"""simple docstring"""






import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


__lowerCAmelCase       :			List[str]   	=logging.get_logger(__name__)





class 					_A		:

      def __init__(		self						,			__lowerCAmelCase						,			__lowerCAmelCase		):







            """simple docstring"""



            lowercase								=      question_encoder
            lowercase								=      generator
            lowercase								=      self.question_encoder

      def 							A__			(		self						,			__lowerCAmelCase		):







            """simple docstring"""



            if os.path.isfile(__lowerCAmelCase		):
                  raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'		)
            os.makedirs(__lowerCAmelCase						,			exist_ok=__lowerCAmelCase		)
            lowercase								=      os.path.join(__lowerCAmelCase						,			"""question_encoder_tokenizer"""		)
            lowercase								=      os.path.join(__lowerCAmelCase						,			"""generator_tokenizer"""		)
            self.question_encoder.save_pretrained(__lowerCAmelCase		)
            self.generator.save_pretrained(__lowerCAmelCase		)

      @classmethod
      def 							A__			(		cls						,			__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            from ..auto.tokenization_auto import AutoTokenizer

            lowercase								=      kwargs.pop("""config"""						,			__lowerCAmelCase		)

            if config is None:
                  lowercase								=      RagConfig.from_pretrained(__lowerCAmelCase		)

            lowercase								=      AutoTokenizer.from_pretrained(
                __lowerCAmelCase						,			config=config.question_encoder						,			subfolder="""question_encoder_tokenizer"""		)
            lowercase								=      AutoTokenizer.from_pretrained(
                __lowerCAmelCase						,			config=config.generator						,			subfolder="""generator_tokenizer"""		)
            return cls(question_encoder=__lowerCAmelCase						,			generator=__lowerCAmelCase		)

      def __call__(		self						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            return self.current_tokenizer(*__lowerCAmelCase						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            return self.generator.batch_decode(*__lowerCAmelCase						,			**__lowerCAmelCase		)

      def 							A__			(		self						,			*__lowerCAmelCase						,			**__lowerCAmelCase		):







            """simple docstring"""



            return self.generator.decode(*__lowerCAmelCase						,			**__lowerCAmelCase		)

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.question_encoder

      def 							A__			(		self		):







            """simple docstring"""



            lowercase								=      self.generator







      def 							A__			(		self						,			__lowerCAmelCase						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = None						,			__lowerCAmelCase = "longest"						,			__lowerCAmelCase = None						,			__lowerCAmelCase = True						,			**__lowerCAmelCase						,			):







            """simple docstring"""



            warnings.warn(
                """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
                """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
                """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
                """details"""						,			__lowerCAmelCase						,			)
            if max_length is None:
                  lowercase								=      self.current_tokenizer.model_max_length
            lowercase								=      self(
                __lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase						,			return_tensors=__lowerCAmelCase						,			max_length=__lowerCAmelCase						,			padding=__lowerCAmelCase						,			truncation=__lowerCAmelCase						,			**__lowerCAmelCase						,			)
            if tgt_texts is None:
                  return model_inputs
            # Process tgt_texts
            if max_target_length is None:
                  lowercase								=      self.current_tokenizer.model_max_length
            lowercase								=      self(
                text_target=__lowerCAmelCase						,			add_special_tokens=__lowerCAmelCase						,			return_tensors=__lowerCAmelCase						,			padding=__lowerCAmelCase						,			max_length=__lowerCAmelCase						,			truncation=__lowerCAmelCase						,			**__lowerCAmelCase						,			)
            lowercase								=      labels["""input_ids"""]
            return model_inputs




"""simple docstring"""






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase       :			Optional[int]   	={
    """configuration_chinese_clip""": [
        """CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """ChineseCLIPConfig""",
        """ChineseCLIPOnnxConfig""",
        """ChineseCLIPTextConfig""",
        """ChineseCLIPVisionConfig""",
    ],
    """processing_chinese_clip""": ["""ChineseCLIPProcessor"""],
}

try:
       if not is_vision_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPFeatureExtractor"""]
       __lowerCAmelCase       :			List[Any]   	=["""ChineseCLIPImageProcessor"""]

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       __lowerCAmelCase       :			Tuple   	=[
           """CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST""",
           """ChineseCLIPModel""",
           """ChineseCLIPPreTrainedModel""",
           """ChineseCLIPTextModel""",
           """ChineseCLIPVisionModel""",
       ]

if TYPE_CHECKING:
       from .configuration_chinese_clip import (
           CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP,
           ChineseCLIPConfig,
           ChineseCLIPOnnxConfig,
           ChineseCLIPTextConfig,
           ChineseCLIPVisionConfig,
       )
       from .processing_chinese_clip import ChineseCLIPProcessor

       try:
              if not is_vision_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .feature_extraction_chinese_clip import ChineseCLIPFeatureExtractor, ChineseCLIPImageProcessor

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_chinese_clip import (
                  CHINESE_CLIP_PRETRAINED_MODEL_ARCHIVE_LIST,
                  ChineseCLIPModel,
                  ChineseCLIPPreTrainedModel,
                  ChineseCLIPTextModel,
                  ChineseCLIPVisionModel,
              )

else:
       import sys

       __lowerCAmelCase       :			Optional[Any]   	=_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)



