



"""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
a_			   =					'''src/transformers'''


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

a_			   =					transformers.models.auto.configuration_auto.CONFIG_MAPPING

a_			   =					{
    # 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__      (		SCREAMING_SNAKE_CASE__ :  Any				,    SCREAMING_SNAKE_CASE__ :  Tuple				,    SCREAMING_SNAKE_CASE__ :  List[Any]				,    SCREAMING_SNAKE_CASE__ :  Union[str, Any]			):



					"""simple docstring"""





					snake_case_					:		Optional[Any] 					=					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
															):
																				snake_case_					:		Dict 					=					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}"'				,    SCREAMING_SNAKE_CASE__				,    )
															    is not None
															):
																				snake_case_					:		Tuple 					=					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:
																									snake_case_					:		Tuple 					=					True
															if attribute_used:
																				break
										if attribute_used:
															break

    # common and important attributes, even if they do not always appear in the modeling files
					snake_case_					:		int 					=					[
					    """bos_index""",
					    """eos_index""",
					    """pad_index""",
					    """unk_index""",
					    """mask_index""",
					    """image_size""",
					    """use_cache""",
					    """out_features""",
					    """out_indices""",
					]
					snake_case_					:		str 					=					["""encoder_no_repeat_ngram_size"""]

					# Special cases to be allowed
					snake_case_					:		str 					=					True
					if not attribute_used:
										snake_case_					:		Dict 					=					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:
																				snake_case_					:		List[str] 					=					True
															elif attribute in ["tie_word_embeddings"] and default_value is False:
																				snake_case_					:		Optional[Any] 					=					True

															# Allow cases without checking the default value in the configuration class
															elif attribute in attributes_to_allow + attributes_used_in_generation:
																				snake_case_					:		Optional[int] 					=					True
															elif attribute.endswith("""_token_id"""			):
																				snake_case_					:		Tuple 					=					True

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

					return attribute_used or case_allowed







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  List[Any]			):



					"""simple docstring"""





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

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

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

					snake_case_					:		Any 					=					[]
					for config_param, default_value in zip(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
										# `attributes` here is all the variant names for `config_param`
										snake_case_					:		int 					=					[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(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			):
															unused_attributes.append(attributes[0]			)

					return sorted(SCREAMING_SNAKE_CASE__			)







def 							SCREAMING_SNAKE_CASE__      (		):



					"""simple docstring"""





					snake_case_					:		Optional[int] 					=					{}
					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.)
										snake_case_					:		str 					=					[
										    cls
										    for name, cls in inspect.getmembers(
										        inspect.getmodule(_config_class			)				,    lambda SCREAMING_SNAKE_CASE__			: inspect.isclass(SCREAMING_SNAKE_CASE__			)
										        and issubclass(SCREAMING_SNAKE_CASE__				,    SCREAMING_SNAKE_CASE__			)
										        and inspect.getmodule(SCREAMING_SNAKE_CASE__			) == inspect.getmodule(_config_class			)				,    )
										]
										for config_class in config_classes_in_module:
															snake_case_					:		int 					=					check_config_attributes_being_used(SCREAMING_SNAKE_CASE__			)
															if len(SCREAMING_SNAKE_CASE__			) > 0:
																				snake_case_					:		Optional[Any] 					=					unused_attributes

					if len(SCREAMING_SNAKE_CASE__			) > 0:
										snake_case_					:		Optional[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(SCREAMING_SNAKE_CASE__			)


if __name__ == "__main__":
		check_config_attributes()









"""simple docstring"""

import random







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int				,    SCREAMING_SNAKE_CASE__ :  float				,    SCREAMING_SNAKE_CASE__ :  bool = False			):



					"""simple docstring"""





					snake_case_					:		dict 					=					{i: [] for i in range(SCREAMING_SNAKE_CASE__			)}

					# if probability is greater or equal than 1, then generate a complete graph
					if probability >= 1:
										return complete_graph(SCREAMING_SNAKE_CASE__			)
					# if probability is lower or equal than 0, then return a graph without edges
					if probability <= 0:
										return graph

					# for each couple of nodes, add an edge from u to v
					# if the number randomly generated is greater than probability probability
					for i in range(SCREAMING_SNAKE_CASE__			):
										for j in range(i + 1				,    SCREAMING_SNAKE_CASE__			):
															if random.random() < probability:
																				graph[i].append(SCREAMING_SNAKE_CASE__			)
																				if not directed:
																									# if the graph is undirected, add an edge in from j to i, either
																									graph[j].append(SCREAMING_SNAKE_CASE__			)
					return graph







def 							SCREAMING_SNAKE_CASE__      (		SCREAMING_SNAKE_CASE__ :  int			):



					"""simple docstring"""





					return {
					    i: [j for j in range(SCREAMING_SNAKE_CASE__			) if i != j] for i in range(SCREAMING_SNAKE_CASE__			)
					}


if __name__ == "__main__":
			import doctest

			doctest.testmod()




