



"""simple docstring"""




import copy
import os
from typing import Union

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


_lowerCAmelCase    : str							=							logging.get_logger(__name__)

_lowerCAmelCase    : List[Any]							=							{
    "google/pix2struct-textcaps-base": (
        "https://huggingface.co/google/pix2struct-textcaps-base/resolve/main/config.json"
    ),
}






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       int								=			'pix2struct_text_model'
      __SCREAMING_SNAKE_CASE						:       str								=			['past_key_values']
      __SCREAMING_SNAKE_CASE						:       int								=			{
          'hidden_size': 'hidden_size',
          'num_attention_heads': 'num_heads',
          'num_hidden_layers': 'num_layers',
      }



      def __init__(					self					:     Optional[Any]					,     A					:     Tuple=5_0_2_4_4					,     A					:     Optional[Any]=7_6_8					,     A					:     str=6_4					,     A					:     int=2_0_4_8					,     A					:     Tuple=1_2					,     A					:     Union[str, Any]=1_2					,     A					:     List[Any]=3_2					,     A					:     Dict=1_2_8					,     A					:     Optional[Any]=0.1					,     A					:     int=1e-6					,     A					:     int=1.0					,     A					:     Dict="gelu_new"					,     A					:     List[str]=0					,     A					:     Optional[int]=False					,     A					:     Optional[Any]=0					,     A					:     Union[str, Any]=1					,     A					:     str=False					,     A					:     List[Any]=True					,     **A					:     Optional[Any]					,     ):
           _UpperCAmelCase      :     str        =   vocab_size
           _UpperCAmelCase      :     Union[str, Any]        =   hidden_size
           _UpperCAmelCase      :     Tuple        =   d_kv
           _UpperCAmelCase      :     Optional[Any]        =   d_ff
           _UpperCAmelCase      :     Union[str, Any]        =   num_layers
           _UpperCAmelCase      :     Union[str, Any]        =   num_heads
           _UpperCAmelCase      :     Tuple        =   relative_attention_num_buckets
           _UpperCAmelCase      :     str        =   relative_attention_max_distance
           _UpperCAmelCase      :     Union[str, Any]        =   dropout_rate
           _UpperCAmelCase      :     str        =   layer_norm_epsilon
           _UpperCAmelCase      :     Union[str, Any]        =   initializer_factor
           _UpperCAmelCase      :     Dict        =   use_cache

           _UpperCAmelCase      :     List[str]        =   eos_token_id
           _UpperCAmelCase      :     Optional[Any]        =   decoder_start_token_id

           # for backwards compatibility
           _UpperCAmelCase      :     Optional[Any]        =   dense_act_fn

           super().__init__(
               pad_token_id=A					,     eos_token_id=A					,     decoder_start_token_id=A					,     tie_word_embeddings=A					,     is_decoder=A					,     **A					,     )



      @classmethod
      def 						snake_case_ (					cls					:     Optional[Any]					,     A					:     Union[str, os.PathLike]					,     **A					:     Tuple						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Dict        =   cls.get_config_dict(A					,     **A						)

           # get the text config dict if we are loading from Pix2StructConfig
           if config_dict.get("model_type"						) == "pix2struct":
                _UpperCAmelCase      :     List[str]        =   config_dict["text_config"]

           if "model_type" in config_dict and hasattr(cls					,     "model_type"						) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

           return cls.from_dict(A					,     **A						)







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       str								=			'pix2struct_vision_model'



      def __init__(					self					:     Tuple					,     A					:     Optional[Any]=7_6_8					,     A					:     str=7_6_8					,     A					:     Any=2_0_4_8					,     A					:     Optional[int]=6_4					,     A					:     Tuple=1_2					,     A					:     Tuple=1_2					,     A					:     List[str]="gelu_new"					,     A					:     Any=1e-6					,     A					:     Union[str, Any]=0.0					,     A					:     Dict=0.0					,     A					:     Optional[Any]=1e-10					,     A					:     List[str]=1.0					,     A					:     Any=4_0_9_6					,     A					:     Optional[int]=3_2					,     A					:     Optional[int]=1_2_8					,     **A					:     Optional[int]					,     ):
           super().__init__(**A						)

           _UpperCAmelCase      :     Any        =   hidden_size
           _UpperCAmelCase      :     str        =   patch_embed_hidden_size
           _UpperCAmelCase      :     Tuple        =   d_ff
           _UpperCAmelCase      :     List[Any]        =   dropout_rate
           _UpperCAmelCase      :     Tuple        =   num_hidden_layers
           _UpperCAmelCase      :     str        =   num_attention_heads
           _UpperCAmelCase      :     str        =   initializer_range
           _UpperCAmelCase      :     Optional[int]        =   initializer_factor
           _UpperCAmelCase      :     Union[str, Any]        =   attention_dropout
           _UpperCAmelCase      :     Optional[Any]        =   layer_norm_eps
           _UpperCAmelCase      :     Tuple        =   dense_act_fn
           _UpperCAmelCase      :     Tuple        =   seq_len
           _UpperCAmelCase      :     Union[str, Any]        =   relative_attention_num_buckets
           _UpperCAmelCase      :     Any        =   relative_attention_max_distance
           _UpperCAmelCase      :     Optional[Any]        =   d_kv



      @classmethod
      def 						snake_case_ (					cls					:     Optional[Any]					,     A					:     Union[str, os.PathLike]					,     **A					:     Any						):
           cls._set_token_in_kwargs(A						)

           _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[int]        =   cls.get_config_dict(A					,     **A						)

           # get the vision config dict if we are loading from Pix2StructConfig
           if config_dict.get("model_type"						) == "pix2struct":
                _UpperCAmelCase      :     int        =   config_dict["vision_config"]

           if "model_type" in config_dict and hasattr(cls					,     "model_type"						) and config_dict["model_type"] != cls.model_type:
                logger.warning(
                    f'You are using a model of type {config_dict["model_type"]} to instantiate a model of type '
                    f'{cls.model_type}. This is not supported for all configurations of models and can yield errors.'						)

           return cls.from_dict(A					,     **A						)







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			'pix2struct'
      __SCREAMING_SNAKE_CASE						:       Dict								=			True



      def __init__(					self					:     Any					,     A					:     List[Any]=None					,     A					:     Optional[int]=None					,     A					:     Tuple=1.0					,     A					:     Union[str, Any]=0.02					,     A					:     Optional[int]=False					,     A					:     int=False					,     A					:     List[Any]=True					,     **A					:     Optional[int]					,     ):
           super().__init__(tie_word_embeddings=A					,     is_encoder_decoder=A					,     **A						)

           if text_config is None:
                _UpperCAmelCase      :     int        =   {}
                logger.info("text_config is None. Initializing the Pix2StructTextConfig with default values."						)

           if vision_config is None:
                _UpperCAmelCase      :     List[str]        =   {}
                logger.info("vision_config is None. Initializing the Pix2StructVisionConfig with default values."						)

           _UpperCAmelCase      :     Tuple        =   PixaStructTextConfig(**A						)
           _UpperCAmelCase      :     Optional[Any]        =   PixaStructVisionConfig(**A						)

           _UpperCAmelCase      :     Any        =   self.text_config.decoder_start_token_id
           _UpperCAmelCase      :     Optional[Any]        =   self.text_config.pad_token_id
           _UpperCAmelCase      :     Any        =   self.text_config.eos_token_id

           _UpperCAmelCase      :     int        =   initializer_factor
           _UpperCAmelCase      :     Optional[int]        =   initializer_range

           _UpperCAmelCase      :     Tuple        =   self.initializer_range
           _UpperCAmelCase      :     Union[str, Any]        =   self.initializer_range

           _UpperCAmelCase      :     int        =   is_vqa



      @classmethod
      def 						snake_case_ (					cls					:     Optional[Any]					,     A					:     PixaStructTextConfig					,     A					:     PixaStructVisionConfig					,     **A					:     Union[str, Any]						):
           return cls(text_config=text_config.to_dict()					,     vision_config=vision_config.to_dict()					,     **A						)



      def 						snake_case_ (					self					:     Tuple						):
           _UpperCAmelCase      :     Optional[int]        =   copy.deepcopy(self.__dict__						)
           _UpperCAmelCase      :     Dict        =   self.text_config.to_dict()
           _UpperCAmelCase      :     Any        =   self.vision_config.to_dict()
           _UpperCAmelCase      :     Optional[int]        =   self.__class__.model_type
           return output





"""simple docstring"""




from typing import Dict, Iterable, List, Optional, Union

import numpy as np

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


_lowerCAmelCase    : Tuple							=							logging.get_logger(__name__)






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       str								=			['pixel_values']



      def __init__(					self					:     List[Any]					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     bool = True					,     A					:     Union[int, float] = 1 / 2_5_5					,     A					:     bool = True					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					,     **A					:     List[Any]					,     ):
           super().__init__(**A						)
           _UpperCAmelCase      :     int        =   size if size is not None else {"shortest_edge": 2_2_4}
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     int        =   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase      :     Union[str, Any]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     str        =   do_resize
           _UpperCAmelCase      :     Union[str, Any]        =   size
           _UpperCAmelCase      :     Tuple        =   resample
           _UpperCAmelCase      :     List[Any]        =   do_center_crop
           _UpperCAmelCase      :     List[Any]        =   crop_size
           _UpperCAmelCase      :     Tuple        =   do_rescale
           _UpperCAmelCase      :     List[Any]        =   rescale_factor
           _UpperCAmelCase      :     Dict        =   do_normalize
           _UpperCAmelCase      :     Optional[Any]        =   image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
           _UpperCAmelCase      :     Any        =   image_std if image_std is not None else IMAGENET_DEFAULT_STD



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Union[str, Any]					,     ):
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           # size_dict is a dict with either keys "height" and "width" or "shortest_edge"
           if "shortest_edge" in size:
                _UpperCAmelCase      :     Any        =   int((2_5_6 / 2_2_4) * size["shortest_edge"]						)
                _UpperCAmelCase      :     Union[str, Any]        =   get_resize_output_image_size(A					,     size=A					,     default_to_square=A						)
                _UpperCAmelCase      :     str        =   {"height": output_size[0], "width": output_size[1]}
           if "height" not in size_dict or "width" not in size_dict:
                raise ValueError(
                    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'						)
           return resize(
               A					,     size=(size_dict["height"], size_dict["width"])					,     resample=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     int					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Optional[Any]					,     ):
           _UpperCAmelCase      :     Dict        =   get_size_dict(A						)
           if "height" not in size or "width" not in size:
                raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'						)
           return center_crop(A					,     size=(size["height"], size["width"])					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[int, float]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Dict					,     ):
           return rescale(A					,     scale=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[float, List[float]]					,     A					:     Union[float, List[float]]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     str					,     ):
           return normalize(A					,     mean=A					,     std=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     ImageInput					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     PILImageResampling = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[float] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[TensorType] = None					,     A					:     ChannelDimension = ChannelDimension.FIRST					,     **A					:     Dict					,     ):
           _UpperCAmelCase      :     Dict        =   do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase      :     List[str]        =   resample if resample is not None else self.resample
           _UpperCAmelCase      :     int        =   do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase      :     str        =   do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase      :     Tuple        =   rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase      :     Optional[int]        =   do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase      :     List[str]        =   image_mean if image_mean is not None else self.image_mean
           _UpperCAmelCase      :     Tuple        =   image_std if image_std is not None else self.image_std

           _UpperCAmelCase      :     Any        =   size if size is not None else self.size
           _UpperCAmelCase      :     Optional[int]        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     List[str]        =   crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase      :     List[str]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     List[Any]        =   make_list_of_images(A						)

           if not valid_images(A						):
                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."						)

           # All transformations expect numpy arrays.
           _UpperCAmelCase      :     Any        =   [to_numpy_array(A						) for image in images]

           if do_resize:
                _UpperCAmelCase      :     str        =   [self.resize(A					,     A					,     A						) for image in images]

           if do_center_crop:
                _UpperCAmelCase      :     Dict        =   [self.center_crop(A					,     A						) for image in images]

           if do_rescale:
                _UpperCAmelCase      :     Dict        =   [self.rescale(A					,     A						) for image in images]

           if do_normalize:
                _UpperCAmelCase      :     Optional[int]        =   [self.normalize(A					,     A					,     A						) for image in images]

           _UpperCAmelCase      :     Union[str, Any]        =   [to_channel_dimension_format(A					,     A						) for image in images]

           _UpperCAmelCase      :     List[Any]        =   {"pixel_values": images}
           return BatchFeature(data=A					,     tensor_type=A						)
