
"""simple docstring"""
import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 			UpperCamelCase      (							__SCREAMING_SNAKE_CASE  ):
     A__      =			["""image_processor""", """tokenizer"""]
     A__      =			"""ViltImageProcessor"""
     A__      =			("""BertTokenizer""", """BertTokenizerFast""")
     def __init__(     self  ,     snake_case__=None  ,     snake_case__=None  ,     **snake_case__				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				List[Any]							    = None
       if "feature_extractor" in kwargs:
         warnings.warn(
             "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
             " instead."  ,     snake_case__  ,     )
         _SCREAMING_SNAKE_CASE   :				Dict							    = kwargs.pop("feature_extractor"				)

       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = image_processor if image_processor is not None else feature_extractor
       if image_processor is None:
         raise ValueError("You need to specify an `image_processor`."				)
       if tokenizer is None:
         raise ValueError("You need to specify a `tokenizer`."				)

       super().__init__(snake_case__  ,     snake_case__				)
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.image_processor
     def __call__(     self  ,     snake_case__  ,     snake_case__ = None  ,     snake_case__ = True  ,     snake_case__ = False  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = 0  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = None  ,     snake_case__ = False  ,     snake_case__ = False  ,     snake_case__ = False  ,     snake_case__ = False  ,     snake_case__ = True  ,     snake_case__ = None  ,     **snake_case__  ,     ):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				Dict							    = self.tokenizer(
           text=snake_case__  ,     add_special_tokens=snake_case__  ,     padding=snake_case__  ,     truncation=snake_case__  ,     max_length=snake_case__  ,     stride=snake_case__  ,     pad_to_multiple_of=snake_case__  ,     return_token_type_ids=snake_case__  ,     return_attention_mask=snake_case__  ,     return_overflowing_tokens=snake_case__  ,     return_special_tokens_mask=snake_case__  ,     return_offsets_mapping=snake_case__  ,     return_length=snake_case__  ,     verbose=snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__  ,     )
       # add pixel_values + pixel_mask
       _SCREAMING_SNAKE_CASE   :				Union[str, Any]							    = self.image_processor(snake_case__  ,     return_tensors=snake_case__				)
       encoding.update(snake_case__				)

       return encoding
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.batch_decode(*snake_case__  ,     **snake_case__				)
     def 				__SCREAMING_SNAKE_CASE							(     self  ,     *snake_case__  ,     **snake_case__				):




       """simple docstring"""


       return self.tokenizer.decode(*snake_case__  ,     **snake_case__				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       _SCREAMING_SNAKE_CASE   :				str							    = self.tokenizer.model_input_names
       _SCREAMING_SNAKE_CASE   :				Optional[int]							    = self.image_processor.model_input_names
       return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names				)				)
     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       warnings.warn(
           "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."  ,     snake_case__  ,     )
       return self.image_processor_class




     @property
     def 				__SCREAMING_SNAKE_CASE							(     self				):




       """simple docstring"""


       warnings.warn(
           "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."  ,     snake_case__  ,     )
       return self.image_processor





"""simple docstring"""
import fire

from transformers import AutoConfig, AutoModelForSeqaSeqLM, AutoTokenizer





def    _lowerCAmelCase    (     lowerCamelCase__							:							str,						lowerCamelCase__							:							str,						**lowerCamelCase__							:							Optional[Any]				)  ->    Union[str, Any]:
  _SCREAMING_SNAKE_CASE   :				Dict							    = AutoConfig.from_pretrained(lowerCamelCase__,						**lowerCamelCase__				)
  _SCREAMING_SNAKE_CASE   :				List[str]							    = AutoModelForSeqaSeqLM.from_config(lowerCamelCase__				)
  model.save_pretrained(lowerCamelCase__				)
  AutoTokenizer.from_pretrained(lowerCamelCase__				).save_pretrained(lowerCamelCase__				)
  return model


if __name__ == "__main__":
      fire.Fire(save_randomly_initialized_version)



