
"""simple docstring"""
from __future__ import annotations

from typing import Generic, TypeVar

lowercase		:						List[Any] =					TypeVar('T')




class    _snake_case  (						_lowercase       ):
							pass






class    _snake_case  (						_lowercase       ):
							pass






class    _snake_case  (						Generic[T]       ):






							def __init__(					self						:			Any						, snake_case__						:			int = 1_0    ):






												"""simple docstring"""



												__a:						list[T]		    =					[]
												__a:						Tuple		    =					limit






							def __bool__(					self						:			List[str]    ):






												"""simple docstring"""



												return bool(self.stack    )






							def __str__(					self						:			Optional[int]    ):






												"""simple docstring"""



												return str(self.stack    )






							def 							__lowercase  (					self						:			Tuple						, snake_case__						:			T    ):






												"""simple docstring"""



												if len(self.stack    ) >= self.limit:
																	raise StackOverflowError
												self.stack.append(snake_case__    )






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												if not self.stack:
																	raise StackUnderflowError
												return self.stack.pop()






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												if not self.stack:
																	raise StackUnderflowError
												return self.stack[-1]






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												return not bool(self.stack    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												return self.size() == self.limit






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												return len(self.stack    )






							def __contains__(					self						:			int						, snake_case__						:			T    ):






												"""simple docstring"""



												return item in self.stack






def     lowerCamelCase_    (	)     ->			None:


					'''simple docstring'''
					__a:						Stack[int]		    =					Stack(10 )
					assert bool(lowerCamelCase_ ) is False
					assert stack.is_empty() is True
					assert stack.is_full() is False
					assert str(lowerCamelCase_ ) == "[]"

					try:
										__a:						List[str]		    =					stack.pop()
										raise AssertionError  # This should not happen
					except StackUnderflowError:
										assert True  # This should happen

					try:
										__a:						Dict		    =					stack.peek()
										raise AssertionError  # This should not happen
					except StackUnderflowError:
										assert True  # This should happen

					for i in range(10 ):
										assert stack.size() == i
										stack.push(lowerCamelCase_ )

					assert bool(lowerCamelCase_ )
					assert not stack.is_empty()
					assert stack.is_full()
					assert str(lowerCamelCase_ ) == str(list(range(10 ) ) )
					assert stack.pop() == 9
					assert stack.peek() == 8

					stack.push(1_00 )
					assert str(lowerCamelCase_ ) == str([0, 1, 2, 3, 4, 5, 6, 7, 8, 1_00] )

					try:
										stack.push(2_00 )
										raise AssertionError  # This should not happen
					except StackOverflowError:
										assert True  # This should happen

					assert not stack.is_empty()
					assert stack.size() == 10

					assert 5 in stack
					assert 55 not in stack


if __name__ == "__main__":
			test_stack()


"""simple docstring"""
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:
			lowercase		:						str =					None

if is_ray_available():
			import ray  # noqa: E402 # isort:skip
			from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
			lowercase		:						Optional[int] =					None
			lowercase		:						Union[str, Any] =					None
			lowercase		:						List[str] =					None


def     lowerCamelCase_    (	lowerCamelCase_ )     ->			Union[str, Any]:


					'''simple docstring'''
					if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
										__a:						int		    =					unittest.skip("""test requires Datasets, Faiss, psutil""" )(lowerCamelCase_ )
					return test_case




@require_distributed_retrieval
class    _snake_case  (						_lowercase       ):






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



												__a:						Optional[Any]		    =					tempfile.mkdtemp()
												__a:						int		    =					8

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

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






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Any    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			Tuple    ):






												"""simple docstring"""



												shutil.rmtree(self.tmpdirname    )






							def 							__lowercase  (					self						:			int    ):






												"""simple docstring"""



												__a:						int		    =					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 							__lowercase  (					self						:			Any						, snake_case__						:			bool						, snake_case__						:			Dict=1_2_3_4_5    ):






												"""simple docstring"""



												__a:						str		    =					self.get_dummy_dataset()
												__a:						int		    =					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:						List[str]		    =					dataset
																	__a:						List[Any]		    =					RagPyTorchDistributedRetriever(
																	    snake_case__						, question_encoder_tokenizer=self.get_dpr_tokenizer()						, generator_tokenizer=self.get_bart_tokenizer()						, )
																	if init_retrieval:
																						retriever.init_retrieval(snake_case__    )
												return retriever






							def 							__lowercase  (					self						:			Dict						, snake_case__						:			bool    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool						, snake_case__						:			Union[str, Any]=1_2_3_4_5    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			str						, snake_case__						:			bool						, snake_case__						:			bool    ):






												"""simple docstring"""



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






							def 							__lowercase  (					self						:			str						, snake_case__						:			RagRetriever						, snake_case__						:			np.array						, snake_case__						:			int    ):






												"""simple docstring"""



												__a    ,				__a    ,				__a:						Optional[Any]		    =					retriever.retrieve(snake_case__						, n_docs=snake_case__    )
												self.assertEqual(retrieved_doc_embeds.shape						, (2, n_docs, self.retrieval_vector_size)    )
												self.assertEqual(len(snake_case__    )						, 2    )
												self.assertEqual(sorted(doc_dicts[0]    )						, ["""embeddings""", """id""", """text""", """title"""]    )
												self.assertEqual(len(doc_dicts[0]["""id"""]    )						, 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 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						Tuple		    =					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=snake_case__    )						, snake_case__						, snake_case__    )






							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						Tuple		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							def 							__lowercase  (					self						:			List[Any]    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						int		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )






							@require_ray
							def 							__lowercase  (					self						:			str    ):






												"""simple docstring"""



												__a:						int		    =					1
												__a:						str		    =					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=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			Dict    ):






												"""simple docstring"""



												__a:						List[Any]		    =					1
												__a:						List[str]		    =					np.array(
												    [np.ones(self.retrieval_vector_size    ), -np.ones(self.retrieval_vector_size    )]						, dtype=np.floataa    )
												with self.assertRaises(snake_case__    ):
																	self.distributed_retriever_check(
																	    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__						, )
												ray.shutdown()






							@require_ray
							def 							__lowercase  (					self						:			List[str]    ):






												"""simple docstring"""



												__a:						Tuple		    =					1
												__a:						str		    =					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=snake_case__						, from_disk=snake_case__    )						, snake_case__						, snake_case__    )
												ray.shutdown()
