
'''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:
					UpperCamelCase_            =    None

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


def 		_UpperCAmelCase			(					_lowerCamelCase  :    Optional[int]						)      ->  Optional[int]:
		if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
				_lowerCAmelCase    :					List[str]							=   unittest.skip("""test requires Datasets, Faiss, psutil"""						)(_lowerCamelCase						)
		return test_case



@require_distributed_retrieval
class 					a_					(_a      ):




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Optional[Any]							=   tempfile.mkdtemp()
									_lowerCAmelCase    :					List[str]							=   8

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

									_lowerCAmelCase    :					Any							=   os.path.join(self.tmpdirname				,				"""bart_tokenizer"""					)
									os.makedirs(snake_case_				,				exist_ok=snake_case_					)
									_lowerCAmelCase    :					Union[str, Any]							=   os.path.join(snake_case_				,				BART_VOCAB_FILES_NAMES["""vocab_file"""]					)
									_lowerCAmelCase    :					Optional[int]							=   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 							__UpperCamelCase    (       self					):
									return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname				,				"""dpr_tokenizer"""					)					)




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




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




							def 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					List[str]							=   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				,				snake_case_				,				snake_case_=1_2_3_4_5					):
									_lowerCAmelCase    :					Dict							=   self.get_dummy_dataset()
									_lowerCAmelCase    :					str							=   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    :					int							=   dataset
											_lowerCAmelCase    :					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 							__UpperCamelCase    (       self				,				snake_case_					):
									# Have to run in local mode because sys.path modifications at top of
									# file are not propogated to remote workers.
									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
									ray.init(local_mode=snake_case_					)
									_lowerCAmelCase    :					List[Any]							=   RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				)
									_lowerCAmelCase    :					Union[str, Any]							=   ray.remote(snake_case_					)
									_lowerCAmelCase    :					Union[str, Any]							=   [remote_cls.remote() for _ in range(1					)]
									with patch("""transformers.models.rag.retrieval_rag.load_dataset"""					) as mock_load_dataset:
											_lowerCAmelCase    :					List[Any]							=   self.get_dummy_dataset()
											_lowerCAmelCase    :					Union[str, 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 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_=1_2_3_4_5					):
									_lowerCAmelCase    :					str							=   self.get_dummy_dataset()
									_lowerCAmelCase    :					Optional[int]							=   RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				index_name="""custom"""				,				)
									if from_disk:
											_lowerCAmelCase    :					int							=   os.path.join(self.tmpdirname				,				"""dataset"""					)
											_lowerCAmelCase    :					Union[str, 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
											_lowerCAmelCase    :					Tuple							=   RagPyTorchDistributedRetriever(
											    snake_case_				,				question_encoder_tokenizer=self.get_dpr_tokenizer()				,				generator_tokenizer=self.get_bart_tokenizer()				,				)
									else:
											_lowerCAmelCase    :					Optional[int]							=   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 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_					):
									# Have to run in local mode because sys.path modifications at top of
									# file are not propogated to remote workers.
									# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
									ray.init(local_mode=snake_case_					)
									_lowerCAmelCase    :					Optional[Any]							=   self.get_dummy_dataset()
									_lowerCAmelCase    :					str							=   RagConfig(
									    retrieval_vector_size=self.retrieval_vector_size				,				question_encoder=DPRConfig().to_dict()				,				generator=BartConfig().to_dict()				,				index_name="""custom"""				,				)
									_lowerCAmelCase    :					List[Any]							=   ray.remote(snake_case_					)
									_lowerCAmelCase    :					Union[str, Any]							=   [remote_cls.remote() for _ in range(1					)]
									if from_disk:
											_lowerCAmelCase    :					str							=   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    :					int							=   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:
											_lowerCAmelCase    :					int							=   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 							__UpperCamelCase    (       self				,				snake_case_				,				snake_case_				,				snake_case_					):
									_lowerCAmelCase    :					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 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Any							=   1
									_lowerCAmelCase    :					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 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Tuple							=   1
									_lowerCAmelCase    :					Optional[Any]							=   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 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Optional[int]							=   1
									_lowerCAmelCase    :					Optional[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 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Tuple							=   1
									_lowerCAmelCase    :					Any							=   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 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Union[str, Any]							=   1
									_lowerCAmelCase    :					Dict							=   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 							__UpperCamelCase    (       self					):
									_lowerCAmelCase    :					Any							=   1
									_lowerCAmelCase    :					Optional[Any]							=   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()




'''simple docstring'''

def 		_UpperCAmelCase			(					_lowerCamelCase  :    list[list[int]]					, _lowerCamelCase  :    list[list[int]]						)      ->  list[list[int]]:
  _lowerCAmelCase    :					int							=   []
  _lowerCAmelCase    :					Union[str, Any]							=   len(_lowerCamelCase						)
  for i in range(_lowerCamelCase						):
    _lowerCAmelCase    :					List[str]							=   []
    for j in range(_lowerCamelCase						):
      _lowerCAmelCase    :					Union[str, Any]							=   0
      for k in range(_lowerCamelCase						):
        _lowerCAmelCase    :					Any							=   val + matrix_a[i][k] * matrix_b[k][j]
      list_a.append(_lowerCamelCase						)
    matrix_c.append(_lowerCamelCase						)
  return matrix_c


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  list[list[int]]:
  return [[int(row == column						) for column in range(_lowerCamelCase						)] for row in range(_lowerCamelCase						)]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Tuple							=   identity(2						)
  _lowerCAmelCase    :					Union[str, Any]							=   [[1, 1], [1, 0]]
  _lowerCAmelCase    :					Optional[int]							=   n - 1
  while n > 0:
    if n % 2 == 1:
      _lowerCAmelCase    :					str							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Tuple							=   multiply(_lowerCamelCase					, _lowerCamelCase						)
    _lowerCAmelCase    :					Dict							=   int(n / 2						)
  return res_matrix[0][0]


def 		_UpperCAmelCase			(					_lowerCamelCase  :    int						)      ->  int:
  if n <= 1:
    return n
  _lowerCAmelCase    :					Any							=   0
  _lowerCAmelCase    :					Dict							=   1
  for _ in range(2					, n + 1						):
    _lowerCAmelCase			,   _lowerCAmelCase    :					int							=   fiba, fiba + fiba
  return fiba


def 		_UpperCAmelCase			(					)      ->  None:
  for ordinal in "0th 1st 2nd 3rd 10th 100th 1000th".split():
    _lowerCAmelCase    :					Union[str, Any]							=   int("""""".join(c for c in ordinal if c in """0123456789"""						)						)  # 1000th --> 1000
    print(
        f'{ordinal} fibonacci number using matrix exponentiation is '
        f'{nth_fibonacci_matrix(_lowerCamelCase						)} and using bruteforce is '
        f'{nth_fibonacci_bruteforce(_lowerCamelCase						)}\n'						)
  # from timeit import timeit
  # print(timeit("nth_fibonacci_matrix(1000000)",
  #              "from main import nth_fibonacci_matrix", number=5))
  # print(timeit("nth_fibonacci_bruteforce(1000000)",
  #              "from main import nth_fibonacci_bruteforce", number=5))
  # 2.3342058970001744
  # 57.256506615000035


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     main()


