





'''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

_A:       List[Any]						    =					logging.get_logger(__name__)






@add_end_docstrings(UpperCAmelCase_				)
class UpperCAmelCase				(		UpperCAmelCase_				):



    def __init__(	self  ,      *__A  ,      **__A       ):
     super().__init__(*UpperCamelCase_  ,      **UpperCamelCase_       )
     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        __lowerCamelCase    (	self  ,      __A=None  ,      __A=None  ,      __A=None       ):
     __UpperCAmelCase       = {}
     __UpperCAmelCase       = {}

     if prompt is not None:
      __UpperCAmelCase       = prompt

     if generate_kwargs is not None:
      __UpperCAmelCase       = generate_kwargs
     if max_new_tokens is not None:
      if "generate_kwargs" not in forward_kwargs:
       __UpperCAmelCase       = {}
      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'       )
      __UpperCAmelCase       = max_new_tokens
     return preprocess_params, forward_kwargs, {}



    def __call__(	self  ,      __A  ,      **__A       ):
     return super().__call__(UpperCamelCase_  ,      **UpperCamelCase_       )



    def        __lowerCamelCase    (	self  ,      __A  ,      __A=None       ):
     __UpperCAmelCase       = load_image(UpperCamelCase_       )

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

      __UpperCAmelCase       = self.model.config.model_type

      if model_type == "git":
       __UpperCAmelCase       = self.image_processor(images=UpperCamelCase_  ,      return_tensors=self.framework       )
       __UpperCAmelCase       = self.tokenizer(text=UpperCamelCase_  ,      add_special_tokens=UpperCamelCase_       ).input_ids
       __UpperCAmelCase       = [self.tokenizer.cls_token_id] + input_ids
       __UpperCAmelCase       = torch.tensor(UpperCamelCase_       ).unsqueeze(0       )
       model_inputs.update({'input_ids': input_ids}       )

      elif model_type == "pix2struct":
       __UpperCAmelCase       = self.image_processor(images=UpperCamelCase_  ,      header_text=UpperCamelCase_  ,      return_tensors=self.framework       )

      elif model_type != "vision-encoder-decoder":
       # vision-encoder-decoder does not support conditional generation
       __UpperCAmelCase       = self.image_processor(images=UpperCamelCase_  ,      return_tensors=self.framework       )
       __UpperCAmelCase       = self.tokenizer(UpperCamelCase_  ,      return_tensors=self.framework       )
       model_inputs.update(UpperCamelCase_       )

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

     else:
      __UpperCAmelCase       = self.image_processor(images=UpperCamelCase_  ,      return_tensors=self.framework       )

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

     return model_inputs



    def        __lowerCamelCase    (	self  ,      __A  ,      __A=None       ):
     # Git model sets `model_inputs["input_ids"] = None` in `preprocess` (when `prompt=None`). In batch model, the
     # pipeline will group them into a list of `None`, which fail `_forward`. Avoid this by checking it first.
     if (
         "input_ids" in model_inputs
         and isinstance(model_inputs['input_ids']  ,      UpperCamelCase_       )
         and all(x is None for x in model_inputs['input_ids']       )
     ):
      __UpperCAmelCase       = None

     if generate_kwargs is None:
      __UpperCAmelCase       = {}
     # 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.
     __UpperCAmelCase       = model_inputs.pop(self.model.main_input_name       )
     __UpperCAmelCase       = self.model.generate(UpperCamelCase_  ,      **UpperCamelCase_  ,      **UpperCamelCase_       )
     return model_outputs




    def        __lowerCamelCase    (	self  ,      __A       ):
     __UpperCAmelCase       = []
     for output_ids in model_outputs:
      __UpperCAmelCase       = {
          'generated_text': self.tokenizer.decode(
              UpperCamelCase_  ,      skip_special_tokens=UpperCamelCase_  ,      )
      }
      records.append(UpperCamelCase_       )
     return records







"""simple docstring"""



from __future__ import annotations

from collections.abc import Iterator





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					value
          UpperCAmelCase__	:      Node | None     							=					None
          UpperCAmelCase__	:      Node | None     							=					None





class 				a			:



      def __init__(   self     ,	UpperCamelCase_      ):
          UpperCAmelCase__	:      str     							=					tree



      def  __snake_case							(   self     ,	UpperCamelCase_      ):
          if node is None:
              return 0
          return node.value + (
              self.depth_first_search(node.left      ) + self.depth_first_search(node.right      )
          )



      def __iter__(   self      ):
          yield self.depth_first_search(self.tree      )


if __name__ == "__main__":
    import doctest

    doctest.testmod()

