




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     __SCREAMING_SNAKE_CASE							(		unittest.TestCase  ):

					def __init__(    self			,	__lowercase			,	__lowercase=7			,	__lowercase=3			,	__lowercase=18			,	__lowercase=30			,	__lowercase=400			,	__lowercase=True			,	__lowercase=None			,	__lowercase=2			,	__lowercase=True			,	__lowercase=[0.5, 0.5, 0.5]			,	__lowercase=[0.5, 0.5, 0.5]			,	):







						'''simple docstring'''




						lowerCamelCase_							:		Tuple							       =							size if size is not None else {'shortest_edge': 30}
						lowerCamelCase_							:		Tuple							       =							parent
						lowerCamelCase_							:		List[Any]							       =							batch_size
						lowerCamelCase_							:		str							       =							num_channels
						lowerCamelCase_							:		List[str]							       =							image_size
						lowerCamelCase_							:		Tuple							       =							min_resolution
						lowerCamelCase_							:		List[str]							       =							max_resolution
						lowerCamelCase_							:		Optional[Any]							       =							do_resize
						lowerCamelCase_							:		Optional[Any]							       =							size
						lowerCamelCase_							:		str							       =							size_divisor
						lowerCamelCase_							:		Optional[Any]							       =							do_normalize
						lowerCamelCase_							:		Any							       =							image_mean
						lowerCamelCase_							:		Any							       =							image_std

					def      lowercase_							(    self ):







						'''simple docstring'''




						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      lowercase_							(    self			,	__lowercase			,	__lowercase=False ):







						'''simple docstring'''




						if not batched:
							lowerCamelCase_							:		str							       =							self.size['shortest_edge']
							lowerCamelCase_							:		List[str]							       =							image_inputs[0]
							if isinstance(__lowercase			,	Image.Image ):
								lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							image.size
							else:
								lowerCamelCase_       ,      lowerCamelCase_							:		Optional[Any]							       =							image.shape[1], image.shape[2]
							lowerCamelCase_							:		List[Any]							       =							size / min(__lowercase			,	__lowercase )
							if h < w:
								lowerCamelCase_       ,      lowerCamelCase_							:		List[Any]							       =							size, scale * w
							else:
								lowerCamelCase_       ,      lowerCamelCase_							:		Dict							       =							scale * h, size

							lowerCamelCase_							:		Union[str, Any]							       =							int((1_333 / 800) * size )
							if max(__lowercase			,	__lowercase ) > max_size:
								lowerCamelCase_							:		Optional[int]							       =							max_size / max(__lowercase			,	__lowercase )
								lowerCamelCase_							:		int							       =							newh * scale
								lowerCamelCase_							:		List[str]							       =							neww * scale

							lowerCamelCase_       ,      lowerCamelCase_							:		str							       =							int(newh + 0.5 ), int(neww + 0.5 )
							lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							(
							    newh // self.size_divisor * self.size_divisor,
							    neww // self.size_divisor * self.size_divisor,
							)

						else:
							lowerCamelCase_							:		List[Any]							       =							[]
							for image in image_inputs:
								lowerCamelCase_       ,      lowerCamelCase_							:		List[str]							       =							self.get_expected_values([image] )
								expected_values.append((expected_height, expected_width) )
							lowerCamelCase_							:		List[str]							       =							max(__lowercase			,	key=lambda __lowercase : item[0] )[0]
							lowerCamelCase_							:		Optional[int]							       =							max(__lowercase			,	key=lambda __lowercase : item[1] )[1]

						return expected_height, expected_width


@require_torch
@require_vision
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			ViltImageProcessor if is_vision_available() else None

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[int]							       =							ViltImageProcessingTester(self )

					@property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return self.image_processor_tester.prepare_image_processor_dict()

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[Any]							       =							self.image_processing_class(**self.image_processor_dict )
						self.assertTrue(hasattr(__lowercase			,	'image_mean' ) )
						self.assertTrue(hasattr(__lowercase			,	'image_std' ) )
						self.assertTrue(hasattr(__lowercase			,	'do_normalize' ) )
						self.assertTrue(hasattr(__lowercase			,	'do_resize' ) )
						self.assertTrue(hasattr(__lowercase			,	'size' ) )
						self.assertTrue(hasattr(__lowercase			,	'size_divisor' ) )

					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Union[str, Any]							       =							self.image_processing_class.from_dict(self.image_processor_dict )
						self.assertEqual(image_processor.size			,	{'shortest_edge': 30} )

						lowerCamelCase_							:		Dict							       =							self.image_processing_class.from_dict(self.image_processor_dict			,	size=42 )
						self.assertEqual(image_processor.size			,	{'shortest_edge': 42} )

					def      lowercase_							(    self ):







						'''simple docstring'''




						pass

					def      lowercase_							(    self ):







						'''simple docstring'''




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

						# Test not batched input
						lowerCamelCase_							:		int							       =							image_processing(image_inputs[0]			,	return_tensors='pt' ).pixel_values

						lowerCamelCase_       ,      lowerCamelCase_							:		List[Any]							       =							self.image_processor_tester.get_expected_values(__lowercase )
						self.assertEqual(
						    encoded_images.shape			,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,	)

						# Test batched
						lowerCamelCase_							:		List[Any]							       =							image_processing(__lowercase			,	return_tensors='pt' ).pixel_values

						lowerCamelCase_       ,      lowerCamelCase_							:		List[str]							       =							self.image_processor_tester.get_expected_values(__lowercase			,	batched=__lowercase )
						self.assertEqual(
						    encoded_images.shape			,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,	)

					def      lowercase_							(    self ):







						'''simple docstring'''




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

						# Test not batched input
						lowerCamelCase_							:		List[Any]							       =							image_processing(image_inputs[0]			,	return_tensors='pt' ).pixel_values

						lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							self.image_processor_tester.get_expected_values(__lowercase )
						self.assertEqual(
						    encoded_images.shape			,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,	)

						# Test batched
						lowerCamelCase_							:		Optional[int]							       =							image_processing(__lowercase			,	return_tensors='pt' ).pixel_values

						lowerCamelCase_       ,      lowerCamelCase_							:		List[str]							       =							self.image_processor_tester.get_expected_values(__lowercase			,	batched=__lowercase )
						self.assertEqual(
						    encoded_images.shape			,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,	)





					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Optional[Any]							       =							self.image_processing_class(**self.image_processor_dict )
						# create random PyTorch tensors
						lowerCamelCase_							:		List[Any]							       =							prepare_image_inputs(self.image_processor_tester			,	equal_resolution=__lowercase			,	torchify=__lowercase )
						for image in image_inputs:
							self.assertIsInstance(__lowercase			,	torch.Tensor )

						# Test not batched input
						lowerCamelCase_							:		Union[str, Any]							       =							image_processing(image_inputs[0]			,	return_tensors='pt' ).pixel_values

						lowerCamelCase_       ,      lowerCamelCase_							:		Union[str, Any]							       =							self.image_processor_tester.get_expected_values(__lowercase )
						self.assertEqual(
						    encoded_images.shape			,	(1, self.image_processor_tester.num_channels, expected_height, expected_width)			,	)

						# Test batched
						lowerCamelCase_							:		Any							       =							image_processing(__lowercase			,	return_tensors='pt' ).pixel_values

						lowerCamelCase_       ,      lowerCamelCase_							:		Any							       =							self.image_processor_tester.get_expected_values(__lowercase			,	batched=__lowercase )
						self.assertEqual(
						    encoded_images.shape			,	(
						        self.image_processor_tester.batch_size,
						        self.image_processor_tester.num_channels,
						        expected_height,
						        expected_width,
						    )			,	)






import sys

from transformers import AutoTokenizer


UpperCamelCase__		=						sys.argv[1]
UpperCamelCase__		=						sys.argv[2]
UpperCamelCase__		=						int(sys.argv[3])

UpperCamelCase__		=						0

UpperCamelCase__		=						AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, 'rt') as f_p:
							for line in f_p:
														UpperCamelCase__		=						line.rstrip()

														if not line:
																					print(line)
																					UpperCamelCase__		=						0
																					continue

														UpperCamelCase__		=						line.split()[0]

														UpperCamelCase__		=						len(tokenizer.tokenize(token))

														# Token contains strange control characters like \x96 or \x95
														# Just filter out the complete line
														if current_subwords_len == 0:
																					continue

														if (subword_len_counter + current_subwords_len) > max_len:
																					print('')
																					print(line)
																					UpperCamelCase__		=						current_subwords_len
																					continue

														subword_len_counter += current_subwords_len

														print(line)
