




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   ):


 '''simple docstring'''







 lowercase_							       =       StableUnCLIPPipeline
 lowercase_							       =       TEXT_TO_IMAGE_PARAMS
 lowercase_							       =       TEXT_TO_IMAGE_BATCH_PARAMS
 lowercase_							       =       TEXT_TO_IMAGE_IMAGE_PARAMS
 lowercase_							       =       TEXT_TO_IMAGE_IMAGE_PARAMS

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



 def  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     32
       SCREAMING_SNAKE_CASE_      		=     embedder_hidden_size

       # prior components

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

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     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=1_000							,							)		)

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

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     DDPMScheduler(
           variance_type='fixed_small_log'							,							prediction_type='sample'							,							num_train_timesteps=1_000							,							clip_sample=_lowerCAmelCase							,							clip_sample_range=5.0							,							beta_schedule='squaredcos_cap_v2'							,							)

       # regular denoising components

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     StableUnCLIPImageNormalizer(embedding_dim=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     DDPMScheduler(beta_schedule='squaredcos_cap_v2'		)

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

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     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=1_000							,							)		)

       torch.manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     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		)
       SCREAMING_SNAKE_CASE_      		=     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		)
       SCREAMING_SNAKE_CASE_      		=     AutoencoderKL()

       SCREAMING_SNAKE_CASE_      		=     {
           # 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    :						str							,							_lowerCAmelCase    :						Tuple							,							_lowerCAmelCase    :						List[Any]=0		):
       if str(_lowerCAmelCase		).startswith('mps'		):
             SCREAMING_SNAKE_CASE_      		=     torch.manual_seed(_lowerCAmelCase		)
       else:
             SCREAMING_SNAKE_CASE_      		=     torch.Generator(device=_lowerCAmelCase		).manual_seed(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     {
           '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    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     torch_device == 'cpu'

       self._test_attention_slicing_forward_pass(test_max_difference=_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Tuple		):
       SCREAMING_SNAKE_CASE_      		=     torch_device in ['cpu', 'mps']

       self._test_inference_batch_single_identical(test_max_difference=_lowerCAmelCase		)





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


 '''simple docstring'''







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



 def  lowerCAmelCase_    (				self    :						int		):
       SCREAMING_SNAKE_CASE_      		=     load_numpy(
           'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy'		)

       SCREAMING_SNAKE_CASE_      		=     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()

       SCREAMING_SNAKE_CASE_      		=     torch.Generator(device='cpu'		).manual_seed(0		)
       SCREAMING_SNAKE_CASE_      		=     pipe('anime turle'							,							generator=_lowerCAmelCase							,							output_type='np'		)

       SCREAMING_SNAKE_CASE_      		=     output.images[0]

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

       assert_mean_pixel_difference(_lowerCAmelCase							,							_lowerCAmelCase		)




 def  lowerCAmelCase_    (				self    :						Any		):
       torch.cuda.empty_cache()
       torch.cuda.reset_max_memory_allocated()
       torch.cuda.reset_peak_memory_stats()

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

       SCREAMING_SNAKE_CASE_      		=     pipe(
           'anime turtle'							,							prior_num_inference_steps=2							,							num_inference_steps=2							,							output_type='np'							,							)

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





from __future__ import annotations

from collections.abc import Generator

def    UpperCAmelCase_				( )   ->					Generator[int, None, None]:
      SCREAMING_SNAKE_CASE_      		=     {}
      SCREAMING_SNAKE_CASE_      		=     2
      while True:
            SCREAMING_SNAKE_CASE_      		=     factor_map.pop(__UpperCAmelCase     ,     __UpperCAmelCase				)
            if factor:
                  SCREAMING_SNAKE_CASE_      		=     factor + prime
                  while x in factor_map:
                        x += factor
                  SCREAMING_SNAKE_CASE_      		=     factor
            else:
                  SCREAMING_SNAKE_CASE_      		=     prime
                  yield prime
            prime += 1

def    UpperCAmelCase_				( __UpperCAmelCase		:      float = 1E10				)   ->					int:
      SCREAMING_SNAKE_CASE_      		=     sieve()

      SCREAMING_SNAKE_CASE_      		=     1
      while True:
            SCREAMING_SNAKE_CASE_      		=     next(__UpperCAmelCase				)
            if (2 * prime * n) > limit:
                  return n
            # Ignore the next prime as the reminder will be 2.
            next(__UpperCAmelCase				)
            n += 2


if __name__ == "__main__":
   print(solution())