


'''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_						:       Tuple      			=				logging.get_logger(__name__)





@add_end_docstrings(lowercase    )
class   snake_case					(					lowercase    ):





  """simple docstring"""




  def __init__(					self    ,						*UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   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 						snake_case	(					self    ,						UpperCamelCase=None    ,						UpperCamelCase=None    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_  				=						{}
   lowerCamelCase_  				=						{}

   if prompt is not None:
    lowerCamelCase_  				=						prompt

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






  def __call__(					self    ,						UpperCamelCase    ,						**UpperCamelCase  ):







   """simple docstring"""
   return super().__call__(UpperCamelCase    ,						**UpperCamelCase  )






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   lowerCamelCase_  				=						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."  )

    lowerCamelCase_  				=						self.model.config.model_type

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

    elif model_type == "pix2struct":
     lowerCamelCase_  				=						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
     lowerCamelCase_  				=						self.image_processor(images=UpperCamelCase    ,						return_tensors=self.framework  )
     lowerCamelCase_  				=						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:
    lowerCamelCase_  				=						self.image_processor(images=UpperCamelCase    ,						return_tensors=self.framework  )

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

   return model_inputs






  def 						snake_case	(					self    ,						UpperCamelCase    ,						UpperCamelCase=None  ):







   """simple docstring"""
   # 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"]  )
   ):
    lowerCamelCase_  				=						None

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







  def 						snake_case	(					self    ,						UpperCamelCase  ):







   """simple docstring"""
   lowerCamelCase_  				=						[]
   for output_ids in model_outputs:
    lowerCamelCase_  				=						{
        "generated_text": self.tokenizer.decode(
            UpperCamelCase    ,						skip_special_tokens=UpperCamelCase    ,						)
    }
    records.append(UpperCamelCase  )
   return records







'''simple docstring'''

import math





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		float	):
 return math.pow(UpperCAmelCase_      ,       2	) - a





def __snake_case      (   UpperCAmelCase_		:		float	):
 return 2 * x





def __snake_case      (   UpperCAmelCase_		:		float	):
 lowerCamelCase_  				=						2.0

 while start <= a:
  lowerCamelCase_  				=						math.pow(UpperCAmelCase_      ,       2	)

 return start





def __snake_case      (   UpperCAmelCase_		:		float      ,       UpperCAmelCase_		:		int = 9999      ,       UpperCAmelCase_		:		float = 0.00_0000_0000_0001	):

 if a < 0:
  raise ValueError("math domain error"	)

 lowerCamelCase_  				=						get_initial_point(UpperCAmelCase_	)

 for _ in range(UpperCAmelCase_	):
  lowerCamelCase_  				=						value
  lowerCamelCase_  				=						value - fx(UpperCAmelCase_      ,       UpperCAmelCase_	) / fx_derivative(UpperCAmelCase_	)
  if abs(prev_value - value	) < tolerance:
   return value

 return value


if __name__ == "__main__":
     from doctest import testmod

     testmod()



