






from cva import destroyAllWindows, imread, imshow, waitKey







def 		SCREAMING_SNAKE_CASE							(					__lowerCAmelCase			) ->	Optional[int]:
     # getting number of pixels in the image
     UpperCamelCase__				,     UpperCamelCase__			:   Any       				=  img.shape[0], img.shape[1]

     # converting each pixel's color to its negative
     for i in range(__lowerCAmelCase			):
          for j in range(__lowerCAmelCase			):
               UpperCamelCase__			:   List[Any]       				=  [255, 255, 255] - img[i][j]

     return img


if __name__ == "__main__":
 # read original image
 lowerCamelCase	:							str				=imread('''image_data/lena.jpg''', 1)

 # convert to its negative
 lowerCamelCase	:							Union[str, Any]				=convert_to_negative(img)

 # show result image
 imshow('''negative of original image''', img)
 waitKey(0)
 destroyAllWindows()







import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
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_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
 import faiss





@require_faiss
class 			__a  (    A__       ):







 def 					__lowercase					(		self					:   Any						):


      '''simple docstring'''






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

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

      UpperCamelCase__			:   Dict       				=  os.path.join(self.tmpdirname							,   "bart_tokenizer"						)
      os.makedirs(SCREAMING_SNAKE_CASE							,   exist_ok=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   List[str]       				=  os.path.join(SCREAMING_SNAKE_CASE							,   BART_VOCAB_FILES_NAMES["vocab_file"]						)
      UpperCamelCase__			:   str       				=  os.path.join(SCREAMING_SNAKE_CASE							,   BART_VOCAB_FILES_NAMES["merges_file"]						)
      with open(self.vocab_file							,   "w"							,   encoding="utf-8"						) as fp:
           fp.write(json.dumps(SCREAMING_SNAKE_CASE						) + "\n"						)
      with open(self.merges_file							,   "w"							,   encoding="utf-8"						) as fp:
           fp.write("\n".join(SCREAMING_SNAKE_CASE						)						)







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname							,   "dpr_tokenizer"						)						)







 def 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






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







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      shutil.rmtree(self.tmpdirname						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Tuple       				=  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 					__lowercase					(		self					:   Optional[int]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  self.get_dummy_dataset()
      UpperCamelCase__			:   List[Any]       				=  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__			:   str       				=  dataset
           UpperCamelCase__			:   Optional[int]       				=  RagRetriever(
               SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()							,   )
      return retriever







 def 					__lowercase					(		self					:   int							,   SCREAMING_SNAKE_CASE					:   bool						):


      '''simple docstring'''






      UpperCamelCase__			:   Dict       				=  self.get_dummy_dataset()
      UpperCamelCase__			:   Tuple       				=  RagConfig(
          retrieval_vector_size=self.retrieval_vector_size							,   question_encoder=DPRConfig().to_dict()							,   generator=BartConfig().to_dict()							,   index_name="custom"							,   )
      if from_disk:
           UpperCamelCase__			:   Optional[int]       				=  os.path.join(self.tmpdirname							,   "dataset"						)
           UpperCamelCase__			:   List[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__			:   str       				=  RagRetriever(
               SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()							,   )
      else:
           UpperCamelCase__			:   List[Any]       				=  RagRetriever(
               SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()							,   index=CustomHFIndex(config.retrieval_vector_size							,   SCREAMING_SNAKE_CASE						)							,   )
      return retriever







 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  Dataset.from_dict(
          {
              "id": ["0", "1"],
              "text": ["foo", "bar"],
              "title": ["Foo", "Bar"],
              "embeddings": [np.ones(self.retrieval_vector_size + 1						), 2 * np.ones(self.retrieval_vector_size + 1						)],
          }						)
      dataset.add_faiss_index("embeddings"							,   string_factory="Flat"							,   metric_type=faiss.METRIC_INNER_PRODUCT						)

      UpperCamelCase__			:   List[str]       				=  os.path.join(self.tmpdirname							,   "hf_bert_base.hnswSQ8_correct_phi_128.c_index"						)
      dataset.save_faiss_index("embeddings"							,   index_file_name + ".index.dpr"						)
      pickle.dump(dataset["id"]							,   open(index_file_name + ".index_meta.dpr"							,   "wb"						)						)

      UpperCamelCase__			:   Optional[int]       				=  os.path.join(self.tmpdirname							,   "psgs_w100.tsv.pkl"						)
      UpperCamelCase__			:   Tuple       				=  {sample["id"]: [sample["text"], sample["title"]] for sample in dataset}
      pickle.dump(SCREAMING_SNAKE_CASE							,   open(SCREAMING_SNAKE_CASE							,   "wb"						)						)

      UpperCamelCase__			:   List[str]       				=  RagConfig(
          retrieval_vector_size=self.retrieval_vector_size							,   question_encoder=DPRConfig().to_dict()							,   generator=BartConfig().to_dict()							,   index_name="legacy"							,   index_path=self.tmpdirname							,   )
      UpperCamelCase__			:   List[str]       				=  RagRetriever(
          SCREAMING_SNAKE_CASE							,   question_encoder_tokenizer=self.get_dpr_tokenizer()							,   generator_tokenizer=self.get_bart_tokenizer()						)
      return retriever







 def 					__lowercase					(		self					:   Optional[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  1
      UpperCamelCase__			:   Tuple       				=  self.get_dummy_canonical_hf_index_retriever()
      UpperCamelCase__			:   str       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["embeddings", "id", "text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["id"]						)							,   SCREAMING_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 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.get_dummy_canonical_hf_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
           with patch("transformers.models.rag.retrieval_rag.load_dataset"						) as mock_load_dataset:
                UpperCamelCase__			:   Optional[int]       				=  self.get_dummy_dataset()
                retriever.save_pretrained(SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   Any       				=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE						)
                self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
                UpperCamelCase__			:   str       				=  np.array(
                    [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
                UpperCamelCase__			:   Any       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=1						)
                self.assertTrue(out is not None						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  1
      UpperCamelCase__			:   Dict       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Tuple       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["embeddings", "id", "text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["id"]						)							,   SCREAMING_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 					__lowercase					(		self					:   str						):


      '''simple docstring'''






      UpperCamelCase__			:   Any       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      with tempfile.TemporaryDirectory() as tmp_dirname:
           retriever.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Tuple       				=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Optional[int]       				=  np.array(
               [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
           UpperCamelCase__			:   Any       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=1						)
           self.assertTrue(out is not None						)







 def 					__lowercase					(		self					:   List[str]						):


      '''simple docstring'''






      UpperCamelCase__			:   int       				=  1
      UpperCamelCase__			:   List[str]       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__			:   Dict       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["embeddings", "id", "text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["id"]						)							,   SCREAMING_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 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      with tempfile.TemporaryDirectory() as tmp_dirname:
           retriever.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Optional[int]       				=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   Dict       				=  np.array(
               [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
           UpperCamelCase__			:   Union[str, Any]       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=1						)
           self.assertTrue(out is not None						)







 def 					__lowercase					(		self					:   Tuple						):


      '''simple docstring'''






      UpperCamelCase__			:   List[str]       				=  1
      UpperCamelCase__			:   Any       				=  self.get_dummy_legacy_index_retriever()
      UpperCamelCase__			:   Optional[int]       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   Tuple       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=SCREAMING_SNAKE_CASE						)
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertEqual(len(SCREAMING_SNAKE_CASE						)							,   2						)
      self.assertEqual(sorted(doc_dicts[0]						)							,   ["text", "title"]						)
      self.assertEqual(len(doc_dicts[0]["text"]						)							,   SCREAMING_SNAKE_CASE						)
      self.assertEqual(doc_dicts[0]["text"][0]							,   "bar"						)  # max inner product is reached with second doc
      self.assertEqual(doc_dicts[1]["text"][0]							,   "foo"						)  # max inner product is reached with first doc
      self.assertListEqual(doc_ids.tolist()							,   [[1], [0]]						)







 def 					__lowercase					(		self					:   List[Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Union[str, Any]       				=  self.get_dummy_legacy_index_retriever()
      with tempfile.TemporaryDirectory() as tmp_dirname:
           retriever.save_pretrained(SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   str       				=  RagRetriever.from_pretrained(SCREAMING_SNAKE_CASE						)
           self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
           UpperCamelCase__			:   int       				=  np.array(
               [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
           UpperCamelCase__			:   int       				=  retriever.retrieve(SCREAMING_SNAKE_CASE							,   n_docs=1						)
           self.assertTrue(out is not None						)







 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 					__lowercase					(		self					:   int						):


      '''simple docstring'''






      import torch

      UpperCamelCase__			:   Optional[Any]       				=  1
      UpperCamelCase__			:   Optional[int]       				=  self.get_dummy_canonical_hf_index_retriever()
      UpperCamelCase__			:   Optional[Any]       				=  [[5, 7], [10, 11]]
      UpperCamelCase__			:   List[str]       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__			:   int       				=  retriever(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   prefix=retriever.config.generator.prefix							,   n_docs=SCREAMING_SNAKE_CASE						)
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[str]       				=  (
          out["context_input_ids"],
          out["context_attention_mask"],
          out["retrieved_doc_embeds"],
      )
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   np.ndarray						)

      UpperCamelCase__			:   List[Any]       				=  retriever(
          SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   prefix=retriever.config.generator.prefix							,   n_docs=SCREAMING_SNAKE_CASE							,   return_tensors="pt"							,   )
      UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__				,     UpperCamelCase__			:   List[Any]       				=  (  # noqa: F841
          out["context_input_ids"],
          out["context_attention_mask"],
          out["retrieved_doc_embeds"],
          out["doc_ids"],
      )
      self.assertEqual(retrieved_doc_embeds.shape							,   (2, n_docs, self.retrieval_vector_size)						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)
      self.assertIsInstance(SCREAMING_SNAKE_CASE							,   torch.Tensor						)







 @require_torch
 @require_tokenizers
 @require_sentencepiece
 def 					__lowercase					(		self					:   Union[str, Any]						):


      '''simple docstring'''






      UpperCamelCase__			:   Optional[int]       				=  self.get_dpr_ctx_encoder_tokenizer()
      UpperCamelCase__			:   Union[str, Any]       				=  1
      UpperCamelCase__			:   Tuple       				=  self.get_dummy_custom_hf_index_retriever(from_disk=SCREAMING_SNAKE_CASE						)
      retriever.set_ctx_encoder_tokenizer(SCREAMING_SNAKE_CASE						)

      UpperCamelCase__			:   Tuple       				=  [[5, 7], [10, 11]]
      UpperCamelCase__			:   List[str]       				=  np.array(
          [np.ones(self.retrieval_vector_size						), -np.ones(self.retrieval_vector_size						)]							,   dtype=np.floataa						)
      UpperCamelCase__			:   Optional[int]       				=  retriever(SCREAMING_SNAKE_CASE							,   SCREAMING_SNAKE_CASE							,   prefix=retriever.config.generator.prefix							,   n_docs=SCREAMING_SNAKE_CASE						)

      self.assertEqual(
          len(SCREAMING_SNAKE_CASE						)							,   6						)  # check whether the retriever output consist of 6 attributes including tokenized docs
      self.assertEqual(
          all(k in out for k in ("tokenized_doc_ids", "tokenized_doc_attention_mask")						)							,   SCREAMING_SNAKE_CASE						)  # check for doc token related keys in dictionary.