

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__  (    snake_case	):
			"""simple docstring"""







			def    UpperCamelCase						(			self				)     ->  int:
							'''simple docstring'''






							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    UpperCamelCase						(			self				)     ->  DPRQuestionEncoderTokenizer:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  BartTokenizer:
							'''simple docstring'''






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


			def    UpperCamelCase						(			self				)     ->  Union[str, Any]:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname				)


			@require_tokenizers
			def    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							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    UpperCamelCase						(			self				)     ->  str:
							'''simple docstring'''






							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    UpperCamelCase						(			self				)     ->  Optional[int]:
							'''simple docstring'''






							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"""



# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
						import torch





class 					_UpperCAmelCase       ( TensorFormatter[Mapping, "torch.Tensor", Mapping]							):




					def __init__(				self     :						Any    ,						A     :						int=None    ,						**A     :						str   )  ->  Union[str, Any]:
							super().__init__(features=A   )
							lowercase_ : Union[str, Any]							       =   torch_tensor_kwargs
							import torch  # noqa import torch at initialization




					def   A  (				self     :						Dict    ,						A     :						int   )  ->  List[Any]:
							import torch

							if isinstance(A    ,						A   ) and column:
									if all(
									    isinstance(A    ,						torch.Tensor   ) and x.shape == column[0].shape and x.dtype == column[0].dtype
									    for x in column   ):
											return torch.stack(A   )
							return column




					def   A  (				self     :						int    ,						A     :						Any   )  ->  Optional[Any]:
							import torch

							if isinstance(A    ,						(str, bytes, type(A   ))   ):
									return value
							elif isinstance(A    ,						(np.character, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.character   ):
									return value.tolist()

							lowercase_ : Any							       =   {}

							if isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.integer   ):
									lowercase_ : Any							       =   {'''dtype''': torch.intaa}
							elif isinstance(A    ,						(np.number, np.ndarray)   ) and np.issubdtype(value.dtype    ,						np.floating   ):
									lowercase_ : Dict							       =   {'''dtype''': torch.floataa}
							elif config.PIL_AVAILABLE and "PIL" in sys.modules:
									import PIL.Image

									if isinstance(A    ,						PIL.Image.Image   ):
											lowercase_ : Dict							       =   np.asarray(A   )
							return torch.tensor(A    ,						**{**default_dtype, **self.torch_tensor_kwargs}   )




					def   A  (				self     :						Union[str, Any]    ,						A     :						Optional[int]   )  ->  str:
							import torch

							# support for torch, tf, jax etc.
							if hasattr(A    ,						'''__array__'''   ) and not isinstance(A    ,						torch.Tensor   ):
									lowercase_ : Optional[int]							       =   data_struct.__array__()
							# support for nested types like struct of list of struct
							if isinstance(A    ,						np.ndarray   ):
									if data_struct.dtype == object:  # torch tensors cannot be instantied from an array of objects
											return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							elif isinstance(A    ,						(list, tuple)   ):
									return self._consolidate([self.recursive_tensorize(A   ) for substruct in data_struct]   )
							return self._tensorize(A   )




					def   A  (				self     :						Dict    ,						A     :						dict   )  ->  Tuple:
							return map_nested(self._recursive_tensorize    ,						A    ,						map_list=A   )




					def   A  (				self     :						str    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[Any]							       =   self.numpy_arrow_extractor().extract_row(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_row(A   )
							return self.recursive_tensorize(A   )




					def   A  (				self     :						List[Any]    ,						A     :						pa.Table   )  ->  "torch.Tensor":
							lowercase_ : List[str]							       =   self.numpy_arrow_extractor().extract_column(A   )
							lowercase_ : str							       =   self.python_features_decoder.decode_column(A    ,						pa_table.column_names[0]   )
							lowercase_ : Optional[int]							       =   self.recursive_tensorize(A   )
							lowercase_ : Any							       =   self._consolidate(A   )
							return column






					def   A  (				self     :						List[str]    ,						A     :						pa.Table   )  ->  Mapping:
							lowercase_ : Optional[int]							       =   self.numpy_arrow_extractor().extract_batch(A   )
							lowercase_ : int							       =   self.python_features_decoder.decode_batch(A   )
							lowercase_ : Dict							       =   self.recursive_tensorize(A   )
							for column_name in batch:
									lowercase_ : Optional[Any]							       =   self._consolidate(batch[column_name]   )
							return batch



