





"""simple docstring"""




from __future__ import annotations

from typing import Generic, TypeVar

lowerCamelCase_	:		Union[str, Any]         =   TypeVar('T')

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      pass


class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''
      pass


class 					_UpperCAmelCase    (  Generic[T]						):



      '''simple docstring'''

      def __init__(	self   , snake_case_ = 1_0    ):

        """simple docstring"""

        A_    :			list[T]									=    []
        A_    :			List[str]									=    limit

      def __bool__(	self    ):

        """simple docstring"""

        return bool(self.stack    )

      def __str__(	self    ):

        """simple docstring"""

        return str(self.stack    )

      def    lowerCamelCase_     (	self   , snake_case_    ):

        """simple docstring"""

        if len(self.stack    ) >= self.limit:
          raise StackOverflowError
        self.stack.append(snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if not self.stack:
          raise StackUnderflowError
        return self.stack.pop()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if not self.stack:
          raise StackUnderflowError
        return self.stack[-1]

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return not bool(self.stack    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return self.size() == self.limit

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        return len(self.stack    )

      def __contains__(	self   , snake_case_    ):

        """simple docstring"""

        return item in self.stack





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			Stack[int]									=    Stack(10   )
  assert bool(_UpperCAmelCase   ) is False
  assert stack.is_empty() is True
  assert stack.is_full() is False
  assert str(_UpperCAmelCase   ) == "[]"

  try:
    A_    :			Optional[Any]									=    stack.pop()
    raise AssertionError  # This should not happen
  except StackUnderflowError:
    assert True  # This should happen

  try:
    A_    :			Union[str, Any]									=    stack.peek()
    raise AssertionError  # This should not happen
  except StackUnderflowError:
    assert True  # This should happen

  for i in range(10   ):
    assert stack.size() == i
    stack.push(_UpperCAmelCase   )

  assert bool(_UpperCAmelCase   )
  assert not stack.is_empty()
  assert stack.is_full()
  assert str(_UpperCAmelCase   ) == str(list(range(10   )   )   )
  assert stack.pop() == 9
  assert stack.peek() == 8

  stack.push(100   )
  assert str(_UpperCAmelCase   ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 100]   )

  try:
    stack.push(200   )
    raise AssertionError  # This should not happen
  except StackOverflowError:
    assert True  # This should happen

  assert not stack.is_empty()
  assert stack.size() == 10

  assert 5 in stack
  assert 55 not in stack


if __name__ == "__main__":
   test_stack()






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


UpperCAmelCase__    :      int       					={
    '''configuration_flaubert''': ['''FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''FlaubertConfig''', '''FlaubertOnnxConfig'''],
    '''tokenization_flaubert''': ['''FlaubertTokenizer'''],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      int       					=[
							    '''FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''FlaubertForMultipleChoice''',
							    '''FlaubertForQuestionAnswering''',
							    '''FlaubertForQuestionAnsweringSimple''',
							    '''FlaubertForSequenceClassification''',
							    '''FlaubertForTokenClassification''',
							    '''FlaubertModel''',
							    '''FlaubertWithLMHeadModel''',
							    '''FlaubertPreTrainedModel''',
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							UpperCAmelCase__    :      List[str]       					=[
							    '''TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST''',
							    '''TFFlaubertForMultipleChoice''',
							    '''TFFlaubertForQuestionAnsweringSimple''',
							    '''TFFlaubertForSequenceClassification''',
							    '''TFFlaubertForTokenClassification''',
							    '''TFFlaubertModel''',
							    '''TFFlaubertPreTrainedModel''',
							    '''TFFlaubertWithLMHeadModel''',
							]


if TYPE_CHECKING:
							from .configuration_flaubert import FLAUBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, FlaubertConfig, FlaubertOnnxConfig
							from .tokenization_flaubert import FlaubertTokenizer

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flaubert import (
														    FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    FlaubertForMultipleChoice,
														    FlaubertForQuestionAnswering,
														    FlaubertForQuestionAnsweringSimple,
														    FlaubertForSequenceClassification,
														    FlaubertForTokenClassification,
														    FlaubertModel,
														    FlaubertPreTrainedModel,
														    FlaubertWithLMHeadModel,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_flaubert import (
														    TF_FLAUBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFFlaubertForMultipleChoice,
														    TFFlaubertForQuestionAnsweringSimple,
														    TFFlaubertForSequenceClassification,
														    TFFlaubertForTokenClassification,
														    TFFlaubertModel,
														    TFFlaubertPreTrainedModel,
														    TFFlaubertWithLMHeadModel,
														)

else:
							import sys

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





