

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_							:					List[str]		 =   'src/transformers'


# This is to make sure the transformers module imported is the one in the repo.
UpperCAmelCase_							:					Union[str, Any]		 =   direct_transformers_import(PATH_TO_TRANSFORMERS)

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

UpperCAmelCase_							:					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 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[int]			,a__    :   List[str]			,a__    :   Tuple			,a__    :   Tuple       )					->				str:
	__A :			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
			):
				__A :			Tuple		    =			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}\""""			,a__			,)
			    is not None
			):
				__A :			Union[str, 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:
					__A :			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
	__A :			Any		    =			[
	    """bos_index""",
	    """eos_index""",
	    """pad_index""",
	    """unk_index""",
	    """mask_index""",
	    """image_size""",
	    """use_cache""",
	    """out_features""",
	    """out_indices""",
	]
	__A :			Tuple		    =			["""encoder_no_repeat_ngram_size"""]

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

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

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

	return attribute_used or case_allowed
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]       )					->				int:
	__A :			Dict		    =			dict(inspect.signature(config_class.__init__       ).parameters       )
	__A :			List[Any]		    =			[x for x in list(signature.keys()       ) if x not in ["""self""", """kwargs"""]]
	__A :			List[str]		    =			[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 :			Tuple		    =			{}
	if len(config_class.attribute_map       ) > 0:
		__A :			Optional[int]		    =			{v: k for k, v in config_class.attribute_map.items()}

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

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

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

	return sorted(a__       )
def 							__SCREAMING_SNAKE_CASE     (   )					->				Optional[int]:
	__A :			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.)
		__A :			Dict		    =			[
		    cls
		    for name, cls in inspect.getmembers(
		        inspect.getmodule(_config_class       )			,lambda a__       : inspect.isclass(a__       )
		        and issubclass(a__			,a__       )
		        and inspect.getmodule(a__       ) == inspect.getmodule(_config_class       )			,)
		]
		for config_class in config_classes_in_module:
			__A :			Any		    =			check_config_attributes_being_used(a__       )
			if len(a__       ) > 0:
				__A :			int		    =			unused_attributes

	if len(a__       ) > 0:
		__A :			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(a__       )


if __name__ == "__main__":
			check_config_attributes()







'''simple docstring'''
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_torch_available,
)


lowerCAmelCase					:    Optional[Any]           = {
    'configuration_falcon': ['FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP', 'FalconConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						lowerCAmelCase					:    str           = [
						    'FALCON_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'FalconForCausalLM',
						    'FalconModel',
						    'FalconPreTrainedModel',
						    'FalconForSequenceClassification',
						    'FalconForTokenClassification',
						    'FalconForQuestionAnswering',
						]


if TYPE_CHECKING:
						from .configuration_falcon import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP, FalconConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_falcon import (
												    FALCON_PRETRAINED_MODEL_ARCHIVE_LIST,
												    FalconForCausalLM,
												    FalconForQuestionAnswering,
												    FalconForSequenceClassification,
												    FalconForTokenClassification,
												    FalconModel,
												    FalconPreTrainedModel,
												)


else:
						import sys

						lowerCAmelCase					:    Tuple           = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




