





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			(      __lowercase			):
				SCREAMING_SNAKE_CASE__     :   Any		      =     ['''image_processor''', '''tokenizer''']
				SCREAMING_SNAKE_CASE__     :   Union[str, Any]		      =     '''BridgeTowerImageProcessor'''
				SCREAMING_SNAKE_CASE__     :   int		      =     ('''RobertaTokenizer''', '''RobertaTokenizerFast''')






				def __init__(  self			, _lowerCAmelCase			, _lowerCAmelCase					):

										"""simple docstring"""




										super().__init__(_lowerCAmelCase			, _lowerCAmelCase					)






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

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, 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
										__SCREAMING_SNAKE_CASE:				Any           =   self.image_processor(
										    _lowerCAmelCase			, return_tensors=_lowerCAmelCase			, do_normalize=_lowerCAmelCase			, do_center_crop=_lowerCAmelCase			, **_lowerCAmelCase					)
										encoding.update(_lowerCAmelCase					)

										return encoding






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




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






				def 			snake_case_    (  self			, *_lowerCAmelCase			, **_lowerCAmelCase					):

										"""simple docstring"""




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






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.tokenizer.model_input_names
										__SCREAMING_SNAKE_CASE:				Dict           =   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,
)


lowerCAmelCase							:       List[str]					 =	{
    """configuration_gpt_bigcode""": ["""GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP""", """GPTBigCodeConfig"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							lowerCAmelCase							:       str					 =	[
							    """GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """GPTBigCodeForSequenceClassification""",
							    """GPTBigCodeForTokenClassification""",
							    """GPTBigCodeForCausalLM""",
							    """GPTBigCodeModel""",
							    """GPTBigCodePreTrainedModel""",
							]

if TYPE_CHECKING:
							from .configuration_gpt_bigcode import GPT_BIGCODE_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTBigCodeConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_gpt_bigcode import (
														    GPT_BIGCODE_PRETRAINED_MODEL_ARCHIVE_LIST,
														    GPTBigCodeForCausalLM,
														    GPTBigCodeForSequenceClassification,
														    GPTBigCodeForTokenClassification,
														    GPTBigCodeModel,
														    GPTBigCodePreTrainedModel,
														)


else:
							import sys

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



