
"""simple docstring"""






from typing import TYPE_CHECKING

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


__UpperCAmelCase 					=    {
    'configuration_retribert': ['RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RetriBertConfig'],
    'tokenization_retribert': ['RetriBertTokenizer'],
}

try:
				if not is_tokenizers_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    ['RetriBertTokenizerFast']

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				__UpperCAmelCase 					=    [
				    'RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST',
				    'RetriBertModel',
				    'RetriBertPreTrainedModel',
				]


if TYPE_CHECKING:
				from .configuration_retribert import RETRIBERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RetriBertConfig
				from .tokenization_retribert import RetriBertTokenizer

				try:
								if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .tokenization_retribert_fast import RetriBertTokenizerFast

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_retribert import (
								    RETRIBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
								    RetriBertModel,
								    RetriBertPreTrainedModel,
								)

else:
				import sys

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










"""simple docstring"""






from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding


class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     Union[str, Any]				  =  ['''image_processor''', '''tokenizer''']
							__magic_name__					:     Optional[int]				  =  '''EfficientNetImageProcessor'''
							__magic_name__					:     Optional[Any]				  =  ('''BertTokenizer''', '''BertTokenizerFast''')




							def __init__(						self	,					lowerCamelCase__	,					lowerCamelCase__)   ->	str:







								'''simple docstring'''




								super().__init__(lowerCamelCase__	,					lowerCamelCase__)




							def __call__(						self	,					lowerCamelCase__=None	,					lowerCamelCase__=None	,					lowerCamelCase__="max_length"	,					lowerCamelCase__=64	,					lowerCamelCase__=None	,					**lowerCamelCase__)   ->	List[str]:







								'''simple docstring'''




								if text is None and images is None:
									raise ValueError("You have to specify either text or images. Both cannot be none.")

								if text is not None:
									snake_case__  :						int		=      self.tokenizer(
									    lowerCamelCase__	,					padding=lowerCamelCase__	,					max_length=lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if images is not None:
									snake_case__  :						int		=      self.image_processor(lowerCamelCase__	,					return_tensors=lowerCamelCase__	,					**lowerCamelCase__)

								if text is not None and images is not None:
									snake_case__  :						List[str]		=      image_features.pixel_values
									return encoding
								elif text is not None:
									return encoding
								else:
									return BatchEncoding(data=dict(**lowerCamelCase__)	,					tensor_type=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	int:







								'''simple docstring'''




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




							def        UpperCAmelCase (						self	,					*lowerCamelCase__	,					**lowerCamelCase__)   ->	Optional[Any]:







								'''simple docstring'''




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







							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								snake_case__  :						Dict		=      self.tokenizer.model_input_names
								snake_case__  :						Optional[int]		=      self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





