
'''simple docstring'''
import os
import re
import shutil
import sys
import tempfile
import unittest

import black


_snake_case         =  os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_copies  # noqa: E402


# This is the reference code that will be used in the tests.
# If DDPMSchedulerOutput is changed in scheduling_ddpm.py, this code needs to be manually updated.
_snake_case         =  '    \"""\n    Output class for the scheduler\'s step function output.\n\n    Args:\n        prev_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            Computed sample (x_{t-1}) of previous timestep. `prev_sample` should be used as next model input in the\n            denoising loop.\n        pred_original_sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` for images):\n            The predicted denoised sample (x_{0}) based on the model output from the current timestep.\n            `pred_original_sample` can be used to preview progress or for guidance.\n    \"""\n\n    prev_sample: torch.FloatTensor\n    pred_original_sample: Optional[torch.FloatTensor] = None\n'


class        a__  (							unittest.TestCase  ):


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Union[str, Any]      					=       tempfile.mkdtemp()
      os.makedirs(os.path.join(self.diffusers_dir      ,    "schedulers/"  )  )
      _lowercase      :				Any      					=       self.diffusers_dir
      shutil.copy(
          os.path.join(_UpperCamelCase      ,    "src/diffusers/schedulers/scheduling_ddpm.py"  )      ,    os.path.join(self.diffusers_dir      ,    "schedulers/scheduling_ddpm.py"  )      ,    )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				str      					=       "src/diffusers"
      shutil.rmtree(self.diffusers_dir  )


   def 						_lowerCamelCase       (			self      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase      ,    _UpperCamelCase=None  ):





      """simple docstring"""





      _lowercase      :				Optional[int]      					=       comment + f'''\nclass {class_name}(nn.Module):\n''' + class_code
      if overwrite_result is not None:
         _lowercase      :				Optional[int]      					=       comment + f'''\nclass {class_name}(nn.Module):\n''' + overwrite_result
      _lowercase      :				Optional[int]      					=       black.Mode(target_versions={black.TargetVersion.PYaa}      ,    line_length=119  )
      _lowercase      :				int      					=       black.format_str(_UpperCamelCase      ,    mode=_UpperCamelCase  )
      _lowercase      :				Dict      					=       os.path.join(self.diffusers_dir      ,    "new_code.py"  )
      with open(_UpperCamelCase      ,    "w"      ,    newline="\n"  ) as f:
         f.write(_UpperCamelCase  )
      if overwrite_result is None:
         self.assertTrue(len(check_copies.is_copy_consistent(_UpperCamelCase  )  ) == 0  )
      else:
         check_copies.is_copy_consistent(f.name      ,    overwrite=_UpperCamelCase  )
         with open(_UpperCamelCase      ,    "r"  ) as f:
            self.assertTrue(f.read()      ,    _UpperCamelCase  )


   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      _lowercase      :				Any      					=       check_copies.find_code_in_diffusers("schedulers.scheduling_ddpm.DDPMSchedulerOutput"  )
      self.assertEqual(_UpperCamelCase      ,    _UpperCamelCase  )






   def 						_lowerCamelCase       (			self  ):





      """simple docstring"""





      self.check_copy_consistency(
          "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"      ,    "DDPMSchedulerOutput"      ,    REFERENCE_CODE + "\n"      ,    )

      # With no empty line at the end
      self.check_copy_consistency(
          "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput"      ,    "DDPMSchedulerOutput"      ,    _UpperCamelCase      ,    )

      # Copy consistency with rename
      self.check_copy_consistency(
          "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"      ,    "TestSchedulerOutput"      ,    re.sub("DDPM"      ,    "Test"      ,    _UpperCamelCase  )      ,    )

      # Copy consistency with a really long name
      _lowercase      :				Optional[Any]      					=       "TestClassWithAReallyLongNameBecauseSomePeopleLikeThatForSomeReason"
      self.check_copy_consistency(
          f'''# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->{long_class_name}'''      ,    f'''{long_class_name}SchedulerOutput'''      ,    re.sub("Bert"      ,    _UpperCamelCase      ,    _UpperCamelCase  )      ,    )

      # Copy consistency with overwrite
      self.check_copy_consistency(
          "# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->Test"      ,    "TestSchedulerOutput"      ,    _UpperCamelCase      ,    overwrite_result=re.sub("DDPM"      ,    "Test"      ,    _UpperCamelCase  )      ,    )









'''simple docstring'''
from . import __version__

# Backward compatibility imports, to make sure all those objects can be found in file_utils
from .utils import (
    CLOUDFRONT_DISTRIB_PREFIX,
    CONFIG_NAME,
    DISABLE_TELEMETRY,
    DUMMY_INPUTS,
    DUMMY_MASK,
    ENV_VARS_TRUE_AND_AUTO_VALUES,
    ENV_VARS_TRUE_VALUES,
    FEATURE_EXTRACTOR_NAME,
    FLAX_WEIGHTS_NAME,
    HF_MODULES_CACHE,
    HUGGINGFACE_CO_PREFIX,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    MODEL_CARD_NAME,
    MULTIPLE_CHOICE_DUMMY_INPUTS,
    PYTORCH_PRETRAINED_BERT_CACHE,
    PYTORCH_TRANSFORMERS_CACHE,
    S3_BUCKET_PREFIX,
    SENTENCEPIECE_UNDERLINE,
    SPIECE_UNDERLINE,
    TF2_WEIGHTS_NAME,
    TF_WEIGHTS_NAME,
    TORCH_FX_REQUIRED_VERSION,
    TRANSFORMERS_CACHE,
    TRANSFORMERS_DYNAMIC_MODULE_NAME,
    USE_JAX,
    USE_TF,
    USE_TORCH,
    WEIGHTS_INDEX_NAME,
    WEIGHTS_NAME,
    ContextManagers,
    DummyObject,
    EntryNotFoundError,
    ExplicitEnum,
    ModelOutput,
    PaddingStrategy,
    PushToHubMixin,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    TensorType,
    _LazyModule,
    add_code_sample_docstrings,
    add_end_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    cached_property,
    copy_func,
    default_cache_path,
    define_sagemaker_information,
    get_cached_models,
    get_file_from_repo,
    get_full_repo_name,
    get_torch_version,
    has_file,
    http_user_agent,
    is_apex_available,
    is_bsa_available,
    is_coloredlogs_available,
    is_datasets_available,
    is_detectrona_available,
    is_faiss_available,
    is_flax_available,
    is_ftfy_available,
    is_in_notebook,
    is_ipex_available,
    is_librosa_available,
    is_offline_mode,
    is_onnx_available,
    is_pandas_available,
    is_phonemizer_available,
    is_protobuf_available,
    is_psutil_available,
    is_pyanvml_available,
    is_pyctcdecode_available,
    is_pytesseract_available,
    is_pytorch_quantization_available,
    is_rjieba_available,
    is_sagemaker_dp_enabled,
    is_sagemaker_mp_enabled,
    is_scipy_available,
    is_sentencepiece_available,
    is_seqio_available,
    is_sklearn_available,
    is_soundfile_availble,
    is_spacy_available,
    is_speech_available,
    is_tensor,
    is_tensorflow_probability_available,
    is_tfaonnx_available,
    is_tf_available,
    is_timm_available,
    is_tokenizers_available,
    is_torch_available,
    is_torch_bfaa_available,
    is_torch_cuda_available,
    is_torch_fx_available,
    is_torch_fx_proxy,
    is_torch_mps_available,
    is_torch_tfaa_available,
    is_torch_tpu_available,
    is_torchaudio_available,
    is_training_run_on_sagemaker,
    is_vision_available,
    replace_return_docstrings,
    requires_backends,
    to_numpy,
    to_py_obj,
    torch_only_method,
)






