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 lowerCamelCase  (   SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE  ,			SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Dict			  =		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     :List[Any]			  =		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}\""""  ,			snake_case__  ,			)
                is not None
            ):
                __UpperCamelCase     :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:
                    __UpperCamelCase     :int			  =		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     :Any			  =		[
        '''bos_index''',
        '''eos_index''',
        '''pad_index''',
        '''unk_index''',
        '''mask_index''',
        '''image_size''',
        '''use_cache''',
        '''out_features''',
        '''out_indices''',
    ]
    __UpperCamelCase     :Optional[Any]			  =		['''encoder_no_repeat_ngram_size''']

    # Special cases to be allowed
    __UpperCamelCase     :Union[str, Any]			  =		True
    if not attribute_used:
        __UpperCamelCase     :int			  =		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     :Optional[Any]			  =		True
            elif attribute in ["tie_word_embeddings"] and default_value is False:
                __UpperCamelCase     :List[str]			  =		True

            # Allow cases without checking the default value in the configuration class
            elif attribute in attributes_to_allow + attributes_used_in_generation:
                __UpperCamelCase     :Union[str, Any]			  =		True
            elif attribute.endswith('''_token_id'''	):
                __UpperCamelCase     :int			  =		True

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

    return attribute_used or case_allowed



def lowerCamelCase  (   SCREAMING_SNAKE_CASE	):
    '''simple docstring'''







    __UpperCamelCase     :Optional[int]			  =		dict(inspect.signature(config_class.__init__	).parameters	)
    __UpperCamelCase     :Any			  =		[x for x in list(signature.keys()	) if x not in ['''self''', '''kwargs''']]
    __UpperCamelCase     :Union[str, Any]			  =		[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     :int			  =		{}
    if len(config_class.attribute_map	) > 0:
        __UpperCamelCase     :Any			  =		{v: k for k, v in config_class.attribute_map.items()}

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

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

    __UpperCamelCase     :Union[str, Any]			  =		[]
    for config_param, default_value in zip(snake_case__  ,			snake_case__	):
        # `attributes` here is all the variant names for `config_param`
        __UpperCamelCase     :Union[str, Any]			  =		[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(snake_case__  ,			snake_case__  ,			snake_case__  ,			snake_case__	):
            unused_attributes.append(attributes[0]	)

    return sorted(snake_case__	)



def lowerCamelCase  (   ):
    '''simple docstring'''







    __UpperCamelCase     :Union[str, 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.)
        __UpperCamelCase     :List[str]			  =		[
            cls
            for name, cls in inspect.getmembers(
                inspect.getmodule(_config_class	)  ,			lambda SCREAMING_SNAKE_CASE	: inspect.isclass(snake_case__	)
                and issubclass(snake_case__  ,			snake_case__	)
                and inspect.getmodule(snake_case__	) == inspect.getmodule(_config_class	)  ,			)
        ]
        for config_class in config_classes_in_module:
            __UpperCamelCase     :Tuple			  =		check_config_attributes_being_used(snake_case__	)
            if len(snake_case__	) > 0:
                __UpperCamelCase     :List[Any]			  =		unused_attributes

    if len(snake_case__	) > 0:
        __UpperCamelCase     :int			  =		'''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(snake_case__	)


if __name__ == "__main__":
    check_config_attributes()







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


__a  			=					logging.get_logger(__name__)
class  lowercase__(				UpperCAmelCase							):



	"""simple docstring"""




	a			:Union[str, Any]         =	'upernet'
	def __init__(       self  :			Dict       ,							SCREAMING_SNAKE_CASE_  :			Dict=None       ,							SCREAMING_SNAKE_CASE_  :			str=5_1_2       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.02       ,							SCREAMING_SNAKE_CASE_  :			Optional[Any]=[1, 2, 3, 6]       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=True       ,							SCREAMING_SNAKE_CASE_  :			Tuple=0.4       ,							SCREAMING_SNAKE_CASE_  :			Optional[int]=3_8_4       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_6       ,							SCREAMING_SNAKE_CASE_  :			Dict=1       ,							SCREAMING_SNAKE_CASE_  :			Tuple=False       ,							SCREAMING_SNAKE_CASE_  :			str=2_5_5       ,							**SCREAMING_SNAKE_CASE_  :			str       ,							)       ->     int:
					super().__init__(**SCREAMING_SNAKE_CASE_       )

					if backbone_config is None:
									logger.info('''`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.'''       )
									lowercase_       =					CONFIG_MAPPING['''resnet'''](out_features=['''stage1''', '''stage2''', '''stage3''', '''stage4''']       )
					elif isinstance(SCREAMING_SNAKE_CASE_       ,							SCREAMING_SNAKE_CASE_       ):
									lowercase_       =					backbone_config.get('''model_type'''       )
									lowercase_       =					CONFIG_MAPPING[backbone_model_type]
									lowercase_       =					config_class.from_dict(SCREAMING_SNAKE_CASE_       )

					lowercase_       =					backbone_config
					lowercase_       =					hidden_size
					lowercase_       =					initializer_range
					lowercase_       =					pool_scales
					lowercase_       =					use_auxiliary_head
					lowercase_       =					auxiliary_loss_weight
					lowercase_       =					auxiliary_in_channels
					lowercase_       =					auxiliary_channels
					lowercase_       =					auxiliary_num_convs
					lowercase_       =					auxiliary_concat_input
					lowercase_       =					loss_ignore_index




	def 						_lowercase  (       self  :			List[str]       )       ->     List[str]:
					lowercase_       =					copy.deepcopy(self.__dict__       )
					lowercase_       =					self.backbone_config.to_dict()
					lowercase_       =					self.__class__.model_type
					return output




