



"""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     A__     (						__SCREAMING_SNAKE_CASE							):




	'''simple docstring'''





	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= tempfile.mkdtemp()
				__lowerCAmelCase   :						int				= 8

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

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> DPRQuestionEncoderTokenizer:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> DPRContextEncoderTokenizer:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> BartTokenizer:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Union[str, Any]:

				"""simple docstring"""

				shutil.rmtree(self.tmpdirname)

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						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 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> List[Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						int				= self.get_dummy_dataset()
				__lowerCAmelCase   :						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:
							__lowerCAmelCase   :						Any				= dataset
							__lowerCAmelCase   :						Any				= RagRetriever(
							    _SCREAMING_SNAKE_CASE ,    question_encoder_tokenizer=self.get_dpr_tokenizer() ,    generator_tokenizer=self.get_bart_tokenizer() ,    )
				return retriever

	def 							_SCREAMING_SNAKE_CASE     (  self:	Any ,    _SCREAMING_SNAKE_CASE:	bool)       -> Optional[Any]:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	Tuple)       -> Any:

				"""simple docstring"""

				__lowerCAmelCase   :						Dict				= 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)

				__lowerCAmelCase   :						Union[str, Any]				= 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"))

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

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	List[Any])       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= 1
				__lowerCAmelCase   :						int				= self.get_dummy_canonical_hf_index_retriever()
				__lowerCAmelCase   :						int				= np.array(
				    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)] ,    dtype=np.floataa)
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= retriever.retrieve(_SCREAMING_SNAKE_CASE ,    n_docs=_SCREAMING_SNAKE_CASE)
				self.assertEqual(retrieved_doc_embeds.shape ,    (2, n_docs, self.retrieval_vector_size))
				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    2)
				self.assertEqual(sorted(doc_dicts[0]) ,    ["embeddings", "id", "text", "title"])
				self.assertEqual(len(doc_dicts[0]["id"]) ,    _SCREAMING_SNAKE_CASE)
				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 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						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:
										__lowerCAmelCase   :						int				= self.get_dummy_dataset()
										retriever.save_pretrained(_SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						str				= RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE)
										self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						List[str]				= np.array(
										    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)] ,    dtype=np.floataa)
										__lowerCAmelCase   :						List[Any]				= retriever.retrieve(_SCREAMING_SNAKE_CASE ,    n_docs=1)
										self.assertTrue(out is not None)

	def 							_SCREAMING_SNAKE_CASE     (  self:	str)       -> Optional[int]:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= 1
				__lowerCAmelCase   :						Tuple				= self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Any				= np.array(
				    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)] ,    dtype=np.floataa)
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Tuple				= retriever.retrieve(_SCREAMING_SNAKE_CASE ,    n_docs=_SCREAMING_SNAKE_CASE)
				self.assertEqual(retrieved_doc_embeds.shape ,    (2, n_docs, self.retrieval_vector_size))
				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    2)
				self.assertEqual(sorted(doc_dicts[0]) ,    ["embeddings", "id", "text", "title"])
				self.assertEqual(len(doc_dicts[0]["id"]) ,    _SCREAMING_SNAKE_CASE)
				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 							_SCREAMING_SNAKE_CASE     (  self:	Optional[int])       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Union[str, Any]				= self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE)
				with tempfile.TemporaryDirectory() as tmp_dirname:
							retriever.save_pretrained(_SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Optional[Any]				= RagRetriever.from_pretrained(_SCREAMING_SNAKE_CASE)
							self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
							__lowerCAmelCase   :						Dict				= np.array(
							    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)] ,    dtype=np.floataa)
							__lowerCAmelCase   :						Tuple				= retriever.retrieve(_SCREAMING_SNAKE_CASE ,    n_docs=1)
							self.assertTrue(out is not None)

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> Union[str, Any]:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= 1
				__lowerCAmelCase   :						List[Any]				= self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						List[str]				= np.array(
				    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)] ,    dtype=np.floataa)
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Union[str, Any]				= retriever.retrieve(_SCREAMING_SNAKE_CASE ,    n_docs=_SCREAMING_SNAKE_CASE)
				self.assertEqual(retrieved_doc_embeds.shape ,    (2, n_docs, self.retrieval_vector_size))
				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    2)
				self.assertEqual(sorted(doc_dicts[0]) ,    ["embeddings", "id", "text", "title"])
				self.assertEqual(len(doc_dicts[0]["id"]) ,    _SCREAMING_SNAKE_CASE)
				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 							_SCREAMING_SNAKE_CASE     (  self:	Union[str, Any])       -> int:

				"""simple docstring"""

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

	def 							_SCREAMING_SNAKE_CASE     (  self:	int)       -> int:

				"""simple docstring"""

				__lowerCAmelCase   :						Tuple				= 1
				__lowerCAmelCase   :						Optional[Any]				= self.get_dummy_legacy_index_retriever()
				__lowerCAmelCase   :						str				= np.array(
				    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)] ,    dtype=np.floataa)
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						List[str]				= retriever.retrieve(_SCREAMING_SNAKE_CASE ,    n_docs=_SCREAMING_SNAKE_CASE)
				self.assertEqual(retrieved_doc_embeds.shape ,    (2, n_docs, self.retrieval_vector_size))
				self.assertEqual(len(_SCREAMING_SNAKE_CASE) ,    2)
				self.assertEqual(sorted(doc_dicts[0]) ,    ["text", "title"])
				self.assertEqual(len(doc_dicts[0]["text"]) ,    _SCREAMING_SNAKE_CASE)
				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 							_SCREAMING_SNAKE_CASE     (  self:	List[str])       -> Optional[Any]:

				"""simple docstring"""

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

	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 							_SCREAMING_SNAKE_CASE     (  self:	Any)       -> Optional[Any]:

				"""simple docstring"""

				import torch

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

				__lowerCAmelCase   :						str				= retriever(
				    _SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    prefix=retriever.config.generator.prefix ,    n_docs=_SCREAMING_SNAKE_CASE ,    return_tensors="pt" ,    )
				__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase		,			__lowerCAmelCase   :						Union[str, 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(_SCREAMING_SNAKE_CASE ,    torch.Tensor)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    torch.Tensor)
				self.assertIsInstance(_SCREAMING_SNAKE_CASE ,    torch.Tensor)







	@require_torch
	@require_tokenizers
	@require_sentencepiece
	def 							_SCREAMING_SNAKE_CASE     (  self:	Optional[Any])       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[Any]				= self.get_dpr_ctx_encoder_tokenizer()
				__lowerCAmelCase   :						Dict				= 1
				__lowerCAmelCase   :						Union[str, Any]				= self.get_dummy_custom_hf_index_retriever(from_disk=_SCREAMING_SNAKE_CASE)
				retriever.set_ctx_encoder_tokenizer(_SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= [[5, 7], [10, 11]]
				__lowerCAmelCase   :						List[Any]				= np.array(
				    [np.ones(self.retrieval_vector_size), -np.ones(self.retrieval_vector_size)] ,    dtype=np.floataa)
				__lowerCAmelCase   :						List[str]				= retriever(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE ,    prefix=retriever.config.generator.prefix ,    n_docs=_SCREAMING_SNAKE_CASE)

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




"""simple docstring"""






def     _lowercase       (						__snake_case = 3      ,__snake_case = 7      ,__snake_case = 1_000_000			)		->     int:
			__lowerCAmelCase   :						Optional[Any]				= 0
			__lowerCAmelCase   :						List[str]				= 1

			for current_denominator in range(1      ,limit + 1			):
						__lowerCAmelCase   :						Optional[int]				= current_denominator * numerator // denominator
						if current_denominator % denominator == 0:
									current_numerator -= 1
						if current_numerator * max_denominator > current_denominator * max_numerator:
									__lowerCAmelCase   :						Union[str, Any]				= current_numerator
									__lowerCAmelCase   :						int				= current_denominator
			return max_numerator


if __name__ == "__main__":
	print(solution(numerator=3, denominator=7, limit=1_000_000))