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	( __lowerCAmelCase ,  __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       StableUnCLIPPipeline
      _snake_case    		=       TEXT_TO_IMAGE_PARAMS
      _snake_case    		=       TEXT_TO_IMAGE_BATCH_PARAMS
      _snake_case    		=       TEXT_TO_IMAGE_IMAGE_PARAMS
      _snake_case    		=       TEXT_TO_IMAGE_IMAGE_PARAMS

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						32
          _lowerCamelCase     :		Optional[int] 						=						embedder_hidden_size

          # prior components

          torch.manual_seed(0)
          _lowerCamelCase     :		Optional[Any] 						=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

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

          torch.manual_seed(0)
          _lowerCamelCase     :		Optional[Any] 						=						DDPMScheduler(
              variance_type="""fixed_small_log"""    ,  prediction_type="""sample"""    ,  num_train_timesteps=1000    ,  clip_sample=_UpperCamelCase    ,  clip_sample_range=5.0    ,  beta_schedule="""squaredcos_cap_v2"""    ,  )

          # regular denoising components

          torch.manual_seed(0)
          _lowerCamelCase     :		Optional[int] 						=						StableUnCLIPImageNormalizer(embedding_dim=_UpperCamelCase)
          _lowerCamelCase     :		Dict 						=						DDPMScheduler(beta_schedule="""squaredcos_cap_v2""")

          torch.manual_seed(0)
          _lowerCamelCase     :		Optional[Any] 						=						CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip""")

          torch.manual_seed(0)
          _lowerCamelCase     :		Optional[int] 						=						CLIPTextModel(
              CLIPTextConfig(
                  bos_token_id=0    ,  eos_token_id=2    ,  hidden_size=_UpperCamelCase    ,  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)
          _lowerCamelCase     :		Optional[int] 						=						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)
          _lowerCamelCase     :		Tuple 						=						DDIMScheduler(
              beta_schedule="""scaled_linear"""    ,  beta_start=0.0_0_0_8_5    ,  beta_end=0.0_1_2    ,  prediction_type="""v_prediction"""    ,  set_alpha_to_one=_UpperCamelCase    ,  steps_offset=1    ,  )

          torch.manual_seed(0)
          _lowerCamelCase     :		str 						=						AutoencoderKL()

          _lowerCamelCase     :		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 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Dict=0)				->Dict:


          """simple docstring"""


          if str(_UpperCamelCase).startswith("""mps"""):
              _lowerCamelCase     :		Any 						=						torch.manual_seed(_UpperCamelCase)
          else:
              _lowerCamelCase     :		Union[str, Any] 						=						torch.Generator(device=_UpperCamelCase).manual_seed(_UpperCamelCase)
          _lowerCamelCase     :		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     :				Tuple)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						torch_device == """cpu"""

          self._test_attention_slicing_forward_pass(test_max_difference=_UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						torch_device in ["""cpu""", """mps"""]

          self._test_inference_batch_single_identical(test_max_difference=_UpperCamelCase)





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





      '''simple docstring'''






      def 	_SCREAMING_SNAKE_CASE    (					self     :				Dict)				->Any:


          """simple docstring"""


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



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


          """simple docstring"""


          _lowerCamelCase     :		List[str] 						=						load_numpy(
              """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_anime_turtle_fp16.npy""")

          _lowerCamelCase     :		int 						=						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()

          _lowerCamelCase     :		List[str] 						=						torch.Generator(device="""cpu""").manual_seed(0)
          _lowerCamelCase     :		Dict 						=						pipe("""anime turle"""    ,  generator=_UpperCamelCase    ,  output_type="""np""")

          _lowerCamelCase     :		Dict 						=						output.images[0]

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

          assert_mean_pixel_difference(_UpperCamelCase    ,  _UpperCamelCase)




      def 	_SCREAMING_SNAKE_CASE    (					self     :				str)				->int:


          """simple docstring"""


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

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

          _lowerCamelCase     :		int 						=						pipe(
              """anime turtle"""    ,  prior_num_inference_steps=2    ,  num_inference_steps=2    ,  output_type="""np"""    ,  )

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





from __future__ import annotations




class 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Tuple    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						key



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->list[str]:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						key or self.__key or 1

          # make sure key is an appropriate size
          key %= 255

          return [chr(ord(_UpperCamelCase) ^ key) for ch in content]



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Any 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->str:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          _lowerCamelCase     :		int 						=						key or self.__key or 1

          # make sure key can be any size
          while key > 255:
              key -= 255

          # This will be returned
          _lowerCamelCase     :		Optional[Any] 						=						""""""

          for ch in content:
              ans += chr(ord(_UpperCamelCase) ^ key)

          return ans



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int = 0)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""encrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.encrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True




      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any]    ,  _UpperCamelCase     :				str    ,  _UpperCamelCase     :				int)				->bool:


          """simple docstring"""


          assert isinstance(_UpperCamelCase    ,  _UpperCamelCase) and isinstance(_UpperCamelCase    ,  _UpperCamelCase)

          try:
              with open(_UpperCamelCase) as fin, open("""decrypt.out"""    ,  """w+""") as fout:
                  # actual encrypt-process
                  for line in fin:
                      fout.write(self.decrypt_string(_UpperCamelCase    ,  _UpperCamelCase))

          except OSError:
              return False

          return True


# Tests
# crypt = XORCipher()
# key = 67

# # test encrypt
# print(crypt.encrypt("hallo welt",key))
# # test decrypt
# print(crypt.decrypt(crypt.encrypt("hallo welt",key), key))

# # test encrypt_string
# print(crypt.encrypt_string("hallo welt",key))

# # test decrypt_string
# print(crypt.decrypt_string(crypt.encrypt_string("hallo welt",key),key))

# if (crypt.encrypt_file("test.txt",key)):
#       print("encrypt successful")
# else:
#       print("encrypt unsuccessful")

# if (crypt.decrypt_file("encrypt.out",key)):
#       print("decrypt successful")
# else:
#       print("decrypt unsuccessful")




