


"""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_image_inputs


if is_torch_available():
	import torch

if is_vision_available():
	from PIL import Image

	from transformers import LevitImageProcessor



class 			lowerCamelCase__						(   unittest.TestCase			):







						def __init__(							self   :    Optional[Any]			,			_lowercase   :    Optional[Any]			,			_lowercase   :    Optional[int]=7			,			_lowercase   :    List[Any]=3			,			_lowercase   :    Tuple=18			,			_lowercase   :    Dict=30			,			_lowercase   :    List[Any]=400			,			_lowercase   :    int=True			,			_lowercase   :    str=None			,			_lowercase   :    List[str]=True			,			_lowercase   :    Dict=None			,			_lowercase   :    Optional[int]=True			,			_lowercase   :    Optional[int]=[0.5, 0.5, 0.5]			,			_lowercase   :    str=[0.5, 0.5, 0.5]			,			):
								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					       = image_size
								A					       = min_resolution
								A					       = max_resolution
								A					       = do_resize
								A					       = size
								A					       = do_center_crop
								A					       = crop_size
								A					       = do_normalize
								A					       = image_mean
								A					       = image_std







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



@require_torch
@require_vision
class 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     LevitImageProcessor if is_vision_available() else None







						def __a						(							self   :    Dict		):
								A					       = LevitImageProcessingTester(self		)







						@property
						def __a						(							self   :    Optional[int]		):
								return self.image_processor_tester.prepare_image_processor_dict()







						def __a						(							self   :    Union[str, Any]		):
								A					       = self.image_processing_class(**self.image_processor_dict		)
								self.assertTrue(hasattr(__A			,			'image_mean'		)		)
								self.assertTrue(hasattr(__A			,			'image_std'		)		)
								self.assertTrue(hasattr(__A			,			'do_normalize'		)		)
								self.assertTrue(hasattr(__A			,			'do_resize'		)		)
								self.assertTrue(hasattr(__A			,			'do_center_crop'		)		)
								self.assertTrue(hasattr(__A			,			'size'		)		)







						def __a						(							self   :    int		):
								A					       = 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}		)

								A					       = 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 __a						(							self   :    List[Any]		):
								pass







						def __a						(							self   :    Optional[int]		):
								# Initialize image_processing
								A					       = self.image_processing_class(**self.image_processor_dict		)
								# create random PIL images
								A					       = prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__A		)
								for image in image_inputs:
										self.assertIsInstance(__A			,			Image.Image		)

								# Test not batched input
								A					       = image_processing(image_inputs[0]			,			return_tensors='pt'		).pixel_values
								self.assertEqual(
								    encoded_images.shape			,			(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,			)

								# Test batched
								A					       = image_processing(__A			,			return_tensors='pt'		).pixel_values
								self.assertEqual(
								    encoded_images.shape			,			(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,			)







						def __a						(							self   :    List[str]		):
								# Initialize image_processing
								A					       = self.image_processing_class(**self.image_processor_dict		)
								# create random numpy tensors
								A					       = prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__A			,			numpify=__A		)
								for image in image_inputs:
										self.assertIsInstance(__A			,			np.ndarray		)

								# Test not batched input
								A					       = image_processing(image_inputs[0]			,			return_tensors='pt'		).pixel_values
								self.assertEqual(
								    encoded_images.shape			,			(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,			)

								# Test batched
								A					       = image_processing(__A			,			return_tensors='pt'		).pixel_values
								self.assertEqual(
								    encoded_images.shape			,			(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,			)







						def __a						(							self   :    List[str]		):
								# Initialize image_processing
								A					       = self.image_processing_class(**self.image_processor_dict		)
								# create random PyTorch tensors
								A					       = prepare_image_inputs(self.image_processor_tester			,			equal_resolution=__A			,			torchify=__A		)
								for image in image_inputs:
										self.assertIsInstance(__A			,			torch.Tensor		)

								# Test not batched input
								A					       = image_processing(image_inputs[0]			,			return_tensors='pt'		).pixel_values
								self.assertEqual(
								    encoded_images.shape			,			(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,			)

								# Test batched
								A					       = image_processing(__A			,			return_tensors='pt'		).pixel_values
								self.assertEqual(
								    encoded_images.shape			,			(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size['height'],
								        self.image_processor_tester.crop_size['width'],
								    )			,			)









"""simple docstring"""


import os
import unittest

from transformers.models.transfo_xl.tokenization_transfo_xl import VOCAB_FILES_NAMES, TransfoXLTokenizer

from ...test_tokenization_common import TokenizerTesterMixin



class 			lowerCamelCase__						(   UpperCAmelCase_							,    unittest.TestCase			):
						lowerCAmelCase       =     TransfoXLTokenizer
						lowerCAmelCase       =     False
						lowerCAmelCase       =     False







						def __a						(							self   :    Union[str, Any]		):
								super().setUp()

								A					       = [
								    '<unk>',
								    '[CLS]',
								    '[SEP]',
								    'want',
								    'unwanted',
								    'wa',
								    'un',
								    'running',
								    ',',
								    'low',
								    'l',
								]
								A					       = os.path.join(self.tmpdirname			,			VOCAB_FILES_NAMES['vocab_file']		)
								with open(self.vocab_file			,			'w'			,			encoding='utf-8'		) as vocab_writer:
										vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]		)		)







						def __a						(							self   :    int			,			**_lowercase   :    List[str]		):
								A					       = True
								return TransfoXLTokenizer.from_pretrained(self.tmpdirname			,			**_lowercase		)







						def __a						(							self   :    int			,			_lowercase   :    List[Any]		):
								A					       = '<unk> UNwanted , running'
								A					       = '<unk> unwanted, running'
								return input_text, output_text







						def __a						(							self   :    List[str]		):
								A					       = TransfoXLTokenizer(vocab_file=self.vocab_file			,			lower_case=_lowercase		)

								A					       = tokenizer.tokenize('<unk> UNwanted , running'		)
								self.assertListEqual(_lowercase			,			['<unk>', 'unwanted', ',', 'running']		)

								self.assertListEqual(tokenizer.convert_tokens_to_ids(_lowercase		)			,			[0, 4, 8, 7]		)







						def __a						(							self   :    int		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '		)			,			['hello', '!', 'how', 'are', 'you', '?']		)







						def __a						(							self   :    Optional[Any]		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)

								self.assertListEqual(
								    tokenizer.tokenize(' \tHeLLo ! how  \n Are yoU ?  '		)			,			['HeLLo', '!', 'how', 'Are', 'yoU', '?']		)







						def __a						(							self   :    str		):
								A					       = TransfoXLTokenizer(lower_case=_lowercase		)
								A					       = 'Hello (bracket) and side-scrolled [and] Henry\'s $5,000 with 3.34 m. What\'s up!?'
								A					       = [
								    'Hello',
								    '(',
								    'bracket',
								    ')',
								    'and',
								    'side',
								    '@-@',
								    'scrolled',
								    '[',
								    'and',
								    ']',
								    'Henry',
								    '\'s',
								    '$',
								    '5',
								    '@,@',
								    '000',
								    'with',
								    '3',
								    '@.@',
								    '34',
								    'm',
								    '.',
								    'What',
								    '\'s',
								    'up',
								    '!',
								    '?',
								]

								self.assertListEqual(tokenizer.tokenize(_lowercase		)			,			_lowercase		)

								self.assertEqual(tokenizer.convert_tokens_to_string(_lowercase		)			,			_lowercase		)







						def __a						(							self   :    Dict		):
								A					       = self.get_tokenizer()
								A					       = len(_lowercase		)

								tokenizer.add_tokens(['new1', 'new2']		)
								tokenizer.move_added_token('new1'			,			1		)

								# Check that moved token is not copied (duplicate)
								self.assertEqual(len(_lowercase		)			,			original_len + 2		)
								# Check that token is moved to specified id
								self.assertEqual(tokenizer.encode('new1'		)			,			[1]		)
								self.assertEqual(tokenizer.decode([1]		)			,			'new1'		)





