputs:
          self.assertIsInstance(_A    ,np.ndarray      )

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

      _lowerCAmelCase       :   Optional[Any]  =    self.image_processor_tester.get_expected_values(_A      )
      self.assertEqual(
          encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

      # Test batched
      _lowerCAmelCase       :   str  =    image_processing(_A    ,return_tensors='pt'      ).pixel_values

      _lowerCAmelCase       :   List[Any]  =    self.image_processor_tester.get_expected_values(_A    ,batched=_A      )
      self.assertEqual(
          encoded_images.shape    ,(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,)







  def __lowerCamelCase		(		self      ):
      '''simple docstring'''

      _lowerCAmelCase       :   List[Any]  =    self.image_processing_class(**self.image_processor_dict      )
      # create random PyTorch tensors
      _lowerCAmelCase       :   Optional[int]  =    prepare_image_inputs(self.image_processor_tester    ,equal_resolution=_A    ,torchify=_A      )
      for image in image_inputs:
          self.assertIsInstance(_A    ,torch.Tensor      )

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

      _lowerCAmelCase       :   int  =    self.image_processor_tester.get_expected_values(_A      )
      self.assertEqual(
          encoded_images.shape    ,(1, self.image_processor_tester.num_channels, expected_height, expected_width)    ,)

      # Test batched
      _lowerCAmelCase       :   List[str]  =    image_processing(_A    ,return_tensors='pt'      ).pixel_values

      _lowerCAmelCase       :   Optional[int]  =    self.image_processor_tester.get_expected_values(_A    ,batched=_A      )
      self.assertEqual(
          encoded_images.shape    ,(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              expected_height,
              expected_width,
          )    ,)








"""simple docstring"""






from __future__ import annotations

def 		lowerCamelCase__		(					_lowerCamelCase   ):

    '''simple docstring'''



    create_state_space_tree(_lowerCamelCase ,   [] ,   0 ,   [0 for i in range(len(_lowerCamelCase   )   )]   )

def 		lowerCamelCase__		(					_lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   _lowerCamelCase ,   ):

    '''simple docstring'''



    if index == len(_lowerCamelCase   ):
        print(_lowerCamelCase   )
        return

    for i in range(len(_lowerCamelCase   )   ):
        if not index_used[i]:
            current_sequence.append(sequence[i]   )
            _lowerCAmelCase       :   List[str]  =    True
            create_state_space_tree(_lowerCamelCase ,   _lowerCamelCase ,   index + 1 ,   _lowerCamelCase   )
            current_sequence.pop()
            _lowerCAmelCase       :   int  =    False
_lowerCAmelCase						=		[3, 1, 2, 4]
generate_all_permutations(sequence)

_lowerCAmelCase						=		["A", "B", "C"]

generate_all_permutations(sequence_a)





