


"""simple docstring"""
from typing import Any



class      _UpperCAmelCase					:






		'''simple docstring'''

		def __init__(self   ):





			'''simple docstring'''

			__snake_case      :		List[Any]         =							[]
			__snake_case      :		Union[str, Any]         =							0



		def __str__(self   ):





			'''simple docstring'''

			__snake_case      :		Optional[Any]         =							'''<''' + str(self.stack   )[1:-1] + '''>'''
			return printed



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			self.stack.append(a_   )
			__snake_case      :		str         =							self.length + 1



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			self.rotate(1   )
			__snake_case      :		List[str]         =							self.stack[self.length - 1]
			__snake_case      :		Union[str, Any]         =							self.stack[:-1]
			self.rotate(self.length - 1   )
			__snake_case      :		int         =							self.length - 1
			return dequeued



		def 							SCREAMING_SNAKE_CASE    (self							,			a_   ):





			'''simple docstring'''

			for _ in range(a_   ):
				__snake_case      :		Optional[int]         =							self.stack[0]
				__snake_case      :		Dict         =							self.stack[1:]
				self.put(a_   )
				__snake_case      :		str         =							self.length - 1



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			__snake_case      :		Union[str, Any]         =							self.get()
			self.put(a_   )
			self.rotate(self.length - 1   )
			return front



		def 							SCREAMING_SNAKE_CASE    (self   ):





			'''simple docstring'''

			return self.length







'''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,
)


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

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

try:
     if not is_tokenizers_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, Any]					=['T5TokenizerFast']

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

try:
     if not is_flax_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     A__ :		Union[str, 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

     A__ :		Dict					=_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



