
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     lowercase (		unittest.TestCase       ):



		def __init__(			self    ,				A_    ,				A_=7    ,				A_=3    ,				A_=18    ,				A_=30    ,				A_=400    ,				A_=True    ,				A_=None    ,				A_=True    ,				A_=None    ,				A_=True    ,				A_=[0.5, 0.5, 0.5]    ,				A_=[0.5, 0.5, 0.5]    ,				)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					size if size is not None else {'shortest_edge': 18}
				UpperCamelCase          =					crop_size if crop_size is not None else {'height': 18, 'width': 18}
				UpperCamelCase          =					parent
				UpperCamelCase          =					batch_size
				UpperCamelCase          =					num_channels
				UpperCamelCase          =					image_size
				UpperCamelCase          =					min_resolution
				UpperCamelCase          =					max_resolution
				UpperCamelCase          =					do_resize
				UpperCamelCase          =					size
				UpperCamelCase          =					do_center_crop
				UpperCamelCase          =					crop_size
				UpperCamelCase          =					do_normalize
				UpperCamelCase          =					image_mean
				UpperCamelCase          =					image_std



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				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     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Tuple     					=    LevitImageProcessor if is_vision_available() else None



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				UpperCamelCase          =					LevitImageProcessingTester(self	)



		@property
		def __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				return self.image_processor_tester.prepare_image_processor_dict()



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					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}	)

				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				pass



		def __UpperCamelCase			(			self	)  -> Union[str, Any]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase          =					self.image_processing_class(**self.image_processor_dict	)
				# create random PIL images
				UpperCamelCase          =					prepare_image_inputs(self.image_processor_tester    ,				equal_resolution=A_	)
				for image in image_inputs:
						self.assertIsInstance(A_    ,				Image.Image	)

				# Test not batched input
				UpperCamelCase          =					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
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase          =					self.image_processing_class(**self.image_processor_dict	)
				# create random numpy tensors
				UpperCamelCase          =					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
				UpperCamelCase          =					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
				UpperCamelCase          =					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 __UpperCamelCase			(			self	)  -> List[str]:
				"""simple docstring"""
				# Initialize image_processing
				UpperCamelCase          =					self.image_processing_class(**self.image_processor_dict	)
				# create random PyTorch tensors
				UpperCamelCase          =					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
				UpperCamelCase          =					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
				UpperCamelCase          =					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'],
				    )    ,				)


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 :			Union[str, Any]              =	get_tests_dir("fixtures/test_sentencepiece_with_bytefallback.model")


@require_sentencepiece
@require_tokenizers
class     lowercase (		_SCREAMING_SNAKE_CASE  ,       unittest.TestCase       ):
		__lowercase	:	Optional[Any]     					=    GPTSwaTokenizer
		__lowercase	:	Optional[Any]     					=    False
		__lowercase	:	Union[str, Any]     					=    True
		__lowercase	:	Tuple     					=    False



		def __UpperCamelCase			(			self	)  -> Optional[int]:
				"""simple docstring"""
				super().setUp()

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

				tokenizer.save_pretrained(self.tmpdirname	)



		def __UpperCamelCase			(			self    ,				A_	)  -> List[str]:
				"""simple docstring"""
				UpperCamelCase          =					'This is a test'
				UpperCamelCase          =					'This is a test'
				return input_text, output_text



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					'<s>'
				UpperCamelCase          =					1

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



		def __UpperCamelCase			(			self	)  -> Dict:
				"""simple docstring"""
				UpperCamelCase          =					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(A_	)    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> Any:
				"""simple docstring"""
				self.assertEqual(self.get_tokenizer().vocab_size    ,				2_000	)



		def __UpperCamelCase			(			self	)  -> int:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)

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

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

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

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

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



		def __UpperCamelCase			(			self	)  -> Tuple:
				"""simple docstring"""
				UpperCamelCase          =					GPTSwaTokenizer(A_	)
				UpperCamelCase          =					['This is a test', 'I was born in 92000, and this is falsé.']
				UpperCamelCase          =					[
				    [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(A_    ,				A_	):
						self.assertListEqual(tokenizer.encode_fast(A_	)    ,				A_	)

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



		@slow
		def __UpperCamelCase			(			self	)  -> List[Any]:
				"""simple docstring"""
				UpperCamelCase          =					[
				    '<|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
				UpperCamelCase          =					{'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=A_    ,				model_name='AI-Sweden/gpt-sw3-126m'    ,				sequences=A_    ,				)
