





import unittest
from typing import Dict, List, Optional, Union

import numpy as np

from transformers.testing_utils import require_torch, require_vision
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 BridgeTowerImageProcessor



class 					__snake_case	(  unittest.TestCase    ):


    '''simple docstring'''







    def __init__(       self   ,				A_   ,				A_ = True   ,				A_ = None   ,				A_ = 32   ,				A_ = True   ,				A_ = 1 / 2_55   ,				A_ = True   ,				A_ = True   ,				A_ = [0.48145466, 0.4578275, 0.40821073]   ,				A_ = [0.26862954, 0.26130258, 0.27577711]   ,				A_ = True   ,				A_=7   ,				A_=30   ,				A_=4_00   ,				A_=3   ,				):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			parent
        SCREAMING_SNAKE_CASE__											=			do_resize
        SCREAMING_SNAKE_CASE__											=			size if size is not None else {'''shortest_edge''': 2_88}
        SCREAMING_SNAKE_CASE__											=			size_divisor
        SCREAMING_SNAKE_CASE__											=			do_rescale
        SCREAMING_SNAKE_CASE__											=			rescale_factor
        SCREAMING_SNAKE_CASE__											=			do_normalize
        SCREAMING_SNAKE_CASE__											=			do_center_crop
        SCREAMING_SNAKE_CASE__											=			image_mean
        SCREAMING_SNAKE_CASE__											=			image_std
        SCREAMING_SNAKE_CASE__											=			do_pad
        SCREAMING_SNAKE_CASE__											=			batch_size
        SCREAMING_SNAKE_CASE__											=			num_channels
        SCREAMING_SNAKE_CASE__											=			min_resolution
        SCREAMING_SNAKE_CASE__											=			max_resolution



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return {
            "image_mean": self.image_mean,
            "image_std": self.image_std,
            "do_normalize": self.do_normalize,
            "do_resize": self.do_resize,
            "size": self.size,
            "size_divisor": self.size_divisor,
        }




    def  lowercase_					(       self   ,				A_   ,				A_=False     ):





        '''simple docstring'''

        if not batched:
            SCREAMING_SNAKE_CASE__											=			self.size['''shortest_edge''']
            SCREAMING_SNAKE_CASE__											=			image_inputs[0]
            if isinstance(A_   ,				Image.Image     ):
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			image.size
            else:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			image.shape[1], image.shape[2]
            SCREAMING_SNAKE_CASE__											=			size / min(A_   ,				A_     )
            if h < w:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			size, scale * w
            else:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			scale * h, size

            SCREAMING_SNAKE_CASE__											=			int((13_33 / 8_00) * size     )
            if max(A_   ,				A_     ) > max_size:
                SCREAMING_SNAKE_CASE__											=			max_size / max(A_   ,				A_     )
                SCREAMING_SNAKE_CASE__											=			newh * scale
                SCREAMING_SNAKE_CASE__											=			neww * scale

            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			int(newh + 0.5     ), int(neww + 0.5     )
            SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			(
                newh // self.size_divisor * self.size_divisor,
                neww // self.size_divisor * self.size_divisor,
            )

        else:
            SCREAMING_SNAKE_CASE__											=			[]
            for image in image_inputs:
                SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			self.get_expected_values([image]     )
                expected_values.append((expected_height, expected_width)     )
            SCREAMING_SNAKE_CASE__											=			max(A_   ,				key=lambda A_     : item[0]     )[0]
            SCREAMING_SNAKE_CASE__											=			max(A_   ,				key=lambda A_     : item[1]     )[1]

        return expected_height, expected_width





@require_torch
@require_vision
class 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Optional[Any]				=	BridgeTowerImageProcessor if is_vision_available() else None



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			BridgeTowerImageProcessingTester(self     )



    @property
    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self.image_processor_tester.prepare_image_processor_dict()



    def  lowercase_					(       self     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			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_   ,				'''size'''     )     )
        self.assertTrue(hasattr(A_   ,				'''size_divisor'''     )     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        pass



    def  lowercase_					(       self     ):





        '''simple docstring'''

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

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

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			image_processing(A_   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.image_processing_class(**self.image_processor_dict     )
        # create random numpy tensors
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			image_processing(image_inputs[0]   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			image_processing(A_   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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     ):





        '''simple docstring'''

        SCREAMING_SNAKE_CASE__											=			self.image_processing_class(**self.image_processor_dict     )
        # create random PyTorch tensors
        SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			image_processing(image_inputs[0]   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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
        SCREAMING_SNAKE_CASE__											=			image_processing(A_   ,				return_tensors='''pt'''     ).pixel_values

        SCREAMING_SNAKE_CASE__				,		SCREAMING_SNAKE_CASE__											=			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,
            )   ,				)







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 					__snake_case	(  __SCREAMING_SNAKE_CASE			,     __SCREAMING_SNAKE_CASE			,     unittest.TestCase    ):


    '''simple docstring'''







    lowerCamelCase__    :		Any				=	IFInpaintingPipeline
    lowerCamelCase__    :		str				=	TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"""width""", """height"""}
    lowerCamelCase__    :		Optional[Any]				=	TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
    lowerCamelCase__    :		Tuple				=	PipelineTesterMixin.required_optional_params - {"""latents"""}



    def  lowercase_					(       self     ):





        '''simple docstring'''

        return self._get_dummy_components()



    def  lowercase_					(       self   ,				A_   ,				A_=0     ):





        '''simple docstring'''

        if str(A_     ).startswith('''mps'''     ):
            SCREAMING_SNAKE_CASE__											=			torch.manual_seed(A_     )
        else:
            SCREAMING_SNAKE_CASE__											=			torch.Generator(device=A_     ).manual_seed(A_     )

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

        SCREAMING_SNAKE_CASE__											=			{
            '''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  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_save_load_optional_components()



    @unittest.skipIf(torch_device != '''cuda'''   ,				reason='''float16 requires CUDA'''     )
    def  lowercase_					(       self     ):





        '''simple docstring'''

        super().test_save_load_floataa(expected_max_diff=1E-1     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_attention_slicing_forward_pass(expected_max_diff=1E-2     )



    def  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_save_load_local()




    def  lowercase_					(       self     ):





        '''simple docstring'''

        self._test_inference_batch_single_identical(
            expected_max_diff=1E-2   ,				)
