





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  A__    (							__UpperCAmelCase	):




   """simple docstring"""



   def 							__lowercase		(					self)			->						Any:







          '''simple docstring'''

          a__						:	Optional[Any] =				tempfile.mkdtemp()
          a__						:	List[Any] =				8

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

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

   def 							__lowercase		(					self)			->						DPRQuestionEncoderTokenizer:







          '''simple docstring'''

          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,  'dpr_tokenizer'))

   def 							__lowercase		(					self)			->						BartTokenizer:







          '''simple docstring'''

          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,  'bart_tokenizer'))

   def 							__lowercase		(					self)			->						str:







          '''simple docstring'''

          shutil.rmtree(self.tmpdirname)

   @require_tokenizers
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				os.path.join(self.tmpdirname					,  'rag_tokenizer')
          a__						:	Optional[int] =				RagConfig(question_encoder=DPRConfig().to_dict()					,  generator=BartConfig().to_dict())
          a__						:	Any =				RagTokenizer(question_encoder=self.get_dpr_tokenizer()					,  generator=self.get_bart_tokenizer())
          rag_config.save_pretrained(lowercase)
          rag_tokenizer.save_pretrained(lowercase)
          a__						:	int =				RagTokenizer.from_pretrained(lowercase					,  config=lowercase)
          self.assertIsInstance(new_rag_tokenizer.question_encoder					,  lowercase)
          self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()					,  rag_tokenizer.question_encoder.get_vocab())
          self.assertIsInstance(new_rag_tokenizer.generator					,  lowercase)
          self.assertEqual(new_rag_tokenizer.generator.get_vocab()					,  rag_tokenizer.generator.get_vocab())

   @slow
   def 							__lowercase		(					self)			->						Tuple:







          '''simple docstring'''

          a__						:	Any =				RagTokenizer.from_pretrained('facebook/rag-token-nq')
          a__						:	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',
          ]
          a__						:	Optional[Any] =				tokenizer(lowercase)
          self.assertIsNotNone(lowercase)

   @slow
   def 							__lowercase		(					self)			->						List[str]:







          '''simple docstring'''

          a__						:	str =				RagTokenizer.from_pretrained('facebook/rag-sequence-nq')
          a__						:	List[str] =				[
              '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',
          ]
          a__						:	Optional[int] =				tokenizer(lowercase)
          self.assertIsNotNone(lowercase)









from ..utils import is_flax_available, is_torch_available


if is_torch_available():
    from .autoencoder_kl import AutoencoderKL
    from .controlnet import ControlNetModel
    from .dual_transformer_ad import DualTransformeraDModel
    from .modeling_utils import ModelMixin
    from .prior_transformer import PriorTransformer
    from .ta_film_transformer import TaFilmDecoder
    from .transformer_ad import TransformeraDModel
    from .unet_ad import UNetaDModel
    from .unet_ad import UNetaDModel
    from .unet_ad_condition import UNetaDConditionModel
    from .unet_ad_condition import UNetaDConditionModel
    from .vq_model import VQModel

if is_flax_available():
    from .controlnet_flax import FlaxControlNetModel
    from .unet_ad_condition_flax import FlaxUNetaDConditionModel
    from .vae_flax import FlaxAutoencoderKL


