
import argparse
import importlib
from pathlib import Path


# Test all the extensions added in the setup
UpperCAmelCase       =					[
    '''kernels/rwkv/wkv_cuda.cu''',
    '''kernels/rwkv/wkv_op.cpp''',
    '''kernels/deformable_detr/ms_deform_attn.h''',
    '''kernels/deformable_detr/cuda/ms_deform_im2col_cuda.cuh''',
    '''models/graphormer/algos_graphormer.pyx''',
]






def 				UpperCAmelCase_						(				__SCREAMING_SNAKE_CASE				):
	# Test all the extensions added in the setup
	for file in FILES_TO_FIND:
		if not (transformers_path / file).exists():
			return False
	return True


if __name__ == "__main__":
	UpperCAmelCase       =					argparse.ArgumentParser()
	parser.add_argument('''--check_lib''', action='''store_true''', help='''Whether to check the build or the actual package.''')
	UpperCAmelCase       =					parser.parse_args()
	if args.check_lib:
		UpperCAmelCase       =					importlib.import_module('''transformers''')
		UpperCAmelCase       =					Path(transformers_module.__file__).parent
	else:
		UpperCAmelCase       =					Path.cwd() / '''build/lib/transformers'''
	if not test_custom_files_are_present(transformers_path):
		raise ValueError('''The built release does not contain the custom files. Fix this before going further!''')



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





