"""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 	UpperCamelCase    (					UpperCAmelCase_		):



				def       _lowercase  (self       :   List[Any])					->			Any:
					__snake_case    :							str   	=				tempfile.mkdtemp()
					__snake_case    :							Union[str, Any]   	=				8

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

					__snake_case    :							Tuple   	=				os.path.join(self.tmpdirname			,						'bart_tokenizer')
					os.makedirs(_lowercase			,						exist_ok=_lowercase)
					__snake_case    :							Any   	=				os.path.join(_lowercase			,						BART_VOCAB_FILES_NAMES['vocab_file'])
					__snake_case    :							Union[str, Any]   	=				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       :   Optional[Any])					->			Tuple:
					return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,						'dpr_tokenizer'))



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



				def       _lowercase  (self       :   str)					->			int:
					shutil.rmtree(self.tmpdirname)



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






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






"""simple docstring"""







from typing import TYPE_CHECKING

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


_a  :  Optional[Any]=				{
    "configuration_wav2vec2": ["WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP", "Wav2Vec2Config"],
    "feature_extraction_wav2vec2": ["Wav2Vec2FeatureExtractor"],
    "processing_wav2vec2": ["Wav2Vec2Processor"],
    "tokenization_wav2vec2": ["Wav2Vec2CTCTokenizer", "Wav2Vec2Tokenizer"],
}


try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  Optional[Any]=				[
	    "WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "Wav2Vec2ForAudioFrameClassification",
	    "Wav2Vec2ForCTC",
	    "Wav2Vec2ForMaskedLM",
	    "Wav2Vec2ForPreTraining",
	    "Wav2Vec2ForSequenceClassification",
	    "Wav2Vec2ForXVector",
	    "Wav2Vec2Model",
	    "Wav2Vec2PreTrainedModel",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[str]=				[
	    "TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST",
	    "TFWav2Vec2ForCTC",
	    "TFWav2Vec2Model",
	    "TFWav2Vec2PreTrainedModel",
	    "TFWav2Vec2ForSequenceClassification",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_a  :  List[Any]=				[
	    "FlaxWav2Vec2ForCTC",
	    "FlaxWav2Vec2ForPreTraining",
	    "FlaxWav2Vec2Model",
	    "FlaxWav2Vec2PreTrainedModel",
	]


if TYPE_CHECKING:
	from .configuration_wavaveca import WAV_2_VEC_2_PRETRAINED_CONFIG_ARCHIVE_MAP, WavaVecaConfig
	from .feature_extraction_wavaveca import WavaVecaFeatureExtractor
	from .processing_wavaveca import WavaVecaProcessor
	from .tokenization_wavaveca import WavaVecaCTCTokenizer, WavaVecaTokenizer

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_wavaveca import (
		    WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    WavaVecaForAudioFrameClassification,
		    WavaVecaForCTC,
		    WavaVecaForMaskedLM,
		    WavaVecaForPreTraining,
		    WavaVecaForSequenceClassification,
		    WavaVecaForXVector,
		    WavaVecaModel,
		    WavaVecaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    TF_WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFWavaVecaForCTC,
		    TFWavaVecaForSequenceClassification,
		    TFWavaVecaModel,
		    TFWavaVecaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_wavaveca import (
		    FlaxWavaVecaForCTC,
		    FlaxWavaVecaForPreTraining,
		    FlaxWavaVecaModel,
		    FlaxWavaVecaPreTrainedModel,
		)


else:
	import sys

	_a  :  Tuple=				_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)





