






"""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
SCREAMING_SNAKE_CASE						:						int					   =       '''src/transformers'''


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

SCREAMING_SNAKE_CASE						:						str					   =       transformers.models.auto.configuration_auto.CONFIG_MAPPING

SCREAMING_SNAKE_CASE						:						Dict					   =       {
    # 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__				):
				A__							 =       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__							 =       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}"'''       ,lowerCAmelCase__       ,)
												    is not None
												):
																A__							 =       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__							 =       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__							 =       [
				    'bos_index',
				    'eos_index',
				    'pad_index',
				    'unk_index',
				    'mask_index',
				    'image_size',
				    'use_cache',
				    'out_features',
				    'out_indices',
				]
				A__							 =       ['encoder_no_repeat_ngram_size']

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

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

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

				return attribute_used or case_allowed



def  __lowerCamelCase					(					lowerCAmelCase__				):
				A__							 =       dict(inspect.signature(config_class.__init__				).parameters				)
				A__							 =       [x for x in list(signature.keys()				) if x not in ['self', 'kwargs']]
				A__							 =       [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__							 =       {}
				if len(config_class.attribute_map				) > 0:
								A__							 =       {v: k for k, v in config_class.attribute_map.items()}

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

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

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

				return sorted(lowerCAmelCase__				)



def  __lowerCamelCase					(					):
				A__							 =       {}
				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__							 =       [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class				)       ,lambda lowerCAmelCase__				: inspect.isclass(lowerCAmelCase__				)
								        and issubclass(lowerCAmelCase__       ,lowerCAmelCase__				)
								        and inspect.getmodule(lowerCAmelCase__				) == inspect.getmodule(_config_class				)       ,)
								]
								for config_class in config_classes_in_module:
												A__							 =       check_config_attributes_being_used(lowerCAmelCase__				)
												if len(lowerCAmelCase__				) > 0:
																A__							 =       unused_attributes

				if len(lowerCAmelCase__				) > 0:
								A__							 =       '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(lowerCAmelCase__				)


if __name__ == "__main__":
					check_config_attributes()










"""simple docstring"""






from ..utils import DummyObject, requires_backends





class       snake_case_       (    metaclass=_lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							List[str]   			=					["""note_seq"""]


	def __init__(      self  ,					*__a  ,					**__a						):


					"""simple docstring"""

					requires_backends(self  ,					['note_seq']						)


	@classmethod
	def       _UpperCAmelCase						(      cls  ,					*__a  ,					**__a						):


					"""simple docstring"""

					requires_backends(cls  ,					['note_seq']						)






	@classmethod
	def       _UpperCAmelCase						(      cls  ,					*__a  ,					**__a						):


					"""simple docstring"""

					requires_backends(cls  ,					['note_seq']						)


