



'''simple docstring'''






import gc
import random
import unittest

import numpy as np
import torch
from transformers import (
    CLIPImageProcessor,
    CLIPTextConfig,
    CLIPTextModel,
    CLIPTokenizer,
    CLIPVisionConfig,
    CLIPVisionModelWithProjection,
)

from diffusers import AutoencoderKL, DDIMScheduler, DDPMScheduler, StableUnCLIPImgaImgPipeline, UNetaDConditionModel
from diffusers.pipelines.pipeline_utils import DiffusionPipeline
from diffusers.pipelines.stable_diffusion.stable_unclip_image_normalizer import StableUnCLIPImageNormalizer
from diffusers.utils.import_utils import is_xformers_available
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    floats_tensor,
    load_image,
    load_numpy,
    require_torch_gpu,
    skip_mps,
    slow,
    torch_device,
)

from ..pipeline_params import TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS, TEXT_GUIDED_IMAGE_VARIATION_PARAMS
from ..test_pipelines_common import (
    PipelineKarrasSchedulerTesterMixin,
    PipelineLatentTesterMixin,
    PipelineTesterMixin,
    assert_mean_pixel_difference,
)


enable_full_determinism()

class 							a__(					__UpperCAmelCase		, __UpperCAmelCase		, __UpperCAmelCase		, unittest.TestCase	):
    lowercase__ =    StableUnCLIPImgaImgPipeline
    lowercase__ =    TEXT_GUIDED_IMAGE_VARIATION_PARAMS
    lowercase__ =    TEXT_GUIDED_IMAGE_VARIATION_BATCH_PARAMS
    lowercase__ =    frozenset(
        []	)  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
    lowercase__ =    frozenset([]	)







    def 			lowercase_       (      self :  Optional[Any]	):
      a						:					Any        =     32
      a						:					Any        =     embedder_hidden_size

      # image encoding components

      a						:					Optional[Any]        =     CLIPImageProcessor(crop_size=32						,			size=32	)

      torch.manual_seed(0	)
      a						:					Tuple        =     CLIPVisionModelWithProjection(
          CLIPVisionConfig(
              hidden_size=__snake_case						,			projection_dim=__snake_case						,			num_hidden_layers=5						,			num_attention_heads=4						,			image_size=32						,			intermediate_size=37						,			patch_size=1						,			)	)

      # regular denoising components

      torch.manual_seed(0	)
      a						:					Union[str, Any]        =     StableUnCLIPImageNormalizer(embedding_dim=__snake_case	)
      a						:					str        =     DDPMScheduler(beta_schedule='squaredcos_cap_v2'	)

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

      torch.manual_seed(0	)
      a						:					List[str]        =     CLIPTextModel(
          CLIPTextConfig(
              bos_token_id=0						,			eos_token_id=2						,			hidden_size=__snake_case						,			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=10_00						,			)	)

      torch.manual_seed(0	)
      a						:					Optional[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=__snake_case						,			layers_per_block=1						,			upcast_attention=__snake_case						,			use_linear_projection=__snake_case						,			)

      torch.manual_seed(0	)
      a						:					List[str]        =     DDIMScheduler(
          beta_schedule='scaled_linear'						,			beta_start=0.00085						,			beta_end=0.012						,			prediction_type='v_prediction'						,			set_alpha_to_one=__snake_case						,			steps_offset=1						,			)

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

      a						:					int        =     {
          # image encoding components
          'feature_extractor': feature_extractor,
          'image_encoder': image_encoder.eval(),
          # image noising components
          'image_normalizer': image_normalizer.eval(),
          'image_noising_scheduler': image_noising_scheduler,
          # regular denoising components
          'tokenizer': tokenizer,
          'text_encoder': text_encoder.eval(),
          'unet': unet.eval(),
          'scheduler': scheduler,
          'vae': vae.eval(),
      }

      return components







    def 			lowercase_       (      self :  Dict						,			__snake_case :  Any						,			__snake_case :  Any=0						,			__snake_case :  Tuple=True	):

      if str(__snake_case	).startswith('mps'	):
        a						:					List[Any]        =     torch.manual_seed(__snake_case	)
      else:
        a						:					Optional[Any]        =     torch.Generator(device=__snake_case	).manual_seed(__snake_case	)

      a						:					Union[str, Any]        =     floats_tensor((1, 3, 32, 32)						,			rng=random.Random(__snake_case	)	).to(__snake_case	)

      if pil_image:
        a						:					Any        =     input_image * 0.5 + 0.5
        a						:					int        =     input_image.clamp(0						,			1	)
        a						:					Any        =     input_image.cpu().permute(0						,			2						,			3						,			1	).float().numpy()
        a						:					Tuple        =     DiffusionPipeline.numpy_to_pil(__snake_case	)[0]

      return {
          "prompt": "An anime racoon running a marathon",
          "image": input_image,
          "generator": generator,
          "num_inference_steps": 2,
          "output_type": "np",
      }







    @skip_mps
    def 			lowercase_       (      self :  Union[str, Any]	):
      a						:					Optional[Any]        =     'cpu'  # ensure determinism for the device-dependent torch.Generator
      a						:					str        =     self.get_dummy_components()
      a						:					Optional[Any]        =     StableUnCLIPImgaImgPipeline(**__snake_case	)
      a						:					Union[str, Any]        =     sd_pipe.to(__snake_case	)
      sd_pipe.set_progress_bar_config(disable=__snake_case	)

      a						:					str        =     self.get_dummy_inputs(__snake_case	)
      inputs.update({'image_embeds': None}	)
      a						:					int        =     sd_pipe(**__snake_case	).images
      a						:					Dict        =     image[0, -3:, -3:, -1]

      assert image.shape == (1, 32, 32, 3)
      a						:					List[Any]        =     np.array([0.3872, 0.7224, 0.5601, 0.4741, 0.6872, 0.5814, 0.4636, 0.3867, 0.5078]	)

      assert np.abs(image_slice.flatten() - expected_slice	).max() < 1e-3







    def 			lowercase_       (      self :  Optional[int]	):
      a						:					str        =     torch_device in ['cpu', 'mps']

      self._test_attention_slicing_forward_pass(test_max_difference=__snake_case	)







    def 			lowercase_       (      self :  int	):
      a						:					int        =     torch_device in ['cpu', 'mps']

      self._test_inference_batch_single_identical(test_max_difference=__snake_case	)







    @unittest.skipIf(
        torch_device != 'cuda' or not is_xformers_available()						,			reason='XFormers attention is only available with CUDA and `xformers` installed'						,			)
    def 			lowercase_       (      self :  List[str]	):
      self._test_xformers_attention_forwardGenerator_pass(test_max_difference=__snake_case	)

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







    def 			lowercase_       (      self :  Union[str, Any]	):
      super().tearDown()
      gc.collect()
      torch.cuda.empty_cache()







    def 			lowercase_       (      self :  Tuple	):
      a						:					List[str]        =     load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'	)

      a						:					int        =     load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_l_img2img_anime_turtle_fp16.npy'	)

      a						:					Any        =     StableUnCLIPImgaImgPipeline.from_pretrained(
          'fusing/stable-unclip-2-1-l-img2img'						,			torch_dtype=torch.floataa	)
      pipe.to(__snake_case	)
      pipe.set_progress_bar_config(disable=__snake_case	)
      # 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						:					Any        =     torch.Generator(device='cpu'	).manual_seed(0	)
      a						:					Dict        =     pipe(__snake_case						,			'anime turle'						,			generator=__snake_case						,			output_type='np'	)

      a						:					Any        =     output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(__snake_case						,			__snake_case	)







    def 			lowercase_       (      self :  str	):
      a						:					Optional[int]        =     load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'	)

      a						:					Tuple        =     load_numpy(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/stable_unclip_2_1_h_img2img_anime_turtle_fp16.npy'	)

      a						:					int        =     StableUnCLIPImgaImgPipeline.from_pretrained(
          'fusing/stable-unclip-2-1-h-img2img'						,			torch_dtype=torch.floataa	)
      pipe.to(__snake_case	)
      pipe.set_progress_bar_config(disable=__snake_case	)
      # 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						:					Dict        =     torch.Generator(device='cpu'	).manual_seed(0	)
      a						:					Dict        =     pipe(__snake_case						,			'anime turle'						,			generator=__snake_case						,			output_type='np'	)

      a						:					Any        =     output.images[0]

      assert image.shape == (7_68, 7_68, 3)

      assert_mean_pixel_difference(__snake_case						,			__snake_case	)







    def 			lowercase_       (      self :  Union[str, Any]	):
      a						:					Optional[Any]        =     load_image(
          'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/stable_unclip/turtle.png'	)

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

      a						:					Optional[int]        =     StableUnCLIPImgaImgPipeline.from_pretrained(
          'fusing/stable-unclip-2-1-h-img2img'						,			torch_dtype=torch.floataa	)
      a						:					Optional[int]        =     pipe.to(__snake_case	)
      pipe.set_progress_bar_config(disable=__snake_case	)
      pipe.enable_attention_slicing()
      pipe.enable_sequential_cpu_offload()

      a						:					List[Any]        =     pipe(
          __snake_case						,			'anime turtle'						,			num_inference_steps=2						,			output_type='np'						,			)

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






from collections.abc import Iterable
from typing import Generic, TypeVar

lowercase      :       Any									=  TypeVar("""_T""")





class  A__    (							Generic[_T]	):




   """simple docstring"""



   def __init__(					self					,  lowercase = None)			->						None:







          '''simple docstring'''

          a__						:	list[_T] =				list(iterable or [])
          a__						:	list[_T] =				[]

   def __len__(					self)			->						int:







          '''simple docstring'''

          return len(self._stacka) + len(self._stacka)

   def __repr__(					self)			->						str:







          '''simple docstring'''

          return F'Queue({tuple(self._stacka[::-1] + self._stacka)})'

   def 							__lowercase		(					self					,  lowercase)			->						None:







          '''simple docstring'''

          self._stacka.append(lowercase)

   def 							__lowercase		(					self)			->						_T:







          '''simple docstring'''

          a__						:	List[str] =				self._stacka.pop
          a__						:	Optional[int] =				self._stacka.append

          if not self._stacka:
                 while self._stacka:
                        stacka_append(stacka_pop())

          if not self._stacka:
                 raise IndexError('Queue is empty')
          return self._stacka.pop()


if __name__ == "__main__":
    from doctest import testmod

    testmod()


