
'''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
__lowerCAmelCase         =    """src/transformers"""


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

__lowerCAmelCase         =    transformers.models.auto.configuration_auto.CONFIG_MAPPING

__lowerCAmelCase         =    {
    # 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 					UpperCAmelCase_		(__a      :    int ,	__a      :    Any ,	__a      :    int ,	__a      :    Optional[int] ):






   """simple docstring"""

   _a				:	Tuple			=   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[str]			=   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				:	Union[str, Any]			=   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				:	Optional[Any]			=   [
       'bos_index',
       'eos_index',
       'pad_index',
       'unk_index',
       'mask_index',
       'image_size',
       'use_cache',
       'out_features',
       'out_indices',
   ]
   _a				:	Dict			=   ['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				:	Any			=   True
         elif attribute in ["tie_word_embeddings"] and default_value is False:
            _a				:	Dict			=   True

         # Allow cases without checking the default value in the configuration class
         elif attribute in attributes_to_allow + attributes_used_in_generation:
            _a				:	List[Any]			=   True
         elif attribute.endswith('_token_id' ):
            _a				:	Optional[Any]			=   True

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

   return attribute_used or case_allowed





def 					UpperCAmelCase_		(__a      :    str ):






   """simple docstring"""

   _a				:	List[Any]			=   dict(inspect.signature(config_class.__init__ ).parameters )
   _a				:	Any			=   [x for x in list(signature.keys() ) if x not in ['self', 'kwargs']]
   _a				:	Dict			=   [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				:	str			=   {v: k for k, v in config_class.attribute_map.items()}

   # Get the path to modeling source files
   _a				:	Dict			=   inspect.getsourcefile(__a )
   _a				:	Dict			=   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				:	Optional[int]			=   []
   for path in modeling_paths:
      if os.path.isfile(__a ):
         with open(__a ) as fp:
            modeling_sources.append(fp.read() )

   _a				:	Tuple			=   []
   for config_param, default_value in zip(__a ,	__a ):
      # `attributes` here is all the variant names for `config_param`
      _a				:	Tuple			=   [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 					UpperCAmelCase_		():






   """simple docstring"""

   _a				:	int			=   {}
   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				:	Tuple			=   [
          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				:	Dict			=   check_config_attributes_being_used(__a )
         if len(__a ) > 0:
            _a				:	List[Any]			=   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()





'''simple docstring'''







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import KarrasVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class UpperCAmelCase__	( lowercase__	):





  """simple docstring"""

  __UpperCAmelCase				:				UNetaDModel
  __UpperCAmelCase				:				KarrasVeScheduler


  def __init__(		self      :     Union[str, Any]							,_a      :     UNetaDModel							,_a      :     KarrasVeScheduler       ):


     '''simple docstring'''






     super().__init__()
     self.register_modules(unet=_a							,scheduler=_a       )


  @torch.no_grad()
  def __call__(		self      :     List[Any]							,_a      :     int = 1							,_a      :     int = 50							,_a      :     Optional[Union[torch.Generator, List[torch.Generator]]] = None							,_a      :     Optional[str] = "pil"							,_a      :     bool = True							,**_a      :     List[Any]							,):


     '''simple docstring'''






     _a				:	Any			=   self.unet.config.sample_size
     _a				:	Optional[int]			=   (batch_size, 3, img_size, img_size)

     _a				:	Dict			=   self.unet

     # sample x_0 ~ N(0, sigma_0^2 * I)
     _a				:	Dict			=   randn_tensor(_a							,generator=_a							,device=self.device       ) * self.scheduler.init_noise_sigma

     self.scheduler.set_timesteps(_a       )

     for t in self.progress_bar(self.scheduler.timesteps       ):
        # here sigma_t == t_i from the paper
        _a				:	Optional[int]			=   self.scheduler.schedule[t]
        _a				:	List[str]			=   self.scheduler.schedule[t - 1] if t > 0 else 0

        # 1. Select temporarily increased noise level sigma_hat
        # 2. Add new noise to move from sample_i to sample_hat
        _a,  _a				:	List[Any]			=   self.scheduler.add_noise_to_input(_a							,_a							,generator=_a       )

        # 3. Predict the noise residual given the noise magnitude `sigma_hat`
        # The model inputs and output are adjusted by following eq. (213) in [1].
        _a				:	Optional[int]			=   (sigma_hat / 2) * model((sample_hat + 1) / 2							,sigma_hat / 2       ).sample

        # 4. Evaluate dx/dt at sigma_hat
        # 5. Take Euler step from sigma to sigma_prev
        _a				:	Tuple			=   self.scheduler.step(_a							,_a							,_a							,_a       )

        if sigma_prev != 0:
           # 6. Apply 2nd order correction
           # The model inputs and output are adjusted by following eq. (213) in [1].
           _a				:	Optional[int]			=   (sigma_prev / 2) * model((step_output.prev_sample + 1) / 2							,sigma_prev / 2       ).sample
           _a				:	Optional[Any]			=   self.scheduler.step_correct(
               _a							,_a							,_a							,_a							,step_output.prev_sample							,step_output['derivative']							,)
        _a				:	Dict			=   step_output.prev_sample

     _a				:	Tuple			=   (sample / 2 + 0.5).clamp(0							,1       )
     _a				:	Optional[Any]			=   sample.cpu().permute(0							,2							,3							,1       ).numpy()
     if output_type == "pil":
        _a				:	List[str]			=   self.numpy_to_pil(_a       )

     if not return_dict:
        return (image,)

     return ImagePipelineOutput(images=_a       )



