






import json
from typing import 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_mvp import MvpTokenizer


__lowerCamelCase	:		List[str]										=   logging.get_logger(__name__)


__lowerCamelCase	:		str										=   {"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt""", """tokenizer_file""": """tokenizer.json"""}

# See all MVP models at https://huggingface.co/models?filter=mvp
__lowerCamelCase	:		str										=   {
    """vocab_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/vocab.json""",
    },
    """added_tokens.json""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/added_tokens.json""",
    },
    """merges_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/merges.txt""",
    },
    """tokenizer_file""": {
        """RUCAIBox/mvp""": """https://huggingface.co/RUCAIBox/mvp/resolve/main/tokenizer.json""",
    },
}

__lowerCamelCase	:		Any										=   {
    """RUCAIBox/mvp""": 10_24,
}







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							VOCAB_FILES_NAMES
      _a							:						Optional[Any]										=							PRETRAINED_VOCAB_FILES_MAP
      _a							:						List[Any]										=							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _a							:						Optional[Any]										=							['input_ids', 'attention_mask']
      _a							:						int										=							MvpTokenizer
      def __init__(     self				,			a=None				,			a=None				,			a=None				,			a="replace"				,			a="<s>"				,			a="</s>"				,			a="</s>"				,			a="<s>"				,			a="<unk>"				,			a="<pad>"				,			a="<mask>"				,			a=False				,			a=True				,			**a				,			):
       super().__init__(
           a				,			a				,			tokenizer_file=a				,			errors=a				,			bos_token=a				,			eos_token=a				,			sep_token=a				,			cls_token=a				,			unk_token=a				,			pad_token=a				,			mask_token=a				,			add_prefix_space=a				,			trim_offsets=a				,			**a				,			)

       snake_case__  :    Dict							       =json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
       if pre_tok_state.get("""add_prefix_space"""				,			a   ) != add_prefix_space:
        snake_case__  :    List[str]							       =getattr(a				,			pre_tok_state.pop("""type"""   )   )
        snake_case__  :    Optional[int]							       =add_prefix_space
        snake_case__  :    Any							       =pre_tok_class(**a   )

       snake_case__  :    Any							       =add_prefix_space

       # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
       snake_case__  :    str							       ="""post_processor"""
       snake_case__  :    Optional[int]							       =getattr(self.backend_tokenizer				,			a				,			a   )
       if tokenizer_component_instance:
        snake_case__  :    Any							       =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							       =tuple(state["""sep"""]   )
        if "cls" in state:
         snake_case__  :    List[str]							       =tuple(state["""cls"""]   )

        snake_case__  :    str							       =False

        if state.get("""add_prefix_space"""				,			a   ) != add_prefix_space:
         snake_case__  :    str							       =add_prefix_space
         snake_case__  :    Optional[int]							       =True

        if state.get("""trim_offsets"""				,			a   ) != trim_offsets:
         snake_case__  :    Union[str, Any]							       =trim_offsets
         snake_case__  :    Union[str, Any]							       =True

        if changes_to_apply:
         snake_case__  :    Dict							       =getattr(a				,			state.pop("""type"""   )   )
         snake_case__  :    Any							       =component_class(**a   )
         setattr(self.backend_tokenizer				,			a				,			a   )
      @property
      def 						lowercase__					(     self   ):
       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				,			a   ):
       snake_case__  :    List[str]							       =AddedToken(a				,			lstrip=a				,			rstrip=a   ) if isinstance(a				,			a   ) else value
       snake_case__  :    Dict							       =value
      def 						lowercase__					(     self				,			*a				,			**a   ):
       snake_case__  :    Dict							       =kwargs.get("""is_split_into_words"""				,			a   )

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

       return super()._batch_encode_plus(*a				,			**a   )
      def 						lowercase__					(     self				,			*a				,			**a   ):
       snake_case__  :    Tuple							       =kwargs.get("""is_split_into_words"""				,			a   )

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

       return super()._encode_plus(*a				,			**a   )
      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    int							       =self._tokenizer.model.save(a				,			name=a   )
       return tuple(a   )
      def 						lowercase__					(     self				,			a				,			a=None   ):
       snake_case__  :    Dict							       =[self.bos_token_id] + token_ids_a + [self.eos_token_id]
       if token_ids_a is None:
        return output

       return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]



      def 						lowercase__					(     self				,			a				,			a = None   ):
       snake_case__  :    Any							       =[self.sep_token_id]
       snake_case__  :    Dict							       =[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]













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	:		Tuple										=   logging.get_logger(__name__)







class  _lowercase   (   _A						):
      _a							:						Optional[int]										=							['pixel_values']
      def __init__(     self				,			a = True				,			a = 3_2				,			a=PILImageResampling.BILINEAR				,			a = True				,			**a				,			):
       snake_case__  :    Any							       =do_resize
       snake_case__  :    Optional[int]							       =do_rescale
       snake_case__  :    Optional[Any]							       =size_divisor
       snake_case__  :    Optional[int]							       =resample
       super().__init__(**a   )
      def 						lowercase__					(     self				,			a				,			a				,			a				,			a = None				,			**a   ):
       snake_case__       ,   snake_case__  :    List[str]							       =get_image_size(a   )
       # Rounds the height and width down to the closest multiple of size_divisor
       snake_case__  :    str							       =height // size_divisor * size_divisor
       snake_case__  :    List[str]							       =width // size_divisor * size_divisor
       snake_case__  :    int							       =resize(a				,			(new_h, new_w)				,			resample=a				,			data_format=a				,			**a   )
       return image
      def 						lowercase__					(     self				,			a				,			a				,			a = None				,			**a   ):
       return rescale(image=a				,			scale=a				,			data_format=a				,			**a   )



      def 						lowercase__					(     self				,			a				,			a = None				,			a = None				,			a=None				,			a = None				,			a = None				,			a = ChannelDimension.FIRST				,			**a				,			):
       snake_case__  :    Optional[Any]							       =do_resize if do_resize is not None else self.do_resize
       snake_case__  :    str							       =do_rescale if do_rescale is not None else self.do_rescale
       snake_case__  :    List[Any]							       =size_divisor if size_divisor is not None else self.size_divisor
       snake_case__  :    Optional[int]							       =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"""   )

       snake_case__  :    Optional[int]							       =make_list_of_images(a   )

       if not valid_images(a   ):
        raise ValueError("""Invalid image(s)"""   )

       # All transformations expect numpy arrays.
       snake_case__  :    str							       =[to_numpy_array(a   ) for img in images]

       if do_resize:
        snake_case__  :    str							       =[self.resize(a				,			size_divisor=a				,			resample=a   ) for image in images]

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

       snake_case__  :    int							       =[to_channel_dimension_format(a				,			a   ) for image in images]

       snake_case__  :    Tuple							       ={"""pixel_values""": images}
       return BatchFeature(data=a				,			tensor_type=a   )





