





"""simple docstring"""






from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType



class 	lowerCAmelCase					(      lowercase__							):
       lowerCAmelCase__						 =  ["""image_processor""", """tokenizer"""]
       lowerCAmelCase__						 =  """BlipImageProcessor"""
       lowerCAmelCase__						 =  ("""BertTokenizer""", """BertTokenizerFast""")






       def __init__(   self    ,  a__    ,  a__     ):
              _UpperCAmelCase     =     False
              super().__init__(UpperCAmelCase__    ,  UpperCAmelCase__     )
              _UpperCAmelCase     =     self.image_processor






       def __call__(   self    ,  a__ = None    ,  a__ = None    ,  a__ = True    ,  a__ = False    ,  a__ = None    ,  a__ = None    ,  a__ = 0    ,  a__ = None    ,  a__ = None    ,  a__ = False    ,  a__ = False    ,  a__ = False    ,  a__ = False    ,  a__ = False    ,  a__ = True    ,  a__ = None    ,  **a__    ,  ):
              if images is None and text is None:
                     raise ValueError('You have to specify either images or text.'     )

              # Get only text
              if images is None:
                     _UpperCAmelCase     =     self.tokenizer
                     _UpperCAmelCase     =     self.tokenizer(
                         text=UpperCAmelCase__    ,  add_special_tokens=UpperCAmelCase__    ,  padding=UpperCAmelCase__    ,  truncation=UpperCAmelCase__    ,  max_length=UpperCAmelCase__    ,  stride=UpperCAmelCase__    ,  pad_to_multiple_of=UpperCAmelCase__    ,  return_attention_mask=UpperCAmelCase__    ,  return_overflowing_tokens=UpperCAmelCase__    ,  return_special_tokens_mask=UpperCAmelCase__    ,  return_offsets_mapping=UpperCAmelCase__    ,  return_token_type_ids=UpperCAmelCase__    ,  return_length=UpperCAmelCase__    ,  verbose=UpperCAmelCase__    ,  return_tensors=UpperCAmelCase__    ,  **UpperCAmelCase__    ,  )
                     return text_encoding

              # add pixel_values
              _UpperCAmelCase     =     self.image_processor(UpperCAmelCase__    ,  return_tensors=UpperCAmelCase__     )

              if text is not None:
                     _UpperCAmelCase     =     self.tokenizer(
                         text=UpperCAmelCase__    ,  add_special_tokens=UpperCAmelCase__    ,  padding=UpperCAmelCase__    ,  truncation=UpperCAmelCase__    ,  max_length=UpperCAmelCase__    ,  stride=UpperCAmelCase__    ,  pad_to_multiple_of=UpperCAmelCase__    ,  return_attention_mask=UpperCAmelCase__    ,  return_overflowing_tokens=UpperCAmelCase__    ,  return_special_tokens_mask=UpperCAmelCase__    ,  return_offsets_mapping=UpperCAmelCase__    ,  return_token_type_ids=UpperCAmelCase__    ,  return_length=UpperCAmelCase__    ,  verbose=UpperCAmelCase__    ,  return_tensors=UpperCAmelCase__    ,  **UpperCAmelCase__    ,  )
              else:
                     _UpperCAmelCase     =     None

              if text_encoding is not None:
                     encoding_image_processor.update(UpperCAmelCase__     )

              return encoding_image_processor






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






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






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













"""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.
from ..models.auto import AutoModelForSeqaSeqLM, AutoTokenizer
from .base import PipelineTool



class 	lowerCAmelCase					(      snake_case							):
       lowerCAmelCase__						 =  """philschmid/bart-large-cnn-samsum"""
       lowerCAmelCase__						 =  (
           """This is a tool that summarizes an English text. It takes an input `text` containing the text to summarize, """
           """and returns a summary of the text."""
       )
       lowerCAmelCase__						 =  """summarizer"""
       lowerCAmelCase__						 =  AutoTokenizer
       lowerCAmelCase__						 =  AutoModelForSeqaSeqLM

       lowerCAmelCase__						 =  ["""text"""]
       lowerCAmelCase__						 =  ["""text"""]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor(a__    ,  return_tensors='pt'    ,  truncation=a__     )






       def 							__A (   self    ,  a__     ):
              return self.model.generate(**a__     )[0]






       def 							__A (   self    ,  a__     ):
              return self.pre_processor.decode(a__    ,  skip_special_tokens=a__    ,  clean_up_tokenization_spaces=a__     )






