


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 DetaImageProcessor






class     _lowerCamelCase					(   unittest.TestCase     ):




       """simple docstring"""



       def __init__( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE=7							,			_SCREAMING_SNAKE_CASE=3							,			_SCREAMING_SNAKE_CASE=30							,			_SCREAMING_SNAKE_CASE=400							,			_SCREAMING_SNAKE_CASE=True							,			_SCREAMING_SNAKE_CASE=None							,			_SCREAMING_SNAKE_CASE=True							,			_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,			_SCREAMING_SNAKE_CASE=[0.5, 0.5, 0.5]							,			_SCREAMING_SNAKE_CASE=True							,			_SCREAMING_SNAKE_CASE=1 / 255							,			_SCREAMING_SNAKE_CASE=True							,			)->int:




         '''simple docstring'''





         A_ :							Optional[int]							=   size if size is not None else {'''shortest_edge''': 18, '''longest_edge''': 1333}
         A_ :							List[str]							=   parent
         A_ :							List[Any]							=   batch_size
         A_ :							Tuple							=   num_channels
         A_ :							int							=   min_resolution
         A_ :							Dict							=   max_resolution
         A_ :							str							=   do_resize
         A_ :							List[Any]							=   size
         A_ :							Dict							=   do_normalize
         A_ :							List[str]							=   image_mean
         A_ :							Dict							=   image_std
         A_ :							Tuple							=   do_rescale
         A_ :							Optional[int]							=   rescale_factor
         A_ :							Optional[int]							=   do_pad



       def 			_snake_case			( self						)->Optional[Any]:




         '''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 			_snake_case			( self							,			_SCREAMING_SNAKE_CASE							,			_SCREAMING_SNAKE_CASE=False						)->Tuple:




         '''simple docstring'''





         if not batched:
           A_ :							Any							=   image_inputs[0]
           if isinstance(_SCREAMING_SNAKE_CASE							,			Image.Image						):
             A_	,			A_ :							Union[str, Any]							=   image.size
           else:
             A_	,			A_ :							Dict							=   image.shape[1], image.shape[2]
           if w < h:
             A_ :							Dict							=   int(self.size['''shortest_edge'''] * h / w						)
             A_ :							Dict							=   self.size['''shortest_edge''']
           elif w > h:
             A_ :							List[Any]							=   self.size['''shortest_edge''']
             A_ :							Union[str, Any]							=   int(self.size['''shortest_edge'''] * w / h						)
           else:
             A_ :							Any							=   self.size['''shortest_edge''']
             A_ :							List[Any]							=   self.size['''shortest_edge''']

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

         return expected_height, expected_width






@require_torch
@require_vision
class     _lowerCamelCase					(   A__   ,				unittest.TestCase     ):




       """simple docstring"""
       snake_case    =       DetaImageProcessor if is_vision_available() else None



       def 			_snake_case			( self						)->List[str]:




         '''simple docstring'''





         A_ :							Union[str, Any]							=   DetaImageProcessingTester(self						)



       @property
       def 			_snake_case			( self						)->Optional[Any]:




         '''simple docstring'''





         return self.image_processor_tester.prepare_image_processor_dict()



       def 			_snake_case			( self						)->List[Any]:




         '''simple docstring'''





         A_ :							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							,			'''do_rescale'''						)						)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE							,			'''do_pad'''						)						)
         self.assertTrue(hasattr(_SCREAMING_SNAKE_CASE							,			'''size'''						)						)



       def 			_snake_case			( self						)->Any:




         '''simple docstring'''





         A_ :							str							=   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							,			_SCREAMING_SNAKE_CASE						)



       def 			_snake_case			( self						)->str:




         '''simple docstring'''





         pass



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




         '''simple docstring'''





         A_ :							List[str]							=   self.image_processing_class(**self.image_processor_dict						)
         # create random PIL images
         A_ :							List[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
         A_ :							str							=   image_processing(image_inputs[0]							,			return_tensors='''pt'''						).pixel_values

         A_	,			A_ :							str							=   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
         A_	,			A_ :							Any							=   self.image_processor_tester.get_expected_values(_SCREAMING_SNAKE_CASE							,			batched=_SCREAMING_SNAKE_CASE						)

         A_ :							int							=   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 			_snake_case			( self						)->int:




         '''simple docstring'''





         A_ :							List[str]							=   self.image_processing_class(**self.image_processor_dict						)
         # create random numpy tensors
         A_ :							Union[str, Any]							=   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
         A_ :							Union[str, Any]							=   image_processing(image_inputs[0]							,			return_tensors='''pt'''						).pixel_values

         A_	,			A_ :							Union[str, 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
         A_ :							List[Any]							=   image_processing(_SCREAMING_SNAKE_CASE							,			return_tensors='''pt'''						).pixel_values

         A_	,			A_ :							str							=   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 			_snake_case			( self						)->str:




         '''simple docstring'''





         A_ :							List[str]							=   self.image_processing_class(**self.image_processor_dict						)
         # create random PyTorch tensors
         A_ :							str							=   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
         A_ :							Tuple							=   image_processing(image_inputs[0]							,			return_tensors='''pt'''						).pixel_values

         A_	,			A_ :							List[str]							=   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
         A_ :							Dict							=   image_processing(_SCREAMING_SNAKE_CASE							,			return_tensors='''pt'''						).pixel_values

         A_	,			A_ :							Dict							=   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,
             )							,			)



       @slow
       def 			_snake_case			( self						)->List[Any]:




         '''simple docstring'''





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

         A_ :							Dict							=   {'''image_id''': 3_9769, '''annotations''': target}

         # encode them
         A_ :							List[Any]							=   DetaImageProcessor()
         A_ :							Any							=   image_processing(images=_SCREAMING_SNAKE_CASE							,			annotations=_SCREAMING_SNAKE_CASE							,			return_tensors='''pt'''						)

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

         A_ :							Any							=   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
         A_ :							List[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
         A_ :							Union[str, Any]							=   torch.Size([6, 4]						)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,			_SCREAMING_SNAKE_CASE						)
         A_ :							List[str]							=   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
         A_ :							Any							=   torch.tensor([3_9769]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify is_crowd
         A_ :							List[Any]							=   torch.tensor([0, 0, 0, 0, 0, 0]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify class_labels
         A_ :							Optional[int]							=   torch.tensor([75, 75, 63, 65, 17, 17]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify orig_size
         A_ :							Optional[Any]							=   torch.tensor([480, 640]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify size
         A_ :							Tuple							=   torch.tensor([800, 1066]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,			_SCREAMING_SNAKE_CASE						)						)







       @slow
       def 			_snake_case			( self						)->str:




         '''simple docstring'''





         A_ :							Optional[Any]							=   Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''						)
         with open('''./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'''							,			'''r'''						) as f:
           A_ :							Optional[int]							=   json.loads(f.read()						)

         A_ :							Optional[Any]							=   {'''file_name''': '''000000039769.png''', '''image_id''': 3_9769, '''segments_info''': target}

         A_ :							Any							=   pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''						)

         # encode them
         A_ :							Optional[Any]							=   DetaImageProcessor(format='''coco_panoptic'''						)
         A_ :							Dict							=   image_processing(images=_SCREAMING_SNAKE_CASE							,			annotations=_SCREAMING_SNAKE_CASE							,			masks_path=_SCREAMING_SNAKE_CASE							,			return_tensors='''pt'''						)

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

         A_ :							List[Any]							=   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
         A_ :							Optional[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
         A_ :							int							=   torch.Size([6, 4]						)
         self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape							,			_SCREAMING_SNAKE_CASE						)
         A_ :							Tuple							=   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
         A_ :							int							=   torch.tensor([3_9769]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify is_crowd
         A_ :							Optional[Any]							=   torch.tensor([0, 0, 0, 0, 0, 0]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify class_labels
         A_ :							List[str]							=   torch.tensor([17, 17, 63, 75, 75, 93]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify masks
         A_ :							Dict							=   82_2873
         self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()							,			_SCREAMING_SNAKE_CASE						)
         # verify orig_size
         A_ :							int							=   torch.tensor([480, 640]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']							,			_SCREAMING_SNAKE_CASE						)						)
         # verify size
         A_ :							int							=   torch.tensor([800, 1066]						)
         self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']							,			_SCREAMING_SNAKE_CASE						)						)












from typing import Dict

from .base import GenericTensor, Pipeline
class  _UpperCAmelCase   (			A__      ):

				"""simple docstring"""




				def 							lowercase__  (	self		:       Any,    lowerCamelCase		:       Tuple=None,    lowerCamelCase		:       Union[str, Any]=None,    lowerCamelCase		:       Union[str, Any]=None,    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								if tokenize_kwargs is None:
												lowercase__  					=     {}

								if truncation is not None:
												if "truncation" in tokenize_kwargs:
																raise ValueError(
																    '''truncation parameter defined twice (given as keyword argument as well as in tokenize_kwargs)'''    )
												lowercase__  					=     truncation

								lowercase__  					=     tokenize_kwargs

								lowercase__  					=     {}
								if return_tensors is not None:
												lowercase__  					=     return_tensors

								return preprocess_params, {}, postprocess_params




				def 							lowercase__  (	self		:       Tuple,    lowerCamelCase		:       Optional[int],    **lowerCamelCase		:       List[Any]    ):

								'''simple docstring'''
								lowercase__  					=     self.framework
								lowercase__  					=     self.tokenizer(lowerCamelCase,    return_tensors=lowerCamelCase,    **lowerCamelCase    )
								return model_inputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict    ):

								'''simple docstring'''
								lowercase__  					=     self.model(**lowerCamelCase    )
								return model_outputs




				def 							lowercase__  (	self		:       Union[str, Any],    lowerCamelCase		:       Dict,    lowerCamelCase		:       Tuple=False    ):

								'''simple docstring'''
								# [0] is the first available tensor, logits or last_hidden_state.
								if return_tensors:
												return model_outputs[0]
								if self.framework == "pt":
												return model_outputs[0].tolist()
								elif self.framework == "tf":
												return model_outputs[0].numpy().tolist()




				def __call__(	self		:       List[str],    *lowerCamelCase		:       List[Any],    **lowerCamelCase		:       str    ):

								'''simple docstring'''
								return super().__call__(*lowerCamelCase,    **lowerCamelCase    )




