


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
    is_vision_available,
)


SCREAMING_SNAKE_CASE							    =							{
    'configuration_clip': [
        'CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'CLIPConfig',
        'CLIPOnnxConfig',
        'CLIPTextConfig',
        'CLIPVisionConfig',
    ],
    'processing_clip': ['CLIPProcessor'],
    'tokenization_clip': ['CLIPTokenizer'],
}

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

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							['CLIPFeatureExtractor']
  SCREAMING_SNAKE_CASE							    =							['CLIPImageProcessor']

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
      'CLIPModel',
      'CLIPPreTrainedModel',
      'CLIPTextModel',
      'CLIPTextModelWithProjection',
      'CLIPVisionModel',
      'CLIPVisionModelWithProjection',
  ]

try:
  if not is_tf_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST',
      'TFCLIPModel',
      'TFCLIPPreTrainedModel',
      'TFCLIPTextModel',
      'TFCLIPVisionModel',
  ]

try:
  if not is_flax_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  SCREAMING_SNAKE_CASE							    =							[
      'FlaxCLIPModel',
      'FlaxCLIPPreTrainedModel',
      'FlaxCLIPTextModel',
      'FlaxCLIPTextPreTrainedModel',
      'FlaxCLIPVisionModel',
      'FlaxCLIPVisionPreTrainedModel',
  ]


if TYPE_CHECKING:
  from .configuration_clip import (
      CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
      CLIPConfig,
      CLIPOnnxConfig,
      CLIPTextConfig,
      CLIPVisionConfig,
  )
  from .processing_clip import CLIPProcessor
  from .tokenization_clip import CLIPTokenizer

  try:
    if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .tokenization_clip_fast import CLIPTokenizerFast

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_clip import CLIPFeatureExtractor
    from .image_processing_clip import CLIPImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_clip import (
        CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        CLIPModel,
        CLIPPreTrainedModel,
        CLIPTextModel,
        CLIPTextModelWithProjection,
        CLIPVisionModel,
        CLIPVisionModelWithProjection,
    )

  try:
    if not is_tf_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_tf_clip import (
        TF_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
        TFCLIPModel,
        TFCLIPPreTrainedModel,
        TFCLIPTextModel,
        TFCLIPVisionModel,
    )

  try:
    if not is_flax_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_flax_clip import (
        FlaxCLIPModel,
        FlaxCLIPPreTrainedModel,
        FlaxCLIPTextModel,
        FlaxCLIPTextPreTrainedModel,
        FlaxCLIPVisionModel,
        FlaxCLIPVisionPreTrainedModel,
    )


else:
  import sys

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









"""simple docstring"""






import operator as op







def 	_snake_case     (							lowercase__						):
				_lowerCamelCase	:     Dict       =       []
				_lowerCamelCase	:     List[str]       =       lambda lowercase__				,       lowercase__						: int(x / y						)  # noqa: E731 integer division operation
				_lowerCamelCase	:     Optional[int]       =       {
				    '^': op.pow,
				    '*': op.mul,
				    '/': div,
				    '+': op.add,
				    '-': op.sub,
				}  # operators & their respective operation

				# print table header
				print('Symbol'.center(8						)				,       'Action'.center(12						)				,       'Stack'				,       sep=' | '						)
				print('-' * (30 + len(lowercase__						))						)

				for x in post_fix:
								if x.isdigit():  # if x in digit
												stack.append(lowercase__						)  # append x to stack
												# output in tabular format
												print(x.rjust(8						)				,       ('push(' + x + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)
								else:
												_lowerCamelCase	:     int       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												_lowerCamelCase	:     Tuple       =       stack.pop()  # pop stack
												# output in tabular format
												print(''.rjust(8						)				,       ('pop(' + a + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '						)

												stack.append(
												    str(opr[x](int(lowercase__						)				,       int(lowercase__						)						)						)						)  # evaluate the 2 values popped from stack & push result to stack
												# output in tabular format
												print(
												    x.rjust(8						)				,       ('push(' + a + x + b + ')').ljust(12						)				,       ','.join(lowercase__						)				,       sep=' | '				,       )

				return int(stack[0]						)


if __name__ == "__main__":
			lowercase__			=		input("""\n\nEnter a Postfix Equation (space separated) = """).split(""" """)
			print("""\n\tResult = """, solve(Postfix))