






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 __A    (      __lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase ,		__lowerCAmelCase	)->  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 __A    (      __lowerCAmelCase	)->  Optional[int]:





						"""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 __A    (      )->  int:





						"""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 __lowerCAmelCase	: 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()








from __future__ import annotations

from itertools import permutations
from random import randint
from timeit import repeat


def __A    (      )->  tuple[list[int], int]:





						"""simple docstring"""



						_UpperCAmelCase									=			[randint(-1_000 ,		1_000	) for i in range(10	)]
						_UpperCAmelCase									=			randint(-5_000 ,		5_000	)
						return (arr, r)


_a      	=  make_dataset()


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, ...]:





						"""simple docstring"""



						for triplet in permutations(__lowerCAmelCase ,		3	):
												if sum(__lowerCAmelCase	) == target:
																		return tuple(sorted(__lowerCAmelCase	)	)
						return (0, 0, 0)


def __A    (      __lowerCAmelCase ,		__lowerCAmelCase	)->  tuple[int, int, int]:





						"""simple docstring"""



						arr.sort()
						_UpperCAmelCase									=			len(__lowerCAmelCase	)
						for i in range(n - 1	):
												_UpperCAmelCase					, _UpperCAmelCase									=			i + 1, n - 1
												while left < right:
																		if arr[i] + arr[left] + arr[right] == target:
																								return (arr[i], arr[left], arr[right])
																		elif arr[i] + arr[left] + arr[right] < target:
																								left += 1
																		elif arr[i] + arr[left] + arr[right] > target:
																								right -= 1
						return (0, 0, 0)


def __A    (      )->  tuple[float, float]:





						"""simple docstring"""



						_UpperCAmelCase									=			'\nfrom __main__ import dataset, triplet_sum1, triplet_sum2\n'
						_UpperCAmelCase									=			'\ntriplet_sum1(*dataset)\n'
						_UpperCAmelCase									=			'\ntriplet_sum2(*dataset)\n'
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						_UpperCAmelCase									=			repeat(setup=__lowerCAmelCase ,		stmt=__lowerCAmelCase ,		repeat=5 ,		number=10_000	)
						return (min(__lowerCAmelCase	), min(__lowerCAmelCase	))


if __name__ == "__main__":
		from doctest import testmod

		testmod()
		_a      	=  solution_times()
		print(F'''The time for naive implementation is {times[0]}.''')
		print(F'''The time for optimized implementation is {times[1]}.''')
