


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 		_snake_case  (				_snake_case						,   _snake_case						,   _snake_case						,   unittest.TestCase							):
 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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	32
       a						:List[Any]										=	embedder_hidden_size

       # prior components

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

       torch.manual_seed(0   )
       a						:Dict										=	CLIPTextModelWithProjection(
           CLIPTextConfig(
               bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=_lowerCamelCase ,  projection_dim=_lowerCamelCase ,  intermediate_size=37 ,  layer_norm_eps=1e-05 ,  num_attention_heads=4 ,  num_hidden_layers=5 ,  pad_token_id=1 ,  vocab_size=1000 ,  )   )

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

       torch.manual_seed(0   )
       a						:str										=	DDPMScheduler(
           variance_type='''fixed_small_log''' ,  prediction_type='''sample''' ,  num_train_timesteps=1000 ,  clip_sample=_lowerCamelCase ,  clip_sample_range=5.0 ,  beta_schedule='''squaredcos_cap_v2''' ,  )

       # regular denoising components

       torch.manual_seed(0   )
       a						:List[Any]										=	StableUnCLIPImageNormalizer(embedding_dim=_lowerCamelCase   )
       a						:List[str]										=	DDPMScheduler(beta_schedule='''squaredcos_cap_v2'''   )

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

       torch.manual_seed(0   )
       a						:Any										=	CLIPTextModel(
           CLIPTextConfig(
               bos_token_id=0 ,  eos_token_id=2 ,  hidden_size=_lowerCamelCase ,  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=1000 ,  )   )

       torch.manual_seed(0   )
       a						: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=_lowerCamelCase ,  layers_per_block=1 ,  upcast_attention=_lowerCamelCase ,  use_linear_projection=_lowerCamelCase ,  )

       torch.manual_seed(0   )
       a						:Any										=	DDIMScheduler(
           beta_schedule='''scaled_linear''' ,  beta_start=0.0_0085 ,  beta_end=0.012 ,  prediction_type='''v_prediction''' ,  set_alpha_to_one=_lowerCamelCase ,  steps_offset=1 ,  )

       torch.manual_seed(0   )
       a						:List[Any]										=	AutoencoderKL()

       a						:Dict										=	{
           # 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 	SCREAMING_SNAKE_CASE__    (		self ,  _lowerCamelCase ,  _lowerCamelCase=0   ):
       if str(_lowerCamelCase   ).startswith('''mps'''   ):
             a						:Optional[int]										=	torch.manual_seed(_lowerCamelCase   )
       else:
             a						:Dict										=	torch.Generator(device=_lowerCamelCase   ).manual_seed(_lowerCamelCase   )
       a						:Optional[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 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:int										=	torch_device == '''cpu'''

       self._test_attention_slicing_forward_pass(test_max_difference=_lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Any										=	torch_device in ['''cpu''', '''mps''']

       self._test_inference_batch_single_identical(test_max_difference=_lowerCamelCase   )







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




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




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       a						:Any										=	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						:Optional[int]										=	StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l''' ,  torch_dtype=torch.floataa   )
       pipe.to(_lowerCamelCase   )
       pipe.set_progress_bar_config(disable=_lowerCamelCase   )
       # 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						:List[str]										=	torch.Generator(device='''cpu'''   ).manual_seed(0   )
       a						:Dict										=	pipe('''anime turle''' ,  generator=_lowerCamelCase ,  output_type='''np'''   )

       a						:Any										=	output.images[0]

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

       assert_mean_pixel_difference(_lowerCamelCase ,  _lowerCamelCase   )




 def 	SCREAMING_SNAKE_CASE__    (		self   ):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       a						:Dict										=	pipe(
           '''anime turtle''' ,  prior_num_inference_steps=2 ,  num_inference_steps=2 ,  output_type='''np''' ,  )

       a						:Optional[Any]										=	torch.cuda.max_memory_allocated()
       # make sure that less than 7 GB is allocated
       assert mem_bytes < 7 * 10**9









def     __lowerCamelCase						(			UpperCAmelCase_						:		int = 100						):




      """simple docstring"""





      a						:List[Any]										=	0
      a						:List[Any]										=	0
      for i in range(1			,  n + 1						):
            sum_of_squares += i**2
            sum_of_ints += i
      return sum_of_ints**2 - sum_of_squares


if __name__ == "__main__":
     print(F"""{solution() = }""")





