import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
   from transformers.models.rag.configuration_rag import RagConfig
   from transformers.models.rag.tokenization_rag import RagTokenizer
@require_faiss
@require_torch
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE				):







       def 				snake_case  (				self     :    Any		):
          lowerCamelCase						:List[str]						  =     tempfile.mkdtemp()
          lowerCamelCase						:List[str]						  =     8

          # DPR tok
          lowerCamelCase						:str						  =     [
              '''[UNK]''',
              '''[CLS]''',
              '''[SEP]''',
              '''[PAD]''',
              '''[MASK]''',
              '''want''',
              '''##want''',
              '''##ed''',
              '''wa''',
              '''un''',
              '''runn''',
              '''##ing''',
              ''',''',
              '''low''',
              '''lowest''',
          ]
          lowerCamelCase						:Dict						  =     os.path.join(self.tmpdirname	,					'''dpr_tokenizer'''		)
          os.makedirs(__snake_case	,					exist_ok=__snake_case		)
          lowerCamelCase						:List[str]						  =     os.path.join(__snake_case	,					DPR_VOCAB_FILES_NAMES['''vocab_file''']		)
          with open(self.vocab_file	,					'''w'''	,					encoding='''utf-8'''		) as vocab_writer:
             vocab_writer.write(''''''.join([x + '''\n''' for x in vocab_tokens]		)		)

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

          lowerCamelCase						:Tuple						  =     os.path.join(self.tmpdirname	,					'''bart_tokenizer'''		)
          os.makedirs(__snake_case	,					exist_ok=__snake_case		)
          lowerCamelCase						:Dict						  =     os.path.join(__snake_case	,					BART_VOCAB_FILES_NAMES['''vocab_file''']		)
          lowerCamelCase						:Optional[int]						  =     os.path.join(__snake_case	,					BART_VOCAB_FILES_NAMES['''merges_file''']		)
          with open(self.vocab_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
             fp.write(json.dumps(__snake_case		) + '''\n'''		)
          with open(self.merges_file	,					'''w'''	,					encoding='''utf-8'''		) as fp:
             fp.write('''\n'''.join(__snake_case		)		)







       def 				snake_case  (				self     :    List[Any]		):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,					'''dpr_tokenizer'''		)		)







       def 				snake_case  (				self     :    Optional[Any]		):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,					'''bart_tokenizer'''		)		)







       def 				snake_case  (				self     :    List[str]		):
          shutil.rmtree(self.tmpdirname		)







       @require_tokenizers
       def 				snake_case  (				self     :    Optional[int]		):
          lowerCamelCase						:Tuple						  =     os.path.join(self.tmpdirname	,					'''rag_tokenizer'''		)
          lowerCamelCase						:Union[str, Any]						  =     RagConfig(question_encoder=DPRConfig().to_dict()	,					generator=BartConfig().to_dict()		)
          lowerCamelCase						:Union[str, Any]						  =     RagTokenizer(question_encoder=self.get_dpr_tokenizer()	,					generator=self.get_bart_tokenizer()		)
          rag_config.save_pretrained(__snake_case		)
          rag_tokenizer.save_pretrained(__snake_case		)
          lowerCamelCase						:int						  =     RagTokenizer.from_pretrained(__snake_case	,					config=__snake_case		)
          self.assertIsInstance(new_rag_tokenizer.question_encoder	,					__snake_case		)
          self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()	,					rag_tokenizer.question_encoder.get_vocab()		)
          self.assertIsInstance(new_rag_tokenizer.generator	,					__snake_case		)
          self.assertEqual(new_rag_tokenizer.generator.get_vocab()	,					rag_tokenizer.generator.get_vocab()		)







       @slow
       def 				snake_case  (				self     :    Any		):
          lowerCamelCase						:Optional[int]						  =     RagTokenizer.from_pretrained('''facebook/rag-token-nq'''		)
          lowerCamelCase						:Any						  =     [
              '''who got the first nobel prize in physics''',
              '''when is the next deadpool movie being released''',
              '''which mode is used for short wave broadcast service''',
              '''who is the owner of reading football club''',
              '''when is the next scandal episode coming out''',
              '''when is the last time the philadelphia won the superbowl''',
              '''what is the most current adobe flash player version''',
              '''how many episodes are there in dragon ball z''',
              '''what is the first step in the evolution of the eye''',
              '''where is gall bladder situated in human body''',
              '''what is the main mineral in lithium batteries''',
              '''who is the president of usa right now''',
              '''where do the greasers live in the outsiders''',
              '''panda is a national animal of which country''',
              '''what is the name of manchester united stadium''',
          ]
          lowerCamelCase						:Dict						  =     tokenizer(__snake_case		)
          self.assertIsNotNone(__snake_case		)







       @slow
       def 				snake_case  (				self     :    Tuple		):
          lowerCamelCase						:Dict						  =     RagTokenizer.from_pretrained('''facebook/rag-sequence-nq'''		)
          lowerCamelCase						:List[Any]						  =     [
              '''who got the first nobel prize in physics''',
              '''when is the next deadpool movie being released''',
              '''which mode is used for short wave broadcast service''',
              '''who is the owner of reading football club''',
              '''when is the next scandal episode coming out''',
              '''when is the last time the philadelphia won the superbowl''',
              '''what is the most current adobe flash player version''',
              '''how many episodes are there in dragon ball z''',
              '''what is the first step in the evolution of the eye''',
              '''where is gall bladder situated in human body''',
              '''what is the main mineral in lithium batteries''',
              '''who is the president of usa right now''',
              '''where do the greasers live in the outsiders''',
              '''panda is a national animal of which country''',
              '''what is the name of manchester united stadium''',
          ]
          lowerCamelCase						:str						  =     tokenizer(__snake_case		)
          self.assertIsNotNone(__snake_case		)




import json
import os
import unittest

from transformers import DebertaTokenizer, DebertaTokenizerFast
from transformers.models.deberta.tokenization_deberta import VOCAB_FILES_NAMES
from transformers.testing_utils import slow

from ...test_tokenization_common import TokenizerTesterMixin
class   _lowerCAmelCase		(     __SCREAMING_SNAKE_CASE      ,   unittest.TestCase				):
							_UpperCAmelCase						    =     DebertaTokenizer
							_UpperCAmelCase						    =     True
							_UpperCAmelCase						    =     DebertaTokenizerFast







							def 				snake_case  (				self     :    List[str]		):
										super().setUp()

										# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
										lowerCamelCase						:Dict						  =     [
										    '''l''',
										    '''o''',
										    '''w''',
										    '''e''',
										    '''r''',
										    '''s''',
										    '''t''',
										    '''i''',
										    '''d''',
										    '''n''',
										    '''\u0120''',
										    '''\u0120l''',
										    '''\u0120n''',
										    '''\u0120lo''',
										    '''\u0120low''',
										    '''er''',
										    '''\u0120lowest''',
										    '''\u0120newer''',
										    '''\u0120wider''',
										    '''[UNK]''',
										]
										lowerCamelCase						:List[str]						  =     dict(zip(__snake_case	,					range(len(__snake_case		)		)		)		)
										lowerCamelCase						:Any						  =     ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
										lowerCamelCase						:Dict						  =     {'''unk_token''': '''[UNK]'''}

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







							def 				snake_case  (				self     :    str	,					**__snake_case     :    Dict		):
										kwargs.update(self.special_tokens_map		)
										return self.tokenizer_class.from_pretrained(self.tmpdirname	,					**__snake_case		)







							def 				snake_case  (				self     :    Optional[Any]	,					__snake_case     :    int		):
										lowerCamelCase						:List[Any]						  =     '''lower newer'''
										lowerCamelCase						:List[str]						  =     '''lower newer'''
										return input_text, output_text







							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Optional[int]						  =     self.get_tokenizer()
										lowerCamelCase						:Union[str, Any]						  =     '''lower newer'''
										lowerCamelCase						:str						  =     ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
										lowerCamelCase						:Optional[int]						  =     tokenizer.tokenize(__snake_case		)
										self.assertListEqual(__snake_case	,					__snake_case		)

										lowerCamelCase						:List[str]						  =     tokens + [tokenizer.unk_token]
										lowerCamelCase						:Optional[int]						  =     [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
										self.assertListEqual(tokenizer.convert_tokens_to_ids(__snake_case		)	,					__snake_case		)







							def 				snake_case  (				self     :    Optional[int]		):
										lowerCamelCase						:List[str]						  =     self.get_tokenizer()
										lowerCamelCase						:Optional[int]						  =     tokenizer('''Hello'''	,					'''World'''		)
										lowerCamelCase						:List[str]						  =     [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
										self.assertListEqual(tokd['''token_type_ids''']	,					__snake_case		)







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:Union[str, Any]						  =     self.tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

										lowerCamelCase						:Optional[Any]						  =     tokenizer.encode('''sequence builders'''	,					add_special_tokens=__snake_case		)
										lowerCamelCase						:Tuple						  =     tokenizer.encode('''multi-sequence build'''	,					add_special_tokens=__snake_case		)

										lowerCamelCase						:Union[str, Any]						  =     tokenizer.encode(
										    '''sequence builders'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)
										lowerCamelCase						:str						  =     tokenizer.encode(
										    '''sequence builders'''	,					'''multi-sequence build'''	,					add_special_tokens=__snake_case	,					add_prefix_space=__snake_case		)

										lowerCamelCase						:Any						  =     tokenizer.build_inputs_with_special_tokens(__snake_case		)
										lowerCamelCase						:Dict						  =     tokenizer.build_inputs_with_special_tokens(__snake_case	,					__snake_case		)

										assert encoded_sentence == encoded_text_from_decode
										assert encoded_pair == encoded_pair_from_decode







							@slow
							def 				snake_case  (				self     :    str		):
										lowerCamelCase						:List[str]						  =     [self.tokenizer_class]
										if self.test_rust_tokenizer:
													tokenizer_classes.append(self.rust_tokenizer_class		)

										for tokenizer_class in tokenizer_classes:
													lowerCamelCase						:int						  =     tokenizer_class.from_pretrained('''microsoft/deberta-base'''		)

													lowerCamelCase						:Tuple						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													lowerCamelCase						:List[Any]						  =     tokenizer(__snake_case	,					padding=__snake_case		)
													lowerCamelCase						:Union[str, Any]						  =     [tokenizer.decode(__snake_case	,					skip_special_tokens=__snake_case		) for seq in encoding['''input_ids''']]

													# fmt: off
													lowerCamelCase						:Any						  =     {
													    '''input_ids''': [
													        [1, 2118, 11126, 565, 35, 83, 25191, 163, 18854, 13, 12156, 12, 16101, 25376, 13807, 9, 22205, 27893, 1635, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 2118, 11126, 565, 24536, 80, 43797, 4878, 7373, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 133, 78, 65, 16, 10, 3724, 1538, 33183, 11303, 43797, 1938, 4, 870, 24165, 29105, 5, 739, 32644, 33183, 11303, 36173, 88, 80, 650, 7821, 45940, 6, 52, 2559, 5, 1836, 9, 5, 7397, 13171, 31, 5, 1836, 9, 32644, 33183, 11303, 4, 2]
													    ],
													    '''token_type_ids''': [
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
													    ],
													    '''attention_mask''': [
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
													        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
													    ]
													}
													# fmt: on

													lowerCamelCase						:Optional[int]						  =     [
													    '''ALBERT: A Lite BERT for Self-supervised Learning of Language Representations''',
													    '''ALBERT incorporates two parameter reduction techniques''',
													    '''The first one is a factorized embedding parameterization. By decomposing the large vocabulary'''
													    ''' embedding matrix into two small matrices, we separate the size of the hidden layers from the size of'''
													    ''' vocabulary embedding.''',
													]

													self.assertDictEqual(encoding.data	,					__snake_case		)

													for expected, decoded in zip(__snake_case	,					__snake_case		):
																self.assertEqual(__snake_case	,					__snake_case		)



