
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					(	__a       ):




				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	tempfile.mkdtemp()
							lowerCAmelCase_						=	8

							# DPR tok
							lowerCAmelCase_						=	[
							    "[UNK]",
							    "[CLS]",
							    "[SEP]",
							    "[PAD]",
							    "[MASK]",
							    "want",
							    "##want",
							    "##ed",
							    "wa",
							    "un",
							    "runn",
							    "##ing",
							    ",",
							    "low",
							    "lowest",
							]
							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				"dpr_tokenizer"     )
							os.makedirs(_UpperCamelCase				,				exist_ok=_UpperCamelCase     )
							lowerCAmelCase_						=	os.path.join(_UpperCamelCase				,				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
							lowerCAmelCase_						=	[
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							lowerCAmelCase_						=	dict(zip(_UpperCamelCase				,				range(len(_UpperCamelCase     )     )     )     )
							lowerCAmelCase_						=	["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
							lowerCAmelCase_						=	{"unk_token": "<unk>"}

							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				"bart_tokenizer"     )
							os.makedirs(_UpperCamelCase				,				exist_ok=_UpperCamelCase     )
							lowerCAmelCase_						=	os.path.join(_UpperCamelCase				,				BART_VOCAB_FILES_NAMES["vocab_file"]     )
							lowerCAmelCase_						=	os.path.join(_UpperCamelCase				,				BART_VOCAB_FILES_NAMES["merges_file"]     )
							with open(self.vocab_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write(json.dumps(_UpperCamelCase     ) + "\n"     )
							with open(self.merges_file				,				"w"				,				encoding="utf-8"     ) as fp:
										fp.write("\n".join(_UpperCamelCase     )     )




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




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




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




				@require_tokenizers
				def      __a						(					self     )				->      List[str]:
							lowerCAmelCase_						=	os.path.join(self.tmpdirname				,				"rag_tokenizer"     )
							lowerCAmelCase_						=	RagConfig(question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()     )
							lowerCAmelCase_						=	RagTokenizer(question_encoder=self.get_dpr_tokenizer()				,				generator=self.get_bart_tokenizer()     )
							rag_config.save_pretrained(_UpperCamelCase     )
							rag_tokenizer.save_pretrained(_UpperCamelCase     )
							lowerCAmelCase_						=	RagTokenizer.from_pretrained(_UpperCamelCase				,				config=_UpperCamelCase     )
							self.assertIsInstance(new_rag_tokenizer.question_encoder				,				_UpperCamelCase     )
							self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()				,				rag_tokenizer.question_encoder.get_vocab()     )
							self.assertIsInstance(new_rag_tokenizer.generator				,				_UpperCamelCase     )
							self.assertEqual(new_rag_tokenizer.generator.get_vocab()				,				rag_tokenizer.generator.get_vocab()     )




				@slow
				def      __a						(					self     )				->      Optional[int]:
							lowerCAmelCase_						=	RagTokenizer.from_pretrained("facebook/rag-token-nq"     )
							lowerCAmelCase_						=	[
							    "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",
							]
							lowerCAmelCase_						=	tokenizer(_UpperCamelCase     )
							self.assertIsNotNone(_UpperCamelCase     )




				@slow
				def      __a						(					self     )				->      Any:
							lowerCAmelCase_						=	RagTokenizer.from_pretrained("facebook/rag-sequence-nq"     )
							lowerCAmelCase_						=	[
							    "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",
							]
							lowerCAmelCase_						=	tokenizer(_UpperCamelCase     )
							self.assertIsNotNone(_UpperCamelCase     )





from typing import List, Optional, TypeVar

from .arrow_dataset import Dataset, _concatenate_map_style_datasets, _interleave_map_style_datasets
from .dataset_dict import DatasetDict, IterableDatasetDict
from .info import DatasetInfo
from .iterable_dataset import IterableDataset, _concatenate_iterable_datasets, _interleave_iterable_datasets
from .splits import NamedSplit
from .utils import logging
from .utils.py_utils import Literal


_A   =     logging.get_logger(__name__)


_A   =     TypeVar("DatasetType", Dataset, IterableDataset)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[List[float]] = None    ,							__lowerCAmelCase  :  Optional[int] = None    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  Literal["first_exhausted", "all_exhausted"] = "first_exhausted"    ,							):




			"""simple docstring"""


			from .arrow_dataset import Dataset
			from .iterable_dataset import IterableDataset

			if not datasets:
						raise ValueError("Unable to interleave an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if stopping_strategy not in ["first_exhausted", "all_exhausted"]:
						raise ValueError(F"""{stopping_strategy} is not supported. Please enter a valid stopping_strategy."""				)
			if dataset_type is Dataset:
						return _interleave_map_style_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)
			else:
						return _interleave_iterable_datasets(
						    __lowerCAmelCase    ,							__lowerCAmelCase    ,							__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							stopping_strategy=__lowerCAmelCase				)






def    lowerCamelCase__				(				__lowerCAmelCase  :  List[DatasetType]    ,							__lowerCAmelCase  :  Optional[DatasetInfo] = None    ,							__lowerCAmelCase  :  Optional[NamedSplit] = None    ,							__lowerCAmelCase  :  int = 0    ,							):




			"""simple docstring"""


			if not dsets:
						raise ValueError("Unable to concatenate an empty list of datasets."				)
			for i, dataset in enumerate(__lowerCAmelCase				):
						if not isinstance(__lowerCAmelCase    ,							(Dataset, IterableDataset)				):
									if isinstance(__lowerCAmelCase    ,							(DatasetDict, IterableDatasetDict)				):
												if not dataset:
															raise ValueError(
															    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} """
															    "is an empty dataset dictionary."				)
												raise ValueError(
												    F"""Dataset at position {i} has at least one split: {list(__lowerCAmelCase				)}\n"""
												    F"""Please pick one to interleave with the other datasets, for example: dataset['{next(iter(__lowerCAmelCase				)				)}']"""				)
									raise ValueError(
									    F"""Expected a list of Dataset objects or a list of IterableDataset objects, but element at position {i} is a {type(__lowerCAmelCase				).__name__}."""				)
						if i == 0:
									lowerCAmelCase_	,  lowerCAmelCase_						=	(
									    (Dataset, IterableDataset) if isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				) else (IterableDataset, Dataset)
									)
						elif not isinstance(__lowerCAmelCase    ,							__lowerCAmelCase				):
									raise ValueError(
									    F"""Unable to interleave a {dataset_type.__name__} (at position 0) with a {other_type.__name__} (at position {i}). Expected a list of Dataset objects or a list of IterableDataset objects."""				)
			if dataset_type is Dataset:
						return _concatenate_map_style_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)
			else:
						return _concatenate_iterable_datasets(__lowerCAmelCase    ,							info=__lowerCAmelCase    ,							split=__lowerCAmelCase    ,							axis=__lowerCAmelCase				)



