



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 					snake_case__						(       unittest.TestCase     ):






     """simple docstring"""


     def __init__(       self  :  Tuple, _snake_case  :  str, _snake_case  :  str=7, _snake_case  :  Optional[Any]=3, _snake_case  :  Any=3_0, _snake_case  :  int=4_0_0, _snake_case  :  Dict=True, _snake_case  :  Tuple=None, _snake_case  :  Optional[Any]=True, _snake_case  :  List[str]=[0.5, 0.5, 0.5], _snake_case  :  List[str]=[0.5, 0.5, 0.5], _snake_case  :  Any=True, _snake_case  :  int=1 / 2_5_5, _snake_case  :  List[str]=True, )   ->int:
            snake_case__							:	Union[str, Any]   	=	size if size is not None else {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}
            snake_case__							:	Optional[int]   	=	parent
            snake_case__							:	Union[str, Any]   	=	batch_size
            snake_case__							:	List[str]   	=	num_channels
            snake_case__							:	List[Any]   	=	min_resolution
            snake_case__							:	Tuple   	=	max_resolution
            snake_case__							:	List[Any]   	=	do_resize
            snake_case__							:	int   	=	size
            snake_case__							:	Union[str, Any]   	=	do_normalize
            snake_case__							:	int   	=	image_mean
            snake_case__							:	str   	=	image_std
            snake_case__							:	Optional[int]   	=	do_rescale
            snake_case__							:	Tuple   	=	rescale_factor
            snake_case__							:	Optional[int]   	=	do_pad






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

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

            return expected_height, expected_width




@require_torch
@require_vision
class 					snake_case__						(       _SCREAMING_SNAKE_CASE				,     unittest.TestCase     ):






     """simple docstring"""


     _SCREAMING_SNAKE_CASE						=	DetaImageProcessor if is_vision_available() else None






     def    lowercase_   (       self  :  Tuple					)   ->Optional[int]:
            snake_case__							:	List[Any]   	=	DetaImageProcessingTester(self					)






     @property
     def    lowercase_   (       self  :  List[Any]					)   ->Union[str, Any]:
            return self.image_processor_tester.prepare_image_processor_dict()






     def    lowercase_   (       self  :  Optional[Any]					)   ->Dict:
            snake_case__							:	Dict   	=	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_, 'do_rescale'					)					)
            self.assertTrue(hasattr(A_, 'do_pad'					)					)
            self.assertTrue(hasattr(A_, 'size'					)					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[Any]:
            snake_case__							:	List[Any]   	=	self.image_processing_class.from_dict(self.image_processor_dict					)
            self.assertEqual(image_processor.size, {'shortest_edge': 1_8, 'longest_edge': 1_3_3_3}					)
            self.assertEqual(image_processor.do_pad, A_					)






     def    lowercase_   (       self  :  Optional[int]					)   ->Optional[int]:
            pass






     def    lowercase_   (       self  :  List[str]					)   ->Tuple:
            snake_case__							:	Tuple   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PIL images
            snake_case__							:	Union[str, Any]   	=	prepare_image_inputs(self.image_processor_tester, equal_resolution=A_					)
            for image in image_inputs:
                   self.assertIsInstance(A_, Image.Image					)

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

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

            snake_case__							:	str   	=	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    lowercase_   (       self  :  str					)   ->int:
            snake_case__							:	int   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random numpy tensors
            snake_case__							:	int   	=	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
            snake_case__							:	str   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values

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

            snake_case__	,				snake_case__							:	List[Any]   	=	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    lowercase_   (       self  :  int					)   ->Union[str, Any]:
            snake_case__							:	Optional[int]   	=	self.image_processing_class(**self.image_processor_dict					)
            # create random PyTorch tensors
            snake_case__							:	Tuple   	=	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
            snake_case__							:	str   	=	image_processing(image_inputs[0], return_tensors='pt'					).pixel_values

            snake_case__	,				snake_case__							:	Dict   	=	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
            snake_case__							:	Dict   	=	image_processing(A_, return_tensors='pt'					).pixel_values

            snake_case__	,				snake_case__							:	Dict   	=	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    lowercase_   (       self  :  Optional[int]					)   ->int:
            snake_case__							:	Any   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
            with open('./tests/fixtures/tests_samples/COCO/coco_annotations.txt', 'r'					) as f:
                   snake_case__							:	List[str]   	=	json.loads(f.read()					)

            snake_case__							:	Tuple   	=	{'image_id': 3_9_7_6_9, 'annotations': target}

            # encode them
            snake_case__							:	str   	=	DetaImageProcessor()
            snake_case__							:	int   	=	image_processing(images=A_, annotations=A_, return_tensors='pt'					)

            # verify pixel values
            snake_case__							:	Union[str, Any]   	=	torch.Size([1, 3, 8_0_0, 1_0_6_6]					)
            self.assertEqual(encoding['pixel_values'].shape, A_					)

            snake_case__							:	Tuple   	=	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], A_, atol=1e-4					)					)

            # verify area
            snake_case__							:	Optional[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'], A_					)					)
            # verify boxes
            snake_case__							:	List[Any]   	=	torch.Size([6, 4]					)
            self.assertEqual(encoding['labels'][0]['boxes'].shape, A_					)
            snake_case__							:	Optional[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], A_, atol=1e-3					)					)
            # verify image_id
            snake_case__							:	List[Any]   	=	torch.tensor([3_9_7_6_9]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'], A_					)					)
            # verify is_crowd
            snake_case__							:	str   	=	torch.tensor([0, 0, 0, 0, 0, 0]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'], A_					)					)
            # verify class_labels
            snake_case__							:	List[str]   	=	torch.tensor([7_5, 7_5, 6_3, 6_5, 1_7, 1_7]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'], A_					)					)
            # verify orig_size
            snake_case__							:	str   	=	torch.tensor([4_8_0, 6_4_0]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'], A_					)					)
            # verify size
            snake_case__							:	List[Any]   	=	torch.tensor([8_0_0, 1_0_6_6]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['size'], A_					)					)






     @slow
     def    lowercase_   (       self  :  List[Any]					)   ->Optional[Any]:
            snake_case__							:	Union[str, Any]   	=	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'					)
            with open('./tests/fixtures/tests_samples/COCO/coco_panoptic_annotations.txt', 'r'					) as f:
                   snake_case__							:	Optional[int]   	=	json.loads(f.read()					)

            snake_case__							:	Any   	=	{'file_name': '000000039769.png', 'image_id': 3_9_7_6_9, 'segments_info': target}

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

            # encode them
            snake_case__							:	Union[str, Any]   	=	DetaImageProcessor(format='coco_panoptic'					)
            snake_case__							:	List[str]   	=	image_processing(images=A_, annotations=A_, masks_path=A_, return_tensors='pt'					)

            # verify pixel values
            snake_case__							:	Union[str, Any]   	=	torch.Size([1, 3, 8_0_0, 1_0_6_6]					)
            self.assertEqual(encoding['pixel_values'].shape, A_					)

            snake_case__							:	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], A_, atol=1e-4					)					)

            # verify area
            snake_case__							:	Tuple   	=	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'], A_					)					)
            # verify boxes
            snake_case__							:	List[str]   	=	torch.Size([6, 4]					)
            self.assertEqual(encoding['labels'][0]['boxes'].shape, A_					)
            snake_case__							:	Optional[int]   	=	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], A_, atol=1e-3					)					)
            # verify image_id
            snake_case__							:	Optional[Any]   	=	torch.tensor([3_9_7_6_9]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['image_id'], A_					)					)
            # verify is_crowd
            snake_case__							:	Optional[int]   	=	torch.tensor([0, 0, 0, 0, 0, 0]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['iscrowd'], A_					)					)
            # verify class_labels
            snake_case__							:	Optional[Any]   	=	torch.tensor([1_7, 1_7, 6_3, 7_5, 7_5, 9_3]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['class_labels'], A_					)					)
            # verify masks
            snake_case__							:	Optional[Any]   	=	8_2_2_8_7_3
            self.assertEqual(encoding['labels'][0]['masks'].sum().item(), A_					)
            # verify orig_size
            snake_case__							:	Optional[int]   	=	torch.tensor([4_8_0, 6_4_0]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['orig_size'], A_					)					)
            # verify size
            snake_case__							:	int   	=	torch.tensor([8_0_0, 1_0_6_6]					)
            self.assertTrue(torch.allclose(encoding['labels'][0]['size'], A_					)					)








import random
import unittest

import torch

from diffusers import IFInpaintingPipeline
from diffusers.utils import floats_tensor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import skip_mps, torch_device

from ..pipeline_params import (
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin
from . import IFPipelineTesterMixin


@skip_mps
class 				lowercase					(		_SCREAMING_SNAKE_CASE  ,   _SCREAMING_SNAKE_CASE  ,   unittest.TestCase   ):
		__lowercase       :       int	     =				IFInpaintingPipeline
		__lowercase       :       str	     =				TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"width", "height"}
		__lowercase       :       Optional[int]	     =				TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		__lowercase       :       Optional[int]	     =				PipelineTesterMixin.required_optional_params - {"latents"}



		def __UpperCamelCase ( self       )  ->					List[str]:
				"""simple docstring"""
				return self._get_dummy_components()



		def __UpperCamelCase ( self				,      A_				,      A_=0       )  ->					List[Any]:
				"""simple docstring"""
				if str(A_       ).startswith('mps'       ):
						UpperCamelCase		=     torch.manual_seed(A_       )
				else:
						UpperCamelCase		=     torch.Generator(device=A_       ).manual_seed(A_       )

				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )
				UpperCamelCase		=     floats_tensor((1, 3, 32, 32)				,      rng=random.Random(A_       )       ).to(A_       )

				UpperCamelCase		=     {
				    'prompt': 'A painting of a squirrel eating a burger',
				    'image': image,
				    'mask_image': mask_image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'output_type': 'numpy',
				}

				return inputs



		@unittest.skipIf(
		    torch_device != 'cuda' or not is_xformers_available()				,      reason='XFormers attention is only available with CUDA and `xformers` installed'				,      )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1e-3       )



		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				self._test_save_load_optional_components()



		@unittest.skipIf(torch_device != 'cuda'				,      reason='float16 requires CUDA'       )
		def __UpperCamelCase ( self       )  ->					str:
				"""simple docstring"""
				# Due to non-determinism in save load of the hf-internal-testing/tiny-random-t5 text encoder
				super().test_save_load_floataa(expected_max_diff=1e-1       )



		def __UpperCamelCase ( self       )  ->					Optional[Any]:
				"""simple docstring"""
				self._test_attention_slicing_forward_pass(expected_max_diff=1e-2       )



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_save_load_local()



		def __UpperCamelCase ( self       )  ->					Dict:
				"""simple docstring"""
				self._test_inference_batch_single_identical(
				    expected_max_diff=1e-2				,      )
