



'''simple docstring'''

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


__lowerCAmelCase						      =       {'''configuration_opt''': ['''OPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''OPTConfig''']}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =       [
							    '''OPT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''OPTForCausalLM''',
							    '''OPTModel''',
							    '''OPTPreTrainedModel''',
							    '''OPTForSequenceClassification''',
							    '''OPTForQuestionAnswering''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =       ['''TFOPTForCausalLM''', '''TFOPTModel''', '''TFOPTPreTrainedModel''']

try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__lowerCAmelCase						      =       [
							    '''FlaxOPTForCausalLM''',
							    '''FlaxOPTModel''',
							    '''FlaxOPTPreTrainedModel''',
							]


if TYPE_CHECKING:
							from .configuration_opt import OPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OPTConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_opt import (
														    OPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    OPTForCausalLM,
														    OPTForQuestionAnswering,
														    OPTForSequenceClassification,
														    OPTModel,
														    OPTPreTrainedModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_opt import TFOPTForCausalLM, TFOPTModel, TFOPTPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_opt import FlaxOPTForCausalLM, FlaxOPTModel, FlaxOPTPreTrainedModel

else:
							import sys

							__lowerCAmelCase						      =       _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)









'''simple docstring'''

from __future__ import annotations

import collections
import pprint
from pathlib import Path



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				str:
		return "".join(sorted(lowerCAmelCase_  )  )



def 					__lowerCamelCase					(				lowerCAmelCase_  )      ->				list[str]:
		return word_by_signature[signature(lowerCAmelCase_  )]


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

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

if __name__ == "__main__":
							__lowerCAmelCase						      =       {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))




