
import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPImageProcessor, CLIPVisionConfig

from diffusers import AutoencoderKL, PaintByExamplePipeline, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.paint_by_example import PaintByExampleImageEncoder
from diffusers.utils import floats_tensor, load_image, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()




class      SCREAMING_SNAKE_CASE__  (			UpperCAmelCase							,unittest.TestCase      ):





	'''simple docstring'''



	_UpperCAmelCase :	Dict					     =      PaintByExamplePipeline
	_UpperCAmelCase :	Tuple					     =      IMAGE_GUIDED_IMAGE_INPAINTING_PARAMS
	_UpperCAmelCase :	List[str]					     =      IMAGE_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
	_UpperCAmelCase :	Union[str, Any]					     =      frozenset([]      )  # TO_DO: update the image_prams once refactored VaeImageProcessor.preprocess



	def 				A							(						self			:					Dict			):


				'''simple docstring'''


				torch.manual_seed(0			)
				_snake_case						=    UNetaDConditionModel(
				    block_out_channels=(32, 64)    ,						layers_per_block=2    ,						sample_size=32    ,						in_channels=9    ,						out_channels=4    ,						down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')    ,						up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')    ,						cross_attention_dim=32    ,						)
				_snake_case						=    PNDMScheduler(skip_prk_steps=lowercase			)
				torch.manual_seed(0			)
				_snake_case						=    AutoencoderKL(
				    block_out_channels=[32, 64]    ,						in_channels=3    ,						out_channels=3    ,						down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']    ,						up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']    ,						latent_channels=4    ,						)
				torch.manual_seed(0			)
				_snake_case						=    CLIPVisionConfig(
				    hidden_size=32    ,						projection_dim=32    ,						intermediate_size=37    ,						layer_norm_eps=1E-05    ,						num_attention_heads=4    ,						num_hidden_layers=5    ,						image_size=32    ,						patch_size=4    ,						)
				_snake_case						=    PaintByExampleImageEncoder(lowercase    ,						proj_size=32			)
				_snake_case						=    CLIPImageProcessor(crop_size=32    ,						size=32			)

				_snake_case						=    {
				    'unet': unet,
				    'scheduler': scheduler,
				    'vae': vae,
				    'image_encoder': image_encoder,
				    'safety_checker': None,
				    'feature_extractor': feature_extractor,
				}
				return components



	def 				A							(						self			:					Union[str, Any]    ,						lowercase			:					Optional[int]			):


				'''simple docstring'''


				_snake_case						=    np.array(image.convert('RGB'			)			)
				_snake_case						=    image[None].transpose(0    ,						3    ,						1    ,						2			)
				_snake_case						=    torch.from_numpy(lowercase			).to(dtype=torch.floataa			) / 127.5 - 1.0
				return image



	def 				A							(						self			:					List[Any]    ,						lowercase			:					Any="cpu"    ,						lowercase			:					Optional[int]=0			):


				'''simple docstring'''


				_snake_case						=    floats_tensor((1, 3, 32, 32)    ,						rng=random.Random(lowercase			)			).to(lowercase			)
				_snake_case						=    image.cpu().permute(0    ,						2    ,						3    ,						1			)[0]
				_snake_case						=    Image.fromarray(np.uinta(lowercase			)			).convert('RGB'			).resize((64, 64)			)
				_snake_case						=    Image.fromarray(np.uinta(image + 4			)			).convert('RGB'			).resize((64, 64)			)
				_snake_case						=    Image.fromarray(np.uinta(lowercase			)			).convert('RGB'			).resize((32, 32)			)

				if str(lowercase			).startswith('mps'			):
							_snake_case						=    torch.manual_seed(lowercase			)
				else:
							_snake_case						=    torch.Generator(device=lowercase			).manual_seed(lowercase			)
				_snake_case						=    {
				    'example_image': example_image,
				    'image': init_image,
				    'mask_image': mask_image,
				    'generator': generator,
				    'num_inference_steps': 2,
				    'guidance_scale': 6.0,
				    'output_type': 'numpy',
				}
				return inputs



	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    self.get_dummy_components()

				# make sure here that pndm scheduler skips prk
				_snake_case						=    PaintByExamplePipeline(**lowercase			)
				_snake_case						=    pipe.to('cpu'			)
				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    self.get_dummy_inputs()
				_snake_case						=    pipe(**lowercase			)
				_snake_case						=    output.images

				_snake_case						=    image[0, -3:, -3:, -1]

				assert image.shape == (1, 64, 64, 3)
				_snake_case						=    np.array([0.4686, 0.5687, 0.4007, 0.5218, 0.5741, 0.4482, 0.4940, 0.4629, 0.4503]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2



	def 				A							(						self			:					Optional[int]			):


				'''simple docstring'''


				_snake_case						=    'cpu'
				_snake_case						=    self.get_dummy_inputs()
				inputs.pop('mask_image'			)
				_snake_case						=    self.convert_to_pt(inputs.pop('image'			)			)
				_snake_case						=    image.clamp(0    ,						1			) / 2

				# make sure here that pndm scheduler skips prk
				_snake_case						=    PaintByExamplePipeline(**self.get_dummy_components()			)
				_snake_case						=    pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    pipe(image=lowercase    ,						mask_image=mask_image[:, 0]    ,						**lowercase			)
				_snake_case						=    output.images

				_snake_case						=    image.cpu().permute(0    ,						2    ,						3    ,						1			)[0]
				_snake_case						=    mask_image.cpu().permute(0    ,						2    ,						3    ,						1			)[0]

				_snake_case						=    Image.fromarray(np.uinta(lowercase			)			).convert('RGB'			)
				_snake_case						=    Image.fromarray(np.uinta(lowercase			)			).convert('RGB'			)

				_snake_case						=    pipe(**self.get_dummy_inputs()			)
				_snake_case						=    output.images

				assert out_a.shape == (1, 64, 64, 3)
				assert np.abs(out_a.flatten() - out_a.flatten()			).max() < 5E-2





	def 				A							(						self			:					List[str]			):


				'''simple docstring'''


				super().test_inference_batch_single_identical(expected_max_diff=3E-3			)




@slow
@require_torch_gpu
class      SCREAMING_SNAKE_CASE__  (			unittest.TestCase      ):





	'''simple docstring'''



	def 				A							(						self			:					Union[str, Any]			):


				'''simple docstring'''


				super().tearDown()
				gc.collect()
				torch.cuda.empty_cache()





	def 				A							(						self			:					List[Any]			):


				'''simple docstring'''


				_snake_case						=    load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/paint_by_example/dog_in_bucket.png'			)
				_snake_case						=    load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/paint_by_example/mask.png'			)
				_snake_case						=    load_image(
				    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
				    '/paint_by_example/panda.jpg'			)

				_snake_case						=    PaintByExamplePipeline.from_pretrained('Fantasy-Studio/Paint-by-Example'			)
				_snake_case						=    pipe.to(lowercase			)
				pipe.set_progress_bar_config(disable=lowercase			)

				_snake_case						=    torch.manual_seed(321			)
				_snake_case						=    pipe(
				    image=lowercase    ,						mask_image=lowercase    ,						example_image=lowercase    ,						generator=lowercase    ,						guidance_scale=5.0    ,						num_inference_steps=50    ,						output_type='np'    ,						)

				_snake_case						=    output.images

				_snake_case						=    image[0, -3:, -3:, -1]

				assert image.shape == (1, 512, 512, 3)
				_snake_case						=    np.array([0.4834, 0.4811, 0.4874, 0.5122, 0.5081, 0.5144, 0.5291, 0.5290, 0.5374]			)

				assert np.abs(image_slice.flatten() - expected_slice			).max() < 1E-2

# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
    is_vision_available,
)


_lowerCamelCase :       int     					=    {
    '''configuration_tvlt''': ['''TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''TvltConfig'''],
    '''feature_extraction_tvlt''': ['''TvltFeatureExtractor'''],
    '''processing_tvlt''': ['''TvltProcessor'''],
}

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    [
			    '''TVLT_PRETRAINED_MODEL_ARCHIVE_LIST''',
			    '''TvltModel''',
			    '''TvltForPreTraining''',
			    '''TvltForAudioVisualClassification''',
			    '''TvltPreTrainedModel''',
			]

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			_lowerCamelCase :       str     					=    ['''TvltImageProcessor''']


if TYPE_CHECKING:
			from .configuration_tvlt import TVLT_PRETRAINED_CONFIG_ARCHIVE_MAP, TvltConfig
			from .processing_tvlt import TvltProcessor
			from .feature_extraction_tvlt import TvltFeatureExtractor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_tvlt import (
						    TVLT_PRETRAINED_MODEL_ARCHIVE_LIST,
						    TvltForAudioVisualClassification,
						    TvltForPreTraining,
						    TvltModel,
						    TvltPreTrainedModel,
						)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .image_processing_tvlt import TvltImageProcessor


else:
			import sys

			_lowerCamelCase :       Dict     					=    _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)