
'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_speech_available,
    is_torch_available,
)


lowercase					:							Union[str, Any]   =      {
    "configuration_trocr": ["TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP", "TrOCRConfig"],
    "processing_trocr": ["TrOCRProcessor"],
}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					lowercase					:							Optional[Any]   =      [
					    "TROCR_PRETRAINED_MODEL_ARCHIVE_LIST",
					    "TrOCRForCausalLM",
					    "TrOCRPreTrainedModel",
					]


if TYPE_CHECKING:
					from .configuration_trocr import TROCR_PRETRAINED_CONFIG_ARCHIVE_MAP, TrOCRConfig
					from .processing_trocr import TrOCRProcessor

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_trocr import TROCR_PRETRAINED_MODEL_ARCHIVE_LIST, TrOCRForCausalLM, TrOCRPreTrainedModel

else:
					import sys

					lowercase					:							Optional[Any]   =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)



'''simple docstring'''







import gc
import random
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionInstructPixaPixPipeline,
    UNetaDConditionModel,
)
from diffusers.image_processor import VaeImageProcessor
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_TO_IMAGE_IMAGE_PARAMS,
    TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineKarrasSchedulerTesterMixin, PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()


class 		__UpperCAmelCase   (      _lowerCamelCase		,     _lowerCamelCase		,     _lowerCamelCase		,     unittest.TestCase		):
							__lowercase   	= StableDiffusionInstructPixaPixPipeline
							__lowercase   	= TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {"""height""", """width""", """cross_attention_kwargs"""}
							__lowercase   	= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
							__lowercase   	= IMAGE_TO_IMAGE_IMAGE_PARAMS
							__lowercase   	= IMAGE_TO_IMAGE_IMAGE_PARAMS


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								torch.manual_seed(0						)
								_snake_case     		=	UNetaDConditionModel(
								    block_out_channels=(32, 64)					,     layers_per_block=2					,     sample_size=32					,     in_channels=8					,     out_channels=4					,     down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')					,     up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')					,     cross_attention_dim=32					,     )
								_snake_case     		=	PNDMScheduler(skip_prk_steps=lowerCAmelCase_						)
								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     		=	CLIPTextConfig(
								    bos_token_id=0					,     eos_token_id=2					,     hidden_size=32					,     intermediate_size=37					,     layer_norm_eps=1E-05					,     num_attention_heads=4					,     num_hidden_layers=5					,     pad_token_id=1					,     vocab_size=10_00					,     )
								_snake_case     		=	CLIPTextModel(lowerCAmelCase_						)
								_snake_case     		=	CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'						)

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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_					,     lowerCAmelCase_=0						):





								"""simple docstring"""






								_snake_case     		=	floats_tensor((1, 3, 32, 32)					,     rng=random.Random(lowerCAmelCase_						)						).to(lowerCAmelCase_						)
								_snake_case     		=	image.cpu().permute(0					,     2					,     3					,     1						)[0]
								_snake_case     		=	Image.fromarray(np.uinta(lowerCAmelCase_						)						).convert('RGB'						)
								if str(lowerCAmelCase_						).startswith('mps'						):
									_snake_case     		=	torch.manual_seed(lowerCAmelCase_						)
								else:
									_snake_case     		=	torch.Generator(device=lowerCAmelCase_						).manual_seed(lowerCAmelCase_						)
								_snake_case     		=	{
								    'prompt': 'A painting of a squirrel eating a burger',
								    'image': image,
								    'generator': generator,
								    'num_inference_steps': 2,
								    'guidance_scale': 6.0,
								    'image_guidance_scale': 1,
								    'output_type': 'numpy',
								}
								return inputs


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	sd_pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1]
								assert image.shape == (1, 32, 32, 3)
								_snake_case     		=	np.array([0.7526, 0.3750, 0.4547, 0.6117, 0.5866, 0.5016, 0.4327, 0.5642, 0.4815]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	'french fries'
								_snake_case     		=	sd_pipe(**lowerCAmelCase_					,     negative_prompt=lowerCAmelCase_						)
								_snake_case     		=	output.images
								_snake_case     		=	image[0, -3:, -3:, -1]

								assert image.shape == (1, 32, 32, 3)
								_snake_case     		=	np.array([0.7511, 0.3642, 0.4553, 0.6236, 0.5797, 0.5013, 0.4343, 0.5611, 0.4831]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	[inputs['prompt']] * 2

								_snake_case     		=	np.array(inputs['image']						).astype(np.floataa						) / 255.0
								_snake_case     		=	torch.from_numpy(lowerCAmelCase_						).unsqueeze(0						).to(lowerCAmelCase_						)
								_snake_case     		=	image / 2 + 0.5
								_snake_case     		=	image.permute(0					,     3					,     1					,     2						)
								_snake_case     		=	image.repeat(2					,     1					,     1					,     1						)

								_snake_case     		=	sd_pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[-1, -3:, -3:, -1]

								assert image.shape == (2, 32, 32, 3)
								_snake_case     		=	np.array([0.5812, 0.5748, 0.5222, 0.5908, 0.5695, 0.7174, 0.6804, 0.5523, 0.5579]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	'cpu'  # ensure determinism for the device-dependent torch.Generator
								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	EulerAncestralDiscreteScheduler(
								    beta_start=0.00085					,     beta_end=0.012					,     beta_schedule='scaled_linear'						)
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	sd_pipe.to(lowerCAmelCase_						)
								sd_pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	self.get_dummy_inputs(lowerCAmelCase_						)
								_snake_case     		=	sd_pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1]

								_snake_case     		=	[round(lowerCAmelCase_					,     4						) for x in image_slice.flatten().tolist()]
								print(','.join([str(lowerCAmelCase_						) for x in slice]						)						)

								assert image.shape == (1, 32, 32, 3)
								_snake_case     		=	np.array([0.7417, 0.3842, 0.4732, 0.5776, 0.5891, 0.5139, 0.4052, 0.5673, 0.4986]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_dummy_components()
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline(**lowerCAmelCase_						)
								_snake_case     		=	VaeImageProcessor(do_resize=lowerCAmelCase_					,     do_normalize=lowerCAmelCase_						)
								_snake_case     		=	pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)

								_snake_case     		=	pipe(**self.get_dummy_inputs_by_type(lowerCAmelCase_					,     input_image_type='pt'						)						)[0]

								_snake_case     		=	components['vae']
								_snake_case     		=	self.get_dummy_inputs_by_type(lowerCAmelCase_					,     input_image_type='pt'						)

								for image_param in self.image_latents_params:
									if image_param in inputs.keys():
										_snake_case     		=	vae.encode(inputs[image_param]						).latent_dist.mode()

								_snake_case     		=	pipe(**lowerCAmelCase_						)[0]

								_snake_case     		=	np.abs(out - out_latents_inputs						).max()
								self.assertLess(lowerCAmelCase_					,     1E-4					,     'passing latents as image input generate different result from passing image'						)


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


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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


							def 				lowerCamelCase       ( self					,     lowerCAmelCase_=0						):





								"""simple docstring"""






								_snake_case     		=	torch.manual_seed(lowerCAmelCase_						)
								_snake_case     		=	load_image(
								    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'						)
								_snake_case     		=	{
								    'prompt': 'turn him into a cyborg',
								    'image': image,
								    'generator': generator,
								    'num_inference_steps': 3,
								    'guidance_scale': 7.5,
								    'image_guidance_scale': 1.0,
								    'output_type': 'numpy',
								}
								return inputs


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case     		=	np.array([0.5902, 0.6015, 0.6027, 0.5983, 0.6092, 0.6061, 0.5765, 0.5785, 0.5555]						)

								assert np.abs(expected_slice - image_slice						).max() < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_						)
								_snake_case     		=	LMSDiscreteScheduler.from_config(pipe.scheduler.config						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case     		=	np.array([0.6578, 0.6817, 0.6972, 0.6761, 0.6856, 0.6916, 0.6428, 0.6516, 0.6301]						)

								assert np.abs(expected_slice - image_slice						).max() < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_						)
								_snake_case     		=	DDIMScheduler.from_config(pipe.scheduler.config						)
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						).images
								_snake_case     		=	image[0, -3:, -3:, -1].flatten()

								assert image.shape == (1, 5_12, 5_12, 3)
								_snake_case     		=	np.array([0.3828, 0.3834, 0.3818, 0.3792, 0.3865, 0.3752, 0.3792, 0.3847, 0.3753]						)

								assert np.abs(expected_slice - image_slice						).max() < 1E-3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	0

								def callback_fn(lowerCAmelCase_					,     lowerCAmelCase_					,     lowerCAmelCase_						) -> None:
									_snake_case     		=	True
									nonlocal number_of_steps
									number_of_steps += 1
									if step == 1:
										_snake_case     		=	latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										_snake_case     		=	latents[0, -3:, -3:, -1]
										_snake_case     		=	np.array([-0.2463, -0.4644, -0.9756, 1.5176, 1.4414, 0.7866, 0.9897, 0.8521, 0.7983]						)

										assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2
									elif step == 2:
										_snake_case     		=	latents.detach().cpu().numpy()
										assert latents.shape == (1, 4, 64, 64)
										_snake_case     		=	latents[0, -3:, -3:, -1]
										_snake_case     		=	np.array([-0.2644, -0.4626, -0.9653, 1.5176, 1.4551, 0.7686, 0.9805, 0.8452, 0.8115]						)

										assert np.abs(latents_slice.flatten() - expected_slice						).max() < 5E-2

								_snake_case     		=	False

								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_					,     torch_dtype=torch.floataa						)
								_snake_case     		=	pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	self.get_inputs()
								pipe(**lowerCAmelCase_					,     callback=lowerCAmelCase_					,     callback_steps=1						)
								assert callback_fn.has_been_called
								assert number_of_steps == 3


							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






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

								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    'timbrooks/instruct-pix2pix'					,     safety_checker=lowerCAmelCase_					,     torch_dtype=torch.floataa						)
								_snake_case     		=	pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing(1						)
								pipe.enable_sequential_cpu_offload()

								_snake_case     		=	self.get_inputs()
								_snake_case     		=	pipe(**lowerCAmelCase_						)

								_snake_case     		=	torch.cuda.max_memory_allocated()
								# make sure that less than 2.2 GB is allocated
								assert mem_bytes < 2.2 * 10**9




							def 				lowerCamelCase       ( self						):





								"""simple docstring"""






								_snake_case     		=	self.get_inputs()
								# resize to resolution that is divisible by 8 but not 16 or 32
								_snake_case     		=	inputs['image'].resize((5_04, 5_04)						)

								_snake_case     		=	'timbrooks/instruct-pix2pix'
								_snake_case     		=	StableDiffusionInstructPixaPixPipeline.from_pretrained(
								    lowerCAmelCase_					,     safety_checker=lowerCAmelCase_					,     )
								pipe.to(lowerCAmelCase_						)
								pipe.set_progress_bar_config(disable=lowerCAmelCase_						)
								pipe.enable_attention_slicing()

								_snake_case     		=	pipe(**lowerCAmelCase_						)
								_snake_case     		=	output.images[0]

								_snake_case     		=	image[2_55:2_58, 3_83:3_86, -1]

								assert image.shape == (5_04, 5_04, 3)
								_snake_case     		=	np.array([0.2726, 0.2529, 0.2664, 0.2655, 0.2641, 0.2642, 0.2591, 0.2649, 0.2590]						)

								assert np.abs(image_slice.flatten() - expected_slice						).max() < 5E-3

