





"""simple docstring"""
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,
)


_snake_case	:						Tuple					=					{'configuration_t5': ['T5_PRETRAINED_CONFIG_ARCHIVE_MAP', 'T5Config', 'T5OnnxConfig']}

try:
		if not is_sentencepiece_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						str					=					['T5Tokenizer']

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						int					=					['T5TokenizerFast']

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						List[Any]					=					[
		    'T5_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'T5EncoderModel',
		    'T5ForConditionalGeneration',
		    'T5Model',
		    'T5PreTrainedModel',
		    'load_tf_weights_in_t5',
		    'T5ForQuestionAnswering',
		]

try:
		if not is_tf_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						Dict					=					[
		    'TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'TFT5EncoderModel',
		    'TFT5ForConditionalGeneration',
		    'TFT5Model',
		    'TFT5PreTrainedModel',
		]

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		_snake_case	:						Optional[Any]					=					[
		    'FlaxT5EncoderModel',
		    'FlaxT5ForConditionalGeneration',
		    'FlaxT5Model',
		    'FlaxT5PreTrainedModel',
		]


if TYPE_CHECKING:
		from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

		try:
				if not is_sentencepiece_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ta import TaTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .tokenization_ta_fast import TaTokenizerFast

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_ta import (
				    T5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TaEncoderModel,
				    TaForConditionalGeneration,
				    TaForQuestionAnswering,
				    TaModel,
				    TaPreTrainedModel,
				    load_tf_weights_in_ta,
				)

		try:
				if not is_tf_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_tf_ta import (
				    TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
				    TFTaEncoderModel,
				    TFTaForConditionalGeneration,
				    TFTaModel,
				    TFTaPreTrainedModel,
				)

		try:
				if not is_flax_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_flax_ta import (
				    FlaxTaEncoderModel,
				    FlaxTaForConditionalGeneration,
				    FlaxTaModel,
				    FlaxTaPreTrainedModel,
				)


else:
		import sys

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




from __future__ import annotations

import math
import random

def    UpperCamelCase_ (	a_  ,				a_  ,				a_		)     ->list[int]:
		A				=d * e - 1
		A				=0
		A				=0
		while p == 0:
				A				=random.randint(2  ,				n - 1		)
				A				=k
				while True:
						if t % 2 == 0:
								A				=t // 2
								A				=(g**t) % n
								A				=math.gcd(x - 1  ,				a_		)
								if x > 1 and y > 1:
										A				=y
										A				=n // y
										break  # find the correct factors
						else:
								break  # t is not divisible by 2, break and choose another g
		return sorted([p, q]		)


if __name__ == "__main__":
	import doctest

	doctest.testmod()




