




import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if TYPE_CHECKING:
    from transformers.pipelines.conversational import Conversation


snake_case_ : Optional[int]    		=       logging.get_logger(__name__)

snake_case_ : Union[str, Any]    		=       {"tokenizer_file": "tokenizer.json"}

snake_case_ : Tuple    		=       {
    "tokenizer_file": {
        "bigscience/tokenizer": "https://huggingface.co/bigscience/tokenizer/blob/main/tokenizer.json",
        "bigscience/bloom-560m": "https://huggingface.co/bigscience/bloom-560m/blob/main/tokenizer.json",
        "bigscience/bloom-1b1": "https://huggingface.co/bigscience/bloom-1b1/blob/main/tokenizer.json",
        "bigscience/bloom-1b7": "https://huggingface.co/bigscience/bloom-1b7/blob/main/tokenizer.json",
        "bigscience/bloom-3b": "https://huggingface.co/bigscience/bloom-3b/blob/main/tokenizer.json",
        "bigscience/bloom-7b1": "https://huggingface.co/bigscience/bloom-7b1/blob/main/tokenizer.json",
        "bigscience/bloom": "https://huggingface.co/bigscience/bloom/blob/main/tokenizer.json",
    },
}







class __snake_case			(			a		):
   UpperCAmelCase__  :    Any          =  VOCAB_FILES_NAMES
   UpperCAmelCase__  :    Union[str, Any]          =  PRETRAINED_VOCAB_FILES_MAP
   UpperCAmelCase__  :    Tuple          =  ['''input_ids''', '''attention_mask''']
   UpperCAmelCase__  :    List[str]          =  None







   def __init__(      self		:						int					,						_snake_case		:						List[Any]=None					,						_snake_case		:						Dict=None					,						_snake_case		:						str=None					,						_snake_case		:						int="<unk>"					,						_snake_case		:						Dict="<s>"					,						_snake_case		:						List[str]="</s>"					,						_snake_case		:						List[str]="<pad>"					,						_snake_case		:						Optional[Any]=False					,						_snake_case		:						int=False					,						**_snake_case		:						Dict					,						):

        """simple docstring"""


        super().__init__(
            _snake_case					,						_snake_case					,						tokenizer_file=_snake_case					,						unk_token=_snake_case					,						bos_token=_snake_case					,						eos_token=_snake_case					,						pad_token=_snake_case					,						add_prefix_space=_snake_case					,						clean_up_tokenization_spaces=_snake_case					,						**_snake_case					,						)
        UpperCAmelCase_          =							json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__())
        if pre_tok_state.get('''add_prefix_space'''					,						_snake_case) != add_prefix_space:
             UpperCAmelCase_          =							getattr(_snake_case					,						pre_tok_state.pop('''type'''))
             UpperCAmelCase_          =							add_prefix_space
             UpperCAmelCase_          =							pre_tok_class(**_snake_case)

        UpperCAmelCase_          =							add_prefix_space







   def lowerCamelCase						(      self		:						Optional[Any]					,						*_snake_case		:						Optional[int]					,						**_snake_case		:						Tuple):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_snake_case)
        if not (self.add_prefix_space or not is_split_into_words):
             raise Exception(
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                 ''' pretokenized inputs.''')

        return super()._batch_encode_plus(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						int					,						*_snake_case		:						str					,						**_snake_case		:						int):

        """simple docstring"""


        UpperCAmelCase_          =							kwargs.get('''is_split_into_words'''					,						_snake_case)

        if not (self.add_prefix_space or not is_split_into_words):
             raise Exception(
                 F"""You need to instantiate {self.__class__.__name__} with add_prefix_space=True to use it with"""
                 ''' pretokenized inputs.''')

        return super()._encode_plus(*_snake_case					,						**_snake_case)







   def lowerCamelCase						(      self		:						Tuple					,						_snake_case		:						str					,						_snake_case		:						Optional[str] = None):

        """simple docstring"""


        UpperCAmelCase_          =							self._tokenizer.model.save(_snake_case					,						name=_snake_case)
        return tuple(_snake_case)







   def lowerCamelCase						(      self		:						Optional[Any]					,						_snake_case		:						"Conversation"):

        """simple docstring"""


        UpperCAmelCase_          =							[]
        for is_user, text in conversation.iter_texts():
             input_ids.extend(self.encode(_snake_case					,						add_special_tokens=_snake_case) + [self.eos_token_id])

        if len(_snake_case) > self.model_max_length:
             UpperCAmelCase_          =							input_ids[-self.model_max_length :]
        return input_ids














"""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 typing import TYPE_CHECKING

from ..models.auto import AutoModelForVisionaSeq
from ..utils import requires_backends
from .base import PipelineTool


if TYPE_CHECKING:
  from PIL import Image






class      a						(					__snake_case		):
    SCREAMING_SNAKE_CASE						:     Dict						= """Salesforce/blip-image-captioning-base"""
    SCREAMING_SNAKE_CASE						:     int						= (
        """This is a tool that generates a description of an image. It takes an input named `image` which should be the """
        """image to caption, and returns a text that contains the description in English."""
    )
    SCREAMING_SNAKE_CASE						:     Optional[int]						= """image_captioner"""
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= AutoModelForVisionaSeq

    SCREAMING_SNAKE_CASE						:     int						= ["""image"""]
    SCREAMING_SNAKE_CASE						:     Optional[Any]						= ["""text"""]






    def __init__(       self     :							Optional[Any]       ,						*__SCREAMING_SNAKE_CASE     :							Optional[Any]       ,						**__SCREAMING_SNAKE_CASE     :							List[Any]					)     ->		Optional[Any]:
      requires_backends(self       ,						['vision']					)
      super().__init__(*__SCREAMING_SNAKE_CASE       ,						**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							List[Any]       ,						__SCREAMING_SNAKE_CASE     :							"Image"					)     ->		Dict:
      return self.pre_processor(images=__SCREAMING_SNAKE_CASE       ,						return_tensors='pt'					)






    def    UpperCamelCase       (       self     :							List[str]       ,						__SCREAMING_SNAKE_CASE     :							Tuple					)     ->		str:
      return self.model.generate(**__SCREAMING_SNAKE_CASE					)






    def    UpperCamelCase       (       self     :							Union[str, Any]       ,						__SCREAMING_SNAKE_CASE     :							List[str]					)     ->		Optional[Any]:
      return self.pre_processor.batch_decode(__SCREAMING_SNAKE_CASE       ,						skip_special_tokens=__SCREAMING_SNAKE_CASE					)[0].strip()




