


"""simple docstring"""


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__						(   UpperCAmelCase_			):







						def __a						(							self   :    List[Any]		):
								A					       = tempfile.mkdtemp()
								A					       = 8

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

								A					       = os.path.join(self.tmpdirname			,			'bart_tokenizer'		)
								os.makedirs(_lowercase			,			exist_ok=_lowercase		)
								A					       = os.path.join(_lowercase			,			BART_VOCAB_FILES_NAMES['vocab_file']		)
								A					       = 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 __a						(							self   :    Optional[Any]		):
								return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,			'dpr_tokenizer'		)		)







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







						def __a						(							self   :    str		):
								shutil.rmtree(self.tmpdirname		)







						@require_tokenizers
						def __a						(							self   :    Optional[Any]		):
								A					       = os.path.join(self.tmpdirname			,			'rag_tokenizer'		)
								A					       = RagConfig(question_encoder=DPRConfig().to_dict()			,			generator=BartConfig().to_dict()		)
								A					       = RagTokenizer(question_encoder=self.get_dpr_tokenizer()			,			generator=self.get_bart_tokenizer()		)
								rag_config.save_pretrained(_lowercase		)
								rag_tokenizer.save_pretrained(_lowercase		)
								A					       = 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 __a						(							self   :    Dict		):
								A					       = RagTokenizer.from_pretrained('facebook/rag-token-nq'		)
								A					       = [
								    '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					       = tokenizer(_lowercase		)
								self.assertIsNotNone(_lowercase		)







						@slow
						def __a						(							self   :    int		):
								A					       = RagTokenizer.from_pretrained('facebook/rag-sequence-nq'		)
								A					       = [
								    '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					       = tokenizer(_lowercase		)
								self.assertIsNotNone(_lowercase		)









"""simple docstring"""


from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCamelCase						:				Any             =      {
    "configuration_lxmert": ["LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP", "LxmertConfig"],
    "tokenization_lxmert": ["LxmertTokenizer"],
}

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				int             =      ["LxmertTokenizerFast"]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Union[str, Any]             =      [
	    "LxmertEncoder",
	    "LxmertForPreTraining",
	    "LxmertForQuestionAnswering",
	    "LxmertModel",
	    "LxmertPreTrainedModel",
	    "LxmertVisualFeatureEncoder",
	    "LxmertXLayer",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCamelCase						:				Union[str, Any]             =      [
	    "TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFLxmertForPreTraining",
	    "TFLxmertMainLayer",
	    "TFLxmertModel",
	    "TFLxmertPreTrainedModel",
	    "TFLxmertVisualFeatureEncoder",
	]


if TYPE_CHECKING:
	from .configuration_lxmert import LXMERT_PRETRAINED_CONFIG_ARCHIVE_MAP, LxmertConfig
	from .tokenization_lxmert import LxmertTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_lxmert_fast import LxmertTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_lxmert import (
		    LxmertEncoder,
		    LxmertForPreTraining,
		    LxmertForQuestionAnswering,
		    LxmertModel,
		    LxmertPreTrainedModel,
		    LxmertVisualFeatureEncoder,
		    LxmertXLayer,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_lxmert import (
		    TF_LXMERT_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFLxmertForPreTraining,
		    TFLxmertMainLayer,
		    TFLxmertModel,
		    TFLxmertPreTrainedModel,
		    TFLxmertVisualFeatureEncoder,
		)

else:
	import sys

	UpperCamelCase						:				Dict             =      _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





