

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        _a		(__magic_name__						,  __magic_name__						,  __magic_name__						,  unittest.TestCase							):


					'''simple docstring'''






					UpperCAmelCase__:  Any    				=			StableDiffusionInstructPixaPixPipeline
					UpperCAmelCase__:  Optional[Any]    				=			TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
					UpperCAmelCase__:  Any    				=			TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					UpperCAmelCase__:  List[str]    				=			IMAGE_TO_IMAGE_IMAGE_PARAMS
					UpperCAmelCase__:  str    				=			IMAGE_TO_IMAGE_IMAGE_PARAMS






					def     __A     (					self     ):
						torch.manual_seed(0     )
						A__				:       List[str]	  =						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       ,			)
						A__				:       str	  =						PNDMScheduler(skip_prk_steps=A__     )
						torch.manual_seed(0     )
						A__				:       Optional[int]	  =						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     )
						A__				:       Dict	  =						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       ,			)
						A__				:       Tuple	  =						CLIPTextModel(A__     )
						A__				:       Optional[Any]	  =						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""     )

						A__				:       str	  =						{
						    """unet""": unet,
						    """scheduler""": scheduler,
						    """vae""": vae,
						    """text_encoder""": text_encoder,
						    """tokenizer""": tokenizer,
						    """safety_checker""": None,
						    """feature_extractor""": None,
						}
						return components






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

						A__				:       Optional[int]	  =						self.get_dummy_inputs(A__     )
						A__				:       Optional[int]	  =						sd_pipe(**A__     ).images
						A__				:       Optional[int]	  =						image[0, -3:, -3:, -1]
						assert image.shape == (1, 32, 32, 3)
						A__				:       Any	  =						np.array([0.7_5_2_6, 0.3_7_5_0, 0.4_5_4_7, 0.6_1_1_7, 0.5_8_6_6, 0.5_0_1_6, 0.4_3_2_7, 0.5_6_4_2, 0.4_8_1_5]     )

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






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__				:       str	  =						self.get_dummy_components()
						A__				:       Optional[int]	  =						StableDiffusionInstructPixaPixPipeline(**A__     )
						A__				:       Dict	  =						sd_pipe.to(A__     )
						sd_pipe.set_progress_bar_config(disable=A__     )

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

						assert image.shape == (1, 32, 32, 3)
						A__				:       List[str]	  =						np.array([0.7_5_1_1, 0.3_6_4_2, 0.4_5_5_3, 0.6_2_3_6, 0.5_7_9_7, 0.5_0_1_3, 0.4_3_4_3, 0.5_6_1_1, 0.4_8_3_1]     )

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






					def     __A     (					self     ):
						A__				:       List[Any]	  =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__				:       Union[str, Any]	  =						self.get_dummy_components()
						A__				:       str	  =						StableDiffusionInstructPixaPixPipeline(**A__     )
						A__				:       List[Any]	  =						sd_pipe.to(A__     )
						sd_pipe.set_progress_bar_config(disable=A__     )

						A__				:       int	  =						self.get_dummy_inputs(A__     )
						A__				:       List[str]	  =						[inputs["""prompt"""]] * 2

						A__				:       List[str]	  =						np.array(inputs["""image"""]     ).astype(np.floataa     ) / 2_5_5.0
						A__				:       str	  =						torch.from_numpy(A__     ).unsqueeze(0     ).to(A__     )
						A__				:       Optional[Any]	  =						image / 2 + 0.5
						A__				:       Union[str, Any]	  =						image.permute(0       ,			3       ,			1       ,			2     )
						A__				:       Optional[int]	  =						image.repeat(2       ,			1       ,			1       ,			1     )

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

						assert image.shape == (2, 32, 32, 3)
						A__				:       Optional[Any]	  =						np.array([0.5_8_1_2, 0.5_7_4_8, 0.5_2_2_2, 0.5_9_0_8, 0.5_6_9_5, 0.7_1_7_4, 0.6_8_0_4, 0.5_5_2_3, 0.5_5_7_9]     )

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






					def     __A     (					self     ):
						A__				:       str	  =						"""cpu"""  # ensure determinism for the device-dependent torch.Generator
						A__				:       str	  =						self.get_dummy_components()
						A__				:       Dict	  =						EulerAncestralDiscreteScheduler(
						    beta_start=0.0_0_0_8_5       ,			beta_end=0.0_1_2       ,			beta_schedule="""scaled_linear"""     )
						A__				:       int	  =						StableDiffusionInstructPixaPixPipeline(**A__     )
						A__				:       Dict	  =						sd_pipe.to(A__     )
						sd_pipe.set_progress_bar_config(disable=A__     )

						A__				:       Optional[int]	  =						self.get_dummy_inputs(A__     )
						A__				:       int	  =						sd_pipe(**A__     ).images
						A__				:       Any	  =						image[0, -3:, -3:, -1]

						A__				:       Optional[Any]	  =						[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)
						A__				:       int	  =						np.array([0.7_4_1_7, 0.3_8_4_2, 0.4_7_3_2, 0.5_7_7_6, 0.5_8_9_1, 0.5_1_3_9, 0.4_0_5_2, 0.5_6_7_3, 0.4_9_8_6]     )

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






					def     __A     (					self     ):
						super().test_inference_batch_single_identical(expected_max_diff=3e-3     )






					def     __A     (					self     ):
						A__				:       Dict	  =						self.get_dummy_components()
						A__				:       Tuple	  =						StableDiffusionInstructPixaPixPipeline(**A__     )
						A__				:       Optional[Any]	  =						VaeImageProcessor(do_resize=A__       ,			do_normalize=A__     )
						A__				:       Tuple	  =						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )

						A__				:       Union[str, Any]	  =						pipe(**self.get_dummy_inputs_by_type(A__       ,			input_image_type="""pt"""     )     )[0]

						A__				:       str	  =						components["""vae"""]
						A__				:       Any	  =						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():
								A__				:       str	  =						vae.encode(inputs[image_param]     ).latent_dist.mode()

						A__				:       str	  =						pipe(**A__     )[0]

						A__				:       Tuple	  =						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        _a		(unittest.TestCase							):


					'''simple docstring'''






					def     __A     (					self     ):
						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()






					def     __A     (					self       ,			A__=0     ):
						A__				:       List[str]	  =						torch.manual_seed(A__     )
						A__				:       Any	  =						load_image(
						    """https://huggingface.co/datasets/diffusers/test-arrays/resolve/main/stable_diffusion_pix2pix/example.jpg"""     )
						A__				:       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     __A     (					self     ):
						A__				:       Any	  =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""       ,			safety_checker=A__     )
						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )
						pipe.enable_attention_slicing()

						A__				:       int	  =						self.get_inputs()
						A__				:       str	  =						pipe(**A__     ).images
						A__				:       Optional[Any]	  =						image[0, -3:, -3:, -1].flatten()

						assert image.shape == (1, 512, 512, 3)
						A__				:       List[Any]	  =						np.array([0.5_9_0_2, 0.6_0_1_5, 0.6_0_2_7, 0.5_9_8_3, 0.6_0_9_2, 0.6_0_6_1, 0.5_7_6_5, 0.5_7_8_5, 0.5_5_5_5]     )

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






					def     __A     (					self     ):
						A__				:       Optional[Any]	  =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""       ,			safety_checker=A__     )
						A__				:       Union[str, Any]	  =						LMSDiscreteScheduler.from_config(pipe.scheduler.config     )
						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 512, 512, 3)
						A__				:       Any	  =						np.array([0.6_5_7_8, 0.6_8_1_7, 0.6_9_7_2, 0.6_7_6_1, 0.6_8_5_6, 0.6_9_1_6, 0.6_4_2_8, 0.6_5_1_6, 0.6_3_0_1]     )

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






					def     __A     (					self     ):
						A__				:       Union[str, Any]	  =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""       ,			safety_checker=A__     )
						A__				:       Any	  =						DDIMScheduler.from_config(pipe.scheduler.config     )
						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )
						pipe.enable_attention_slicing()

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

						assert image.shape == (1, 512, 512, 3)
						A__				:       Union[str, Any]	  =						np.array([0.3_8_2_8, 0.3_8_3_4, 0.3_8_1_8, 0.3_7_9_2, 0.3_8_6_5, 0.3_7_5_2, 0.3_7_9_2, 0.3_8_4_7, 0.3_7_5_3]     )

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






					def     __A     (					self     ):
						A__				:       List[str]	  =						0

						def callback_fn(A__       ,			A__       ,			A__     ) -> None:
							A__				:       Optional[Any]	  =						True
							nonlocal number_of_steps
							number_of_steps += 1
							if step == 1:
								A__				:       List[Any]	  =						latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 64, 64)
								A__				:       str	  =						latents[0, -3:, -3:, -1]
								A__				:       List[str]	  =						np.array([-0.2_4_6_3, -0.4_6_4_4, -0.9_7_5_6, 1.5_1_7_6, 1.4_4_1_4, 0.7_8_6_6, 0.9_8_9_7, 0.8_5_2_1, 0.7_9_8_3]     )

								assert np.abs(latents_slice.flatten() - expected_slice     ).max() < 5e-2
							elif step == 2:
								A__				:       List[Any]	  =						latents.detach().cpu().numpy()
								assert latents.shape == (1, 4, 64, 64)
								A__				:       List[str]	  =						latents[0, -3:, -3:, -1]
								A__				:       Any	  =						np.array([-0.2_6_4_4, -0.4_6_2_6, -0.9_6_5_3, 1.5_1_7_6, 1.4_5_5_1, 0.7_6_8_6, 0.9_8_0_5, 0.8_4_5_2, 0.8_1_1_5]     )

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

						A__				:       List[str]	  =						False

						A__				:       int	  =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    """timbrooks/instruct-pix2pix"""       ,			safety_checker=A__       ,			torch_dtype=torch.floataa     )
						A__				:       Optional[Any]	  =						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )
						pipe.enable_attention_slicing()

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






					def     __A     (					self     ):
						torch.cuda.empty_cache()
						torch.cuda.reset_max_memory_allocated()
						torch.cuda.reset_peak_memory_stats()

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

						A__				:       List[str]	  =						self.get_inputs()
						A__				:       str	  =						pipe(**A__     )

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

						A__				:       Dict	  =						"""timbrooks/instruct-pix2pix"""
						A__				:       Optional[int]	  =						StableDiffusionInstructPixaPixPipeline.from_pretrained(
						    A__       ,			safety_checker=A__       ,			)
						pipe.to(A__     )
						pipe.set_progress_bar_config(disable=A__     )
						pipe.enable_attention_slicing()

						A__				:       Any	  =						pipe(**A__     )
						A__				:       str	  =						output.images[0]

						A__				:       Union[str, Any]	  =						image[255:258, 383:386, -1]

						assert image.shape == (504, 504, 3)
						A__				:       Dict	  =						np.array([0.2_7_2_6, 0.2_5_2_9, 0.2_6_6_4, 0.2_6_5_5, 0.2_6_4_1, 0.2_6_4_2, 0.2_5_9_1, 0.2_6_4_9, 0.2_5_9_0]     )

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






A_			:					Union[str, Any]    			=     '\n# Installazione di Transformers\n! pip install transformers datasets\n# Per installare dalla fonte invece dell\'ultima versione rilasciata, commenta il comando sopra e\n# rimuovi la modalità commento al comando seguente.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A_			:					str    			=     [{'type': 'code', 'content': INSTALL_CONTENT}]
A_			:					Any    			=     {
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}



