"""simple docstring"""


import argparse
import os
import re


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_dummies.py
UpperCAmelCase__     						=  """src/diffusers"""

# Matches is_xxx_available()
UpperCAmelCase__     						=  re.compile(r"""is\_([a-z_]*)_available\(\)""")
# Matches from xxx import bla
UpperCAmelCase__     						=  re.compile(r"""\s+from\s+\S*\s+import\s+([^\(\s].*)\n""")


UpperCAmelCase__     						=  """
{0} = None
"""

UpperCAmelCase__     						=  """
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})

    @classmethod
    def from_config(cls, *args, **kwargs):
        requires_backends(cls, {1})

    @classmethod
    def from_pretrained(cls, *args, **kwargs):
        requires_backends(cls, {1})
"""


UpperCAmelCase__     						=  """
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
"""






def 			__UpperCAmelCase    (	lowercase						):







  """simple docstring"""
  _UpperCAmelCase         =					_re_backend.findall(lowercase						)
  if len(lowercase						) == 0:
    return None

  return "_and_".join(lowercase						)






def 			__UpperCAmelCase    (	):







  """simple docstring"""
  with open(os.path.join(lowercase      ,"""__init__.py"""						)      ,"""r"""      ,encoding="""utf-8"""      ,newline="""\n"""						) as f:
    _UpperCAmelCase         =					f.readlines()

  # Get to the point we do the actual imports for type checking
  _UpperCAmelCase         =					0
  _UpperCAmelCase         =					{}
  # Go through the end of the file
  while line_index < len(lowercase						):
    # If the line contains is_backend_available, we grab all objects associated with the `else` block
    _UpperCAmelCase         =					find_backend(lines[line_index]						)
    if backend is not None:
      while not lines[line_index].startswith("""else:"""						):
        line_index += 1
      line_index += 1
      _UpperCAmelCase         =					[]
      # Until we unindent, add backend objects to the list
      while line_index < len(lowercase						) and len(lines[line_index]						) > 1:
        _UpperCAmelCase         =					lines[line_index]
        _UpperCAmelCase         =					_re_single_line_import.search(lowercase						)
        if single_line_import_search is not None:
          objects.extend(single_line_import_search.groups()[0].split(""", """						)						)
        elif line.startswith(""" """ * 8						):
          objects.append(line[8:-2]						)
        line_index += 1

      if len(lowercase						) > 0:
        _UpperCAmelCase         =					objects
    else:
      line_index += 1

  return backend_specific_objects






def 			__UpperCAmelCase    (	lowercase      ,lowercase						):







  """simple docstring"""
  if name.isupper():
    return DUMMY_CONSTANT.format(lowercase						)
  elif name.islower():
    return DUMMY_FUNCTION.format(lowercase      ,lowercase						)
  else:
    return DUMMY_CLASS.format(lowercase      ,lowercase						)






def 			__UpperCAmelCase    (	lowercase=None						):







  """simple docstring"""
  if backend_specific_objects is None:
    _UpperCAmelCase         =					read_init()
  # For special correspondence backend to module name as used in the function requires_modulename
  _UpperCAmelCase         =					{}

  for backend, objects in backend_specific_objects.items():
    _UpperCAmelCase         =					"""[""" + """, """.join(f'''"{b}"''' for b in backend.split("""_and_"""						)						) + """]"""
    _UpperCAmelCase         =					"""# This file is autogenerated by the command `make fix-copies`, do not edit.\n"""
    dummy_file += "from ..utils import DummyObject, requires_backends\n\n"
    dummy_file += "\n".join([create_dummy_object(lowercase      ,lowercase						) for o in objects]						)
    _UpperCAmelCase         =					dummy_file

  return dummy_files






def 			__UpperCAmelCase    (	lowercase=False						):







  """simple docstring"""
  _UpperCAmelCase         =					create_dummy_files()
  # For special correspondence backend to shortcut as used in utils/dummy_xxx_objects.py
  _UpperCAmelCase         =					{"""torch""": """pt"""}

  # Locate actual dummy modules and read their content.
  _UpperCAmelCase         =					os.path.join(lowercase      ,"""utils"""						)
  _UpperCAmelCase         =					{
      backend: os.path.join(lowercase      ,f'''dummy_{short_names.get(lowercase      ,lowercase						)}_objects.py'''						)
      for backend in dummy_files.keys()
  }

  _UpperCAmelCase         =					{}
  for backend, file_path in dummy_file_paths.items():
    if os.path.isfile(lowercase						):
      with open(lowercase      ,"""r"""      ,encoding="""utf-8"""      ,newline="""\n"""						) as f:
        _UpperCAmelCase         =					f.read()
    else:
      _UpperCAmelCase         =					""""""

  for backend in dummy_files.keys():
    if dummy_files[backend] != actual_dummies[backend]:
      if overwrite:
        print(
            f'''Updating diffusers.utils.dummy_{short_names.get(lowercase      ,lowercase						)}_objects.py as the main '''
            """__init__ has new objects."""						)
        with open(dummy_file_paths[backend]      ,"""w"""      ,encoding="""utf-8"""      ,newline="""\n"""						) as f:
          f.write(dummy_files[backend]						)
      else:
        raise ValueError(
            """The main __init__ has objects that are not present in """
            f'''diffusers.utils.dummy_{short_names.get(lowercase      ,lowercase						)}_objects.py. Run `make fix-copies` '''
            """to fix this."""						)


if __name__ == "__main__":
    UpperCAmelCase__     						=  argparse.ArgumentParser()
    parser.add_argument("""--fix_and_overwrite""", action="""store_true""", help="""Whether to fix inconsistencies.""")
    UpperCAmelCase__     						=  parser.parse_args()

    check_dummies(args.fix_and_overwrite)

"""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
