


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       __magic_name__	(     __lowerCAmelCase					,				__lowerCAmelCase					,				__lowerCAmelCase					,				unittest.TestCase):
     A:		int			=      StableUnCLIPPipeline
     A:		List[str]			=      TEXT_TO_IMAGE_PARAMS
     A:		Union[str, Any]			=      TEXT_TO_IMAGE_BATCH_PARAMS
     A:		Optional[int]			=      TEXT_TO_IMAGE_IMAGE_PARAMS
     A:		str			=      TEXT_TO_IMAGE_IMAGE_PARAMS

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


     def      UpperCAmelCase__	(      self							:     Union[str, Any]			)  ->      Dict:





      '''simple docstring'''






      UpperCamelCase__					:    Dict          =   32
      UpperCamelCase__					:    Any          =   embedder_hidden_size

      # prior components

      torch.manual_seed(0			)
      UpperCamelCase__					:    Optional[int]          =   CLIPTokenizer.from_pretrained('''hf-internal-testing/tiny-random-clip'''			)

      torch.manual_seed(0			)
      UpperCamelCase__					:    int          =   CLIPTextModelWithProjection(
          CLIPTextConfig(
              bos_token_id=0							,	eos_token_id=2							,	hidden_size=lowerCamelCase__							,	projection_dim=lowerCamelCase__							,	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			)
      UpperCamelCase__					:    List[str]          =   PriorTransformer(
          num_attention_heads=2							,	attention_head_dim=12							,	embedding_dim=lowerCamelCase__							,	num_layers=1							,	)

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

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

      torch.manual_seed(0			)
      UpperCamelCase__					:    Dict          =   CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0							,	eos_token_id=2							,	hidden_size=lowerCamelCase__							,	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			)
      UpperCamelCase__					:    List[Any]          =   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			)
      UpperCamelCase__					:    str          =   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			)
      UpperCamelCase__					:    List[str]          =   AutoencoderKL()

      UpperCamelCase__					:    List[str]          =   {
          # 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      UpperCAmelCase__	(      self							:     Union[str, Any]							,	lowerCamelCase__							:     Optional[int]							,	lowerCamelCase__							:     Optional[Any]=0			)  ->      str:





      '''simple docstring'''






      if str(lowerCamelCase__			).startswith('''mps'''			):
       UpperCamelCase__					:    Union[str, Any]          =   torch.manual_seed(lowerCamelCase__			)
      else:
       UpperCamelCase__					:    Tuple          =   torch.Generator(device=lowerCamelCase__			).manual_seed(lowerCamelCase__			)
      UpperCamelCase__					:    List[str]          =   {
          '''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      UpperCAmelCase__	(      self							:     List[str]			)  ->      Optional[Any]:





      '''simple docstring'''






      UpperCamelCase__					:    List[str]          =   torch_device == '''cpu'''

      self._test_attention_slicing_forward_pass(test_max_difference=lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     Tuple			)  ->      Tuple:





      '''simple docstring'''






      UpperCamelCase__					:    int          =   torch_device in ['''cpu''', '''mps''']

      self._test_inference_batch_single_identical(test_max_difference=lowerCamelCase__			)



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


     def      UpperCAmelCase__	(      self							:     Optional[int]			)  ->      Dict:





      '''simple docstring'''






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


     def      UpperCAmelCase__	(      self							:     Any			)  ->      Any:





      '''simple docstring'''






      UpperCamelCase__					:    Union[str, 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'''			)

      UpperCamelCase__					:    Any          =   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()

      UpperCamelCase__					:    Dict          =   torch.Generator(device='''cpu'''			).manual_seed(0			)
      UpperCamelCase__					:    Tuple          =   pipe('''anime turle'''							,	generator=lowerCamelCase__							,	output_type='''np'''			)

      UpperCamelCase__					:    Union[str, Any]          =   output.images[0]

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

      assert_mean_pixel_difference(lowerCamelCase__							,	lowerCamelCase__			)


     def      UpperCAmelCase__	(      self							:     str			)  ->      Tuple:





      '''simple docstring'''






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

      UpperCamelCase__					:    Dict          =   StableUnCLIPPipeline.from_pretrained('''fusing/stable-unclip-2-1-l'''							,	torch_dtype=torch.floataa			)
      UpperCamelCase__					:    List[str]          =   pipe.to(lowerCamelCase__			)
      pipe.set_progress_bar_config(disable=lowerCamelCase__			)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

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

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






__UpperCamelCase       :       List[Any]						       =				256
# Modulus to hash a string
__UpperCamelCase       :       Union[str, Any]						       =				100_0003

def 			_a  (				SCREAMING_SNAKE_CASE	:				str    , SCREAMING_SNAKE_CASE	:				str     ):







 """simple docstring"""



 UpperCamelCase__					:    Optional[int]          =   len(SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    List[Any]          =   len(SCREAMING_SNAKE_CASE     )
 if p_len > t_len:
  return False

 UpperCamelCase__					:    Any          =   0
 UpperCamelCase__					:    str          =   0
 UpperCamelCase__					:    List[Any]          =   1

 # Calculating the hash of pattern and substring of text
 for i in range(SCREAMING_SNAKE_CASE     ):
  UpperCamelCase__					:    Any          =   (ord(pattern[i]     ) + p_hash * alphabet_size) % modulus
  UpperCamelCase__					:    List[str]          =   (ord(text[i]     ) + text_hash * alphabet_size) % modulus
  if i == p_len - 1:
   continue
  UpperCamelCase__					:    Dict          =   (modulus_power * alphabet_size) % modulus

 for i in range(0    , t_len - p_len + 1     ):
  if text_hash == p_hash and text[i : i + p_len] == pattern:
   return True
  if i == t_len - p_len:
   continue
  # Calculate the https://en.wikipedia.org/wiki/Rolling_hash
  UpperCamelCase__					:    Optional[int]          =   (
      (text_hash - ord(text[i]     ) * modulus_power) * alphabet_size
      + ord(text[i + p_len]     )
  ) % modulus
 return False

def 			_a  (				):







 """simple docstring"""



 UpperCamelCase__					:    Tuple          =   '''abc1abc12'''
 UpperCamelCase__					:    Dict          =   '''alskfjaldsabc1abc1abc12k23adsfabcabc'''
 UpperCamelCase__					:    List[str]          =   '''alskfjaldsk23adsfabcabc'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     ) and not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 2)
 UpperCamelCase__					:    Optional[int]          =   '''ABABX'''
 UpperCamelCase__					:    int          =   '''ABABZABABYABABX'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 3)
 UpperCamelCase__					:    int          =   '''AAAB'''
 UpperCamelCase__					:    str          =   '''ABAAAAAB'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 4)
 UpperCamelCase__					:    Union[str, Any]          =   '''abcdabcy'''
 UpperCamelCase__					:    List[str]          =   '''abcxabcdabxabcdabcdabcy'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )

 # Test 5)
 UpperCamelCase__					:    Tuple          =   '''Lü'''
 UpperCamelCase__					:    Any          =   '''Lüsai'''
 assert rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 UpperCamelCase__					:    Dict          =   '''Lue'''
 assert not rabin_karp(SCREAMING_SNAKE_CASE    , SCREAMING_SNAKE_CASE     )
 print('''Success.'''     )


if __name__ == "__main__":
       test_rabin_karp()


