



from typing import List

import PIL
import torch
from PIL import Image

from ...configuration_utils import ConfigMixin
from ...models.modeling_utils import ModelMixin
from ...utils import PIL_INTERPOLATION
class       _lowerCAmelCase (			_UpperCAmelCase     ,       _UpperCAmelCase      ):






			"""simple docstring"""





			def __init__(					self						:							List[str] )    ->       Optional[int]:
									"""simple docstring"""







									super().__init__()

									self.register_buffer("""watermark_image"""      ,  torch.zeros((62, 62, 4) ) )
									__lowercase											=					None







			def 		snake_case__      (					self						:							Optional[int]      ,  lowercase						:							List[PIL.Image.Image]      ,  lowercase						:							Optional[int]=None )    ->       Dict:
									"""simple docstring"""







									__lowercase											=					images[0].height
									__lowercase											=					images[0].width

									__lowercase											=					sample_size or h

									__lowercase											=					min(h / sample_size      ,  w / sample_size )
									__lowercase						,      __lowercase											=					(int(h / coef ), int(w / coef )) if coef < 1 else (h, w)

									__lowercase						,      __lowercase											=					1_024**2, img_w * img_h
									__lowercase											=					(Sa / Sa) ** 0.5
									__lowercase						,      __lowercase						,      __lowercase											=					int(K * 62 ), img_w - int(14 * K ), img_h - int(14 * K )

									if self.watermark_image_as_pil is None:
															__lowercase											=					self.watermark_image.to(torch.uinta ).cpu().numpy()
															__lowercase											=					Image.fromarray(lowercase      ,  mode="""RGBA""" )
															__lowercase											=					watermark_image

									__lowercase											=					self.watermark_image_as_pil.resize(
									    (wm_size, wm_size)      ,  PIL_INTERPOLATION["""bicubic"""]      ,  reducing_gap=lowercase )

									for pil_img in images:
															pil_img.paste(lowercase      ,  box=(wm_x - wm_size, wm_y - wm_size, wm_x, wm_y)      ,  mask=wm_img.split()[-1] )

									return images










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


UpperCamelCase__			=    logging.get_logger(__name__)

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

UpperCamelCase__			=    {
    "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"
        ),
    },
}

UpperCamelCase__			=    {
    "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,
}

UpperCamelCase__			=    {
    "microsoft/deberta-base": {"do_lower_case": False},
    "microsoft/deberta-large": {"do_lower_case": False},
}
class       _lowerCAmelCase (			_UpperCAmelCase      ):






			"""simple docstring"""





			lowercase__   :	Dict  			=      VOCAB_FILES_NAMES
			lowercase__   :	Dict  			=      PRETRAINED_VOCAB_FILES_MAP
			lowercase__   :	str  			=      PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowercase__   :	Tuple  			=      ["""input_ids""", """attention_mask""", """token_type_ids"""]
			lowercase__   :	List[Any]  			=      DebertaTokenizer





			def __init__(					self						:							Dict      ,  lowercase						:							Union[str, Any]=None      ,  lowercase						:							Union[str, Any]=None      ,  lowercase						:							Any=None      ,  lowercase						:							Optional[Any]="replace"      ,  lowercase						:							Optional[Any]="[CLS]"      ,  lowercase						:							List[Any]="[SEP]"      ,  lowercase						:							int="[SEP]"      ,  lowercase						:							str="[CLS]"      ,  lowercase						:							Any="[UNK]"      ,  lowercase						:							List[Any]="[PAD]"      ,  lowercase						:							List[str]="[MASK]"      ,  lowercase						:							Tuple=False      ,  **lowercase						:							Tuple      ,  )    ->       List[Any]:
									"""simple docstring"""







									super().__init__(
									    lowercase      ,  lowercase      ,  tokenizer_file=lowercase      ,  errors=lowercase      ,  bos_token=lowercase      ,  eos_token=lowercase      ,  unk_token=lowercase      ,  sep_token=lowercase      ,  cls_token=lowercase      ,  pad_token=lowercase      ,  mask_token=lowercase      ,  add_prefix_space=lowercase      ,  **lowercase      ,  )
									__lowercase											=					kwargs.pop("""add_bos_token"""      ,  lowercase )

									__lowercase											=					json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__() )
									if pre_tok_state.get("""add_prefix_space"""      ,  lowercase ) != add_prefix_space:
															__lowercase											=					getattr(lowercase      ,  pre_tok_state.pop("""type""" ) )
															__lowercase											=					add_prefix_space
															__lowercase											=					pre_tok_class(**lowercase )

									__lowercase											=					add_prefix_space





			@property
			def 		snake_case__      (					self						:							Tuple )    ->       str:
									"""simple docstring"""







									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 		snake_case__      (					self						:							List[Any]      ,  lowercase						:							Tuple )    ->       Optional[int]:
									"""simple docstring"""







									__lowercase											=					AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase ) if isinstance(lowercase      ,  lowercase ) else value
									__lowercase											=					value





			def 		snake_case__      (					self						:							List[str]      ,  lowercase						:							List[int]      ,  lowercase						:							Optional[List[int]] = None )    ->       List[int]:
									"""simple docstring"""







									if token_ids_a is None:
															return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
									__lowercase											=					[self.cls_token_id]
									__lowercase											=					[self.sep_token_id]
									return cls + token_ids_a + sep + token_ids_a + sep





			def 		snake_case__      (					self						:							Dict      ,  lowercase						:							List[int]      ,  lowercase						:							Optional[List[int]] = None )    ->       List[int]:
									"""simple docstring"""







									__lowercase											=					[self.sep_token_id]
									__lowercase											=					[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 		snake_case__      (					self						:							Optional[int]      ,  *lowercase						:							List[str]      ,  **lowercase						:							Tuple )    ->       BatchEncoding:
									"""simple docstring"""







									__lowercase											=					kwargs.get("""is_split_into_words"""      ,  lowercase )
									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(*lowercase      ,  **lowercase )





			def 		snake_case__      (					self						:							List[Any]      ,  *lowercase						:							str      ,  **lowercase						:							List[Any] )    ->       BatchEncoding:
									"""simple docstring"""







									__lowercase											=					kwargs.get("""is_split_into_words"""      ,  lowercase )

									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(*lowercase      ,  **lowercase )





			def 		snake_case__      (					self						:							Union[str, Any]      ,  lowercase						:							str      ,  lowercase						:							Optional[str] = None )    ->       Tuple[str]:
									"""simple docstring"""







									__lowercase											=					self._tokenizer.model.save(lowercase      ,  name=lowercase )
									return tuple(lowercase )







			def 		snake_case__      (					self						:							List[str]      ,  lowercase						:							"Conversation" )    ->       List[int]:
									"""simple docstring"""







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

									if len(lowercase ) > self.model_max_length:
															__lowercase											=					input_ids[-self.model_max_length :]
									return input_ids





