

'''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_       						=							logging.get_logger(__name__)





@add_end_docstrings(_a						)
class 							_snake_case (       _a						):
   def __init__( self			:      Any     ,*SCREAMING_SNAKE_CASE__			:      Any     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     super().__init__(*A__     ,**A__							)
     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     __UpperCamelCase						( self			:      List[Any]     ,SCREAMING_SNAKE_CASE__			:      int=None     ,SCREAMING_SNAKE_CASE__			:      Dict=None     ,SCREAMING_SNAKE_CASE__			:      Any=None							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					{}
     SCREAMING_SNAKE_CASE:Any						      =					{}

     if prompt is not None:
       SCREAMING_SNAKE_CASE:str						      =					prompt

     if generate_kwargs is not None:
       SCREAMING_SNAKE_CASE:Any						      =					generate_kwargs
     if max_new_tokens is not None:
       if "generate_kwargs" not in forward_kwargs:
         SCREAMING_SNAKE_CASE:Any						      =					{}
       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"							)
       SCREAMING_SNAKE_CASE:int						      =					max_new_tokens
     return preprocess_params, forward_kwargs, {}
   def __call__( self			:      str     ,SCREAMING_SNAKE_CASE__			:      Any     ,**SCREAMING_SNAKE_CASE__			:      Union[str, Any]							):
     return super().__call__(A__     ,**A__							)
   def     __UpperCamelCase						( self			:      Tuple     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      Optional[Any]=None							):
     SCREAMING_SNAKE_CASE:List[Any]						      =					load_image(A__							)

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

       SCREAMING_SNAKE_CASE:Any						      =					self.model.config.model_type

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

       elif model_type == "pix2struct":
         SCREAMING_SNAKE_CASE:Any						      =					self.image_processor(images=A__     ,header_text=A__     ,return_tensors=self.framework							)

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

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

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

     if self.model.config.model_type == "git" and prompt is None:
       SCREAMING_SNAKE_CASE:List[str]						      =					None

     return model_inputs
   def     __UpperCamelCase						( self			:      Any     ,SCREAMING_SNAKE_CASE__			:      str     ,SCREAMING_SNAKE_CASE__			:      int=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"]     ,A__							)
         and all(x is None for x in model_inputs["input_ids"]							)
     ):
       SCREAMING_SNAKE_CASE:Optional[Any]						      =					None

     if generate_kwargs is None:
       SCREAMING_SNAKE_CASE:int						      =					{}
     # 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.
     SCREAMING_SNAKE_CASE:List[Any]						      =					model_inputs.pop(self.model.main_input_name							)
     SCREAMING_SNAKE_CASE:int						      =					self.model.generate(A__     ,**A__     ,**A__							)
     return model_outputs







   def     __UpperCamelCase						( self			:      List[str]     ,SCREAMING_SNAKE_CASE__			:      Optional[int]							):
     SCREAMING_SNAKE_CASE:Optional[Any]						      =					[]
     for output_ids in model_outputs:
       SCREAMING_SNAKE_CASE:Dict						      =					{
           """generated_text""": self.tokenizer.decode(
               A__     ,skip_special_tokens=A__     ,)
       }
       records.append(A__							)
     return records





'''simple docstring'''

import PIL.Image
import PIL.ImageOps
from packaging import version
from PIL import Image


if version.parse(version.parse(PIL.__version__).base_version) >= version.parse("9.1.0"):
    A_       						=							{
        "linear": PIL.Image.Resampling.BILINEAR,
        "bilinear": PIL.Image.Resampling.BILINEAR,
        "bicubic": PIL.Image.Resampling.BICUBIC,
        "lanczos": PIL.Image.Resampling.LANCZOS,
        "nearest": PIL.Image.Resampling.NEAREST,
    }
else:
    A_       						=							{
        "linear": PIL.Image.LINEAR,
        "bilinear": PIL.Image.BILINEAR,
        "bicubic": PIL.Image.BICUBIC,
        "lanczos": PIL.Image.LANCZOS,
        "nearest": PIL.Image.NEAREST,
    }





def    A_  ( snake_case ):
  SCREAMING_SNAKE_CASE:Any						      =					(images / 2 + 0.5).clamp(0      ,       1 )
  SCREAMING_SNAKE_CASE:Optional[Any]						      =					images.cpu().permute(0      ,       2      ,       3      ,       1 ).float().numpy()
  SCREAMING_SNAKE_CASE:str						      =					numpy_to_pil(snake_case )
  return images





def    A_  ( snake_case ):
  if images.ndim == 3:
    SCREAMING_SNAKE_CASE:List[str]						      =					images[None, ...]
  SCREAMING_SNAKE_CASE:List[Any]						      =					(images * 255).round().astype("uint8" )
  if images.shape[-1] == 1:
    # special case for grayscale (single channel) images
    SCREAMING_SNAKE_CASE:Any						      =					[Image.fromarray(image.squeeze()      ,       mode="L" ) for image in images]
  else:
    SCREAMING_SNAKE_CASE:int						      =					[Image.fromarray(snake_case ) for image in images]

  return pil_images






