
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   SCREAMING_SNAKE_CASE__	(      UpperCAmelCase   ):





	'''simple docstring'''



	def 					A				(    self      :       List[Any]      ):


				'''simple docstring'''


				_snake_case            =     tempfile.mkdtemp()
				_snake_case            =     8

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

				_snake_case            =     os.path.join(self.tmpdirname     ,    'bart_tokenizer'      )
				os.makedirs(lowercase     ,    exist_ok=lowercase      )
				_snake_case            =     os.path.join(lowercase     ,    BART_VOCAB_FILES_NAMES['vocab_file']      )
				_snake_case            =     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      :       Any      ):


				'''simple docstring'''


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



	def 					A				(    self      :       Union[str, Any]      ):


				'''simple docstring'''


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



	def 					A				(    self      :       str      ):


				'''simple docstring'''


				shutil.rmtree(self.tmpdirname      )



	@require_tokenizers
	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				_snake_case            =     os.path.join(self.tmpdirname     ,    'rag_tokenizer'      )
				_snake_case            =     RagConfig(question_encoder=DPRConfig().to_dict()     ,    generator=BartConfig().to_dict()      )
				_snake_case            =     RagTokenizer(question_encoder=self.get_dpr_tokenizer()     ,    generator=self.get_bart_tokenizer()      )
				rag_config.save_pretrained(lowercase      )
				rag_tokenizer.save_pretrained(lowercase      )
				_snake_case            =     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      :       Any      ):


				'''simple docstring'''


				_snake_case            =     RagTokenizer.from_pretrained('facebook/rag-token-nq'      )
				_snake_case            =     [
				    "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            =     tokenizer(lowercase      )
				self.assertIsNotNone(lowercase      )





	@slow
	def 					A				(    self      :       Any      ):


				'''simple docstring'''


				_snake_case            =     RagTokenizer.from_pretrained('facebook/rag-sequence-nq'      )
				_snake_case            =     [
				    "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            =     tokenizer(lowercase      )
				self.assertIsNotNone(lowercase      )


"""simple docstring"""

from typing import TYPE_CHECKING

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


if is_sentencepiece_available():
    from ..ta.tokenization_ta import TaTokenizer
else:
    from ...utils.dummy_sentencepiece_objects import TaTokenizer

_UpperCamelCase		     =    TaTokenizer

if is_tokenizers_available():
    from ..ta.tokenization_ta_fast import TaTokenizerFast
else:
    from ...utils.dummy_tokenizers_objects import TaTokenizerFast

_UpperCamelCase		     =    TaTokenizerFast

_UpperCamelCase		     =    {"""configuration_mt5""": ["""MT5Config""", """MT5OnnxConfig"""]}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    [
        """MT5EncoderModel""",
        """MT5ForConditionalGeneration""",
        """MT5ForQuestionAnswering""",
        """MT5Model""",
        """MT5PreTrainedModel""",
        """MT5Stack""",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""TFMT5EncoderModel""", """TFMT5ForConditionalGeneration""", """TFMT5Model"""]

try:
    if not is_flax_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _UpperCamelCase		     =    ["""FlaxMT5EncoderModel""", """FlaxMT5ForConditionalGeneration""", """FlaxMT5Model"""]


if TYPE_CHECKING:
    from .configuration_mta import MTaConfig, MTaOnnxConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mta import (
            MTaEncoderModel,
            MTaForConditionalGeneration,
            MTaForQuestionAnswering,
            MTaModel,
            MTaPreTrainedModel,
            MTaStack,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mta import TFMTaEncoderModel, TFMTaForConditionalGeneration, TFMTaModel

    try:
        if not is_flax_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_flax_mta import FlaxMTaEncoderModel, FlaxMTaForConditionalGeneration, FlaxMTaModel

else:
    import sys

    _UpperCamelCase		     =    _LazyModule(
        __name__,
        globals()["""__file__"""],
        _import_structure,
        extra_objects={"""MT5Tokenizer""": MTaTokenizer, """MT5TokenizerFast""": MTaTokenizerFast},
        module_spec=__spec__,
    )






