




'''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 		snake_case_    (UpperCamelCase       :  Union[str, Any] ,						UpperCamelCase       :  Any=10 ,						UpperCamelCase       :  Dict=10 ,						UpperCamelCase       :  List[str]=False ,						UpperCamelCase       :  int=False      ):





							'''simple docstring'''


							_a	     =				[]
							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	     =				[Image.fromarray(np.moveaxis(UpperCamelCase ,						0 ,						-1      )      ) for frame in video]

							if torchify:
														_a	     =				[torch.from_numpy(UpperCamelCase      ) for frame in video]

							return video
def 		snake_case_    (UpperCamelCase       :  int ,						UpperCamelCase       :  str=False ,						UpperCamelCase       :  Optional[int]=False ,						UpperCamelCase       :  List[str]=False      ):





							'''simple docstring'''


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

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

							return video_inputs

class 			A    (		unittest.TestCase    ):







							def __init__(  self       :  str							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Dict=7							,			lowerCAmelCase_       :  Dict=3							,			lowerCAmelCase_       :  int=4							,			lowerCAmelCase_       :  Tuple=18							,			lowerCAmelCase_       :  Any=30							,			lowerCAmelCase_       :  Optional[Any]=4_00							,			lowerCAmelCase_       :  List[Any]=True							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  int=True							,			lowerCAmelCase_       :  str=[0.5, 0.5, 0.5]							,			lowerCAmelCase_       :  Optional[Any]=[0.5, 0.5, 0.5]							,			lowerCAmelCase_       :  Tuple=True							,			lowerCAmelCase_       :  int=None							,			)			->							Any:


														"""simple docstring"""


														_a	     =				size if size is not None else {'''shortest_edge''': 18}
														_a	     =				crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}

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







							def    __lowerCAmelCase			(  self       :  Dict					)			->							List[Any]:


														"""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 			A    (		_a     ,unittest.TestCase    ):
							lowercase_							=				TvltImageProcessor if is_vision_available() else None







							def    __lowerCAmelCase			(  self       :  str					)			->							Dict:


														"""simple docstring"""


														_a	     =				TvltImageProcessorTester(self					)







							@property
							def    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Any:


														"""simple docstring"""


														return self.image_processor_tester.prepare_image_processor_dict()







							def    __lowerCAmelCase			(  self       :  Any					)			->							Optional[Any]:


														"""simple docstring"""


														_a	     =				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    __lowerCAmelCase			(  self       :  str					)			->							List[str]:


														"""simple docstring"""


														_a	     =				self.image_processing_class(**self.image_processor_dict					)
														# create random PIL videos
														_a	     =				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	     =				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	     =				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    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Optional[Any]:


														"""simple docstring"""


														_a	     =				self.image_processing_class(**self.image_processor_dict					)
														# create random numpy tensors
														_a	     =				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	     =				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	     =				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    __lowerCAmelCase			(  self       :  Optional[Any]					)			->							Tuple:


														"""simple docstring"""


														_a	     =				self.image_processing_class(**self.image_processor_dict					)
														# create random PyTorch tensors
														_a	     =				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	     =				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	     =				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:
				_snake_case      :      int   							=		None


_snake_case      :      Union[str, Any]   							=		logging.get_logger(__name__)


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

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

_snake_case      :      List[str]   							=		{
    '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'
        )
    },
}

_snake_case      :      Union[str, Any]   							=		{
    'google/reformer-crime-and-punishment': 524288,
}

class 			A    (		_a    ):
							lowercase_							=				VOCAB_FILES_NAMES
							lowercase_							=				PRETRAINED_VOCAB_FILES_MAP
							lowercase_							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							lowercase_							=				['input_ids', 'attention_mask']
							lowercase_							=				ReformerTokenizer







							def __init__(  self       :  Any							,			lowerCAmelCase_       :  int=None							,			lowerCAmelCase_       :  Optional[Any]=None							,			lowerCAmelCase_       :  List[str]="</s>"							,			lowerCAmelCase_       :  Any="<unk>"							,			lowerCAmelCase_       :  List[str]=[]							,			**lowerCAmelCase_       :  List[Any]							,			)			->							Dict:


														"""simple docstring"""


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

														_a	     =				vocab_file
														_a	     =				False if not self.vocab_file else True







							def    __lowerCAmelCase			(  self       :  List[Any]							,			lowerCAmelCase_       :  str							,			lowerCAmelCase_       :  Optional[str] = None					)			->							Tuple[str]:


														"""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	     =				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,)

