


'''simple docstring'''



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 						_snake_case   (       a__		):




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   int       				=       tempfile.mkdtemp()
										UpperCAmelCase__					:   Optional[int]       				=       8

										# DPR tok
										UpperCAmelCase__					:   Union[str, Any]       				=       [
										    """[UNK]""",
										    """[CLS]""",
										    """[SEP]""",
										    """[PAD]""",
										    """[MASK]""",
										    """want""",
										    """##want""",
										    """##ed""",
										    """wa""",
										    """un""",
										    """runn""",
										    """##ing""",
										    """,""",
										    """low""",
										    """lowest""",
										]
										UpperCAmelCase__					:   List[Any]       				=       os.path.join(self.tmpdirname							,							"""dpr_tokenizer""")
										os.makedirs(_lowerCamelCase							,							exist_ok=_lowerCamelCase)
										UpperCAmelCase__					:   Tuple       				=       os.path.join(_lowerCamelCase							,							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
										UpperCAmelCase__					:   Union[str, Any]       				=       [
										    """l""",
										    """o""",
										    """w""",
										    """e""",
										    """r""",
										    """s""",
										    """t""",
										    """i""",
										    """d""",
										    """n""",
										    """\u0120""",
										    """\u0120l""",
										    """\u0120n""",
										    """\u0120lo""",
										    """\u0120low""",
										    """er""",
										    """\u0120lowest""",
										    """\u0120newer""",
										    """\u0120wider""",
										    """<unk>""",
										]
										UpperCAmelCase__					:   Optional[Any]       				=       dict(zip(_lowerCamelCase							,							range(len(_lowerCamelCase))))
										UpperCAmelCase__					:   Optional[int]       				=       ["""#version: 0.2""", """\u0120 l""", """\u0120l o""", """\u0120lo w""", """e r""", """"""]
										UpperCAmelCase__					:   Tuple       				=       {"""unk_token""": """<unk>"""}

										UpperCAmelCase__					:   List[Any]       				=       os.path.join(self.tmpdirname							,							"""bart_tokenizer""")
										os.makedirs(_lowerCamelCase							,							exist_ok=_lowerCamelCase)
										UpperCAmelCase__					:   List[str]       				=       os.path.join(_lowerCamelCase							,							BART_VOCAB_FILES_NAMES["""vocab_file"""])
										UpperCAmelCase__					:   Any       				=       os.path.join(_lowerCamelCase							,							BART_VOCAB_FILES_NAMES["""merges_file"""])
										with open(self.vocab_file							,							"""w"""							,							encoding="""utf-8""") as fp:
													fp.write(json.dumps(_lowerCamelCase) + """\n""")
										with open(self.merges_file							,							"""w"""							,							encoding="""utf-8""") as fp:
													fp.write("""\n""".join(_lowerCamelCase))




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




							def 			snake_case__  ( self):
										return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,							"""dpr_tokenizer"""))




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




							def 			snake_case__  ( self):
										shutil.rmtree(self.tmpdirname)




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Optional[Any]       				=       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 			snake_case__  ( self):
										UpperCAmelCase__					:   Union[str, Any]       				=       self.get_dummy_dataset()
										UpperCAmelCase__					:   Any       				=       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:
													UpperCAmelCase__					:   List[Any]       				=       dataset
													UpperCAmelCase__					:   Optional[Any]       				=       RagRetriever(
													    _lowerCamelCase							,							question_encoder_tokenizer=self.get_dpr_tokenizer()							,							generator_tokenizer=self.get_bart_tokenizer()							,							)
										return retriever




							def 			snake_case__  ( self							,							_lowerCamelCase):
										UpperCAmelCase__					:   Union[str, Any]       				=       self.get_dummy_dataset()
										UpperCAmelCase__					:   Any       				=       RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size							,							question_encoder=DPRConfig().to_dict()							,							generator=BartConfig().to_dict()							,							index_name="""custom"""							,							)
										if from_disk:
													UpperCAmelCase__					:   Any       				=       os.path.join(self.tmpdirname							,							"""dataset""")
													UpperCAmelCase__					:   Optional[Any]       				=       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
													UpperCAmelCase__					:   Dict       				=       RagRetriever(
													    _lowerCamelCase							,							question_encoder_tokenizer=self.get_dpr_tokenizer()							,							generator_tokenizer=self.get_bart_tokenizer()							,							)
										else:
													UpperCAmelCase__					:   List[str]       				=       RagRetriever(
													    _lowerCamelCase							,							question_encoder_tokenizer=self.get_dpr_tokenizer()							,							generator_tokenizer=self.get_bart_tokenizer()							,							index=CustomHFIndex(config.retrieval_vector_size							,							_lowerCamelCase)							,							)
										return retriever




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Optional[Any]       				=       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)

										UpperCAmelCase__					:   str       				=       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"""))

										UpperCAmelCase__					:   int       				=       os.path.join(self.tmpdirname							,							"""psgs_w100.tsv.pkl""")
										UpperCAmelCase__					:   Optional[Any]       				=       {sample["""id"""]: [sample["""text"""], sample["""title"""]] for sample in dataset}
										pickle.dump(_lowerCamelCase							,							open(_lowerCamelCase							,							"""wb"""))

										UpperCAmelCase__					:   List[Any]       				=       RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size							,							question_encoder=DPRConfig().to_dict()							,							generator=BartConfig().to_dict()							,							index_name="""legacy"""							,							index_path=self.tmpdirname							,							)
										UpperCAmelCase__					:   Union[str, Any]       				=       RagRetriever(
										    _lowerCamelCase							,							question_encoder_tokenizer=self.get_dpr_tokenizer()							,							generator_tokenizer=self.get_bart_tokenizer())
										return retriever




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Optional[Any]       				=       1
										UpperCAmelCase__					:   Optional[Any]       				=       self.get_dummy_canonical_hf_index_retriever()
										UpperCAmelCase__					:   Dict       				=       np.array(
										    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
										UpperCAmelCase__					:   int       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=_lowerCamelCase)
										self.assertEqual(retrieved_doc_embeds.shape							,							(2, n_docs, self.retrieval_vector_size))
										self.assertEqual(len(_lowerCamelCase)							,							2)
										self.assertEqual(sorted(doc_dicts[0])							,							["""embeddings""", """id""", """text""", """title"""])
										self.assertEqual(len(doc_dicts[0]["""id"""])							,							_lowerCamelCase)
										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 			snake_case__  ( self):
										UpperCAmelCase__					:   Union[str, Any]       				=       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:
																UpperCAmelCase__					:   Optional[int]       				=       self.get_dummy_dataset()
																retriever.save_pretrained(_lowerCamelCase)
																UpperCAmelCase__					:   List[str]       				=       RagRetriever.from_pretrained(_lowerCamelCase)
																self.assertIsInstance(_lowerCamelCase							,							_lowerCamelCase)
																UpperCAmelCase__					:   Dict       				=       np.array(
																    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
																UpperCAmelCase__					:   str       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=1)
																self.assertTrue(out is not None)




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Tuple       				=       1
										UpperCAmelCase__					:   Optional[int]       				=       self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase)
										UpperCAmelCase__					:   str       				=       np.array(
										    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
										UpperCAmelCase__					:   Union[str, Any]       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=_lowerCamelCase)
										self.assertEqual(retrieved_doc_embeds.shape							,							(2, n_docs, self.retrieval_vector_size))
										self.assertEqual(len(_lowerCamelCase)							,							2)
										self.assertEqual(sorted(doc_dicts[0])							,							["""embeddings""", """id""", """text""", """title"""])
										self.assertEqual(len(doc_dicts[0]["""id"""])							,							_lowerCamelCase)
										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 			snake_case__  ( self):
										UpperCAmelCase__					:   List[str]       				=       self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase)
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(_lowerCamelCase)
													UpperCAmelCase__					:   List[str]       				=       RagRetriever.from_pretrained(_lowerCamelCase)
													self.assertIsInstance(_lowerCamelCase							,							_lowerCamelCase)
													UpperCAmelCase__					:   List[Any]       				=       np.array(
													    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
													UpperCAmelCase__					:   Optional[int]       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=1)
													self.assertTrue(out is not None)




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   List[str]       				=       1
										UpperCAmelCase__					:   List[str]       				=       self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase)
										UpperCAmelCase__					:   Any       				=       np.array(
										    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
										UpperCAmelCase__					:   Dict       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=_lowerCamelCase)
										self.assertEqual(retrieved_doc_embeds.shape							,							(2, n_docs, self.retrieval_vector_size))
										self.assertEqual(len(_lowerCamelCase)							,							2)
										self.assertEqual(sorted(doc_dicts[0])							,							["""embeddings""", """id""", """text""", """title"""])
										self.assertEqual(len(doc_dicts[0]["""id"""])							,							_lowerCamelCase)
										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 			snake_case__  ( self):
										UpperCAmelCase__					:   Optional[Any]       				=       self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase)
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(_lowerCamelCase)
													UpperCAmelCase__					:   Optional[Any]       				=       RagRetriever.from_pretrained(_lowerCamelCase)
													self.assertIsInstance(_lowerCamelCase							,							_lowerCamelCase)
													UpperCAmelCase__					:   Optional[int]       				=       np.array(
													    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
													UpperCAmelCase__					:   Any       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=1)
													self.assertTrue(out is not None)




							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Tuple       				=       1
										UpperCAmelCase__					:   Any       				=       self.get_dummy_legacy_index_retriever()
										UpperCAmelCase__					:   Optional[Any]       				=       np.array(
										    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
										UpperCAmelCase__					:   Union[str, Any]       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=_lowerCamelCase)
										self.assertEqual(retrieved_doc_embeds.shape							,							(2, n_docs, self.retrieval_vector_size))
										self.assertEqual(len(_lowerCamelCase)							,							2)
										self.assertEqual(sorted(doc_dicts[0])							,							["""text""", """title"""])
										self.assertEqual(len(doc_dicts[0]["""text"""])							,							_lowerCamelCase)
										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 			snake_case__  ( self):
										UpperCAmelCase__					:   str       				=       self.get_dummy_legacy_index_retriever()
										with tempfile.TemporaryDirectory() as tmp_dirname:
													retriever.save_pretrained(_lowerCamelCase)
													UpperCAmelCase__					:   Optional[int]       				=       RagRetriever.from_pretrained(_lowerCamelCase)
													self.assertIsInstance(_lowerCamelCase							,							_lowerCamelCase)
													UpperCAmelCase__					:   str       				=       np.array(
													    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
													UpperCAmelCase__					:   Union[str, Any]       				=       retriever.retrieve(_lowerCamelCase							,							n_docs=1)
													self.assertTrue(out is not None)




							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def 			snake_case__  ( self):
										import torch

										UpperCAmelCase__					:   Dict       				=       1
										UpperCAmelCase__					:   str       				=       self.get_dummy_canonical_hf_index_retriever()
										UpperCAmelCase__					:   Optional[Any]       				=       [[5, 7], [10, 11]]
										UpperCAmelCase__					:   Optional[Any]       				=       np.array(
										    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
										UpperCAmelCase__					:   Optional[Any]       				=       retriever(_lowerCamelCase							,							_lowerCamelCase							,							prefix=retriever.config.generator.prefix							,							n_docs=_lowerCamelCase)
										UpperCAmelCase__					:   Any       				=       (
										    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(_lowerCamelCase							,							_lowerCamelCase)
										self.assertIsInstance(_lowerCamelCase							,							_lowerCamelCase)
										self.assertIsInstance(_lowerCamelCase							,							np.ndarray)

										UpperCAmelCase__					:   Optional[int]       				=       retriever(
										    _lowerCamelCase							,							_lowerCamelCase							,							prefix=retriever.config.generator.prefix							,							n_docs=_lowerCamelCase							,							return_tensors="""pt"""							,							)
										UpperCAmelCase__					:   Any       				=       (  # 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(_lowerCamelCase							,							torch.Tensor)
										self.assertIsInstance(_lowerCamelCase							,							torch.Tensor)
										self.assertIsInstance(_lowerCamelCase							,							torch.Tensor)




							@require_torch
							@require_tokenizers
							@require_sentencepiece
							def 			snake_case__  ( self):
										UpperCAmelCase__					:   Optional[Any]       				=       self.get_dpr_ctx_encoder_tokenizer()
										UpperCAmelCase__					:   Union[str, Any]       				=       1
										UpperCAmelCase__					:   List[Any]       				=       self.get_dummy_custom_hf_index_retriever(from_disk=_lowerCamelCase)
										retriever.set_ctx_encoder_tokenizer(_lowerCamelCase)

										UpperCAmelCase__					:   Tuple       				=       [[5, 7], [10, 11]]
										UpperCAmelCase__					:   Optional[int]       				=       np.array(
										    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)]							,							dtype=np.floataa)
										UpperCAmelCase__					:   Optional[Any]       				=       retriever(_lowerCamelCase							,							_lowerCamelCase							,							prefix=retriever.config.generator.prefix							,							n_docs=_lowerCamelCase)

										self.assertEqual(
										    len(_lowerCamelCase)							,							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"""))							,							_lowerCamelCase)  # check for doc token related keys in dictionary.



'''simple docstring'''



import os
from datetime import datetime as dt

from github import Github


__A		=[
    'good first issue',
    'good second issue',
    'good difficult issue',
    'enhancement',
    'new pipeline/model',
    'new scheduler',
    'wip',
]







def       _UpperCamelCase (						):
			UpperCAmelCase__					:   Union[str, Any]       				=       Github(os.environ["""GITHUB_TOKEN"""]   )
			UpperCAmelCase__					:   Any       				=       g.get_repo("""huggingface/diffusers"""   )
			UpperCAmelCase__					:   Optional[int]       				=       repo.get_issues(state="""open"""   )

			for issue in open_issues:
						UpperCAmelCase__					:   Any       				=       sorted(issue.get_comments()	, key=lambda UpperCamelCase__   : i.created_at	, reverse=UpperCamelCase__   )
						UpperCAmelCase__					:   List[Any]       				=       comments[0] if len(UpperCamelCase__   ) > 0 else None
						if (
						    last_comment is not None
						    and last_comment.user.login == "github-actions[bot]"
						    and (dt.utcnow() - issue.updated_at).days > 7
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Closes the issue after 7 days of inactivity since the Stalebot notification.
									issue.edit(state="""closed"""   )
						elif (
						    "stale" in issue.get_labels()
						    and last_comment is not None
						    and last_comment.user.login != "github-actions[bot]"
						):
									# Opens the issue if someone other than Stalebot commented.
									issue.edit(state="""open"""   )
									issue.remove_from_labels("""stale"""   )
						elif (
						    (dt.utcnow() - issue.updated_at).days > 2_3
						    and (dt.utcnow() - issue.created_at).days >= 3_0
						    and not any(label.name.lower() in LABELS_TO_EXEMPT for label in issue.get_labels()   )
						):
									# Post a Stalebot notification after 23 days of inactivity.
									issue.create_comment(
									    """This issue has been automatically marked as stale because it has not had """
									    """recent activity. If you think this still needs to be addressed """
									    """please comment on this thread.\n\nPlease note that issues that do not follow the """
									    """[contributing guidelines](https://github.com/huggingface/diffusers/blob/main/CONTRIBUTING.md) """
									    """are likely to be ignored."""   )
									issue.add_to_labels("""stale"""   )


if __name__ == "__main__":
		main()