


'''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      lowercase			(							A__			,	A__			,	A__			,	unittest.TestCase      ):

		"""simple docstring"""

		_a									=       StableDiffusionInstructPixaPixPipeline
		_a									=       TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width', 'cross_attention_kwargs'}
		_a									=       TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
		_a									=       IMAGE_TO_IMAGE_IMAGE_PARAMS
		_a									=       IMAGE_TO_IMAGE_IMAGE_PARAMS




		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


							torch.manual_seed(0							)
							UpperCamelCase__     :Union[str, Any]							      =				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					,     )
							UpperCamelCase__     :Union[str, Any]							      =				PNDMScheduler(skip_prk_steps=UpperCamelCase_							)
							torch.manual_seed(0							)
							UpperCamelCase__     :List[str]							      =				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							)
							UpperCamelCase__     :int							      =				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=1000					,     )
							UpperCamelCase__     :Any							      =				CLIPTextModel(UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''							)

							UpperCamelCase__     :List[Any]							      =				{
							    '''unet''': unet,
							    '''scheduler''': scheduler,
							    '''vae''': vae,
							    '''text_encoder''': text_encoder,
							    '''tokenizer''': tokenizer,
							    '''safety_checker''': None,
							    '''feature_extractor''': None,
							}
							return components




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_					,     UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__     :Optional[int]							      =				floats_tensor((1, 3, 32, 32)					,     rng=random.Random(UpperCamelCase_							)							).to(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				image.cpu().permute(0					,     2					,     3					,     1							)[0]
							UpperCamelCase__     :Tuple							      =				Image.fromarray(np.uinta(UpperCamelCase_							)							).convert('''RGB'''							)
							if str(UpperCamelCase_							).startswith('''mps'''							):
												UpperCamelCase__     :int							      =				torch.manual_seed(UpperCamelCase_							)
							else:
												UpperCamelCase__     :Any							      =				torch.Generator(device=UpperCamelCase_							).manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :Optional[int]							      =				{
							    '''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'''


							UpperCamelCase__     :List[Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :Optional[int]							      =				self.get_dummy_components()
							UpperCamelCase__     :Tuple							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[Any]							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				sd_pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :str							      =				image[0, -3:, -3:, -1]
							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__     :Union[str, Any]							      =				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'''


							UpperCamelCase__     :List[Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :List[Any]							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :int							      =				'''french fries'''
							UpperCamelCase__     :Union[str, Any]							      =				sd_pipe(**UpperCamelCase_					,     negative_prompt=UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				output.images
							UpperCamelCase__     :Optional[Any]							      =				image[0, -3:, -3:, -1]

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__     :int							      =				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'''


							UpperCamelCase__     :Dict							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :List[str]							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :int							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :Any							      =				[inputs['''prompt''']] * 2

							UpperCamelCase__     :Dict							      =				np.array(inputs['''image''']							).astype(np.floataa							) / 255.0
							UpperCamelCase__     :Dict							      =				torch.from_numpy(UpperCamelCase_							).unsqueeze(0							).to(UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				image / 2 + 0.5
							UpperCamelCase__     :Dict							      =				image.permute(0					,     3					,     1					,     2							)
							UpperCamelCase__     :List[str]							      =				image.repeat(2					,     1					,     1					,     1							)

							UpperCamelCase__     :Dict							      =				sd_pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :Optional[Any]							      =				image[-1, -3:, -3:, -1]

							assert image.shape == (2, 32, 32, 3)
							UpperCamelCase__     :int							      =				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'''


							UpperCamelCase__     :Union[str, Any]							      =				'''cpu'''  # ensure determinism for the device-dependent torch.Generator
							UpperCamelCase__     :Optional[int]							      =				self.get_dummy_components()
							UpperCamelCase__     :Optional[Any]							      =				EulerAncestralDiscreteScheduler(
							    beta_start=0.00085					,     beta_end=0.012					,     beta_schedule='''scaled_linear'''							)
							UpperCamelCase__     :List[Any]							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				sd_pipe.to(UpperCamelCase_							)
							sd_pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Any							      =				self.get_dummy_inputs(UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				sd_pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :List[Any]							      =				image[0, -3:, -3:, -1]

							UpperCamelCase__     :Dict							      =				[round(UpperCamelCase_					,     4							) for x in image_slice.flatten().tolist()]
							print(''','''.join([str(UpperCamelCase_							) for x in slice]							)							)

							assert image.shape == (1, 32, 32, 3)
							UpperCamelCase__     :Optional[Any]							      =				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'''


							UpperCamelCase__     :str							      =				self.get_dummy_components()
							UpperCamelCase__     :Any							      =				StableDiffusionInstructPixaPixPipeline(**UpperCamelCase_							)
							UpperCamelCase__     :Union[str, Any]							      =				VaeImageProcessor(do_resize=UpperCamelCase_					,     do_normalize=UpperCamelCase_							)
							UpperCamelCase__     :List[Any]							      =				pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				pipe(**self.get_dummy_inputs_by_type(UpperCamelCase_					,     input_image_type='''pt'''							)							)[0]

							UpperCamelCase__     :Dict							      =				components['''vae''']
							UpperCamelCase__     :int							      =				self.get_dummy_inputs_by_type(UpperCamelCase_					,     input_image_type='''pt'''							)

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

							UpperCamelCase__     :Any							      =				pipe(**UpperCamelCase_							)[0]

							UpperCamelCase__     :str							      =				np.abs(out - out_latents_inputs							).max()
							self.assertLess(UpperCamelCase_					,     1e-4					,     '''passing latents as image input generate different result from passing image'''							)



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

		"""simple docstring"""

		def 					lowerCAmelCase__       (	self							):

							'''simple docstring'''


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




		def 					lowerCAmelCase__       (	self					,     UpperCamelCase_=0							):

							'''simple docstring'''


							UpperCamelCase__     :List[Any]							      =				torch.manual_seed(UpperCamelCase_							)
							UpperCamelCase__     :List[str]							      =				load_image(
							    '''https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg'''							)
							UpperCamelCase__     :List[str]							      =				{
							    '''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'''


							UpperCamelCase__     :Any							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :Dict							      =				self.get_inputs()
							UpperCamelCase__     :Any							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :int							      =				image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__     :List[Any]							      =				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'''


							UpperCamelCase__     :Optional[int]							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				LMSDiscreteScheduler.from_config(pipe.scheduler.config							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :List[Any]							      =				self.get_inputs()
							UpperCamelCase__     :Union[str, Any]							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :str							      =				image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__     :Any							      =				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'''


							UpperCamelCase__     :Optional[int]							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_							)
							UpperCamelCase__     :Optional[Any]							      =				DDIMScheduler.from_config(pipe.scheduler.config							)
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :Dict							      =				self.get_inputs()
							UpperCamelCase__     :Any							      =				pipe(**UpperCamelCase_							).images
							UpperCamelCase__     :str							      =				image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 512, 512, 3)
							UpperCamelCase__     :Optional[int]							      =				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'''


							UpperCamelCase__     :Optional[int]							      =				0

							def callback_fn(UpperCamelCase_					,     UpperCamelCase_					,     UpperCamelCase_							) -> None:
												UpperCamelCase__     :str							      =				True
												nonlocal number_of_steps
												number_of_steps += 1
												if step == 1:
																	UpperCamelCase__     :Optional[int]							      =				latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__     :Tuple							      =				latents[0, -3:, -3:, -1]
																	UpperCamelCase__     :Tuple							      =				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:
																	UpperCamelCase__     :List[Any]							      =				latents.detach().cpu().numpy()
																	assert latents.shape == (1, 4, 64, 64)
																	UpperCamelCase__     :Any							      =				latents[0, -3:, -3:, -1]
																	UpperCamelCase__     :List[str]							      =				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

							UpperCamelCase__     :Dict							      =				False

							UpperCamelCase__     :Tuple							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_					,     torch_dtype=torch.floataa							)
							UpperCamelCase__     :List[str]							      =				pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :List[str]							      =				self.get_inputs()
							pipe(**UpperCamelCase_					,     callback=UpperCamelCase_					,     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()

							UpperCamelCase__     :Optional[Any]							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    '''timbrooks/instruct-pix2pix'''					,     safety_checker=UpperCamelCase_					,     torch_dtype=torch.floataa							)
							UpperCamelCase__     :Dict							      =				pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing(1							)
							pipe.enable_sequential_cpu_offload()

							UpperCamelCase__     :str							      =				self.get_inputs()
							UpperCamelCase__     :Dict							      =				pipe(**UpperCamelCase_							)

							UpperCamelCase__     :Optional[int]							      =				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'''


							UpperCamelCase__     :Optional[Any]							      =				self.get_inputs()
							# resize to resolution that is divisible by 8 but not 16 or 32
							UpperCamelCase__     :Any							      =				inputs['''image'''].resize((504, 504)							)

							UpperCamelCase__     :List[str]							      =				'''timbrooks/instruct-pix2pix'''
							UpperCamelCase__     :Dict							      =				StableDiffusionInstructPixaPixPipeline.from_pretrained(
							    UpperCamelCase_					,     safety_checker=UpperCamelCase_					,     )
							pipe.to(UpperCamelCase_							)
							pipe.set_progress_bar_config(disable=UpperCamelCase_							)
							pipe.enable_attention_slicing()

							UpperCamelCase__     :Optional[Any]							      =				pipe(**UpperCamelCase_							)
							UpperCamelCase__     :Tuple							      =				output.images[0]

							UpperCamelCase__     :List[Any]							      =				image[255:258, 383:386, -1]

							assert image.shape == (504, 504, 3)
							UpperCamelCase__     :Dict							      =				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



'''simple docstring'''
from typing import TYPE_CHECKING

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


__snake_case							   =  {'''configuration_reformer''': ['''REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ReformerConfig''']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizer''']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  ['''ReformerTokenizerFast''']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__snake_case							   =  [
		    '''REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''ReformerAttention''',
		    '''ReformerForMaskedLM''',
		    '''ReformerForQuestionAnswering''',
		    '''ReformerForSequenceClassification''',
		    '''ReformerLayer''',
		    '''ReformerModel''',
		    '''ReformerModelWithLMHead''',
		    '''ReformerPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer import ReformerTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_reformer_fast import ReformerTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_reformer import (
				    REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
				    ReformerAttention,
				    ReformerForMaskedLM,
				    ReformerForQuestionAnswering,
				    ReformerForSequenceClassification,
				    ReformerLayer,
				    ReformerModel,
				    ReformerModelWithLMHead,
				    ReformerPreTrainedModel,
				)

else:
		import sys

		__snake_case							   =  _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)