



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     _UpperCAmelCase			(       A__     ):
     UpperCamelCase__		    =				['''image_processor''', '''tokenizer''']
     UpperCamelCase__		    =				'''BridgeTowerImageProcessor'''
     UpperCamelCase__		    =				('''RobertaTokenizer''', '''RobertaTokenizerFast''')




     def __init__(				self ,						a__ ,						a__):
         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__ ,						):
         A__		 =					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
         A__		 =					self.image_processor(
             a__ ,						return_tensors=a__ ,						do_normalize=a__ ,						do_center_crop=a__ ,						**a__)
         encoding.update(a__)

         return encoding




     def 		snake_case_		(				self ,						*a__ ,						**a__):
         return self.tokenizer.batch_decode(*a__ ,						**a__)




     def 		snake_case_		(				self ,						*a__ ,						**a__):
         return self.tokenizer.decode(*a__ ,						**a__)




     @property
     def 		snake_case_		(				self):
         A__		 =					self.tokenizer.model_input_names
         A__		 =					self.image_processor.model_input_names
         return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names))





# Copyright 2022 The HuggingFace Team and The OpenBMB Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_lowercase						  =		{
    "configuration_cpmant": ["CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP", "CpmAntConfig"],
    "tokenization_cpmant": ["CpmAntTokenizer"],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _lowercase						  =		[
       "CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST",
       "CpmAntForCausalLM",
       "CpmAntModel",
       "CpmAntPreTrainedModel",
   ]


if TYPE_CHECKING:
   from .configuration_cpmant import CPMANT_PRETRAINED_CONFIG_ARCHIVE_MAP, CpmAntConfig
   from .tokenization_cpmant import CpmAntTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_cpmant import (
          CPMANT_PRETRAINED_MODEL_ARCHIVE_LIST,
          CpmAntForCausalLM,
          CpmAntModel,
          CpmAntPreTrainedModel,
      )


else:
   import sys

   _lowercase						  =		_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
