
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       (    __UpperCAmelCase    ,	__UpperCAmelCase    ,	__UpperCAmelCase    ,	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       _UpperCamelCase   (			self )		->							Any:
			torch.manual_seed(0 )
			lowerCamelCase					:      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		,			)
			lowerCamelCase					:      Optional[int]          =   PNDMScheduler(skip_prk_steps=__A )
			torch.manual_seed(0 )
			lowerCamelCase					:      Dict          =   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 )
			lowerCamelCase					:      Optional[int]          =   CLIPTextConfig(
			    bos_token_id=0		,			eos_token_id=2		,			hidden_size=32		,			intermediate_size=37		,			layer_norm_eps=1E-0_5		,			num_attention_heads=4		,			num_hidden_layers=5		,			pad_token_id=1		,			vocab_size=1000		,			)
			lowerCamelCase					:      Tuple          =   CLIPTextModel(__A )
			lowerCamelCase					:      List[Any]          =   CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip' )

			lowerCamelCase					:      Optional[int]          =   {
			    'unet': unet,
			    'scheduler': scheduler,
			    'vae': vae,
			    'text_encoder': text_encoder,
			    'tokenizer': tokenizer,
			    'safety_checker': None,
			    'feature_extractor': None,
			}
			return components




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_		,			UpperCAmelCase_=0 )		->							List[str]:
			lowerCamelCase					:      int          =   floats_tensor((1, 3, 32, 32)		,			rng=random.Random(__A ) ).to(__A )
			lowerCamelCase					:      str          =   image.cpu().permute(0		,			2		,			3		,			1 )[0]
			lowerCamelCase					:      str          =   Image.fromarray(np.uinta(__A ) ).convert('RGB' )
			if str(__A ).startswith('mps' ):
					lowerCamelCase					:      List[str]          =   torch.manual_seed(__A )
			else:
					lowerCamelCase					:      Tuple          =   torch.Generator(device=__A ).manual_seed(__A )
			lowerCamelCase					:      str          =   {
			    '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       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase					:      List[str]          =   self.get_dummy_components()
			lowerCamelCase					:      Any          =   StableDiffusionInstructPixaPixPipeline(**__A )
			lowerCamelCase					:      Optional[int]          =   sd_pipe.to(__A )
			sd_pipe.set_progress_bar_config(disable=__A )

			lowerCamelCase					:      Union[str, Any]          =   self.get_dummy_inputs(__A )
			lowerCamelCase					:      Optional[Any]          =   sd_pipe(**__A ).images
			lowerCamelCase					:      Any          =   image[0, -3:, -3:, -1]
			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase					:      str          =   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       _UpperCamelCase   (			self )		->							int:
			lowerCamelCase					:      Union[str, Any]          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase					:      Optional[Any]          =   self.get_dummy_components()
			lowerCamelCase					:      Optional[Any]          =   StableDiffusionInstructPixaPixPipeline(**__A )
			lowerCamelCase					:      Optional[Any]          =   sd_pipe.to(__A )
			sd_pipe.set_progress_bar_config(disable=__A )

			lowerCamelCase					:      Dict          =   self.get_dummy_inputs(__A )
			lowerCamelCase					:      Tuple          =   'french fries'
			lowerCamelCase					:      Union[str, Any]          =   sd_pipe(**__A		,			negative_prompt=__A )
			lowerCamelCase					:      str          =   output.images
			lowerCamelCase					:      Tuple          =   image[0, -3:, -3:, -1]

			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase					:      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       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      Optional[int]          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase					:      Any          =   self.get_dummy_components()
			lowerCamelCase					:      List[str]          =   StableDiffusionInstructPixaPixPipeline(**__A )
			lowerCamelCase					:      List[Any]          =   sd_pipe.to(__A )
			sd_pipe.set_progress_bar_config(disable=__A )

			lowerCamelCase					:      Union[str, Any]          =   self.get_dummy_inputs(__A )
			lowerCamelCase					:      Any          =   [inputs['prompt']] * 2

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

			lowerCamelCase					:      Union[str, Any]          =   sd_pipe(**__A ).images
			lowerCamelCase					:      List[Any]          =   image[-1, -3:, -3:, -1]

			assert image.shape == (2, 32, 32, 3)
			lowerCamelCase					:      Dict          =   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       _UpperCamelCase   (			self )		->							Any:
			lowerCamelCase					:      List[Any]          =   'cpu'  # ensure determinism for the device-dependent torch.Generator
			lowerCamelCase					:      Tuple          =   self.get_dummy_components()
			lowerCamelCase					:      Dict          =   EulerAncestralDiscreteScheduler(
			    beta_start=0.00085		,			beta_end=0.012		,			beta_schedule='scaled_linear' )
			lowerCamelCase					:      Dict          =   StableDiffusionInstructPixaPixPipeline(**__A )
			lowerCamelCase					:      int          =   sd_pipe.to(__A )
			sd_pipe.set_progress_bar_config(disable=__A )

			lowerCamelCase					:      List[str]          =   self.get_dummy_inputs(__A )
			lowerCamelCase					:      Dict          =   sd_pipe(**__A ).images
			lowerCamelCase					:      List[Any]          =   image[0, -3:, -3:, -1]

			lowerCamelCase					:      List[str]          =   [round(__A		,			4 ) for x in image_slice.flatten().tolist()]
			print(','.join([str(__A ) for x in slice] ) )

			assert image.shape == (1, 32, 32, 3)
			lowerCamelCase					:      Union[str, 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       _UpperCamelCase   (			self )		->							Any:
			super().test_inference_batch_single_identical(expected_max_diff=3E-3 )




	def       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Optional[int]          =   self.get_dummy_components()
			lowerCamelCase					:      Union[str, Any]          =   StableDiffusionInstructPixaPixPipeline(**__A )
			lowerCamelCase					:      List[str]          =   VaeImageProcessor(do_resize=__A		,			do_normalize=__A )
			lowerCamelCase					:      Optional[int]          =   pipe.to(__A )
			pipe.set_progress_bar_config(disable=__A )

			lowerCamelCase					:      Tuple          =   pipe(**self.get_dummy_inputs_by_type(__A		,			input_image_type='pt' ) )[0]

			lowerCamelCase					:      List[Any]          =   components['vae']
			lowerCamelCase					:      List[str]          =   self.get_dummy_inputs_by_type(__A		,			input_image_type='pt' )

			for image_param in self.image_latents_params:
					if image_param in inputs.keys():
							lowerCamelCase					:      Union[str, Any]          =   vae.encode(inputs[image_param] ).latent_dist.mode()

			lowerCamelCase					:      Dict          =   pipe(**__A )[0]

			lowerCamelCase					:      List[str]          =   np.abs(out - out_latents_inputs ).max()
			self.assertLess(__A		,			1E-4		,			'passing latents as image input generate different result from passing image' )






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




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			super().tearDown()
			gc.collect()
			torch.cuda.empty_cache()




	def       _UpperCamelCase   (			self		,			UpperCAmelCase_=0 )		->							Optional[int]:
			lowerCamelCase					:      str          =   torch.manual_seed(__A )
			lowerCamelCase					:      Optional[int]          =   load_image(
			    'https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg' )
			lowerCamelCase					:      Tuple          =   {
			    '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       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      Dict          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    'timbrooks/instruct-pix2pix'		,			safety_checker=__A )
			pipe.to(__A )
			pipe.set_progress_bar_config(disable=__A )
			pipe.enable_attention_slicing()

			lowerCamelCase					:      Any          =   self.get_inputs()
			lowerCamelCase					:      Dict          =   pipe(**__A ).images
			lowerCamelCase					:      Optional[Any]          =   image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase					:      Optional[int]          =   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       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase					:      Dict          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    'timbrooks/instruct-pix2pix'		,			safety_checker=__A )
			lowerCamelCase					:      List[str]          =   LMSDiscreteScheduler.from_config(pipe.scheduler.config )
			pipe.to(__A )
			pipe.set_progress_bar_config(disable=__A )
			pipe.enable_attention_slicing()

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

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase					:      int          =   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       _UpperCamelCase   (			self )		->							str:
			lowerCamelCase					:      str          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    'timbrooks/instruct-pix2pix'		,			safety_checker=__A )
			lowerCamelCase					:      Union[str, Any]          =   DDIMScheduler.from_config(pipe.scheduler.config )
			pipe.to(__A )
			pipe.set_progress_bar_config(disable=__A )
			pipe.enable_attention_slicing()

			lowerCamelCase					:      List[str]          =   self.get_inputs()
			lowerCamelCase					:      Tuple          =   pipe(**__A ).images
			lowerCamelCase					:      Optional[int]          =   image[0, -3:, -3:, -1].flatten()

			assert image.shape == (1, 512, 512, 3)
			lowerCamelCase					:      Union[str, Any]          =   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       _UpperCamelCase   (			self )		->							List[str]:
			lowerCamelCase					:      Any          =   0

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

			lowerCamelCase					:      int          =   False

			lowerCamelCase					:      int          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    'timbrooks/instruct-pix2pix'		,			safety_checker=__A		,			torch_dtype=torch.floataa )
			lowerCamelCase					:      Tuple          =   pipe.to(__A )
			pipe.set_progress_bar_config(disable=__A )
			pipe.enable_attention_slicing()

			lowerCamelCase					:      Optional[Any]          =   self.get_inputs()
			pipe(**__A		,			callback=__A		,			callback_steps=1 )
			assert callback_fn.has_been_called
			assert number_of_steps == 3




	def       _UpperCamelCase   (			self )		->							List[str]:
			torch.cuda.empty_cache()
			torch.cuda.reset_max_memory_allocated()
			torch.cuda.reset_peak_memory_stats()

			lowerCamelCase					:      Union[str, Any]          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    'timbrooks/instruct-pix2pix'		,			safety_checker=__A		,			torch_dtype=torch.floataa )
			lowerCamelCase					:      Any          =   pipe.to(__A )
			pipe.set_progress_bar_config(disable=__A )
			pipe.enable_attention_slicing(1 )
			pipe.enable_sequential_cpu_offload()

			lowerCamelCase					:      List[str]          =   self.get_inputs()
			lowerCamelCase					:      Optional[Any]          =   pipe(**__A )

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




	def       _UpperCamelCase   (			self )		->							Tuple:
			lowerCamelCase					:      Tuple          =   self.get_inputs()
			# resize to resolution that is divisible by 8 but not 16 or 32
			lowerCamelCase					:      str          =   inputs['image'].resize((504, 504) )

			lowerCamelCase					:      int          =   'timbrooks/instruct-pix2pix'
			lowerCamelCase					:      str          =   StableDiffusionInstructPixaPixPipeline.from_pretrained(
			    __A		,			safety_checker=__A		,			)
			pipe.to(__A )
			pipe.set_progress_bar_config(disable=__A )
			pipe.enable_attention_slicing()

			lowerCamelCase					:      List[Any]          =   pipe(**__A )
			lowerCamelCase					:      Any          =   output.images[0]

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

			assert image.shape == (504, 504, 3)
			lowerCamelCase					:      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"""

import warnings

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     ['image_processor', 'tokenizer']
	lowercase_				=     'ChineseCLIPImageProcessor'
	lowercase_				=     ('BertTokenizer', 'BertTokenizerFast')




	def __init__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							List[str]:
			lowerCamelCase					:      Tuple          =   None
			if "feature_extractor" in kwargs:
					warnings.warn(
					    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
					    ' instead.'		,			UpperCAmelCase_		,			)
					lowerCamelCase					:      int          =   kwargs.pop('feature_extractor' )

			lowerCamelCase					:      Any          =   image_processor if image_processor is not None else feature_extractor
			if image_processor is None:
					raise ValueError('You need to specify an `image_processor`.' )
			if tokenizer is None:
					raise ValueError('You need to specify a `tokenizer`.' )

			super().__init__(UpperCAmelCase_		,			UpperCAmelCase_ )
			lowerCamelCase					:      Tuple          =   self.image_processor




	def __call__(			self		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			UpperCAmelCase_=None		,			**UpperCAmelCase_ )		->							str:

			if text is None and images is None:
					raise ValueError('You have to specify either text or images. Both cannot be none.' )

			if text is not None:
					lowerCamelCase					:      Any          =   self.tokenizer(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if images is not None:
					lowerCamelCase					:      Dict          =   self.image_processor(UpperCAmelCase_		,			return_tensors=UpperCAmelCase_		,			**UpperCAmelCase_ )

			if text is not None and images is not None:
					lowerCamelCase					:      Tuple          =   image_features.pixel_values
					return encoding
			elif text is not None:
					return encoding
			else:
					return BatchEncoding(data=dict(**UpperCAmelCase_ )		,			tensor_type=UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							List[str]:
			return self.tokenizer.batch_decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self		,			*UpperCAmelCase_		,			**UpperCAmelCase_ )		->							Optional[int]:
			return self.tokenizer.decode(*UpperCAmelCase_		,			**UpperCAmelCase_ )




	@property
	def       _UpperCamelCase   (			self )		->							Optional[Any]:
			lowerCamelCase					:      List[Any]          =   self.tokenizer.model_input_names
			lowerCamelCase					:      Union[str, Any]          =   self.image_processor.model_input_names
			return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names ) )




	@property
	def       _UpperCamelCase   (			self )		->							List[str]:
			warnings.warn(
			    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'		,			UpperCAmelCase_		,			)
			return self.image_processor_class






