



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__       =	"src/transformers"


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

UpperCAmelCase__       =	transformers.models.auto.configuration_auto.CONFIG_MAPPING

UpperCAmelCase__       =	{
    # 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     (					_UpperCAmelCase    :       Tuple							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       List[str]   )    ->			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}\""							,       _UpperCAmelCase							,       )
									    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 							A     (					_UpperCAmelCase    :       Union[str, Any]   )    ->			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(_UpperCAmelCase   )
			_UpperCAmelCase			=       os.path.dirname(_UpperCAmelCase   )
			# Let's check against all frameworks: as long as one framework uses an attribute, we are good.
			_UpperCAmelCase			=       [os.path.join(_UpperCAmelCase							,       _UpperCAmelCase   ) for fn in os.listdir(_UpperCAmelCase   ) if fn.startswith('modeling_'   )]

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

			_UpperCAmelCase			=       []
			for config_param, default_value in zip(_UpperCAmelCase							,       _UpperCAmelCase   ):
						# `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(_UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase							,       _UpperCAmelCase   ):
									unused_attributes.append(attributes[0]   )

			return sorted(_UpperCAmelCase   )





def 							A     (					)    ->			str:







			'''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 _UpperCAmelCase   : inspect.isclass(_UpperCAmelCase   )
						        and issubclass(_UpperCAmelCase							,       _UpperCAmelCase   )
						        and inspect.getmodule(_UpperCAmelCase   ) == inspect.getmodule(_config_class   )							,       )
						]
						for config_class in config_classes_in_module:
									_UpperCAmelCase			=       check_config_attributes_being_used(_UpperCAmelCase   )
									if len(_UpperCAmelCase   ) > 0:
												_UpperCAmelCase			=       unused_attributes

			if len(_UpperCAmelCase   ) > 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(_UpperCAmelCase   )


if __name__ == "__main__":
							check_config_attributes()





UpperCAmelCase__       =	{
    "A": ["B", "C", "E"],
    "B": ["A", "D", "E"],
    "C": ["A", "F", "G"],
    "D": ["B"],
    "E": ["A", "B", "D"],
    "F": ["C"],
    "G": ["C"],
}





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[Any]   )    ->			list[str]:







			'''simple docstring'''
			_UpperCAmelCase			=       set()
			# keep track of all the paths to be checked
			_UpperCAmelCase			=       [[start]]

			# return path if start is goal
			if start == goal:
						return [start]

			# keeps looping until all possible paths have been checked
			while queue:
						# pop the first path from the queue
						_UpperCAmelCase			=       queue.pop(0   )
						# get the last node from the path
						_UpperCAmelCase			=       path[-1]
						if node not in explored:
									_UpperCAmelCase			=       graph[node]
									# go through all neighbour nodes, construct a new path and
									# push it into the queue
									for neighbour in neighbours:
												_UpperCAmelCase			=       list(_UpperCAmelCase   )
												new_path.append(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												# return path if neighbour is goal
												if neighbour == goal:
															return new_path

            # mark node as explored
									explored.add(_UpperCAmelCase   )

    # in case there's no path between the 2 nodes
			return []





def 							A     (					_UpperCAmelCase    :       dict							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[str]   )    ->			int:







			'''simple docstring'''
			if not graph or start not in graph or target not in graph:
						return -1
			if start == target:
						return 0
			_UpperCAmelCase			=       [start]
			_UpperCAmelCase			=       set(_UpperCAmelCase   )
			# Keep tab on distances from `start` node.
			_UpperCAmelCase			=       {start: 0, target: -1}
			while queue:
						_UpperCAmelCase			=       queue.pop(0   )
						if node == target:
									_UpperCAmelCase			=       (
									    dist[node] if dist[target] == -1 else min(dist[target]							,       dist[node]   )
									)
						for adjacent in graph[node]:
									if adjacent not in visited:
												visited.add(_UpperCAmelCase   )
												queue.append(_UpperCAmelCase   )
												_UpperCAmelCase			=       dist[node] + 1
			return dist[target]


if __name__ == "__main__":
							print(bfs_shortest_path(demo_graph, "G", "D"))  # returns ['G', 'C', 'A', 'B', 'D']
							print(bfs_shortest_path_distance(demo_graph, "G", "D"))  # returns 4
