


from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


snake_case			:      Union[str, Any] 							=   logging.get_logger(__name__)



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					int        =						["pixel_values"]





							def __init__(    self				, A_ = True				, A_ = None				, A_ = PILImageResampling.BILINEAR				, A_ = True				, A_ = 1 / 255				, A_ = True				, A_ = None				, A_ = None				, **A_				, )->	None:
											super().__init__(**A_	)
											_SCREAMING_SNAKE_CASE      = size if size is not None else {'height': 224, 'width': 224}
											_SCREAMING_SNAKE_CASE      = get_size_dict(A_	)
											_SCREAMING_SNAKE_CASE      = do_resize
											_SCREAMING_SNAKE_CASE      = do_rescale
											_SCREAMING_SNAKE_CASE      = do_normalize
											_SCREAMING_SNAKE_CASE      = size
											_SCREAMING_SNAKE_CASE      = resample
											_SCREAMING_SNAKE_CASE      = rescale_factor
											_SCREAMING_SNAKE_CASE      = image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
											_SCREAMING_SNAKE_CASE      = image_std if image_std is not None else IMAGENET_STANDARD_STD





							def 		__magic_name__			(    self				, A_				, A_				, A_ = PILImageResampling.BILINEAR				, A_ = None				, **A_				, )->	np.ndarray:
											_SCREAMING_SNAKE_CASE      = get_size_dict(A_	)
											if "height" not in size or "width" not in size:
															raise ValueError(F'''The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}'''	)
											return resize(
											    A_				, size=(size['height'], size['width'])				, resample=A_				, data_format=A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_				, A_ = None				, **A_	)->	np.ndarray:
											return rescale(A_				, scale=A_				, data_format=A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_				, A_				, A_ = None				, **A_				, )->	np.ndarray:
											return normalize(A_				, mean=A_				, std=A_				, data_format=A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = None				, A_ = ChannelDimension.FIRST				, **A_				, )->	List[Any]:
											_SCREAMING_SNAKE_CASE      = do_resize if do_resize is not None else self.do_resize
											_SCREAMING_SNAKE_CASE      = do_rescale if do_rescale is not None else self.do_rescale
											_SCREAMING_SNAKE_CASE      = do_normalize if do_normalize is not None else self.do_normalize
											_SCREAMING_SNAKE_CASE      = resample if resample is not None else self.resample
											_SCREAMING_SNAKE_CASE      = rescale_factor if rescale_factor is not None else self.rescale_factor
											_SCREAMING_SNAKE_CASE      = image_mean if image_mean is not None else self.image_mean
											_SCREAMING_SNAKE_CASE      = image_std if image_std is not None else self.image_std

											_SCREAMING_SNAKE_CASE      = size if size is not None else self.size
											_SCREAMING_SNAKE_CASE      = get_size_dict(A_	)

											_SCREAMING_SNAKE_CASE      = make_list_of_images(A_	)

											if not valid_images(A_	):
															raise ValueError(
															    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
															    'torch.Tensor, tf.Tensor or jax.ndarray.'	)

											if do_resize and size is None:
															raise ValueError('Size must be specified if do_resize is True.'	)

											if do_rescale and rescale_factor is None:
															raise ValueError('Rescale factor must be specified if do_rescale is True.'	)

											# All transformations expect numpy arrays.
											_SCREAMING_SNAKE_CASE      = [to_numpy_array(A_	) for image in images]

											if do_resize:
															_SCREAMING_SNAKE_CASE      = [self.resize(image=A_				, size=A_				, resample=A_	) for image in images]

											if do_rescale:
															_SCREAMING_SNAKE_CASE      = [self.rescale(image=A_				, scale=A_	) for image in images]

											if do_normalize:
															_SCREAMING_SNAKE_CASE      = [self.normalize(image=A_				, mean=A_				, std=A_	) for image in images]

											_SCREAMING_SNAKE_CASE      = [to_channel_dimension_format(A_				, A_	) for image in images]

											_SCREAMING_SNAKE_CASE      = {'pixel_values': images}
											return BatchFeature(data=A_				, tensor_type=A_	)




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

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_deberta import DebertaTokenizer


if TYPE_CHECKING:
							from transformers.pipelines.conversational import Conversation


snake_case			:      Dict 							=   logging.get_logger(__name__)

snake_case			:      List[str] 							=   {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

snake_case			:      Any 							=   {
    'vocab_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/vocab.json',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/vocab.json',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/vocab.json',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/vocab.json',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/vocab.json',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/vocab.json'
        ),
    },
    'merges_file': {
        'microsoft/deberta-base': 'https://huggingface.co/microsoft/deberta-base/resolve/main/merges.txt',
        'microsoft/deberta-large': 'https://huggingface.co/microsoft/deberta-large/resolve/main/merges.txt',
        'microsoft/deberta-xlarge': 'https://huggingface.co/microsoft/deberta-xlarge/resolve/main/merges.txt',
        'microsoft/deberta-base-mnli': 'https://huggingface.co/microsoft/deberta-base-mnli/resolve/main/merges.txt',
        'microsoft/deberta-large-mnli': 'https://huggingface.co/microsoft/deberta-large-mnli/resolve/main/merges.txt',
        'microsoft/deberta-xlarge-mnli': (
            'https://huggingface.co/microsoft/deberta-xlarge-mnli/resolve/main/merges.txt'
        ),
    },
}

snake_case			:      Optional[int] 							=   {
    'microsoft/deberta-base': 5_12,
    'microsoft/deberta-large': 5_12,
    'microsoft/deberta-xlarge': 5_12,
    'microsoft/deberta-base-mnli': 5_12,
    'microsoft/deberta-large-mnli': 5_12,
    'microsoft/deberta-xlarge-mnli': 5_12,
}

snake_case			:      Union[str, Any] 							=   {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}



class    __lowercase  (       UpperCamelCase							):

							"""simple docstring"""


							SCREAMING_SNAKE_CASE   :					Optional[int]        =						VOCAB_FILES_NAMES
							SCREAMING_SNAKE_CASE   :					List[str]        =						PRETRAINED_VOCAB_FILES_MAP
							SCREAMING_SNAKE_CASE   :					Optional[Any]        =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							SCREAMING_SNAKE_CASE   :					List[Any]        =						["input_ids", "attention_mask", "token_type_ids"]
							SCREAMING_SNAKE_CASE   :					Any        =						DebertaTokenizer





							def __init__(    self				, A_=None				, A_=None				, A_=None				, A_="replace"				, A_="[CLS]"				, A_="[SEP]"				, A_="[SEP]"				, A_="[CLS]"				, A_="[UNK]"				, A_="[PAD]"				, A_="[MASK]"				, A_=False				, **A_				, )->	List[str]:
											super().__init__(
											    A_				, A_				, tokenizer_file=A_				, errors=A_				, bos_token=A_				, eos_token=A_				, unk_token=A_				, sep_token=A_				, cls_token=A_				, pad_token=A_				, mask_token=A_				, add_prefix_space=A_				, **A_				, )
											_SCREAMING_SNAKE_CASE      = kwargs.pop('add_bos_token'				, A_	)

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

											_SCREAMING_SNAKE_CASE      = add_prefix_space





							@property
							def 		__magic_name__			(    self	)->	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 		__magic_name__			(    self				, A_	)->	int:
											_SCREAMING_SNAKE_CASE      = AddedToken(A_				, lstrip=A_				, rstrip=A_	) if isinstance(A_				, A_	) else value
											_SCREAMING_SNAKE_CASE      = value





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
											_SCREAMING_SNAKE_CASE      = [self.cls_token_id]
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											return cls + token_ids_a + sep + token_ids_a + sep





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = [self.sep_token_id]
											_SCREAMING_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	) * [0] + len(token_ids_a + sep	) * [1]





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)
											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(*A_				, **A_	)





							def 		__magic_name__			(    self				, *A_				, **A_	)->	BatchEncoding:
											_SCREAMING_SNAKE_CASE      = kwargs.get('is_split_into_words'				, A_	)

											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(*A_				, **A_	)





							def 		__magic_name__			(    self				, A_				, A_ = None	)->	Tuple[str]:
											_SCREAMING_SNAKE_CASE      = self._tokenizer.model.save(A_				, name=A_	)
											return tuple(A_	)





							def 		__magic_name__			(    self				, A_	)->	List[int]:
											_SCREAMING_SNAKE_CASE      = []
											for is_user, text in conversation.iter_texts():
															input_ids.extend(self.encode(A_				, add_special_tokens=A_	) + [self.eos_token_id]	)

											if len(A_	) > self.model_max_length:
															_SCREAMING_SNAKE_CASE      = input_ids[-self.model_max_length :]
											return input_ids
