




"""simple docstring"""







from __future__ import annotations

import collections
import pprint
from pathlib import Path




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return "".join(sorted(lowerCamelCase_       )       )




def        UpperCAmelCase__	(lowerCAmelCase_       ):




					'''simple docstring'''


					return word_by_signature[signature(lowerCamelCase_       )]


a__    :							str            =					Path(__file__).parent.joinpath('''words.txt''').read_text(encoding='''utf-8''')
a__    :							Tuple            =					sorted({word.strip().lower() for word in data.splitlines()})

a__    :							str            =					collections.defaultdict(list)
for word in word_list:
		word_by_signature[signature(word)].append(word)

if __name__ == "__main__":
		a__    :							Dict            =					{word: anagram(word) for word in word_list if len(anagram(word)) > 1}

		with open('''anagrams.txt''', '''w''') as file:
				file.write('''all_anagrams = \n ''')
				file.write(pprint.pformat(all_anagrams))




"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


a__    :							List[str]            =					{
    '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''],
    '''tokenization_biogpt''': ['''BioGptTokenizer'''],
}

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		a__    :							Union[str, Any]            =					[
		    '''BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
		    '''BioGptForCausalLM''',
		    '''BioGptForTokenClassification''',
		    '''BioGptForSequenceClassification''',
		    '''BioGptModel''',
		    '''BioGptPreTrainedModel''',
		]


if TYPE_CHECKING:
		from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
		from .tokenization_biogpt import BioGptTokenizer

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_biogpt import (
				    BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    BioGptForCausalLM,
				    BioGptForSequenceClassification,
				    BioGptForTokenClassification,
				    BioGptModel,
				    BioGptPreTrainedModel,
				)


else:
		import sys

		a__    :							Optional[int]            =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)
