






"""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_	:    Tuple     =      '''src/transformers'''


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

lowerCAmelCase_	:    Optional[int]     =      transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCAmelCase_	:    List[Any]     =      {
    # 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       _lowerCAmelCase       (      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase	,      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			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
                     ):
                            UpperCAmelCase	    =			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}"'''	,      SCREAMING_SNAKE_CASE__	,      )
                         is not None
                     ):
                            UpperCAmelCase	    =			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:
                                   UpperCAmelCase	    =			True
                     if attribute_used:
                            break
              if attribute_used:
                     break

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

       # Special cases to be allowed
       UpperCAmelCase	    =			True
       if not attribute_used:
              UpperCAmelCase	    =			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:
                            UpperCAmelCase	    =			True
                     elif attribute in ["tie_word_embeddings"] and default_value is False:
                            UpperCAmelCase	    =			True

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

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

       return attribute_used or case_allowed






def       _lowerCAmelCase       (      lowerCAmelCase		):







       '''simple docstring'''
       UpperCAmelCase	    =			dict(inspect.signature(config_class.__init__		).parameters		)
       UpperCAmelCase	    =			[x for x in list(signature.keys()		) if x not in ["""self""", """kwargs"""]]
       UpperCAmelCase	    =			[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
       UpperCAmelCase	    =			{}
       if len(config_class.attribute_map		) > 0:
              UpperCAmelCase	    =			{v: k for k, v in config_class.attribute_map.items()}

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

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

       UpperCAmelCase	    =			[]
       for config_param, default_value in zip(SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__		):
              # `attributes` here is all the variant names for `config_param`
              UpperCAmelCase	    =			[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(SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__		):
                     unused_attributes.append(attributes[0]		)

       return sorted(SCREAMING_SNAKE_CASE__		)






def       _lowerCAmelCase       (      ):







       '''simple docstring'''
       UpperCAmelCase	    =			{}
       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.)
              UpperCAmelCase	    =			[
                  cls
                  for name, cls in inspect.getmembers(
                      inspect.getmodule(_config_class		)	,      lambda lowerCAmelCase		: inspect.isclass(SCREAMING_SNAKE_CASE__		)
                      and issubclass(SCREAMING_SNAKE_CASE__	,      SCREAMING_SNAKE_CASE__		)
                      and inspect.getmodule(SCREAMING_SNAKE_CASE__		) == inspect.getmodule(_config_class		)	,      )
              ]
              for config_class in config_classes_in_module:
                     UpperCAmelCase	    =			check_config_attributes_being_used(SCREAMING_SNAKE_CASE__		)
                     if len(SCREAMING_SNAKE_CASE__		) > 0:
                            UpperCAmelCase	    =			unused_attributes

       if len(SCREAMING_SNAKE_CASE__		) > 0:
              UpperCAmelCase	    =			"""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(SCREAMING_SNAKE_CASE__		)


if __name__ == "__main__":
 check_config_attributes()








"""simple docstring"""







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
 lowerCAmelCase_	:    Tuple            =      argparse.ArgumentParser()

 parser.add_argument(
     '''--checkpoint_path''', default=None, type=str, required=True, help='''Path to the checkpoint to convert.'''
 )
 parser.add_argument(
     '''--original_config_file''',
     type=str,
     required=True,
     help='''The YAML config file corresponding to the original architecture.''',
 )
 parser.add_argument(
     '''--num_in_channels''',
     default=None,
     type=int,
     help='''The number of input channels. If `None` number of input channels will be automatically inferred.''',
 )
 parser.add_argument(
     '''--image_size''',
     default=5_1_2,
     type=int,
     help=(
         '''The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2'''
         ''' Base. Use 768 for Stable Diffusion v2.'''
     ),
 )
 parser.add_argument(
     '''--extract_ema''',
     action='''store_true''',
     help=(
         '''Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights'''
         ''' or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield'''
         ''' higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning.'''
     ),
 )
 parser.add_argument(
     '''--upcast_attention''',
     action='''store_true''',
     help=(
         '''Whether the attention computation should always be upcasted. This is necessary when running stable'''
         ''' diffusion 2.1.'''
     ),
 )
 parser.add_argument(
     '''--from_safetensors''',
     action='''store_true''',
     help='''If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.''',
 )
 parser.add_argument(
     '''--to_safetensors''',
     action='''store_true''',
     help='''Whether to store pipeline in safetensors format or not.''',
 )
 parser.add_argument('''--dump_path''', default=None, type=str, required=True, help='''Path to the output model.''')
 parser.add_argument('''--device''', type=str, help='''Device to use (e.g. cpu, cuda:0, cuda:1, etc.)''')






 def       _lowerCAmelCase       (      lowerCAmelCase		):







        '''simple docstring'''
        if string == "True":
               return True
        elif string == "False":
               return False
        else:
               raise ValueError(F'''could not parse string as bool {string}'''		)

 parser.add_argument(
     '''--use_linear_projection''', help='''Override for use linear projection''', required=False, type=parse_bool
 )

 parser.add_argument('''--cross_attention_dim''', help='''Override for cross attention_dim''', required=False, type=int)

 lowerCAmelCase_	:    int            =      parser.parse_args()

 lowerCAmelCase_	:    Optional[int]            =      download_controlnet_from_original_ckpt(
     checkpoint_path=args.checkpoint_path,
     original_config_file=args.original_config_file,
     image_size=args.image_size,
     extract_ema=args.extract_ema,
     num_in_channels=args.num_in_channels,
     upcast_attention=args.upcast_attention,
     from_safetensors=args.from_safetensors,
     device=args.device,
     use_linear_projection=args.use_linear_projection,
     cross_attention_dim=args.cross_attention_dim,
 )





 controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)
