

from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
					from ..ta.tokenization_ta import TaTokenizer
else:
					from ...utils.dummy_sentencepiece_objects import TaTokenizer

_UpperCAmelCase		:   List[str]										=       TaTokenizer

if is_tokenizers_available():
					from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
					from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_UpperCAmelCase		:   int										=       TaTokenizerFast

_UpperCAmelCase		:   str										=       {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:   Union[str, Any]										=       [
					    """MT5EncoderModel""",
					    """MT5ForConditionalGeneration""",
					    """MT5ForQuestionAnswering""",
					    """MT5Model""",
					    """MT5PreTrainedModel""",
					    """MT5Stack""",
					]

try:
					if not is_tf_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:   List[str]										=       ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
					if not is_flax_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					_UpperCAmelCase		:   int										=       ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
					from .configuration_mta import MTaConfig, MTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_mta import (
										    MTaEncoderModel,
										    MTaForConditionalGeneration,
										    MTaForQuestionAnswering,
										    MTaModel,
										    MTaPreTrainedModel,
										    MTaStack,
										)

					try:
										if not is_tf_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

					try:
										if not is_flax_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
					import sys

					_UpperCAmelCase		:   Tuple										=       _LazyModule(
					    __name__,
					    globals()["""__file__"""],
					    _import_structure,
					    extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
					    module_spec=__spec__,
					)







from __future__ import annotations

import collections
import pprint
from pathlib import Path





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return "".join(sorted(UpperCamelCase__      )      )





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							return word_by_signature[signature(UpperCamelCase__      )]


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

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

if __name__ == "__main__":
					_UpperCAmelCase		:   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))




