


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      :      Dict            =					'src/transformers'


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

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

_lowercase      :      Optional[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__				(	A       :      Dict				,       A       :      List[str]				,       A       :      Dict				,       A       :      Dict					):




						'''simple docstring'''






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

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

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

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

						return attribute_used or case_allowed


def 				lowerCamelCase__				(	A       :      Optional[Any]					):




						'''simple docstring'''






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

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

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

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






						UpperCAmelCase					    =     {}
						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					    =     [
												    cls
												    for name, cls in inspect.getmembers(
												        inspect.getmodule(_config_class					)				,       lambda A					: inspect.isclass(lowerCAmelCase__					)
												        and issubclass(lowerCAmelCase__				,       lowerCAmelCase__					)
												        and inspect.getmodule(lowerCAmelCase__					) == inspect.getmodule(_config_class					)				,       )
												]
												for config_class in config_classes_in_module:
																		UpperCAmelCase					    =     check_config_attributes_being_used(lowerCAmelCase__					)
																		if len(lowerCAmelCase__					) > 0:
																								UpperCAmelCase					    =     unused_attributes

						if len(lowerCAmelCase__					) > 0:
												UpperCAmelCase					    =     """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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


_lowercase      :      List[str]            =					{
    """configuration_time_series_transformer""": [
        """TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TimeSeriesTransformerConfig""",
    ],
}

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Dict            =					[
	    """TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TimeSeriesTransformerForPrediction""",
	    """TimeSeriesTransformerModel""",
	    """TimeSeriesTransformerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_time_series_transformer import (
	    TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    TimeSeriesTransformerConfig,
	)

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_time_series_transformer import (
		    TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TimeSeriesTransformerForPrediction,
		    TimeSeriesTransformerModel,
		    TimeSeriesTransformerPreTrainedModel,
		)

else:
	import sys

	_lowercase      :      Tuple            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)





