


"""simple docstring"""



from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
class 					__magic_name__		(			__UpperCAmelCase     ):
       __A			:      Any        =   ["image_processor", "tokenizer"]
       __A			:      List[str]        =   "AutoImageProcessor"
       __A			:      List[str]        =   "AutoTokenizer"






       def __init__(   self  :    Dict  ,     snake_case__  :    Optional[Any]  ,     snake_case__  :    Optional[Any]				):

              '''simple docstring'''






              super().__init__(snake_case__  ,     snake_case__				)
              lowercase		:Union[str, Any]       							=				self.image_processor






       def __call__(   self  :    List[Any]  ,     snake_case__  :    Optional[int]=None  ,     snake_case__  :    Optional[int]=None  ,     snake_case__  :    List[str]=None  ,     **snake_case__  :    List[Any]				):

              '''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:
                     lowercase		:int       							=				self.tokenizer(snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__				)

              if images is not None:
                     lowercase		:List[Any]       							=				self.image_processor(snake_case__  ,     return_tensors=snake_case__  ,     **snake_case__				)

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






       def  __snake_case						(   self  :    Any  ,     *snake_case__  :    Union[str, Any]  ,     **snake_case__  :    Optional[Any]				):

              '''simple docstring'''






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






       def  __snake_case						(   self  :    Optional[Any]  ,     *snake_case__  :    Optional[int]  ,     **snake_case__  :    Tuple				):

              '''simple docstring'''






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







       @property
       def  __snake_case						(   self  :    List[Any]				):

              '''simple docstring'''






              return ["input_ids", "attention_mask", "pixel_values"]





"""simple docstring"""



# Copyright 2023 The HuggingFace Inc. 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.
import re

from ..utils import cached_file


# docstyle-ignore
UpperCAmelCase      		=			'''
Human: <<task>>

Assistant: '''


UpperCAmelCase      		=			'''huggingface-tools/default-prompts'''
UpperCAmelCase      		=			{'''chat''': '''chat_prompt_template.txt''', '''run''': '''run_prompt_template.txt'''}






def       lowerCamelCase					(a_						:int				,				a_						:str				,				a_						:Dict="run")     ->							Optional[Any]:
       if prompt_or_repo_id is None:
              lowercase		:Tuple       							=				DEFAULT_PROMPTS_REPO

       # prompt is considered a repo ID when it does not contain any kind of space
       if re.search('''\\s'''				,				a_) is not None:
              return prompt_or_repo_id

       lowercase		:List[str]       							=				cached_file(
           a_				,				PROMPT_FILES[mode]				,				repo_type='''dataset'''				,				user_agent={'''agent''': agent_name})
       with open(a_				,				'''r'''				,				encoding='''utf-8''') as f:
              return f.read()

