





'''simple docstring'''


import shutil
import tempfile
import unittest

import numpy as np
import pytest

from transformers.testing_utils import require_vision
from transformers.utils import is_vision_available


if is_vision_available():
				from PIL import Image

				from transformers import AutoProcessor, BertTokenizer, CLIPImageProcessor, GitProcessor, PreTrainedTokenizerFast



@require_vision
class 				__UpperCAmelCase	(							unittest.TestCase    ):







		'''simple docstring'''







		def 							A  (self    :   Optional[int]   ):
			A					=   tempfile.mkdtemp()

			A					=   CLIPImageProcessor()
			A					=   BertTokenizer.from_pretrained(
			    """hf-internal-testing/tiny-random-BertModel"""   ,   model_input_names=["""input_ids""", """attention_mask"""]   )

			A					=   GitProcessor(_lowerCAmelCase   ,   _lowerCAmelCase   )

			processor.save_pretrained(self.tmpdirname   )







		def 							A  (self    :   List[str]   ,   **_lowerCAmelCase    :   Optional[Any]   ):
			return AutoProcessor.from_pretrained(self.tmpdirname   ,   **_lowerCAmelCase   ).tokenizer







		def 							A  (self    :   Dict   ,   **_lowerCAmelCase    :   Tuple   ):
			return AutoProcessor.from_pretrained(self.tmpdirname   ,   **_lowerCAmelCase   ).image_processor







		def 							A  (self    :   List[Any]   ):
			shutil.rmtree(self.tmpdirname   )







		def 							A  (self    :   Tuple   ):
			A					=   [np.random.randint(255   ,   size=(3, 30, 400)   ,   dtype=np.uinta   )]

			A					=   [Image.fromarray(np.moveaxis(_lowerCAmelCase   ,   0   ,   -1   )   ) for x in image_inputs]

			return image_inputs







		def 							A  (self    :   List[str]   ):
			A					=   GitProcessor(tokenizer=self.get_tokenizer()   ,   image_processor=self.get_image_processor()   )
			processor.save_pretrained(self.tmpdirname   )

			A					=   self.get_tokenizer(bos_token="""(BOS)"""   ,   eos_token="""(EOS)"""   )
			A					=   self.get_image_processor(do_normalize=_lowerCAmelCase   ,   padding_value=1.0   )

			A					=   GitProcessor.from_pretrained(
			    self.tmpdirname   ,   bos_token="""(BOS)"""   ,   eos_token="""(EOS)"""   ,   do_normalize=_lowerCAmelCase   ,   padding_value=1.0   )

			self.assertEqual(processor.tokenizer.get_vocab()   ,   tokenizer_add_kwargs.get_vocab()   )
			self.assertIsInstance(processor.tokenizer   ,   _lowerCAmelCase   )

			self.assertEqual(processor.image_processor.to_json_string()   ,   image_processor_add_kwargs.to_json_string()   )
			self.assertIsInstance(processor.image_processor   ,   _lowerCAmelCase   )







		def 							A  (self    :   List[str]   ):
			A					=   self.get_image_processor()
			A					=   self.get_tokenizer()

			A					=   GitProcessor(tokenizer=_lowerCAmelCase   ,   image_processor=_lowerCAmelCase   )

			A					=   self.prepare_image_inputs()

			A					=   image_processor(_lowerCAmelCase   ,   return_tensors="""np"""   )
			A					=   processor(images=_lowerCAmelCase   ,   return_tensors="""np"""   )

			for key in input_feat_extract.keys():
				self.assertAlmostEqual(input_feat_extract[key].sum()   ,   input_processor[key].sum()   ,   delta=1e-2   )







		def 							A  (self    :   str   ):
			A					=   self.get_image_processor()
			A					=   self.get_tokenizer()

			A					=   GitProcessor(tokenizer=_lowerCAmelCase   ,   image_processor=_lowerCAmelCase   )

			A					=   """lower newer"""

			A					=   processor(text=_lowerCAmelCase   )

			A					=   tokenizer(_lowerCAmelCase   ,   return_token_type_ids=_lowerCAmelCase   )

			for key in encoded_tok.keys():
				self.assertListEqual(encoded_tok[key]   ,   encoded_processor[key]   )







		def 							A  (self    :   Dict   ):
			A					=   self.get_image_processor()
			A					=   self.get_tokenizer()

			A					=   GitProcessor(tokenizer=_lowerCAmelCase   ,   image_processor=_lowerCAmelCase   )

			A					=   """lower newer"""
			A					=   self.prepare_image_inputs()

			A					=   processor(text=_lowerCAmelCase   ,   images=_lowerCAmelCase   )

			self.assertListEqual(list(inputs.keys()   )   ,   ["""input_ids""", """attention_mask""", """pixel_values"""]   )

			# test if it raises when no input is passed
			with pytest.raises(_lowerCAmelCase   ):
				processor()







		def 							A  (self    :   Union[str, Any]   ):
			A					=   self.get_image_processor()
			A					=   self.get_tokenizer()

			A					=   GitProcessor(tokenizer=_lowerCAmelCase   ,   image_processor=_lowerCAmelCase   )

			A					=   [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]

			A					=   processor.batch_decode(_lowerCAmelCase   )
			A					=   tokenizer.batch_decode(_lowerCAmelCase   )

			self.assertListEqual(_lowerCAmelCase   ,   _lowerCAmelCase   )







		def 							A  (self    :   List[str]   ):
			A					=   self.get_image_processor()
			A					=   self.get_tokenizer()

			A					=   GitProcessor(tokenizer=_lowerCAmelCase   ,   image_processor=_lowerCAmelCase   )

			A					=   """lower newer"""
			A					=   self.prepare_image_inputs()

			A					=   processor(text=_lowerCAmelCase   ,   images=_lowerCAmelCase   )

			# For now the processor supports only ['input_ids', 'attention_mask', 'pixel_values']
			self.assertListEqual(list(inputs.keys()   )   ,   ["""input_ids""", """attention_mask""", """pixel_values"""]   )








'''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)

