





'''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

__magic_name__   :						Optional[int]    =			logging.get_logger(__name__)


@add_end_docstrings(__UpperCamelCase			)
class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     def __init__(					self						,   *lowerCamelCase						,   **lowerCamelCase	):
       super().__init__(*lowerCamelCase						,   **lowerCamelCase	)
       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						,   lowerCamelCase=None						,   lowerCamelCase=None						,   lowerCamelCase=None	):
       _snake_case     		=				{}
       _snake_case     		=				{}

       if prompt is not None:
         _snake_case     		=				prompt

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

     def __call__(					self						,   lowerCamelCase						,   **lowerCamelCase	):
       return super().__call__(lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=None	):
       _snake_case     		=				load_image(lowerCamelCase	)

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

         _snake_case     		=				self.model.config.model_type

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

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

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

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

       else:
         _snake_case     		=				self.image_processor(images=lowerCamelCase						,   return_tensors=self.framework	)

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

       return model_inputs

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase=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"]						,   lowerCamelCase	)
           and all(x is None for x in model_inputs["input_ids"]	)
       ):
         _snake_case     		=				None

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






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









'''simple docstring'''



from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


__magic_name__   :						Tuple    =			logging.get_logger(__name__)


if is_vision_available():
 import PIL


class       __SCREAMING_SNAKE_CASE	(						__UpperCamelCase			):



     '''simple docstring'''






     UpperCAmelCase__       :						str      =   ['''pixel_values''']

     def __init__(					self						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = True						,   lowerCamelCase = 1 / 255						,   lowerCamelCase = True						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = True						,   **lowerCamelCase						,   ):
       super().__init__(**lowerCamelCase	)
       _snake_case     		=				size if size is not None else {"shortest_edge": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				crop_size if crop_size is not None else {"height": 224, "width": 224}
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase						,   param_name="crop_size"	)

       _snake_case     		=				do_resize
       _snake_case     		=				size
       _snake_case     		=				resample
       _snake_case     		=				do_center_crop
       _snake_case     		=				crop_size
       _snake_case     		=				do_rescale
       _snake_case     		=				rescale_factor
       _snake_case     		=				do_normalize
       _snake_case     		=				image_mean if image_mean is not None else OPENAI_CLIP_MEAN
       _snake_case     		=				image_std if image_std is not None else OPENAI_CLIP_STD
       _snake_case     		=				do_convert_rgb

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = PILImageResampling.BICUBIC						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase						,   default_to_square=lowerCamelCase	)
       if "shortest_edge" not in size:
         raise ValueError(F'''The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'''	)
       _snake_case     		=				get_resize_output_image_size(lowerCamelCase						,   size=size["shortest_edge"]						,   default_to_square=lowerCamelCase	)
       return resize(lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       _snake_case     		=				get_size_dict(lowerCamelCase	)
       if "height" not in size or "width" not in size:
         raise ValueError(F'''The `size` parameter must contain the keys (height, width). Got {size.keys()}'''	)
       return center_crop(lowerCamelCase						,   size=(size["height"], size["width"])						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return rescale(lowerCamelCase						,   scale=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)

     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase						,   lowerCamelCase = None						,   **lowerCamelCase						,   ):
       return normalize(lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase						,   data_format=lowerCamelCase						,   **lowerCamelCase	)






     def 	UpperCamelCase(					self						,   lowerCamelCase						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = None						,   lowerCamelCase = ChannelDimension.FIRST						,   **lowerCamelCase						,   ):
       _snake_case     		=				do_resize if do_resize is not None else self.do_resize
       _snake_case     		=				size if size is not None else self.size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				resample if resample is not None else self.resample
       _snake_case     		=				do_center_crop if do_center_crop is not None else self.do_center_crop
       _snake_case     		=				crop_size if crop_size is not None else self.crop_size
       _snake_case     		=				get_size_dict(lowerCamelCase						,   param_name="crop_size"						,   default_to_square=lowerCamelCase	)
       _snake_case     		=				do_rescale if do_rescale is not None else self.do_rescale
       _snake_case     		=				rescale_factor if rescale_factor is not None else self.rescale_factor
       _snake_case     		=				do_normalize if do_normalize is not None else self.do_normalize
       _snake_case     		=				image_mean if image_mean is not None else self.image_mean
       _snake_case     		=				image_std if image_std is not None else self.image_std
       _snake_case     		=				do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

       _snake_case     		=				make_list_of_images(lowerCamelCase	)

       if not valid_images(lowerCamelCase	):
         raise ValueError(
             "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
             "torch.Tensor, tf.Tensor or jax.ndarray."	)

       if do_resize and size is None:
         raise ValueError("Size must be specified if do_resize is True."	)

       if do_center_crop and crop_size is None:
         raise ValueError("Crop size must be specified if do_center_crop is True."	)

       if do_rescale and rescale_factor is None:
         raise ValueError("Rescale factor must be specified if do_rescale is True."	)

       if do_normalize and (image_mean is None or image_std is None):
         raise ValueError("Image mean and std must be specified if do_normalize is True."	)

       # PIL RGBA images are converted to RGB
       if do_convert_rgb:
         _snake_case     		=				[convert_to_rgb(lowerCamelCase	) for image in images]

       # All transformations expect numpy arrays.
       _snake_case     		=				[to_numpy_array(lowerCamelCase	) for image in images]

       if do_resize:
         _snake_case     		=				[self.resize(image=lowerCamelCase						,   size=lowerCamelCase						,   resample=lowerCamelCase	) for image in images]

       if do_center_crop:
         _snake_case     		=				[self.center_crop(image=lowerCamelCase						,   size=lowerCamelCase	) for image in images]

       if do_rescale:
         _snake_case     		=				[self.rescale(image=lowerCamelCase						,   scale=lowerCamelCase	) for image in images]

       if do_normalize:
         _snake_case     		=				[self.normalize(image=lowerCamelCase						,   mean=lowerCamelCase						,   std=lowerCamelCase	) for image in images]

       _snake_case     		=				[to_channel_dimension_format(lowerCamelCase						,   lowerCamelCase	) for image in images]

       _snake_case     		=				{"pixel_values": images}
       return BatchFeature(data=lowerCamelCase						,   tensor_type=lowerCamelCase	)


