






"""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		:  List[Any]						=		None

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





  '''simple docstring'''





  if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
    lowercase							   =	unittest.skip('test requires Datasets, Faiss, psutil'   )(__snake_case   )
  return test_case



@require_distributed_retrieval
class 	a					(       a_     ):







    def       UpperCamelCase_				(      self	):
      lowercase							   =	tempfile.mkdtemp()
      lowercase							   =	8

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

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







    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	):
      lowercase							   =	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							,		_lowerCamelCase							,		_lowerCamelCase=1_2_3_4_5	):
      lowercase							   =	self.get_dummy_dataset()
      lowercase							   =	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:
        lowercase							   =	dataset
        lowercase							   =	RagPyTorchDistributedRetriever(
            _lowerCamelCase							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
        if init_retrieval:
          retriever.init_retrieval(_lowerCamelCase	)
      return retriever







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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase=1_2_3_4_5	):
      lowercase							   =	self.get_dummy_dataset()
      lowercase							   =	RagConfig(
          retrieval_vector_size=self.retrieval_vector_size							,		question_encoder=DPRConfig().to_dict()							,		generator=BartConfig().to_dict()							,		index_name='custom'							,		)
      if from_disk:
        lowercase							   =	os.path.join(self.tmpdirname							,		'dataset'	)
        lowercase							   =	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
        lowercase							   =	RagPyTorchDistributedRetriever(
            _lowerCamelCase							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		)
      else:
        lowercase							   =	RagPyTorchDistributedRetriever(
            _lowerCamelCase							,		question_encoder_tokenizer=self.get_dpr_tokenizer()							,		generator_tokenizer=self.get_bart_tokenizer()							,		index=CustomHFIndex(config.retrieval_vector_size							,		_lowerCamelCase	)							,		)
      if init_retrieval:
        retriever.init_retrieval(_lowerCamelCase	)
      return retriever







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







    def       UpperCamelCase_				(      self							,		_lowerCamelCase							,		_lowerCamelCase							,		_lowerCamelCase	):
      lowercase   ,					lowercase   ,					lowercase							   =	retriever.retrieve(_lowerCamelCase							,		n_docs=_lowerCamelCase	)
      self.assertEqual(retrieved_doc_embeds.shape							,		(2, n_docs, self.retrieval_vector_size)	)
      self.assertEqual(len(_lowerCamelCase	)							,		2	)
      self.assertEqual(sorted(doc_dicts[0]	)							,		['embeddings', 'id', 'text', 'title']	)
      self.assertEqual(len(doc_dicts[0]['id']	)							,		_lowerCamelCase	)
      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	):
      lowercase							   =	1
      lowercase							   =	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=_lowerCamelCase	)							,		_lowerCamelCase							,		_lowerCamelCase	)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	1
      lowercase							   =	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=_lowerCamelCase							,		from_disk=_lowerCamelCase	)							,		_lowerCamelCase							,		_lowerCamelCase							,		)







    def       UpperCamelCase_				(      self	):
      lowercase							   =	1
      lowercase							   =	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=_lowerCamelCase							,		from_disk=_lowerCamelCase	)							,		_lowerCamelCase							,		_lowerCamelCase							,		)







    @require_ray
    def       UpperCamelCase_				(      self	):
      lowercase							   =	1
      lowercase							   =	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=_lowerCamelCase	)							,		_lowerCamelCase							,		_lowerCamelCase	)
      ray.shutdown()







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







    @require_ray
    def       UpperCamelCase_				(      self	):
      lowercase							   =	1
      lowercase							   =	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=_lowerCamelCase							,		from_disk=_lowerCamelCase	)							,		_lowerCamelCase							,		_lowerCamelCase	)
      ray.shutdown()










"""simple docstring"""



def 						_SCREAMING_SNAKE_CASE			(   __snake_case      :  str   ):





  '''simple docstring'''





  lowercase							   =	len(__snake_case   )
  lowercase							   =	[0] * length
  lowercase							   =	[[False for i in range(__snake_case   )] for j in range(__snake_case   )]
  for i, c in enumerate(__snake_case   ):
    lowercase							   =	i
    for j in range(i + 1   ):
      if c == string[j] and (i - j < 2 or is_palindromic[j + 1][i - 1]):
        lowercase							   =	True
        lowercase							   =	min(__snake_case		, 0 if j == 0 else (cut[j - 1] + 1)   )
    lowercase							   =	mincut
  return cut[length - 1]


if __name__ == "__main__":
    _UpperCamelCase		:  List[Any]						=		input('Enter the string: ').strip()
    _UpperCamelCase		:  List[Any]						=		find_minimum_partitions(s)
    print(F'''Minimum number of partitions required for the \'{s}\' is {ans}''')


