





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


UpperCAmelCase     :     int							=	logging.get_logger(__name__)


if is_vision_available():
     import PIL







class     __lowerCAmelCase	(   UpperCamelCase__):
       _lowercase    : int            =      ["""pixel_values"""]







       def __init__(   self		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = PILImageResampling.BICUBIC		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = 1 / 2_5_5		,				lowerCAmelCase__ = True		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = None		,				lowerCAmelCase__ = True		,				**lowerCAmelCase__		,				)       ->						None:



         '''simple docstring'''




         super().__init__(**lowerCAmelCase__							)
         a__     : Dict  =size if size is not None else {"shortest_edge": 2_2_4}
         a__     : int  =get_size_dict(lowerCAmelCase__		,				default_to_square=lowerCAmelCase__							)
         a__     : List[str]  =crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
         a__     : str  =get_size_dict(lowerCAmelCase__		,				default_to_square=lowerCAmelCase__		,				param_name="crop_size"							)

         a__     : Union[str, Any]  =do_resize
         a__     : Tuple  =size
         a__     : List[str]  =resample
         a__     : int  =do_center_crop
         a__     : Any  =crop_size
         a__     : Tuple  =do_rescale
         a__     : Optional[int]  =rescale_factor
         a__     : str  =do_normalize
         a__     : Optional[Any]  =image_mean if image_mean is not None else OPENAI_CLIP_MEAN
         a__     : List[str]  =image_std if image_std is not None else OPENAI_CLIP_STD
         a__     : str  =do_convert_rgb







       def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = PILImageResampling.BICUBIC		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



         '''simple docstring'''




         a__     : List[Any]  =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()}'''							)
         a__     : Dict  =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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



         '''simple docstring'''




         a__     : str  =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        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						Dict:



         '''simple docstring'''




         return rescale(lowerCAmelCase__		,				scale=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







       def        _lowercase  (   self		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__		,				lowerCAmelCase__ = None		,				**lowerCAmelCase__		,				)       ->						np.ndarray:



         '''simple docstring'''




         return normalize(lowerCAmelCase__		,				mean=lowerCAmelCase__		,				std=lowerCAmelCase__		,				data_format=lowerCAmelCase__		,				**lowerCAmelCase__							)







       def        _lowercase  (   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__		,				)       ->						PIL.Image.Image:



         '''simple docstring'''




         a__     : List[str]  =do_resize if do_resize is not None else self.do_resize
         a__     : Optional[Any]  =size if size is not None else self.size
         a__     : int  =get_size_dict(lowerCAmelCase__		,				param_name="size"		,				default_to_square=lowerCAmelCase__							)
         a__     : str  =resample if resample is not None else self.resample
         a__     : Tuple  =do_center_crop if do_center_crop is not None else self.do_center_crop
         a__     : Optional[Any]  =crop_size if crop_size is not None else self.crop_size
         a__     : Any  =get_size_dict(lowerCAmelCase__		,				param_name="crop_size"		,				default_to_square=lowerCAmelCase__							)
         a__     : Tuple  =do_rescale if do_rescale is not None else self.do_rescale
         a__     : Optional[int]  =rescale_factor if rescale_factor is not None else self.rescale_factor
         a__     : Union[str, Any]  =do_normalize if do_normalize is not None else self.do_normalize
         a__     : int  =image_mean if image_mean is not None else self.image_mean
         a__     : List[Any]  =image_std if image_std is not None else self.image_std
         a__     : Tuple  =do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

         a__     : Optional[int]  =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:
           a__     : List[str]  =[convert_to_rgb(lowerCAmelCase__							) for image in images]

         # All transformations expect numpy arrays.
         a__     : List[str]  =[to_numpy_array(lowerCAmelCase__							) for image in images]

         if do_resize:
           a__     : List[Any]  =[self.resize(image=lowerCAmelCase__		,				size=lowerCAmelCase__		,				resample=lowerCAmelCase__							) for image in images]

         if do_center_crop:
           a__     : Tuple  =[self.center_crop(image=lowerCAmelCase__		,				size=lowerCAmelCase__							) for image in images]

         if do_rescale:
           a__     : List[Any]  =[self.rescale(image=lowerCAmelCase__		,				scale=lowerCAmelCase__							) for image in images]

         if do_normalize:
           a__     : Optional[Any]  =[self.normalize(image=lowerCAmelCase__		,				mean=lowerCAmelCase__		,				std=lowerCAmelCase__							) for image in images]

         a__     : Union[str, Any]  =[to_channel_dimension_format(lowerCAmelCase__		,				lowerCAmelCase__							) for image in images]

         a__     : List[str]  ={"pixel_values": images}
         return BatchFeature(data=lowerCAmelCase__		,				tensor_type=lowerCAmelCase__							)













import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


UpperCAmelCase     :     Dict							=	logging.get_logger(__name__)

UpperCAmelCase     :     Dict							=	{
    """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json""",
}







class     __lowerCAmelCase	(   UpperCamelCase__):
      _lowercase    : List[Any]            =      """mvp"""
      _lowercase    : Tuple            =      ["""past_key_values"""]
      _lowercase    : Dict            =      {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







      def __init__(   self		,				lowerCAmelCase__=5_0_2_6_7		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=1_2		,				lowerCAmelCase__=4_0_9_6		,				lowerCAmelCase__=1_6		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__="gelu"		,				lowerCAmelCase__=1_0_2_4		,				lowerCAmelCase__=0.1		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=0.02		,				lowerCAmelCase__=0.0		,				lowerCAmelCase__=False		,				lowerCAmelCase__=True		,				lowerCAmelCase__=1		,				lowerCAmelCase__=0		,				lowerCAmelCase__=2		,				lowerCAmelCase__=True		,				lowerCAmelCase__=2		,				lowerCAmelCase__=2		,				lowerCAmelCase__=False		,				lowerCAmelCase__=1_0_0		,				lowerCAmelCase__=8_0_0		,				**lowerCAmelCase__		,				)       ->						str:



        '''simple docstring'''




        a__     : Dict  =vocab_size
        a__     : List[str]  =max_position_embeddings
        a__     : List[str]  =d_model
        a__     : Optional[Any]  =encoder_ffn_dim
        a__     : Dict  =encoder_layers
        a__     : List[str]  =encoder_attention_heads
        a__     : List[str]  =decoder_ffn_dim
        a__     : Tuple  =decoder_layers
        a__     : Tuple  =decoder_attention_heads
        a__     : Any  =dropout
        a__     : str  =attention_dropout
        a__     : str  =activation_dropout
        a__     : Optional[int]  =activation_function
        a__     : Union[str, Any]  =init_std
        a__     : Dict  =encoder_layerdrop
        a__     : List[str]  =decoder_layerdrop
        a__     : Union[str, Any]  =classifier_dropout
        a__     : Union[str, Any]  =use_cache
        a__     : Optional[Any]  =encoder_layers
        a__     : List[str]  =scale_embedding  # scale factor will be sqrt(d_model) if True
        a__     : int  =use_prompt
        a__     : Union[str, Any]  =prompt_length
        a__     : Dict  =prompt_mid_dim

        super().__init__(
            pad_token_id=lowerCAmelCase__		,				bos_token_id=lowerCAmelCase__		,				eos_token_id=lowerCAmelCase__		,				is_encoder_decoder=lowerCAmelCase__		,				decoder_start_token_id=lowerCAmelCase__		,				forced_eos_token_id=lowerCAmelCase__		,				**lowerCAmelCase__		,				)

        if self.forced_bos_token_id is None and kwargs.get("force_bos_token_to_be_generated"		,				lowerCAmelCase__							):
          a__     : Tuple  =self.bos_token_id
          warnings.warn(
              F'''Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. '''
              "The config can simply be saved and uploaded again to be fixed."							)






