





'''simple docstring'''


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 :					Dict 							=logging.get_logger(__name__)
@add_end_docstrings(_lowerCAmelCase							)
class 				a_       (    _lowerCAmelCase							):





     def __init__(			self		: Dict     ,				*lowercase		: Optional[Any]     ,				**lowercase		: List[Any]						):





        """simple docstring"""




        super().__init__(*lowercase     ,				**lowercase						)
        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 							lowercase__				(			self		: Optional[Any]     ,				lowercase		: Tuple=None     ,				lowercase		: Dict=None     ,				lowercase		: str=None						):





        """simple docstring"""




        lowercase_      :Optional[Any]         =			{}
        lowercase_      :Tuple         =			{}

        if prompt is not None:
           lowercase_      :Union[str, Any]         =			prompt

        if generate_kwargs is not None:
           lowercase_      :Any         =			generate_kwargs
        if max_new_tokens is not None:
           if "generate_kwargs" not in forward_kwargs:
              lowercase_      :Optional[int]         =			{}
           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"						)
           lowercase_      :List[str]         =			max_new_tokens
        return preprocess_params, forward_kwargs, {}





     def __call__(			self		: Any     ,				lowercase		: Union[str, List[str], "Image.Image", List["Image.Image"]]     ,				**lowercase		: List[Any]						):





        """simple docstring"""




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





     def 							lowercase__				(			self		: int     ,				lowercase		: Dict     ,				lowercase		: List[str]=None						):





        """simple docstring"""




        lowercase_      :Any         =			load_image(lowercase						)

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

           lowercase_      :Dict         =			self.model.config.model_type

           if model_type == "git":
              lowercase_      :Optional[Any]         =			self.image_processor(images=lowercase     ,				return_tensors=self.framework						)
              lowercase_      :Tuple         =			self.tokenizer(text=lowercase     ,				add_special_tokens=lowercase						).input_ids
              lowercase_      :int         =			[self.tokenizer.cls_token_id] + input_ids
              lowercase_      :str         =			torch.tensor(lowercase						).unsqueeze(0						)
              model_inputs.update({"input_ids": input_ids}						)

           elif model_type == "pix2struct":
              lowercase_      :Dict         =			self.image_processor(images=lowercase     ,				header_text=lowercase     ,				return_tensors=self.framework						)

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

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

        else:
           lowercase_      :Any         =			self.image_processor(images=lowercase     ,				return_tensors=self.framework						)

        if self.model.config.model_type == "git" and prompt is None:
           lowercase_      :Optional[Any]         =			None

        return model_inputs





     def 							lowercase__				(			self		: List[Any]     ,				lowercase		: int     ,				lowercase		: Dict=None						):





        """simple docstring"""




        if (
            "input_ids" in model_inputs
            and isinstance(model_inputs["input_ids"]     ,				lowercase						)
            and all(x is None for x in model_inputs["input_ids"]						)
        ):
           lowercase_      :Dict         =			None

        if generate_kwargs is None:
           lowercase_      :Optional[Any]         =			{}
        # 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.
        lowercase_      :List[Any]         =			model_inputs.pop(self.model.main_input_name						)
        lowercase_      :Tuple         =			self.model.generate(lowercase     ,				**lowercase     ,				**lowercase						)
        return model_outputs







     def 							lowercase__				(			self		: Optional[int]     ,				lowercase		: str						):





        """simple docstring"""




        lowercase_      :str         =			[]
        for output_ids in model_outputs:
           lowercase_      :Tuple         =			{
               "generated_text": self.tokenizer.decode(
                   lowercase     ,				skip_special_tokens=lowercase     ,				)
           }
           records.append(lowercase						)
        return records









'''simple docstring'''


import sys

lowerCAmelCase :					List[Any] 							=(
    '''73167176531330624919225119674426574742355349194934'''
    '''96983520312774506326239578318016984801869478851843'''
    '''85861560789112949495459501737958331952853208805511'''
    '''12540698747158523863050715693290963295227443043557'''
    '''66896648950445244523161731856403098711121722383113'''
    '''62229893423380308135336276614282806444486645238749'''
    '''30358907296290491560440772390713810515859307960866'''
    '''70172427121883998797908792274921901699720888093776'''
    '''65727333001053367881220235421809751254540594752243'''
    '''52584907711670556013604839586446706324415722155397'''
    '''53697817977846174064955149290862569321978468622482'''
    '''83972241375657056057490261407972968652414535100474'''
    '''82166370484403199890008895243450658541227588666881'''
    '''16427171479924442928230863465674813919123162824586'''
    '''17866458359124566529476545682848912883142607690042'''
    '''24219022671055626321111109370544217506941658960408'''
    '''07198403850962455444362981230987879927244284909188'''
    '''84580156166097919133875499200524063689912560717606'''
    '''05886116467109405077541002256983155200055935729725'''
    '''71636269561882670428252483600823257530420752963450'''
)
def 							UpperCAmelCase_  (						__lowerCamelCase : str			):
   lowercase_      :List[str]         =			1
   for digit in s:
      product *= int(__lowerCamelCase			)
   return product
def 							UpperCAmelCase_  (						__lowerCamelCase : str = N			):
   lowercase_      :Any         =			-sys.maxsize - 1
   lowercase_      :int         =			n[:13]
   lowercase_      :Optional[int]         =			13
   while cur_index < len(__lowerCamelCase			) - 13:
      if int(n[cur_index]			) >= int(substr[0]			):
         lowercase_      :str         =			substr[1:] + n[cur_index]
         cur_index += 1
      else:
         lowercase_      :List[str]         =			max(__lowerCamelCase     ,str_eval(__lowerCamelCase			)			)
         lowercase_      :Any         =			n[cur_index : cur_index + 13]
         cur_index += 13
   return largest_product


if __name__ == "__main__":
 print(F'''{solution() = }''')


