

import random



def   __UpperCAmelCase					(      __a :     int      )     ->	bool:

					"""simple docstring"""

					_a      :       str   =						num - 1
					_a      :       Dict   =						0

					while s % 2 == 0:
										_a      :       Dict   =						s // 2
										t += 1

					for _ in range(5      ):
										_a      :       Dict   =						random.randrange(2						,num - 1      )
										_a      :       List[str]   =						pow(__a						,__a						,__a      )
										if v != 1:
															_a      :       Any   =						0
															while v != (num - 1):
																				if i == t - 1:
																									return False
																				else:
																									_a      :       List[Any]   =						i + 1
																									_a      :       List[Any]   =						(v**2) % num
					return True



def   __UpperCAmelCase					(      __a :     int      )     ->	bool:

					"""simple docstring"""

					if num < 2:
										return False

					_a      :       str   =						[
					    2,
					    3,
					    5,
					    7,
					    11,
					    13,
					    17,
					    19,
					    23,
					    29,
					    31,
					    37,
					    41,
					    43,
					    47,
					    53,
					    59,
					    61,
					    67,
					    71,
					    73,
					    79,
					    83,
					    89,
					    97,
					    101,
					    103,
					    107,
					    109,
					    113,
					    127,
					    131,
					    137,
					    139,
					    149,
					    151,
					    157,
					    163,
					    167,
					    173,
					    179,
					    181,
					    191,
					    193,
					    197,
					    199,
					    211,
					    223,
					    227,
					    229,
					    233,
					    239,
					    241,
					    251,
					    257,
					    263,
					    269,
					    271,
					    277,
					    281,
					    283,
					    293,
					    307,
					    311,
					    313,
					    317,
					    331,
					    337,
					    347,
					    349,
					    353,
					    359,
					    367,
					    373,
					    379,
					    383,
					    389,
					    397,
					    401,
					    409,
					    419,
					    421,
					    431,
					    433,
					    439,
					    443,
					    449,
					    457,
					    461,
					    463,
					    467,
					    479,
					    487,
					    491,
					    499,
					    503,
					    509,
					    521,
					    523,
					    541,
					    547,
					    557,
					    563,
					    569,
					    571,
					    577,
					    587,
					    593,
					    599,
					    601,
					    607,
					    613,
					    617,
					    619,
					    631,
					    641,
					    643,
					    647,
					    653,
					    659,
					    661,
					    673,
					    677,
					    683,
					    691,
					    701,
					    709,
					    719,
					    727,
					    733,
					    739,
					    743,
					    751,
					    757,
					    761,
					    769,
					    773,
					    787,
					    797,
					    809,
					    811,
					    821,
					    823,
					    827,
					    829,
					    839,
					    853,
					    857,
					    859,
					    863,
					    877,
					    881,
					    883,
					    887,
					    907,
					    911,
					    919,
					    929,
					    937,
					    941,
					    947,
					    953,
					    967,
					    971,
					    977,
					    983,
					    991,
					    997,
					]

					if num in low_primes:
										return True

					for prime in low_primes:
										if (num % prime) == 0:
															return False

					return rabin_miller(__a      )



def   __UpperCAmelCase					(      __a :     int = 1_024      )     ->	int:

					"""simple docstring"""

					while True:
										_a      :       List[Any]   =						random.randrange(2 ** (keysize - 1)						,2 ** (keysize)      )
										if is_prime_low_num(__a      ):
															return num


if __name__ == "__main__":
			a__												=						generate_large_prime()
			print(('''Prime number:''', num))
			print(('''is_prime_low_num:''', is_prime_low_num(num)))











'''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
__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 		_UpperCAmelCase (				__A				: Optional[Any]    ,		__A				: List[str]    ,		__A				: List[str]    ,		__A				: Union[str, Any]   ):
				a_		:	List[str]									=   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
												):
																a_		:	Tuple									=   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}"'    ,		__A    ,		)
												    is not None
												):
																a_		:	Union[str, Any]									=   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:
																				a_		:	Any									=   True
												if attribute_used:
																break
								if attribute_used:
												break

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

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

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

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

				return attribute_used or case_allowed





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

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

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

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

				return sorted(__A   )





def 		_UpperCAmelCase (				):
				a_		:	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.)
								a_		:	int									=   [
								    cls
								    for name, cls in inspect.getmembers(
								        inspect.getmodule(_config_class   )    ,		lambda __A   : inspect.isclass(__A   )
								        and issubclass(__A    ,		__A   )
								        and inspect.getmodule(__A   ) == inspect.getmodule(_config_class   )    ,		)
								]
								for config_class in config_classes_in_module:
												a_		:	int									=   check_config_attributes_being_used(__A   )
												if len(__A   ) > 0:
																a_		:	Dict									=   unused_attributes

				if len(__A   ) > 0:
								a_		:	Dict									=   '''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(__A   )


if __name__ == "__main__":
		check_config_attributes()




