





from typing import List, Union

from ..utils import (
    add_end_docstrings,
    is_tf_available,
    is_torch_available,
    is_vision_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
   from PIL import Image

   from ..image_utils import load_image

if is_tf_available():
   from ..models.auto.modeling_tf_auto import TF_MODEL_FOR_VISION_2_SEQ_MAPPING

if is_torch_available():
   import torch

   from ..models.auto.modeling_auto import MODEL_FOR_VISION_2_SEQ_MAPPING

lowerCAmelCase_				= logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase					)
class 					A     (__UpperCAmelCase					):



      def __init__(       self  ,	*lowercase_  ,	**lowercase_       )   -> Union[str, Any]:



             '''simple docstring'''

             super().__init__(*snake_case__  ,	**snake_case__       )
             requires_backends(self  ,	'''vision'''       )
             self.check_model_type(
                 TF_MODEL_FOR_VISION_2_SEQ_MAPPING if self.framework == '''tf''' else MODEL_FOR_VISION_2_SEQ_MAPPING       )



      def  __a			(       self  ,	lowercase_=None  ,	lowercase_=None  ,	lowercase_=None       )   -> List[str]:



             '''simple docstring'''

             _snake_case   :				int  					=    {}
             _snake_case   :				Any  					=    {}

             if prompt is not None:
                    _snake_case   :				Optional[Any]  					=    prompt

             if generate_kwargs is not None:
                    _snake_case   :				Optional[int]  					=    generate_kwargs
             if max_new_tokens is not None:
                    if "generate_kwargs" not in forward_kwargs:
                           _snake_case   :				str  					=    {}
                    if "max_new_tokens" in forward_kwargs["generate_kwargs"]:
                           raise ValueError(
                               '''\'max_new_tokens\' is defined twice, once in \'generate_kwargs\' and once as a direct parameter,'''
                               ''' please use only one'''       )
                    _snake_case   :				Optional[Any]  					=    max_new_tokens
             return preprocess_params, forward_kwargs, {}



      def __call__(       self  ,	lowercase_  ,	**lowercase_       )   -> int:



             '''simple docstring'''

             return super().__call__(snake_case__  ,	**snake_case__       )



      def  __a			(       self  ,	lowercase_  ,	lowercase_=None       )   -> str:



             '''simple docstring'''

             _snake_case   :				List[str]  					=    load_image(snake_case__       )

             if prompt is not None:
                    if not isinstance(snake_case__  ,	snake_case__       ):
                           raise ValueError(
                               F'''Received an invalid text input, got - {type(snake_case__       )} - but expected a single string. '''
                               '''Note also that one single text can be provided for conditional image to text generation.'''       )

                    _snake_case   :				Union[str, Any]  					=    self.model.config.model_type

                    if model_type == "git":
                           _snake_case   :				List[Any]  					=    self.image_processor(images=snake_case__  ,	return_tensors=self.framework       )
                           _snake_case   :				Dict  					=    self.tokenizer(text=snake_case__  ,	add_special_tokens=snake_case__       ).input_ids
                           _snake_case   :				Tuple  					=    [self.tokenizer.cls_token_id] + input_ids
                           _snake_case   :				int  					=    torch.tensor(snake_case__       ).unsqueeze(0       )
                           model_inputs.update({'''input_ids''': input_ids}       )

                    elif model_type == "pix2struct":
                           _snake_case   :				Optional[int]  					=    self.image_processor(images=snake_case__  ,	header_text=snake_case__  ,	return_tensors=self.framework       )

                    elif model_type != "vision-encoder-decoder":
                           # vision-encoder-decoder does not support conditional generation
                           _snake_case   :				Any  					=    self.image_processor(images=snake_case__  ,	return_tensors=self.framework       )
                           _snake_case   :				List[Any]  					=    self.tokenizer(snake_case__  ,	return_tensors=self.framework       )
                           model_inputs.update(snake_case__       )

                    else:
                           raise ValueError(F'''Model type {model_type} does not support conditional text generation'''       )

             else:
                    _snake_case   :				Union[str, Any]  					=    self.image_processor(images=snake_case__  ,	return_tensors=self.framework       )

             if self.model.config.model_type == "git" and prompt is None:
                    _snake_case   :				int  					=    None

             return model_inputs



      def  __a			(       self  ,	lowercase_  ,	lowercase_=None       )   -> Union[str, Any]:



             '''simple docstring'''

             if (
                 "input_ids" in model_inputs
                 and isinstance(model_inputs['''input_ids''']  ,	snake_case__       )
                 and all(x is None for x in model_inputs['''input_ids''']       )
             ):
                    _snake_case   :				Optional[Any]  					=    None

             if generate_kwargs is None:
                    _snake_case   :				List[str]  					=    {}
             # FIXME: We need to pop here due to a difference in how `generation.py` and `generation.tf_utils.py`
             #  parse inputs. In the Tensorflow version, `generate` raises an error if we don't use `input_ids` whereas
             #  the PyTorch version matches it with `self.model.main_input_name` or `self.model.encoder.main_input_name`
             #  in the `_prepare_model_inputs` method.
             _snake_case   :				List[Any]  					=    model_inputs.pop(self.model.main_input_name       )
             _snake_case   :				Optional[Any]  					=    self.model.generate(snake_case__  ,	**snake_case__  ,	**snake_case__       )
             return model_outputs



      def  __a			(       self  ,	lowercase_       )   -> Any:



             '''simple docstring'''

             _snake_case   :				Optional[int]  					=    []
             for output_ids in model_outputs:
                    _snake_case   :				List[str]  					=    {
                        "generated_text": self.tokenizer.decode(
                            snake_case__  ,	skip_special_tokens=snake_case__  ,	)
                    }
                    records.append(snake_case__       )
             return records








'''simple docstring'''





import operator as op







def snake_case_     (					lowercase__		):
  UpperCAmelCase__      :					Optional[Any]				       =							[]
  UpperCAmelCase__      :					Any				       =							lambda lowercase__   ,       lowercase__		: int(x / y		)  # noqa: E731 integer division operation
  UpperCAmelCase__      :					Any				       =							{
      "^": op.pow,
      "*": op.mul,
      "/": div,
      "+": op.add,
      "-": op.sub,
  }  # operators & their respective operation

  # print table header
  print("Symbol".center(8		)   ,       "Action".center(1_2		)   ,       "Stack"   ,       sep=" | "		)
  print("-" * (3_0 + 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(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)
    else:
      UpperCAmelCase__      :					str				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + b + ")").ljust(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "		)

      UpperCAmelCase__      :					List[Any]				       =							stack.pop()  # pop stack
      # output in tabular format
      print("".rjust(8		)   ,       ("pop(" + a + ")").ljust(1_2		)   ,       ",".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(1_2		)   ,       ",".join(lowercase__		)   ,       sep=" | "   ,       )

  return int(stack[0]		)


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



