




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:
 a_										=       None

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




def    __SCREAMING_SNAKE_CASE				(					lowercase_ )    ->		int:

    """simple docstring"""


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


@require_distributed_retrieval
class 	_lowerCamelCase  (     _SCREAMING_SNAKE_CASE				):

      """simple docstring"""

      def  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  tempfile.mkdtemp()
          __UpperCamelCase											=  8

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

          __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''bart_tokenizer'''			)
          os.makedirs(snake_case	,    exist_ok=snake_case			)
          __UpperCamelCase											=  os.path.join(snake_case	,    BART_VOCAB_FILES_NAMES['''vocab_file''']			)
          __UpperCamelCase											=  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  snake_case		(       self   :	Optional[Any]			):
          return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname	,    '''dpr_tokenizer'''			)			)







      def  snake_case		(       self   :	Tuple			):
          return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname	,    '''bart_tokenizer'''			)			)







      def  snake_case		(       self   :	List[str]			):
          shutil.rmtree(self.tmpdirname			)







      def  snake_case		(       self   :	int			):
          __UpperCamelCase											=  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   :	List[Any]	,    snake_case   :	bool	,    snake_case   :	str=12345			):
          __UpperCamelCase											=  self.get_dummy_dataset()
          __UpperCamelCase											=  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											=  dataset
              __UpperCamelCase											=  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  snake_case		(       self   :	Optional[int]	,    snake_case   :	bool			):
          # 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			)
          __UpperCamelCase											=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    )
          __UpperCamelCase											=  ray.remote(snake_case			)
          __UpperCamelCase											=  [remote_cls.remote() for _ in range(1			)]
          with patch('''transformers.models.rag.retrieval_rag.load_dataset'''			) as mock_load_dataset:
              __UpperCamelCase											=  self.get_dummy_dataset()
              __UpperCamelCase											=  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  snake_case		(       self   :	int	,    snake_case   :	bool	,    snake_case   :	bool	,    snake_case   :	List[Any]=12345			):
          __UpperCamelCase											=  self.get_dummy_dataset()
          __UpperCamelCase											=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    index_name='''custom'''	,    )
          if from_disk:
              __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''dataset'''			)
              __UpperCamelCase											=  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											=  RagPyTorchDistributedRetriever(
                  snake_case	,    question_encoder_tokenizer=self.get_dpr_tokenizer()	,    generator_tokenizer=self.get_bart_tokenizer()	,    )
          else:
              __UpperCamelCase											=  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  snake_case		(       self   :	str	,    snake_case   :	bool	,    snake_case   :	bool			):
          # 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			)
          __UpperCamelCase											=  self.get_dummy_dataset()
          __UpperCamelCase											=  RagConfig(
              retrieval_vector_size=self.retrieval_vector_size	,    question_encoder=DPRConfig().to_dict()	,    generator=BartConfig().to_dict()	,    index_name='''custom'''	,    )
          __UpperCamelCase											=  ray.remote(snake_case			)
          __UpperCamelCase											=  [remote_cls.remote() for _ in range(1			)]
          if from_disk:
              __UpperCamelCase											=  os.path.join(self.tmpdirname	,    '''dataset'''			)
              __UpperCamelCase											=  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											=  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:
              __UpperCamelCase											=  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  snake_case		(       self   :	Dict	,    snake_case   :	RagRetriever	,    snake_case   :	np.array	,    snake_case   :	int			):
          __UpperCamelCase						, __UpperCamelCase						, __UpperCamelCase											=  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  snake_case		(       self   :	Tuple			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  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  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  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  snake_case		(       self   :	Union[str, Any]			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  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  snake_case		(       self   :	Tuple			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  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  snake_case		(       self   :	List[Any]			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  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  snake_case		(       self   :	int			):
          __UpperCamelCase											=  1
          __UpperCamelCase											=  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()












import cva
import numpy as np




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    try:
        return int(image[x_coordinate][y_coordinate] >= center )
    except (IndexError, TypeError):
        return 0




def    __SCREAMING_SNAKE_CASE				(					lowercase_   ,       lowercase_   ,       lowercase_ )    ->		int:

    """simple docstring"""


    __UpperCamelCase											=  image[x_coordinate][y_coordinate]
    __UpperCamelCase											=  [1, 2, 4, 8, 16, 32, 64, 1_28]

    # skip get_neighbors_pixel if center is null
    if center is None:
        return 0

    # Starting from the top right, assigning value to pixels clockwise
    __UpperCamelCase											=  [
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate + 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       lowercase_   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate + 1   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       lowercase_   ,       y_coordinate - 1   ,       lowercase_ ),
        get_neighbors_pixel(lowercase_   ,       x_coordinate - 1   ,       y_coordinate - 1   ,       lowercase_ ),
    ]

    # Converting the binary value to decimal.
    return sum(
        binary_value * power for binary_value, power in zip(lowercase_   ,       lowercase_ ) )


if __name__ == "__main__":
 # Reading the image and converting it to grayscale.
 a_										=       cva.imread(
     "digital_image_processing/image_data/lena.jpg", cva.IMREAD_GRAYSCALE
 )

 # Create a numpy array as the same height and width of read image
 a_										=       np.zeros((image.shape[0], image.shape[1]))

 # Iterating through the image and calculating the
 # local binary pattern value for each pixel.
 for i in range(0, image.shape[0]):
  for j in range(0, image.shape[1]):
   a_										=       local_binary_value(image, i, j)

 cva.imshow("local binary pattern", lbp_image)
 cva.waitKey(0)
 cva.destroyAllWindows()






