

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

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
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_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
	from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
	lowerCAmelCase__						=	None

if is_ray_available():
	import ray  # noqa: E402 # isort:skip
	from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
	lowerCAmelCase__						=	None
	lowerCAmelCase__						=	None
	lowerCAmelCase__						=	None



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							Tuple )     -> List[Any]:




				'''simple docstring'''





				if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
								A__            =     unittest.skip("test requires Datasets, Faiss, psutil" )(SCREAMING_SNAKE_CASE_ )
				return test_case





@require_distributed_retrieval
class    a__  (   snake_case     ):
			"""simple docstring"""







			def        UpperCamelCase						(      self    )					->  List[Any]:
							'''simple docstring'''






							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(lowercase , exist_ok=lowercase    )
							A__            =     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
							A__            =     [
							    "l",
							    "o",
							    "w",
							    "e",
							    "r",
							    "s",
							    "t",
							    "i",
							    "d",
							    "n",
							    "\u0120",
							    "\u0120l",
							    "\u0120n",
							    "\u0120lo",
							    "\u0120low",
							    "er",
							    "\u0120lowest",
							    "\u0120newer",
							    "\u0120wider",
							    "<unk>",
							]
							A__            =     dict(zip(lowercase , range(len(lowercase    )    )    )    )
							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(lowercase , exist_ok=lowercase    )
							A__            =     os.path.join(lowercase , BART_VOCAB_FILES_NAMES["vocab_file"]    )
							A__            =     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        UpperCamelCase						(      self    )					->  DPRQuestionEncoderTokenizer:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  BartTokenizer:
							'''simple docstring'''






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


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							shutil.rmtree(self.tmpdirname    )


			def        UpperCamelCase						(      self    )					->  Union[str, Any]:
							'''simple docstring'''






							A__            =     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        UpperCamelCase						(      self , lowercase , lowercase=12345    )					->  RagPyTorchDistributedRetriever:
							'''simple docstring'''






							A__            =     self.get_dummy_dataset()
							A__            =     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:
											A__            =     dataset
											A__            =     RagPyTorchDistributedRetriever(
											    lowercase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
											if init_retrieval:
															retriever.init_retrieval(lowercase    )
							return retriever


			def        UpperCamelCase						(      self , lowercase    )					->  RagRayDistributedRetriever:
							'''simple docstring'''






							ray.init(local_mode=lowercase    )
							A__            =     RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , )
							A__            =     ray.remote(lowercase    )
							A__            =     [remote_cls.remote() for _ in range(1    )]
							with patch("transformers.models.rag.retrieval_rag.load_dataset"    ) as mock_load_dataset:
											A__            =     self.get_dummy_dataset()
											A__            =     RagRayDistributedRetriever(
											    lowercase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , retrieval_workers=lowercase , )
											if init_retrieval:
															retriever.init_retrieval()
							return retriever


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase=12345    )					->  int:
							'''simple docstring'''






							A__            =     self.get_dummy_dataset()
							A__            =     RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="custom" , )
							if from_disk:
											A__            =     os.path.join(self.tmpdirname , "dataset"    )
											A__            =     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
											A__            =     RagPyTorchDistributedRetriever(
											    lowercase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , )
							else:
											A__            =     RagPyTorchDistributedRetriever(
											    lowercase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , index=CustomHFIndex(config.retrieval_vector_size , lowercase    ) , )
							if init_retrieval:
											retriever.init_retrieval(lowercase    )
							return retriever


			def        UpperCamelCase						(      self , lowercase , lowercase    )					->  int:
							'''simple docstring'''






							ray.init(local_mode=lowercase    )
							A__            =     self.get_dummy_dataset()
							A__            =     RagConfig(
							    retrieval_vector_size=self.retrieval_vector_size , question_encoder=DPRConfig().to_dict() , generator=BartConfig().to_dict() , index_name="custom" , )
							A__            =     ray.remote(lowercase    )
							A__            =     [remote_cls.remote() for _ in range(1    )]
							if from_disk:
											A__            =     os.path.join(self.tmpdirname , "dataset"    )
											A__            =     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
											A__            =     RagRayDistributedRetriever(
											    lowercase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , retrieval_workers=lowercase , index=CustomHFIndex.load_from_disk(
											        vector_size=config.retrieval_vector_size , dataset_path=config.passages_path , index_path=config.index_path , ) , )
							else:
											A__            =     RagRayDistributedRetriever(
											    lowercase , question_encoder_tokenizer=self.get_dpr_tokenizer() , generator_tokenizer=self.get_bart_tokenizer() , retrieval_workers=lowercase , index=CustomHFIndex(config.retrieval_vector_size , lowercase    ) , )
							if init_retrieval:
											retriever.init_retrieval()
							return retriever


			def        UpperCamelCase						(      self , lowercase , lowercase , lowercase    )					->  None:
							'''simple docstring'''






							A__      ,			A__      ,			A__            =     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        UpperCamelCase						(      self    )					->  Tuple:
							'''simple docstring'''






							A__            =     1
							A__            =     np.array(
							    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )] , dtype=np.floataa    )

							self.distributed_retriever_check(
							    self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowercase    ) , lowercase , lowercase    )


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     1
							A__            =     np.array(
							    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )] , dtype=np.floataa    )

							self.distributed_retriever_check(
							    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowercase , from_disk=lowercase    ) , lowercase , lowercase , )


			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     1
							A__            =     np.array(
							    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )] , dtype=np.floataa    )

							self.distributed_retriever_check(
							    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowercase , from_disk=lowercase    ) , lowercase , lowercase , )


			@require_ray
			def        UpperCamelCase						(      self    )					->  Optional[Any]:
							'''simple docstring'''






							A__            =     1
							A__            =     np.array(
							    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )] , dtype=np.floataa    )

							self.distributed_retriever_check(
							    self.get_dummy_ray_distributed_retriever(init_retrieval=lowercase    ) , lowercase , lowercase    )
							ray.shutdown()


			@require_ray
			def        UpperCamelCase						(      self    )					->  Dict:
							'''simple docstring'''






							A__            =     1
							A__            =     np.array(
							    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )] , dtype=np.floataa    )
							with self.assertRaises(lowercase    ):
											self.distributed_retriever_check(
											    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowercase , from_disk=lowercase    ) , lowercase , lowercase , )
							ray.shutdown()


			@require_ray
			def        UpperCamelCase						(      self    )					->  int:
							'''simple docstring'''






							A__            =     1
							A__            =     np.array(
							    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )] , dtype=np.floataa    )

							self.distributed_retriever_check(
							    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowercase , from_disk=lowercase    ) , lowercase , lowercase    )
							ray.shutdown()




from .balanced_parentheses import balanced_parentheses
from .stack import Stack



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> int:




				'''simple docstring'''





				return {"+": 1, "-": 1, "*": 2, "/": 2, "^": 3}.get(SCREAMING_SNAKE_CASE_		,    -1 )



def    lowerCAmelCase__							(		SCREAMING_SNAKE_CASE_:							str )     -> str:




				'''simple docstring'''





				if not balanced_parentheses(SCREAMING_SNAKE_CASE_ ):
								raise ValueError("Mismatched parentheses" )
				A__            =     Stack()
				A__            =     []
				for char in expression_str:
								if char.isalpha() or char.isdigit():
												postfix.append(SCREAMING_SNAKE_CASE_ )
								elif char == "(":
												stack.push(SCREAMING_SNAKE_CASE_ )
								elif char == ")":
												while not stack.is_empty() and stack.peek() != "(":
																postfix.append(stack.pop() )
												stack.pop()
								else:
												while not stack.is_empty() and precedence(SCREAMING_SNAKE_CASE_ ) <= precedence(stack.peek() ):
																postfix.append(stack.pop() )
												stack.push(SCREAMING_SNAKE_CASE_ )
				while not stack.is_empty():
								postfix.append(stack.pop() )
				return " ".join(SCREAMING_SNAKE_CASE_ )


if __name__ == "__main__":
	from doctest import testmod

	testmod()
	lowerCAmelCase__						=	"""a+b*(c^d-e)^(f+g*h)-i"""

	print("""Infix to Postfix Notation demonstration:\n""")
	print("""Infix notation: """ + expression)
	print("""Postfix notation: """ + infix_to_postfix(expression))

