


import copy
from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ..auto.configuration_auto import AutoConfig


if TYPE_CHECKING:
    from ... import PreTrainedTokenizerBase, TensorType

__UpperCamelCase    :						Tuple     =							logging.get_logger(__name__)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Dict    = "vision-encoder-decoder"
   a_				:							Tuple    = True

   def __init__(  self  : List[Any]       ,  **_lowerCamelCase  : List[str]							):






         '''simple docstring'''



         super().__init__(**_lowerCamelCase							)
         if "encoder" not in kwargs or "decoder" not in kwargs:
               raise ValueError(
                   F"""A configuraton of type {self.model_type} cannot be instantiated because """
                   F"""not both `encoder` and `decoder` sub-configurations are passed, but only {kwargs}"""							)

         __lowerCamelCase     :      Union[str, Any]       						=						kwargs.pop("""encoder"""							)
         __lowerCamelCase     :      Union[str, Any]       						=						encoder_config.pop("""model_type"""							)
         __lowerCamelCase     :      Optional[int]       						=						kwargs.pop("""decoder"""							)
         __lowerCamelCase     :      str       						=						decoder_config.pop("""model_type"""							)

         __lowerCamelCase     :      str       						=						AutoConfig.for_model(_lowerCamelCase       ,  **_lowerCamelCase							)
         __lowerCamelCase     :      Tuple       						=						AutoConfig.for_model(_lowerCamelCase       ,  **_lowerCamelCase							)
         __lowerCamelCase     :      Union[str, Any]       						=						True

   @classmethod
   def   _snake_case    (  cls  : int       ,  _lowerCamelCase  : PretrainedConfig       ,  _lowerCamelCase  : PretrainedConfig       ,  **_lowerCamelCase  : str							):






         '''simple docstring'''



         logger.info("""Setting `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config"""							)
         __lowerCamelCase     :      Optional[int]       						=						True
         __lowerCamelCase     :      Tuple       						=						True

         return cls(encoder=encoder_config.to_dict()       ,  decoder=decoder_config.to_dict()       ,  **_lowerCamelCase							)


   def   _snake_case    (  self  : Optional[Any]							):






         '''simple docstring'''



         __lowerCamelCase     :      Optional[Any]       						=						copy.deepcopy(self.__dict__							)
         __lowerCamelCase     :      List[Any]       						=						self.encoder.to_dict()
         __lowerCamelCase     :      int       						=						self.decoder.to_dict()
         __lowerCamelCase     :      List[str]       						=						self.__class__.model_type
         return output



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   a_				:							Optional[Any]    = version.parse("1.11"						)

   @property
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         return OrderedDict(
             [
                 ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
             ]							)

   @property
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         return 1E-4


   @property
   def   _snake_case    (  self  : Optional[int]							):






         '''simple docstring'''



         return OrderedDict({"""last_hidden_state""": {0: """batch""", 1: """encoder_sequence"""}}							)



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   @property
   def   _snake_case    (  self  : Dict							):






         '''simple docstring'''



         __lowerCamelCase     :      Dict       						=						OrderedDict()
         __lowerCamelCase     :      Any       						=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
         __lowerCamelCase     :      int       						=						{0: """batch""", 1: """past_decoder_sequence + sequence"""}
         __lowerCamelCase     :      Optional[int]       						=						{0: """batch""", 1: """encoder_sequence"""}

         return common_inputs


   def   _snake_case    (  self  : Optional[int]       ,  _lowerCamelCase  : "PreTrainedTokenizerBase"       ,  _lowerCamelCase  : int = -1       ,  _lowerCamelCase  : int = -1       ,  _lowerCamelCase  : bool = False       ,  _lowerCamelCase  : Optional["TensorType"] = None       ,  ):






         '''simple docstring'''



         import torch

         __lowerCamelCase     :      List[str]       						=						OrderedDict()

         __lowerCamelCase     :      List[str]       						=						super().generate_dummy_inputs(
             _lowerCamelCase       ,  batch_size=_lowerCamelCase       ,  seq_length=_lowerCamelCase       ,  is_pair=_lowerCamelCase       ,  framework=_lowerCamelCase							)

         __lowerCamelCase     :      List[Any]       						=						dummy_input["""input_ids"""].shape
         __lowerCamelCase     :      Optional[Any]       						=						(batch, encoder_sequence, self._config.encoder_hidden_size)
         __lowerCamelCase     :      Any       						=						dummy_input.pop("""input_ids"""							)
         __lowerCamelCase     :      Dict       						=						dummy_input.pop("""attention_mask"""							)
         __lowerCamelCase     :      int       						=						torch.zeros(_lowerCamelCase							)

         return common_inputs



class 							_UpperCamelCase (						A						):

   '''simple docstring'''







   @property
   def   _snake_case    (  self  : Any							):






         '''simple docstring'''



         pass

   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : PretrainedConfig							):






         '''simple docstring'''



         return VisionEncoderDecoderEncoderOnnxConfig(_lowerCamelCase							)


   def   _snake_case    (  self  : str       ,  _lowerCamelCase  : PretrainedConfig       ,  _lowerCamelCase  : PretrainedConfig       ,  _lowerCamelCase  : str = "default"							):






         '''simple docstring'''



         __lowerCamelCase     :      List[Any]       						=						encoder_config.hidden_size
         return VisionEncoderDecoderDecoderOnnxConfig(_lowerCamelCase       ,  _lowerCamelCase							)










import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer





def   _UpperCAmelCase       (     UpperCAmelCase      :			Any						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			int						,	UpperCAmelCase      :			Any=1_024      ):
      """simple docstring"""




      __lowerCamelCase						, __lowerCamelCase     :      str       						=						[], []

      __lowerCamelCase     :      Any       						=						list(zip(UpperCAmelCase						,	UpperCAmelCase      )      )
      __lowerCamelCase						, __lowerCamelCase     :      List[str]       						=						sorted_examples[0]

      def is_too_big(UpperCAmelCase      :			Optional[Any]      ):
            return tok(UpperCAmelCase						,	return_tensors="""pt"""      ).input_ids.shape[1] > max_tokens

      for src, tgt in tqdm(sorted_examples[1:]      ):
            __lowerCamelCase     :      Union[str, Any]       						=						new_src + """ """ + src
            __lowerCamelCase     :      str       						=						new_tgt + """ """ + tgt
            if is_too_big(UpperCAmelCase      ) or is_too_big(UpperCAmelCase      ):  # cant fit, finalize example
                  finished_src.append(UpperCAmelCase      )
                  finished_tgt.append(UpperCAmelCase      )
                  __lowerCamelCase						, __lowerCamelCase     :      str       						=						src, tgt
            else:  # can fit, keep adding
                  __lowerCamelCase						, __lowerCamelCase     :      int       						=						cand_src, cand_tgt

    # cleanup
      if new_src:
            assert new_tgt
            finished_src.append(UpperCAmelCase      )
            finished_tgt.append(UpperCAmelCase      )
      return finished_src, finished_tgt





def   _UpperCAmelCase       (     UpperCAmelCase      :			Tuple						,	UpperCAmelCase      :			Path						,	UpperCAmelCase      :			Dict						,	UpperCAmelCase      :			Optional[Any]      ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						Path(UpperCAmelCase      )
      save_path.mkdir(exist_ok=UpperCAmelCase      )
      for split in ["train"]:
            __lowerCamelCase						, __lowerCamelCase     :      List[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase     :      Tuple       						=						[x.rstrip() for x in Path(UpperCAmelCase      ).open().readlines()]
            __lowerCamelCase						, __lowerCamelCase     :      int       						=						pack_examples(UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase						,	UpperCAmelCase      )
            print(f"""packed {split} split from {len(UpperCAmelCase      )} examples -> {len(UpperCAmelCase      )}."""      )
            Path(save_path / f"""{split}.source"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
            Path(save_path / f"""{split}.target"""      ).open("""w"""      ).write("""\n""".join(UpperCAmelCase      )      )
      for split in ["val", "test"]:
            __lowerCamelCase						, __lowerCamelCase     :      Optional[Any]       						=						data_dir / f"""{split}.source""", data_dir / f"""{split}.target"""
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.source"""      )
            shutil.copyfile(UpperCAmelCase						,	save_path / f"""{split}.target"""      )





def   _UpperCAmelCase       (     ):
      """simple docstring"""




      __lowerCamelCase     :      List[Any]       						=						argparse.ArgumentParser()
      parser.add_argument("""--tok_name"""						,	type=UpperCAmelCase						,	help="""like facebook/bart-large-cnn,t5-base, etc."""      )
      parser.add_argument("""--max_seq_len"""						,	type=UpperCAmelCase						,	default=128      )
      parser.add_argument("""--data_dir"""						,	type=UpperCAmelCase      )
      parser.add_argument("""--save_path"""						,	type=UpperCAmelCase      )
      __lowerCamelCase     :      Union[str, Any]       						=						parser.parse_args()
      __lowerCamelCase     :      Tuple       						=						AutoTokenizer.from_pretrained(args.tok_name      )
      return pack_data_dir(UpperCAmelCase						,	Path(args.data_dir      )						,	args.max_seq_len						,	args.save_path      )


if __name__ == "__main__":
    packer_cli()



