





'''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 DeiTImageProcessor



class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		def __init__(self    :   Union[str, Any]   ,   _lowerCAmelCase    :   str   ,   _lowerCAmelCase    :   str=7   ,   _lowerCAmelCase    :   int=3   ,   _lowerCAmelCase    :   str=18   ,   _lowerCAmelCase    :   Any=30   ,   _lowerCAmelCase    :   List[Any]=400   ,   _lowerCAmelCase    :   Dict=True   ,   _lowerCAmelCase    :   Optional[Any]=None   ,   _lowerCAmelCase    :   str=True   ,   _lowerCAmelCase    :   List[Any]=None   ,   _lowerCAmelCase    :   Any=True   ,   _lowerCAmelCase    :   List[str]=[0.5, 0.5, 0.5]   ,   _lowerCAmelCase    :   List[Any]=[0.5, 0.5, 0.5]   ,   ):
			A					=   size if size is not None else {"""height""": 20, """width""": 20}
			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    :   Union[str, Any]   ):
			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,
			}






@require_torch
@require_vision
class 				__UpperCAmelCase	(							A__		,       unittest.TestCase    ):







		'''simple docstring'''







		__lowerCAmelCase								=     DeiTImageProcessor if is_vision_available() else None
		__lowerCAmelCase								=     True







		def 							A  (self    :   List[str]   ):
			A					=   DeiTImageProcessingTester(self   )







		@property
		def 							A  (self    :   int   ):
			return self.image_processor_tester.prepare_image_processor_dict()







		def 							A  (self    :   int   ):
			A					=   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 							A  (self    :   List[str]   ):
			A					=   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}   )

			A					=   self.image_processing_class.from_dict(self.image_processor_dict   ,   size=42   ,   crop_size=84   )
			self.assertEqual(image_processor.size   ,   {"""height""": 42, """width""": 42}   )
			self.assertEqual(image_processor.crop_size   ,   {"""height""": 84, """width""": 84}   )







		def 							A  (self    :   Any   ):
			pass







		def 							A  (self    :   List[str]   ):
			# 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=_lowerCAmelCase   )
			for image in image_inputs:
				self.assertIsInstance(_lowerCAmelCase   ,   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(_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 							A  (self    :   int   ):
			# 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=_lowerCAmelCase   ,   numpify=_lowerCAmelCase   )
			for image in image_inputs:
				self.assertIsInstance(_lowerCAmelCase   ,   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(_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 							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=_lowerCAmelCase   ,   torchify=_lowerCAmelCase   )
			for image in image_inputs:
				self.assertIsInstance(_lowerCAmelCase   ,   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(_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"""],
			    )   ,   )








'''simple docstring'''


import sys

from transformers import AutoTokenizer


_lowerCamelCase					:						Any					     =							sys.argv[1]
_lowerCamelCase					:						Union[str, Any]					     =							sys.argv[2]
_lowerCamelCase					:						Optional[Any]					     =							int(sys.argv[3])

_lowerCamelCase					:						Union[str, Any]					     =							0

_lowerCamelCase					:						Tuple					     =							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:
								_lowerCamelCase					:						Optional[int]					     =							line.rstrip()

								if not line:
												print(line)
												_lowerCamelCase					:						Optional[int]					     =							0
												continue

								_lowerCamelCase					:						int					     =							line.split()[0]

								_lowerCamelCase					:						Union[str, Any]					     =							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)
												_lowerCamelCase					:						Optional[int]					     =							current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

