import gc
import unittest

import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    DDPMScheduler,
    PriorTransformer,
    StableUnCLIPPipeline,
    UNetaDConditionModel,
)
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.testing_utils import enable_full_determinism, load_numpy, require_torch_gpu, slow, torch_device

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     StableUnCLIPPipeline
							_UpperCAmelCase						    =     TEXT_TO_IMAGE_PARAMS
							_UpperCAmelCase						    =     TEXT_TO_IMAGE_BATCH_PARAMS
							_UpperCAmelCase						    =     TEXT_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase						    =     TEXT_TO_IMAGE_IMAGE_PARAMS

							# TODO(will) Expected attn_bias.stride(1) == 0 to be true, but got false
							_UpperCAmelCase						    =     False







							def 				snake_case  (				self     :    Union[str, Any]		):
										lowerCamelCase						:List[str]						  =     32
										lowerCamelCase						:List[Any]						  =     embedder_hidden_size

										# prior components

										torch.manual_seed(0		)
										lowerCamelCase						:Any						  =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

										torch.manual_seed(0		)
										lowerCamelCase						:Union[str, Any]						  =     CLIPTextModelWithProjection(
										    CLIPTextConfig(
										        bos_token_id=0	,					eos_token_id=2	,					hidden_size=__snake_case	,					projection_dim=__snake_case	,					intermediate_size=37	,					layer_norm_eps=1e-0_5	,					num_attention_heads=4	,					num_hidden_layers=5	,					pad_token_id=1	,					vocab_size=1000	,					)		)

										torch.manual_seed(0		)
										lowerCamelCase						:Optional[int]						  =     PriorTransformer(
										    num_attention_heads=2	,					attention_head_dim=12	,					embedding_dim=__snake_case	,					num_layers=1	,					)

										torch.manual_seed(0		)
										lowerCamelCase						:List[Any]						  =     DDPMScheduler(
										    variance_type='''fixed_small_log'''	,					prediction_type='''sample'''	,					num_train_timesteps=1000	,					clip_sample=__snake_case	,					clip_sample_range=5.0	,					beta_schedule='''squaredcos_cap_v2'''	,					)

										# regular denoising components

										torch.manual_seed(0		)
										lowerCamelCase						:Dict						  =     StableUnCLIPImageNormalizer(embedding_dim=__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''		)

										torch.manual_seed(0		)
										lowerCamelCase						:Union[str, Any]						  =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''		)

										torch.manual_seed(0		)
										lowerCamelCase						:Any						  =     CLIPTextModel(
										    CLIPTextConfig(
										        bos_token_id=0	,					eos_token_id=2	,					hidden_size=__snake_case	,					projection_dim=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	,					)		)

										torch.manual_seed(0		)
										lowerCamelCase						:List[str]						  =     UNetaDConditionModel(
										    sample_size=32	,					in_channels=4	,					out_channels=4	,					down_block_types=('''CrossAttnDownBlock2D''', '''DownBlock2D''')	,					up_block_types=('''UpBlock2D''', '''CrossAttnUpBlock2D''')	,					block_out_channels=(32, 64)	,					attention_head_dim=(2, 4)	,					class_embed_type='''projection'''	,					projection_class_embeddings_input_dim=embedder_projection_dim * 2	,					cross_attention_dim=__snake_case	,					layers_per_block=1	,					upcast_attention=__snake_case	,					use_linear_projection=__snake_case	,					)

										torch.manual_seed(0		)
										lowerCamelCase						:List[str]						  =     DDIMScheduler(
										    beta_schedule='''scaled_linear'''	,					beta_start=0.0_0_0_8_5	,					beta_end=0.0_1_2	,					prediction_type='''v_prediction'''	,					set_alpha_to_one=__snake_case	,					steps_offset=1	,					)

										torch.manual_seed(0		)
										lowerCamelCase						:Union[str, Any]						  =     AutoencoderKL()

										lowerCamelCase						:int						  =     {
										    # prior components
										    '''prior_tokenizer''': prior_tokenizer,
										    '''prior_text_encoder''': prior_text_encoder,
										    '''prior''': prior,
										    '''prior_scheduler''': prior_scheduler,
										    # image noising components
										    '''image_normalizer''': image_normalizer,
										    '''image_noising_scheduler''': image_noising_scheduler,
										    # regular denoising components
										    '''tokenizer''': tokenizer,
										    '''text_encoder''': text_encoder,
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										}

										return components







							def 				snake_case  (				self     :    int	,					__snake_case     :    Tuple	,					__snake_case     :    str=0		):
										if str(__snake_case		).startswith('''mps'''		):
													lowerCamelCase						:int						  =     torch.manual_seed(__snake_case		)
										else:
													lowerCamelCase						:str						  =     torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
										lowerCamelCase						:int						  =     {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''prior_num_inference_steps''': 2,
										    '''output_type''': '''numpy''',
										}
										return inputs







							def 				snake_case  (				self     :    Optional[Any]		):
										lowerCamelCase						:List[Any]						  =     torch_device == '''cpu'''

										self._test_attention_slicing_forward_pass(test_max_difference=__snake_case		)







							def 				snake_case  (				self     :    Dict		):
										lowerCamelCase						:Dict						  =     torch_device in ['''cpu''', '''mps''']

										self._test_inference_batch_single_identical(test_max_difference=__snake_case		)


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







							def 				snake_case  (				self     :    Tuple		):
										# clean up the VRAM after each test
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()







							def 				snake_case  (				self     :    Any		):
										lowerCamelCase						:List[str]						  =     load_numpy(
										    '''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'''		)

										lowerCamelCase						:List[str]						  =     StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''	,					torch_dtype=torch.floataa		)
										pipe.to(__snake_case		)
										pipe.set_progress_bar_config(disable=__snake_case		)
										# stable unclip will oom when integration tests are run on a V100,
										# so turn on memory savings
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										lowerCamelCase						:Union[str, Any]						  =     torch.Generator(device='''cpu'''		).manual_seed(0		)
										lowerCamelCase						:Optional[Any]						  =     pipe('''anime turle'''	,					generator=__snake_case	,					output_type='''np'''		)

										lowerCamelCase						:Any						  =     output.images[0]

										assert image.shape == (768, 768, 3)

										assert_mean_pixel_difference(__snake_case	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										torch.cuda.empty_cache()
										torch.cuda.reset_max_memory_allocated()
										torch.cuda.reset_peak_memory_stats()

										lowerCamelCase						:str						  =     StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''	,					torch_dtype=torch.floataa		)
										lowerCamelCase						:Union[str, Any]						  =     pipe.to(__snake_case		)
										pipe.set_progress_bar_config(disable=__snake_case		)
										pipe.enable_attention_slicing()
										pipe.enable_sequential_cpu_offload()

										lowerCamelCase						:Optional[int]						  =     pipe(
										    '''anime turtle'''	,					prior_num_inference_steps=2	,					num_inference_steps=2	,					output_type='''np'''	,					)

										lowerCamelCase						:str						  =     torch.cuda.max_memory_allocated()
										# make sure that less than 7 GB is allocated
										assert mem_bytes < 7 * 10**9




import numpy as np
from cva import COLOR_BGR2GRAY, cvtColor, imread
from numpy import array, uinta
from PIL import Image

from digital_image_processing import change_contrast as cc
from digital_image_processing import convert_to_negative as cn
from digital_image_processing import sepia as sp
from digital_image_processing.dithering import burkes as bs
from digital_image_processing.edge_detection import canny
from digital_image_processing.filters import convolve as conv
from digital_image_processing.filters import gaussian_filter as gg
from digital_image_processing.filters import local_binary_pattern as lbp
from digital_image_processing.filters import median_filter as med
from digital_image_processing.filters import sobel_filter as sob
from digital_image_processing.resize import resize as rs

A__           =							imread(R"""digital_image_processing/image_data/lena_small.jpg""")
A__           =							cvtColor(img, COLOR_BGR2GRAY)





def  _lowerCamelCase					(   ):
			lowerCamelCase						:int						  =     cn.convert_to_negative(a_)
			# assert negative_img array for at least one True
			assert negative_img.any()





def  _lowerCamelCase					(   ):
			with Image.open('''digital_image_processing/image_data/lena_small.jpg''') as img:
						# Work around assertion for response
						assert str(cc.change_contrast(a_						,			1_10)).startswith(
						    '''<PIL.Image.Image image mode=RGB size=100x100 at''')





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Optional[Any]						  =     canny.gen_gaussian_kernel(9						,			sigma=1.4)
			# Assert ambiguous array
			assert resp.all()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:str						  =     imread('''digital_image_processing/image_data/lena_small.jpg'''						,			0)
			# assert ambiguous array for all == True
			assert canny_img.all()
			lowerCamelCase						:Optional[Any]						  =     canny.canny(a_)
			# assert canny array for at least one True
			assert canny_array.any()





def  _lowerCamelCase					(   ):
			assert gg.gaussian_filter(a_						,			5						,			sigma=0.9).all()





def  _lowerCamelCase					(   ):
			# laplace diagonals
			lowerCamelCase						:List[Any]						  =     array([[0.25, 0.5, 0.25], [0.5, -3, 0.5], [0.25, 0.5, 0.25]])
			lowerCamelCase						:List[Any]						  =     conv.img_convolve(a_						,			a_).astype(a_)
			assert res.any()





def  _lowerCamelCase					(   ):
			assert med.median_filter(a_						,			3).any()





def  _lowerCamelCase					(   ):
			lowerCamelCase  ,    lowerCamelCase						:Union[str, Any]						  =     sob.sobel_filter(a_)
			assert grad.any() and theta.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Dict						  =     sp.make_sepia(a_						,			20)
			assert sepia.all()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"):
			lowerCamelCase						:Any						  =     bs.Burkes(imread(a_						,			1)						,			1_20)
			burkes.process()
			assert burkes.output_img.any()





def  _lowerCamelCase					(   a_				: str = "digital_image_processing/image_data/lena_small.jpg"						,			):
			lowerCamelCase						:Tuple						  =     rs.NearestNeighbour(imread(a_						,			1)						,			4_00						,			2_00)
			nn.process()
			assert nn.output.any()





def  _lowerCamelCase					(   ):
			lowerCamelCase						:Tuple						  =     '''digital_image_processing/image_data/lena.jpg'''

			# Reading the image and converting it to grayscale.
			lowerCamelCase						:Tuple						  =     imread(a_						,			0)

			# Test for get_neighbors_pixel function() return not None
			lowerCamelCase						:Dict						  =     0
			lowerCamelCase						:Optional[Any]						  =     0
			lowerCamelCase						:str						  =     image[x_coordinate][y_coordinate]

			lowerCamelCase						:Any						  =     lbp.get_neighbors_pixel(
			    a_						,			a_						,			a_						,			a_)

			assert neighbors_pixels is not None

			# Test for local_binary_pattern function()
			# Create a numpy array as the same height and width of read image
			lowerCamelCase						:int						  =     np.zeros((image.shape[0], image.shape[1]))

			# Iterating through the image and calculating the local binary pattern value
			# for each pixel.
			for i in range(0						,			image.shape[0]):
						for j in range(0						,			image.shape[1]):
									lowerCamelCase						:Optional[int]						  =     lbp.local_binary_value(a_						,			a_						,			a_)

			assert lbp_image.any()



