






'''simple docstring'''
import json
import sys




def 		__SCREAMING_SNAKE_CASE       (	_UpperCamelCase    ,		_UpperCamelCase				):


   """simple docstring"""




   with open(_UpperCamelCase    ,		encoding="utf-8"				) as f:
      lowercase_						:     Optional[int]							     =  json.load(_UpperCamelCase				)

   lowercase_						:     str							     =  ["<details>", "<summary>Show updated benchmarks!</summary>", " "]

   for benchmark_name in sorted(_UpperCamelCase				):
      lowercase_						:     List[str]							     =  results[benchmark_name]

      lowercase_						:     Union[str, Any]							     =  benchmark_name.split("/"				)[-1]
      output_md.append(F"""### Benchmark: {benchmark_file_name}"""				)

      lowercase_						:     Optional[int]							     =  "| metric |"
      lowercase_						:     int							     =  "|--------|"
      lowercase_						:     Tuple							     =  "| new / old (diff) |"
      for metric_name in sorted(_UpperCamelCase				):
         lowercase_						:     Optional[int]							     =  benchmark_res[metric_name]
         lowercase_						:     List[str]							     =  metric_vals["new"]
         lowercase_						:     str							     =  metric_vals.get("old"    ,		_UpperCamelCase				)
         lowercase_						:     Dict							     =  metric_vals.get("diff"    ,		_UpperCamelCase				)

         lowercase_						:     Tuple							     =  F""" {new_val:f}""" if isinstance(_UpperCamelCase    ,		(int, float)				) else "None"

         if old_val is not None:
            val_str += F""" / {old_val:f}""" if isinstance(_UpperCamelCase    ,		(int, float)				) else "None"
         if dif_val is not None:
            val_str += F""" ({dif_val:f})""" if isinstance(_UpperCamelCase    ,		(int, float)				) else "None"

         title += " " + metric_name + " |"
         lines += "---|"
         value += val_str + " |"

      output_md += [title, lines, value, " "]

   output_md.append("</details>"				)

   with open(_UpperCamelCase    ,		"w"    ,		encoding="utf-8"				) as f:
      f.writelines("\n".join(_UpperCamelCase				)				)


if __name__ == "__main__":
 UpperCamelCase__			=      sys.argv[1]
 UpperCamelCase__			=      sys.argv[2]

 format_json_to_md(input_json_file, output_md_file)












'''simple docstring'''
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 							_UpperCAmelCase    (		snake_case      ):




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  tempfile.mkdtemp()
       lowercase_						:     str							     =  8

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

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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Tuple      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       shutil.rmtree(self.tmpdirname      )




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  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 					lowerCAmelCase__					(      self	:							List[Any]      ):






       '''simple docstring'''






       lowercase_						:     Optional[int]							     =  self.get_dummy_dataset()
       lowercase_						:     Optional[int]							     =  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_						:     Any							     =  dataset
          lowercase_						:     List[str]							     =  RagRetriever(
              a	,  question_encoder_tokenizer=self.get_dpr_tokenizer()	,  generator_tokenizer=self.get_bart_tokenizer()	,  )
       return retriever




    def 					lowerCAmelCase__					(      self	:							Union[str, Any]	,  a	:							bool      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  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      )

       lowercase_						:     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"      )      )

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

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




    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     Tuple							     =  self.get_dummy_canonical_hf_index_retriever()
       lowercase_						:     Any							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["embeddings", "id", "text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["id"]      )	,  a      )
       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 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






       lowercase_						:     Union[str, Any]							     =  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:
             lowercase_						:     int							     =  self.get_dummy_dataset()
             retriever.save_pretrained(a      )
             lowercase_						:     Union[str, Any]							     =  RagRetriever.from_pretrained(a      )
             self.assertIsInstance(a	,  a      )
             lowercase_						:     Optional[Any]							     =  np.array(
                 [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
             lowercase_						:     List[Any]							     =  retriever.retrieve(a	,  n_docs=1      )
             self.assertTrue(out is not None      )




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Any							     =  1
       lowercase_						:     Optional[int]							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       lowercase_						:     str							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     List[str]							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["embeddings", "id", "text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["id"]      )	,  a      )
       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 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							List[str]      ):






       '''simple docstring'''






       lowercase_						:     List[str]							     =  1
       lowercase_						:     Optional[int]							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       lowercase_						:     Any							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     Optional[int]							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["embeddings", "id", "text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["id"]      )	,  a      )
       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 					lowerCAmelCase__					(      self	:							int      ):






       '''simple docstring'''






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




    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     int							     =  self.get_dummy_legacy_index_retriever()
       lowercase_						:     Dict							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     List[str]							     =  retriever.retrieve(a	,  n_docs=a      )
       self.assertEqual(retrieved_doc_embeds.shape	,  (2, n_docs, self.retrieval_vector_size)      )
       self.assertEqual(len(a      )	,  2      )
       self.assertEqual(sorted(doc_dicts[0]      )	,  ["text", "title"]      )
       self.assertEqual(len(doc_dicts[0]["text"]      )	,  a      )
       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 					lowerCAmelCase__					(      self	:							Optional[int]      ):






       '''simple docstring'''






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




    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Dict      ):






       '''simple docstring'''






       import torch

       lowercase_						:     Optional[Any]							     =  1
       lowercase_						:     str							     =  self.get_dummy_canonical_hf_index_retriever()
       lowercase_						:     Optional[int]							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     str							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     List[str]							     =  retriever(a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a      )
       lowercase_     ,		lowercase_     ,		lowercase_						:     Union[str, Any]							     =  (
           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(a	,  a      )
       self.assertIsInstance(a	,  a      )
       self.assertIsInstance(a	,  np.ndarray      )

       lowercase_						:     Tuple							     =  retriever(
           a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a	,  return_tensors="pt"	,  )
       lowercase_     ,		lowercase_     ,		lowercase_     ,		lowercase_						:     Tuple							     =  (  # 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(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )
       self.assertIsInstance(a	,  torch.Tensor      )





    @require_torch
    @require_tokenizers
    @require_sentencepiece
    def 					lowerCAmelCase__					(      self	:							Any      ):






       '''simple docstring'''






       lowercase_						:     List[Any]							     =  self.get_dpr_ctx_encoder_tokenizer()
       lowercase_						:     Tuple							     =  1
       lowercase_						:     int							     =  self.get_dummy_custom_hf_index_retriever(from_disk=a      )
       retriever.set_ctx_encoder_tokenizer(a      )

       lowercase_						:     Tuple							     =  [[5, 7], [1_0, 1_1]]
       lowercase_						:     Optional[Any]							     =  np.array(
           [np.ones(self.retrieval_vector_size      ), -np.ones(self.retrieval_vector_size      )]	,  dtype=np.floataa      )
       lowercase_						:     Dict							     =  retriever(a	,  a	,  prefix=retriever.config.generator.prefix	,  n_docs=a      )

       self.assertEqual(
           len(a      )	,  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")      )	,  a      )  # check for doc token related keys in dictionary.




