"""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  _snake_case     (					a__		):
						snake_case__			  =			["image_processor", "tokenizer"]
						snake_case__			  =			"ViltImageProcessor"
						snake_case__			  =			("BertTokenizer", "BertTokenizerFast")






						def __init__(       self      :			Optional[int]							,					UpperCAmelCase      :			Tuple=None							,					UpperCAmelCase      :			Optional[Any]=None							,					**UpperCAmelCase      :			Optional[Any]    ):
													__lowerCamelCase					:		Optional[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."							,					UpperCAmelCase							,					)
																				__lowerCamelCase					:		Dict		   =     kwargs.pop("feature_extractor"    )

													__lowerCamelCase					:		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__(UpperCAmelCase							,					UpperCAmelCase    )
													__lowerCamelCase					:		List[str]		   =     self.image_processor






						def __call__(       self      :			Any							,					UpperCAmelCase      :			Any							,					UpperCAmelCase      :			Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Union[bool, str, PaddingStrategy] = False							,					UpperCAmelCase      :			Union[bool, str, TruncationStrategy] = None							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			int = 0							,					UpperCAmelCase      :			Optional[int] = None							,					UpperCAmelCase      :			Optional[bool] = None							,					UpperCAmelCase      :			Optional[bool] = None							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = False							,					UpperCAmelCase      :			bool = True							,					UpperCAmelCase      :			Optional[Union[str, TensorType]] = None							,					**UpperCAmelCase      :			Optional[int]							,					):
													__lowerCamelCase					:		List[Any]		   =     self.tokenizer(
													    text=UpperCAmelCase							,					add_special_tokens=UpperCAmelCase							,					padding=UpperCAmelCase							,					truncation=UpperCAmelCase							,					max_length=UpperCAmelCase							,					stride=UpperCAmelCase							,					pad_to_multiple_of=UpperCAmelCase							,					return_token_type_ids=UpperCAmelCase							,					return_attention_mask=UpperCAmelCase							,					return_overflowing_tokens=UpperCAmelCase							,					return_special_tokens_mask=UpperCAmelCase							,					return_offsets_mapping=UpperCAmelCase							,					return_length=UpperCAmelCase							,					verbose=UpperCAmelCase							,					return_tensors=UpperCAmelCase							,					**UpperCAmelCase							,					)
													# add pixel_values + pixel_mask
													__lowerCamelCase					:		str		   =     self.image_processor(UpperCAmelCase							,					return_tensors=UpperCAmelCase    )
													encoding.update(UpperCAmelCase    )

													return encoding






						def       lowerCamelCase__     (       self      :			Optional[int]							,					*UpperCAmelCase      :			Union[str, Any]							,					**UpperCAmelCase      :			Optional[Any]    ):
													return self.tokenizer.batch_decode(*UpperCAmelCase							,					**UpperCAmelCase    )






						def       lowerCamelCase__     (       self      :			Dict							,					*UpperCAmelCase      :			Any							,					**UpperCAmelCase      :			str    ):
													return self.tokenizer.decode(*UpperCAmelCase							,					**UpperCAmelCase    )






						@property
						def       lowerCamelCase__     (       self      :			Union[str, Any]    ):
													__lowerCamelCase					:		Dict		   =     self.tokenizer.model_input_names
													__lowerCamelCase					:		Any		   =     self.image_processor.model_input_names
													return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names    )    )






						@property
						def       lowerCamelCase__     (       self      :			str    ):
													warnings.warn(
													    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."							,					UpperCAmelCase							,					)
													return self.image_processor_class







						@property
						def       lowerCamelCase__     (       self      :			Optional[Any]    ):
													warnings.warn(
													    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."							,					UpperCAmelCase							,					)
													return self.image_processor
"""simple docstring"""





from typing import TYPE_CHECKING

from ...utils import _LazyModule


__A  =				{'''processing_wav2vec2_with_lm''': ['''Wav2Vec2ProcessorWithLM''']}


if TYPE_CHECKING:
				from .processing_wavaveca_with_lm import WavaVecaProcessorWithLM
else:
				import sys

				__A  =				_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)