import gc
import random
import unittest

import torch

from diffusers import (
    IFImgaImgPipeline,
    IFImgaImgSuperResolutionPipeline,
    IFInpaintingPipeline,
    IFInpaintingSuperResolutionPipeline,
    IFPipeline,
    IFSuperResolutionPipeline,
)
from diffusers.models.attention_processor import AttnAddedKVProcessor
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import floats_tensor, load_numpy, require_torch_gpu, skip_mps, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineTesterMixin, assert_mean_pixel_difference
from . import IFPipelineTesterMixin






@skip_mps
class      __lowercase					(    _A       ,     _A       ,     unittest.TestCase    ):
						lowercase					 =     IFPipeline
						lowercase					 =     TEXT_TO_IMAGE_PARAMS - {'width', 'height', 'latents'}
						lowercase					 =     TEXT_TO_IMAGE_BATCH_PARAMS
						lowercase					 =     PipelineTesterMixin.required_optional_params - {'latents'}

						def        __a		(	self				:      Any   )							->		Dict:


									'''simple docstring'''

									return self._get_dummy_components()

						def        __a		(	self				:      Tuple							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      Tuple=0   )							->		str:


									'''simple docstring'''

									if str(__lowerCamelCase   ).startswith('''mps'''   ):
												lowercase							=		torch.manual_seed(__lowerCamelCase   )
									else:
												lowercase							=		torch.Generator(device=__lowerCamelCase   ).manual_seed(__lowerCamelCase   )

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

									return inputs

						def        __a		(	self				:      Optional[Any]   )							->		str:


									'''simple docstring'''

									self._test_save_load_optional_components()

						@unittest.skipIf(torch_device != '''cuda'''							,							reason='''float16 requires CUDA'''   )
						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

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

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


									'''simple docstring'''

									self._test_attention_slicing_forward_pass(expected_max_diff=1E-2   )

						def        __a		(	self				:      Optional[int]   )							->		Tuple:


									'''simple docstring'''

									self._test_save_load_local()

						def        __a		(	self				:      Any   )							->		int:


									'''simple docstring'''

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



						@unittest.skipIf(
						    torch_device != '''cuda''' or not is_xformers_available()							,							reason='''XFormers attention is only available with CUDA and `xformers` installed'''							,							)
						def        __a		(	self				:      str   )							->		Optional[int]:


									'''simple docstring'''

									self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )






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

						def        __a		(	self				:      int   )							->		Any:


									'''simple docstring'''

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

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


									'''simple docstring'''

									lowercase							=		IFPipeline.from_pretrained('''DeepFloyd/IF-I-XL-v1.0'''							,							variant='''fp16'''							,							torch_dtype=torch.floataa   )

									lowercase							=		IFSuperResolutionPipeline.from_pretrained(
									    '''DeepFloyd/IF-II-L-v1.0'''							,							variant='''fp16'''							,							torch_dtype=torch.floataa							,							text_encoder=__lowerCamelCase							,							tokenizer=__lowerCamelCase   )

									# pre compute text embeddings and remove T5 to save memory

									pipe_a.text_encoder.to('''cuda'''   )

									lowercase		,lowercase							=		pipe_a.encode_prompt('''anime turtle'''							,							device='''cuda'''   )

									del pipe_a.tokenizer
									del pipe_a.text_encoder
									gc.collect()

									lowercase							=		None
									lowercase							=		None

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

									self._test_if(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# img2img

									lowercase							=		IFImgaImgPipeline(**pipe_a.components   )
									lowercase							=		IFImgaImgSuperResolutionPipeline(**pipe_a.components   )

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

									self._test_if_imgaimg(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

									pipe_a.remove_all_hooks()
									pipe_a.remove_all_hooks()

									# inpainting

									lowercase							=		IFInpaintingPipeline(**pipe_a.components   )
									lowercase							=		IFInpaintingSuperResolutionPipeline(**pipe_a.components   )

									pipe_a.enable_model_cpu_offload()
									pipe_a.enable_model_cpu_offload()

									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )
									pipe_a.unet.set_attn_processor(AttnAddedKVProcessor()   )

									self._test_if_inpainting(__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      List[str]							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Dict							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Optional[int]   )							->		Union[str, Any]:


									'''simple docstring'''

									_start_torch_memory_measurement()

									lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )
									lowercase							=		pipe_a(
									    prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							num_inference_steps=2							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)

									lowercase							=		output.images[0]

									assert image.shape == (64, 64, 3)

									lowercase							=		torch.cuda.max_memory_allocated()
									assert mem_bytes < 13 * 10**9

									lowercase							=		load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if.npy'''   )
									assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

									# pipeline 2

									_start_torch_memory_measurement()

									lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

									lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )

									lowercase							=		pipe_a(
									    prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=2							,							output_type='''np'''							,							)

									lowercase							=		output.images[0]

									assert image.shape == (2_56, 2_56, 3)

									lowercase							=		torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									lowercase							=		load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_superresolution_stage_II.npy'''   )
									assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

						def        __a		(	self				:      str							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      List[str]							,							__lowerCamelCase				:      Tuple							,							__lowerCamelCase				:      List[Any]   )							->		Optional[Any]:


									'''simple docstring'''

									_start_torch_memory_measurement()

									lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )

									lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

									lowercase							=		pipe_a(
									    prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							num_inference_steps=2							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)

									lowercase							=		output.images[0]

									assert image.shape == (64, 64, 3)

									lowercase							=		torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									lowercase							=		load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img.npy'''   )
									assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

									# pipeline 2

									_start_torch_memory_measurement()

									lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

									lowercase							=		floats_tensor((1, 3, 2_56, 2_56)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
									lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )

									lowercase							=		pipe_a(
									    prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							original_image=__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=2							,							output_type='''np'''							,							)

									lowercase							=		output.images[0]

									assert image.shape == (2_56, 2_56, 3)

									lowercase							=		torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									lowercase							=		load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_img2img_superresolution_stage_II.npy'''   )
									assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )



						def        __a		(	self				:      str							,							__lowerCamelCase				:      str							,							__lowerCamelCase				:      Union[str, Any]							,							__lowerCamelCase				:      Any							,							__lowerCamelCase				:      Any   )							->		int:


									'''simple docstring'''

									_start_torch_memory_measurement()

									lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
									lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(1   )   ).to(__lowerCamelCase   )

									lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )
									lowercase							=		pipe_a(
									    prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							mask_image=__lowerCamelCase							,							num_inference_steps=2							,							generator=__lowerCamelCase							,							output_type='''np'''							,							)

									lowercase							=		output.images[0]

									assert image.shape == (64, 64, 3)

									lowercase							=		torch.cuda.max_memory_allocated()
									assert mem_bytes < 10 * 10**9

									lowercase							=		load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting.npy'''   )
									assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )

									# pipeline 2

									_start_torch_memory_measurement()

									lowercase							=		torch.Generator(device='''cpu'''   ).manual_seed(0   )

									lowercase							=		floats_tensor((1, 3, 64, 64)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
									lowercase							=		floats_tensor((1, 3, 2_56, 2_56)							,							rng=random.Random(0   )   ).to(__lowerCamelCase   )
									lowercase							=		floats_tensor((1, 3, 2_56, 2_56)							,							rng=random.Random(1   )   ).to(__lowerCamelCase   )

									lowercase							=		pipe_a(
									    prompt_embeds=__lowerCamelCase							,							negative_prompt_embeds=__lowerCamelCase							,							image=__lowerCamelCase							,							mask_image=__lowerCamelCase							,							original_image=__lowerCamelCase							,							generator=__lowerCamelCase							,							num_inference_steps=2							,							output_type='''np'''							,							)

									lowercase							=		output.images[0]

									assert image.shape == (2_56, 2_56, 3)

									lowercase							=		torch.cuda.max_memory_allocated()
									assert mem_bytes < 4 * 10**9

									lowercase							=		load_numpy(
									    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/if/test_if_inpainting_superresolution_stage_II.npy'''   )
									assert_mean_pixel_difference(__lowerCamelCase							,							__lowerCamelCase   )






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



			"""simple docstring"""







			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()






import unittest

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 MobileViTImageProcessor






class      __lowercase					(    unittest.TestCase    ):

						def __init__(	self				:      int							,							__lowerCamelCase				:      Optional[Any]							,							__lowerCamelCase				:      int=7							,							__lowerCamelCase				:      Dict=3							,							__lowerCamelCase				:      Optional[int]=18							,							__lowerCamelCase				:      List[Any]=30							,							__lowerCamelCase				:      Union[str, Any]=4_00							,							__lowerCamelCase				:      Optional[int]=True							,							__lowerCamelCase				:      List[str]=None							,							__lowerCamelCase				:      Dict=True							,							__lowerCamelCase				:      Dict=None							,							__lowerCamelCase				:      Tuple=True							,							)							->		Tuple:


									'''simple docstring'''

									lowercase							=		size if size is not None else {'''shortest_edge''': 20}
									lowercase							=		crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
									lowercase							=		parent
									lowercase							=		batch_size
									lowercase							=		num_channels
									lowercase							=		image_size
									lowercase							=		min_resolution
									lowercase							=		max_resolution
									lowercase							=		do_resize
									lowercase							=		size
									lowercase							=		do_center_crop
									lowercase							=		crop_size
									lowercase							=		do_flip_channel_order



						def        __a		(	self				:      Optional[int]   )							->		Any:


									'''simple docstring'''

									return {
									    "do_resize": self.do_resize,
									    "size": self.size,
									    "do_center_crop": self.do_center_crop,
									    "crop_size": self.crop_size,
									    "do_flip_channel_order": self.do_flip_channel_order,
									}






@require_torch
@require_vision
class      __lowercase					(    _A       ,     unittest.TestCase    ):
						lowercase					 =     MobileViTImageProcessor if is_vision_available() else None

						def        __a		(	self				:      Dict   )							->		Dict:


									'''simple docstring'''

									lowercase							=		MobileViTImageProcessingTester(self   )

						@property
						def        __a		(	self				:      Tuple   )							->		Dict:


									'''simple docstring'''

									return self.image_processor_tester.prepare_image_processor_dict()

						def        __a		(	self				:      Dict   )							->		List[Any]:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_resize'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''size'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_center_crop'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''center_crop'''   )   )
									self.assertTrue(hasattr(__lowerCamelCase							,							'''do_flip_channel_order'''   )   )

						def        __a		(	self				:      List[str]   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 20}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 18, '''width''': 18}   )

									lowercase							=		self.image_processing_class.from_dict(self.image_processor_dict							,							size=42							,							crop_size=84   )
									self.assertEqual(image_processor.size							,							{'''shortest_edge''': 42}   )
									self.assertEqual(image_processor.crop_size							,							{'''height''': 84, '''width''': 84}   )

						def        __a		(	self				:      Any   )							->		Optional[Any]:


									'''simple docstring'''

									pass

						def        __a		(	self				:      Any   )							->		List[str]:


									'''simple docstring'''

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

									# Test not batched input
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		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,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

						def        __a		(	self				:      int   )							->		str:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random numpy tensors
									lowercase							=		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
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		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,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)



						def        __a		(	self				:      Dict   )							->		Any:


									'''simple docstring'''

									lowercase							=		self.image_processing_class(**self.image_processor_dict   )
									# create random PyTorch tensors
									lowercase							=		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
									lowercase							=		image_processing(image_inputs[0]							,							return_tensors='''pt'''   ).pixel_values
									self.assertEqual(
									    encoded_images.shape							,							(
									        1,
									        self.image_processor_tester.num_channels,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)

									# Test batched
									lowercase							=		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,
									        self.image_processor_tester.crop_size['''height'''],
									        self.image_processor_tester.crop_size['''width'''],
									    )							,							)





