"""simple docstring"""



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

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot import BlenderbotTokenizer


if TYPE_CHECKING:
       from transformers.pipelines.conversational import Conversation

__lowercase		:		Optional[Any]								=				logging.get_logger(__name__)


__lowercase		:		int								=				{
    "vocab_file": "vocab.json",
    "merges_file": "merges.txt",
    "tokenizer_config_file": "tokenizer_config.json",
}

__lowercase		:		Any								=				{
    "vocab_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json"},
    "merges_file": {"facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt"},
    "tokenizer_config_file": {
        "facebook/blenderbot-3B": "https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json"
    },
}

__lowercase		:		Optional[int]								=				{"facebook/blenderbot-3B": 128}



class   _A						(    UpperCamelCase_       ):




    """simple docstring"""




    UpperCamelCase_						:       str      					=       VOCAB_FILES_NAMES
    UpperCamelCase_						:       int      					=       PRETRAINED_VOCAB_FILES_MAP
    UpperCamelCase_						:       Tuple      					=       PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCamelCase_						:       str      					=       ['''input_ids''', '''attention_mask''']
    UpperCamelCase_						:       Optional[int]      					=       BlenderbotTokenizer
    def __init__(		self				:  Dict						,   A_				:  int=None						,   A_				:  Any=None						,   A_				:  str=None						,   A_				:  Any="replace"						,   A_				:  List[str]="<s>"						,   A_				:  Union[str, Any]="</s>"						,   A_				:  List[str]="</s>"						,   A_				:  List[Any]="<s>"						,   A_				:  Optional[Any]="<unk>"						,   A_				:  List[str]="<pad>"						,   A_				:  int="<mask>"						,   A_				:  List[Any]=False						,   A_				:  str=True						,   **A_				:  Tuple						,   )     ->							int:
         super().__init__(
             __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE						,   tokenizer_file=__SCREAMING_SNAKE_CASE						,   errors=__SCREAMING_SNAKE_CASE						,   bos_token=__SCREAMING_SNAKE_CASE						,   eos_token=__SCREAMING_SNAKE_CASE						,   sep_token=__SCREAMING_SNAKE_CASE						,   cls_token=__SCREAMING_SNAKE_CASE						,   unk_token=__SCREAMING_SNAKE_CASE						,   pad_token=__SCREAMING_SNAKE_CASE						,   mask_token=__SCREAMING_SNAKE_CASE						,   add_prefix_space=__SCREAMING_SNAKE_CASE						,   trim_offsets=__SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE						,   )

         __snake_case				=				json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()     )
         if pre_tok_state.get('''add_prefix_space'''						,   __SCREAMING_SNAKE_CASE     ) != add_prefix_space:
              __snake_case				=				getattr(__SCREAMING_SNAKE_CASE						,   pre_tok_state.pop('''type'''     )     )
              __snake_case				=				add_prefix_space
              __snake_case				=				pre_tok_class(**__SCREAMING_SNAKE_CASE     )

         __snake_case				=				add_prefix_space

         __snake_case				=				'''post_processor'''
         __snake_case				=				getattr(self.backend_tokenizer						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
         if tokenizer_component_instance:
              __snake_case				=				json.loads(tokenizer_component_instance.__getstate__()     )

              # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
              if "sep" in state:
                   __snake_case				=				tuple(state['''sep''']     )
              if "cls" in state:
                   __snake_case				=				tuple(state['''cls''']     )

              __snake_case				=				False

              if state.get('''add_prefix_space'''						,   __SCREAMING_SNAKE_CASE     ) != add_prefix_space:
                   __snake_case				=				add_prefix_space
                   __snake_case				=				True

              if state.get('''trim_offsets'''						,   __SCREAMING_SNAKE_CASE     ) != trim_offsets:
                   __snake_case				=				trim_offsets
                   __snake_case				=				True

              if changes_to_apply:
                   __snake_case				=				getattr(__SCREAMING_SNAKE_CASE						,   state.pop('''type'''     )     )
                   __snake_case				=				component_class(**__SCREAMING_SNAKE_CASE     )
                   setattr(self.backend_tokenizer						,   __SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     )
    @property
    # Copied from transformers.models.roberta.tokenization_roberta_fast.RobertaTokenizerFast.mask_token with Roberta->Blenderbot, RoBERTa->Blenderbot
    def 			lowercase       (		self				:  Union[str, Any]     )     ->							str:







         if self._mask_token is None:
              if self.verbose:
                   logger.error('''Using mask_token, but it is not set yet.'''     )
              return None
         return str(self._mask_token     )
    @mask_token.setter
    def 			lowercase       (		self				:  Tuple						,   A_				:  Any     )     ->							List[str]:
         __snake_case				=				AddedToken(__SCREAMING_SNAKE_CASE						,   lstrip=__SCREAMING_SNAKE_CASE						,   rstrip=__SCREAMING_SNAKE_CASE     ) if isinstance(__SCREAMING_SNAKE_CASE						,   __SCREAMING_SNAKE_CASE     ) else value
         __snake_case				=				value
    def 			lowercase       (		self				:  Union[str, Any]						,   *A_				:  List[str]						,   **A_				:  List[str]     )     ->							BatchEncoding:
         __snake_case				=				kwargs.get('''is_split_into_words'''						,   __SCREAMING_SNAKE_CASE     )
         assert self.add_prefix_space or not is_split_into_words, (
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Dict						,   *A_				:  Tuple						,   **A_				:  Tuple     )     ->							BatchEncoding:
         __snake_case				=				kwargs.get('''is_split_into_words'''						,   __SCREAMING_SNAKE_CASE     )

         assert self.add_prefix_space or not is_split_into_words, (
             f"You need to instantiate {self.__class__.__name__} with add_prefix_space=True "
             "to use it with pretokenized inputs."
         )

         return super()._encode_plus(*__SCREAMING_SNAKE_CASE						,   **__SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Tuple						,   A_				:  Any						,   A_				:  List[str] = None     )     ->							Tuple[str]:
         __snake_case				=				self._tokenizer.model.save(__SCREAMING_SNAKE_CASE						,   name=__SCREAMING_SNAKE_CASE     )
         return tuple(__SCREAMING_SNAKE_CASE     )
    def 			lowercase       (		self				:  Dict						,   A_				:  Union[str, Any]						,   A_				:  Union[str, Any] = None     )     ->							List[int]:
         __snake_case				=				[self.sep_token_id]
         __snake_case				=				[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep     ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep     ) * [0]
    def 			lowercase       (		self				:  str						,   A_				:  Optional[int]						,   A_				:  List[Any] = None     )     ->							Union[str, Any]:
         return token_ids_a + [self.eos_token_id]







    def 			lowercase       (		self				:  Any						,   A_				:  List[str]     )     ->							List[int]:
         __snake_case				=				[]
         for is_user, text in conversation.iter_texts():
              if is_user:
                   # We need to space prefix as it's being done within blenderbot
                   inputs.append(''' ''' + text     )
              else:
                   # Generated responses should contain them already.
                   inputs.append(__SCREAMING_SNAKE_CASE     )

         __snake_case				=				'''  '''.join(__SCREAMING_SNAKE_CASE     )
         __snake_case				=				self.encode(__SCREAMING_SNAKE_CASE     )
         if len(__SCREAMING_SNAKE_CASE     ) > self.model_max_length:
              __snake_case				=				input_ids[-self.model_max_length :]
              logger.warning(f"Trimmed input from conversation as it was longer than {self.model_max_length} tokens."     )
         return input_ids

from typing import List, Optional, Union

import numpy as np
import PIL.Image

from ...image_processing_utils import BaseImageProcessor, BatchFeature
from ...image_transforms import rescale, resize, to_channel_dimension_format
from ...image_utils import (
    ChannelDimension,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCamelCase  =logging.get_logger(__name__)




class 					_lowerCamelCase			(   UpperCamelCase_			):





     """simple docstring"""







     SCREAMING_SNAKE_CASE_		 =	['''pixel_values''']






     def __init__(				self    ,    __SCREAMING_SNAKE_CASE = True    ,    __SCREAMING_SNAKE_CASE = 3_2    ,    __SCREAMING_SNAKE_CASE=PILImageResampling.BILINEAR    ,    __SCREAMING_SNAKE_CASE = True    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> None:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize
           UpperCamelCase__     : Tuple											=					do_rescale
           UpperCamelCase__     : Dict											=					size_divisor
           UpperCamelCase__     : str											=					resample
           super().__init__(**__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           UpperCamelCase__					,UpperCamelCase__     : Optional[Any]											=					get_image_size(__SCREAMING_SNAKE_CASE	)
           # Rounds the height and width down to the closest multiple of size_divisor
           UpperCamelCase__     : List[Any]											=					height // size_divisor * size_divisor
           UpperCamelCase__     : Dict											=					width // size_divisor * size_divisor
           UpperCamelCase__     : str											=					resize(__SCREAMING_SNAKE_CASE    ,    (new_h, new_w)    ,    resample=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)
           return image






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    **__SCREAMING_SNAKE_CASE	)	-> np.ndarray:




           """simple docstring"""







           return rescale(image=__SCREAMING_SNAKE_CASE    ,    scale=__SCREAMING_SNAKE_CASE    ,    data_format=__SCREAMING_SNAKE_CASE    ,    **__SCREAMING_SNAKE_CASE	)






     def  __SCREAMING_SNAKE_CASE      (				self    ,    __SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE=None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = None    ,    __SCREAMING_SNAKE_CASE = ChannelDimension.FIRST    ,    **__SCREAMING_SNAKE_CASE    ,    )	-> BatchFeature:




           """simple docstring"""







           UpperCamelCase__     : Dict											=					do_resize if do_resize is not None else self.do_resize
           UpperCamelCase__     : List[Any]											=					do_rescale if do_rescale is not None else self.do_rescale
           UpperCamelCase__     : Union[str, Any]											=					size_divisor if size_divisor is not None else self.size_divisor
           UpperCamelCase__     : List[str]											=					resample if resample is not None else self.resample

           if do_resize and size_divisor is None:
                 raise ValueError('''size_divisor is required for resizing'''	)

           UpperCamelCase__     : List[Any]											=					make_list_of_images(__SCREAMING_SNAKE_CASE	)

           if not valid_images(__SCREAMING_SNAKE_CASE	):
                 raise ValueError('''Invalid image(s)'''	)

           # All transformations expect numpy arrays.
           UpperCamelCase__     : Dict											=					[to_numpy_array(__SCREAMING_SNAKE_CASE	) for img in images]

           if do_resize:
                 UpperCamelCase__     : Optional[int]											=					[self.resize(__SCREAMING_SNAKE_CASE    ,    size_divisor=__SCREAMING_SNAKE_CASE    ,    resample=__SCREAMING_SNAKE_CASE	) for image in images]

           if do_rescale:
                 UpperCamelCase__     : Union[str, Any]											=					[self.rescale(__SCREAMING_SNAKE_CASE    ,    scale=1 / 2_5_5	) for image in images]

           UpperCamelCase__     : List[str]											=					[to_channel_dimension_format(__SCREAMING_SNAKE_CASE    ,    __SCREAMING_SNAKE_CASE	) for image in images]

           UpperCamelCase__     : Optional[Any]											=					{'''pixel_values''': images}
           return BatchFeature(data=__SCREAMING_SNAKE_CASE    ,    tensor_type=__SCREAMING_SNAKE_CASE	)


