




from typing import Dict, List, Optional, Tuple

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase			 =						logging.get_logger(__name__)



class 			a     (   _SCREAMING_SNAKE_CASE			):

     '''simple docstring'''





     lowerCAmelCase				:       List[Any]   		=  ["input_ids", "attention_mask"]
     def __init__(	self   :			int					,    __snake_case   :			Union[str, Any]="[PAD]"					,    __snake_case   :			int="[BOS]"					,    __snake_case   :			int="[EOS]"					,    __snake_case   :			str="[MASK]"					,    __snake_case   :			Dict="[CLS]"					,    __snake_case   :			Any="[SEP]"					,    __snake_case   :			Optional[Any]=20_48					,    **__snake_case   :			Union[str, Any]					,    ):
         UpperCAmelCase_							 =     AddedToken(__snake_case					,    lstrip=__snake_case					,    rstrip=__snake_case				) if isinstance(__snake_case					,    __snake_case				) else pad_token
         UpperCAmelCase_							 =     AddedToken(__snake_case					,    lstrip=__snake_case					,    rstrip=__snake_case				) if isinstance(__snake_case					,    __snake_case				) else bos_token
         UpperCAmelCase_							 =     AddedToken(__snake_case					,    lstrip=__snake_case					,    rstrip=__snake_case				) if isinstance(__snake_case					,    __snake_case				) else eos_token
         UpperCAmelCase_							 =     AddedToken(__snake_case					,    lstrip=__snake_case					,    rstrip=__snake_case				) if isinstance(__snake_case					,    __snake_case				) else mask_token
         UpperCAmelCase_							 =     AddedToken(__snake_case					,    lstrip=__snake_case					,    rstrip=__snake_case				) if isinstance(__snake_case					,    __snake_case				) else cls_token
         UpperCAmelCase_							 =     AddedToken(__snake_case					,    lstrip=__snake_case					,    rstrip=__snake_case				) if isinstance(__snake_case					,    __snake_case				) else sep_token

         super().__init__(
             pad_token=__snake_case					,    bos_token=__snake_case					,    eos_token=__snake_case					,    mask_token=__snake_case					,    cls_token=__snake_case					,    sep_token=__snake_case					,    model_max_length=__snake_case					,    **__snake_case					,    )

         UpperCAmelCase_							 =     2**8  # utf is 8 bits

         # define special tokens dict
         UpperCAmelCase_							 =     {
             self.pad_token: 0,
             self.bos_token: 1,
             self.eos_token: 2,
             self.mask_token: 3,
             self.cls_token: 4,
             self.sep_token: 5,
         }
         UpperCAmelCase_							 =     len(self.special_tokens_encoder				)
         UpperCAmelCase_							 =     {v: k for k, v in self.special_tokens_encoder.items()}
     def   lowerCamelCase_   (	self   :			Optional[Any]				):
         UpperCAmelCase_							 =     self.special_tokens_encoder.copy()
         vocab.update(self.added_tokens_encoder				)
         for i in range(self._utf_vocab_size				):
             UpperCAmelCase_							 =     chr(__snake_case				)
             UpperCAmelCase_							 =     i + len(self.special_tokens_encoder				)
         return vocab
     @property
     def   lowerCamelCase_   (	self   :			List[str]				):
         return self._utf_vocab_size + self._num_special_tokens
     def   lowerCamelCase_   (	self   :			Tuple					,    __snake_case   :			List[int]					,    __snake_case   :			Optional[List[int]] = None					,    __snake_case   :			bool = False				):
         if already_has_special_tokens:
             return super().get_special_tokens_mask(
                 token_ids_a=__snake_case					,    token_ids_a=__snake_case					,    already_has_special_tokens=__snake_case				)

         # normal case: some special tokens
         if token_ids_a is None:
             return [1] + [0] * len(__snake_case				) + [1]
         return [1] + ([0] * len(__snake_case				)) + [1] + ([0] * len(__snake_case				)) + [1]
     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			List[int]					,    __snake_case   :			Optional[List[int]] = None				):
         if token_ids_a is None:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         else:
             return [self.cls_token_id] + token_ids_a + [self.sep_token_id] + token_ids_a + [self.sep_token_id]
     def   lowerCamelCase_   (	self   :			Optional[Any]					,    __snake_case   :			str				):
         UpperCAmelCase_							 =     [chr(__snake_case				) for i in text.encode('''utf-8'''				)]
         return tokens
     def   lowerCamelCase_   (	self   :			Dict					,    __snake_case   :			str				):
         if token in self.special_tokens_encoder:
             UpperCAmelCase_							 =     self.special_tokens_encoder[token]
         elif token in self.added_tokens_encoder:
             UpperCAmelCase_							 =     self.added_tokens_encoder[token]
         elif len(__snake_case				) != 1:
             UpperCAmelCase_							 =     self.unk_token_id
         else:
             UpperCAmelCase_							 =     ord(__snake_case				) + self._num_special_tokens
         return token_id
     def   lowerCamelCase_   (	self   :			str					,    __snake_case   :			Dict				):
         if index in self.special_tokens_decoder:
             UpperCAmelCase_							 =     self.special_tokens_decoder[index]
         elif index in self.added_tokens_decoder:
             UpperCAmelCase_							 =     self.added_tokens_decoder[index]
         else:
             UpperCAmelCase_							 =     chr(index - self._num_special_tokens				)
         return token
     def   lowerCamelCase_   (	self   :			Tuple					,    __snake_case   :			Optional[Any]				):
         UpperCAmelCase_							 =     B''''''
         for token in tokens:
             if token in self.special_tokens_decoder:
                 UpperCAmelCase_							 =     self.special_tokens_decoder[token].encode('''utf-8'''				)
             elif token in self.added_tokens_decoder:
                 UpperCAmelCase_							 =     self.special_tokens_decoder[token].encode('''utf-8'''				)
             elif token in self.special_tokens_encoder:
                 UpperCAmelCase_							 =     token.encode('''utf-8'''				)
             elif token in self.added_tokens_encoder:
                 UpperCAmelCase_							 =     token.encode('''utf-8'''				)
             else:
                 UpperCAmelCase_							 =     bytes([ord(__snake_case				)]				)
             bstring += tok_string
         UpperCAmelCase_							 =     bstring.decode('''utf-8'''					,    errors='''replace'''				)
         return string




     def   lowerCamelCase_   (	self   :			int					,    __snake_case   :			str					,    __snake_case   :			Optional[str] = None				):
         return ()











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 TvltImageProcessor




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=10    ,   lowercase_=10    ,   lowercase_=False    ,   lowercase_=False					)				->		Dict:

    """simple docstring"""


    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.num_frames					):
        video.append(np.random.randint(2_55    ,   size=(image_processor_tester.num_channels, width, height)    ,   dtype=np.uinta					)					)

    if not numpify and not torchify:
        # PIL expects the channel dimension as last dimension
        __UpperCamelCase     						=  [Image.fromarray(np.moveaxis(lowercase_    ,   0    ,   -1					)					) for frame in video]

    if torchify:
        __UpperCamelCase     						=  [torch.from_numpy(lowercase_					) for frame in video]

    return video




def     __SCREAMING_SNAKE_CASE      (     lowercase_    ,   lowercase_=False    ,   lowercase_=False    ,   lowercase_=False					)				->		Tuple:

    """simple docstring"""


    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    __UpperCamelCase     						=  []
    for i in range(image_processor_tester.batch_size					):
        if equal_resolution:
            __UpperCamelCase     						=  __UpperCamelCase     						=  image_processor_tester.max_resolution
        else:
            __UpperCamelCase						,					__UpperCamelCase     						=  np.random.choice(
                np.arange(image_processor_tester.min_resolution    ,   image_processor_tester.max_resolution					)    ,   2					)
            __UpperCamelCase     						=  prepare_video(
                image_processor_tester=lowercase_    ,   width=lowercase_    ,   height=lowercase_    ,   numpify=lowercase_    ,   torchify=lowercase_    ,   )
        video_inputs.append(lowercase_					)

    return video_inputs

class        _lowerCamelCase  (	unittest.TestCase      ):

      """simple docstring"""

      def __init__(   self    :       List[str]       ,				snake_case    :       int       ,				snake_case    :       int=7       ,				snake_case    :       int=3       ,				snake_case    :       Any=4       ,				snake_case    :       Tuple=18       ,				snake_case    :       Optional[int]=30       ,				snake_case    :       List[Any]=400       ,				snake_case    :       str=True       ,				snake_case    :       str=None       ,				snake_case    :       Optional[Any]=True       ,				snake_case    :       str=[0.5, 0.5, 0.5]       ,				snake_case    :       Optional[int]=[0.5, 0.5, 0.5]       ,				snake_case    :       Any=True       ,				snake_case    :       str=None       ,				):
          __UpperCamelCase     						=  size if size is not None else {'''shortest_edge''': 18}
          __UpperCamelCase     						=  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

          __UpperCamelCase     						=  parent
          __UpperCamelCase     						=  batch_size
          __UpperCamelCase     						=  num_channels
          __UpperCamelCase     						=  num_frames
          __UpperCamelCase     						=  image_size
          __UpperCamelCase     						=  min_resolution
          __UpperCamelCase     						=  max_resolution
          __UpperCamelCase     						=  do_resize
          __UpperCamelCase     						=  size
          __UpperCamelCase     						=  do_normalize
          __UpperCamelCase     						=  image_mean
          __UpperCamelCase     						=  image_std
          __UpperCamelCase     						=  do_center_crop
          __UpperCamelCase     						=  crop_size







      def 					snake_case		(   self    :       Any ):
          return {
              "image_mean": self.image_mean,
              "image_std": self.image_std,
              "do_normalize": self.do_normalize,
              "do_resize": self.do_resize,
              "size": self.size,
              "do_center_crop": self.do_center_crop,
              "crop_size": self.crop_size,
          }







@require_torch
@require_vision
class        _lowerCamelCase  (	_SCREAMING_SNAKE_CASE							,	unittest.TestCase      ):

      """simple docstring"""

      lowerCAmelCase__	:       str			=    TvltImageProcessor if is_vision_available() else None







      def 					snake_case		(   self    :       Dict ):
          __UpperCamelCase     						=  TvltImageProcessorTester(self )







      @property
      def 					snake_case		(   self    :       Dict ):
          return self.image_processor_tester.prepare_image_processor_dict()







      def 					snake_case		(   self    :       int ):
          __UpperCamelCase     						=  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 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PIL videos
          __UpperCamelCase     						=  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     						=  image_processor(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     						=  image_processor(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 					snake_case		(   self    :       List[Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random numpy tensors
          __UpperCamelCase     						=  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     						=  image_processor(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     						=  image_processor(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 					snake_case		(   self    :       Union[str, Any] ):
          # Initialize image_processor
          __UpperCamelCase     						=  self.image_processing_class(**self.image_processor_dict )
          # create random PyTorch tensors
          __UpperCamelCase     						=  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     						=  image_processor(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     						=  image_processor(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'''],
              )       ,				)






