






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:
  SCREAMING_SNAKE_CASE								=  None

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

def        _lowerCamelCase		(  __A      :							Union[str, Any]	) ->   Dict:
    if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
        _UpperCAmelCase			: List[str]     					=			unittest.skip('''test requires Datasets, Faiss, psutil'''	)(__A	)
    return test_case


@require_distributed_retrieval
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 def 	snake_case__			(		self)	->						Tuple:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			tempfile.mkdtemp()
     _UpperCAmelCase			: str     					=			8

     # DPR tok
     _UpperCAmelCase			: Union[str, Any]     					=			[
         '''[UNK]''',
         '''[CLS]''',
         '''[SEP]''',
         '''[PAD]''',
         '''[MASK]''',
         '''want''',
         '''##want''',
         '''##ed''',
         '''wa''',
         '''un''',
         '''runn''',
         '''##ing''',
         ''',''',
         '''low''',
         '''lowest''',
     ]
     _UpperCAmelCase			: Union[str, Any]     					=			os.path.join(self.tmpdirname							,					'''dpr_tokenizer''')
     os.makedirs(_A							,					exist_ok=_A)
     _UpperCAmelCase			: List[Any]     					=			os.path.join(_A							,					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
     _UpperCAmelCase			: Dict     					=			[
         '''l''',
         '''o''',
         '''w''',
         '''e''',
         '''r''',
         '''s''',
         '''t''',
         '''i''',
         '''d''',
         '''n''',
         '''\u0120''',
         '''\u0120l''',
         '''\u0120n''',
         '''\u0120lo''',
         '''\u0120low''',
         '''er''',
         '''\u0120lowest''',
         '''\u0120newer''',
         '''\u0120wider''',
         '''<unk>''',
     ]
     _UpperCAmelCase			: Dict     					=			dict(zip(_A							,					range(len(_A))))
     _UpperCAmelCase			: str     					=			['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
     _UpperCAmelCase			: Optional[Any]     					=			{'''unk_token''': '''<unk>'''}

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


 def 	snake_case__			(		self)	->						DPRQuestionEncoderTokenizer:

     """simple docstring"""



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


 def 	snake_case__			(		self)	->						BartTokenizer:

     """simple docstring"""



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


 def 	snake_case__			(		self)	->						List[str]:

     """simple docstring"""



     shutil.rmtree(self.tmpdirname)


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: 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 	snake_case__			(		self							,					_A							,					_A=12345)	->						RagPyTorchDistributedRetriever:

     """simple docstring"""



     _UpperCAmelCase			: int     					=			self.get_dummy_dataset()
     _UpperCAmelCase			: Optional[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:
         _UpperCAmelCase			: Optional[Any]     					=			dataset
         _UpperCAmelCase			: Optional[int]     					=			RagPyTorchDistributedRetriever(
             _A							,					question_encoder_tokenizer=self.get_dpr_tokenizer()							,					generator_tokenizer=self.get_bart_tokenizer()							,					)
         if init_retrieval:
             retriever.init_retrieval(_A)
     return retriever


 def 	snake_case__			(		self							,					_A)	->						RagRayDistributedRetriever:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A=12345)	->						Optional[int]:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A)	->						int:

     """simple docstring"""



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


 def 	snake_case__			(		self							,					_A							,					_A							,					_A)	->						None:

     """simple docstring"""



     _UpperCAmelCase					,	_UpperCAmelCase					,	_UpperCAmelCase			: Tuple     					=			retriever.retrieve(_A							,					n_docs=_A)
     self.assertEqual(retrieved_doc_embeds.shape							,					(2, n_docs, self.retrieval_vector_size))
     self.assertEqual(len(_A)							,					2)
     self.assertEqual(sorted(doc_dicts[0])							,					['''embeddings''', '''id''', '''text''', '''title'''])
     self.assertEqual(len(doc_dicts[0]['''id'''])							,					_A)
     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 	snake_case__			(		self)	->						List[Any]:

     """simple docstring"""



     _UpperCAmelCase			: str     					=			1
     _UpperCAmelCase			: 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_pytorch_distributed_retriever(init_retrieval=_A)							,					_A							,					_A)


 def 	snake_case__			(		self)	->						Dict:

     """simple docstring"""



     _UpperCAmelCase			: Tuple     					=			1
     _UpperCAmelCase			: List[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=_A							,					from_disk=_A)							,					_A							,					_A							,					)


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

     """simple docstring"""



     _UpperCAmelCase			: List[str]     					=			1
     _UpperCAmelCase			: List[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_pytorch_retriever(init_retrieval=_A							,					from_disk=_A)							,					_A							,					_A							,					)


 @require_ray
 def 	snake_case__			(		self)	->						Optional[Any]:

     """simple docstring"""



     _UpperCAmelCase			: List[Any]     					=			1
     _UpperCAmelCase			: 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=_A)							,					_A							,					_A)
     ray.shutdown()


 @require_ray
 def 	snake_case__			(		self)	->						Any:

     """simple docstring"""



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


 @require_ray
 def 	snake_case__			(		self)	->						str:

     """simple docstring"""



     _UpperCAmelCase			: Optional[Any]     					=			1
     _UpperCAmelCase			: Dict     					=			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=_A							,					from_disk=_A)							,					_A							,					_A)
     ray.shutdown()












def        _lowerCamelCase		(  __A      :							list	) ->   list:
    _UpperCAmelCase			: Dict     					=			1.3
    _UpperCAmelCase			: Any     					=			len(__A	)
    _UpperCAmelCase			: List[Any]     					=			False

    while not completed:
        # Update the gap value for a next comb
        _UpperCAmelCase			: Optional[Any]     					=			int(gap / shrink_factor	)
        if gap <= 1:
            _UpperCAmelCase			: Any     					=			True

        _UpperCAmelCase			: Optional[Any]     					=			0
        while index + gap < len(__A	):
            if data[index] > data[index + gap]:
                # Swap values
                _UpperCAmelCase					,	_UpperCAmelCase			: Union[str, Any]     					=			data[index + gap], data[index]
                _UpperCAmelCase			: Any     					=			False
            index += 1

    return data


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  SCREAMING_SNAKE_CASE								=  input('Enter numbers separated by a comma:\n').strip()
  SCREAMING_SNAKE_CASE								=  [int(item) for item in user_input.split(',')]
  print(comb_sort(unsorted))




