




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
__UpperCamelCase   :			int          = "src/transformers"


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

__UpperCamelCase   :			List[Any]          = transformers.models.auto.configuration_auto.CONFIG_MAPPING

__UpperCamelCase   :			Tuple          = {
    # 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 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Optional[int]:
	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 						__A						(      __lowerCamelCase       ) ->			Optional[int]:
	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 						__A						(      ) ->			int:
	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()







from bisect import bisect
from itertools import accumulate





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	a									=				sorted(zip(__lowerCamelCase				,     __lowerCamelCase       )				,     key=lambda __lowerCamelCase       : x[0] / x[1]				,     reverse=__lowerCamelCase       )
	a							,					a									=				[i[0] for i in r], [i[1] for i in r]
	a									=				list(accumulate(__lowerCamelCase       )       )
	a									=				bisect(__lowerCamelCase				,     __lowerCamelCase       )
	return (
	    0
	    if k == 0
	    else sum(vl[:k]       ) + (w - acc[k - 1]) * (vl[k]) / (wt[k])
	    if k != n
	    else sum(vl[:k]       )
	)


if __name__ == "__main__":
		import doctest

		doctest.testmod()

