

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'''







import warnings
from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class 				__SCREAMING_SNAKE_CASE  (lowerCamelCase_      ):






				"""simple docstring"""







				__a            =['image_processor', 'tokenizer']
				__a            ='LayoutLMv3ImageProcessor'
				__a            =('LayoutLMv3Tokenizer', 'LayoutLMv3TokenizerFast')




				def __init__(  self     :					Tuple					,				__a     :					int=None					,				__a     :					Union[str, Any]=None					,				**__a     :					Optional[Any]					):
								_a								= None
								if "feature_extractor" in kwargs:
												warnings.warn(
												    "The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`"
												    " instead."					,				__a					,				)
												_a								= kwargs.pop("feature_extractor"					)

								_a								= image_processor if image_processor is not None else feature_extractor
								if image_processor is None:
												raise ValueError("You need to specify an `image_processor`."					)
								if tokenizer is None:
												raise ValueError("You need to specify a `tokenizer`."					)

								super().__init__(__a					,				__a					)




				def __call__(  self     :					Any					,				__a     :					List[str]					,				__a     :					Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None					,				__a     :					Optional[Union[PreTokenizedInput, List[PreTokenizedInput]]] = None					,				__a     :					Union[List[List[int]], List[List[List[int]]]] = None					,				__a     :					Optional[Union[List[int], List[List[int]]]] = None					,				__a     :					bool = True					,				__a     :					Union[bool, str, PaddingStrategy] = False					,				__a     :					Union[bool, str, TruncationStrategy] = None					,				__a     :					Optional[int] = None					,				__a     :					int = 0					,				__a     :					Optional[int] = None					,				__a     :					Optional[bool] = None					,				__a     :					Optional[bool] = None					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = False					,				__a     :					bool = True					,				__a     :					Optional[Union[str, TensorType]] = None					,				**__a     :					Dict					,				):
								# verify input
								if self.image_processor.apply_ocr and (boxes is not None):
												raise ValueError(
												    "You cannot provide bounding boxes if you initialized the image processor with apply_ocr set to True."					)

								if self.image_processor.apply_ocr and (word_labels is not None):
												raise ValueError(
												    "You cannot provide word labels if you initialized the image processor with apply_ocr set to True."					)

								# first, apply the image processor
								_a								= self.image_processor(images=__a					,				return_tensors=__a					)

								# second, apply the tokenizer
								if text is not None and self.image_processor.apply_ocr and text_pair is None:
												if isinstance(__a					,				__a					):
																_a								= [text]  # add batch dimension (as the image processor always adds a batch dimension)
												_a								= features["words"]

								_a								= self.tokenizer(
								    text=text if text is not None else features["words"]					,				text_pair=text_pair if text_pair is not None else None					,				boxes=boxes if boxes is not None else features["boxes"]					,				word_labels=__a					,				add_special_tokens=__a					,				padding=__a					,				truncation=__a					,				max_length=__a					,				stride=__a					,				pad_to_multiple_of=__a					,				return_token_type_ids=__a					,				return_attention_mask=__a					,				return_overflowing_tokens=__a					,				return_special_tokens_mask=__a					,				return_offsets_mapping=__a					,				return_length=__a					,				verbose=__a					,				return_tensors=__a					,				**__a					,				)

								# add pixel values
								_a								= features.pop("pixel_values"					)
								if return_overflowing_tokens is True:
												_a								= self.get_overflowing_images(__a					,				encoded_inputs["overflow_to_sample_mapping"]					)
								_a								= images

								return encoded_inputs




				def   UpperCamelCase__   (  self     :					Optional[int]					,				__a     :					str					,				__a     :					List[Any]					):
								# in case there's an overflow, ensure each `input_ids` sample is mapped to its corresponding image
								_a								= []
								for sample_idx in overflow_to_sample_mapping:
												images_with_overflow.append(images[sample_idx]					)

								if len(__a					) != len(__a					):
												raise ValueError(
												    "Expected length of images to be the same as the length of `overflow_to_sample_mapping`, but got"
												    f' {len(__a					)} and {len(__a					)}'					)

								return images_with_overflow




				def   UpperCamelCase__   (  self     :					int					,				*__a     :					str					,				**__a     :					Tuple					):
								return self.tokenizer.batch_decode(*__a					,				**__a					)




				def   UpperCamelCase__   (  self     :					str					,				*__a     :					List[Any]					,				**__a     :					List[str]					):
								return self.tokenizer.decode(*__a					,				**__a					)




				@property
				def   UpperCamelCase__   (  self     :					Tuple					):
								return ["input_ids", "bbox", "attention_mask", "pixel_values"]




				@property
				def   UpperCamelCase__   (  self     :					int					):
								warnings.warn(
								    "`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead."					,				__a					,				)
								return self.image_processor_class




				@property
				def   UpperCamelCase__   (  self     :					List[str]					):
								warnings.warn(
								    "`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead."					,				__a					,				)
								return self.image_processor
