






"""simple docstring"""



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		:  List[Any]						=		logging.get_logger(__name__)

_UpperCamelCase		:  Dict						=		{'vocab_file': 'vocab.json', 'merges_file': 'merges.txt', 'tokenizer_file': 'tokenizer.json'}

_UpperCamelCase		:  Optional[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'
        ),
    },
}

_UpperCamelCase		:  List[Any]						=		{
    'microsoft/deberta-base': 5_1_2,
    'microsoft/deberta-large': 5_1_2,
    'microsoft/deberta-xlarge': 5_1_2,
    'microsoft/deberta-base-mnli': 5_1_2,
    'microsoft/deberta-large-mnli': 5_1_2,
    'microsoft/deberta-xlarge-mnli': 5_1_2,
}

_UpperCamelCase		:  Optional[int]						=		{
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}

class 	a					(       lowercase_     ):
    UpperCAmelCase_			:					str     					=VOCAB_FILES_NAMES
    UpperCAmelCase_			:					Union[str, Any]     					=PRETRAINED_VOCAB_FILES_MAP
    UpperCAmelCase_			:					Dict     					=PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    UpperCAmelCase_			:					Any     					=["""input_ids""", """attention_mask""", """token_type_ids"""]
    UpperCAmelCase_			:					Dict     					=DebertaTokenizer







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

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

      lowercase							   =	add_prefix_space







    @property
    def       UpperCamelCase_				(      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       UpperCamelCase_				(      self							,		_lowerCamelCase	):
      lowercase							   =	AddedToken(_lowerCamelCase							,		lstrip=_lowerCamelCase							,		rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase							,		_lowerCamelCase	) else value
      lowercase							   =	value







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):







      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       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      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       UpperCamelCase_				(      self							,		*_lowerCamelCase							,		**_lowerCamelCase	):
      lowercase							   =	kwargs.get('is_split_into_words'							,		_lowerCamelCase	)
      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(*_lowerCamelCase							,		**_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		*_lowerCamelCase							,		**_lowerCamelCase	):
      lowercase							   =	kwargs.get('is_split_into_words'							,		_lowerCamelCase	)

      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(*_lowerCamelCase							,		**_lowerCamelCase	)







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase = None	):
      lowercase							   =	self._tokenizer.model.save(_lowerCamelCase							,		name=_lowerCamelCase	)
      return tuple(_lowerCamelCase	)







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

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










"""simple docstring"""



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import ViTImageProcessor

class 	a					(       unittest.TestCase     ):







    def __init__(      self							,		_lowerCamelCase							,		_lowerCamelCase=7							,		_lowerCamelCase=3							,		_lowerCamelCase=1_8							,		_lowerCamelCase=3_0							,		_lowerCamelCase=4_0_0							,		_lowerCamelCase=True							,		_lowerCamelCase=None							,		_lowerCamelCase=True							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		_lowerCamelCase=[0.5, 0.5, 0.5]							,		):
      lowercase							   =	size if size is not None else {'height': 1_8, 'width': 1_8}
      lowercase							   =	parent
      lowercase							   =	batch_size
      lowercase							   =	num_channels
      lowercase							   =	image_size
      lowercase							   =	min_resolution
      lowercase							   =	max_resolution
      lowercase							   =	do_resize
      lowercase							   =	size
      lowercase							   =	do_normalize
      lowercase							   =	image_mean
      lowercase							   =	image_std







    def       UpperCamelCase_				(      self	):
      return {
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_normalize": self.do_normalize,
          "do_resize": self.do_resize,
          "size": self.size,
      }


@require_torch
@require_vision
class 	a					(       a_,    unittest.TestCase     ):
    UpperCAmelCase_			:					int     					=ViTImageProcessor if is_vision_available() else None







    def       UpperCamelCase_				(      self	):
      lowercase							   =	ViTImageProcessingTester(self	)







    @property
    def       UpperCamelCase_				(      self	):
      return self.image_processor_tester.prepare_image_processor_dict()







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_mean'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'image_std'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_normalize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'do_resize'	)	)
      self.assertTrue(hasattr(_lowerCamelCase							,		'size'	)	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict	)
      self.assertEqual(image_processor.size							,		{'height': 1_8, 'width': 1_8}	)

      lowercase							   =	self.image_processing_class.from_dict(self.image_processor_dict							,		size=4_2	)
      self.assertEqual(image_processor.size							,		{'height': 4_2, 'width': 4_2}	)







    def       UpperCamelCase_				(      self	):
      pass







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PIL images
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		Image.Image	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random numpy tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		numpify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		np.ndarray	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)







    def       UpperCamelCase_				(      self	):
      # Initialize image_processing
      lowercase							   =	self.image_processing_class(**self.image_processor_dict	)
      # create random PyTorch tensors
      lowercase							   =	prepare_image_inputs(self.image_processor_tester							,		equal_resolution=_lowerCamelCase							,		torchify=_lowerCamelCase	)
      for image in image_inputs:
        self.assertIsInstance(_lowerCamelCase							,		torch.Tensor	)

      # Test not batched input
      lowercase							   =	image_processing(image_inputs[0]							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)

      # Test batched
      lowercase							   =	image_processing(_lowerCamelCase							,		return_tensors='pt'	).pixel_values
      self.assertEqual(
          encoded_images.shape							,		(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.size['height'],
              self.image_processor_tester.size['width'],
          )							,		)


