






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				(_snake_case      ):







		'''simple docstring'''



		def  __UpperCAmelCase      (							self       )     ->							int:
							UpperCAmelCase_   :				List[Any]						=						tempfile.mkdtemp()
							UpperCAmelCase_   :				Any						=						8

							# DPR tok
							UpperCAmelCase_   :				Optional[int]						=						[
							    '[UNK]',
							    '[CLS]',
							    '[SEP]',
							    '[PAD]',
							    '[MASK]',
							    'want',
							    '##want',
							    '##ed',
							    'wa',
							    'un',
							    'runn',
							    '##ing',
							    ',',
							    'low',
							    'lowest',
							]
							UpperCAmelCase_   :				List[Any]						=						os.path.join(self.tmpdirname    ,		'dpr_tokenizer'       )
							os.makedirs(lowerCamelCase_    ,		exist_ok=lowerCamelCase_       )
							UpperCAmelCase_   :				Tuple						=						os.path.join(lowerCamelCase_    ,		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
							UpperCAmelCase_   :				Tuple						=						[
							    'l',
							    'o',
							    'w',
							    'e',
							    'r',
							    's',
							    't',
							    'i',
							    'd',
							    'n',
							    '\u0120',
							    '\u0120l',
							    '\u0120n',
							    '\u0120lo',
							    '\u0120low',
							    'er',
							    '\u0120lowest',
							    '\u0120newer',
							    '\u0120wider',
							    '<unk>',
							]
							UpperCAmelCase_   :				Any						=						dict(zip(lowerCamelCase_    ,		range(len(lowerCamelCase_       )       )       )       )
							UpperCAmelCase_   :				Union[str, Any]						=						['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
							UpperCAmelCase_   :				Any						=						{'unk_token': '<unk>'}

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



		def  __UpperCAmelCase      (							self       )     ->							DPRQuestionEncoderTokenizer:
							return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,		'dpr_tokenizer'       )       )



		def  __UpperCAmelCase      (							self       )     ->							BartTokenizer:
							return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname    ,		'bart_tokenizer'       )       )



		def  __UpperCAmelCase      (							self       )     ->							Any:
							shutil.rmtree(self.tmpdirname       )



		@require_tokenizers
		def  __UpperCAmelCase      (							self       )     ->							List[Any]:
							UpperCAmelCase_   :				Optional[Any]						=						os.path.join(self.tmpdirname    ,		'rag_tokenizer'       )
							UpperCAmelCase_   :				Tuple						=						RagConfig(question_encoder=DPRConfig().to_dict()    ,		generator=BartConfig().to_dict()       )
							UpperCAmelCase_   :				int						=						RagTokenizer(question_encoder=self.get_dpr_tokenizer()    ,		generator=self.get_bart_tokenizer()       )
							rag_config.save_pretrained(lowerCamelCase_       )
							rag_tokenizer.save_pretrained(lowerCamelCase_       )
							UpperCAmelCase_   :				Any						=						RagTokenizer.from_pretrained(lowerCamelCase_    ,		config=lowerCamelCase_       )
							self.assertIsInstance(new_rag_tokenizer.question_encoder    ,		lowerCamelCase_       )
							self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()    ,		rag_tokenizer.question_encoder.get_vocab()       )
							self.assertIsInstance(new_rag_tokenizer.generator    ,		lowerCamelCase_       )
							self.assertEqual(new_rag_tokenizer.generator.get_vocab()    ,		rag_tokenizer.generator.get_vocab()       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Union[str, Any]:
							UpperCAmelCase_   :				Tuple						=						RagTokenizer.from_pretrained('facebook/rag-token-nq'       )
							UpperCAmelCase_   :				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',
							]
							UpperCAmelCase_   :				Any						=						tokenizer(lowerCamelCase_       )
							self.assertIsNotNone(lowerCamelCase_       )



		@slow
		def  __UpperCAmelCase      (							self       )     ->							Tuple:
							UpperCAmelCase_   :				Union[str, Any]						=						RagTokenizer.from_pretrained('facebook/rag-sequence-nq'       )
							UpperCAmelCase_   :				Optional[int]						=						[
							    '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',
							]
							UpperCAmelCase_   :				Optional[Any]						=						tokenizer(lowerCamelCase_       )
							self.assertIsNotNone(lowerCamelCase_       )














from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__UpperCAmelCase     							=						{
    'configuration_roc_bert': ['ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP', 'RoCBertConfig'],
    'tokenization_roc_bert': ['RoCBertTokenizer'],
}

try:
		if not is_tokenizers_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		pass

try:
		if not is_torch_available():
				raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
		pass
else:
		__UpperCAmelCase     							=						[
		    'ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST',
		    'RoCBertForCausalLM',
		    'RoCBertForMaskedLM',
		    'RoCBertForMultipleChoice',
		    'RoCBertForPreTraining',
		    'RoCBertForQuestionAnswering',
		    'RoCBertForSequenceClassification',
		    'RoCBertForTokenClassification',
		    'RoCBertLayer',
		    'RoCBertModel',
		    'RoCBertPreTrainedModel',
		    'load_tf_weights_in_roc_bert',
		]

if TYPE_CHECKING:
		from .configuration_roc_bert import ROC_BERT_PRETRAINED_CONFIG_ARCHIVE_MAP, RoCBertConfig
		from .tokenization_roc_bert import RoCBertTokenizer

		try:
				if not is_tokenizers_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				raise OptionalDependencyNotAvailable()

		try:
				if not is_torch_available():
						raise OptionalDependencyNotAvailable()
		except OptionalDependencyNotAvailable:
				pass
		else:
				from .modeling_roc_bert import (
				    ROC_BERT_PRETRAINED_MODEL_ARCHIVE_LIST,
				    RoCBertForCausalLM,
				    RoCBertForMaskedLM,
				    RoCBertForMultipleChoice,
				    RoCBertForPreTraining,
				    RoCBertForQuestionAnswering,
				    RoCBertForSequenceClassification,
				    RoCBertForTokenClassification,
				    RoCBertLayer,
				    RoCBertModel,
				    RoCBertPreTrainedModel,
				    load_tf_weights_in_roc_bert,
				)


else:
		import sys

		__UpperCAmelCase     							=						_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)






