


'''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,
)


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

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

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

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

try:
		if not is_flax_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase    				=							[
		    '''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

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



'''simple docstring'''






from __future__ import annotations


def 					_snake_case   (     A ,    A       )						->    list[int]:
							lowerCAmelCase__          =	{}
							for index, val in enumerate(A       ):
														lowerCAmelCase__          =	target - val
														if compl in chk_map:
																					return [chk_map[compl], index]
														lowerCAmelCase__          =	index
							return []


if __name__ == "__main__":
		import doctest

		doctest.testmod()
		print(f"""{two_sum([2, 7, 11, 15], 9) = }""")