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 					__snake_case    (     _lowerCamelCase       ,unittest.TestCase      ):
					__lowerCamelCase			   =  BartTokenizer
					__lowerCamelCase			   =  BartTokenizerFast
					__lowerCamelCase			   =  True
					__lowerCamelCase			   =  filter_roberta_detectors




					def      __a     (  self						)	->    Union[str, Any]:



									'''simple docstring'''







									super().setUp()
									snake_case__   :      Union[str, Any]   				= [
									    "l",
									    "o",
									    "w",
									    "e",
									    "r",
									    "s",
									    "t",
									    "i",
									    "d",
									    "n",
									    "\u0120",
									    "\u0120l",
									    "\u0120n",
									    "\u0120lo",
									    "\u0120low",
									    "er",
									    "\u0120lowest",
									    "\u0120newer",
									    "\u0120wider",
									    "<unk>",
									]
									snake_case__   :      List[str]   				= dict(zip(_A       ,			range(len(_A						)						)						)						)
									snake_case__   :      List[str]   				= ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
									snake_case__   :      str   				= {"unk_token": "<unk>"}

									snake_case__   :      Dict   				= os.path.join(self.tmpdirname       ,			VOCAB_FILES_NAMES['vocab_file']						)
									snake_case__   :      Optional[Any]   				= 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(_A						) + '\n'						)
									with open(self.merges_file       ,			'w'       ,			encoding='utf-8'						) as fp:
													fp.write('\n'.join(_A						)						)




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Optional[Any]:



									'''simple docstring'''







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




					def      __a     (  self       ,			**__UpperCamelCase						)	->    Tuple:



									'''simple docstring'''







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




					def      __a     (  self       ,			__UpperCamelCase						)	->    Dict:



									'''simple docstring'''







									return "lower newer", "lower newer"




					@cached_property
					def      __a     (  self						)	->    Any:



									'''simple docstring'''







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




					@cached_property
					def      __a     (  self						)	->    Dict:



									'''simple docstring'''







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




					@require_torch
					def      __a     (  self						)	->    int:



									'''simple docstring'''







									snake_case__   :      Optional[int]   				= ["A long paragraph for summarization.", "Another paragraph for summarization."]
									snake_case__   :      List[Any]   				= [0, 250, 251, 17818, 13, 39186, 1938, 4, 2]

									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													snake_case__   :      Tuple   				= tokenizer(_A       ,			max_length=len(_A						)       ,			padding=_A       ,			return_tensors='pt'						)
													self.assertIsInstance(_A       ,			_A						)

													self.assertEqual((2, 9)       ,			batch.input_ids.shape						)
													self.assertEqual((2, 9)       ,			batch.attention_mask.shape						)
													snake_case__   :      Dict   				= batch.input_ids.tolist()[0]
													self.assertListEqual(_A       ,			_A						)
													# Test that special tokens are reset




					@require_torch
					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







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




					@require_torch
					def      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									snake_case__   :      Optional[Any]   				= [
									    "Summary of the text.",
									    "Another summary.",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													snake_case__   :      int   				= tokenizer(text_target=_A       ,			max_length=32       ,			padding='max_length'       ,			return_tensors='pt'						)
													self.assertEqual(32       ,			targets['input_ids'].shape[1]						)




					@require_torch
					def      __a     (  self						)	->    List[Any]:



									'''simple docstring'''







									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													snake_case__   :      List[str]   				= tokenizer(
													    ['I am a small frog' * 1024, 'I am a small frog']       ,			padding=_A       ,			truncation=_A       ,			return_tensors='pt'						)
													self.assertIsInstance(_A       ,			_A						)
													self.assertEqual(batch.input_ids.shape       ,			(2, 1024)						)




					@require_torch
					def      __a     (  self						)	->    str:



									'''simple docstring'''







									snake_case__   :      int   				= ["A long paragraph for summarization."]
									snake_case__   :      List[str]   				= [
									    "Summary of the text.",
									]
									for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
													snake_case__   :      Optional[Any]   				= tokenizer(_A       ,			return_tensors='pt'						)
													snake_case__   :      Union[str, Any]   				= tokenizer(text_target=_A       ,			return_tensors='pt'						)
													snake_case__   :      Tuple   				= inputs["input_ids"]
													snake_case__   :      str   				= 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      __a     (  self						)	->    List[str]:



									'''simple docstring'''







									pass




					def      __a     (  self						)	->    str:



									'''simple docstring'''







									for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
													with self.subTest(F"""{tokenizer.__class__.__name__} ({pretrained_name})"""						):
																	snake_case__   :      Union[str, Any]   				= self.rust_tokenizer_class.from_pretrained(_A       ,			**_A						)
																	snake_case__   :      int   				= self.tokenizer_class.from_pretrained(_A       ,			**_A						)
																	snake_case__   :      Union[str, Any]   				= "A, <mask> AllenNLP sentence."
																	snake_case__   :      Dict   				= tokenizer_r.encode_plus(_A       ,			add_special_tokens=_A       ,			return_token_type_ids=_A						)
																	snake_case__   :      Optional[Any]   				= tokenizer_p.encode_plus(_A       ,			add_special_tokens=_A       ,			return_token_type_ids=_A						)

																	# 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']						)       ,			)

																	snake_case__   :      Optional[int]   				= tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']						)
																	snake_case__   :      Dict   				= 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, 50264, 3823, 487, 21992, 3645, 4, 2]						)
																	self.assertSequenceEqual(tokens_r['input_ids']       ,			[0, 250, 6, 50264, 3823, 487, 21992, 3645, 4, 2]						)

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







def 						snake_case__							(       __lowercase							)		->		int:







						"""simple docstring"""

						A__						:				Any         =			0
						for plain_chr in plain_text:
												A__						:				List[str]         =			(
												    ord(__lowercase							) + (hash_value << 6) + (hash_value << 1_6) - hash_value
												)
						return hash_value