




'''simple docstring'''

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							(  _lowerCamelCase	):


				def     A_					(						self       :   List[str]   ):
									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       :   str   ):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname			,							'''dpr_tokenizer'''   )   )


				def     A_					(						self       :   Tuple   ):
									return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname			,							'''bart_tokenizer'''   )   )


				def     A_					(						self       :   Union[str, Any]   ):
									shutil.rmtree(self.tmpdirname   )


				@require_tokenizers
				def     A_					(						self       :   Optional[Any]   ):
									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       :   str   ):
									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       :   Tuple   ):
									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'''

import argparse
from collections import defaultdict

import yaml


a    :							List[Any]										=    'docs/source/en/_toctree.yml'


def 						__magic_name__       (      __UpperCAmelCase      )						->   str:

					'''simple docstring'''

					snake_case_		     =							defaultdict(__UpperCAmelCase      )
					for doc in model_doc:
										counts[doc["local"]] += 1
					snake_case_		     =							[key for key, value in counts.items() if value > 1]

					snake_case_		     =							[]
					for duplicate_key in duplicates:
										snake_case_		     =							list({doc['''title'''] for doc in model_doc if doc['''local'''] == duplicate_key}      )
										if len(__UpperCAmelCase      ) > 1:
															raise ValueError(
															    F"{duplicate_key} is present several times in the documentation table of content at "
															    '''`docs/source/en/_toctree.yml` with different *Title* values. Choose one of those and remove the '''
															    '''others.'''      )
										# Only add this once
										new_doc.append({'''local''': duplicate_key, '''title''': titles[0]}      )

					# Add none duplicate-keys
					new_doc.extend([doc for doc in model_doc if counts[doc['''local''']] == 1]      )

					# Sort
					return sorted(__UpperCAmelCase, key=lambda __UpperCAmelCase      : s["title"].lower()      )


def 						__magic_name__       (      __UpperCAmelCase=False      )						->   List[Any]:

					'''simple docstring'''

					with open(__UpperCAmelCase, encoding='''utf-8'''      ) as f:
										snake_case_		     =							yaml.safe_load(f.read()      )

					# Get to the API doc
					snake_case_		     =							0
					while content[api_idx]["title"] != "API":
										api_idx += 1
					snake_case_		     =							content[api_idx]['''sections''']

					# Then to the model doc
					snake_case_		     =							0
					while api_doc[model_idx]["title"] != "Models":
										model_idx += 1

					snake_case_		     =							api_doc[model_idx]['''sections''']

					snake_case_		     =							[(idx, section) for idx, section in enumerate(__UpperCAmelCase      ) if '''sections''' in section]
					snake_case_		     =							False
					for idx, modality_doc in modalities_docs:
										snake_case_		     =							modality_doc['''sections''']
										snake_case_		     =							clean_model_doc_toc(__UpperCAmelCase      )

										if old_modality_doc != new_modality_doc:
															snake_case_		     =							True
															if overwrite:
																				snake_case_		     =							new_modality_doc

					if diff:
										if overwrite:
															snake_case_		     =							model_doc
															snake_case_		     =							api_doc
															with open(__UpperCAmelCase, '''w''', encoding='''utf-8'''      ) as f:
																				f.write(yaml.dump(__UpperCAmelCase, allow_unicode=__UpperCAmelCase      )      )
										else:
															raise ValueError(
															    '''The model doc part of the table of content is not properly sorted, run `make style` to fix this.'''      )


if __name__ == "__main__":
							a    :							Dict										=    argparse.ArgumentParser()
							parser.add_argument('--fix_and_overwrite', action='store_true', help='Whether to fix inconsistencies.')
							a    :							Union[str, Any]										=    parser.parse_args()

							check_model_doc(args.fix_and_overwrite)

