






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 convert_to_rgb, 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


if is_vision_available():
   import PIL


__A					=	logging.get_logger(__name__)



class 				_A			(     UpperCamelCase				):
    """simple docstring"""

    lowerCamelCase	:							Union[str, Any]				= ['pixel_values']



    def __init__(				self      :     str ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] = None ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = PILImageResampling.BICUBIC ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Union[int, float] = 1 / 255 ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     bool = True ,     **__SCREAMING_SNAKE_CASE      :     Tuple ,     )      ->							None:
           super().__init__(**__SCREAMING_SNAKE_CASE      )
           __UpperCAmelCase     				=size if size is not None else {"""height""": 384, """width""": 384}
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )

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



    def _a							(				self      :     List[str] ,     __SCREAMING_SNAKE_CASE      :     np.ndarray ,     __SCREAMING_SNAKE_CASE      :     Dict[str, int] ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = PILImageResampling.BICUBIC ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None ,     **__SCREAMING_SNAKE_CASE      :     Dict ,     )      ->							np.ndarray:
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )
           if "height" not in size or "width" not in size:
                  raise ValueError(f'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''      )
           __UpperCAmelCase     				=(size["""height"""], size["""width"""])
           return resize(__SCREAMING_SNAKE_CASE ,     size=__SCREAMING_SNAKE_CASE ,     resample=__SCREAMING_SNAKE_CASE ,     data_format=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     np.ndarray ,     __SCREAMING_SNAKE_CASE      :     Union[int, float] ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None ,     **__SCREAMING_SNAKE_CASE      :     Any ,     )      ->							Dict:
           return rescale(__SCREAMING_SNAKE_CASE ,     scale=__SCREAMING_SNAKE_CASE ,     data_format=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     Any ,     __SCREAMING_SNAKE_CASE      :     np.ndarray ,     __SCREAMING_SNAKE_CASE      :     Union[float, List[float]] ,     __SCREAMING_SNAKE_CASE      :     Union[float, List[float]] ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, ChannelDimension]] = None ,     **__SCREAMING_SNAKE_CASE      :     Any ,     )      ->							np.ndarray:
           return normalize(__SCREAMING_SNAKE_CASE ,     mean=__SCREAMING_SNAKE_CASE ,     std=__SCREAMING_SNAKE_CASE ,     data_format=__SCREAMING_SNAKE_CASE ,     **__SCREAMING_SNAKE_CASE      )



    def _a							(				self      :     int ,     __SCREAMING_SNAKE_CASE      :     ImageInput ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Dict[str, int]] = None ,     __SCREAMING_SNAKE_CASE      :     PILImageResampling = None ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[float] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[bool] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[float, List[float]]] = None ,     __SCREAMING_SNAKE_CASE      :     Optional[Union[str, TensorType]] = None ,     __SCREAMING_SNAKE_CASE      :     bool = None ,     __SCREAMING_SNAKE_CASE      :     ChannelDimension = ChannelDimension.FIRST ,     **__SCREAMING_SNAKE_CASE      :     List[str] ,     )      ->							PIL.Image.Image:
           __UpperCAmelCase     				=do_resize if do_resize is not None else self.do_resize
           __UpperCAmelCase     				=resample if resample is not None else self.resample
           __UpperCAmelCase     				=do_rescale if do_rescale is not None else self.do_rescale
           __UpperCAmelCase     				=rescale_factor if rescale_factor is not None else self.rescale_factor
           __UpperCAmelCase     				=do_normalize if do_normalize is not None else self.do_normalize
           __UpperCAmelCase     				=image_mean if image_mean is not None else self.image_mean
           __UpperCAmelCase     				=image_std if image_std is not None else self.image_std
           __UpperCAmelCase     				=do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

           __UpperCAmelCase     				=size if size is not None else self.size
           __UpperCAmelCase     				=get_size_dict(__SCREAMING_SNAKE_CASE ,     default_to_square=__SCREAMING_SNAKE_CASE      )

           __UpperCAmelCase     				=make_list_of_images(__SCREAMING_SNAKE_CASE      )

           if not valid_images(__SCREAMING_SNAKE_CASE      ):
                  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 or resample is None:
                  raise ValueError("""Size and resample must be specified if do_resize 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:
                  __UpperCAmelCase     				=[convert_to_rgb(__SCREAMING_SNAKE_CASE      ) for image in images]

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

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

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

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

           __UpperCAmelCase     				=[to_channel_dimension_format(__SCREAMING_SNAKE_CASE ,     __SCREAMING_SNAKE_CASE      ) for image in images]

           __UpperCAmelCase     				=BatchFeature(data={"""pixel_values""": images} ,     tensor_type=__SCREAMING_SNAKE_CASE      )

           return encoded_outputs







import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration





def      A__					(			__A  :						Any     ,				__A  :						str     ,				__A  :						str     ,				__A  :						Path     ,				__A  :						str = None     ,				__A  :						str = None     ,				__A  :						str = None     ,				)					->Optional[Any]:
      if config_name_or_path is None:
            __A    ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            __A    =generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            __A    =question_encoder_name_or_path

      __A    =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      __A    =RagConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)

      __A    =gen_config
      __A    =question_encoder_config

      __A    =model_class.from_pretrained_question_encoder_generator(
          __A     ,				__A     ,				config=__A			)
      rag_model.save_pretrained(__A			)

      # Sanity check.
      model_class.from_pretrained(__A			)

      # Save tokenizers.
      __A    =AutoTokenizer.from_pretrained(__A			)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
      __A    =AutoTokenizer.from_pretrained(__A			)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type''',
       choices=['''rag_sequence''', '''rag_token'''],
       required=True,
       type=str,
       help='''RAG model type: rag_sequence, rag_token''',
   )
   parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
   parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
   parser.add_argument(
       '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
   )

   parser.add_argument(
       '''--generator_tokenizer_name_or_path''',
       type=str,
       help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
   )
   parser.add_argument(
       '''--question_encoder_tokenizer_name_or_path''',
       type=str,
       help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
   )
   parser.add_argument(
       '''--config_name_or_path''',
       type=str,
       help=(
           '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
           ''' ``model_type``'''
       ),
   )

   _lowerCamelCase      :					str					    =      parser.parse_args()

   _lowerCamelCase      :					Optional[int]					    =      Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )


