




import json
import os
import unittest

from transformers import BartTokenizer, BartTokenizerFast, BatchEncoding
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, require_torch
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin, filter_roberta_detectors


@require_tokenizers
class     __SCREAMING_SNAKE_CASE							(		lowerCamelCase       ,					unittest.TestCase  ):
					_lowerCamelCase		  =			BartTokenizer
					_lowerCamelCase		  =			BartTokenizerFast
					_lowerCamelCase		  =			True
					_lowerCamelCase		  =			filter_roberta_detectors

					def      lowercase_							(    self ):







						'''simple docstring'''




						super().setUp()
						lowerCamelCase_							:		str							       =							[
						    'l',
						    'o',
						    'w',
						    'e',
						    'r',
						    's',
						    't',
						    'i',
						    'd',
						    'n',
						    '\u0120',
						    '\u0120l',
						    '\u0120n',
						    '\u0120lo',
						    '\u0120low',
						    'er',
						    '\u0120lowest',
						    '\u0120newer',
						    '\u0120wider',
						    '<unk>',
						]
						lowerCamelCase_							:		Union[str, Any]							       =							dict(zip(__lowercase			,	range(len(__lowercase ) ) ) )
						lowerCamelCase_							:		Optional[Any]							       =							['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
						lowerCamelCase_							:		Any							       =							{'unk_token': '<unk>'}

						lowerCamelCase_							:		List[str]							       =							os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES['vocab_file'] )
						lowerCamelCase_							:		Tuple							       =							os.path.join(self.tmpdirname			,	VOCAB_FILES_NAMES['merges_file'] )
						with open(self.vocab_file			,	'w'			,	encoding='utf-8' ) as fp:
							fp.write(json.dumps(__lowercase ) + '\n' )
						with open(self.merges_file			,	'w'			,	encoding='utf-8' ) as fp:
							fp.write('\n'.join(__lowercase ) )

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						kwargs.update(self.special_tokens_map )
						return self.tokenizer_class.from_pretrained(self.tmpdirname			,	**__lowercase )

					def      lowercase_							(    self			,	**__lowercase ):







						'''simple docstring'''




						kwargs.update(self.special_tokens_map )
						return self.rust_tokenizer_class.from_pretrained(self.tmpdirname			,	**__lowercase )

					def      lowercase_							(    self			,	__lowercase ):







						'''simple docstring'''




						return "lower newer", "lower newer"

					@cached_property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return BartTokenizer.from_pretrained('facebook/bart-large' )

					@cached_property
					def      lowercase_							(    self ):







						'''simple docstring'''




						return BartTokenizerFast.from_pretrained('facebook/bart-large' )

					@require_torch
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		Any							       =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
						lowerCamelCase_							:		Tuple							       =							[0, 250, 251, 17_818, 13, 39_186, 1_938, 4, 2]

						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCamelCase_							:		Dict							       =							tokenizer(__lowercase			,	max_length=len(__lowercase )			,	padding=__lowercase			,	return_tensors='pt' )
							self.assertIsInstance(__lowercase			,	__lowercase )

							self.assertEqual((2, 9)			,	batch.input_ids.shape )
							self.assertEqual((2, 9)			,	batch.attention_mask.shape )
							lowerCamelCase_							:		List[Any]							       =							batch.input_ids.tolist()[0]
							self.assertListEqual(__lowercase			,	__lowercase )
							# Test that special tokens are reset

					@require_torch
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		str							       =							['A long paragraph for summarization.', 'Another paragraph for summarization.']
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCamelCase_							:		Tuple							       =							tokenizer(__lowercase			,	padding=__lowercase			,	return_tensors='pt' )
							# check if input_ids are returned and no labels
							self.assertIn('input_ids'			,	__lowercase )
							self.assertIn('attention_mask'			,	__lowercase )
							self.assertNotIn('labels'			,	__lowercase )
							self.assertNotIn('decoder_attention_mask'			,	__lowercase )

					@require_torch
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		List[str]							       =							[
						    'Summary of the text.',
						    'Another summary.',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCamelCase_							:		Dict							       =							tokenizer(text_target=__lowercase			,	max_length=32			,	padding='max_length'			,	return_tensors='pt' )
							self.assertEqual(32			,	targets['input_ids'].shape[1] )

					@require_torch
					def      lowercase_							(    self ):







						'''simple docstring'''




						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCamelCase_							:		Tuple							       =							tokenizer(
							    ['I am a small frog' * 1_024, 'I am a small frog']			,	padding=__lowercase			,	truncation=__lowercase			,	return_tensors='pt' )
							self.assertIsInstance(__lowercase			,	__lowercase )
							self.assertEqual(batch.input_ids.shape			,	(2, 1_024) )

					@require_torch
					def      lowercase_							(    self ):







						'''simple docstring'''




						lowerCamelCase_							:		int							       =							['A long paragraph for summarization.']
						lowerCamelCase_							:		List[str]							       =							[
						    'Summary of the text.',
						]
						for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
							lowerCamelCase_							:		int							       =							tokenizer(__lowercase			,	return_tensors='pt' )
							lowerCamelCase_							:		Optional[Any]							       =							tokenizer(text_target=__lowercase			,	return_tensors='pt' )
							lowerCamelCase_							:		Dict							       =							inputs['input_ids']
							lowerCamelCase_							:		Optional[Any]							       =							targets['input_ids']
							self.assertTrue((input_ids[:, 0] == tokenizer.bos_token_id).all().item() )
							self.assertTrue((labels[:, 0] == tokenizer.bos_token_id).all().item() )
							self.assertTrue((input_ids[:, -1] == tokenizer.eos_token_id).all().item() )
							self.assertTrue((labels[:, -1] == tokenizer.eos_token_id).all().item() )

					def      lowercase_							(    self ):







						'''simple docstring'''




						pass





					def      lowercase_							(    self ):







						'''simple docstring'''




						for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
							with self.subTest(f"{tokenizer.__class__.__name__} ({pretrained_name})" ):
								lowerCamelCase_							:		Tuple							       =							self.rust_tokenizer_class.from_pretrained(__lowercase			,	**__lowercase )
								lowerCamelCase_							:		List[str]							       =							self.tokenizer_class.from_pretrained(__lowercase			,	**__lowercase )
								lowerCamelCase_							:		List[str]							       =							'A, <mask> AllenNLP sentence.'
								lowerCamelCase_							:		Optional[int]							       =							tokenizer_r.encode_plus(__lowercase			,	add_special_tokens=__lowercase			,	return_token_type_ids=__lowercase )
								lowerCamelCase_							:		str							       =							tokenizer_p.encode_plus(__lowercase			,	add_special_tokens=__lowercase			,	return_token_type_ids=__lowercase )

								# token_type_ids should put 0 everywhere
								self.assertEqual(sum(tokens_r['token_type_ids'] )			,	sum(tokens_p['token_type_ids'] ) )

								# attention_mask should put 1 everywhere, so sum over length should be 1
								self.assertEqual(
								    sum(tokens_r['attention_mask'] ) / len(tokens_r['attention_mask'] )			,	sum(tokens_p['attention_mask'] ) / len(tokens_p['attention_mask'] )			,	)

								lowerCamelCase_							:		List[Any]							       =							tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids'] )
								lowerCamelCase_							:		List[str]							       =							tokenizer_p.convert_ids_to_tokens(tokens_p['input_ids'] )

								# Rust correctly handles the space before the mask while python doesnt
								self.assertSequenceEqual(tokens_p['input_ids']			,	[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )
								self.assertSequenceEqual(tokens_r['input_ids']			,	[0, 250, 6, 50_264, 3_823, 487, 21_992, 3_645, 4, 2] )

								self.assertSequenceEqual(
								    __lowercase			,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )
								self.assertSequenceEqual(
								    __lowercase			,	['<s>', 'A', ',', '<mask>', 'ĠAllen', 'N', 'LP', 'Ġsentence', '.', '</s>'] )






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)
