


'''simple docstring'''







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 DeformableDetrImageProcessor
class 	UpperCamelCase__					( unittest.TestCase	):



     """simple docstring"""






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

            '''simple docstring'''

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







     def a	( self   ):

            '''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 a	( self      ,	snake_case__      ,	snake_case__=False   ):

            '''simple docstring'''

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

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

            return expected_height, expected_width
@require_torch
@require_vision
class 	UpperCamelCase__					( SCREAMING_SNAKE_CASE_    ,					unittest.TestCase	):



     """simple docstring"""






     __magic_name__    			=    DeformableDetrImageProcessor if is_vision_available() else None







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Union[str, Any]   			=       DeformableDetrImageProcessingTester(self   )







     @property
     def a	( self   ):

            '''simple docstring'''

            return self.image_processor_tester.prepare_image_processor_dict()







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[str]   			=       self.image_processing_class(**self.image_processor_dict   )
            self.assertTrue(hasattr(snake_case__      ,	'image_mean'   )   )
            self.assertTrue(hasattr(snake_case__      ,	'image_std'   )   )
            self.assertTrue(hasattr(snake_case__      ,	'do_normalize'   )   )
            self.assertTrue(hasattr(snake_case__      ,	'do_resize'   )   )
            self.assertTrue(hasattr(snake_case__      ,	'do_rescale'   )   )
            self.assertTrue(hasattr(snake_case__      ,	'do_pad'   )   )
            self.assertTrue(hasattr(snake_case__      ,	'size'   )   )







     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							List[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      ,	snake_case__   )

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







     def a	( self   ):

            '''simple docstring'''

            pass







     def a	( self   ):

            '''simple docstring'''

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

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

            _lowerCAmelCase			,     _lowerCAmelCase    :							int   			=       self.image_processor_tester.get_expected_values(snake_case__   )

            self.assertEqual(
                encoded_images.shape      ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,	)

            # Test batched
            _lowerCAmelCase			,     _lowerCAmelCase    :							List[str]   			=       self.image_processor_tester.get_expected_values(snake_case__      ,	batched=snake_case__   )

            _lowerCAmelCase    :							Union[str, Any]   			=       image_processing(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 a	( self   ):

            '''simple docstring'''

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

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

            _lowerCAmelCase			,     _lowerCAmelCase    :							Tuple   			=       self.image_processor_tester.get_expected_values(snake_case__   )

            self.assertEqual(
                encoded_images.shape      ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,	)

            # Test batched
            _lowerCAmelCase    :							int   			=       image_processing(snake_case__      ,	return_tensors='pt'   ).pixel_values

            _lowerCAmelCase			,     _lowerCAmelCase    :							Dict   			=       self.image_processor_tester.get_expected_values(snake_case__      ,	batched=snake_case__   )

            self.assertEqual(
                encoded_images.shape      ,	(
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )      ,	)







     def a	( self   ):

            '''simple docstring'''

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

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

            _lowerCAmelCase			,     _lowerCAmelCase    :							str   			=       self.image_processor_tester.get_expected_values(snake_case__   )

            self.assertEqual(
                encoded_images.shape      ,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,	)

            # Test batched
            _lowerCAmelCase    :							int   			=       image_processing(snake_case__      ,	return_tensors='pt'   ).pixel_values

            _lowerCAmelCase			,     _lowerCAmelCase    :							List[Any]   			=       self.image_processor_tester.get_expected_values(snake_case__      ,	batched=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 a	( self   ):

            '''simple docstring'''

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

            _lowerCAmelCase    :							Optional[Any]   			=       {'image_id': 3_9769, 'annotations': target}

            # encode them
            _lowerCAmelCase    :							Union[str, Any]   			=       DeformableDetrImageProcessor()
            _lowerCAmelCase    :							Optional[Any]   			=       image_processing(images=snake_case__      ,	annotations=snake_case__      ,	return_tensors='pt'   )

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

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

            # verify area
            _lowerCAmelCase    :							Union[str, Any]   			=       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']      ,	snake_case__   )   )
            # verify boxes
            _lowerCAmelCase    :							Any   			=       torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	snake_case__   )
            _lowerCAmelCase    :							Any   			=       torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,	snake_case__      ,	atol=1E-3   )   )
            # verify image_id
            _lowerCAmelCase    :							Dict   			=       torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	snake_case__   )   )
            # verify is_crowd
            _lowerCAmelCase    :							Dict   			=       torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	snake_case__   )   )
            # verify class_labels
            _lowerCAmelCase    :							Optional[int]   			=       torch.tensor([75, 75, 63, 65, 17, 17]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	snake_case__   )   )
            # verify orig_size
            _lowerCAmelCase    :							Dict   			=       torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	snake_case__   )   )
            # verify size
            _lowerCAmelCase    :							str   			=       torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	snake_case__   )   )







     @slow
     def a	( self   ):

            '''simple docstring'''

            _lowerCAmelCase    :							Tuple   			=       Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
            with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt'      ,	'r'   ) as f:
                   _lowerCAmelCase    :							str   			=       json.loads(f.read()   )

            _lowerCAmelCase    :							Tuple   			=       {'file_name': '000000039769.png', 'image_id': 3_9769, 'segments_info': target}

            _lowerCAmelCase    :							Optional[int]   			=       pathlib.Path('./tests/fixtures/tests_samples/COCO/coco_panoptic'   )

            # encode them
            _lowerCAmelCase    :							List[str]   			=       DeformableDetrImageProcessor(format='coco_panoptic'   )
            _lowerCAmelCase    :							Tuple   			=       image_processing(images=snake_case__      ,	annotations=snake_case__      ,	masks_path=snake_case__      ,	return_tensors='pt'   )

            # verify pixel values
            _lowerCAmelCase    :							Optional[Any]   			=       torch.Size([1, 3, 800, 1066]   )
            self.assertEqual(encoding['pixel_values'].shape      ,	snake_case__   )

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

            # verify area
            _lowerCAmelCase    :							Dict   			=       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']      ,	snake_case__   )   )
            # verify boxes
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.Size([6, 4]   )
            self.assertEqual(encoding['labels'][0]['boxes'].shape      ,	snake_case__   )
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['boxes'][0]      ,	snake_case__      ,	atol=1E-3   )   )
            # verify image_id
            _lowerCAmelCase    :							List[str]   			=       torch.tensor([3_9769]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id']      ,	snake_case__   )   )
            # verify is_crowd
            _lowerCAmelCase    :							int   			=       torch.tensor([0, 0, 0, 0, 0, 0]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd']      ,	snake_case__   )   )
            # verify class_labels
            _lowerCAmelCase    :							Union[str, Any]   			=       torch.tensor([17, 17, 63, 75, 75, 93]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels']      ,	snake_case__   )   )
            # verify masks
            _lowerCAmelCase    :							Optional[int]   			=       82_2873
            self.assertEqual(encoding['labels'][0]['masks'].sum().item()      ,	snake_case__   )
            # verify orig_size
            _lowerCAmelCase    :							Dict   			=       torch.tensor([480, 640]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size']      ,	snake_case__   )   )
            # verify size
            _lowerCAmelCase    :							List[Any]   			=       torch.tensor([800, 1066]   )
            self.assertTrue(torch.allclose(encoding['labels'][0]['size']      ,	snake_case__   )   )





'''simple docstring'''







from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


lowerCAmelCase    :     Optional[int]					=						{
    """configuration_mctct""": ["""MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """MCTCTConfig"""],
    """feature_extraction_mctct""": ["""MCTCTFeatureExtractor"""],
    """processing_mctct""": ["""MCTCTProcessor"""],
}


try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       lowerCAmelCase    :     List[str]					=						[
           """MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """MCTCTForCTC""",
           """MCTCTModel""",
           """MCTCTPreTrainedModel""",
       ]


if TYPE_CHECKING:
       from .configuration_mctct import MCTCT_PRETRAINED_CONFIG_ARCHIVE_MAP, MCTCTConfig
       from .feature_extraction_mctct import MCTCTFeatureExtractor
       from .processing_mctct import MCTCTProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_mctct import MCTCT_PRETRAINED_MODEL_ARCHIVE_LIST, MCTCTForCTC, MCTCTModel, MCTCTPreTrainedModel

else:
       import sys

       lowerCAmelCase    :     Tuple					=						_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

