



'''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	:		Dict				=   '''src/transformers'''


# This is to make sure the transformers module imported is the one in the repo.
lowerCAmelCase	:		List[str]				=   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   :						int				,		lowerCamelCase   :						Dict				,		lowerCamelCase   :						Union[str, Any]				,		lowerCamelCase   :						str					):






	'''simple docstring'''




	__lowerCAmelCase												=  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												=  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
			):
				__lowerCAmelCase												=  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												=  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												=  [
	    "bos_index",
	    "eos_index",
	    "pad_index",
	    "unk_index",
	    "mask_index",
	    "image_size",
	    "use_cache",
	    "out_features",
	    "out_indices",
	]
	__lowerCAmelCase												=  ["encoder_no_repeat_ngram_size"]

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

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

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

	return attribute_used or case_allowed



def   __lowerCAmelCase					(							lowerCamelCase   :						int					):






	'''simple docstring'''




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

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

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

	__lowerCAmelCase												=  []
	for config_param, default_value in zip(lowerCamelCase				,		lowerCamelCase					):
		# `attributes` here is all the variant names for `config_param`
		__lowerCAmelCase												=  [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					(							):






	'''simple docstring'''




	__lowerCAmelCase												=  {}
	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												=  [
		    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:
			__lowerCAmelCase												=  check_config_attributes_being_used(lowerCamelCase					)
			if len(lowerCamelCase					) > 0:
				__lowerCAmelCase												=  unused_attributes

	if len(lowerCamelCase					) > 0:
		__lowerCAmelCase												=  "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 ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase	:		Optional[Any]				=   logging.get_logger(__name__)

lowerCAmelCase	:		Optional[int]				=   {
    '''facebook/dpr-ctx_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-single-nq-base''': (
        '''https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-ctx_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-question_encoder-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/config.json'''
    ),
    '''facebook/dpr-reader-multiset-base''': (
        '''https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/config.json'''
    ),
}






class 					UpperCAmelCase__	(		UpperCamelCase__ ):
					a      :     Optional[Any]        =							"""dpr"""



					def __init__(    self			,				UpperCamelCase=3_0522			,				UpperCamelCase=768			,				UpperCamelCase=12			,				UpperCamelCase=12			,				UpperCamelCase=3072			,				UpperCamelCase="gelu"			,				UpperCamelCase=0.1			,				UpperCamelCase=0.1			,				UpperCamelCase=512			,				UpperCamelCase=2			,				UpperCamelCase=0.02			,				UpperCamelCase=1E-12			,				UpperCamelCase=0			,				UpperCamelCase="absolute"			,				UpperCamelCase = 0			,				**UpperCamelCase			,				)     ->			Tuple:
						super().__init__(pad_token_id=UpperCamelCase			,				**UpperCamelCase     )

						__lowerCAmelCase												=  vocab_size
						__lowerCAmelCase												=  hidden_size
						__lowerCAmelCase												=  num_hidden_layers
						__lowerCAmelCase												=  num_attention_heads
						__lowerCAmelCase												=  hidden_act
						__lowerCAmelCase												=  intermediate_size
						__lowerCAmelCase												=  hidden_dropout_prob
						__lowerCAmelCase												=  attention_probs_dropout_prob
						__lowerCAmelCase												=  max_position_embeddings
						__lowerCAmelCase												=  type_vocab_size
						__lowerCAmelCase												=  initializer_range
						__lowerCAmelCase												=  layer_norm_eps
						__lowerCAmelCase												=  projection_dim
						__lowerCAmelCase												=  position_embedding_type