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 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   __a       =  tempfile.mkdtemp()
   __a       =  8

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

   __a       =  os.path.join(self.tmpdirname     ,							"bart_tokenizer"       )
   os.makedirs(__SCREAMING_SNAKE_CASE     ,							exist_ok=__SCREAMING_SNAKE_CASE       )
   __a       =  os.path.join(__SCREAMING_SNAKE_CASE     ,							BART_VOCAB_FILES_NAMES["vocab_file"]       )
   __a       =  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 		_UpperCAmelCase   (		self :      Dict       ):
   return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							"dpr_tokenizer"       )       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							"dpr_tokenizer"       )       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname     ,							"bart_tokenizer"       )       )

  def 		_UpperCAmelCase   (		self :      str       ):
   shutil.rmtree(self.tmpdirname       )

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  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 :      List[Any]       ):
   __a       =  self.get_dummy_dataset()
   __a       =  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:
    __a       =  dataset
    __a       =  RagRetriever(
        __SCREAMING_SNAKE_CASE     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							)
   return retriever

  def 		_UpperCAmelCase   (		self :      List[str]     ,							__SCREAMING_SNAKE_CASE :      bool       ):
   __a       =  self.get_dummy_dataset()
   __a       =  RagConfig(
       retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							index_name="custom"     ,							)
   if from_disk:
    __a       =  os.path.join(self.tmpdirname     ,							"dataset"       )
    __a       =  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
    __a       =  RagRetriever(
        __SCREAMING_SNAKE_CASE     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()     ,							)
   else:
    __a       =  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 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  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       )

   __a       =  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"       )       )

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

   __a       =  RagConfig(
       retrieval_vector_size=self.retrieval_vector_size     ,							question_encoder=DPRConfig().to_dict()     ,							generator=BartConfig().to_dict()     ,							index_name="legacy"     ,							index_path=self.tmpdirname     ,							)
   __a       =  RagRetriever(
       __SCREAMING_SNAKE_CASE     ,							question_encoder_tokenizer=self.get_dpr_tokenizer()     ,							generator_tokenizer=self.get_bart_tokenizer()       )
   return retriever

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  1
   __a       =  self.get_dummy_canonical_hf_index_retriever()
   __a       =  np.array(
       [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
   __a       ,     __a       ,     __a       =  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 		_UpperCAmelCase   (		self :      Any       ):
   __a       =  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:
     __a       =  self.get_dummy_dataset()
     retriever.save_pretrained(__SCREAMING_SNAKE_CASE       )
     __a       =  RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE       )
     self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
     __a       =  np.array(
         [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
     __a       =  retriever.retrieve(__SCREAMING_SNAKE_CASE     ,							n_docs=1       )
     self.assertTrue(out is not None       )

  def 		_UpperCAmelCase   (		self :      str       ):
   __a       =  1
   __a       =  self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE       )
   __a       =  np.array(
       [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
   __a       ,     __a       ,     __a       =  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 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE       )
   with tempfile.TemporaryDirectory() as tmp_dirname:
    retriever.save_pretrained(__SCREAMING_SNAKE_CASE       )
    __a       =  RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    __a       =  np.array(
        [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
    __a       =  retriever.retrieve(__SCREAMING_SNAKE_CASE     ,							n_docs=1       )
    self.assertTrue(out is not None       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  1
   __a       =  self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE       )
   __a       =  np.array(
       [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
   __a       ,     __a       ,     __a       =  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 		_UpperCAmelCase   (		self :      Dict       ):
   __a       =  self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE       )
   with tempfile.TemporaryDirectory() as tmp_dirname:
    retriever.save_pretrained(__SCREAMING_SNAKE_CASE       )
    __a       =  RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    __a       =  np.array(
        [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
    __a       =  retriever.retrieve(__SCREAMING_SNAKE_CASE     ,							n_docs=1       )
    self.assertTrue(out is not None       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   __a       =  1
   __a       =  self.get_dummy_legacy_index_retriever()
   __a       =  np.array(
       [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
   __a       ,     __a       ,     __a       =  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 		_UpperCAmelCase   (		self :      List[Any]       ):
   __a       =  self.get_dummy_legacy_index_retriever()
   with tempfile.TemporaryDirectory() as tmp_dirname:
    retriever.save_pretrained(__SCREAMING_SNAKE_CASE       )
    __a       =  RagRetriever.from_pretrained(__SCREAMING_SNAKE_CASE       )
    self.assertIsInstance(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       )
    __a       =  np.array(
        [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
    __a       =  retriever.retrieve(__SCREAMING_SNAKE_CASE     ,							n_docs=1       )
    self.assertTrue(out is not None       )

  @require_torch
  @require_tokenizers
  @require_sentencepiece
  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   import torch

   __a       =  1
   __a       =  self.get_dummy_canonical_hf_index_retriever()
   __a       =  [[5, 7], [10, 11]]
   __a       =  np.array(
       [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
   __a       =  retriever(__SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__SCREAMING_SNAKE_CASE       )
   __a       ,     __a       ,     __a       =  (
       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       )

   __a       =  retriever(
       __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE     ,							prefix=retriever.config.generator.prefix     ,							n_docs=__SCREAMING_SNAKE_CASE     ,							return_tensors="pt"     ,							)
   __a       ,     __a       ,     __a       ,     __a       =  (  # 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 		_UpperCAmelCase   (		self :      Tuple       ):
   __a       =  self.get_dpr_ctx_encoder_tokenizer()
   __a       =  1
   __a       =  self.get_dummy_custom_hf_index_retriever(from_disk=__SCREAMING_SNAKE_CASE       )
   retriever.set_ctx_encoder_tokenizer(__SCREAMING_SNAKE_CASE       )

   __a       =  [[5, 7], [10, 11]]
   __a       =  np.array(
       [np.ones(self.retrieval_vector_size       ), -np.ones(self.retrieval_vector_size       )]     ,							dtype=np.floataa       )
   __a       =  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.



# Function to print upper half of diamond (pyramid)



def   __A		(			_A					):



 """simple docstring"""


 for i in range(0							,				_A					):
  for _ in range(0							,				n - i - 1					):  # printing spaces
   print(" "							,				end=""					)
  for _ in range(0							,				i + 1					):  # printing stars
   print("* "							,				end=""					)
  print()



def   __A		(			_A					):



 """simple docstring"""


 for i in range(_A							,				0							,				-1					):
  for _ in range(_A							,				0							,				-1					):  # printing stars
   print("* "							,				end=""					)
  print()
  for _ in range(n - i + 1							,				0							,				-1					):  # printing spaces
   print(" "							,				end=""					)



def   __A		(			_A					):



 """simple docstring"""


 if n <= 0:
  print("       ...       ....        nothing printing :("					)
  return
 floyd(_A					)  # upper half
 reverse_floyd(_A					)  # lower half


if __name__ == "__main__":
  print(R"""| /\ | |- |  |-  |--| |\  /| |-""")
  print(R"""|/  \| |- |_ |_  |__| | \/ | |_""")
  SCREAMING_SNAKE_CASE :	Tuple							     =		1
  while K:
    SCREAMING_SNAKE_CASE :	Tuple							     =		int(input("""enter the number and , and see the magic : """))
    print()
    pretty_print(user_number)
    SCREAMING_SNAKE_CASE :	Optional[int]							     =		int(input("""press 0 to exit... and 1 to continue..."""))

  print("""Good Bye...""")


