
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,
)


_lowerCamelCase          ={"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

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

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

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

try:
      if not is_flax_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      _lowerCamelCase          =[
          """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

      _lowerCamelCase          =_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






import sys





def    _a (						lowerCamelCase,     lowerCamelCase							):
 if not numbers:
  return 0
 if target == 0 and target in numbers:
  return 0
 if not isinstance(lowerCamelCase,     (list, tuple)							) or not all(
     isinstance(lowerCamelCase,     lowerCamelCase							) for number in numbers							):
  raise ValueError("""numbers must be an iterable of integers"""							)

 lowerCamelCase		:     str          =				0
 lowerCamelCase		:     List[str]          =				sys.maxsize

 while right < len(lowerCamelCase							):
  curr_sum += numbers[right]
  while curr_sum >= target and left <= right:
   lowerCamelCase		:     Any          =				min(lowerCamelCase,     right - left + 1							)
   curr_sum -= numbers[left]
   left += 1
  right += 1

 return 0 if min_len == sys.maxsize else min_len




