



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 ViltImageProcessor

class 			__snake_case   ( unittest.TestCase):







							'''simple docstring'''






							def __init__(				self	,	a_	,	a_=7	,	a_=3	,	a_=18	,	a_=30	,	a_=400	,	a_=True	,	a_=None	,	a_=2	,	a_=True	,	a_=[0.5, 0.5, 0.5]	,	a_=[0.5, 0.5, 0.5]	,	):
											a__					   =		size if size is not None else {"""shortest_edge""": 30}
											a__					   =		parent
											a__					   =		batch_size
											a__					   =		num_channels
											a__					   =		image_size
											a__					   =		min_resolution
											a__					   =		max_resolution
											a__					   =		do_resize
											a__					   =		size
											a__					   =		size_divisor
											a__					   =		do_normalize
											a__					   =		image_mean
											a__					   =		image_std


							def _a   (				self		):
											return {
											    "image_mean": self.image_mean,
											    "image_std": self.image_std,
											    "do_normalize": self.do_normalize,
											    "do_resize": self.do_resize,
											    "size": self.size,
											    "size_divisor": self.size_divisor,
											}







							def _a   (				self	,	a_	,	a_=False		):
											if not batched:
															a__					   =		self.size["""shortest_edge"""]
															a__					   =		image_inputs[0]
															if isinstance(a_	,	Image.Image		):
																			a__   ,      a__					   =		image.size
															else:
																			a__   ,      a__					   =		image.shape[1], image.shape[2]
															a__					   =		size / min(a_	,	a_		)
															if h < w:
																			a__   ,      a__					   =		size, scale * w
															else:
																			a__   ,      a__					   =		scale * h, size

															a__					   =		int((1_333 / 800) * size		)
															if max(a_	,	a_		) > max_size:
																			a__					   =		max_size / max(a_	,	a_		)
																			a__					   =		newh * scale
																			a__					   =		neww * scale

															a__   ,      a__					   =		int(newh + 0.5		), int(neww + 0.5		)
															a__   ,      a__					   =		(
															    newh // self.size_divisor * self.size_divisor,
															    neww // self.size_divisor * self.size_divisor,
															)

											else:
															a__					   =		[]
															for image in image_inputs:
																			a__   ,      a__					   =		self.get_expected_values([image]		)
																			expected_values.append((expected_height, expected_width)		)
															a__					   =		max(a_	,	key=lambda a_		: item[0]		)[0]
															a__					   =		max(a_	,	key=lambda a_		: item[1]		)[1]

											return expected_height, expected_width




@require_torch
@require_vision
class 			__snake_case   ( SCREAMING_SNAKE_CASE      ,unittest.TestCase):







							'''simple docstring'''






							UpperCamelCase__					:   List[str]       =       ViltImageProcessor if is_vision_available() else None


							def _a   (				self		):
											a__					   =		ViltImageProcessingTester(self		)


							@property
							def _a   (				self		):
											return self.image_processor_tester.prepare_image_processor_dict()


							def _a   (				self		):
											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_	,	"""size"""		)		)
											self.assertTrue(hasattr(a_	,	"""size_divisor"""		)		)


							def _a   (				self		):
											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 30}		)

											a__					   =		self.image_processing_class.from_dict(self.image_processor_dict	,	size=42		)
											self.assertEqual(image_processor.size	,	{"""shortest_edge""": 42}		)


							def _a   (				self		):
											pass


							def _a   (				self		):
											# 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

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)


							def _a   (				self		):
											# 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

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)







							def _a   (				self		):
											# 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

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_		)
											self.assertEqual(
											    encoded_images.shape	,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)	,	)

											# Test batched
											a__					   =		image_processing(a_	,	return_tensors="""pt"""		).pixel_values

											a__   ,      a__					   =		self.image_processor_tester.get_expected_values(a_	,	batched=a_		)
											self.assertEqual(
											    encoded_images.shape	,	(
											        self.image_processor_tester.batch_size,
											        self.image_processor_tester.num_channels,
											        expected_height,
											        expected_width,
											    )	,	)



import json
import os
import unittest

from transformers import HerbertTokenizer, HerbertTokenizerFast
from transformers.models.herbert.tokenization_herbert import VOCAB_FILES_NAMES
from transformers.testing_utils import get_tests_dir, require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin







@require_tokenizers
class  _lowerCamelCase			(lowerCamelCase							,					unittest.TestCase					):
     lowercase__				  =			HerbertTokenizer
     lowercase__				  =			HerbertTokenizerFast
     lowercase__				  =			True

     def 						__lowerCamelCase				(  self    ):
            super().setUp()

            # Use a simpler test file without japanese/chinese characters
            with open(f'''{get_tests_dir()}/fixtures/sample_text_no_unicode.txt'''							,       encoding='utf-8'    ) as f_data:
                   __snake_case						       =   f_data.read().replace('\n\n'							,       '\n'    ).strip()

            __snake_case						       =   [
                '<s>',
                '</s>',
                'l',
                'o',
                'w',
                'e',
                'r',
                's',
                't',
                'i',
                'd',
                'n',
                'w</w>',
                'r</w>',
                't</w>',
                'lo',
                'low',
                'er</w>',
                'low</w>',
                'lowest</w>',
                'newer</w>',
                'wider</w>',
                ',</w>',
                '<unk>',
            ]
            __snake_case						       =   dict(zip(SCREAMING_SNAKE_CASE_							,       range(len(SCREAMING_SNAKE_CASE_    )    )    )    )
            __snake_case						       =   ['l o 123', 'lo w 1456', 'e r</w> 1789', '']

            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['vocab_file']    )
            __snake_case						       =   os.path.join(self.tmpdirname							,       VOCAB_FILES_NAMES['merges_file']    )
            with open(self.vocab_file							,       'w'    ) as fp:
                   fp.write(json.dumps(SCREAMING_SNAKE_CASE_    )    )
            with open(self.merges_file							,       'w'    ) as fp:
                   fp.write('\n'.join(SCREAMING_SNAKE_CASE_    )    )

     def 						__lowerCamelCase				(  self							,       SCREAMING_SNAKE_CASE_    ):
            __snake_case						       =   'lower newer'
            __snake_case						       =   'lower newer'
            return input_text, output_text

     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.tokenizer_class(vocab_file=self.vocab_file							,       merges_file=self.merges_file    )

            __snake_case						       =   'lower'
            __snake_case						       =   ['low', 'er</w>']
            __snake_case						       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokens + ['<unk>']
            __snake_case						       =   [16, 17, 23]
            self.assertListEqual(tokenizer.convert_tokens_to_ids(SCREAMING_SNAKE_CASE_    )							,       SCREAMING_SNAKE_CASE_    )

     def 						__lowerCamelCase				(  self    ):
            if not self.test_rust_tokenizer:
                   return

            __snake_case						       =   self.get_tokenizer()
            __snake_case						       =   self.get_rust_tokenizer()

            __snake_case						       =   'lower,newer'

            __snake_case						       =   tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.tokenize(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokenizer.encode(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   self.get_rust_tokenizer()
            __snake_case						       =   tokenizer.encode(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   rust_tokenizer.encode(SCREAMING_SNAKE_CASE_    )
            self.assertListEqual(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

     @slow
     def 						__lowerCamelCase				(  self    ):
            __snake_case						       =   self.tokenizer_class.from_pretrained('allegro/herbert-base-cased'    )

            __snake_case						       =   tokenizer.encode('konstruowanie sekwencji'							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   tokenizer.encode('konstruowanie wielu sekwencji'							,       add_special_tokens=SCREAMING_SNAKE_CASE_    )

            __snake_case						       =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_    )
            __snake_case						       =   tokenizer.build_inputs_with_special_tokens(SCREAMING_SNAKE_CASE_							,       SCREAMING_SNAKE_CASE_    )

            assert encoded_sentence == [0] + text + [2]
            assert encoded_pair == [0] + text + [2] + text_a + [2]
