
import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
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.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
			import faiss




@require_faiss
class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_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     :   List[str]     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


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



	def 			A							(  self     :   int     ):


				'''simple docstring'''


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



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				shutil.rmtree(self.tmpdirname     )



	def 			A							(  self     :   List[Any]     ):


				'''simple docstring'''


				_snake_case          =      Dataset.from_dict(
				    {
				        'id': ['0', '1'],
				        'text': ['foo', 'bar'],
				        'title': ['Foo', 'Bar'],
				        'embeddings': [np.ones(self.retrieval_vector_size     ), 2 * np.ones(self.retrieval_vector_size     )],
				    }     )
				dataset.add_faiss_index('embeddings'      ,  string_factory='Flat'      ,  metric_type=faiss.METRIC_INNER_PRODUCT     )
				return dataset



	def 			A							(  self     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      self.get_dummy_dataset()
				_snake_case          =      RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,  question_encoder=DPRConfig().to_dict()      ,  generator=BartConfig().to_dict()      ,  )
				with patch('transformers.models.rag.retrieval_rag.load_dataset'     ) as mock_load_dataset:
							_snake_case          =      dataset
							_snake_case          =      RagRetriever(
							    lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  )
				return retriever



	def 			A							(  self     :   List[Any]      ,  lowercase     :   bool     ):


				'''simple docstring'''


				_snake_case          =      self.get_dummy_dataset()
				_snake_case          =      RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,  question_encoder=DPRConfig().to_dict()      ,  generator=BartConfig().to_dict()      ,  index_name='custom'      ,  )
				if from_disk:
							_snake_case          =      os.path.join(self.tmpdirname      ,  'dataset'     )
							_snake_case          =      os.path.join(self.tmpdirname      ,  'index.faiss'     )
							dataset.get_index('embeddings'     ).save(os.path.join(self.tmpdirname      ,  'index.faiss'     )     )
							dataset.drop_index('embeddings'     )
							dataset.save_to_disk(os.path.join(self.tmpdirname      ,  'dataset'     )     )
							del dataset
							_snake_case          =      RagRetriever(
							    lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  )
				else:
							_snake_case          =      RagRetriever(
							    lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()      ,  index=CustomHFIndex(config.retrieval_vector_size      ,  lowercase     )      ,  )
				return retriever



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      Dataset.from_dict(
				    {
				        'id': ['0', '1'],
				        'text': ['foo', 'bar'],
				        'title': ['Foo', 'Bar'],
				        'embeddings': [np.ones(self.retrieval_vector_size + 1     ), 2 * np.ones(self.retrieval_vector_size + 1     )],
				    }     )
				dataset.add_faiss_index('embeddings'      ,  string_factory='Flat'      ,  metric_type=faiss.METRIC_INNER_PRODUCT     )

				_snake_case          =      os.path.join(self.tmpdirname      ,  'hf_bert_base.hnswSQ8_correct_phi_128.c_index'     )
				dataset.save_faiss_index('embeddings'      ,  index_file_name + '.index.dpr'     )
				pickle.dump(dataset['id']      ,  open(index_file_name + '.index_meta.dpr'      ,  'wb'     )     )

				_snake_case          =      os.path.join(self.tmpdirname      ,  'psgs_w100.tsv.pkl'     )
				_snake_case          =      {sample['id']: [sample['text'], sample['title']] for sample in dataset}
				pickle.dump(lowercase      ,  open(lowercase      ,  'wb'     )     )

				_snake_case          =      RagConfig(
				    retrieval_vector_size=self.retrieval_vector_size      ,  question_encoder=DPRConfig().to_dict()      ,  generator=BartConfig().to_dict()      ,  index_name='legacy'      ,  index_path=self.tmpdirname      ,  )
				_snake_case          =      RagRetriever(
				    lowercase      ,  question_encoder_tokenizer=self.get_dpr_tokenizer()      ,  generator_tokenizer=self.get_bart_tokenizer()     )
				return retriever



	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				_snake_case          =      1
				_snake_case          =      self.get_dummy_canonical_hf_index_retriever()
				_snake_case          =      np.array(
				    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
				_snake_case     , _snake_case     , _snake_case          =      retriever.retrieve(lowercase      ,  n_docs=lowercase     )
				self.assertEqual(retrieved_doc_embeds.shape      ,  (2, n_docs, self.retrieval_vector_size)     )
				self.assertEqual(len(lowercase     )      ,  2     )
				self.assertEqual(sorted(doc_dicts[0]     )      ,  ['embeddings', 'id', 'text', 'title']     )
				self.assertEqual(len(doc_dicts[0]['id']     )      ,  lowercase     )
				self.assertEqual(doc_dicts[0]['id'][0]      ,  '1'     )  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['id'][0]      ,  '0'     )  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()      ,  [[1], [0]]     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.get_dummy_canonical_hf_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
							with patch('transformers.models.rag.retrieval_rag.load_dataset'     ) as mock_load_dataset:
										_snake_case          =      self.get_dummy_dataset()
										retriever.save_pretrained(lowercase     )
										_snake_case          =      RagRetriever.from_pretrained(lowercase     )
										self.assertIsInstance(lowercase      ,  lowercase     )
										_snake_case          =      np.array(
										    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
										_snake_case          =      retriever.retrieve(lowercase      ,  n_docs=1     )
										self.assertTrue(out is not None     )



	def 			A							(  self     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      1
				_snake_case          =      self.get_dummy_custom_hf_index_retriever(from_disk=lowercase     )
				_snake_case          =      np.array(
				    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
				_snake_case     , _snake_case     , _snake_case          =      retriever.retrieve(lowercase      ,  n_docs=lowercase     )
				self.assertEqual(retrieved_doc_embeds.shape      ,  (2, n_docs, self.retrieval_vector_size)     )
				self.assertEqual(len(lowercase     )      ,  2     )
				self.assertEqual(sorted(doc_dicts[0]     )      ,  ['embeddings', 'id', 'text', 'title']     )
				self.assertEqual(len(doc_dicts[0]['id']     )      ,  lowercase     )
				self.assertEqual(doc_dicts[0]['id'][0]      ,  '1'     )  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['id'][0]      ,  '0'     )  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()      ,  [[1], [0]]     )



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.get_dummy_custom_hf_index_retriever(from_disk=lowercase     )
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(lowercase     )
							_snake_case          =      RagRetriever.from_pretrained(lowercase     )
							self.assertIsInstance(lowercase      ,  lowercase     )
							_snake_case          =      np.array(
							    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
							_snake_case          =      retriever.retrieve(lowercase      ,  n_docs=1     )
							self.assertTrue(out is not None     )



	def 			A							(  self     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      1
				_snake_case          =      self.get_dummy_custom_hf_index_retriever(from_disk=lowercase     )
				_snake_case          =      np.array(
				    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
				_snake_case     , _snake_case     , _snake_case          =      retriever.retrieve(lowercase      ,  n_docs=lowercase     )
				self.assertEqual(retrieved_doc_embeds.shape      ,  (2, n_docs, self.retrieval_vector_size)     )
				self.assertEqual(len(lowercase     )      ,  2     )
				self.assertEqual(sorted(doc_dicts[0]     )      ,  ['embeddings', 'id', 'text', 'title']     )
				self.assertEqual(len(doc_dicts[0]['id']     )      ,  lowercase     )
				self.assertEqual(doc_dicts[0]['id'][0]      ,  '1'     )  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['id'][0]      ,  '0'     )  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()      ,  [[1], [0]]     )



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case          =      self.get_dummy_custom_hf_index_retriever(from_disk=lowercase     )
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(lowercase     )
							_snake_case          =      RagRetriever.from_pretrained(lowercase     )
							self.assertIsInstance(lowercase      ,  lowercase     )
							_snake_case          =      np.array(
							    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
							_snake_case          =      retriever.retrieve(lowercase      ,  n_docs=1     )
							self.assertTrue(out is not None     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      1
				_snake_case          =      self.get_dummy_legacy_index_retriever()
				_snake_case          =      np.array(
				    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
				_snake_case     , _snake_case     , _snake_case          =      retriever.retrieve(lowercase      ,  n_docs=lowercase     )
				self.assertEqual(retrieved_doc_embeds.shape      ,  (2, n_docs, self.retrieval_vector_size)     )
				self.assertEqual(len(lowercase     )      ,  2     )
				self.assertEqual(sorted(doc_dicts[0]     )      ,  ['text', 'title']     )
				self.assertEqual(len(doc_dicts[0]['text']     )      ,  lowercase     )
				self.assertEqual(doc_dicts[0]['text'][0]      ,  'bar'     )  # max inner product is reached with second doc
				self.assertEqual(doc_dicts[1]['text'][0]      ,  'foo'     )  # max inner product is reached with first doc
				self.assertListEqual(doc_ids.tolist()      ,  [[1], [0]]     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				_snake_case          =      self.get_dummy_legacy_index_retriever()
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(lowercase     )
							_snake_case          =      RagRetriever.from_pretrained(lowercase     )
							self.assertIsInstance(lowercase      ,  lowercase     )
							_snake_case          =      np.array(
							    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
							_snake_case          =      retriever.retrieve(lowercase      ,  n_docs=1     )
							self.assertTrue(out is not None     )



	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 			A							(  self     :   Tuple     ):


				'''simple docstring'''


				import torch

				_snake_case          =      1
				_snake_case          =      self.get_dummy_canonical_hf_index_retriever()
				_snake_case          =      [[5, 7], [10, 11]]
				_snake_case          =      np.array(
				    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
				_snake_case          =      retriever(lowercase      ,  lowercase      ,  prefix=retriever.config.generator.prefix      ,  n_docs=lowercase     )
				_snake_case     , _snake_case     , _snake_case          =      (
				    out['context_input_ids'],
				    out['context_attention_mask'],
				    out['retrieved_doc_embeds'],
				)
				self.assertEqual(retrieved_doc_embeds.shape      ,  (2, n_docs, self.retrieval_vector_size)     )
				self.assertIsInstance(lowercase      ,  lowercase     )
				self.assertIsInstance(lowercase      ,  lowercase     )
				self.assertIsInstance(lowercase      ,  np.ndarray     )

				_snake_case          =      retriever(
				    lowercase      ,  lowercase      ,  prefix=retriever.config.generator.prefix      ,  n_docs=lowercase      ,  return_tensors='pt'      ,  )
				_snake_case     , _snake_case     , _snake_case     , _snake_case          =      (  # noqa: F841
				    out['context_input_ids'],
				    out['context_attention_mask'],
				    out['retrieved_doc_embeds'],
				    out['doc_ids'],
				)
				self.assertEqual(retrieved_doc_embeds.shape      ,  (2, n_docs, self.retrieval_vector_size)     )
				self.assertIsInstance(lowercase      ,  torch.Tensor     )
				self.assertIsInstance(lowercase      ,  torch.Tensor     )
				self.assertIsInstance(lowercase      ,  torch.Tensor     )





	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				_snake_case          =      self.get_dpr_ctx_encoder_tokenizer()
				_snake_case          =      1
				_snake_case          =      self.get_dummy_custom_hf_index_retriever(from_disk=lowercase     )
				retriever.set_ctx_encoder_tokenizer(lowercase     )

				_snake_case          =      [[5, 7], [10, 11]]
				_snake_case          =      np.array(
				    [np.ones(self.retrieval_vector_size     ), -np.ones(self.retrieval_vector_size     )]      ,  dtype=np.floataa     )
				_snake_case          =      retriever(lowercase      ,  lowercase      ,  prefix=retriever.config.generator.prefix      ,  n_docs=lowercase     )

				self.assertEqual(
				    len(lowercase     )      ,  6     )  # check whether the retriever output consist of 6 attributes including tokenized docs
				self.assertEqual(
				    all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')     )      ,  lowercase     )  # check for doc token related keys in dictionary.

from __future__ import annotations

import requests

_lowerCamelCase	:    List[str]          =      set(
    '''approved_at_utc approved_by author_flair_background_color
author_flair_css_class author_flair_richtext author_flair_template_id author_fullname
author_premium can_mod_post category clicked content_categories created_utc downs
edited gilded gildings hidden hide_score is_created_from_ads_ui is_meta
is_original_content is_reddit_media_domain is_video link_flair_css_class
link_flair_richtext link_flair_text link_flair_text_color media_embed mod_reason_title
name permalink pwls quarantine saved score secure_media secure_media_embed selftext
subreddit subreddit_name_prefixed subreddit_type thumbnail title top_awarded_type
total_awards_received ups upvote_ratio url user_reports'''.split()
)






def 	a_		(	__lowercase       :     str	, __lowercase       :     int = 1	, __lowercase       :     str = "new"	, __lowercase       :     list | None = None			)			->  dict:
			_snake_case          =      wanted_data or []
			if invalid_search_terms := ", ".join(sorted(set(__lowercase			) - valid_terms			)			):
						_snake_case          =      f'''Invalid search term: {invalid_search_terms}'''
						raise ValueError(__lowercase			)
			_snake_case          =      requests.get(
			    f'''https://reddit.com/r/{subreddit}/{age}.json?limit={limit}'''	, headers={'User-agent': 'A random string'}	, )
			if response.status_code == 429:
						raise requests.HTTPError

			_snake_case          =      response.json()
			if not wanted_data:
						return {id_: data["data"]["children"][id_] for id_ in range(__lowercase			)}

			_snake_case          =      {}
			for id_ in range(__lowercase			):
						_snake_case          =      {
						    item: data['data']['children'][id_]['data'][item] for item in wanted_data
						}
			return data_dict


if __name__ == "__main__":
			# If you get Error 429, that means you are rate limited.Try after some time
			print(get_subreddit_data('''learnpython''', wanted_data=['''title''', '''url''', '''selftext''']))