




import unittest

import numpy as np
from datasets import load_dataset

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 BeitImageProcessor







class a (     unittest.TestCase  ):
						"""simple docstring"""

						def __init__( self :     Union[str, Any]				,							lowerCamelCase__ :     int				,							lowerCamelCase__ :     int=7				,							lowerCamelCase__ :     Optional[Any]=3				,							lowerCamelCase__ :     str=18				,							lowerCamelCase__ :     Union[str, Any]=30				,							lowerCamelCase__ :     int=400				,							lowerCamelCase__ :     str=True				,							lowerCamelCase__ :     str=None				,							lowerCamelCase__ :     List[str]=True				,							lowerCamelCase__ :     Tuple=None				,							lowerCamelCase__ :     Tuple=True				,							lowerCamelCase__ :     List[str]=[0.5, 0.5, 0.5]				,							lowerCamelCase__ :     Optional[Any]=[0.5, 0.5, 0.5]				,							lowerCamelCase__ :     List[str]=False				,							)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    size if size is not None else {'''height''': 20, '''width''': 20}
													__lowercase      =    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
													__lowercase      =    parent
													__lowercase      =    batch_size
													__lowercase      =    num_channels
													__lowercase      =    image_size
													__lowercase      =    min_resolution
													__lowercase      =    max_resolution
													__lowercase      =    do_resize
													__lowercase      =    size
													__lowercase      =    do_center_crop
													__lowercase      =    crop_size
													__lowercase      =    do_normalize
													__lowercase      =    image_mean
													__lowercase      =    image_std
													__lowercase      =    do_reduce_labels






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[Any]:
													"""simple docstring"""


													return {
													    "do_resize": self.do_resize,
													    "size": self.size,
													    "do_center_crop": self.do_center_crop,
													    "crop_size": self.crop_size,
													    "do_normalize": self.do_normalize,
													    "image_mean": self.image_mean,
													    "image_std": self.image_std,
													    "do_reduce_labels": self.do_reduce_labels,
													}







def 		_A(       )    -> List[str]:
							'''simple docstring'''


							__lowercase      =    load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''				)

							__lowercase      =    Image.open(dataset[0]['''file''']				)
							__lowercase      =    Image.open(dataset[1]['''file''']				)

							return image, map







def 		_A(       )    -> int:
							'''simple docstring'''


							__lowercase      =    load_dataset('''hf-internal-testing/fixtures_ade20k'''				,       split='''test'''				)

							__lowercase      =    Image.open(ds[0]['''file''']				)
							__lowercase      =    Image.open(ds[1]['''file''']				)
							__lowercase      =    Image.open(ds[2]['''file''']				)
							__lowercase      =    Image.open(ds[3]['''file''']				)

							return [imagea, imagea], [mapa, mapa]







@require_torch
@require_vision
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[Any]       	=		BeitImageProcessor if is_vision_available() else None






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    BeitImageProcessingTester(self				)






						@property
						def     UpperCAmelCase_      ( self :     Any				)    ->						str:
													"""simple docstring"""


													return self.image_processor_tester.prepare_image_processor_dict()






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						str:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''do_resize'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''size'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''do_center_crop'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''center_crop'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''do_normalize'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''image_mean'''				)				)
													self.assertTrue(hasattr(lowerCamelCase__				,							'''image_std'''				)				)






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						int:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class.from_dict(self.image_processor_dict				)
													self.assertEqual(image_processor.size				,							{'''height''': 20, '''width''': 20}				)
													self.assertEqual(image_processor.crop_size				,							{'''height''': 18, '''width''': 18}				)
													self.assertEqual(image_processor.do_reduce_labels				,							lowerCamelCase__				)

													__lowercase      =    self.image_processing_class.from_dict(
													    self.image_processor_dict				,							size=42				,							crop_size=84				,							reduce_labels=lowerCamelCase__				)
													self.assertEqual(image_processor.size				,							{'''height''': 42, '''width''': 42}				)
													self.assertEqual(image_processor.crop_size				,							{'''height''': 84, '''width''': 84}				)
													self.assertEqual(image_processor.do_reduce_labels				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						List[str]:
													"""simple docstring"""


													pass






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						List[Any]:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)
													# create random PIL images
													__lowercase      =    prepare_image_inputs(self.image_processor_tester				,							equal_resolution=lowerCamelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCamelCase__				,							Image.Image				)

													# Test not batched input
													__lowercase      =    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
													__lowercase      =    image_processing(lowerCamelCase__				,							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     UpperCAmelCase_      ( self :     List[Any]				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)
													# create random numpy tensors
													__lowercase      =    prepare_image_inputs(self.image_processor_tester				,							equal_resolution=lowerCamelCase__				,							numpify=lowerCamelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCamelCase__				,							np.ndarray				)

													# Test not batched input
													__lowercase      =    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
													__lowercase      =    image_processing(lowerCamelCase__				,							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     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													__lowercase      =    prepare_image_inputs(self.image_processor_tester				,							equal_resolution=lowerCamelCase__				,							torchify=lowerCamelCase__				)
													for image in image_inputs:
																				self.assertIsInstance(lowerCamelCase__				,							torch.Tensor				)

													# Test not batched input
													__lowercase      =    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
													__lowercase      =    image_processing(lowerCamelCase__				,							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     UpperCAmelCase_      ( self :     Union[str, Any]				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)
													# create random PyTorch tensors
													__lowercase      =    prepare_image_inputs(self.image_processor_tester				,							equal_resolution=lowerCamelCase__				,							torchify=lowerCamelCase__				)
													__lowercase      =    []
													for image in image_inputs:
																				self.assertIsInstance(lowerCamelCase__				,							torch.Tensor				)
																				maps.append(torch.zeros(image.shape[-2:]				).long()				)

													# Test not batched input
													__lowercase      =    image_processing(image_inputs[0]				,							maps[0]				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].shape				,							(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        1,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)

													# Test batched
													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].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'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        self.image_processor_tester.batch_size,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)

													# Test not batched input (PIL images)
													__lowercase     ,     __lowercase      =    prepare_semantic_single_inputs()

													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].shape				,							(
													        1,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        1,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)

													# Test batched input (PIL images)
													__lowercase     ,     __lowercase      =    prepare_semantic_batch_inputs()

													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertEqual(
													    encoding['''pixel_values'''].shape				,							(
													        2,
													        self.image_processor_tester.num_channels,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(
													    encoding['''labels'''].shape				,							(
													        2,
													        self.image_processor_tester.crop_size['''height'''],
													        self.image_processor_tester.crop_size['''width'''],
													    )				,							)
													self.assertEqual(encoding['''labels'''].dtype				,							torch.long				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)






						def     UpperCAmelCase_      ( self :     List[Any]				)    ->						Any:
													"""simple docstring"""


													__lowercase      =    self.image_processing_class(**self.image_processor_dict				)

													# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
													__lowercase     ,     __lowercase      =    prepare_semantic_single_inputs()
													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 150				)

													__lowercase      =    True
													__lowercase      =    image_processing(lowerCamelCase__				,							lowerCamelCase__				,							return_tensors='''pt'''				)
													self.assertTrue(encoding['''labels'''].min().item() >= 0				)
													self.assertTrue(encoding['''labels'''].max().item() <= 255				)












import unittest

from transformers import GPTSwaTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin


UpperCAmelCase__				     = get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")







@require_sentencepiece
@require_tokenizers
class a (     __SCREAMING_SNAKE_CASE       ,						unittest.TestCase  ):
						"""simple docstring"""

						UpperCamelCase_	:       List[str]       	=		GPTSwaTokenizer
						UpperCamelCase_	:       Union[str, Any]       	=		False
						UpperCamelCase_	:       Union[str, Any]       	=		True
						UpperCamelCase_	:       Optional[Any]       	=		False






						def     UpperCAmelCase_      ( self :     int				)    ->						Optional[int]:
													"""simple docstring"""


													super().setUp()

													# We have a SentencePiece fixture for testing
													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				,							eos_token='''<unk>'''				,							bos_token='''<unk>'''				,							pad_token='''<unk>'''				)

													tokenizer.save_pretrained(self.tmpdirname				)






						def     UpperCAmelCase_      ( self :     int				,							lowerCamelCase__ :     Dict				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    '''This is a test'''
													__lowercase      =    '''This is a test'''
													return input_text, output_text






						def     UpperCAmelCase_      ( self :     Optional[Any]				)    ->						Union[str, Any]:
													"""simple docstring"""


													__lowercase      =    '''<s>'''
													__lowercase      =    1

													self.assertEqual(self.get_tokenizer()._convert_token_to_id(lowerCamelCase__				)				,							lowerCamelCase__				)
													self.assertEqual(self.get_tokenizer()._convert_id_to_token(lowerCamelCase__				)				,							lowerCamelCase__				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						Optional[int]:
													"""simple docstring"""


													__lowercase      =    list(self.get_tokenizer().get_vocab().keys()				)

													self.assertEqual(vocab_keys[0]				,							'''<unk>'''				)
													self.assertEqual(vocab_keys[1]				,							'''<s>'''				)
													self.assertEqual(vocab_keys[-1]				,							'''j'''				)
													self.assertEqual(len(lowerCamelCase__				)				,							2_000				)






						def     UpperCAmelCase_      ( self :     Dict				)    ->						str:
													"""simple docstring"""


													self.assertEqual(self.get_tokenizer().vocab_size				,							2_000				)






						def     UpperCAmelCase_      ( self :     List[str]				)    ->						Dict:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)

													__lowercase      =    tokenizer.tokenize('''This is a test'''				)
													self.assertListEqual(lowerCamelCase__				,							['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est''']				)

													self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCamelCase__				)				,							[465, 287, 265, 631, 842]				)

													__lowercase      =    tokenizer.tokenize('''I was born in 92000, and this is falsé.'''				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				,							)
													# fmt: on

													__lowercase      =    tokenizer.convert_tokens_to_ids(lowerCamelCase__				)
													self.assertListEqual(
													    lowerCamelCase__				,							[262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260]				,							)

													__lowercase      =    tokenizer.convert_ids_to_tokens(lowerCamelCase__				)
													# fmt: off
													self.assertListEqual(
													    lowerCamelCase__				,							['''▁I''', '''▁was''', '''▁bor''', '''n''', '''▁in''', '''▁''', '''<0x39>''', '''2''', '''0''', '''0''', '''0''', ''',''', '''▁and''', '''▁this''', '''▁is''', '''▁f''', '''al''', '''s''', '''<0xC3>''', '''<0xA9>''', '''.''']				)
													# fmt: on






						def     UpperCAmelCase_      ( self :     Tuple				)    ->						Tuple:
													"""simple docstring"""


													__lowercase      =    GPTSwaTokenizer(lowerCamelCase__				)
													__lowercase      =    ['''This is a test''', '''I was born in 92000, and this is falsé.''']
													__lowercase      =    [
													    [465, 287, 265, 631, 842],
													    [262, 272, 1_525, 286, 271, 268, 60, 916, 633, 633, 633, 259, 266, 301, 287, 384, 367, 263, 198, 172, 260],
													]

													# Test that encode_fast returns the same as tokenize + convert_tokens_to_ids
													for text, expected_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertListEqual(tokenizer.encode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)

													# Test that decode_fast returns the input text
													for text, token_ids in zip(lowerCamelCase__				,							lowerCamelCase__				):
																				self.assertEqual(tokenizer.decode_fast(lowerCamelCase__				)				,							lowerCamelCase__				)






						@slow
						def     UpperCAmelCase_      ( self :     List[str]				)    ->						int:
													"""simple docstring"""


													__lowercase      =    [
													    '''<|python|>def fibonacci(n)\n    if n < 0:\n        print(\'Incorrect input\')''',
													    '''Hey there, how are you doing this fine day?''',
													    '''This is a text with a trailing spaces followed by a dot     .''',
													    '''Häj sväjs lillebrör! =)''',
													    '''Det är inget fel på Mr. Cool''',
													]

													# fmt: off
													__lowercase      =    {'''input_ids''': [[63_423, 5, 6_811, 14_954, 282, 816, 3_821, 63_466, 63_425, 63_462, 18, 63_978, 678, 301, 1_320, 63_423, 63_455, 63_458, 18, 63_982, 4_246, 3_940, 1_901, 47_789, 5_547, 18_994], [19_630, 1_100, 63_446, 1_342, 633, 544, 4_488, 593, 5_102, 2_416, 63_495, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1_652, 428, 268, 1_936, 515, 268, 58_593, 22_413, 9_106, 546, 268, 33_213, 63_979, 698, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [55_130, 63_450, 924, 63_449, 2_249, 4_062, 1_558, 318, 63_504, 21_498, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [509, 377, 2_827, 2_559, 332, 6_575, 63_443, 26_801, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''token_type_ids''': [[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}
													# fmt: on
													self.tokenizer_integration_test_util(
													    expected_encoding=lowerCamelCase__				,							model_name='''AI-Sweden/gpt-sw3-126m'''				,							sequences=lowerCamelCase__				,							)






