

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 				a  (	UpperCAmelCase				):
      _lowercase    						=			["image_processor", "tokenizer"]
      _lowercase    						=			"BridgeTowerImageProcessor"
      _lowercase    						=			("RobertaTokenizer", "RobertaTokenizerFast")


      def __init__(       self						,					A_						,					A_	):







          '''simple docstring'''


          super().__init__(A_						,					A_	)


      def __call__(       self						,					A_						,					A_ = None						,					A_ = True						,					A_ = False						,					A_ = None						,					A_ = None						,					A_ = 0						,					A_ = None						,					A_ = None						,					A_ = None						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = False						,					A_ = True						,					A_ = None						,					**A_						,					):







          '''simple docstring'''


          _UpperCAmelCase       :		int 		=    self.tokenizer(
              text=A_						,					add_special_tokens=A_						,					padding=A_						,					truncation=A_						,					max_length=A_						,					stride=A_						,					pad_to_multiple_of=A_						,					return_token_type_ids=A_						,					return_attention_mask=A_						,					return_overflowing_tokens=A_						,					return_special_tokens_mask=A_						,					return_offsets_mapping=A_						,					return_length=A_						,					verbose=A_						,					return_tensors=A_						,					**A_						,					)
          # add pixel_values + pixel_mask
          _UpperCAmelCase       :		Any 		=    self.image_processor(
              A_						,					return_tensors=A_						,					do_normalize=A_						,					do_center_crop=A_						,					**A_	)
          encoding.update(A_	)

          return encoding


      def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







          '''simple docstring'''


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


      def        _UpperCAmelCase				(       self						,					*A_						,					**A_	):







          '''simple docstring'''


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





      @property
      def        _UpperCAmelCase				(       self	):







          '''simple docstring'''


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





from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


SCREAMING_SNAKE_CASE_        =      {
    'configuration_nllb_moe': [
        'NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'NllbMoeConfig',
    ]
}

try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					SCREAMING_SNAKE_CASE_        =      [
					    'NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST',
					    'NllbMoeForConditionalGeneration',
					    'NllbMoeModel',
					    'NllbMoePreTrainedModel',
					    'NllbMoeTop2Router',
					    'NllbMoeSparseMLP',
					]


if TYPE_CHECKING:
					from .configuration_nllb_moe import (
					    NLLB_MOE_PRETRAINED_CONFIG_ARCHIVE_MAP,
					    NllbMoeConfig,
					)

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_nllb_moe import (
										    NLLB_MOE_PRETRAINED_MODEL_ARCHIVE_LIST,
										    NllbMoeForConditionalGeneration,
										    NllbMoeModel,
										    NllbMoePreTrainedModel,
										    NllbMoeSparseMLP,
										    NllbMoeTopaRouter,
										)


else:
					import sys

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


