





from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
    import torch

if is_tf_available():
    import tensorflow as tf



class       A__     ( __snake_case    ):
      _UpperCAmelCase			:str							=     ['image_processor']
      _UpperCAmelCase			:Tuple							=     'SamImageProcessor'



      def __init__(				self ,							A_     ):
       '''simple docstring'''




       super().__init__(A_     )
       UpperCamelCase			:      List[Any] 			=     self.image_processor
       UpperCamelCase			:      Tuple 			=     -10
       UpperCamelCase			:      Optional[int] 			=     self.image_processor.size["longest_edge"]



      def __call__(				self ,							A_=None ,							A_=None ,							A_=None ,							A_=None ,							A_ = None ,							**A_ ,							):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     self.image_processor(
           A_ ,							return_tensors=A_ ,							**A_ ,							)

       # pop arguments that are not used in the foward but used nevertheless
       UpperCamelCase			:      Union[str, Any] 			=     encoding_image_processor["original_sizes"]

       if hasattr(A_ ,							"numpy"     ):  # Checks if Torch or TF tensor
        UpperCamelCase			:      Tuple 			=     original_sizes.numpy()

       UpperCamelCase			:      Dict 			=     self._check_and_preprocess_points(
           input_points=A_ ,							input_labels=A_ ,							input_boxes=A_ ,							)

       UpperCamelCase			:      List[str] 			=     self._normalize_and_convert(
           A_ ,							A_ ,							input_points=A_ ,							input_labels=A_ ,							input_boxes=A_ ,							return_tensors=A_ ,							)

       return encoding_image_processor



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_=None ,							A_=None ,							A_=None ,							A_="pt" ,							):
       '''simple docstring'''




       if input_points is not None:
        if len(A_     ) != len(A_     ):
         UpperCamelCase			:      Union[str, Any] 			=     [
             self._normalize_coordinates(self.target_size ,							A_ ,							original_sizes[0]     ) for point in input_points
         ]
        else:
         UpperCamelCase			:      Optional[int] 			=     [
             self._normalize_coordinates(self.target_size ,							A_ ,							A_     )
             for point, original_size in zip(A_ ,							A_     )
         ]
        # check that all arrays have the same shape
        if not all(point.shape == input_points[0].shape for point in input_points     ):
         if input_labels is not None:
          UpperCamelCase			:      int 			=     self._pad_points_and_labels(A_ ,							A_     )

        UpperCamelCase			:      Optional[Any] 			=     np.array(A_     )

       if input_labels is not None:
        UpperCamelCase			:      Optional[Any] 			=     np.array(A_     )

       if input_boxes is not None:
        if len(A_     ) != len(A_     ):
         UpperCamelCase			:      Any 			=     [
             self._normalize_coordinates(self.target_size ,							A_ ,							original_sizes[0] ,							is_bounding_box=A_     )
             for box in input_boxes
         ]
        else:
         UpperCamelCase			:      Dict 			=     [
             self._normalize_coordinates(self.target_size ,							A_ ,							A_ ,							is_bounding_box=A_     )
             for box, original_size in zip(A_ ,							A_     )
         ]
        UpperCamelCase			:      Dict 			=     np.array(A_     )

       if input_boxes is not None:
        if return_tensors == "pt":
         UpperCamelCase			:      Optional[int] 			=     torch.from_numpy(A_     )
         # boxes batch size of 1 by default
         UpperCamelCase			:      Optional[Any] 			=     input_boxes.unsqueeze(1     ) if len(input_boxes.shape     ) != 3 else input_boxes
        elif return_tensors == "tf":
         UpperCamelCase			:      str 			=     tf.convert_to_tensor(A_     )
         # boxes batch size of 1 by default
         UpperCamelCase			:      Any 			=     tf.expand_dims(A_ ,							1     ) if len(input_boxes.shape     ) != 3 else input_boxes
        encoding_image_processor.update({"input_boxes": input_boxes}     )
       if input_points is not None:
        if return_tensors == "pt":
         UpperCamelCase			:      int 			=     torch.from_numpy(A_     )
         # point batch size of 1 by default
         UpperCamelCase			:      int 			=     input_points.unsqueeze(1     ) if len(input_points.shape     ) != 4 else input_points
        elif return_tensors == "tf":
         UpperCamelCase			:      Optional[int] 			=     tf.convert_to_tensor(A_     )
         # point batch size of 1 by default
         UpperCamelCase			:      Optional[Any] 			=     tf.expand_dims(A_ ,							1     ) if len(input_points.shape     ) != 4 else input_points
        encoding_image_processor.update({"input_points": input_points}     )
       if input_labels is not None:
        if return_tensors == "pt":
         UpperCamelCase			:      str 			=     torch.from_numpy(A_     )
         # point batch size of 1 by default
         UpperCamelCase			:      Optional[int] 			=     input_labels.unsqueeze(1     ) if len(input_labels.shape     ) != 3 else input_labels
        elif return_tensors == "tf":
         UpperCamelCase			:      Any 			=     tf.convert_to_tensor(A_     )
         # point batch size of 1 by default
         UpperCamelCase			:      Dict 			=     tf.expand_dims(A_ ,							1     ) if len(input_labels.shape     ) != 3 else input_labels
        encoding_image_processor.update({"input_labels": input_labels}     )

       return encoding_image_processor



      def  __UpperCamelCase(				self ,							A_ ,							A_     ):
       '''simple docstring'''




       UpperCamelCase			:      Dict 			=     max([point.shape[0] for point in input_points]     )
       UpperCamelCase			:      List[Any] 			=     []
       for i, point in enumerate(A_     ):
        if point.shape[0] != expected_nb_points:
         UpperCamelCase			:      str 			=     np.concatenate(
             [point, np.zeros((expected_nb_points - point.shape[0], 2)     ) + self.point_pad_value] ,							axis=0     )
         UpperCamelCase			:      Dict 			=     np.append(input_labels[i] ,							[self.point_pad_value]     )
        processed_input_points.append(A_     )
       UpperCamelCase			:      Union[str, Any] 			=     processed_input_points
       return input_points, input_labels



      def  __UpperCamelCase(				self ,							A_ ,							A_ ,							A_ ,							A_=False     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     original_size
       UpperCamelCase			:      str 			=     self.image_processor._get_preprocess_shape(A_ ,							longest_edge=A_     )
       UpperCamelCase			:      List[Any] 			=     deepcopy(A_     ).astype(A_     )

       if is_bounding_box:
        UpperCamelCase			:      Optional[Any] 			=     coords.reshape(-1 ,							2 ,							2     )

       UpperCamelCase			:      Dict 			=     coords[..., 0] * (new_w / old_w)
       UpperCamelCase			:      int 			=     coords[..., 1] * (new_h / old_h)

       if is_bounding_box:
        UpperCamelCase			:      str 			=     coords.reshape(-1 ,							4     )

       return coords



      def  __UpperCamelCase(				self ,							A_=None ,							A_=None ,							A_=None ,							):
       '''simple docstring'''




       if input_points is not None:
        if hasattr(A_ ,							"numpy"     ):  # Checks for TF or Torch tensor
         UpperCamelCase			:      List[Any] 			=     input_points.numpy().tolist()

        if not isinstance(A_ ,							A_     ) or not isinstance(input_points[0] ,							A_     ):
         raise ValueError("Input points must be a list of list of floating points."     )
        UpperCamelCase			:      Any 			=     [np.array(A_     ) for input_point in input_points]
       else:
        UpperCamelCase			:      Union[str, Any] 			=     None

       if input_labels is not None:
        if hasattr(A_ ,							"numpy"     ):
         UpperCamelCase			:      str 			=     input_labels.numpy().tolist()

        if not isinstance(A_ ,							A_     ) or not isinstance(input_labels[0] ,							A_     ):
         raise ValueError("Input labels must be a list of list integers."     )
        UpperCamelCase			:      str 			=     [np.array(A_     ) for label in input_labels]
       else:
        UpperCamelCase			:      Optional[Any] 			=     None

       if input_boxes is not None:
        if hasattr(A_ ,							"numpy"     ):
         UpperCamelCase			:      Union[str, Any] 			=     input_boxes.numpy().tolist()

        if (
            not isinstance(A_ ,							A_     )
            or not isinstance(input_boxes[0] ,							A_     )
            or not isinstance(input_boxes[0][0] ,							A_     )
        ):
         raise ValueError("Input boxes must be a list of list of list of floating points."     )
        UpperCamelCase			:      Optional[int] 			=     [np.array(A_     ).astype(np.floataa     ) for box in input_boxes]
       else:
        UpperCamelCase			:      Dict 			=     None

       return input_points, input_labels, input_boxes



      @property
      def  __UpperCamelCase(				self     ):
       '''simple docstring'''




       UpperCamelCase			:      Optional[int] 			=     self.image_processor.model_input_names
       return list(dict.fromkeys(A_     )     )



      def  __UpperCamelCase(				self ,							*A_ ,							**A_     ):
       '''simple docstring'''




       return self.image_processor.post_process_masks(*A_ ,							**A_     )







import argparse
from pathlib import Path

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



def 	A_       (  _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     _lowerCAmelCase = None							,     )		->				str:
 if config_name_or_path is None:
  UpperCamelCase			:      Dict 			=     "facebook/rag-token-base" if model_type == "rag_token" else "facebook/rag-sequence-base"

 if generator_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     generator_name_or_path

 if question_encoder_tokenizer_name_or_path is None:
  UpperCamelCase			:      Tuple 			=     question_encoder_name_or_path

 UpperCamelCase			:      Any 			=     RagTokenForGeneration if model_type == "rag_token" else RagSequenceForGeneration

 # Save model.
 UpperCamelCase			:      Optional[Any] 			=     RagConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )
 UpperCamelCase			:      Tuple 			=     AutoConfig.from_pretrained(_lowerCAmelCase  )

 UpperCamelCase			:      int 			=     gen_config
 UpperCamelCase			:      Dict 			=     question_encoder_config

 UpperCamelCase			:      Tuple 			=     model_class.from_pretrained_question_encoder_generator(
     _lowerCAmelCase							,     _lowerCAmelCase							,     config=_lowerCAmelCase  )
 rag_model.save_pretrained(_lowerCAmelCase  )

 # Sanity check.
 model_class.from_pretrained(_lowerCAmelCase  )

 # Save tokenizers.
 UpperCamelCase			:      Optional[Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 gen_tokenizer.save_pretrained(dest_dir / "generator_tokenizer/"  )
 UpperCamelCase			:      Union[str, Any] 			=     AutoTokenizer.from_pretrained(_lowerCAmelCase  )
 question_encoder_tokenizer.save_pretrained(dest_dir / "question_encoder_tokenizer/"  )


if __name__ == "__main__":
    __lowerCamelCase :  Tuple										=  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 :  Dict										=  parser.parse_args()

    __lowerCamelCase :  Dict										=  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,
    )
