



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 DetrImageProcessor



class 	lowerCamelCase__      (							unittest.TestCase):




		'''simple docstring'''



		def __init__(self		,__lowerCamelCase		,__lowerCamelCase=7		,__lowerCamelCase=3		,__lowerCamelCase=30		,__lowerCamelCase=4_00		,__lowerCamelCase=True		,__lowerCamelCase=None		,__lowerCamelCase=True		,__lowerCamelCase=1 / 2_55		,__lowerCamelCase=True		,__lowerCamelCase=[0.5, 0.5, 0.5]		,__lowerCamelCase=[0.5, 0.5, 0.5]		,__lowerCamelCase=True		,)					->      int:

						"""simple docstring"""






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



		def 		lowerCAmelCase__				(self			)					->      Dict:

						"""simple docstring"""






						return {
						    "do_resize": self.do_resize,
						    "size": self.size,
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_normalize": self.do_normalize,
						    "image_mean": self.image_mean,
						    "image_std": self.image_std,
						    "do_pad": self.do_pad,
						}



		def 		lowerCAmelCase__				(self		,__lowerCamelCase		,__lowerCamelCase=False			)					->      List[str]:

						"""simple docstring"""






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

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

						return expected_height, expected_width



@require_torch
@require_vision
class 	lowerCamelCase__      (							lowerCamelCase__				,    unittest.TestCase):




		'''simple docstring'''



		snake_case_     =DetrImageProcessor if is_vision_available() else None



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					DetrImageProcessingTester(self			)



		@property
		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






						return self.image_processor_tester.prepare_image_processor_dict()



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

						"""simple docstring"""






						lowerCAmelCase__					:    Union[str, Any]												=					self.image_processing_class(**self.image_processor_dict			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_mean'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''image_std'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_normalize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_rescale'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''rescale_factor'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_resize'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''size'''			)			)
						self.assertTrue(hasattr(__lowerCamelCase		,'''do_pad'''			)			)



		def 		lowerCAmelCase__				(self			)					->      Tuple:

						"""simple docstring"""






						lowerCAmelCase__					:    Optional[int]												=					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		,__lowerCamelCase			)

						lowerCAmelCase__					:    Optional[int]												=					self.image_processing_class.from_dict(
						    self.image_processor_dict		,size=42		,max_size=84		,pad_and_return_pixel_mask=__lowerCamelCase			)
						self.assertEqual(image_processor.size		,{'''shortest_edge''': 42, '''longest_edge''': 84}			)
						self.assertEqual(image_processor.do_pad		,__lowerCamelCase			)



		def 		lowerCAmelCase__				(self			)					->      str:

						"""simple docstring"""






						pass



		def 		lowerCAmelCase__				(self			)					->      int:

						"""simple docstring"""






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

						# 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(__lowerCamelCase			)

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

						# Test batched
						lowerCAmelCase__						,		lowerCAmelCase__					:    Union[str, Any]												=					self.image_processor_tester.get_expected_values(__lowerCamelCase		,batched=__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					image_processing(__lowerCamelCase		,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 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    int												=					self.image_processing_class(**self.image_processor_dict			)
						# create random numpy tensors
						lowerCAmelCase__					:    int												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,numpify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,np.ndarray			)

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

						lowerCAmelCase__						,		lowerCAmelCase__					:    Any												=					self.image_processor_tester.get_expected_values(__lowerCamelCase			)

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

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

						lowerCAmelCase__						,		lowerCAmelCase__					:    Union[str, Any]												=					self.image_processor_tester.get_expected_values(__lowerCamelCase		,batched=__lowerCamelCase			)

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



		def 		lowerCAmelCase__				(self			)					->      Any:

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					self.image_processing_class(**self.image_processor_dict			)
						# create random PyTorch tensors
						lowerCAmelCase__					:    Tuple												=					prepare_image_inputs(self.image_processor_tester		,equal_resolution=__lowerCamelCase		,torchify=__lowerCamelCase			)
						for image in image_inputs:
										self.assertIsInstance(__lowerCamelCase		,torch.Tensor			)

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

						lowerCAmelCase__						,		lowerCAmelCase__					:    Optional[Any]												=					self.image_processor_tester.get_expected_values(__lowerCamelCase			)

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

						# Test batched
						lowerCAmelCase__					:    int												=					image_processing(__lowerCamelCase		,return_tensors='''pt'''			).pixel_values

						lowerCAmelCase__						,		lowerCAmelCase__					:    int												=					self.image_processor_tester.get_expected_values(__lowerCamelCase		,batched=__lowerCamelCase			)

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



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

						"""simple docstring"""






						lowerCAmelCase__					:    Dict												=					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__					:    List[str]												=					{'''image_id''': 3_97_69, '''annotations''': target}

						# encode them
						lowerCAmelCase__					:    List[Any]												=					DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50'''			)
						lowerCAmelCase__					:    Dict												=					image_processing(images=__lowerCamelCase		,annotations=__lowerCamelCase		,return_tensors='''pt'''			)

						# verify pixel values
						lowerCAmelCase__					:    Any												=					torch.Size([1, 3, 8_00, 10_66]			)
						self.assertEqual(encoding['''pixel_values'''].shape		,__lowerCamelCase			)

						lowerCAmelCase__					:    str												=					torch.tensor([0.2796, 0.3138, 0.3481]			)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,__lowerCamelCase		,atol=1e-4			)			)

						# verify area
						lowerCAmelCase__					:    List[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''']		,__lowerCamelCase			)			)
						# verify boxes
						lowerCAmelCase__					:    Optional[int]												=					torch.Size([6, 4]			)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,__lowerCamelCase			)
						lowerCAmelCase__					:    Dict												=					torch.tensor([0.5503, 0.2765, 0.0604, 0.2215]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,__lowerCamelCase		,atol=1e-3			)			)
						# verify image_id
						lowerCAmelCase__					:    Any												=					torch.tensor([3_97_69]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,__lowerCamelCase			)			)
						# verify is_crowd
						lowerCAmelCase__					:    Tuple												=					torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,__lowerCamelCase			)			)
						# verify class_labels
						lowerCAmelCase__					:    Tuple												=					torch.tensor([75, 75, 63, 65, 17, 17]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,__lowerCamelCase			)			)
						# verify orig_size
						lowerCAmelCase__					:    List[Any]												=					torch.tensor([4_80, 6_40]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,__lowerCamelCase			)			)
						# verify size
						lowerCAmelCase__					:    Any												=					torch.tensor([8_00, 10_66]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,__lowerCamelCase			)			)



		@slow
		def 		lowerCAmelCase__				(self			)					->      List[str]:

						"""simple docstring"""






						lowerCAmelCase__					:    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:
										lowerCAmelCase__					:    List[Any]												=					json.loads(f.read()			)

						lowerCAmelCase__					:    Any												=					{'''file_name''': '''000000039769.png''', '''image_id''': 3_97_69, '''segments_info''': target}

						lowerCAmelCase__					:    Union[str, Any]												=					pathlib.Path('''./tests/fixtures/tests_samples/COCO/coco_panoptic'''			)

						# encode them
						lowerCAmelCase__					:    str												=					DetrImageProcessor.from_pretrained('''facebook/detr-resnet-50-panoptic'''			)
						lowerCAmelCase__					:    Union[str, Any]												=					image_processing(images=__lowerCamelCase		,annotations=__lowerCamelCase		,masks_path=__lowerCamelCase		,return_tensors='''pt'''			)

						# verify pixel values
						lowerCAmelCase__					:    Optional[int]												=					torch.Size([1, 3, 8_00, 10_66]			)
						self.assertEqual(encoding['''pixel_values'''].shape		,__lowerCamelCase			)

						lowerCAmelCase__					:    List[Any]												=					torch.tensor([0.2796, 0.3138, 0.3481]			)
						self.assertTrue(torch.allclose(encoding['''pixel_values'''][0, 0, 0, :3]		,__lowerCamelCase		,atol=1e-4			)			)

						# verify area
						lowerCAmelCase__					:    List[str]												=					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''']		,__lowerCamelCase			)			)
						# verify boxes
						lowerCAmelCase__					:    List[str]												=					torch.Size([6, 4]			)
						self.assertEqual(encoding['''labels'''][0]['''boxes'''].shape		,__lowerCamelCase			)
						lowerCAmelCase__					:    Union[str, Any]												=					torch.tensor([0.2625, 0.5437, 0.4688, 0.8625]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''boxes'''][0]		,__lowerCamelCase		,atol=1e-3			)			)
						# verify image_id
						lowerCAmelCase__					:    Dict												=					torch.tensor([3_97_69]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''image_id''']		,__lowerCamelCase			)			)
						# verify is_crowd
						lowerCAmelCase__					:    List[str]												=					torch.tensor([0, 0, 0, 0, 0, 0]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''iscrowd''']		,__lowerCamelCase			)			)
						# verify class_labels
						lowerCAmelCase__					:    str												=					torch.tensor([17, 17, 63, 75, 75, 93]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''class_labels''']		,__lowerCamelCase			)			)
						# verify masks
						lowerCAmelCase__					:    Optional[Any]												=					82_28_73
						self.assertEqual(encoding['''labels'''][0]['''masks'''].sum().item()		,__lowerCamelCase			)
						# verify orig_size
						lowerCAmelCase__					:    List[str]												=					torch.tensor([4_80, 6_40]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''orig_size''']		,__lowerCamelCase			)			)
						# verify size
						lowerCAmelCase__					:    Dict												=					torch.tensor([8_00, 10_66]			)
						self.assertTrue(torch.allclose(encoding['''labels'''][0]['''size''']		,__lowerCamelCase			)			)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__snake_case    :		Any  ={
    'configuration_blenderbot_small': [
        'BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'BlenderbotSmallConfig',
        'BlenderbotSmallOnnxConfig',
    ],
    'tokenization_blenderbot_small': ['BlenderbotSmallTokenizer'],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Union[str, Any]  =['BlenderbotSmallTokenizerFast']

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		List[str]  =[
							    'BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST',
							    'BlenderbotSmallForCausalLM',
							    'BlenderbotSmallForConditionalGeneration',
							    'BlenderbotSmallModel',
							    'BlenderbotSmallPreTrainedModel',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		str  =[
							    'TFBlenderbotSmallForConditionalGeneration',
							    'TFBlenderbotSmallModel',
							    'TFBlenderbotSmallPreTrainedModel',
							]

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__snake_case    :		Dict  =[
							    'FlaxBlenderbotSmallForConditionalGeneration',
							    'FlaxBlenderbotSmallModel',
							    'FlaxBlenderbotSmallPreTrainedModel',
							]

if TYPE_CHECKING:
							from .configuration_blenderbot_small import (
							    BLENDERBOT_SMALL_PRETRAINED_CONFIG_ARCHIVE_MAP,
							    BlenderbotSmallConfig,
							    BlenderbotSmallOnnxConfig,
							)
							from .tokenization_blenderbot_small import BlenderbotSmallTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_blenderbot_small_fast import BlenderbotSmallTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_blenderbot_small import (
														    BLENDERBOT_SMALL_PRETRAINED_MODEL_ARCHIVE_LIST,
														    BlenderbotSmallForCausalLM,
														    BlenderbotSmallForConditionalGeneration,
														    BlenderbotSmallModel,
														    BlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_blenderbot_small import (
														    TFBlenderbotSmallForConditionalGeneration,
														    TFBlenderbotSmallModel,
														    TFBlenderbotSmallPreTrainedModel,
														)

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_blenderbot_small import (
														    FlaxBlenderbotSmallForConditionalGeneration,
														    FlaxBlenderbotSmallModel,
														    FlaxBlenderbotSmallPreTrainedModel,
														)

else:
							import sys

							__snake_case    :		Optional[int]  =_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)

