"""simple docstring"""


import importlib
import os
import sys


# This is required to make the module import works (when the python process is running from the root of the repo)
sys.path.append(""".""")






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					test_file.split(os.path.sep						)
  if components[0:2] != ["tests", "models"]:
    raise ValueError(
        """`test_file` should start with `tests/models/` (with `/` being the OS specific path separator). Got """
        f'''{test_file} instead.'''						)
  _UpperCAmelCase         =					components[-1]
  if not test_fn.endswith("""py"""						):
    raise ValueError(f'''`test_file` should be a python file. Got {test_fn} instead.'''						)
  if not test_fn.startswith("""test_modeling_"""						):
    raise ValueError(
        f'''`test_file` should point to a file name of the form `test_modeling_*.py`. Got {test_fn} instead.'''						)

  _UpperCAmelCase         =					components[:-1] + [test_fn.replace(""".py"""      ,""""""						)]
  _UpperCAmelCase         =					""".""".join(lowercase						)

  return test_module_path






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_module_path(lowercase						)
  _UpperCAmelCase         =					importlib.import_module(lowercase						)

  return test_module






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					get_test_module(lowercase						)
  for attr in dir(lowercase						):
    if attr.endswith("""ModelTester"""						):
      tester_classes.append(getattr(lowercase      ,lowercase						)						)

    # sort with class names
  return sorted(lowercase      ,key=lambda lowercase						: x.__name__						)






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					[]
  _UpperCAmelCase         =					get_test_module(lowercase						)
  for attr in dir(lowercase						):
    _UpperCAmelCase         =					getattr(lowercase      ,lowercase						)
    # (TF/Flax)ModelTesterMixin is also an attribute in specific model test module. Let's exclude them by checking
    # `all_model_classes` is not empty (which also excludes other special classes).
    _UpperCAmelCase         =					getattr(lowercase      ,"""all_model_classes"""      ,[]						)
    if len(lowercase						) > 0:
      test_classes.append(lowercase						)

    # sort with class names
  return sorted(lowercase      ,key=lambda lowercase						: x.__name__						)






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_test_classes(lowercase						)
  _UpperCAmelCase         =					set()
  for test_class in test_classes:
    model_classes.update(test_class.all_model_classes						)

  # sort with class names
  return sorted(lowercase      ,key=lambda lowercase						: x.__name__						)






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					test_class()
  if hasattr(lowercase      ,"""setUp"""						):
    test.setUp()

  _UpperCAmelCase         =					None
  if hasattr(lowercase      ,"""model_tester"""						):
    # `(TF/Flax)ModelTesterMixin` has this attribute default to `None`. Let's skip this case.
    if test.model_tester is not None:
      _UpperCAmelCase         =					test.model_tester.__class__

  return model_tester






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_test_classes(lowercase						)

  _UpperCAmelCase         =					[]
  for test_class in test_classes:
    if model_class in test_class.all_model_classes:
      target_test_classes.append(lowercase						)

    # sort with class names
  return sorted(lowercase      ,key=lambda lowercase						: x.__name__						)






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_test_classes_for_model(lowercase      ,lowercase						)

  _UpperCAmelCase         =					[]
  for test_class in test_classes:
    _UpperCAmelCase         =					get_model_tester_from_test_class(lowercase						)
    if tester_class is not None:
      tester_classes.append(lowercase						)

    # sort with class names
  return sorted(lowercase      ,key=lambda lowercase						: x.__name__						)






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_test_classes(lowercase						)
  _UpperCAmelCase         =					{test_class: get_model_tester_from_test_class(lowercase						) for test_class in test_classes}
  return test_tester_mapping






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_model_classes(lowercase						)
  _UpperCAmelCase         =					{
      model_class: get_test_classes_for_model(lowercase      ,lowercase						) for model_class in model_classes
  }
  return model_test_mapping






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					get_model_classes(lowercase						)
  _UpperCAmelCase         =					{
      model_class: get_tester_classes_for_model(lowercase      ,lowercase						) for model_class in model_classes
  }
  return model_to_tester_mapping






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  if isinstance(lowercase      ,lowercase						):
    return o
  elif isinstance(lowercase      ,lowercase						):
    return o.__name__
  elif isinstance(lowercase      ,(list, tuple)						):
    return [to_json(lowercase						) for x in o]
  elif isinstance(lowercase      ,lowercase						):
    return {to_json(lowercase						): to_json(lowercase						) for k, v in o.items()}
  else:
    return o

"""simple docstring"""


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 				a (						lowerCAmelCase_		,							lowerCAmelCase_		,							lowerCAmelCase_		,							unittest.TestCase      ):
    _snake_case   :     int   							=							StableUnCLIPPipeline
    _snake_case   :     str   							=							TEXT_TO_IMAGE_PARAMS
    _snake_case   :     Any   							=							TEXT_TO_IMAGE_BATCH_PARAMS
    _snake_case   :     Optional[Any]   							=							TEXT_TO_IMAGE_IMAGE_PARAMS
    _snake_case   :     str   							=							TEXT_TO_IMAGE_IMAGE_PARAMS

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


    def    lowerCAmelCase_     (    self		:     Optional[int]    ):
      _UpperCAmelCase         =					32
      _UpperCAmelCase         =					embedder_hidden_size

      # prior components

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

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

      torch.manual_seed(0    )
      _UpperCAmelCase         =					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         =					StableUnCLIPImageNormalizer(embedding_dim=__lowerCAmelCase    )
      _UpperCAmelCase         =					DDPMScheduler(beta_schedule="""squaredcos_cap_v2"""    )

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

      torch.manual_seed(0    )
      _UpperCAmelCase         =					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         =					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         =					DDIMScheduler(
          beta_schedule="""scaled_linear"""      , beta_start=0.00_085      , beta_end=0.012      , prediction_type="""v_prediction"""      , set_alpha_to_one=__lowerCAmelCase      , steps_offset=1      , )

      torch.manual_seed(0    )
      _UpperCAmelCase         =					AutoencoderKL()

      _UpperCAmelCase         =					{
          # 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		:     Optional[int]      , __lowerCAmelCase		:     List[Any]      , __lowerCAmelCase		:     str=0    ):
      if str(__lowerCAmelCase    ).startswith("""mps"""    ):
        _UpperCAmelCase         =					torch.manual_seed(__lowerCAmelCase    )
      else:
        _UpperCAmelCase         =					torch.Generator(device=__lowerCAmelCase    ).manual_seed(__lowerCAmelCase    )
      _UpperCAmelCase         =					{
          """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]    ):
      _UpperCAmelCase         =					torch_device == """cpu"""

      self._test_attention_slicing_forward_pass(test_max_difference=__lowerCAmelCase    )


    def    lowerCAmelCase_     (    self		:     List[str]    ):
      _UpperCAmelCase         =					torch_device in ["""cpu""", """mps"""]

      self._test_inference_batch_single_identical(test_max_difference=__lowerCAmelCase    )



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


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


    def    lowerCAmelCase_     (    self		:     List[Any]    ):
      _UpperCAmelCase         =					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         =					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         =					torch.Generator(device="""cpu"""    ).manual_seed(0    )
      _UpperCAmelCase         =					pipe("""anime turle"""      , generator=__lowerCAmelCase      , output_type="""np"""    )

      _UpperCAmelCase         =					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()

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

      _UpperCAmelCase         =					pipe(
          """anime turtle"""      , prior_num_inference_steps=2      , num_inference_steps=2      , output_type="""np"""      , )

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