





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  __lowerCAmelCase	(      _a,					unittest.TestCase					):
			lowerCamelCase_					:					List[str]	      =				MvpTokenizer
			lowerCamelCase_					:					str	      =				MvpTokenizerFast
			lowerCamelCase_					:					Tuple	      =				True
			lowerCamelCase_					:					List[str]	      =				filter_roberta_detectors






			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



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

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






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       Dict:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							**__magic_name__   )					->       List[Any]:


							'''simple docstring'''



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






			def       lowerCamelCase  (self ,							__magic_name__   )					->       str:


							'''simple docstring'''



							return "lower newer", "lower newer"






			@cached_property
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



							return MvpTokenizer.from_pretrained('''RUCAIBox/mvp'''   )






			@cached_property
			def       lowerCamelCase  (self   )					->       List[str]:


							'''simple docstring'''



							return MvpTokenizerFast.from_pretrained('''RUCAIBox/mvp'''   )






			@require_torch
			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



							snake_case_						:   int           =  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
							snake_case_						:   int           =  [0, 250, 251, 1_7818, 13, 3_9186, 1938, 4, 2]

							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											snake_case_						:   List[Any]           =  tokenizer(__magic_name__ ,							max_length=len(__magic_name__   ) ,							padding=__magic_name__ ,							return_tensors='''pt'''   )
											self.assertIsInstance(__magic_name__ ,							__magic_name__   )

											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(__magic_name__ ,							__magic_name__   )
											# Test that special tokens are reset






			@require_torch
			def       lowerCamelCase  (self   )					->       Dict:


							'''simple docstring'''



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






			@require_torch
			def       lowerCamelCase  (self   )					->       Optional[int]:


							'''simple docstring'''



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






			@require_torch
			def       lowerCamelCase  (self   )					->       Any:


							'''simple docstring'''



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






			@require_torch
			def       lowerCamelCase  (self   )					->       int:


							'''simple docstring'''



							snake_case_						:   Optional[Any]           =  ['''A long paragraph for summarization.''']
							snake_case_						:   int           =  [
							    '''Summary of the text.''',
							]
							for tokenizer in [self.default_tokenizer, self.default_tokenizer_fast]:
											snake_case_						:   List[Any]           =  tokenizer(__magic_name__ ,							text_target=__magic_name__ ,							return_tensors='''pt'''   )
											snake_case_						:   Optional[int]           =  inputs['''input_ids''']
											snake_case_						:   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   )					->       int:


							'''simple docstring'''



							pass






			def       lowerCamelCase  (self   )					->       List[Any]:


							'''simple docstring'''



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

															# 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_						:   int           =  tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']   )
															snake_case_						:   Optional[Any]           =  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, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )
															self.assertSequenceEqual(tokens_r['''input_ids'''] ,							[0, 250, 6, 5_0264, 3823, 487, 2_1992, 3645, 4, 2]   )

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












from typing import List, Optional

from tokenizers import ByteLevelBPETokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_blenderbot_small import BlenderbotSmallTokenizer


lowerCAmelCase_    =    logging.get_logger(__name__)

lowerCAmelCase_    =    {
    '''vocab_file''': '''vocab.json''',
    '''merges_file''': '''merges.txt''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

lowerCAmelCase_    =    {
    '''vocab_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/vocab.json'''
    },
    '''merges_file''': {
        '''facebook/blenderbot_small-90M''': '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/merges.txt'''
    },
    '''tokenizer_config_file''': {
        '''facebook/blenderbot_small-90M''': (
            '''https://huggingface.co/facebook/blenderbot_small-90M/resolve/main/tokenizer_config.json'''
        )
    },
}

lowerCAmelCase_    =    {
    '''facebook/blenderbot_small-90M''': 5_1_2,
}



class  __lowerCAmelCase	(      _a					):
			lowerCamelCase_					:					int	      =				VOCAB_FILES_NAMES
			lowerCamelCase_					:					List[Any]	      =				PRETRAINED_VOCAB_FILES_MAP
			lowerCamelCase_					:					int	      =				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
			lowerCamelCase_					:					Optional[Any]	      =				BlenderbotSmallTokenizer






			def __init__(self ,							__magic_name__=None ,							__magic_name__=None ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__="<|endoftext|>" ,							__magic_name__=False ,							__magic_name__=True ,							**__magic_name__ ,							)					->       Union[str, Any]:


							'''simple docstring'''



							super().__init__(
							    ByteLevelBPETokenizer(
							        vocab=__magic_name__ ,							merges=__magic_name__ ,							add_prefix_space=__magic_name__ ,							trim_offsets=__magic_name__ ,							) ,							bos_token=__magic_name__ ,							eos_token=__magic_name__ ,							unk_token=__magic_name__ ,							**__magic_name__ ,							)
							snake_case_						:   Optional[int]           =  add_prefix_space






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__=None   )					->       Any:


							'''simple docstring'''



							snake_case_						:   Optional[int]           =  [self.bos_token_id] + token_ids_a + [self.eos_token_id]
							if token_ids_a is None:
											return output

							return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]






			def       lowerCamelCase  (self ,							__magic_name__ ,							__magic_name__ = None   )					->       List[int]:


							'''simple docstring'''



							snake_case_						:   List[str]           =  [self.sep_token_id]
							snake_case_						:   Optional[Any]           =  [self.cls_token_id]

							if token_ids_a is None:
											return len(cls + token_ids_a + sep   ) * [0]
							return len(cls + token_ids_a + sep + sep + token_ids_a + sep   ) * [0]
