'''simple docstring'''

import os
from shutil import copyfile
from typing import Optional, Tuple

from ...file_utils import is_sentencepiece_available
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging


if is_sentencepiece_available():
      from .tokenization_deberta_va import DebertaVaTokenizer
else:
      _lowerCamelCase :				List[str]														= None

_lowerCamelCase :				Tuple														= logging.get_logger(__name__)

_lowerCamelCase :				List[Any]														= {"""vocab_file""": """spm.model""", """tokenizer_file""": """tokenizer.json"""}

_lowerCamelCase :				List[str]														= {
    """vocab_file""": {
        """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/spm.model""",
        """microsoft/deberta-v2-xlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/spm.model"""
        ),
        """microsoft/deberta-v2-xxlarge-mnli""": (
            """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/spm.model"""
        ),
    }
}

_lowerCamelCase :				Optional[Any]														= {
    """microsoft/deberta-v2-xlarge""": 5_12,
    """microsoft/deberta-v2-xxlarge""": 5_12,
    """microsoft/deberta-v2-xlarge-mnli""": 5_12,
    """microsoft/deberta-v2-xxlarge-mnli""": 5_12,
}

_lowerCamelCase :				Optional[int]														= {
    """microsoft/deberta-v2-xlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xlarge-mnli""": {"""do_lower_case""": False},
    """microsoft/deberta-v2-xxlarge-mnli""": {"""do_lower_case""": False},
}




class    lowerCamelCase__  (   __snake_case     ):
   __UpperCAmelCase           =						VOCAB_FILES_NAMES
   __UpperCAmelCase           =						PRETRAINED_VOCAB_FILES_MAP
   __UpperCAmelCase           =						PRETRAINED_INIT_CONFIGURATION
   __UpperCAmelCase           =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
   __UpperCAmelCase           =						DebertaVaTokenizer



   def __init__(    self					,      lowerCAmelCase__=None					,      lowerCAmelCase__=None					,      lowerCAmelCase__=False					,      lowerCAmelCase__=False					,      lowerCAmelCase__="[CLS]"					,      lowerCAmelCase__="[SEP]"					,      lowerCAmelCase__="[UNK]"					,      lowerCAmelCase__="[SEP]"					,      lowerCAmelCase__="[PAD]"					,      lowerCAmelCase__="[CLS]"					,      lowerCAmelCase__="[MASK]"					,      **lowerCAmelCase__					,      )				->     None:
        """simple docstring"""





        super().__init__(
            lowerCAmelCase__					,      tokenizer_file=lowerCAmelCase__					,      do_lower_case=lowerCAmelCase__					,      bos_token=lowerCAmelCase__					,      eos_token=lowerCAmelCase__					,      unk_token=lowerCAmelCase__					,      sep_token=lowerCAmelCase__					,      pad_token=lowerCAmelCase__					,      cls_token=lowerCAmelCase__					,      mask_token=lowerCAmelCase__					,      split_by_punct=lowerCAmelCase__					,      **lowerCAmelCase__					,      )

        _UpperCamelCase  :int       =do_lower_case
        _UpperCamelCase  :Dict       =split_by_punct
        _UpperCamelCase  :Any       =vocab_file
        _UpperCamelCase  :Any       =False if not self.vocab_file else True



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None     )				->     int:
        """simple docstring"""





        if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        _UpperCamelCase  :List[Any]       =[self.cls_token_id]
        _UpperCamelCase  :List[str]       =[self.sep_token_id]
        return cls + token_ids_a + sep + token_ids_a + sep



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None					,      lowerCAmelCase__=False     )				->     Optional[Any]:
        """simple docstring"""





        if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=lowerCAmelCase__					,      token_ids_a=lowerCAmelCase__					,      already_has_special_tokens=lowerCAmelCase__     )

        if token_ids_a is not None:
             return [1] + ([0] * len(lowerCAmelCase__     )) + [1] + ([0] * len(lowerCAmelCase__     )) + [1]
        return [1] + ([0] * len(lowerCAmelCase__     )) + [1]



   def  _UpperCamelCase		(    self					,      lowerCAmelCase__					,      lowerCAmelCase__=None     )				->     str:
        """simple docstring"""





        _UpperCamelCase  :Tuple       =[self.sep_token_id]
        _UpperCamelCase  :List[str]       =[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__ = None     )				->     Tuple[str]:
        """simple docstring"""





        if not self.can_save_slow_tokenizer:
             raise ValueError(
                 """Your fast tokenizer does not have the necessary information to save the vocabulary for a slow """
                 """tokenizer."""     )

        if not os.path.isdir(lowerCAmelCase__     ):
             logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
             return
        _UpperCamelCase  :str       =os.path.join(
            lowerCAmelCase__					,      (filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]     )

        if os.path.abspath(self.vocab_file     ) != os.path.abspath(lowerCAmelCase__     ):
             copyfile(self.vocab_file					,      lowerCAmelCase__     )

        return (out_vocab_file,)
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 EfficientNetImageProcessor







class       __snake_case (  unittest.TestCase ):
 def __init__(	self		, snake_case__		, snake_case__=13		, snake_case__=3		, snake_case__=18		, snake_case__=30		, snake_case__=400		, snake_case__=True		, snake_case__=None		, snake_case__=True		, snake_case__=[0.5, 0.5, 0.5]		, snake_case__=[0.5, 0.5, 0.5]		, )						->				Any:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=size if size is not None else {'''height''': 18, '''width''': 18}
       UpperCAmelCase     :			Tuple     	=parent
       UpperCAmelCase     :			List[str]     	=batch_size
       UpperCAmelCase     :			int     	=num_channels
       UpperCAmelCase     :			List[str]     	=image_size
       UpperCAmelCase     :			str     	=min_resolution
       UpperCAmelCase     :			Optional[int]     	=max_resolution
       UpperCAmelCase     :			Optional[int]     	=do_resize
       UpperCAmelCase     :			int     	=size
       UpperCAmelCase     :			Union[str, Any]     	=do_normalize
       UpperCAmelCase     :			Dict     	=image_mean
       UpperCAmelCase     :			List[Any]     	=image_std






 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       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       __snake_case (  lowerCamelCase__       , unittest.TestCase ):
 __lowerCamelCase     : Any 	=   EfficientNetImageProcessor if is_vision_available() else None
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			Dict     	=EfficientNetImageProcessorTester(self    )
 @property
 def 						UpperCAmelCase__							(	self    )						->				int:
       '''simple docstring'''





       return self.image_processor_tester.prepare_image_processor_dict()
 def 						UpperCAmelCase__							(	self    )						->				List[str]:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       self.assertTrue(hasattr(snake_case__		, '''image_mean'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''image_std'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_normalize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''do_resize'''    )    )
       self.assertTrue(hasattr(snake_case__		, '''size'''    )    )
 def 						UpperCAmelCase__							(	self    )						->				Tuple:
       '''simple docstring'''





       UpperCAmelCase     :			str     	=self.image_processing_class.from_dict(self.image_processor_dict    )
       self.assertEqual(image_processor.size		, {'''height''': 18, '''width''': 18}    )

       UpperCAmelCase     :			List[str]     	=self.image_processing_class.from_dict(self.image_processor_dict		, size=42    )
       self.assertEqual(image_processor.size		, {'''height''': 42, '''width''': 42}    )
 def 						UpperCAmelCase__							(	self    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			List[Any]     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PIL images
       UpperCAmelCase     :			Optional[Any]     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, Image.Image    )

       # Test not batched input
       UpperCAmelCase     :			int     	=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
       UpperCAmelCase     :			int     	=image_processing(snake_case__		, 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    )						->				Dict:
       '''simple docstring'''





       UpperCAmelCase     :			Tuple     	=self.image_processing_class(**self.image_processor_dict    )
       # create random numpy tensors
       UpperCAmelCase     :			List[str]     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, numpify=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, np.ndarray    )

       # Test not batched input
       UpperCAmelCase     :			str     	=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
       UpperCAmelCase     :			Union[str, Any]     	=image_processing(snake_case__		, 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    )						->				str:
       '''simple docstring'''





       UpperCAmelCase     :			int     	=self.image_processing_class(**self.image_processor_dict    )
       # create random PyTorch tensors
       UpperCAmelCase     :			int     	=prepare_image_inputs(self.image_processor_tester		, equal_resolution=snake_case__		, torchify=snake_case__    )
       for image in image_inputs:
             self.assertIsInstance(snake_case__		, torch.Tensor    )

       # Test not batched input
       UpperCAmelCase     :			List[Any]     	=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
       UpperCAmelCase     :			List[str]     	=image_processing(snake_case__		, 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'''],
           )		, )
