
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 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=10     ,						SCREAMING_SNAKE_CASE=10     ,						SCREAMING_SNAKE_CASE=False     ,						SCREAMING_SNAKE_CASE=False							):




			"""simple docstring"""


			UpperCAmelCase__		: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
						UpperCAmelCase__		:List[str] 		=    [Image.fromarray(np.moveaxis(UpperCAmelCase_     ,						0     ,						-1							)							) for frame in video]

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

			return video




def 		A						(  SCREAMING_SNAKE_CASE     ,						SCREAMING_SNAKE_CASE=False     ,						SCREAMING_SNAKE_CASE=False     ,						SCREAMING_SNAKE_CASE=False							):




			"""simple docstring"""


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

			UpperCAmelCase__		:Optional[int] 		=    []
			for i in range(image_processor_tester.batch_size							):
						if equal_resolution:
									UpperCAmelCase__		:Any 		=    image_processor_tester.max_resolution
						else:
									UpperCAmelCase__		:Union[str, Any] 		=    np.random.choice(
									    np.arange(image_processor_tester.min_resolution     ,						image_processor_tester.max_resolution							)     ,						2							)
									UpperCAmelCase__		: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):

						'''simple docstring'''



						def __init__(  self  , A  , A=7  , A=3  , A=4  , A=18  , A=30  , A=4_00  , A=True  , A=None  , A=True  , A=[0.5, 0.5, 0.5]  , A=[0.5, 0.5, 0.5]  , A=True  , A=None  , )  ->Any:
									UpperCAmelCase__		:List[str] 		=    size if size is not None else {"""shortest_edge""": 18}
									UpperCAmelCase__		:Optional[int] 		=    crop_size if crop_size is not None else {"""height""": 18, """width""": 18}

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


						def    A__       (  self	)  ->Tuple:
									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):

						'''simple docstring'''



						__a     :    Dict												=			TvltImageProcessor if is_vision_available() else None


						def    A__       (  self	)  ->Dict:
									UpperCAmelCase__		:List[str] 		=    TvltImageProcessorTester(self	)


						@property
						def    A__       (  self	)  ->List[Any]:
									return self.image_processor_tester.prepare_image_processor_dict()


						def    A__       (  self	)  ->Optional[int]:
									UpperCAmelCase__		: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	)  ->Optional[int]:
									UpperCAmelCase__		:str 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PIL videos
									UpperCAmelCase__		: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
									UpperCAmelCase__		: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
									UpperCAmelCase__		: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	)  ->List[Any]:
									UpperCAmelCase__		:Optional[Any] 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random numpy tensors
									UpperCAmelCase__		: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
									UpperCAmelCase__		: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
									UpperCAmelCase__		: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	)  ->str:
									UpperCAmelCase__		:Any 		=    self.image_processing_class(**self.image_processor_dict	)
									# create random PyTorch tensors
									UpperCAmelCase__		: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
									UpperCAmelCase__		: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
									UpperCAmelCase__		: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,)


