
import os
import sys

lowercase   =	os.path.join(os.path.dirname(__file__), '''src''')
sys.path.append(SRC_DIR)


from transformers import (
    AutoConfig,
    AutoModel,
    AutoModelForCausalLM,
    AutoModelForMaskedLM,
    AutoModelForQuestionAnswering,
    AutoModelForSequenceClassification,
    AutoTokenizer,
    add_start_docstrings,
)


lowercase   =	[
    '''torch''',
    '''numpy''',
    '''tokenizers''',
    '''filelock''',
    '''requests''',
    '''tqdm''',
    '''regex''',
    '''sentencepiece''',
    '''sacremoses''',
    '''importlib_metadata''',
    '''huggingface_hub''',
]



@add_start_docstrings(AutoConfig.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   List[str]							)	->     List[str]:
							return AutoConfig.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoTokenizer.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Any:
							return AutoTokenizer.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModel.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Union[str, Any]:
							return AutoModel.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForCausalLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[str]      ,					**UpperCAmelCase__					:   List[Any]							)	->     Optional[Any]:
							return AutoModelForCausalLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForMaskedLM.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Union[str, Any]      ,					**UpperCAmelCase__					:   Dict							)	->     int:
							return AutoModelForMaskedLM.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForSequenceClassification.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   List[Any]      ,					**UpperCAmelCase__					:   Dict							)	->     Dict:
							return AutoModelForSequenceClassification.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)



@add_start_docstrings(AutoModelForQuestionAnswering.__doc__							)
def   __lowerCAmelCase    (				*UpperCAmelCase__					:   Any      ,					**UpperCAmelCase__					:   List[str]							)	->     Union[str, Any]:
							return AutoModelForQuestionAnswering.from_pretrained(*UpperCAmelCase__      ,					**UpperCAmelCase__							)







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(					UpperCAmelCase  ,		UpperCAmelCase  ,		UpperCAmelCase  ,		unittest.TestCase							):
					SCREAMING_SNAKE_CASE           =     StableDiffusionInstructPixaPixPipeline
					SCREAMING_SNAKE_CASE           =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'''height''', '''width''', '''cross_attention_kwargs'''}
					SCREAMING_SNAKE_CASE           =     TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS
					SCREAMING_SNAKE_CASE           =     IMAGE_TO_IMAGE_IMAGE_PARAMS
					SCREAMING_SNAKE_CASE           =     IMAGE_TO_IMAGE_IMAGE_PARAMS

					def  lowercase__   (						self       :  List[Any] ):
												torch.manual_seed(0 )
												lowerCamelCase_					=       UNetaDConditionModel(
												    block_out_channels=(3_2, 6_4) ,       layers_per_block=2 ,       sample_size=3_2 ,       in_channels=8 ,       out_channels=4 ,       down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""") ,       up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""") ,       cross_attention_dim=3_2 ,       )
												lowerCamelCase_					=       PNDMScheduler(skip_prk_steps=__UpperCamelCase )
												torch.manual_seed(0 )
												lowerCamelCase_					=       AutoencoderKL(
												    block_out_channels=[3_2, 6_4] ,       in_channels=3 ,       out_channels=3 ,       down_block_types=["""DownEncoderBlock2D""", """DownEncoderBlock2D"""] ,       up_block_types=["""UpDecoderBlock2D""", """UpDecoderBlock2D"""] ,       latent_channels=4 ,       )
												torch.manual_seed(0 )
												lowerCamelCase_					=       CLIPTextConfig(
												    bos_token_id=0 ,       eos_token_id=2 ,       hidden_size=3_2 ,       intermediate_size=3_7 ,       layer_norm_eps=1E-05 ,       num_attention_heads=4 ,       num_hidden_layers=5 ,       pad_token_id=1 ,       vocab_size=1_0_0_0 ,       )
												lowerCamelCase_					=       CLIPTextModel(__UpperCamelCase )
												lowerCamelCase_					=       CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""" )

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

					def  lowercase__   (						self       :  Optional[int] ,       __UpperCamelCase       :  Optional[int] ,       __UpperCamelCase       :  Any=0 ):
												lowerCamelCase_					=       floats_tensor((1, 3, 3_2, 3_2) ,       rng=random.Random(__UpperCamelCase ) ).to(__UpperCamelCase )
												lowerCamelCase_					=       image.cpu().permute(0 ,       2 ,       3 ,       1 )[0]
												lowerCamelCase_					=       Image.fromarray(np.uinta(__UpperCamelCase ) ).convert("""RGB""" )
												if str(__UpperCamelCase ).startswith("""mps""" ):
																			lowerCamelCase_					=       torch.manual_seed(__UpperCamelCase )
												else:
																			lowerCamelCase_					=       torch.Generator(device=__UpperCamelCase ).manual_seed(__UpperCamelCase )
												lowerCamelCase_					=       {
												    """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  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]
												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       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  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       """french fries"""
												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ,       negative_prompt=__UpperCamelCase )
												lowerCamelCase_					=       output.images
												lowerCamelCase_					=       image[0, -3:, -3:, -1]

												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       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  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       self.get_dummy_inputs(__UpperCamelCase )
												lowerCamelCase_					=       [inputs["""prompt"""]] * 2

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

												lowerCamelCase_					=       sd_pipe(**__UpperCamelCase ).images
												lowerCamelCase_					=       image[-1, -3:, -3:, -1]

												assert image.shape == (2, 3_2, 3_2, 3)
												lowerCamelCase_					=       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  lowercase__   (						self       :  Dict ):
												lowerCamelCase_					=       """cpu"""  # ensure determinism for the device-dependent torch.Generator
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       EulerAncestralDiscreteScheduler(
												    beta_start=0.00085 ,       beta_end=0.012 ,       beta_schedule="""scaled_linear""" )
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       sd_pipe.to(__UpperCamelCase )
												sd_pipe.set_progress_bar_config(disable=__UpperCamelCase )

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

												lowerCamelCase_					=       [round(__UpperCamelCase ,       4 ) for x in image_slice.flatten().tolist()]
												print(""",""".join([str(__UpperCamelCase ) for x in slice] ) )

												assert image.shape == (1, 3_2, 3_2, 3)
												lowerCamelCase_					=       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  lowercase__   (						self       :  str ):
												super().test_inference_batch_single_identical(expected_max_diff=3E-3 )



					def  lowercase__   (						self       :  Optional[int] ):
												lowerCamelCase_					=       self.get_dummy_components()
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline(**__UpperCamelCase )
												lowerCamelCase_					=       VaeImageProcessor(do_resize=__UpperCamelCase ,       do_normalize=__UpperCamelCase )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )

												lowerCamelCase_					=       pipe(**self.get_dummy_inputs_by_type(__UpperCamelCase ,       input_image_type="""pt""" ) )[0]

												lowerCamelCase_					=       components["""vae"""]
												lowerCamelCase_					=       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():
																										lowerCamelCase_					=       vae.encode(inputs[image_param] ).latent_dist.mode()

												lowerCamelCase_					=       pipe(**__UpperCamelCase )[0]

												lowerCamelCase_					=       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    __A(					unittest.TestCase							):

					def  lowercase__   (						self       :  List[Any] ):
												super().tearDown()
												gc.collect()
												torch.cuda.empty_cache()

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

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												lowerCamelCase_					=       LMSDiscreteScheduler.from_config(pipe.scheduler.config )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[Any] ):
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase )
												lowerCamelCase_					=       DDIMScheduler.from_config(pipe.scheduler.config )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

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

												assert image.shape == (1, 5_1_2, 5_1_2, 3)
												lowerCamelCase_					=       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  lowercase__   (						self       :  List[str] ):
												lowerCamelCase_					=       0

												def callback_fn(__UpperCamelCase       :  int ,       __UpperCamelCase       :  int ,       __UpperCamelCase       :  torch.FloatTensor ) -> None:
																			lowerCamelCase_					=       True
																			nonlocal number_of_steps
																			number_of_steps += 1
																			if step == 1:
																										lowerCamelCase_					=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       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_					=       latents.detach().cpu().numpy()
																										assert latents.shape == (1, 4, 6_4, 6_4)
																										lowerCamelCase_					=       latents[0, -3:, -3:, -1]
																										lowerCamelCase_					=       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_					=       False

												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase ,       torch_dtype=torch.floataa )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

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

					def  lowercase__   (						self       :  int ):
												torch.cuda.empty_cache()
												torch.cuda.reset_max_memory_allocated()
												torch.cuda.reset_peak_memory_stats()

												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    """timbrooks/instruct-pix2pix""" ,       safety_checker=__UpperCamelCase ,       torch_dtype=torch.floataa )
												lowerCamelCase_					=       pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing(1 )
												pipe.enable_sequential_cpu_offload()

												lowerCamelCase_					=       self.get_inputs()
												lowerCamelCase_					=       pipe(**__UpperCamelCase )

												lowerCamelCase_					=       torch.cuda.max_memory_allocated()
												# make sure that less than 2.2 GB is allocated
												assert mem_bytes < 2.2 * 1_0**9



					def  lowercase__   (						self       :  Tuple ):
												lowerCamelCase_					=       self.get_inputs()
												# resize to resolution that is divisible by 8 but not 16 or 32
												lowerCamelCase_					=       inputs["""image"""].resize((5_0_4, 5_0_4) )

												lowerCamelCase_					=       """timbrooks/instruct-pix2pix"""
												lowerCamelCase_					=       StableDiffusionInstructPixaPixPipeline.from_pretrained(
												    __UpperCamelCase ,       safety_checker=__UpperCamelCase ,       )
												pipe.to(__UpperCamelCase )
												pipe.set_progress_bar_config(disable=__UpperCamelCase )
												pipe.enable_attention_slicing()

												lowerCamelCase_					=       pipe(**__UpperCamelCase )
												lowerCamelCase_					=       output.images[0]

												lowerCamelCase_					=       image[2_5_5:2_5_8, 3_8_3:3_8_6, -1]

												assert image.shape == (5_0_4, 5_0_4, 3)
												lowerCamelCase_					=       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





