


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 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			["image_processor", "tokenizer"]
       A__											=			"ViltImageProcessor"
       A__											=			("BertTokenizer", "BertTokenizerFast")





       def __init__(   self							:			Optional[Any]					,      __lowerCamelCase							:			List[str]=None					,      __lowerCamelCase							:			str=None					,      **__lowerCamelCase							:			List[str]       ):


             '''simple docstring'''



             lowerCamelCase__		:				str    =  None
             if "feature_extractor" in kwargs:
                   warnings.warn(
                       "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
                       " instead."					,      __lowerCamelCase					,      )
                   lowerCamelCase__		:				Union[str, Any]    =  kwargs.pop("feature_extractor"       )

             lowerCamelCase__		:				Optional[Any]    =  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__(__lowerCamelCase					,      __lowerCamelCase       )
             lowerCamelCase__		:				Any    =  self.image_processor





       def __call__(   self							:			Union[str, Any]					,      __lowerCamelCase							:			Tuple					,      __lowerCamelCase							:			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,      __lowerCamelCase							:			bool = True					,      __lowerCamelCase							:			Union[bool, str, PaddingStrategy] = False					,      __lowerCamelCase							:			Union[bool, str, TruncationStrategy] = None					,      __lowerCamelCase							:			Optional[int] = None					,      __lowerCamelCase							:			int = 0					,      __lowerCamelCase							:			Optional[int] = None					,      __lowerCamelCase							:			Optional[bool] = None					,      __lowerCamelCase							:			Optional[bool] = None					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = False					,      __lowerCamelCase							:			bool = True					,      __lowerCamelCase							:			Optional[Union[str, TensorType]] = None					,      **__lowerCamelCase							:			List[str]					,      ):


             '''simple docstring'''



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

             return encoding





       def 						lowerCAmelCase     (   self							:			Optional[int]					,      *__lowerCamelCase							:			Optional[Any]					,      **__lowerCamelCase							:			Optional[int]       ):


             '''simple docstring'''



             return self.tokenizer.batch_decode(*__lowerCamelCase					,      **__lowerCamelCase       )





       def 						lowerCAmelCase     (   self							:			int					,      *__lowerCamelCase							:			Optional[int]					,      **__lowerCamelCase							:			Any       ):


             '''simple docstring'''



             return self.tokenizer.decode(*__lowerCamelCase					,      **__lowerCamelCase       )





       @property
       def 						lowerCAmelCase     (   self							:			List[Any]       ):


             '''simple docstring'''



             lowerCamelCase__		:				Optional[Any]    =  self.tokenizer.model_input_names
             lowerCamelCase__		:				str    =  self.image_processor.model_input_names
             return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names       )       )





       @property
       def 						lowerCAmelCase     (   self							:			Union[str, Any]       ):


             '''simple docstring'''



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





       @property
       def 						lowerCAmelCase     (   self							:			Tuple       ):


             '''simple docstring'''



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






import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, Features, Value
from .base import TaskTemplate







@dataclass(frozen=lowercase__)
class 			_lowercase (							lowercase__):


       """simple docstring"""




       A__											=			field(default="automatic-speech-recognition"							,  metadata={"include_in_asdict_even_if_is_default": True})
       A__											=			Features({"audio": Audio()})
       A__											=			Features({"transcription": Value("string")})
       A__											=			"audio"
       A__											=			"transcription"





       def 						lowerCAmelCase     (   self							:			Any					,      __lowerCamelCase							:			int       ):


             '''simple docstring'''



             if self.audio_column not in features:
                   raise ValueError(f"Column {self.audio_column} is not present in features."       )
             if not isinstance(features[self.audio_column]					,      __lowerCamelCase       ):
                   raise ValueError(f"Column {self.audio_column} is not an Audio type."       )
             lowerCamelCase__		:				Tuple    =  copy.deepcopy(self       )
             lowerCamelCase__		:				Tuple    =  self.input_schema.copy()
             lowerCamelCase__		:				Optional[int]    =  features[self.audio_column]
             lowerCamelCase__		:				int    =  input_schema
             return task_template





       @property
       def 						lowerCAmelCase     (   self							:			int       ):


             '''simple docstring'''



             return {self.audio_column: "audio", self.transcription_column: "transcription"}


