

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
lowerCAmelCase_			      =      '''src/transformers'''


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

lowerCAmelCase_			      =      transformers.models.auto.configuration_auto.CONFIG_MAPPING

lowerCAmelCase_			      =      {
    # 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__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__    ,						SCREAMING_SNAKE_CASE__			):
						snake_case_  =      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_  =      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_  =      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_  =      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_  =      [
						    '''bos_index''',
						    '''eos_index''',
						    '''pad_index''',
						    '''unk_index''',
						    '''mask_index''',
						    '''image_size''',
						    '''use_cache''',
						    '''out_features''',
						    '''out_indices''',
						]
						snake_case_  =      ['''encoder_no_repeat_ngram_size''']

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

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

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

						return attribute_used or case_allowed


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

						# Get the path to modeling source files
						snake_case_  =      inspect.getsourcefile(SCREAMING_SNAKE_CASE__			)
						snake_case_  =      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_  =      [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_  =      []
						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_  =      []
						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_  =      [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   ():
						snake_case_  =      {}
						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_  =      [
												    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_  =      check_config_attributes_being_used(SCREAMING_SNAKE_CASE__			)
																		if len(SCREAMING_SNAKE_CASE__			) > 0:
																								snake_case_  =      unused_attributes

						if len(SCREAMING_SNAKE_CASE__			) > 0:
												snake_case_  =      '''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"""






from __future__ import annotations

from random import random

class 					__a     :



							'''simple docstring'''







							def __init__(     self  ,    _lowerCamelCase = None       )			->							str:




										'''simple docstring'''
										__lowercase       =       value
										__lowercase       =       random()
										__lowercase       =       None
										__lowercase       =       None
							def __repr__(     self       )			->							str:




										'''simple docstring'''
										from pprint import pformat

										if self.left is None and self.right is None:
													return f'''\'{self.value}: {self.prior:.5}\''''
										else:
													return pformat(
													    {f'''{self.value}: {self.prior:.5}''': (self.left, self.right)}  ,    indent=1       )





							def __str__(     self       )			->							str:




										'''simple docstring'''
										__lowercase       =       str(self.value       ) + " "
										__lowercase       =       str(self.left or ""       )
										__lowercase       =       str(self.right or ""       )
										return value + left + right





def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->tuple[Node | None, Node | None]:
			if root is None:  # None tree is split into 2 Nones
						return None, None
			elif root.value is None:
						return None, None
			else:
						if value < root.value:
									__lowercase   ,					__lowercase       =       split(root.left			,   __magic_name__      )
									return left, root
						else:
									__lowercase   ,					__lowercase       =       split(root.right			,   __magic_name__      )
									return root, right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			if (not left) or (not right):  # If one node is None, return the other
						return left or right
			elif left.prior < right.prior:
						__lowercase       =       merge(left.right			,   __magic_name__      )
						return left
			else:
						__lowercase       =       merge(__magic_name__			,   right.left      )
						return right



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase       =       Node(__magic_name__      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(merge(__magic_name__			,   __magic_name__      )			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   value - 1      )
			__lowercase   ,					__lowercase       =       split(__magic_name__			,   __magic_name__      )
			return merge(__magic_name__			,   __magic_name__      )



def      lowerCAmelCase__			(		__magic_name__      )						->None:
			if not root:  # None
						return
			else:
						inorder(root.left      )
						print(root.value			,   end=","      )
						inorder(root.right      )



def      lowerCAmelCase__			(		__magic_name__			,   __magic_name__      )						->Node | None:
			for arg in args.split():
						if arg[0] == "+":
									__lowercase       =       insert(__magic_name__			,   int(arg[1:]      )      )

						elif arg[0] == "-":
									__lowercase       =       erase(__magic_name__			,   int(arg[1:]      )      )

						else:
									print("Unknown command"      )

			return root



def      lowerCAmelCase__			(		)						->None:
			__lowercase       =       None
			print(
			    "enter numbers to create a tree, + value to add value into treap, "
			    "- value to erase all nodes with value. 'q' to quit. "      )

			__lowercase       =       input()
			while args != "q":
						__lowercase       =       interact_treap(__magic_name__			,   __magic_name__      )
						print(__magic_name__      )
						__lowercase       =       input()

			print("good by!"      )


if __name__ == "__main__":
					import doctest

					doctest.testmod()
					main()





