


"""simple docstring"""







import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest






class 						_A  (  lowerCAmelCase__  ):







     """simple docstring"""




     UpperCAmelCase	:							List[str]     =	(KDPMaDiscreteScheduler,)
     UpperCAmelCase	:							List[str]     =	1_0






     def      __snake_case							( self    :   Any    ,			**__UpperCAmelCase    :   List[str]):
       a						:		Union[str, Any]											=      {
           "num_train_timesteps": 1100,
           "beta_start": 0.0_001,
           "beta_end": 0.02,
           "beta_schedule": "linear",
       }

       config.update(**a__)
       return config






     def      __snake_case							( self    :   int):
       for timesteps in [10, 50, 100, 1000]:
         self.check_over_configs(num_train_timesteps=a__)






     def      __snake_case							( self    :   Any):
       for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001]    ,			[0.0_002, 0.002, 0.02]):
         self.check_over_configs(beta_start=a__    ,			beta_end=a__)






     def      __snake_case							( self    :   Optional[int]):
       for schedule in ["linear", "scaled_linear"]:
         self.check_over_configs(beta_schedule=a__)






     def      __snake_case							( self    :   int):
       for prediction_type in ["epsilon", "v_prediction"]:
         self.check_over_configs(prediction_type=a__)






     def      __snake_case							( self    :   Tuple):
       a						:		str											=      self.scheduler_classes[0]
       a						:		Optional[int]											=      self.get_scheduler_config(prediction_type="v_prediction")
       a						:		Union[str, Any]											=      scheduler_class(**a__)

       scheduler.set_timesteps(self.num_inference_steps)

       a						:		str											=      self.dummy_model()
       a						:		Tuple											=      self.dummy_sample_deter * scheduler.init_noise_sigma
       a						:		Dict											=      sample.to(a__)

       for i, t in enumerate(scheduler.timesteps):
         a						:		Optional[Any]											=      scheduler.scale_model_input(a__    ,			a__)

         a						:		List[Any]											=      model(a__    ,			a__)

         a						:		Any											=      scheduler.step(a__    ,			a__    ,			a__)
         a						:		Optional[int]											=      output.prev_sample

       a						:		Optional[Any]											=      torch.sum(torch.abs(a__))
       a						:		Tuple											=      torch.mean(torch.abs(a__))

       if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 4.6934e-07) < 1e-2
         assert abs(result_mean.item() - 6.1112e-10) < 1e-3
       else:
         # CUDA
         assert abs(result_sum.item() - 4.693428650170972e-07) < 1e-2
         assert abs(result_mean.item() - 0.0_002) < 1e-3






     def      __snake_case							( self    :   Dict):
       if torch_device == "mps":
         return
       a						:		List[str]											=      self.scheduler_classes[0]
       a						:		Dict											=      self.get_scheduler_config()
       a						:		str											=      scheduler_class(**a__)

       scheduler.set_timesteps(self.num_inference_steps)

       a						:		Optional[Any]											=      self.dummy_model()
       a						:		str											=      self.dummy_sample_deter * scheduler.init_noise_sigma
       a						:		Tuple											=      sample.to(a__)

       for i, t in enumerate(scheduler.timesteps):
         a						:		Union[str, Any]											=      scheduler.scale_model_input(a__    ,			a__)

         a						:		Any											=      model(a__    ,			a__)

         a						:		str											=      scheduler.step(a__    ,			a__    ,			a__)
         a						:		Union[str, Any]											=      output.prev_sample

       a						:		Optional[int]											=      torch.sum(torch.abs(a__))
       a						:		Dict											=      torch.mean(torch.abs(a__))

       if torch_device in ["cpu", "mps"]:
         assert abs(result_sum.item() - 20.4_125) < 1e-2
         assert abs(result_mean.item() - 0.0_266) < 1e-3
       else:
         # CUDA
         assert abs(result_sum.item() - 20.4_125) < 1e-2
         assert abs(result_mean.item() - 0.0_266) < 1e-3






     def      __snake_case							( self    :   int):
       if torch_device == "mps":
         return
       a						:		Tuple											=      self.scheduler_classes[0]
       a						:		Tuple											=      self.get_scheduler_config()
       a						:		int											=      scheduler_class(**a__)

       scheduler.set_timesteps(self.num_inference_steps    ,			device=a__)

       a						:		Dict											=      self.dummy_model()
       a						:		List[Any]											=      self.dummy_sample_deter.to(a__) * scheduler.init_noise_sigma

       for t in scheduler.timesteps:
         a						:		Optional[int]											=      scheduler.scale_model_input(a__    ,			a__)

         a						:		Optional[int]											=      model(a__    ,			a__)

         a						:		Any											=      scheduler.step(a__    ,			a__    ,			a__)
         a						:		List[Any]											=      output.prev_sample

       a						:		Optional[int]											=      torch.sum(torch.abs(a__))
       a						:		str											=      torch.mean(torch.abs(a__))

       if str(a__).startswith("cpu"):
         # The following sum varies between 148 and 156 on mps. Why?
         assert abs(result_sum.item() - 20.4_125) < 1e-2
         assert abs(result_mean.item() - 0.0_266) < 1e-3
       else:
         # CUDA
         assert abs(result_sum.item() - 20.4_125) < 1e-2
         assert abs(result_mean.item() - 0.0_266) < 1e-3










"""simple docstring"""







import inspect
import os
import re

from transformers.configuration_utils import PretrainedConfig
from transformers.utils import direct_transformers_import


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


# This is to make sure the transformers module imported is the one in the repo.
__lowercase    =				direct_transformers_import(PATH_TO_TRANSFORMERS)

__lowercase    =				transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowercase    =				{
    # used to compute the property `self.chunk_length`
    """EncodecConfig""": ["""overlap"""],
    # used as `self.bert_model = BertModel(config, ...)`
    """DPRConfig""": True,
    # not used in modeling files, but it's an important information
    """FSMTConfig""": ["""langs"""],
    # used internally in the configuration class file
    """GPTNeoConfig""": ["""attention_types"""],
    # used internally in the configuration class file
    """EsmConfig""": ["""is_folding_model"""],
    # used during training (despite we don't have training script for these models yet)
    """Mask2FormerConfig""": ["""ignore_value"""],
    # `ignore_value` used during training (despite we don't have training script for these models yet)
    # `norm` used in conversion script (despite not using in the modeling file)
    """OneFormerConfig""": ["""ignore_value""", """norm"""],
    # used during preprocessing and collation, see `collating_graphormer.py`
    """GraphormerConfig""": ["""spatial_pos_max"""],
    # used internally in the configuration class file
    """T5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    # `tokenizer_class` get default value `T5Tokenizer` intentionally
    """MT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    """UMT5Config""": ["""feed_forward_proj""", """tokenizer_class"""],
    # used internally in the configuration class file
    """LongT5Config""": ["""feed_forward_proj"""],
    # used internally in the configuration class file
    """SwitchTransformersConfig""": ["""feed_forward_proj"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """BioGptConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """GLPNConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """SegformerConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """CvtConfig""": ["""layer_norm_eps"""],
    # having default values other than `1e-5` - we can't fix them without breaking
    """PerceiverConfig""": ["""layer_norm_eps"""],
    # used internally to calculate the feature size
    """InformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """TimeSeriesTransformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate the feature size
    """AutoformerConfig""": ["""num_static_real_features""", """num_time_features"""],
    # used internally to calculate `mlp_dim`
    """SamVisionConfig""": ["""mlp_ratio"""],
    # For (head) training, but so far not implemented
    """ClapAudioConfig""": ["""num_classes"""],
    # Not used, but providing useful information to users
    """SpeechT5HifiGanConfig""": ["""sampling_rate"""],
}


# TODO (ydshieh): Check the failing cases, try to fix them or move some cases to the above block once we are sure
SPECIAL_CASES_TO_ALLOW.update(
    {
        """CLIPSegConfig""": True,
        """DeformableDetrConfig""": True,
        """DetaConfig""": True,
        """DinatConfig""": True,
        """DonutSwinConfig""": True,
        """EfficientFormerConfig""": True,
        """FSMTConfig""": True,
        """JukeboxConfig""": True,
        """LayoutLMv2Config""": True,
        """MaskFormerSwinConfig""": True,
        """MT5Config""": True,
        """NatConfig""": True,
        """OneFormerConfig""": True,
        """PerceiverConfig""": True,
        """RagConfig""": True,
        """SpeechT5Config""": True,
        """SwinConfig""": True,
        """Swin2SRConfig""": True,
        """Swinv2Config""": True,
        """SwitchTransformersConfig""": True,
        """TableTransformerConfig""": True,
        """TapasConfig""": True,
        """TransfoXLConfig""": True,
        """UniSpeechConfig""": True,
        """UniSpeechSatConfig""": True,
        """WavLMConfig""": True,
        """WhisperConfig""": True,
        # TODO: @Arthur (for `alignment_head` and `alignment_layer`)
        """JukeboxPriorConfig""": True,
        # TODO: @Younes (for `is_decoder`)
        """Pix2StructTextConfig""": True,
    }
)


def 					lowercase      (    A_  ,       A_  ,       A_  ,       A_	)->				Optional[Any]:
  '''simple docstring'''







  a						:		List[str]											=      False
  for attribute in attributes:
    for modeling_source in source_strings:
      # check if we can find `config.xxx`, `getattr(config, "xxx", ...)` or `getattr(self.config, "xxx", ...)`
      if (
          F'''config.{attribute}''' in modeling_source
          or F'''getattr(config, "{attribute}"''' in modeling_source
          or F'''getattr(self.config, "{attribute}"''' in modeling_source
      ):
        a						:		str											=      True
      # Deal with multi-line cases
      elif (
          re.search(
              RF'''getattr[ \t\v\n\r\f]*\([ \t\v\n\r\f]*(self\.)?config,[ \t\v\n\r\f]*"{attribute}"'''  ,       A_  ,       )
          is not None
      ):
        a						:		List[Any]											=      True
      # `SequenceSummary` is called with `SequenceSummary(config)`
      elif attribute in [
          "summary_type",
          "summary_use_proj",
          "summary_activation",
          "summary_last_dropout",
          "summary_proj_to_labels",
          "summary_first_dropout",
      ]:
        if "SequenceSummary" in modeling_source:
          a						:		str											=      True
      if attribute_used:
        break
    if attribute_used:
      break

    # common and important attributes, even if they do not always appear in the modeling files
  a						:		Tuple											=      [
      "bos_index",
      "eos_index",
      "pad_index",
      "unk_index",
      "mask_index",
      "image_size",
      "use_cache",
      "out_features",
      "out_indices",
  ]
  a						:		str											=      ["encoder_no_repeat_ngram_size"]

  # Special cases to be allowed
  a						:		int											=      True
  if not attribute_used:
    a						:		Dict											=      False
    for attribute in attributes:
      # Allow if the default value in the configuration class is different from the one in `PretrainedConfig`
      if attribute in ["is_encoder_decoder"] and default_value is True:
        a						:		Optional[int]											=      True
      elif attribute in ["tie_word_embeddings"] and default_value is False:
        a						:		List[Any]											=      True

      # Allow cases without checking the default value in the configuration class
      elif attribute in attributes_to_allow + attributes_used_in_generation:
        a						:		str											=      True
      elif attribute.endswith("_token_id"	):
        a						:		str											=      True

      # configuration class specific cases
      if not case_allowed:
        a						:		Union[str, Any]											=      SPECIAL_CASES_TO_ALLOW.get(config_class.__name__  ,       []	)
        a						:		Optional[Any]											=      allowed_cases is True or attribute in allowed_cases

  return attribute_used or case_allowed


def 					lowercase      (    A_	)->				Tuple:
  '''simple docstring'''







  a						:		Optional[int]											=      dict(inspect.signature(config_class.__init__	).parameters	)
  a						:		Any											=      [x for x in list(signature.keys()	) if x not in ["self", "kwargs"]]
  a						:		str											=      [signature[param].default for param in parameter_names]

  # If `attribute_map` exists, an attribute can have different names to be used in the modeling files, and as long
  # as one variant is used, the test should pass
  a						:		Dict											=      {}
  if len(config_class.attribute_map	) > 0:
    a						:		int											=      {v: k for k, v in config_class.attribute_map.items()}

  # Get the path to modeling source files
  a						:		int											=      inspect.getsourcefile(A_	)
  a						:		Union[str, Any]											=      os.path.dirname(A_	)
  # Let's check against all frameworks: as long as one framework uses an attribute, we are good.
  a						:		Optional[Any]											=      [os.path.join(A_  ,       A_	) for fn in os.listdir(A_	) if fn.startswith("modeling_"	)]

  # Get the source code strings
  a						:		Tuple											=      []
  for path in modeling_paths:
    if os.path.isfile(A_	):
      with open(A_	) as fp:
        modeling_sources.append(fp.read()	)

  a						:		Optional[Any]											=      []
  for config_param, default_value in zip(A_  ,       A_	):
    # `attributes` here is all the variant names for `config_param`
    a						:		str											=      [config_param]
    # some configuration classes have non-empty `attribute_map`, and both names could be used in the
    # corresponding modeling files. As long as one of them appears, it is fine.
    if config_param in reversed_attribute_map:
      attributes.append(reversed_attribute_map[config_param]	)

    if not check_attribute_being_used(A_  ,       A_  ,       A_  ,       A_	):
      unused_attributes.append(attributes[0]	)

  return sorted(A_	)


def 					lowercase      (    )->				str:
  '''simple docstring'''







  a						:		List[Any]											=      {}
  for _config_class in list(CONFIG_MAPPING.values()	):
    # Skip deprecated models
    if "models.deprecated" in _config_class.__module__:
      continue
    # Some config classes are not in `CONFIG_MAPPING` (e.g. `CLIPVisionConfig`, `Blip2VisionConfig`, etc.)
    a						:		str											=      [
        cls
        for name, cls in inspect.getmembers(
            inspect.getmodule(_config_class	)  ,       lambda A_	: inspect.isclass(A_	)
            and issubclass(A_  ,       A_	)
            and inspect.getmodule(A_	) == inspect.getmodule(_config_class	)  ,       )
    ]
    for config_class in config_classes_in_module:
      a						:		Union[str, Any]											=      check_config_attributes_being_used(A_	)
      if len(A_	) > 0:
        a						:		Dict											=      unused_attributes

  if len(A_	) > 0:
    a						:		Union[str, Any]											=      "The following configuration classes contain unused attributes in the corresponding modeling files:\n"
    for name, attributes in configs_with_unused_attributes.items():
      error += F'''{name}: {attributes}\n'''

    raise ValueError(A_	)


if __name__ == "__main__":
 check_config_attributes()






