



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      UpperCamelCase    (						_UpperCAmelCase     ):



	def 			__A							(       self		):
							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(UpperCAmelCase__			,	exist_ok=UpperCAmelCase__		)
							A__							=       os.path.join(UpperCAmelCase__			,	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(UpperCAmelCase__			,	range(len(UpperCAmelCase__		)		)		)		)
							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(UpperCAmelCase__			,	exist_ok=UpperCAmelCase__		)
							A__							=       os.path.join(UpperCAmelCase__			,	BART_VOCAB_FILES_NAMES["vocab_file"]		)
							A__							=       os.path.join(UpperCAmelCase__			,	BART_VOCAB_FILES_NAMES["merges_file"]		)
							with open(self.vocab_file			,	"w"			,	encoding="utf-8"		) as fp:
													fp.write(json.dumps(UpperCAmelCase__		) + "\n"		)
							with open(self.merges_file			,	"w"			,	encoding="utf-8"		) as fp:
													fp.write("\n".join(UpperCAmelCase__		)		)



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



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



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



	@require_tokenizers
	def 			__A							(       self		):
							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(UpperCAmelCase__		)
							rag_tokenizer.save_pretrained(UpperCAmelCase__		)
							A__							=       RagTokenizer.from_pretrained(UpperCAmelCase__			,	config=UpperCAmelCase__		)
							self.assertIsInstance(new_rag_tokenizer.question_encoder			,	UpperCAmelCase__		)
							self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()			,	rag_tokenizer.question_encoder.get_vocab()		)
							self.assertIsInstance(new_rag_tokenizer.generator			,	UpperCAmelCase__		)
							self.assertEqual(new_rag_tokenizer.generator.get_vocab()			,	rag_tokenizer.generator.get_vocab()		)



	@slow
	def 			__A							(       self		):
							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(UpperCAmelCase__		)
							self.assertIsNotNone(UpperCAmelCase__		)



	@slow
	def 			__A							(       self		):
							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(UpperCAmelCase__		)
							self.assertIsNotNone(UpperCAmelCase__		)










import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links
def  UpperCamelCase						(       _A			:				List[Any]			,							_A			:				List[str]=7    )->       Optional[Any]:





						"""simple docstring"""





						A__							=       None
						if token is not None:
												A__							=       {"Accept": "application/vnd.github+json", "Authorization": f"""Bearer {token}"""}

						# The id of a workflow (not of a workflow run)
						A__							=       "636036"

						A__							=       f"""https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs"""
						# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
						url += f"""?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}"""

						A__							=       requests.get(_A			,							headers=_A    ).json()

						return result["workflow_runs"]
def  UpperCamelCase						(       _A			:				str    )->       Dict:





						"""simple docstring"""





						A__							=       get_daily_ci_runs(_A    )
						A__							=       None
						for workflow_run in workflow_runs:
												if workflow_run["status"] == "completed":
																		A__							=       workflow_run["id"]
																		break

						return workflow_run_id
def  UpperCamelCase						(       _A			:				int			,							_A			:				List[str]			,							_A			:				str    )->       Any:





						"""simple docstring"""





						A__							=       get_last_daily_ci_runs(_A    )
						if workflow_run_id is not None:
												A__							=       get_artifacts_links(worflow_run_id=_A			,							token=_A    )
												for artifact_name in artifact_names:
																		if artifact_name in artifacts_links:
																								A__							=       artifacts_links[artifact_name]
																								download_artifact(
																								    artifact_name=_A			,							artifact_url=_A			,							output_dir=_A			,							token=_A    )
def  UpperCamelCase						(       _A			:				Optional[Any]			,							_A			:				Any			,							_A			:				List[Any]    )->       Optional[int]:





						"""simple docstring"""





						get_last_daily_ci_artifacts(_A			,							_A			,							_A    )

						A__							=       {}
						for artifact_name in artifact_names:
												A__							=       os.path.join(_A			,							f"""{artifact_name}.zip"""    )
												if os.path.isfile(_A    ):
																		A__							=       {}
																		with zipfile.ZipFile(_A    ) as z:
																								for filename in z.namelist():
																														if not os.path.isdir(_A    ):
																																				# read the file
																																				with z.open(_A    ) as f:
																																										A__							=       f.read().decode("UTF-8"    )

						return results





