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_video_inputs


if is_torch_available():
   import torch

if is_vision_available():
   from PIL import Image

   from transformers import VideoMAEImageProcessor





class 	_lowercase		(      unittest.TestCase ):

 def __init__(				self			:							Union[str, Any]     ,   snake_case			:							Optional[Any]     ,   snake_case			:							Optional[Any]=7     ,   snake_case			:							Optional[int]=3     ,   snake_case			:							Any=1_0     ,   snake_case			:							Tuple=1_8     ,   snake_case			:							List[Any]=3_0     ,   snake_case			:							Optional[Any]=4_0_0     ,   snake_case			:							Optional[Any]=True     ,   snake_case			:							str=None     ,   snake_case			:							int=True     ,   snake_case			:							Tuple=[0.5, 0.5, 0.5]     ,   snake_case			:							Any=[0.5, 0.5, 0.5]     ,   snake_case			:							Dict=None     ,   )     ->		Any:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      size if size is not None else {'shortest_edge': 1_8}
  UpperCamelCase_      :		Union[str, Any]				=      crop_size if crop_size is not None else {'height': 1_8, 'width': 1_8}

  UpperCamelCase_      :		Dict				=      parent
  UpperCamelCase_      :		Any				=      batch_size
  UpperCamelCase_      :		Optional[int]				=      num_channels
  UpperCamelCase_      :		Tuple				=      num_frames
  UpperCamelCase_      :		Optional[int]				=      image_size
  UpperCamelCase_      :		Tuple				=      min_resolution
  UpperCamelCase_      :		Optional[Any]				=      max_resolution
  UpperCamelCase_      :		Any				=      do_resize
  UpperCamelCase_      :		Optional[Any]				=      size
  UpperCamelCase_      :		Tuple				=      do_normalize
  UpperCamelCase_      :		str				=      image_mean
  UpperCamelCase_      :		List[Any]				=      image_std
  UpperCamelCase_      :		Dict				=      crop_size


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]    )     ->		Tuple:

  """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,
      "crop_size": self.crop_size,
  }





@require_torch
@require_vision
class 	_lowercase		(      snake_case_		,   unittest.TestCase ):
 lowercase         =     VideoMAEImageProcessor if is_vision_available() else None

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		Dict:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      VideoMAEImageProcessingTester(self    )

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		str:

  """simple docstring"""





  return self.image_processor_tester.prepare_image_processor_dict()

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Union[str, Any]				=      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     ,   'do_center_crop'    )    )
  self.assertTrue(hasattr(snake_case     ,   'size'    )    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int    )     ->		Dict:

  """simple docstring"""





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

  UpperCamelCase_      :		Optional[Any]				=      self.image_processing_class.from_dict(self.image_processor_dict     ,   size=4_2     ,   crop_size=8_4    )
  self.assertEqual(image_processor.size     ,   {'shortest_edge': 4_2}    )
  self.assertEqual(image_processor.crop_size     ,   {'height': 8_4, 'width': 8_4}    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		int:

  """simple docstring"""





  pass

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Tuple    )     ->		Tuple:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      self.image_processing_class(**self.image_processor_dict    )
  # create random PIL videos
  UpperCamelCase_      :		List[str]				=      prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=snake_case    )
  for video in video_inputs:
   self.assertIsInstance(snake_case     ,   snake_case    )
   self.assertIsInstance(video[0]     ,   Image.Image    )

  # Test not batched input
  UpperCamelCase_      :		Optional[Any]				=      image_processing(video_inputs[0]     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

  # Test batched
  UpperCamelCase_      :		Any				=      image_processing(snake_case     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[Any]    )     ->		Optional[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      self.image_processing_class(**self.image_processor_dict    )
  # create random numpy tensors
  UpperCamelCase_      :		List[Any]				=      prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=snake_case     ,   numpify=snake_case    )
  for video in video_inputs:
   self.assertIsInstance(snake_case     ,   snake_case    )
   self.assertIsInstance(video[0]     ,   np.ndarray    )

  # Test not batched input
  UpperCamelCase_      :		Any				=      image_processing(video_inputs[0]     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

  # Test batched
  UpperCamelCase_      :		int				=      image_processing(snake_case     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict    )     ->		List[str]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      self.image_processing_class(**self.image_processor_dict    )
  # create random PyTorch tensors
  UpperCamelCase_      :		Union[str, Any]				=      prepare_video_inputs(self.image_processor_tester     ,   equal_resolution=snake_case     ,   torchify=snake_case    )
  for video in video_inputs:
   self.assertIsInstance(snake_case     ,   snake_case    )
   self.assertIsInstance(video[0]     ,   torch.Tensor    )

  # Test not batched input
  UpperCamelCase_      :		int				=      image_processing(video_inputs[0]     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          1,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )

  # Test batched
  UpperCamelCase_      :		Union[str, Any]				=      image_processing(snake_case     ,   return_tensors='pt'    ).pixel_values
  self.assertEqual(
      encoded_videos.shape     ,   (
          self.image_processor_tester.batch_size,
          self.image_processor_tester.num_frames,
          self.image_processor_tester.num_channels,
          self.image_processor_tester.crop_size['height'],
          self.image_processor_tester.crop_size['width'],
      )     ,   )




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


a_					      = logging.get_logger(__name__)

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

a_					      = {
    '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'
        ),
    },
}

a_					      = {
    'microsoft/deberta-base': 512,
    'microsoft/deberta-large': 512,
    'microsoft/deberta-xlarge': 512,
    'microsoft/deberta-base-mnli': 512,
    'microsoft/deberta-large-mnli': 512,
    'microsoft/deberta-xlarge-mnli': 512,
}

a_					      = {
    'microsoft/deberta-base': {'do_lower_case': False},
    'microsoft/deberta-large': {'do_lower_case': False},
}





class 	_lowercase		(      snake_case_ ):
 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			:							Optional[int]     ,   snake_case			:							Any=None     ,   snake_case			:							Union[str, Any]=None     ,   snake_case			:							Optional[Any]=None     ,   snake_case			:							List[str]="replace"     ,   snake_case			:							int="[CLS]"     ,   snake_case			:							Any="[SEP]"     ,   snake_case			:							int="[SEP]"     ,   snake_case			:							Union[str, Any]="[CLS]"     ,   snake_case			:							List[Any]="[UNK]"     ,   snake_case			:							List[str]="[PAD]"     ,   snake_case			:							Tuple="[MASK]"     ,   snake_case			:							int=False     ,   **snake_case			:							List[str]     ,   )     ->		Any:

  """simple docstring"""





  super().__init__(
      snake_case     ,   snake_case     ,   tokenizer_file=snake_case     ,   errors=snake_case     ,   bos_token=snake_case     ,   eos_token=snake_case     ,   unk_token=snake_case     ,   sep_token=snake_case     ,   cls_token=snake_case     ,   pad_token=snake_case     ,   mask_token=snake_case     ,   add_prefix_space=snake_case     ,   **snake_case     ,   )
  UpperCamelCase_      :		str				=      kwargs.pop('add_bos_token'     ,   snake_case    )

  UpperCamelCase_      :		Tuple				=      json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()    )
  if pre_tok_state.get('add_prefix_space'     ,   snake_case    ) != add_prefix_space:
   UpperCamelCase_      :		str				=      getattr(snake_case     ,   pre_tok_state.pop('type'    )    )
   UpperCamelCase_      :		List[Any]				=      add_prefix_space
   UpperCamelCase_      :		List[str]				=      pre_tok_class(**snake_case    )

  UpperCamelCase_      :		int				=      add_prefix_space

 @property
 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]    )     ->		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 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							Optional[int]    )     ->		str:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      AddedToken(snake_case     ,   lstrip=snake_case     ,   rstrip=snake_case    ) if isinstance(snake_case     ,   snake_case    ) else value
  UpperCamelCase_      :		Optional[int]				=      value

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							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]
  UpperCamelCase_      :		Dict				=      [self.cls_token_id]
  UpperCamelCase_      :		Tuple				=      [self.sep_token_id]
  return cls + token_ids_a + sep + token_ids_a + sep

 def 		SCREAMING_SNAKE_CASE__     (				self			:							str     ,   snake_case			:							List[int]     ,   snake_case			:							Optional[List[int]] = None    )     ->		List[int]:

  """simple docstring"""





  UpperCamelCase_      :		Optional[int]				=      [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 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[int]     ,   *snake_case			:							Any     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		Tuple				=      kwargs.get('is_split_into_words'     ,   snake_case    )
  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(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							int     ,   *snake_case			:							List[str]     ,   **snake_case			:							Union[str, Any]    )     ->		BatchEncoding:

  """simple docstring"""





  UpperCamelCase_      :		List[Any]				=      kwargs.get('is_split_into_words'     ,   snake_case    )

  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(*snake_case     ,   **snake_case    )

 def 		SCREAMING_SNAKE_CASE__     (				self			:							Optional[Any]     ,   snake_case			:							str     ,   snake_case			:							Optional[str] = None    )     ->		Tuple[str]:

  """simple docstring"""





  UpperCamelCase_      :		Dict				=      self._tokenizer.model.save(snake_case     ,   name=snake_case    )
  return tuple(snake_case    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Any     ,   snake_case			:							"Conversation"    )     ->		List[int]:

  """simple docstring"""





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

  if len(snake_case    ) > self.model_max_length:
   UpperCamelCase_      :		Optional[int]				=      input_ids[-self.model_max_length :]
  return input_ids



