




'''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 TvltImageProcessor





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=10   ,	UpperCAmelCase_=10   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=False						):
    _a				:							Dict							       =      []
    for i in range(image_processor_tester.num_frames						):
        video.append(np.random.randint(255   ,	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
        _a				:							List[str]							       =      [Image.fromarray(np.moveaxis(UpperCAmelCase_   ,	0   ,	-1						)						) for frame in video]

    if torchify:
        _a				:							Union[str, Any]							       =      [torch.from_numpy(UpperCAmelCase_						) for frame in video]

    return video





def UpperCAmelCase    (       UpperCAmelCase_   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=False   ,	UpperCAmelCase_=False						):
    assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

    _a				:							Optional[int]							       =      []
    for i in range(image_processor_tester.batch_size						):
        if equal_resolution:
            _a				:							Any							       =      image_processor_tester.max_resolution
        else:
            _a       , _a				:							Union[str, Any]							       =      np.random.choice(
                np.arange(image_processor_tester.min_resolution   ,	image_processor_tester.max_resolution						)   ,	2						)
            _a				:							Dict							       =      prepare_video(
                image_processor_tester=UpperCAmelCase_   ,	width=UpperCAmelCase_   ,	height=UpperCAmelCase_   ,	numpify=UpperCAmelCase_   ,	torchify=UpperCAmelCase_   ,	)
        video_inputs.append(UpperCAmelCase_						)

    return video_inputs




class      UpperCamelCase       (	unittest.TestCase       ):



  def __init__(			self		,     __lowerCAmelCase		,     __lowerCAmelCase=7		,     __lowerCAmelCase=3		,     __lowerCAmelCase=4		,     __lowerCAmelCase=18		,     __lowerCAmelCase=30		,     __lowerCAmelCase=400		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     __lowerCAmelCase=True		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     __lowerCAmelCase=[0.5, 0.5, 0.5]		,     __lowerCAmelCase=True		,     __lowerCAmelCase=None		,     ):







      '''simple docstring'''


      _a				:							List[str]							       =      size if size is not None else {"""shortest_edge""": 18}
      _a				:							Optional[int]							       =      crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

      _a				:							int							       =      parent
      _a				:							Tuple							       =      batch_size
      _a				:							Optional[Any]							       =      num_channels
      _a				:							Any							       =      num_frames
      _a				:							Any							       =      image_size
      _a				:							Tuple							       =      min_resolution
      _a				:							Union[str, Any]							       =      max_resolution
      _a				:							Tuple							       =      do_resize
      _a				:							Optional[int]							       =      size
      _a				:							List[Any]							       =      do_normalize
      _a				:							Tuple							       =      image_mean
      _a				:							Optional[Any]							       =      image_std
      _a				:							Optional[Any]							       =      do_center_crop
      _a				:							Optional[int]							       =      crop_size



  def     a_							(			self ):







      '''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,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
      }






@require_torch
@require_vision
class      UpperCamelCase       (	snake_case__      ,		unittest.TestCase       ):
  a     :Dict          =					TvltImageProcessor if is_vision_available() else None



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							List[str]							       =      TvltImageProcessorTester(self )



  @property
  def     a_							(			self ):







      '''simple docstring'''


      return self.image_processor_tester.prepare_image_processor_dict()



  def     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.image_processing_class(**self.image_processor_dict )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_mean""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """image_std""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_normalize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_resize""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """do_center_crop""" ) )
      self.assertTrue(hasattr(__lowerCAmelCase		,     """size""" ) )



  def     a_							(			self ):







      '''simple docstring'''


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

      # Test not batched input
      _a				:							List[str]							       =      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
      _a				:							Tuple							       =      image_processor(__lowerCAmelCase		,     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     a_							(			self ):







      '''simple docstring'''


      _a				:							Optional[Any]							       =      self.image_processing_class(**self.image_processor_dict )
      # create random numpy tensors
      _a				:							Union[str, Any]							       =      prepare_video_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     numpify=__lowerCAmelCase )
      for video in video_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     __lowerCAmelCase )
          self.assertIsInstance(video[0]		,     np.ndarray )

      # Test not batched input
      _a				:							Optional[int]							       =      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
      _a				:							Tuple							       =      image_processor(__lowerCAmelCase		,     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     a_							(			self ):







      '''simple docstring'''


      _a				:							Any							       =      self.image_processing_class(**self.image_processor_dict )
      # create random PyTorch tensors
      _a				:							List[Any]							       =      prepare_video_inputs(self.image_processor_tester		,     equal_resolution=__lowerCAmelCase		,     torchify=__lowerCAmelCase )
      for video in video_inputs:
          self.assertIsInstance(__lowerCAmelCase		,     __lowerCAmelCase )
          self.assertIsInstance(video[0]		,     torch.Tensor )

      # Test not batched input
      _a				:							Optional[Any]							       =      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
      _a				:							List[Any]							       =      image_processor(__lowerCAmelCase		,     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"""],
          )		,     )








'''simple docstring'''





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

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


if is_sentencepiece_available():
  from .tokenization_reformer import ReformerTokenizer
else:
  a		:	Optional[int]       =      None


a		:	Optional[Any]       =      logging.get_logger(__name__)


a		:	Union[str, Any]       =      '''▁'''

a		:	Any       =      {'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

a		:	List[Any]       =      {
    '''vocab_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/spiece.model'''
        )
    },
    '''tokenizer_file''': {
        '''google/reformer-crime-and-punishment''': (
            '''https://huggingface.co/google/reformer-crime-and-punishment/resolve/main/tokenizer.json'''
        )
    },
}

a		:	Any       =      {
    '''google/reformer-crime-and-punishment''': 5_2_4_2_8_8,
}




class      UpperCamelCase       (	snake_case__       ):
  a     :str          =					VOCAB_FILES_NAMES
  a     :List[Any]          =					PRETRAINED_VOCAB_FILES_MAP
  a     :Optional[int]          =					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  a     :Any          =					['input_ids', 'attention_mask']
  a     :int          =					ReformerTokenizer



  def __init__(			self		,     __lowerCAmelCase=None		,     __lowerCAmelCase=None		,     __lowerCAmelCase="</s>"		,     __lowerCAmelCase="<unk>"		,     __lowerCAmelCase=[]		,     **__lowerCAmelCase		,     ):







      '''simple docstring'''


      super().__init__(
          __lowerCAmelCase		,     tokenizer_file=__lowerCAmelCase		,     eos_token=__lowerCAmelCase		,     unk_token=__lowerCAmelCase		,     additional_special_tokens=__lowerCAmelCase		,     **__lowerCAmelCase		,     )

      _a				:							List[str]							       =      vocab_file
      _a				:							Optional[int]							       =      False if not self.vocab_file else True



  def     a_							(			self		,     __lowerCAmelCase		,     __lowerCAmelCase = None ):







      '''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
      _a				:							List[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,)


