import gc
import random
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTextModelWithProjection, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DiffusionPipeline,
    EulerDiscreteScheduler,
    StableDiffusionXLImgaImgPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import floats_tensor, 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_VARIATION_BATCH_PARAMS,
    TEXT_GUIDED_IMAGE_VARIATION_PARAMS,
)
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     StableDiffusionXLImgaImgPipeline
							_UpperCAmelCase						    =     TEXT_GUIDED_IMAGE_VARIATION_PARAMS - {'height', 'width'}
							_UpperCAmelCase						    =     PipelineTesterMixin.required_optional_params - {'latents'}
							_UpperCAmelCase						    =     TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
							_UpperCAmelCase						    =     IMAGE_TO_IMAGE_IMAGE_PARAMS
							_UpperCAmelCase						    =     IMAGE_TO_IMAGE_IMAGE_PARAMS







							def 				snake_case  (				self     :    Dict		):
										torch.manual_seed(0		)
										lowerCamelCase						:List[Any]						  =     UNetaDConditionModel(
										    block_out_channels=(32, 64)	,					layers_per_block=2	,					sample_size=32	,					in_channels=4	,					out_channels=4	,					down_block_types=('''DownBlock2D''', '''CrossAttnDownBlock2D''')	,					up_block_types=('''CrossAttnUpBlock2D''', '''UpBlock2D''')	,					attention_head_dim=(2, 4)	,					use_linear_projection=__snake_case	,					addition_embed_type='''text_time'''	,					addition_time_embed_dim=8	,					transformer_layers_per_block=(1, 2)	,					projection_class_embeddings_input_dim=80	,					cross_attention_dim=64	,					)
										lowerCamelCase						:str						  =     EulerDiscreteScheduler(
										    beta_start=0.0_0_0_8_5	,					beta_end=0.0_1_2	,					steps_offset=1	,					beta_schedule='''scaled_linear'''	,					timestep_spacing='''leading'''	,					)
										torch.manual_seed(0		)
										lowerCamelCase						: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	,					sample_size=128	,					)
										torch.manual_seed(0		)
										lowerCamelCase						:Dict						  =     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	,					hidden_act='''gelu'''	,					projection_dim=32	,					)
										lowerCamelCase						:List[str]						  =     CLIPTextModel(__snake_case		)
										lowerCamelCase						:str						  =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,					local_files_only=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     CLIPTextModelWithProjection(__snake_case		)
										lowerCamelCase						:Any						  =     CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''	,					local_files_only=__snake_case		)

										lowerCamelCase						:Dict						  =     {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										    '''vae''': vae,
										    '''text_encoder''': text_encoder,
										    '''tokenizer''': tokenizer,
										    '''text_encoder_2''': text_encoder_a,
										    '''tokenizer_2''': tokenizer_a,
										    # "safety_checker": None,
										    # "feature_extractor": None,
										}
										return components







							def 				snake_case  (				self     :    Tuple	,					__snake_case     :    Optional[int]	,					__snake_case     :    Any=0		):
										lowerCamelCase						:str						  =     floats_tensor((1, 3, 32, 32)	,					rng=random.Random(__snake_case		)		).to(__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     image / 2 + 0.5
										if str(__snake_case		).startswith('''mps'''		):
													lowerCamelCase						:Optional[Any]						  =     torch.manual_seed(__snake_case		)
										else:
													lowerCamelCase						:Dict						  =     torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
										lowerCamelCase						:Union[str, Any]						  =     {
										    '''prompt''': '''A painting of a squirrel eating a burger''',
										    '''image''': image,
										    '''generator''': generator,
										    '''num_inference_steps''': 2,
										    '''guidance_scale''': 5.0,
										    '''output_type''': '''numpy''',
										    '''strength''': 0.7_5,
										}
										return inputs







							def 				snake_case  (				self     :    List[Any]		):
										lowerCamelCase						:List[Any]						  =     '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										lowerCamelCase						:str						  =     self.get_dummy_components()
										lowerCamelCase						:Dict						  =     StableDiffusionXLImgaImgPipeline(**__snake_case		)
										lowerCamelCase						:List[str]						  =     sd_pipe.to(__snake_case		)
										sd_pipe.set_progress_bar_config(disable=__snake_case		)

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

										assert image.shape == (1, 32, 32, 3)

										lowerCamelCase						:str						  =     np.array([0.4_6_5_6, 0.4_8_4_0, 0.4_4_3_9, 0.6_6_9_8, 0.5_5_7_4, 0.4_5_2_4, 0.5_7_9_9, 0.5_9_4_3, 0.5_1_6_5]		)

										assert np.abs(image_slice.flatten() - expected_slice		).max() < 1e-2







							def 				snake_case  (				self     :    Optional[Any]		):
										super().test_attention_slicing_forward_pass(expected_max_diff=3e-3		)







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







							def 				snake_case  (				self     :    Tuple		):
										pass







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[int]						  =     self.get_dummy_components()
										lowerCamelCase						:int						  =     StableDiffusionXLImgaImgPipeline(**__snake_case		)
										lowerCamelCase						:Any						  =     sd_pipe.to(__snake_case		)
										lowerCamelCase						:str						  =     sd_pipe.to(__snake_case		)
										sd_pipe.set_progress_bar_config(disable=__snake_case		)

										# forward without prompt embeds
										lowerCamelCase						:Dict						  =     self.get_dummy_inputs(__snake_case		)
										lowerCamelCase						:Tuple						  =     3 * ['''this is a negative prompt''']
										lowerCamelCase						:Tuple						  =     negative_prompt
										lowerCamelCase						:List[Any]						  =     3 * [inputs['''prompt''']]

										lowerCamelCase						:str						  =     sd_pipe(**__snake_case		)
										lowerCamelCase						:Optional[int]						  =     output.images[0, -3:, -3:, -1]

										# forward with prompt embeds
										lowerCamelCase						:Any						  =     self.get_dummy_inputs(__snake_case		)
										lowerCamelCase						:Dict						  =     3 * ['''this is a negative prompt''']
										lowerCamelCase						:Any						  =     3 * [inputs.pop('''prompt'''		)]

										(
										    (
										    lowerCamelCase
										)  ,    (
										    lowerCamelCase
										)  ,    (
										    lowerCamelCase
										)  ,    (
										    lowerCamelCase
										)  ,    
										)						:Optional[Any]						  =     sd_pipe.encode_prompt(__snake_case	,					negative_prompt=__snake_case		)

										lowerCamelCase						:Any						  =     sd_pipe(
										    **__snake_case	,					prompt_embeds=__snake_case	,					negative_prompt_embeds=__snake_case	,					pooled_prompt_embeds=__snake_case	,					negative_pooled_prompt_embeds=__snake_case	,					)
										lowerCamelCase						:str						  =     output.images[0, -3:, -3:, -1]

										# make sure that it's equal
										assert np.abs(image_slice_a.flatten() - image_slice_a.flatten()		).max() < 1e-4


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







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







							def 				snake_case  (				self     :    Optional[int]	,					__snake_case     :    List[str]	,					__snake_case     :    Dict="cpu"	,					__snake_case     :    Dict=torch.floataa	,					__snake_case     :    Optional[Any]=0		):
										lowerCamelCase						:Dict						  =     torch.Generator(device=__snake_case		).manual_seed(__snake_case		)
										lowerCamelCase						:Dict						  =     np.random.RandomState(__snake_case		).standard_normal((1, 4, 64, 64)		)
										lowerCamelCase						:int						  =     torch.from_numpy(__snake_case		).to(device=__snake_case	,					dtype=__snake_case		)
										lowerCamelCase						:Optional[int]						  =     {
										    '''prompt''': '''a photograph of an astronaut riding a horse''',
										    '''latents''': latents,
										    '''generator''': generator,
										    '''num_inference_steps''': 3,
										    '''guidance_scale''': 7.5,
										    '''output_type''': '''numpy''',
										}
										return inputs







							def 				snake_case  (				self     :    List[str]		):
										lowerCamelCase						:List[Any]						  =     DiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-base'''		)
										pipe.to(__snake_case		)
										pipe.set_progress_bar_config(disable=__snake_case		)

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

										assert image.shape == (1, 512, 512, 3)
										lowerCamelCase						:Dict						  =     np.array([0.4_9_4_9_3, 0.4_7_8_9_6, 0.4_0_7_9_8, 0.5_4_2_1_4, 0.5_3_2_1_2, 0.4_8_2_0_2, 0.4_7_6_5_6, 0.4_6_3_2_9, 0.4_8_5_0_6]		)
										assert np.abs(image_slice - expected_slice		).max() < 7e-3




from maths.prime_factors import prime_factors





def  _lowerCamelCase					(   a_				: int):
			if not isinstance(a_						,			a_):
						lowerCamelCase						:Tuple						  =     F"Input value of [number={number}] must be an integer"
						raise TypeError(a_)
			if number < 1:
						raise ValueError('''Input must be a positive integer''')
			return -1 if len(prime_factors(a_)) % 2 else 1


if __name__ == "__main__":
			import doctest

			doctest.testmod()



