


'''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, prepare_video_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import VideoMAEImageProcessor






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						def __init__(  self:	Any      ,							__UpperCamelCase:	Dict      ,							__UpperCamelCase:	int=7      ,							__UpperCamelCase:	List[Any]=3      ,							__UpperCamelCase:	Dict=10      ,							__UpperCamelCase:	List[Any]=18      ,							__UpperCamelCase:	Dict=30      ,							__UpperCamelCase:	str=400      ,							__UpperCamelCase:	int=True      ,							__UpperCamelCase:	Tuple=None      ,							__UpperCamelCase:	Union[str, Any]=True      ,							__UpperCamelCase:	Dict=[0.5, 0.5, 0.5]      ,							__UpperCamelCase:	List[Any]=[0.5, 0.5, 0.5]      ,							__UpperCamelCase:	Any=None      ,							)							->	Any:
										__magic_name__				:					List[str]						       =       size if size is not None else {"shortest_edge": 18}
										__magic_name__				:					Tuple						       =       crop_size if crop_size is not None else {"height": 18, "width": 18}

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







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Dict:
										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 		_snake_case    (				snake_case_  ,  unittest.TestCase		):






						'''simple docstring'''







						__snake_case   =      VideoMAEImageProcessor if is_vision_available() else None







						def lowerCAmelCase__ (  self:	List[str]     )							->	str:
										__magic_name__				:					str						       =       VideoMAEImageProcessingTester(self     )







						@property
						def lowerCAmelCase__ (  self:	Any     )							->	int:
										return self.image_processor_tester.prepare_image_processor_dict()







						def lowerCAmelCase__ (  self:	Dict     )							->	Union[str, Any]:
										__magic_name__				:					Optional[int]						       =       self.image_processing_class(**self.image_processor_dict     )
										self.assertTrue(hasattr(__UpperCamelCase      ,							"image_mean"     )     )
										self.assertTrue(hasattr(__UpperCamelCase      ,							"image_std"     )     )
										self.assertTrue(hasattr(__UpperCamelCase      ,							"do_normalize"     )     )
										self.assertTrue(hasattr(__UpperCamelCase      ,							"do_resize"     )     )
										self.assertTrue(hasattr(__UpperCamelCase      ,							"do_center_crop"     )     )
										self.assertTrue(hasattr(__UpperCamelCase      ,							"size"     )     )







						def lowerCAmelCase__ (  self:	Any     )							->	List[Any]:
										__magic_name__				:					str						       =       self.image_processing_class.from_dict(self.image_processor_dict     )
										self.assertEqual(image_processor.size      ,							{"shortest_edge": 18}     )
										self.assertEqual(image_processor.crop_size      ,							{"height": 18, "width": 18}     )

										__magic_name__				:					int						       =       self.image_processing_class.from_dict(self.image_processor_dict      ,							size=42      ,							crop_size=84     )
										self.assertEqual(image_processor.size      ,							{"shortest_edge": 42}     )
										self.assertEqual(image_processor.crop_size      ,							{"height": 84, "width": 84}     )







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







						def lowerCAmelCase__ (  self:	int     )							->	List[Any]:
										# Initialize image_processing
										__magic_name__				:					Any						       =       self.image_processing_class(**self.image_processor_dict     )
										# create random PIL videos
										__magic_name__				:					Any						       =       prepare_video_inputs(self.image_processor_tester      ,							equal_resolution=__UpperCamelCase     )
										for video in video_inputs:
														self.assertIsInstance(__UpperCamelCase      ,							__UpperCamelCase     )
														self.assertIsInstance(video[0]      ,							Image.Image     )

										# Test not batched input
										__magic_name__				:					str						       =       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
										__magic_name__				:					int						       =       image_processing(__UpperCamelCase      ,							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:	Tuple     )							->	Any:
										# Initialize image_processing
										__magic_name__				:					List[str]						       =       self.image_processing_class(**self.image_processor_dict     )
										# create random numpy tensors
										__magic_name__				:					Optional[Any]						       =       prepare_video_inputs(self.image_processor_tester      ,							equal_resolution=__UpperCamelCase      ,							numpify=__UpperCamelCase     )
										for video in video_inputs:
														self.assertIsInstance(__UpperCamelCase      ,							__UpperCamelCase     )
														self.assertIsInstance(video[0]      ,							np.ndarray     )

										# Test not batched input
										__magic_name__				:					Dict						       =       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
										__magic_name__				:					str						       =       image_processing(__UpperCamelCase      ,							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:	Tuple     )							->	Optional[Any]:
										# Initialize image_processing
										__magic_name__				:					List[Any]						       =       self.image_processing_class(**self.image_processor_dict     )
										# create random PyTorch tensors
										__magic_name__				:					List[Any]						       =       prepare_video_inputs(self.image_processor_tester      ,							equal_resolution=__UpperCamelCase      ,							torchify=__UpperCamelCase     )
										for video in video_inputs:
														self.assertIsInstance(__UpperCamelCase      ,							__UpperCamelCase     )
														self.assertIsInstance(video[0]      ,							torch.Tensor     )

										# Test not batched input
										__magic_name__				:					Optional[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
										__magic_name__				:					List[Any]						       =       image_processing(__UpperCamelCase      ,							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 unittest

from transformers import BlenderbotTokenizer, BlenderbotTokenizerFast
from transformers.utils import cached_property






class 		_snake_case    (				unittest.TestCase		):






						'''simple docstring'''







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	List[Any]:
										return BlenderbotTokenizer.from_pretrained("facebook/blenderbot-3B"     )







						@cached_property
						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										return BlenderbotTokenizerFast.from_pretrained("facebook/blenderbot-3B"     )







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	Optional[int]:
										__magic_name__				:					Tuple						       =       self.tokenizer_ab
										__magic_name__				:					Optional[int]						       =       " I am a small frog."
										__magic_name__				:					Optional[int]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					int						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[int]     )							->	Any:
										__magic_name__				:					Tuple						       =       self.rust_tokenizer_ab
										__magic_name__				:					str						       =       " I am a small frog."
										__magic_name__				:					Union[str, Any]						       =       tok([src_text]      ,							padding=__UpperCamelCase      ,							truncation=__UpperCamelCase     )["input_ids"]
										__magic_name__				:					Any						       =       tok.batch_decode(__UpperCamelCase      ,							skip_special_tokens=__UpperCamelCase      ,							clean_up_tokenization_spaces=__UpperCamelCase     )[0]
										assert src_text == decoded







						def lowerCAmelCase__ (  self:	Optional[Any]     )							->	str:
										assert self.tokenizer_ab.add_prefix_space
										assert self.tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]







						def lowerCAmelCase__ (  self:	List[str]     )							->	List[Any]:
										assert self.rust_tokenizer_ab.add_prefix_space
										assert self.rust_tokenizer_ab([" Sam", "Sam"]     ).input_ids == [[5502, 2], [5502, 2]]