'''simple docstring'''







import json
import os
import unittest

from transformers import BatchEncoding, MvpTokenizer, MvpTokenizerFast
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(							_SCREAMING_SNAKE_CASE						,						unittest.TestCase					):
						A_			:							List[str]     		= MvpTokenizer
						A_			:							Any     		= MvpTokenizerFast
						A_			:							Tuple     		= True
						A_			:							Dict     		= filter_roberta_detectors




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

													SCREAMING_SNAKE_CASE__     :List[str]						    = os.path.join(self.tmpdirname							,							VOCAB_FILES_NAMES['vocab_file']      )
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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(UpperCamelCase_      ) + '\n'      )
													with open(self.merges_file							,							'w'							,							encoding='utf-8'      ) as fp:
																				fp.write('\n'.join(UpperCamelCase_      )      )




						def      __lowerCamelCase       (    self :       Optional[int]							,							**UpperCamelCase_ :       Optional[Any]      )     ->				Any:
													kwargs.update(self.special_tokens_map      )
													return self.tokenizer_class.from_pretrained(self.tmpdirname							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       Tuple							,							**UpperCamelCase_ :       Union[str, Any]      )     ->				int:
													kwargs.update(self.special_tokens_map      )
													return self.rust_tokenizer_class.from_pretrained(self.tmpdirname							,							**UpperCamelCase_      )




						def      __lowerCamelCase       (    self :       List[str]							,							UpperCamelCase_ :       Optional[Any]      )     ->				str:
													return "lower newer", "lower newer"




						@cached_property
						def      __lowerCamelCase       (    self :       List[str]      )     ->				List[str]:
													return MvpTokenizer.from_pretrained('RUCAIBox/mvp'      )




						@cached_property
						def      __lowerCamelCase       (    self :       Tuple      )     ->				int:
													return MvpTokenizerFast.from_pretrained('RUCAIBox/mvp'      )




						@require_torch
						def      __lowerCamelCase       (    self :       List[str]      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
													SCREAMING_SNAKE_CASE__     :str						    = [0, 2_50, 2_51, 1_78_18, 13, 3_91_86, 19_38, 4, 2]

													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				SCREAMING_SNAKE_CASE__     :str						    = tokenizer(UpperCamelCase_							,							max_length=len(UpperCamelCase_      )							,							padding=UpperCamelCase_							,							return_tensors='pt'      )
																				self.assertIsInstance(UpperCamelCase_							,							UpperCamelCase_      )

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




						@require_torch
						def      __lowerCamelCase       (    self :       Tuple      )     ->				Union[str, Any]:
													SCREAMING_SNAKE_CASE__     :List[Any]						    = ['A long paragraph for summarization.', 'Another paragraph for summarization.']
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer(UpperCamelCase_							,							padding=UpperCamelCase_							,							return_tensors='pt'      )
																				# check if input_ids are returned and no labels
																				self.assertIn('input_ids'							,							UpperCamelCase_      )
																				self.assertIn('attention_mask'							,							UpperCamelCase_      )
																				self.assertNotIn('labels'							,							UpperCamelCase_      )
																				self.assertNotIn('decoder_attention_mask'							,							UpperCamelCase_      )




						@require_torch
						def      __lowerCamelCase       (    self :       Optional[int]      )     ->				Any:
													SCREAMING_SNAKE_CASE__     :Any						    = [
													    'Summary of the text.',
													    'Another summary.',
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer(text_target=UpperCamelCase_							,							max_length=32							,							padding='max_length'							,							return_tensors='pt'      )
																				self.assertEqual(32							,							targets['input_ids'].shape[1]      )




						@require_torch
						def      __lowerCamelCase       (    self :       Optional[Any]      )     ->				int:
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				SCREAMING_SNAKE_CASE__     :Optional[Any]						    = tokenizer(
																				    ['I am a small frog' * 10_24, 'I am a small frog']							,							padding=UpperCamelCase_							,							truncation=UpperCamelCase_							,							return_tensors='pt'      )
																				self.assertIsInstance(UpperCamelCase_							,							UpperCamelCase_      )
																				self.assertEqual(batch.input_ids.shape							,							(2, 10_24)      )




						@require_torch
						def      __lowerCamelCase       (    self :       Union[str, Any]      )     ->				List[Any]:
													SCREAMING_SNAKE_CASE__     :Tuple						    = ['A long paragraph for summarization.']
													SCREAMING_SNAKE_CASE__     :List[str]						    = [
													    'Summary of the text.',
													]
													for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = tokenizer(UpperCamelCase_							,							text_target=UpperCamelCase_							,							return_tensors='pt'      )
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = inputs['input_ids']
																				SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = inputs['labels']
																				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      __lowerCamelCase       (    self :       Optional[Any]      )     ->				Optional[int]:
													pass




						def      __lowerCamelCase       (    self :       Any      )     ->				List[Any]:
													for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																				with self.subTest(f'''{tokenizer.__class__.__name__} ({pretrained_name})'''      ):
																											SCREAMING_SNAKE_CASE__     :List[str]						    = self.rust_tokenizer_class.from_pretrained(UpperCamelCase_							,							**UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :str						    = self.tokenizer_class.from_pretrained(UpperCamelCase_							,							**UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Dict						    = 'A, <mask> AllenNLP sentence.'
																											SCREAMING_SNAKE_CASE__     :Dict						    = tokenizer_r.encode_plus(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_							,							return_token_type_ids=UpperCamelCase_      )
																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer_p.encode_plus(UpperCamelCase_							,							add_special_tokens=UpperCamelCase_							,							return_token_type_ids=UpperCamelCase_      )

																											# 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']      )							,							)

																											SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = tokenizer_r.convert_ids_to_tokens(tokens_r['input_ids']      )
																											SCREAMING_SNAKE_CASE__     :Optional[int]						    = 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, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )
																											self.assertSequenceEqual(tokens_r['input_ids']							,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]      )

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







'''simple docstring'''







import os
from shutil import copyfile
from typing import List, Optional, Tuple

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import is_sentencepiece_available, logging


if is_sentencepiece_available():
		from .tokenization_pegasus import PegasusTokenizer
else:
		UpperCamelCase_      	=	None


UpperCamelCase_      	=	logging.get_logger(__name__)


UpperCamelCase_      	=	'''▁'''

UpperCamelCase_      	=	{'''vocab_file''': '''spiece.model''', '''tokenizer_file''': '''tokenizer.json'''}

UpperCamelCase_      	=	{
    '''vocab_file''': {'''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/spiece.model'''},
    '''tokenizer_file''': {
        '''google/pegasus-xsum''': '''https://huggingface.co/google/pegasus-xsum/resolve/main/tokenizer.json'''
    },
}

UpperCamelCase_      	=	{
    '''google/pegasus-xsum''': 5_12,
}




class _SCREAMING_SNAKE_CASE(							_SCREAMING_SNAKE_CASE					):
						A_			:							Union[str, Any]     		= VOCAB_FILES_NAMES
						A_			:							str     		= PRETRAINED_VOCAB_FILES_MAP
						A_			:							List[str]     		= PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						A_			:							int     		= PegasusTokenizer
						A_			:							Dict     		= ['input_ids', 'attention_mask']




						def __init__(    self :       List[str]							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       str=None							,							UpperCamelCase_ :       Union[str, Any]="<pad>"							,							UpperCamelCase_ :       List[Any]="</s>"							,							UpperCamelCase_ :       Tuple="<unk>"							,							UpperCamelCase_ :       List[Any]="<mask_2>"							,							UpperCamelCase_ :       int="<mask_1>"							,							UpperCamelCase_ :       List[str]=None							,							UpperCamelCase_ :       Optional[Any]=1_03							,							**UpperCamelCase_ :       Optional[Any]							,							)     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :Dict						    = offset

													if additional_special_tokens is not None:
																				if not isinstance(UpperCamelCase_							,							UpperCamelCase_      ):
																											raise TypeError(
																											    f'''additional_special_tokens should be of type {type(UpperCamelCase_      )}, but is'''
																											    f''' {type(UpperCamelCase_      )}'''      )

																				SCREAMING_SNAKE_CASE__     :List[str]						    = (
																				    ([mask_token_sent] + additional_special_tokens)
																				    if mask_token_sent not in additional_special_tokens and mask_token_sent is not None
																				    else additional_special_tokens
																				)
																				# fill additional tokens with ..., <unk_token_102> in case not all additional tokens are already taken
																				additional_special_tokens_extended += [
																				    f'''<unk_{i}>''' for i in range(len(UpperCamelCase_      )							,							self.offset - 1      )
																				]

																				if len(set(UpperCamelCase_      )      ) != len(UpperCamelCase_      ):
																											raise ValueError(
																											    'Please make sure that the provided additional_special_tokens do not contain an incorrectly'
																											    f''' shifted list of <unk_x> tokens. Found {additional_special_tokens_extended}.'''      )
																				SCREAMING_SNAKE_CASE__     :List[str]						    = additional_special_tokens_extended
													else:
																				SCREAMING_SNAKE_CASE__     :Tuple						    = [mask_token_sent] if mask_token_sent is not None else []
																				additional_special_tokens += [f'''<unk_{i}>''' for i in range(2							,							self.offset      )]

													super().__init__(
													    UpperCamelCase_							,							tokenizer_file=UpperCamelCase_							,							pad_token=UpperCamelCase_							,							eos_token=UpperCamelCase_							,							unk_token=UpperCamelCase_							,							mask_token=UpperCamelCase_							,							mask_token_sent=UpperCamelCase_							,							offset=UpperCamelCase_							,							additional_special_tokens=UpperCamelCase_							,							**UpperCamelCase_							,							)
													SCREAMING_SNAKE_CASE__     :int						    = vocab_file
													SCREAMING_SNAKE_CASE__     :Union[str, Any]						    = False if not self.vocab_file else True




						def      __lowerCamelCase       (    self :       int							,							UpperCamelCase_ :       Any      )     ->				Optional[int]:
													SCREAMING_SNAKE_CASE__     :int						    = set(self.all_special_ids      )  # call it once instead of inside list comp
													all_special_ids.remove(self.unk_token_id      )  # <unk> is only sometimes special

													if all_special_ids != set(range(len(self.additional_special_tokens      ) + 3      )      ):
																				raise ValueError(
																				    'There should be 3 special tokens: mask_token, pad_token, and eos_token +'
																				    f''' {len(self.additional_special_tokens      )} additional_special_tokens, but got {all_special_ids}'''      )

													return [1 if x in all_special_ids else 0 for x in seq]




						def      __lowerCamelCase       (    self :       Optional[int]							,							UpperCamelCase_ :       List							,							UpperCamelCase_ :       Optional[List] = None							,							UpperCamelCase_ :       bool = False      )     ->				List[int]:
													if already_has_special_tokens:
																				return self._special_token_mask(UpperCamelCase_      )
													elif token_ids_a is None:
																				return self._special_token_mask(UpperCamelCase_      ) + [1]
													else:
																				return self._special_token_mask(token_ids_a + token_ids_a      ) + [1]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       Any							,							UpperCamelCase_ :       Dict=None      )     ->				List[int]:
													if token_ids_a is None:
																				return token_ids_a + [self.eos_token_id]
													# We don't expect to process pairs, but leave the pair logic for API consistency
													return token_ids_a + token_ids_a + [self.eos_token_id]




						def      __lowerCamelCase       (    self :       Union[str, Any]							,							UpperCamelCase_ :       str							,							UpperCamelCase_ :       Optional[str] = None      )     ->				Tuple[str]:
													if not self.can_save_slow_tokenizer:
																				raise ValueError(
																				    'Your fast tokenizer does not have the necessary information to save the vocabulary for a slow '
																				    'tokenizer.'      )

													if not os.path.isdir(UpperCamelCase_      ):
																				logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''      )
																				return
													SCREAMING_SNAKE_CASE__     :Optional[int]						    = os.path.join(
													    UpperCamelCase_							,							(filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['vocab_file']      )

													if os.path.abspath(self.vocab_file      ) != os.path.abspath(UpperCamelCase_      ):
																				copyfile(self.vocab_file							,							UpperCamelCase_      )

													return (out_vocab_file,)






