





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 EfficientNetImageProcessor
class 							snake_case_		(      unittest.TestCase):







		def __init__(		self      ,  __lowercase      ,  __lowercase=1_3      ,  __lowercase=3      ,  __lowercase=1_8      ,  __lowercase=3_0      ,  __lowercase=4_0_0      ,  __lowercase=True      ,  __lowercase=None      ,  __lowercase=True      ,  __lowercase=[0.5, 0.5, 0.5]      ,  __lowercase=[0.5, 0.5, 0.5]      ,  )					-> Optional[int]:
				lowerCamelCase :			int    		=size if size is not None else {'''height''': 1_8, '''width''': 1_8}
				lowerCamelCase :			Dict    		=parent
				lowerCamelCase :			List[str]    		=batch_size
				lowerCamelCase :			Dict    		=num_channels
				lowerCamelCase :			int    		=image_size
				lowerCamelCase :			Any    		=min_resolution
				lowerCamelCase :			Optional[Any]    		=max_resolution
				lowerCamelCase :			Any    		=do_resize
				lowerCamelCase :			Optional[Any]    		=size
				lowerCamelCase :			str    		=do_normalize
				lowerCamelCase :			Dict    		=image_mean
				lowerCamelCase :			Union[str, Any]    		=image_std







		def       __lowercase (		self )					-> Any:
				return {
				    "image_mean": self.image_mean,
				    "image_std": self.image_std,
				    "do_normalize": self.do_normalize,
				    "do_resize": self.do_resize,
				    "size": self.size,
				}

@require_torch
@require_vision
class 							snake_case_		(      _A    ,       unittest.TestCase):
		lowerCamelCase     :int							=  EfficientNetImageProcessor if is_vision_available() else None







		def       __lowercase (		self )					-> str:
				lowerCamelCase :			Any    		=EfficientNetImageProcessorTester(self )







		@property
		def       __lowercase (		self )					-> Union[str, Any]:
				return self.image_processor_tester.prepare_image_processor_dict()







		def       __lowercase (		self )					-> int:
				lowerCamelCase :			int    		=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''' ) )







		def       __lowercase (		self )					-> Tuple:
				lowerCamelCase :			Optional[Any]    		=self.image_processing_class.from_dict(self.image_processor_dict )
				self.assertEqual(image_processor.size      ,  {'''height''': 1_8, '''width''': 1_8} )

				lowerCamelCase :			Any    		=self.image_processing_class.from_dict(self.image_processor_dict      ,  size=4_2 )
				self.assertEqual(image_processor.size      ,  {'''height''': 4_2, '''width''': 4_2} )







		def       __lowercase (		self )					-> List[Any]:
				# Initialize image_processing
				lowerCamelCase :			Optional[Any]    		=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 :			Optional[Any]    		=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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )      ,  )

				# Test batched
				lowerCamelCase :			Optional[int]    		=image_processing(__lowercase      ,  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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )      ,  )







		def       __lowercase (		self )					-> Optional[Any]:
				# Initialize image_processing
				lowerCamelCase :			Optional[int]    		=self.image_processing_class(**self.image_processor_dict )
				# create random numpy tensors
				lowerCamelCase :			Tuple    		=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 :			int    		=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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )      ,  )

				# Test batched
				lowerCamelCase :			Optional[Any]    		=image_processing(__lowercase      ,  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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )      ,  )







		def       __lowercase (		self )					-> Any:
				# Initialize image_processing
				lowerCamelCase :			Any    		=self.image_processing_class(**self.image_processor_dict )
				# create random PyTorch tensors
				lowerCamelCase :			Dict    		=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 :			Optional[Any]    		=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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )      ,  )

				# Test batched
				lowerCamelCase :			int    		=image_processing(__lowercase      ,  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.size['''height'''],
				        self.image_processor_tester.size['''width'''],
				    )      ,  )








import sys

from transformers import AutoTokenizer


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

snake_case_				=      0

snake_case_				=      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:
								snake_case_				=      line.rstrip()

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

								snake_case_				=      line.split()[0]

								snake_case_				=      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)
												snake_case_				=      current_subwords_len
												continue

								subword_len_counter += current_subwords_len

								print(line)

