

from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_torch_available,
)


_lowerCAmelCase					:			Dict  				=			{
    "configuration_roberta_prelayernorm": [
        "ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "RobertaPreLayerNormConfig",
        "RobertaPreLayerNormOnnxConfig",
    ],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Dict  				=			[
						    "ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "RobertaPreLayerNormForCausalLM",
						    "RobertaPreLayerNormForMaskedLM",
						    "RobertaPreLayerNormForMultipleChoice",
						    "RobertaPreLayerNormForQuestionAnswering",
						    "RobertaPreLayerNormForSequenceClassification",
						    "RobertaPreLayerNormForTokenClassification",
						    "RobertaPreLayerNormModel",
						    "RobertaPreLayerNormPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			Optional[int]  				=			[
						    "TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFRobertaPreLayerNormForCausalLM",
						    "TFRobertaPreLayerNormForMaskedLM",
						    "TFRobertaPreLayerNormForMultipleChoice",
						    "TFRobertaPreLayerNormForQuestionAnswering",
						    "TFRobertaPreLayerNormForSequenceClassification",
						    "TFRobertaPreLayerNormForTokenClassification",
						    "TFRobertaPreLayerNormMainLayer",
						    "TFRobertaPreLayerNormModel",
						    "TFRobertaPreLayerNormPreTrainedModel",
						]

try:
						if not is_flax_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						_lowerCAmelCase					:			List[str]  				=			[
						    "FlaxRobertaPreLayerNormForCausalLM",
						    "FlaxRobertaPreLayerNormForMaskedLM",
						    "FlaxRobertaPreLayerNormForMultipleChoice",
						    "FlaxRobertaPreLayerNormForQuestionAnswering",
						    "FlaxRobertaPreLayerNormForSequenceClassification",
						    "FlaxRobertaPreLayerNormForTokenClassification",
						    "FlaxRobertaPreLayerNormModel",
						    "FlaxRobertaPreLayerNormPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_roberta_prelayernorm import (
						    ROBERTA_PRELAYERNORM_PRETRAINED_CONFIG_ARCHIVE_MAP,
						    RobertaPreLayerNormConfig,
						    RobertaPreLayerNormOnnxConfig,
						)

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_roberta_prelayernorm import (
												    ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    RobertaPreLayerNormForCausalLM,
												    RobertaPreLayerNormForMaskedLM,
												    RobertaPreLayerNormForMultipleChoice,
												    RobertaPreLayerNormForQuestionAnswering,
												    RobertaPreLayerNormForSequenceClassification,
												    RobertaPreLayerNormForTokenClassification,
												    RobertaPreLayerNormModel,
												    RobertaPreLayerNormPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_roberta_prelayernorm import (
												    TF_ROBERTA_PRELAYERNORM_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFRobertaPreLayerNormForCausalLM,
												    TFRobertaPreLayerNormForMaskedLM,
												    TFRobertaPreLayerNormForMultipleChoice,
												    TFRobertaPreLayerNormForQuestionAnswering,
												    TFRobertaPreLayerNormForSequenceClassification,
												    TFRobertaPreLayerNormForTokenClassification,
												    TFRobertaPreLayerNormMainLayer,
												    TFRobertaPreLayerNormModel,
												    TFRobertaPreLayerNormPreTrainedModel,
												)

						try:
												if not is_flax_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_flax_roberta_prelayernorm import (
												    FlaxRobertaPreLayerNormForCausalLM,
												    FlaxRobertaPreLayerNormForMaskedLM,
												    FlaxRobertaPreLayerNormForMultipleChoice,
												    FlaxRobertaPreLayerNormForQuestionAnswering,
												    FlaxRobertaPreLayerNormForSequenceClassification,
												    FlaxRobertaPreLayerNormForTokenClassification,
												    FlaxRobertaPreLayerNormModel,
												    FlaxRobertaPreLayerNormPreTrainedModel,
												)

else:
						import sys

						_lowerCAmelCase					:			str  				=			_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)









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 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						['image_processor', 'tokenizer']
			SCREAMING_SNAKE_CASE							   =						'BridgeTowerImageProcessor'
			SCREAMING_SNAKE_CASE							   =						('RobertaTokenizer', 'RobertaTokenizerFast')





			def __init__(		self					,  __snake_case					,  __snake_case					)   -> Optional[int]:




						'''simple docstring'''
						super().__init__(__snake_case					,  __snake_case					)





			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					,  )   -> BatchEncoding:




						'''simple docstring'''
						__a        =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
						__a        =self.image_processor(
						    __snake_case					,  return_tensors=__snake_case					,  do_normalize=__snake_case					,  do_center_crop=__snake_case					,  **__snake_case					)
						encoding.update(__snake_case					)

						return encoding





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> str:




						'''simple docstring'''
						return self.tokenizer.batch_decode(*__snake_case					,  **__snake_case					)





			def 		__magic_name__    (		self					,  *__snake_case					,  **__snake_case					)   -> Optional[Any]:




						'''simple docstring'''
						return self.tokenizer.decode(*__snake_case					,  **__snake_case					)





			@property
			def 		__magic_name__    (		self					)   -> Union[str, Any]:




						'''simple docstring'''
						__a        =self.tokenizer.model_input_names
						__a        =self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)






