

import re
import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin






class        A_		(       SCREAMING_SNAKE_CASE				):
							_UpperCAmelCase    :  Any						      =      ['''image_processor''', '''tokenizer''']
							_UpperCAmelCase    :  List[Any]						      =      '''AutoImageProcessor'''
							_UpperCAmelCase    :  Dict						      =      '''AutoTokenizer'''
							def __init__(		self		:			Union[str, Any]   ,SCREAMING_SNAKE_CASE__		:			Optional[int]=None   ,SCREAMING_SNAKE_CASE__		:			List[Any]=None   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
								__lowerCamelCase				:				List[str]					       = None
								if "feature_extractor" in kwargs:
									warnings.warn(
									    'The `feature_extractor` argument is deprecated and will be removed in v5, use `image_processor`'
									    ' instead.'   ,SCREAMING_SNAKE_CASE__   ,)
									__lowerCamelCase				:				Union[str, Any]					       = kwargs.pop('feature_extractor')

								__lowerCamelCase				:				Dict					       = 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__(SCREAMING_SNAKE_CASE__   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Dict					       = self.image_processor
								__lowerCamelCase				:				Optional[int]					       = False
							def __call__(		self		:			int   ,*SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,**SCREAMING_SNAKE_CASE__		:			Union[str, Any]):
								# For backward compatibility
								if self._in_target_context_manager:
									return self.current_processor(*SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)

								__lowerCamelCase				:				Optional[int]					       = kwargs.pop('images'   ,SCREAMING_SNAKE_CASE__)
								__lowerCamelCase				:				Union[str, Any]					       = kwargs.pop('text'   ,SCREAMING_SNAKE_CASE__)
								if len(SCREAMING_SNAKE_CASE__) > 0:
									__lowerCamelCase				:				int					       = args[0]
									__lowerCamelCase				:				List[str]					       = args[1:]

								if images is None and text is None:
									raise ValueError('You need to specify either an `images` or `text` input to process.')

								if images is not None:
									__lowerCamelCase				:				Optional[int]					       = self.image_processor(SCREAMING_SNAKE_CASE__   ,*SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
								if text is not None:
									__lowerCamelCase				:				List[Any]					       = self.tokenizer(SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)

								if text is None:
									return inputs
								elif images is None:
									return encodings
								else:
									__lowerCamelCase				:				Optional[Any]					       = encodings['input_ids']
									return inputs
							def     lowerCAmelCase     (		self		:			int   ,*SCREAMING_SNAKE_CASE__		:			Union[str, Any]   ,**SCREAMING_SNAKE_CASE__		:			Dict):
								return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							def     lowerCAmelCase     (		self		:			Optional[Any]   ,*SCREAMING_SNAKE_CASE__		:			List[Any]   ,**SCREAMING_SNAKE_CASE__		:			Any):
								return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__   ,**SCREAMING_SNAKE_CASE__)
							@contextmanager
							def     lowerCAmelCase     (		self		:			Tuple):
								warnings.warn(
								    '`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your '
								    'labels by using the argument `text` of the regular `__call__` method (either in the same call as '
								    'your images inputs, or in a separate call.')
								__lowerCamelCase				:				List[Any]					       = True
								__lowerCamelCase				:				str					       = self.tokenizer
								yield
								__lowerCamelCase				:				Tuple					       = self.image_processor
								__lowerCamelCase				:				Tuple					       = False
							def     lowerCAmelCase     (		self		:			Any   ,SCREAMING_SNAKE_CASE__		:			str   ,SCREAMING_SNAKE_CASE__		:			int=False   ,SCREAMING_SNAKE_CASE__		:			List[Any]=None):
								if added_vocab is None:
									__lowerCamelCase				:				str					       = self.tokenizer.get_added_vocab()

								__lowerCamelCase				:				Union[str, Any]					       = {}

								while tokens:
									__lowerCamelCase				:				Tuple					       = re.search(R'<s_(.*?)>'   ,SCREAMING_SNAKE_CASE__   ,re.IGNORECASE)
									if start_token is None:
										break
									__lowerCamelCase				:				Dict					       = start_token.group(1)
									__lowerCamelCase				:				List[str]					       = re.search(RF"</s_{key}>"   ,SCREAMING_SNAKE_CASE__   ,re.IGNORECASE)
									__lowerCamelCase				:				Optional[int]					       = start_token.group()
									if end_token is None:
										__lowerCamelCase				:				List[Any]					       = tokens.replace(SCREAMING_SNAKE_CASE__   ,'')
									else:
										__lowerCamelCase				:				Tuple					       = end_token.group()
										__lowerCamelCase				:				int					       = re.escape(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase				:				str					       = re.escape(SCREAMING_SNAKE_CASE__)
										__lowerCamelCase				:				Union[str, Any]					       = re.search(F"{start_token_escaped}(.*?){end_token_escaped}"   ,SCREAMING_SNAKE_CASE__   ,re.IGNORECASE)
										if content is not None:
											__lowerCamelCase				:				List[Any]					       = content.group(1).strip()
											if r"<s_" in content and r"</s_" in content:  # non-leaf node
												__lowerCamelCase				:				str					       = self.tokenajson(SCREAMING_SNAKE_CASE__   ,is_inner_value=SCREAMING_SNAKE_CASE__   ,added_vocab=SCREAMING_SNAKE_CASE__)
												if value:
													if len(SCREAMING_SNAKE_CASE__) == 1:
														__lowerCamelCase				:				Tuple					       = value[0]
													__lowerCamelCase				:				int					       = value
											else:  # leaf nodes
												__lowerCamelCase				:				Tuple					       = []
												for leaf in content.split(R'<sep/>'):
													__lowerCamelCase				:				List[Any]					       = leaf.strip()
													if leaf in added_vocab and leaf[0] == "<" and leaf[-2:] == "/>":
														__lowerCamelCase				:				str					       = leaf[1:-2]  # for categorical special tokens
													output[key].append(SCREAMING_SNAKE_CASE__)
												if len(output[key]) == 1:
													__lowerCamelCase				:				Dict					       = output[key][0]

										__lowerCamelCase				:				Dict					       = tokens[tokens.find(SCREAMING_SNAKE_CASE__) + len(SCREAMING_SNAKE_CASE__) :].strip()
										if tokens[:6] == r"<sep/>":  # non-leaf nodes
											return [output] + self.tokenajson(tokens[6:]   ,is_inner_value=SCREAMING_SNAKE_CASE__   ,added_vocab=SCREAMING_SNAKE_CASE__)

								if len(SCREAMING_SNAKE_CASE__):
									return [output] if is_inner_value else output
								else:
									return [] if is_inner_value else {"text_sequence": tokens}
							@property
							def     lowerCAmelCase     (		self		:			List[str]):
								warnings.warn(
								    '`feature_extractor_class` is deprecated and will be removed in v5. Use `image_processor_class` instead.'   ,SCREAMING_SNAKE_CASE__   ,)
								return self.image_processor_class


							@property
							def     lowerCAmelCase     (		self		:			List[Any]):
								warnings.warn(
								    '`feature_extractor` is deprecated and will be removed in v5. Use `image_processor` instead.'   ,SCREAMING_SNAKE_CASE__   ,)
								return self.image_processor






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__							(     __magic_name__	):






				def 							_lowerCamelCase		(						self					:      Tuple   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Optional[Any]         =		tempfile.mkdtemp()
							lowerCAmelCase__      :      Dict         =		8

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

							lowerCAmelCase__      :      Dict         =		os.path.join(self.tmpdirname    ,     'bart_tokenizer'   )
							os.makedirs(a    ,     exist_ok=a   )
							lowerCAmelCase__      :      Optional[Any]         =		os.path.join(a    ,     BART_VOCAB_FILES_NAMES['vocab_file']   )
							lowerCAmelCase__      :      Any         =		os.path.join(a    ,     BART_VOCAB_FILES_NAMES['merges_file']   )
							with open(self.vocab_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write(json.dumps(a   ) + '\n'   )
							with open(self.merges_file    ,     'w'    ,     encoding='utf-8'   ) as fp:
										fp.write('\n'.join(a   )   )






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







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






				def 							_lowerCamelCase		(						self					:      Optional[Any]   ):






							'''simple docstring'''







							shutil.rmtree(self.tmpdirname   )






				@require_tokenizers
				def 							_lowerCamelCase		(						self					:      Any   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		os.path.join(self.tmpdirname    ,     'rag_tokenizer'   )
							lowerCAmelCase__      :      Any         =		RagConfig(question_encoder=DPRConfig().to_dict()    ,     generator=BartConfig().to_dict()   )
							lowerCAmelCase__      :      Any         =		RagTokenizer(question_encoder=self.get_dpr_tokenizer()    ,     generator=self.get_bart_tokenizer()   )
							rag_config.save_pretrained(a   )
							rag_tokenizer.save_pretrained(a   )
							lowerCAmelCase__      :      List[str]         =		RagTokenizer.from_pretrained(a    ,     config=a   )
							self.assertIsInstance(new_rag_tokenizer.question_encoder    ,     a   )
							self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()    ,     rag_tokenizer.question_encoder.get_vocab()   )
							self.assertIsInstance(new_rag_tokenizer.generator    ,     a   )
							self.assertEqual(new_rag_tokenizer.generator.get_vocab()    ,     rag_tokenizer.generator.get_vocab()   )






				@slow
				def 							_lowerCamelCase		(						self					:      int   ):






							'''simple docstring'''







							lowerCAmelCase__      :      Union[str, Any]         =		RagTokenizer.from_pretrained('facebook/rag-token-nq'   )
							lowerCAmelCase__      :      Any         =		[
							    '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__      :      List[str]         =		tokenizer(a   )
							self.assertIsNotNone(a   )






				@slow
				def 							_lowerCamelCase		(						self					:      List[Any]   ):






							'''simple docstring'''







							lowerCAmelCase__      :      List[str]         =		RagTokenizer.from_pretrained('facebook/rag-sequence-nq'   )
							lowerCAmelCase__      :      List[str]         =		[
							    '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__      :      int         =		tokenizer(a   )
							self.assertIsNotNone(a   )