






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 YolosImageProcessor





class 			__a  (    unittest.TestCase       ):







 def __init__(		self					:   Optional[int]							,   SCREAMING_SNAKE_CASE					:   int							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=7							,   SCREAMING_SNAKE_CASE					:   Dict=3							,   SCREAMING_SNAKE_CASE					:   Tuple=30							,   SCREAMING_SNAKE_CASE					:   Tuple=4_00							,   SCREAMING_SNAKE_CASE					:   Optional[int]=True							,   SCREAMING_SNAKE_CASE					:   Dict=None							,   SCREAMING_SNAKE_CASE					:   Optional[Any]=True							,   SCREAMING_SNAKE_CASE					:   int=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   Dict=[0.5, 0.5, 0.5]							,   SCREAMING_SNAKE_CASE					:   List[str]=True							,   SCREAMING_SNAKE_CASE					:   List[str]=1 / 2_55							,   SCREAMING_SNAKE_CASE					:   List[Any]=True							,   ):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  size if size is not None else {"shortest_edge": 18, "longest_edge": 13_33}
      UpperCamelCase__			:   List[Any]       				=  parent
      UpperCamelCase__			:   Optional[int]       				=  batch_size
      UpperCamelCase__			:   Union[str, Any]       				=  num_channels
      UpperCamelCase__			:   Optional[int]       				=  min_resolution
      UpperCamelCase__			:   int       				=  max_resolution
      UpperCamelCase__			:   Any       				=  do_resize
      UpperCamelCase__			:   Any       				=  size
      UpperCamelCase__			:   Any       				=  do_normalize
      UpperCamelCase__			:   Union[str, Any]       				=  image_mean
      UpperCamelCase__			:   str       				=  image_std
      UpperCamelCase__			:   Dict       				=  do_rescale
      UpperCamelCase__			:   Any       				=  rescale_factor
      UpperCamelCase__			:   List[Any]       				=  do_pad







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      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 					__lowercase					(		self					:   Tuple							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]							,   SCREAMING_SNAKE_CASE					:   Union[str, Any]=False						):


      '''simple docstring'''






      if not batched:
           UpperCamelCase__			:   str       				=  image_inputs[0]
           if isinstance(SCREAMING_SNAKE_CASE							,   Image.Image						):
                UpperCamelCase__				,     UpperCamelCase__			:   Optional[int]       				=  image.size
           else:
                UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  image.shape[1], image.shape[2]
           if w < h:
                UpperCamelCase__			:   Dict       				=  int(self.size["shortest_edge"] * h / w						)
                UpperCamelCase__			:   Dict       				=  self.size["shortest_edge"]
           elif w > h:
                UpperCamelCase__			:   List[str]       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   List[str]       				=  int(self.size["shortest_edge"] * w / h						)
           else:
                UpperCamelCase__			:   Any       				=  self.size["shortest_edge"]
                UpperCamelCase__			:   Tuple       				=  self.size["shortest_edge"]

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

      return expected_height, expected_width







@require_torch
@require_vision
class 			__a  (    A__       ,   unittest.TestCase       ):
 _lowerCAmelCase       :       str        = YolosImageProcessor if is_vision_available() else None







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  YolosImageProcessingTester(self						)







 @property
 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      return self.image_processor_tester.prepare_image_processor_dict()







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   List[Any]       				=  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"						)						)







 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   str       				=  self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size							,   {"shortest_edge": 18, "longest_edge": 13_33}						)
      self.assertEqual(image_processor.do_pad							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   List[str]       				=  self.image_processing_class.from_dict(
          self.image_processor_dict							,   size=42							,   max_size=84							,   pad_and_return_pixel_mask=SCREAMING_SNAKE_CASE						)
      self.assertEqual(image_processor.size							,   {"shortest_edge": 42, "longest_edge": 84}						)
      self.assertEqual(image_processor.do_pad							,   SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      pass







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      UpperCamelCase__			:   Any       				=  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
      UpperCamelCase__			:   Optional[int]       				=  image_processing(image_inputs[0]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  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
      UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  self.image_processor_tester.get_expected_values(SCREAMING_SNAKE_CASE							,   batched=SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   List[str]       				=  image_processing(SCREAMING_SNAKE_CASE							,   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 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      UpperCamelCase__			:   str       				=  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
      UpperCamelCase__			:   int       				=  image_processing(image_inputs[0]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Dict       				=  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
      UpperCamelCase__			:   Tuple       				=  image_processing(SCREAMING_SNAKE_CASE							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  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 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      UpperCamelCase__			:   List[Any]       				=  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
      UpperCamelCase__			:   Optional[Any]       				=  image_processing(image_inputs[0]							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  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
      UpperCamelCase__			:   str       				=  image_processing(SCREAMING_SNAKE_CASE							,   return_tensors="pt"						).pixel_values

      UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  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 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[Any]       				=  self.image_processing_class(**self.image_processor_dict						)
      UpperCamelCase__			:   Dict       				=  self.image_processing_class(do_resize=SCREAMING_SNAKE_CASE							,   do_normalize=SCREAMING_SNAKE_CASE							,   do_rescale=SCREAMING_SNAKE_CASE						)
      # create random PyTorch tensors
      UpperCamelCase__			:   List[Any]       				=  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 whether the method "pad" and calling the image processor return the same tensors
      UpperCamelCase__			:   Dict       				=  image_processing_a.pad(SCREAMING_SNAKE_CASE							,   return_tensors="pt"						)
      UpperCamelCase__			:   int       				=  image_processing_a(SCREAMING_SNAKE_CASE							,   return_tensors="pt"						)

      self.assertTrue(
          torch.allclose(encoded_images_with_method["pixel_values"]							,   encoded_images["pixel_values"]							,   atol=1e-4						)						)







 @slow
 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
      with open("./tests/fixtures/tests_samples/COCO/coco_annotations.txt"							,   "r"						) as f:
           UpperCamelCase__			:   str       				=  json.loads(f.read()						)

      UpperCamelCase__			:   Dict       				=  {"image_id": 3_97_69, "annotations": target}

      # encode them
      UpperCamelCase__			:   Any       				=  YolosImageProcessor.from_pretrained("hustvl/yolos-small"						)
      UpperCamelCase__			:   int       				=  image_processing(images=SCREAMING_SNAKE_CASE							,   annotations=SCREAMING_SNAKE_CASE							,   return_tensors="pt"						)

      # verify pixel values
      UpperCamelCase__			:   List[Any]       				=  torch.Size([1, 3, 8_00, 10_66]						)
      self.assertEqual(encoding["pixel_values"].shape							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Dict       				=  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
      self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,   SCREAMING_SNAKE_CASE							,   atol=1e-4						)						)

      # verify area
      UpperCamelCase__			:   Union[str, Any]       				=  torch.tensor([5_8_8_7.9_6_0_0, 1_1_2_5_0.2_0_6_1, 4_8_9_3_5_3.8_4_3_8, 8_3_7_1_2_2.7_5_0_0, 1_4_7_9_6_7.5_1_5_6, 1_6_5_7_3_2.3_4_3_8]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify boxes
      UpperCamelCase__			:   Any       				=  torch.Size([6, 4]						)
      self.assertEqual(encoding["labels"][0]["boxes"].shape							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Any       				=  torch.tensor([0.5_5_0_3, 0.2_7_6_5, 0.0_6_0_4, 0.2_2_1_5]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,   SCREAMING_SNAKE_CASE							,   atol=1e-3						)						)
      # verify image_id
      UpperCamelCase__			:   Any       				=  torch.tensor([3_97_69]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify is_crowd
      UpperCamelCase__			:   Tuple       				=  torch.tensor([0, 0, 0, 0, 0, 0]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify class_labels
      UpperCamelCase__			:   Tuple       				=  torch.tensor([75, 75, 63, 65, 17, 17]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify orig_size
      UpperCamelCase__			:   Tuple       				=  torch.tensor([4_80, 6_40]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify size
      UpperCamelCase__			:   int       				=  torch.tensor([8_00, 10_66]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,   SCREAMING_SNAKE_CASE						)						)







 @slow
 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"						)
      with open("./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt"							,   "r"						) as f:
           UpperCamelCase__			:   Union[str, Any]       				=  json.loads(f.read()						)

      UpperCamelCase__			:   Union[str, Any]       				=  {"file_name": "000000039769.png", "image_id": 3_97_69, "segments_info": target}

      UpperCamelCase__			:   int       				=  pathlib.Path("./tests/fixtures/tests_samples/COCO/coco_panoptic"						)

      # encode them
      UpperCamelCase__			:   Dict       				=  YolosImageProcessor(format="coco_panoptic"						)
      UpperCamelCase__			:   str       				=  image_processing(images=SCREAMING_SNAKE_CASE							,   annotations=SCREAMING_SNAKE_CASE							,   masks_path=SCREAMING_SNAKE_CASE							,   return_tensors="pt"						)

      # verify pixel values
      UpperCamelCase__			:   Optional[Any]       				=  torch.Size([1, 3, 8_00, 10_66]						)
      self.assertEqual(encoding["pixel_values"].shape							,   SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   List[str]       				=  torch.tensor([0.2_7_9_6, 0.3_1_3_8, 0.3_4_8_1]						)
      self.assertTrue(torch.allclose(encoding["pixel_values"][0, 0, 0, :3]							,   SCREAMING_SNAKE_CASE							,   atol=1e-4						)						)

      # verify area
      UpperCamelCase__			:   int       				=  torch.tensor([1_4_7_9_7_9.6_8_7_5, 1_6_5_5_2_7.0_4_6_9, 4_8_4_6_3_8.5_9_3_8, 1_1_2_9_2.9_3_7_5, 5_8_7_9.6_5_6_2, 7_6_3_4.1_1_4_7]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["area"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify boxes
      UpperCamelCase__			:   Union[str, Any]       				=  torch.Size([6, 4]						)
      self.assertEqual(encoding["labels"][0]["boxes"].shape							,   SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  torch.tensor([0.2_6_2_5, 0.5_4_3_7, 0.4_6_8_8, 0.8_6_2_5]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["boxes"][0]							,   SCREAMING_SNAKE_CASE							,   atol=1e-3						)						)
      # verify image_id
      UpperCamelCase__			:   Any       				=  torch.tensor([3_97_69]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["image_id"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify is_crowd
      UpperCamelCase__			:   Optional[Any]       				=  torch.tensor([0, 0, 0, 0, 0, 0]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["iscrowd"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify class_labels
      UpperCamelCase__			:   Tuple       				=  torch.tensor([17, 17, 63, 75, 75, 93]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["class_labels"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify masks
      UpperCamelCase__			:   Any       				=  82_28_73
      self.assertEqual(encoding["labels"][0]["masks"].sum().item()							,   SCREAMING_SNAKE_CASE						)
      # verify orig_size
      UpperCamelCase__			:   Tuple       				=  torch.tensor([4_80, 6_40]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["orig_size"]							,   SCREAMING_SNAKE_CASE						)						)
      # verify size
      UpperCamelCase__			:   List[Any]       				=  torch.tensor([8_00, 10_66]						)
      self.assertTrue(torch.allclose(encoding["labels"][0]["size"]							,   SCREAMING_SNAKE_CASE						)						)







from __future__ import annotations

import sys
from collections import deque
from typing import Generic, TypeVar

lowerCamelCase	:							int				=TypeVar('''T''')





class 			__a  (    Generic[T]       ):
 _lowerCAmelCase       :       deque[T]  # Cache store of keys
 _lowerCAmelCase       :       set[T]  # References of the keys in cache
 _lowerCAmelCase       :       int        = 1_0  # Maximum capacity of cache







 def __init__(		self					:   List[str]							,   SCREAMING_SNAKE_CASE					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  deque()
      UpperCamelCase__			:   Optional[int]       				=  set()
      if not n:
           UpperCamelCase__			:   Tuple       				=  sys.maxsize
      elif n < 0:
           raise ValueError("n should be an integer greater than 0."						)
      else:
           UpperCamelCase__			:   Dict       				=  n







 def 					__lowercase					(		self					:   List[Any]							,   SCREAMING_SNAKE_CASE					:   T						):


      '''simple docstring'''






      if x not in self.key_reference:
           if len(self.dq_store						) == LRUCache._MAX_CAPACITY:
                UpperCamelCase__			:   int       				=  self.dq_store.pop()
                self.key_reference.remove(SCREAMING_SNAKE_CASE						)
      else:
           self.dq_store.remove(SCREAMING_SNAKE_CASE						)

      self.dq_store.appendleft(SCREAMING_SNAKE_CASE						)
      self.key_reference.add(SCREAMING_SNAKE_CASE						)







 def 					__lowercase					(		self					:   Dict						):


      '''simple docstring'''






      for k in self.dq_store:
           print(SCREAMING_SNAKE_CASE						)







 def __repr__(		self					:   Union[str, Any]						):


      '''simple docstring'''






      return F'LRUCache({self._MAX_CAPACITY}) => {list(self.dq_store						)}'


if __name__ == "__main__":
 import doctest

 doctest.testmod()

 lowerCamelCase	:							LRUCache[str | int]				=LRUCache(4)
 lru_cache.refer('''A''')
 lru_cache.refer(2)
 lru_cache.refer(3)
 lru_cache.refer('''A''')
 lru_cache.refer(4)
 lru_cache.refer(5)
 lru_cache.display()

 print(lru_cache)
 assert str(lru_cache) == "LRUCache(4) => [5, 4, 'A', 3]"