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       __lowercase      (	lowercase_			,      lowercase_			,      lowercase_			,      unittest.TestCase  ):







     '''simple docstring'''
     SCREAMING_SNAKE_CASE   						=				StableUnCLIPPipeline
     SCREAMING_SNAKE_CASE   						=				TEXT_TO_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE   						=				TEXT_TO_IMAGE_BATCH_PARAMS
     SCREAMING_SNAKE_CASE   						=				TEXT_TO_IMAGE_IMAGE_PARAMS
     SCREAMING_SNAKE_CASE   						=				TEXT_TO_IMAGE_IMAGE_PARAMS

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




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	32
         __A         =	embedder_hidden_size

         # prior components

         torch.manual_seed(0				)
         __A         =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

         torch.manual_seed(0				)
         __A         =	CLIPTextModelWithProjection(
             CLIPTextConfig(
                 bos_token_id=0 ,	eos_token_id=2 ,	hidden_size=UpperCamelCase_ ,	projection_dim=UpperCamelCase_ ,	intermediate_size=37 ,	layer_norm_eps=1e-05 ,	num_attention_heads=4 ,	num_hidden_layers=5 ,	pad_token_id=1 ,	vocab_size=1_000 ,	)				)

         torch.manual_seed(0				)
         __A         =	PriorTransformer(
             num_attention_heads=2 ,	attention_head_dim=12 ,	embedding_dim=UpperCamelCase_ ,	num_layers=1 ,	)

         torch.manual_seed(0				)
         __A         =	DDPMScheduler(
             variance_type="""fixed_small_log""" ,	prediction_type="""sample""" ,	num_train_timesteps=1_000 ,	clip_sample=UpperCamelCase_ ,	clip_sample_range=5.0 ,	beta_schedule="""squaredcos_cap_v2""" ,	)

         # regular denoising components

         torch.manual_seed(0				)
         __A         =	StableUnCLIPImageNormalizer(embedding_dim=UpperCamelCase_				)
         __A         =	DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""				)

         torch.manual_seed(0				)
         __A         =	CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""				)

         torch.manual_seed(0				)
         __A         =	CLIPTextModel(
             CLIPTextConfig(
                 bos_token_id=0 ,	eos_token_id=2 ,	hidden_size=UpperCamelCase_ ,	projection_dim=32 ,	intermediate_size=37 ,	layer_norm_eps=1e-05 ,	num_attention_heads=4 ,	num_hidden_layers=5 ,	pad_token_id=1 ,	vocab_size=1_000 ,	)				)

         torch.manual_seed(0				)
         __A         =	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=UpperCamelCase_ ,	layers_per_block=1 ,	upcast_attention=UpperCamelCase_ ,	use_linear_projection=UpperCamelCase_ ,	)

         torch.manual_seed(0				)
         __A         =	DDIMScheduler(
             beta_schedule="""scaled_linear""" ,	beta_start=0.00085 ,	beta_end=0.012 ,	prediction_type="""v_prediction""" ,	set_alpha_to_one=UpperCamelCase_ ,	steps_offset=1 ,	)

         torch.manual_seed(0				)
         __A         =	AutoencoderKL()

         __A         =	{
             # 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 		lowerCAmelCase_     (				self       :			Any ,	UpperCamelCase_       :			Optional[Any] ,	UpperCamelCase_       :			Union[str, Any]=0				):


         """simple docstring"""



         if str(UpperCamelCase_				).startswith("""mps"""				):
             __A         =	torch.manual_seed(UpperCamelCase_				)
         else:
             __A         =	torch.Generator(device=UpperCamelCase_				).manual_seed(UpperCamelCase_				)
         __A         =	{
             """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 		lowerCAmelCase_     (				self       :			List[str]				):


         """simple docstring"""



         __A         =	torch_device == """cpu"""

         self._test_attention_slicing_forward_pass(test_max_difference=UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			int				):


         """simple docstring"""



         __A         =	torch_device in ["""cpu""", """mps"""]

         self._test_inference_batch_single_identical(test_max_difference=UpperCamelCase_				)


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







     '''simple docstring'''




     def 		lowerCAmelCase_     (				self       :			Union[str, Any]				):


         """simple docstring"""



         super().tearDown()
         gc.collect()
         torch.cuda.empty_cache()




     def 		lowerCAmelCase_     (				self       :			Any				):


         """simple docstring"""



         __A         =	load_numpy(
             """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy"""				)

         __A         =	StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" ,	torch_dtype=torch.floataa				)
         pipe.to(UpperCamelCase_				)
         pipe.set_progress_bar_config(disable=UpperCamelCase_				)
         # 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()

         __A         =	torch.Generator(device="""cpu"""				).manual_seed(0				)
         __A         =	pipe("""anime turle""" ,	generator=UpperCamelCase_ ,	output_type="""np"""				)

         __A         =	output.images[0]

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

         assert_mean_pixel_difference(UpperCamelCase_ ,	UpperCamelCase_				)





     def 		lowerCAmelCase_     (				self       :			Optional[int]				):


         """simple docstring"""



         torch.cuda.empty_cache()
         torch.cuda.reset_max_memory_allocated()
         torch.cuda.reset_peak_memory_stats()

         __A         =	StableUnCLIPPipeline.from_pretrained("""fusing/stable-unclip-2-1-l""" ,	torch_dtype=torch.floataa				)
         __A         =	pipe.to(UpperCamelCase_				)
         pipe.set_progress_bar_config(disable=UpperCamelCase_				)
         pipe.enable_attention_slicing()
         pipe.enable_sequential_cpu_offload()

         __A         =	pipe(
             """anime turtle""" ,	prior_num_inference_steps=2 ,	num_inference_steps=2 ,	output_type="""np""" ,	)

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












def 			_SCREAMING_SNAKE_CASE						( __lowercase   :			List[Any]			,				__lowercase   :			Dict			,				__lowercase   :			str    )						->      Dict:





    """simple docstring"""

    if n == 0:
        return 1

    elif n % 2 == 1:
        return (binary_exponentiation(__lowercase			,				n - 1			,				__lowercase    ) * a) % mod

    else:
        __A         =	binary_exponentiation(__lowercase			,				n / 2			,				__lowercase    )
        return (b * b) % mod


# a prime number
__a      :       Union[str, Any]				  =   701

__a      :       Dict				  =   1000000000
__a      :       Union[str, Any]				  =   10

# using binary exponentiation function, O(log(p)):
print((a / b) % p == (a * binary_exponentiation(b, p - 2, p)) % p)






print((a / b) % p == (a * b ** (p - 2)) % p)




