


import json
import pathlib
import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision, slow
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 ConditionalDetrImageProcessor






class     UpperCAmelCase__       (					unittest.TestCase		):



    """simple docstring"""






    def __init__(			self					,   A_					,   A_=7					,   A_=3					,   A_=30					,   A_=400					,   A_=True					,   A_=None					,   A_=True					,   A_=[0.5, 0.5, 0.5]					,   A_=[0.5, 0.5, 0.5]					,   A_=True					,   A_=1 / 255					,   A_=True					,   )  ->       Optional[int]:
         # by setting size["longest_edge"] > max_resolution we're effectively not testing this :p
         __UpperCamelCase    							=size if size is not None else {'shortest_edge': 18, 'longest_edge': 1333}
         __UpperCamelCase    							=parent
         __UpperCamelCase    							=batch_size
         __UpperCamelCase    							=num_channels
         __UpperCamelCase    							=min_resolution
         __UpperCamelCase    							=max_resolution
         __UpperCamelCase    							=do_resize
         __UpperCamelCase    							=size
         __UpperCamelCase    							=do_normalize
         __UpperCamelCase    							=image_mean
         __UpperCamelCase    							=image_std
         __UpperCamelCase    							=do_rescale
         __UpperCamelCase    							=rescale_factor
         __UpperCamelCase    							=do_pad






    def _a					(			self    )  ->       Union[str, Any]:
         return {
             "do_resize": self.do_resize,
             "size": self.size,
             "do_normalize": self.do_normalize,
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_rescale": self.do_rescale,
             "rescale_factor": self.rescale_factor,
             "do_pad": self.do_pad,
         }






    def _a					(			self					,   A_					,   A_=False    )  ->       Optional[int]:
         if not batched:
              __UpperCamelCase    							=image_inputs[0]
              if isinstance(A_					,   Image.Image    ):
                   __UpperCamelCase							,      __UpperCamelCase    							=image.size
              else:
                   __UpperCamelCase							,      __UpperCamelCase    							=image.shape[1], image.shape[2]
              if w < h:
                   __UpperCamelCase    							=int(self.size['shortest_edge'] * h / w    )
                   __UpperCamelCase    							=self.size['shortest_edge']
              elif w > h:
                   __UpperCamelCase    							=self.size['shortest_edge']
                   __UpperCamelCase    							=int(self.size['shortest_edge'] * w / h    )
              else:
                   __UpperCamelCase    							=self.size['shortest_edge']
                   __UpperCamelCase    							=self.size['shortest_edge']

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

         return expected_height, expected_width






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



    """simple docstring"""






    UpperCAmelCase__						:		List[str]      =  ConditionalDetrImageProcessor if is_vision_available() else None






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=ConditionalDetrImageProcessingTester(self    )






    @property
    def _a					(			self    )  ->       Any:
         return self.image_processor_tester.prepare_image_processor_dict()






    def _a					(			self    )  ->       Optional[Any]:
         __UpperCamelCase    							=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'    )    )






    def _a					(			self    )  ->       List[str]:
         __UpperCamelCase    							=self.image_processing_class.from_dict(self.image_processor_dict    )
         self.assertEqual(image_processor.size					,   {'shortest_edge': 18, 'longest_edge': 1333}    )
         self.assertEqual(image_processor.do_pad					,   A_    )

         __UpperCamelCase    							=self.image_processing_class.from_dict(
             self.image_processor_dict					,   size=42					,   max_size=84					,   pad_and_return_pixel_mask=A_    )
         self.assertEqual(image_processor.size					,   {'shortest_edge': 42, 'longest_edge': 84}    )
         self.assertEqual(image_processor.do_pad					,   A_    )






    def _a					(			self    )  ->       Dict:
         pass






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

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

         __UpperCamelCase							,      __UpperCamelCase    							=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
         __UpperCamelCase							,      __UpperCamelCase    							=self.image_processor_tester.get_expected_values(A_					,   batched=A_    )

         __UpperCamelCase    							=image_processing(A_					,   return_tensors='pt'    ).pixel_values
         self.assertEqual(
             encoded_images.shape					,   (
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )					,   )






    def _a					(			self    )  ->       Any:
         # Initialize image_processing
         __UpperCamelCase    							=self.image_processing_class(**self.image_processor_dict    )
         # create random numpy tensors
         __UpperCamelCase    							=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
         __UpperCamelCase    							=image_processing(image_inputs[0]					,   return_tensors='pt'    ).pixel_values

         __UpperCamelCase							,      __UpperCamelCase    							=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
         __UpperCamelCase    							=image_processing(A_					,   return_tensors='pt'    ).pixel_values

         __UpperCamelCase							,      __UpperCamelCase    							=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 _a					(			self    )  ->       Tuple:
         # Initialize image_processing
         __UpperCamelCase    							=self.image_processing_class(**self.image_processor_dict    )
         # create random PyTorch tensors
         __UpperCamelCase    							=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
         __UpperCamelCase    							=image_processing(image_inputs[0]					,   return_tensors='pt'    ).pixel_values

         __UpperCamelCase							,      __UpperCamelCase    							=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
         __UpperCamelCase    							=image_processing(A_					,   return_tensors='pt'    ).pixel_values

         __UpperCamelCase							,      __UpperCamelCase    							=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,
             )					,   )






    @slow
    def _a					(			self    )  ->       Union[str, Any]:
         # prepare image and target
         __UpperCamelCase    							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
         with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt'					,   'r'    ) as f:
              __UpperCamelCase    							=json.loads(f.read()    )

         __UpperCamelCase    							={'image_id': 39769, 'annotations': target}

         # encode them
         __UpperCamelCase    							=ConditionalDetrImageProcessor.from_pretrained('microsoft/conditional-detr-resnet-50'    )
         __UpperCamelCase    							=image_processing(images=A_					,   annotations=A_					,   return_tensors='pt'    )

         # verify pixel values
         __UpperCamelCase    							=torch.Size([1, 3, 800, 1066]    )
         self.assertEqual(encoding['pixel_values'].shape					,   A_    )

         __UpperCamelCase    							=torch.tensor([0.2796, 0.3138, 0.3481]    )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,   A_					,   atol=1E-4    )    )

         # verify area
         __UpperCamelCase    							=torch.tensor([5887.9600, 1_1250.2061, 48_9353.8438, 83_7122.7500, 14_7967.5156, 16_5732.3438]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']					,   A_    )    )
         # verify boxes
         __UpperCamelCase    							=torch.Size([6, 4]    )
         self.assertEqual(encoding['labels'][0]['boxes'].shape					,   A_    )
         __UpperCamelCase    							=torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,   A_					,   atol=1E-3    )    )
         # verify image_id
         __UpperCamelCase    							=torch.tensor([39769]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,   A_    )    )
         # verify is_crowd
         __UpperCamelCase    							=torch.tensor([0, 0, 0, 0, 0, 0]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,   A_    )    )
         # verify class_labels
         __UpperCamelCase    							=torch.tensor([75, 75, 63, 65, 17, 17]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,   A_    )    )
         # verify orig_size
         __UpperCamelCase    							=torch.tensor([480, 640]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,   A_    )    )
         # verify size
         __UpperCamelCase    							=torch.tensor([800, 1066]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,   A_    )    )






    @slow
    def _a					(			self    )  ->       Dict:
         # prepare image, target and masks_path
         __UpperCamelCase    							=Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'    )
         with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'					,   'r'    ) as f:
              __UpperCamelCase    							=json.loads(f.read()    )

         __UpperCamelCase    							={'file_name': '000000039769.png', 'image_id': 39769, 'segments_info': target}

         __UpperCamelCase    							=pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'    )

         # encode them
         __UpperCamelCase    							=ConditionalDetrImageProcessor(format='coco_panoptic'    )
         __UpperCamelCase    							=image_processing(images=A_					,   annotations=A_					,   masks_path=A_					,   return_tensors='pt'    )

         # verify pixel values
         __UpperCamelCase    							=torch.Size([1, 3, 800, 1066]    )
         self.assertEqual(encoding['pixel_values'].shape					,   A_    )

         __UpperCamelCase    							=torch.tensor([0.2796, 0.3138, 0.3481]    )
         self.assertTrue(torch.allclose(encoding['pixel_values'][0, 0, 0, :3]					,   A_					,   atol=1E-4    )    )

         # verify area
         __UpperCamelCase    							=torch.tensor([14_7979.6875, 16_5527.0469, 48_4638.5938, 1_1292.9375, 5879.6562, 7634.1147]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['area']					,   A_    )    )
         # verify boxes
         __UpperCamelCase    							=torch.Size([6, 4]    )
         self.assertEqual(encoding['labels'][0]['boxes'].shape					,   A_    )
         __UpperCamelCase    							=torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]					,   A_					,   atol=1E-3    )    )
         # verify image_id
         __UpperCamelCase    							=torch.tensor([39769]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']					,   A_    )    )
         # verify is_crowd
         __UpperCamelCase    							=torch.tensor([0, 0, 0, 0, 0, 0]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']					,   A_    )    )
         # verify class_labels
         __UpperCamelCase    							=torch.tensor([17, 17, 63, 75, 75, 93]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']					,   A_    )    )
         # verify masks
         __UpperCamelCase    							=822873
         self.assertEqual(encoding['labels'][0]['masks'].sum().item()					,   A_    )
         # verify orig_size
         __UpperCamelCase    							=torch.tensor([480, 640]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']					,   A_    )    )
         # verify size
         __UpperCamelCase    							=torch.tensor([800, 1066]    )
         self.assertTrue(torch.allclose(encoding['labels'][0]['size']					,   A_    )    )






import builtins
import sys

from ...utils.imports import _is_package_available
from . import cursor, input
from .helpers import Direction, clear_line, forceWrite, linebreak, move_cursor, reset_cursor, writeColor
from .keymap import KEYMAP


a_    					=       False
try:
   a_    					=       _is_package_available('google.colab')
except ModuleNotFoundError:
   pass





@input.register
class 		_lowercase						:

 def __init__( self	:							Dict   ,      snake_case	:							str = None   ,      snake_case	:							list = []			)   ->						Union[str, Any]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					0
  UpperCamelCase_					:						Optional[Any] =					choices
  UpperCamelCase_					:						Any =					prompt
  if sys.platform == "win32":
   UpperCamelCase_					:						Optional[Any] =					'*'
  else:
   UpperCamelCase_					:						str =					'➔ '

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]   ,      snake_case	:							List[str]   ,      snake_case	:							str = ""			)   ->						List[Any]:

  """simple docstring"""





  if sys.platform != "win32":
   writeColor(self.choices[index]   ,      3_2   ,      snake_case			)
  else:
   forceWrite(self.choices[index]   ,      snake_case			)

 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[str]   ,      snake_case	:							int			)   ->						List[Any]:

  """simple docstring"""





  if index == self.position:
   forceWrite(f" {self.arrow_char} "			)
   self.write_choice(snake_case			)
  else:
   forceWrite(f"    {self.choices[index]}"			)
  reset_cursor()

 def 						SCREAMING_SNAKE_CASE__   ( self	:							Any   ,      snake_case	:							Direction   ,      snake_case	:							int = 1			)   ->						List[str]:

  """simple docstring"""





  UpperCamelCase_					:						Any =					self.position
  if direction == Direction.DOWN:
   if self.position + 1 >= len(self.choices			):
    return
   self.position += num_spaces
  else:
   if self.position - 1 < 0:
    return
   self.position -= num_spaces
  clear_line()
  self.print_choice(snake_case			)
  move_cursor(snake_case   ,      direction.name			)
  self.print_choice(self.position			)

 @input.mark(KEYMAP['up']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						List[Any]:

  """simple docstring"""





  self.move_direction(Direction.UP			)

 @input.mark(KEYMAP['down']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[int]			)   ->						Optional[Any]:

  """simple docstring"""





  self.move_direction(Direction.DOWN			)

 @input.mark(KEYMAP['newline']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							str			)   ->						Optional[int]:

  """simple docstring"""





  move_cursor(len(self.choices			) - self.position   ,      'DOWN'			)
  return self.position

 @input.mark(KEYMAP['interrupt']			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							Optional[Any]			)   ->						Optional[Any]:

  """simple docstring"""





  move_cursor(len(self.choices			) - self.position   ,      'DOWN'			)
  raise KeyboardInterrupt

 @input.mark_multiple(*[KEYMAP[str(snake_case			)] for number in range(1_0			)]			)
 def 						SCREAMING_SNAKE_CASE__   ( self	:							int			)   ->						Dict:

  """simple docstring"""





  UpperCamelCase_					:						List[Any] =					int(chr(self.current_selection			)			)
  UpperCamelCase_					:						Optional[int] =					index - self.position
  if index == self.position:
   return
  if index < len(self.choices			):
   if self.position > index:
    self.move_direction(Direction.UP   ,      -movement			)
   elif self.position < index:
    self.move_direction(Direction.DOWN   ,      snake_case			)
   else:
    return
  else:
   return


 def 						SCREAMING_SNAKE_CASE__   ( self	:							List[Any]   ,      snake_case	:							int = 0			)   ->						Union[str, Any]:

  """simple docstring"""





  if self.prompt:
   linebreak()
   forceWrite(self.prompt   ,      '\n'			)
   if in_colab:
    forceWrite('Please input a choice index (starting from 0), and press enter'   ,      '\n'			)
   else:
    forceWrite('Please select a choice using the arrow or number keys, and selecting with enter'   ,      '\n'			)
  UpperCamelCase_					:						Optional[int] =					default_choice
  for i in range(len(self.choices			)			):
   self.print_choice(snake_case			)
   forceWrite('\n'			)
  move_cursor(len(self.choices			) - self.position   ,      'UP'			)
  with cursor.hide():
   while True:
    if in_colab:
     try:
      UpperCamelCase_					:						Tuple =					int(builtins.input()			)
     except ValueError:
      UpperCamelCase_					:						Tuple =					default_choice
    else:
     UpperCamelCase_					:						Optional[Any] =					self.handle_input()
    if choice is not None:
     reset_cursor()
     for _ in range(len(self.choices			) + 1			):
      move_cursor(1   ,      'UP'			)
      clear_line()
     self.write_choice(snake_case   ,      '\n'			)
     return choice



