
"""simple docstring"""







import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


lowerCamelCase__           =	logging.get_logger(__name__)

lowerCamelCase__           =	{
    """Intel/dpt-large""": """https://huggingface.co/Intel/dpt-large/resolve/main/config.json""",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}




class 				A__ (			SCREAMING_SNAKE_CASE__):
  A_		: Optional[int]            =			'dpt'

  def __init__(   self ,	_SCREAMING_SNAKE_CASE=7_68 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=12 ,	_SCREAMING_SNAKE_CASE=30_72 ,	_SCREAMING_SNAKE_CASE="gelu" ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.0 ,	_SCREAMING_SNAKE_CASE=0.02 ,	_SCREAMING_SNAKE_CASE=1E-12 ,	_SCREAMING_SNAKE_CASE=3_84 ,	_SCREAMING_SNAKE_CASE=16 ,	_SCREAMING_SNAKE_CASE=3 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=[2, 5, 8, 11] ,	_SCREAMING_SNAKE_CASE="project" ,	_SCREAMING_SNAKE_CASE=[4, 2, 1, 0.5] ,	_SCREAMING_SNAKE_CASE=[96, 1_92, 3_84, 7_68] ,	_SCREAMING_SNAKE_CASE=2_56 ,	_SCREAMING_SNAKE_CASE=-1 ,	_SCREAMING_SNAKE_CASE=False ,	_SCREAMING_SNAKE_CASE=True ,	_SCREAMING_SNAKE_CASE=0.4 ,	_SCREAMING_SNAKE_CASE=2_55 ,	_SCREAMING_SNAKE_CASE=0.1 ,	_SCREAMING_SNAKE_CASE=[1, 10_24, 24, 24] ,	_SCREAMING_SNAKE_CASE=[0, 1] ,	_SCREAMING_SNAKE_CASE=None ,	**_SCREAMING_SNAKE_CASE ,	):
   super().__init__(**snake_case__				)

   __lowerCAmelCase    :		Union[str, Any]		    =   hidden_size
   __lowerCAmelCase    :		List[str]		    =   is_hybrid

   if self.is_hybrid:
    if backbone_config is None:
     logger.info('Initializing the config with a `BiT` backbone.'				)
     __lowerCAmelCase    :		Optional[Any]		    =   {
         "global_padding": "same",
         "layer_type": "bottleneck",
         "depths": [3, 4, 9],
         "out_features": ["stage1", "stage2", "stage3"],
         "embedding_dynamic_padding": True,
     }
     __lowerCAmelCase    :		str		    =   BitConfig(**snake_case__				)
    elif isinstance(snake_case__ ,	snake_case__				):
     logger.info('Initializing the config with a `BiT` backbone.'				)
     __lowerCAmelCase    :		Optional[Any]		    =   BitConfig(**snake_case__				)
    elif isinstance(snake_case__ ,	snake_case__				):
     __lowerCAmelCase    :		str		    =   backbone_config
    else:
     raise ValueError(
         f"backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}."				)

    __lowerCAmelCase    :		Any		    =   backbone_featmap_shape
    __lowerCAmelCase    :		Optional[Any]		    =   neck_ignore_stages

    if readout_type != "project":
     raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'				)
   else:
    __lowerCAmelCase    :		List[Any]		    =   None
    __lowerCAmelCase    :		int		    =   None
    __lowerCAmelCase    :		List[Any]		    =   []

   __lowerCAmelCase    :		Tuple		    =   num_hidden_layers
   __lowerCAmelCase    :		Union[str, Any]		    =   num_attention_heads
   __lowerCAmelCase    :		Union[str, Any]		    =   intermediate_size
   __lowerCAmelCase    :		Union[str, Any]		    =   hidden_act
   __lowerCAmelCase    :		int		    =   hidden_dropout_prob
   __lowerCAmelCase    :		str		    =   attention_probs_dropout_prob
   __lowerCAmelCase    :		List[str]		    =   initializer_range
   __lowerCAmelCase    :		Union[str, Any]		    =   layer_norm_eps
   __lowerCAmelCase    :		List[Any]		    =   image_size
   __lowerCAmelCase    :		List[str]		    =   patch_size
   __lowerCAmelCase    :		str		    =   num_channels
   __lowerCAmelCase    :		Dict		    =   qkv_bias
   __lowerCAmelCase    :		List[Any]		    =   backbone_out_indices
   if readout_type not in ["ignore", "add", "project"]:
    raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'				)
   __lowerCAmelCase    :		List[Any]		    =   readout_type
   __lowerCAmelCase    :		List[str]		    =   reassemble_factors
   __lowerCAmelCase    :		Optional[Any]		    =   neck_hidden_sizes
   __lowerCAmelCase    :		Union[str, Any]		    =   fusion_hidden_size
   __lowerCAmelCase    :		Any		    =   head_in_index
   __lowerCAmelCase    :		int		    =   use_batch_norm_in_fusion_residual
   # auxiliary head attributes (semantic segmentation)
   __lowerCAmelCase    :		Optional[int]		    =   use_auxiliary_head
   __lowerCAmelCase    :		Tuple		    =   auxiliary_loss_weight
   __lowerCAmelCase    :		Dict		    =   semantic_loss_ignore_index
   __lowerCAmelCase    :		Any		    =   semantic_classifier_dropout







  def 							__lowerCamelCase							(   self				):
   __lowerCAmelCase    :		int		    =   copy.deepcopy(self.__dict__				)

   if output["backbone_config"] is not None:
    __lowerCAmelCase    :		Union[str, Any]		    =   self.backbone_config.to_dict()

   __lowerCAmelCase    :		int		    =   self.__class__.model_type
   return output

"""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 					__lowerCAmelCase       (_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase ,						_UpperCamelCase	):
	__lowerCAmelCase    :		Optional[int]		    =   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
			):
				__lowerCAmelCase    :		Union[str, 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}\"" ,						_UpperCamelCase ,						)
			    is not None
			):
				__lowerCAmelCase    :		Optional[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:
					__lowerCAmelCase    :		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
	__lowerCAmelCase    :		Optional[Any]		    =   [
	    'bos_index',
	    'eos_index',
	    'pad_index',
	    'unk_index',
	    'mask_index',
	    'image_size',
	    'use_cache',
	    'out_features',
	    'out_indices',
	]
	__lowerCAmelCase    :		Tuple		    =   ['encoder_no_repeat_ngram_size']

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

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

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

	return attribute_used or case_allowed
def 					__lowerCAmelCase       (_UpperCamelCase	):
	__lowerCAmelCase    :		List[str]		    =   dict(inspect.signature(config_class.__init__	).parameters	)
	__lowerCAmelCase    :		Tuple		    =   [x for x in list(signature.keys()	) if x not in ['self', 'kwargs']]
	__lowerCAmelCase    :		Tuple		    =   [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
	__lowerCAmelCase    :		List[Any]		    =   {}
	if len(config_class.attribute_map	) > 0:
		__lowerCAmelCase    :		Union[str, Any]		    =   {v: k for k, v in config_class.attribute_map.items()}

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

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

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

	return sorted(_UpperCamelCase	)
def 					__lowerCAmelCase       ():
	__lowerCAmelCase    :		List[str]		    =   {}
	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.)
		__lowerCAmelCase    :		int		    =   [
		    cls
		    for name, cls in inspect.getmembers(
		        inspect.getmodule(_config_class	) ,						lambda _UpperCamelCase	: inspect.isclass(_UpperCamelCase	)
		        and issubclass(_UpperCamelCase ,						_UpperCamelCase	)
		        and inspect.getmodule(_UpperCamelCase	) == inspect.getmodule(_config_class	) ,						)
		]
		for config_class in config_classes_in_module:
			__lowerCAmelCase    :		List[Any]		    =   check_config_attributes_being_used(_UpperCamelCase	)
			if len(_UpperCamelCase	) > 0:
				__lowerCAmelCase    :		str		    =   unused_attributes

	if len(_UpperCamelCase	) > 0:
		__lowerCAmelCase    :		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(_UpperCamelCase	)


if __name__ == "__main__":
							check_config_attributes()