

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 ViltImageProcessor

class      _a				( unittest.TestCase							):




      """simple docstring"""

      def __init__(      self       :							List[str]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Any=7	, UpperCAmelCase       :							Any=3	, UpperCAmelCase       :							Tuple=18	, UpperCAmelCase       :							Optional[int]=30	, UpperCAmelCase       :							Dict=400	, UpperCAmelCase       :							Optional[int]=True	, UpperCAmelCase       :							Dict=None	, UpperCAmelCase       :							Dict=2	, UpperCAmelCase       :							Dict=True	, UpperCAmelCase       :							Dict=[0.5, 0.5, 0.5]	, UpperCAmelCase       :							Any=[0.5, 0.5, 0.5]	, ):
            A_	     =							size if size is not None else {"shortest_edge": 30}
            A_	     =							parent
            A_	     =							batch_size
            A_	     =							num_channels
            A_	     =							image_size
            A_	     =							min_resolution
            A_	     =							max_resolution
            A_	     =							do_resize
            A_	     =							size
            A_	     =							size_divisor
            A_	     =							do_normalize
            A_	     =							image_mean
            A_	     =							image_std





      def 		__A  (      self       :							Optional[int]  ):
            return {
                "image_mean": self.image_mean,
                "image_std": self.image_std,
                "do_normalize": self.do_normalize,
                "do_resize": self.do_resize,
                "size": self.size,
                "size_divisor": self.size_divisor,
            }







      def 		__A  (      self       :							List[str]	, UpperCAmelCase       :							Optional[Any]	, UpperCAmelCase       :							Tuple=False  ):
            if not batched:
                  A_	     =							self.size["shortest_edge"]
                  A_	     =							image_inputs[0]
                  if isinstance(UpperCAmelCase	, Image.Image  ):
                        A_     ,    A_	     =							image.size
                  else:
                        A_     ,    A_	     =							image.shape[1], image.shape[2]
                  A_	     =							size / min(UpperCAmelCase	, UpperCAmelCase  )
                  if h < w:
                        A_     ,    A_	     =							size, scale * w
                  else:
                        A_     ,    A_	     =							scale * h, size

                  A_	     =							int((1333 / 800) * size  )
                  if max(UpperCAmelCase	, UpperCAmelCase  ) > max_size:
                        A_	     =							max_size / max(UpperCAmelCase	, UpperCAmelCase  )
                        A_	     =							newh * scale
                        A_	     =							neww * scale

                  A_     ,    A_	     =							int(newh + 0.5  ), int(neww + 0.5  )
                  A_     ,    A_	     =							(
                      newh // self.size_divisor * self.size_divisor,
                      neww // self.size_divisor * self.size_divisor,
                  )

            else:
                  A_	     =							[]
                  for image in image_inputs:
                        A_     ,    A_	     =							self.get_expected_values([image]  )
                        expected_values.append((expected_height, expected_width)  )
                  A_	     =							max(UpperCAmelCase	, key=lambda UpperCAmelCase  : item[0]  )[0]
                  A_	     =							max(UpperCAmelCase	, key=lambda UpperCAmelCase  : item[1]  )[1]

            return expected_height, expected_width




@require_torch
@require_vision
class      _a				( _a    ,							unittest.TestCase							):




      """simple docstring"""

      _lowerCamelCase					: List[Any]					=					ViltImageProcessor if is_vision_available() else None





      def 		__A  (      self       :							Optional[int]  ):
            A_	     =							ViltImageProcessingTester(self  )





      @property
      def 		__A  (      self       :							List[str]  ):
            return self.image_processor_tester.prepare_image_processor_dict()





      def 		__A  (      self       :							Tuple  ):
            A_	     =							self.image_processing_class(**self.image_processor_dict  )
            self.assertTrue(hasattr(UpperCAmelCase	, "image_mean"  )  )
            self.assertTrue(hasattr(UpperCAmelCase	, "image_std"  )  )
            self.assertTrue(hasattr(UpperCAmelCase	, "do_normalize"  )  )
            self.assertTrue(hasattr(UpperCAmelCase	, "do_resize"  )  )
            self.assertTrue(hasattr(UpperCAmelCase	, "size"  )  )
            self.assertTrue(hasattr(UpperCAmelCase	, "size_divisor"  )  )





      def 		__A  (      self       :							List[Any]  ):
            A_	     =							self.image_processing_class.from_dict(self.image_processor_dict  )
            self.assertEqual(image_processor.size	, {"shortest_edge": 30}  )

            A_	     =							self.image_processing_class.from_dict(self.image_processor_dict	, size=42  )
            self.assertEqual(image_processor.size	, {"shortest_edge": 42}  )





      def 		__A  (      self       :							Any  ):
            pass





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

            # Test not batched input
            A_	     =							image_processing(image_inputs[0]	, return_tensors="pt"  ).pixel_values

            A_     ,    A_	     =							self.image_processor_tester.get_expected_values(UpperCAmelCase  )
            self.assertEqual(
                encoded_images.shape	, (1, self.image_processor_tester.num_channels, expected_height, expected_width)	, )

            # Test batched
            A_	     =							image_processing(UpperCAmelCase	, return_tensors="pt"  ).pixel_values

            A_     ,    A_	     =							self.image_processor_tester.get_expected_values(UpperCAmelCase	, batched=UpperCAmelCase  )
            self.assertEqual(
                encoded_images.shape	, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	, )





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

            # Test not batched input
            A_	     =							image_processing(image_inputs[0]	, return_tensors="pt"  ).pixel_values

            A_     ,    A_	     =							self.image_processor_tester.get_expected_values(UpperCAmelCase  )
            self.assertEqual(
                encoded_images.shape	, (1, self.image_processor_tester.num_channels, expected_height, expected_width)	, )

            # Test batched
            A_	     =							image_processing(UpperCAmelCase	, return_tensors="pt"  ).pixel_values

            A_     ,    A_	     =							self.image_processor_tester.get_expected_values(UpperCAmelCase	, batched=UpperCAmelCase  )
            self.assertEqual(
                encoded_images.shape	, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	, )







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

            # Test not batched input
            A_	     =							image_processing(image_inputs[0]	, return_tensors="pt"  ).pixel_values

            A_     ,    A_	     =							self.image_processor_tester.get_expected_values(UpperCAmelCase  )
            self.assertEqual(
                encoded_images.shape	, (1, self.image_processor_tester.num_channels, expected_height, expected_width)	, )

            # Test batched
            A_	     =							image_processing(UpperCAmelCase	, return_tensors="pt"  ).pixel_values

            A_     ,    A_	     =							self.image_processor_tester.get_expected_values(UpperCAmelCase	, batched=UpperCAmelCase  )
            self.assertEqual(
                encoded_images.shape	, (
                    self.image_processor_tester.batch_size,
                    self.image_processor_tester.num_channels,
                    expected_height,
                    expected_width,
                )	, )


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:
      __a						:int            = None

__a						:List[str]            = logging.get_logger(__name__)

__a						:str            = {'vocab_file': 'spm.model', 'tokenizer_file': 'tokenizer.json'}

__a						:List[Any]            = {
    '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'
        ),
    }
}

__a						:Dict            = {
    'microsoft/deberta-v2-xlarge': 512,
    'microsoft/deberta-v2-xxlarge': 512,
    'microsoft/deberta-v2-xlarge-mnli': 512,
    'microsoft/deberta-v2-xxlarge-mnli': 512,
}

__a						:List[Any]            = {
    '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      _a				( snake_case_							):




      """simple docstring"""

      _lowerCamelCase					: Dict					=					VOCAB_FILES_NAMES
      _lowerCamelCase					: Optional[Any]					=					PRETRAINED_VOCAB_FILES_MAP
      _lowerCamelCase					: Tuple					=					PRETRAINED_INIT_CONFIGURATION
      _lowerCamelCase					: Tuple					=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _lowerCamelCase					: Optional[int]					=					DebertaVaTokenizer





      def __init__(      self       :							List[Any]	, UpperCAmelCase       :							str=None	, UpperCAmelCase       :							int=None	, UpperCAmelCase       :							List[str]=False	, UpperCAmelCase       :							Optional[Any]=False	, UpperCAmelCase       :							List[str]="[CLS]"	, UpperCAmelCase       :							str="[SEP]"	, UpperCAmelCase       :							List[str]="[UNK]"	, UpperCAmelCase       :							Tuple="[SEP]"	, UpperCAmelCase       :							Union[str, Any]="[PAD]"	, UpperCAmelCase       :							Union[str, Any]="[CLS]"	, UpperCAmelCase       :							Any="[MASK]"	, **UpperCAmelCase       :							Dict	, ):
            super().__init__(
                UpperCAmelCase	, tokenizer_file=UpperCAmelCase	, do_lower_case=UpperCAmelCase	, bos_token=UpperCAmelCase	, eos_token=UpperCAmelCase	, unk_token=UpperCAmelCase	, sep_token=UpperCAmelCase	, pad_token=UpperCAmelCase	, cls_token=UpperCAmelCase	, mask_token=UpperCAmelCase	, split_by_punct=UpperCAmelCase	, **UpperCAmelCase	, )

            A_	     =							do_lower_case
            A_	     =							split_by_punct
            A_	     =							vocab_file
            A_	     =							False if not self.vocab_file else True





      def 		__A  (      self       :							Optional[int]	, UpperCAmelCase       :							Tuple	, UpperCAmelCase       :							List[str]=None  ):

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





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Any	, UpperCAmelCase       :							Optional[int]=None	, UpperCAmelCase       :							int=False  ):

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

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





      def 		__A  (      self       :							List[Any]	, UpperCAmelCase       :							Union[str, Any]	, UpperCAmelCase       :							Optional[int]=None  ):
            A_	     =							[self.sep_token_id]
            A_	     =							[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 		__A  (      self       :							str	, UpperCAmelCase       :							str	, UpperCAmelCase       :							Optional[str] = None  ):
            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(UpperCAmelCase  ):
                  logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''  )
                  return
            A_	     =							os.path.join(
                UpperCAmelCase	, (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]  )

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

            return (out_vocab_file,)