

"""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						:   str							   =	logging.get_logger(__name__)

__UpperCamelCase						:   int							   =	{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

__UpperCamelCase						:   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						:   Dict							   =	{
    '''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						:   List[str]							   =	{
    '''microsoft/deberta-base''': {'''do_lower_case''': False},
    '''microsoft/deberta-large''': {'''do_lower_case''': False},
}







class        SCREAMING_SNAKE_CASE      (	lowerCAmelCase__ ):






     """simple docstring"""



     lowercase__       				= VOCAB_FILES_NAMES
     lowercase__       				= PRETRAINED_VOCAB_FILES_MAP
     lowercase__       				= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
     lowercase__       				= ["input_ids", "attention_mask", "token_type_ids"]
     lowercase__       				= DebertaTokenizer







     def __init__( self    :       str     ,lowercase_    :       Dict=None     ,lowercase_    :       List[Any]=None     ,lowercase_    :       List[Any]=None     ,lowercase_    :       Union[str, Any]="replace"     ,lowercase_    :       Any="[CLS]"     ,lowercase_    :       List[Any]="[SEP]"     ,lowercase_    :       Optional[int]="[SEP]"     ,lowercase_    :       Optional[Any]="[CLS]"     ,lowercase_    :       Tuple="[UNK]"     ,lowercase_    :       str="[PAD]"     ,lowercase_    :       List[Any]="[MASK]"     ,lowercase_    :       Tuple=False     ,**lowercase_    :       int     ,):
      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     ,)
      lowerCAmelCase__			:		str						       =			kwargs.pop('''add_bos_token'''     ,_lowerCamelCase	)

      lowerCAmelCase__			:		Tuple						       =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
      if pre_tok_state.get('''add_prefix_space'''     ,_lowerCamelCase	) != add_prefix_space:
       lowerCAmelCase__			:		Optional[int]						       =			getattr(_lowerCamelCase     ,pre_tok_state.pop('''type'''	)	)
       lowerCAmelCase__			:		Dict						       =			add_prefix_space
       lowerCAmelCase__			:		int						       =			pre_tok_class(**_lowerCamelCase	)

      lowerCAmelCase__			:		Dict						       =			add_prefix_space







     @property
     def  __lowerCAmelCase				( self    :       int	):

      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  __lowerCAmelCase				( self    :       str     ,lowercase_    :       str	):
      lowerCAmelCase__			:		Tuple						       =			AddedToken(_lowerCamelCase     ,lstrip=_lowerCamelCase     ,rstrip=_lowerCamelCase	) if isinstance(_lowerCamelCase     ,_lowerCamelCase	) else value
      lowerCAmelCase__			:		List[str]						       =			value







     def  __lowerCAmelCase				( self    :       int     ,lowercase_    :       int     ,lowercase_    :       List[str] = None	):

      if token_ids_a is None:
       return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
      lowerCAmelCase__			:		int						       =			[self.cls_token_id]
      lowerCAmelCase__			:		Union[str, Any]						       =			[self.sep_token_id]
      return cls + token_ids_a + sep + token_ids_a + sep







     def  __lowerCAmelCase				( self    :       List[Any]     ,lowercase_    :       int     ,lowercase_    :       str = None	):
      lowerCAmelCase__			:		List[Any]						       =			[self.sep_token_id]
      lowerCAmelCase__			:		int						       =			[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  __lowerCAmelCase				( self    :       Union[str, Any]     ,*lowercase_    :       List[Any]     ,**lowercase_    :       List[Any]	):
      lowerCAmelCase__			:		Union[str, Any]						       =			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  __lowerCAmelCase				( self    :       str     ,*lowercase_    :       str     ,**lowercase_    :       Dict	):
      lowerCAmelCase__			:		Tuple						       =			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  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       str     ,lowercase_    :       int = None	):
      lowerCAmelCase__			:		List[str]						       =			self._tokenizer.model.save(_lowerCamelCase     ,name=_lowerCamelCase	)
      return tuple(_lowerCamelCase	)







     def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       Union[str, Any]	):
      lowerCAmelCase__			:		Tuple						       =			[]
      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:
       lowerCAmelCase__			:		Any						       =			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


if is_torch_available():
							import torch

if is_vision_available():
							from PIL import Image

							from transformers import SwinaSRImageProcessor
							from transformers.image_transforms import get_image_size







class        SCREAMING_SNAKE_CASE      (	unittest.TestCase ):






					"""simple docstring"""



					def __init__( self    :       Dict     ,lowercase_    :       List[str]     ,lowercase_    :       List[str]=7     ,lowercase_    :       List[Any]=3     ,lowercase_    :       str=1_8     ,lowercase_    :       Any=3_0     ,lowercase_    :       int=4_0_0     ,lowercase_    :       Tuple=True     ,lowercase_    :       Optional[int]=1 / 2_5_5     ,lowercase_    :       int=True     ,lowercase_    :       Union[str, Any]=8     ,):
						lowerCAmelCase__			:		Tuple						       =			parent
						lowerCAmelCase__			:		Optional[Any]						       =			batch_size
						lowerCAmelCase__			:		int						       =			num_channels
						lowerCAmelCase__			:		Union[str, Any]						       =			image_size
						lowerCAmelCase__			:		Optional[Any]						       =			min_resolution
						lowerCAmelCase__			:		Union[str, Any]						       =			max_resolution
						lowerCAmelCase__			:		int						       =			do_rescale
						lowerCAmelCase__			:		Tuple						       =			rescale_factor
						lowerCAmelCase__			:		Dict						       =			do_pad
						lowerCAmelCase__			:		str						       =			pad_size







					def  __lowerCAmelCase				( self    :       int	):
						return {
						    "do_rescale": self.do_rescale,
						    "rescale_factor": self.rescale_factor,
						    "do_pad": self.do_pad,
						    "pad_size": self.pad_size,
						}







					def  __lowerCAmelCase				( self    :       Union[str, Any]     ,lowercase_    :       List[str]=False     ,lowercase_    :       Any=False     ,lowercase_    :       Dict=False	):
						assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

						if equal_resolution:
							lowerCAmelCase__			:		int						       =			[]
							for i in range(self.batch_size	):
								image_inputs.append(
								    np.random.randint(
								        2_5_5     ,size=(self.num_channels, self.max_resolution, self.max_resolution)     ,dtype=np.uinta	)	)
						else:
							lowerCAmelCase__			:		str						       =			[]
							for i in range(self.batch_size	):
								lowerCAmelCase__       ,lowerCAmelCase__			:		Any						       =			np.random.choice(np.arange(self.min_resolution     ,self.max_resolution	)     ,2	)
								image_inputs.append(np.random.randint(2_5_5     ,size=(self.num_channels, width, height)     ,dtype=np.uinta	)	)

						if not numpify and not torchify:
							# PIL expects the channel dimension as last dimension
							lowerCAmelCase__			:		Optional[int]						       =			[Image.fromarray(np.moveaxis(lowercase_     ,0     ,-1	)	) for x in image_inputs]

						if torchify:
							lowerCAmelCase__			:		int						       =			[torch.from_numpy(lowercase_	) for x in image_inputs]

						return image_inputs







@require_torch
@require_vision
class        SCREAMING_SNAKE_CASE      (	a_					,							unittest.TestCase ):






					"""simple docstring"""



					lowercase__       				= SwinaSRImageProcessor if is_vision_available() else None







					def  __lowerCAmelCase				( self    :       Dict	):
						lowerCAmelCase__			:		Dict						       =			SwinaSRImageProcessingTester(self	)







					@property
					def  __lowerCAmelCase				( self    :       str	):
						return self.image_processor_tester.prepare_image_processor_dict()







					def  __lowerCAmelCase				( self    :       List[str]	):
						lowerCAmelCase__			:		Any						       =			self.image_processing_class(**self.image_processor_dict	)
						self.assertTrue(hasattr(lowercase_     ,'''do_rescale'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''rescale_factor'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''do_pad'''	)	)
						self.assertTrue(hasattr(lowercase_     ,'''pad_size'''	)	)







					def  __lowerCAmelCase				( self    :       Dict	):
						pass







					def  __lowerCAmelCase				( self    :       List[str]     ,lowercase_    :       Optional[Any]	):
						lowerCAmelCase__       ,lowerCAmelCase__			:		Tuple						       =			get_image_size(lowercase_	)
						lowerCAmelCase__			:		Dict						       =			self.image_processor_tester.pad_size

						lowerCAmelCase__			:		List[Any]						       =			(old_height // size + 1) * size - old_height
						lowerCAmelCase__			:		Union[str, Any]						       =			(old_width // size + 1) * size - old_width
						return old_height + pad_height, old_width + pad_width







					def  __lowerCAmelCase				( self    :       List[Any]	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PIL images
						lowerCAmelCase__			:		int						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,Image.Image	)

						# Test not batched input
						lowerCAmelCase__			:		List[str]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(np.array(image_inputs[0]	)	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       int	):
						# Initialize image_processor
						lowerCAmelCase__			:		str						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random numpy tensors
						lowerCAmelCase__			:		Tuple						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,numpify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,np.ndarray	)

						# Test not batched input
						lowerCAmelCase__			:		Optional[Any]						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		List[Any]						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)







					def  __lowerCAmelCase				( self    :       Dict	):
						# Initialize image_processor
						lowerCAmelCase__			:		Tuple						       =			self.image_processing_class(**self.image_processor_dict	)
						# create random PyTorch tensors
						lowerCAmelCase__			:		Union[str, Any]						       =			self.image_processor_tester.prepare_inputs(equal_resolution=lowercase_     ,torchify=lowercase_	)
						for image in image_inputs:
							self.assertIsInstance(lowercase_     ,torch.Tensor	)

						# Test not batched input
						lowerCAmelCase__			:		Tuple						       =			image_processor(image_inputs[0]     ,return_tensors='''pt'''	).pixel_values
						lowerCAmelCase__       ,lowerCAmelCase__			:		int						       =			self.calculate_expected_size(image_inputs[0]	)
						self.assertEqual(
						    encoded_images.shape     ,(
						        1,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )     ,)




