


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_ (							__snake_case						)					->      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 				lowerCamelCase__       (	lowerCAmelCase):





     def 					__A (self	)					->       Any:
      _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(UpperCAmelCase		,					exist_ok=UpperCAmelCase	)
      _lowercase											=os.path.join(UpperCAmelCase		,					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(UpperCAmelCase		,					range(len(UpperCAmelCase	)	)	)	)
      _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(UpperCAmelCase		,					exist_ok=UpperCAmelCase	)
      _lowercase											=os.path.join(UpperCAmelCase		,					BART_VOCAB_FILES_NAMES['''vocab_file''']	)
      _lowercase											=os.path.join(UpperCAmelCase		,					BART_VOCAB_FILES_NAMES['''merges_file''']	)
      with open(self.vocab_file		,					'''w'''		,					encoding='''utf-8'''	) as fp:
       fp.write(json.dumps(UpperCAmelCase	) + '''\n'''	)
      with open(self.merges_file		,					'''w'''		,					encoding='''utf-8'''	) as fp:
       fp.write('''\n'''.join(UpperCAmelCase	)	)





     def 					__A (self	)					->       DPRQuestionEncoderTokenizer:
      return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname		,					'''dpr_tokenizer'''	)	)





     def 					__A (self	)					->       BartTokenizer:
      return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname		,					'''bart_tokenizer'''	)	)





     def 					__A (self	)					->       List[str]:
      shutil.rmtree(self.tmpdirname	)





     def 					__A (self	)					->       Optional[Any]:
      _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 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase=1_2_3_4_5	)					->       RagPyTorchDistributedRetriever:
      _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(
           UpperCAmelCase		,					question_encoder_tokenizer=self.get_dpr_tokenizer()		,					generator_tokenizer=self.get_bart_tokenizer()		,					)
       if init_retrieval:
        retriever.init_retrieval(UpperCAmelCase	)
      return retriever





     def 					__A (self		,					UpperCAmelCase	)					->       RagRayDistributedRetriever:
      # 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=UpperCAmelCase	)
      _lowercase											=RagConfig(
          retrieval_vector_size=self.retrieval_vector_size		,					question_encoder=DPRConfig().to_dict()		,					generator=BartConfig().to_dict()		,					)
      _lowercase											=ray.remote(UpperCAmelCase	)
      _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(
           UpperCAmelCase		,					question_encoder_tokenizer=self.get_dpr_tokenizer()		,					generator_tokenizer=self.get_bart_tokenizer()		,					retrieval_workers=UpperCAmelCase		,					)
       if init_retrieval:
        retriever.init_retrieval()
      return retriever





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase=1_2_3_4_5	)					->       List[str]:
      _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(
           UpperCAmelCase		,					question_encoder_tokenizer=self.get_dpr_tokenizer()		,					generator_tokenizer=self.get_bart_tokenizer()		,					)
      else:
       _lowercase											=RagPyTorchDistributedRetriever(
           UpperCAmelCase		,					question_encoder_tokenizer=self.get_dpr_tokenizer()		,					generator_tokenizer=self.get_bart_tokenizer()		,					index=CustomHFIndex(config.retrieval_vector_size		,					UpperCAmelCase	)		,					)
      if init_retrieval:
       retriever.init_retrieval(UpperCAmelCase	)
      return retriever





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase	)					->       List[str]:
      # 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=UpperCAmelCase	)
      _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(UpperCAmelCase	)
      _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(
           UpperCAmelCase		,					question_encoder_tokenizer=self.get_dpr_tokenizer()		,					generator_tokenizer=self.get_bart_tokenizer()		,					retrieval_workers=UpperCAmelCase		,					index=CustomHFIndex.load_from_disk(
               vector_size=config.retrieval_vector_size		,					dataset_path=config.passages_path		,					index_path=config.index_path		,					)		,					)
      else:
       _lowercase											=RagRayDistributedRetriever(
           UpperCAmelCase		,					question_encoder_tokenizer=self.get_dpr_tokenizer()		,					generator_tokenizer=self.get_bart_tokenizer()		,					retrieval_workers=UpperCAmelCase		,					index=CustomHFIndex(config.retrieval_vector_size		,					UpperCAmelCase	)		,					)
      if init_retrieval:
       retriever.init_retrieval()
      return retriever





     def 					__A (self		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase	)					->       None:
      _lowercase				,   _lowercase				,   _lowercase											=retriever.retrieve(UpperCAmelCase		,					n_docs=UpperCAmelCase	)
      self.assertEqual(retrieved_doc_embeds.shape		,					(2, n_docs, self.retrieval_vector_size)	)
      self.assertEqual(len(UpperCAmelCase	)		,					2	)
      self.assertEqual(sorted(doc_dicts[0]	)		,					['''embeddings''', '''id''', '''text''', '''title''']	)
      self.assertEqual(len(doc_dicts[0]['''id''']	)		,					UpperCAmelCase	)
      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 					__A (self	)					->       int:
      _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=UpperCAmelCase	)		,					UpperCAmelCase		,					UpperCAmelCase	)





     def 					__A (self	)					->       Union[str, Any]:
      _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=UpperCAmelCase		,					from_disk=UpperCAmelCase	)		,					UpperCAmelCase		,					UpperCAmelCase		,					)





     def 					__A (self	)					->       Optional[Any]:
      _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=UpperCAmelCase		,					from_disk=UpperCAmelCase	)		,					UpperCAmelCase		,					UpperCAmelCase		,					)





     @require_ray
     def 					__A (self	)					->       Dict:
      _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=UpperCAmelCase	)		,					UpperCAmelCase		,					UpperCAmelCase	)
      ray.shutdown()





     @require_ray
     def 					__A (self	)					->       List[Any]:
      _lowercase											=1
      _lowercase											=np.array(
          [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]		,					dtype=np.floataa	)
      with self.assertRaises(UpperCAmelCase	):
       self.distributed_retriever_check(
           self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=UpperCAmelCase		,					from_disk=UpperCAmelCase	)		,					UpperCAmelCase		,					UpperCAmelCase		,					)
      ray.shutdown()





     @require_ray
     def 					__A (self	)					->       Union[str, Any]:
      _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=UpperCAmelCase		,					from_disk=UpperCAmelCase	)		,					UpperCAmelCase		,					UpperCAmelCase	)
      ray.shutdown()




UpperCAmelCase__            =							[
    [0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 0, 0],
    [0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0],
    [0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0],
]
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      bool:







 """simple docstring"""


 return 0 <= row < rows and 0 <= col < cols
def     UpperCAmelCase_ (							__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											=len(__snake_case						)
 _lowercase											=len(mat[0]						)
 if is_safe(__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						) and (row, col) not in seen and mat[row][col] == 1:
  seen.add((row, col)						)
  return (
      1
      + depth_first_search(row + 1					,						__snake_case					,						__snake_case					,						__snake_case						)
      + depth_first_search(row - 1					,						__snake_case					,						__snake_case					,						__snake_case						)
      + depth_first_search(__snake_case					,						col + 1					,						__snake_case					,						__snake_case						)
      + depth_first_search(__snake_case					,						col - 1					,						__snake_case					,						__snake_case						)
  )
 else:
  return 0
def     UpperCAmelCase_ (							__snake_case						)					->      int:







 """simple docstring"""


 _lowercase											=set()

 _lowercase											=0
 for row, line in enumerate(__snake_case						):
  for col, item in enumerate(__snake_case						):
   if item == 1 and (row, col) not in seen:
    # Maximizing the area
    _lowercase											=max(__snake_case					,						depth_first_search(__snake_case					,						__snake_case					,						__snake_case					,						__snake_case						)						)
 return max_area


if __name__ == "__main__":
  import doctest

  print(find_max_area(matrix))  # Output -> 6
  doctest.testmod()

