
'''simple docstring'''

import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


snake_case									=    pytest.mark.integration




@require_faiss
class    lowerCAmelCase       (     UpperCamelCase_  ):


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Union[str, Any]     		=	Dataset.from_dict({"filename": ["my_name-train" + "_" + str(a__    ) for x in np.arange(30    ).tolist()]}    )
  return dset


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  import faiss

  lowerCAmelCase__     :   Dataset     		=	self._create_dummy_dataset()
  lowerCAmelCase__     :   List[Any]     		=	dset.map(
      lambda a__			,							a__    : {"vecs": i * np.ones(5			,							dtype=np.floataa    )}			,							with_indices=a__			,							keep_in_memory=a__    )
  lowerCAmelCase__     :   str     		=	dset.add_faiss_index("vecs"			,							batch_size=100			,							metric_type=faiss.METRIC_INNER_PRODUCT    )
  lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	dset.get_nearest_examples("vecs"			,							np.ones(5			,							dtype=np.floataa    )    )
  self.assertEqual(examples["filename"][0]			,							"my_name-train_29"    )
  dset.drop_index("vecs"    )


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  import faiss

  lowerCAmelCase__     :   Dataset     		=	self._create_dummy_dataset()
  dset.add_faiss_index_from_external_arrays(
      external_arrays=np.ones((30, 5)    ) * np.arange(30    ).reshape(-1			,							1    )			,							index_name="vecs"			,							batch_size=100			,							metric_type=faiss.METRIC_INNER_PRODUCT			,							)
  lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	dset.get_nearest_examples("vecs"			,							np.ones(5			,							dtype=np.floataa    )    )
  self.assertEqual(examples["filename"][0]			,							"my_name-train_29"    )


 def    _A	(    self  : Union[str, Any]    ):
  '''simple docstring'''

  import faiss

  lowerCAmelCase__     :   Dataset     		=	self._create_dummy_dataset()
  dset.add_faiss_index_from_external_arrays(
      external_arrays=np.ones((30, 5)    ) * np.arange(30    ).reshape(-1			,							1    )			,							index_name="vecs"			,							metric_type=faiss.METRIC_INNER_PRODUCT			,							)

  # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
  # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
  # see https://bugs.python.org/issue14243 and
  # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
  with tempfile.NamedTemporaryFile(delete=a__    ) as tmp_file:
   dset.save_faiss_index("vecs"			,							tmp_file.name    )
   dset.load_faiss_index("vecs2"			,							tmp_file.name    )
  os.unlink(tmp_file.name    )

  lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	dset.get_nearest_examples("vecs2"			,							np.ones(5			,							dtype=np.floataa    )    )
  self.assertEqual(examples["filename"][0]			,							"my_name-train_29"    )


 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Dataset     		=	self._create_dummy_dataset()
  dset.add_faiss_index_from_external_arrays(
      external_arrays=np.ones((30, 5)    ) * np.arange(30    ).reshape(-1			,							1    )			,							index_name="vecs"    )
  dset.drop_index("vecs"    )
  self.assertRaises(a__			,							partial(dset.get_nearest_examples			,							"vecs2"			,							np.ones(5			,							dtype=np.floataa    )    )    )


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  from elasticsearch import Elasticsearch

  lowerCAmelCase__     :   Dataset     		=	self._create_dummy_dataset()
  with patch("elasticsearch.Elasticsearch.search"    ) as mocked_search, patch(
      "elasticsearch.client.IndicesClient.create"    ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"    ) as mocked_bulk:
   lowerCAmelCase__     :   Optional[Any]     		=	{"acknowledged": True}
   mocked_bulk.return_value([(True, None)] * 30    )
   lowerCAmelCase__     :   Any     		=	{"hits": {"hits": [{"_score": 1, "_id": 29}]}}
   lowerCAmelCase__     :   List[Any]     		=	Elasticsearch()

   dset.add_elasticsearch_index("filename"			,							es_client=a__    )
   lowerCAmelCase__		,     lowerCAmelCase__     :   str     		=	dset.get_nearest_examples("filename"			,							"my_name-train_29"    )
   self.assertEqual(examples["filename"][0]			,							"my_name-train_29"    )




@require_faiss
class    lowerCAmelCase       (     UpperCamelCase_  ):


 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  import faiss

  lowerCAmelCase__     :   Optional[int]     		=	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT    )

  # add vectors
  index.add_vectors(np.eye(5			,							dtype=np.floataa    )    )
  self.assertIsNotNone(index.faiss_index    )
  self.assertEqual(index.faiss_index.ntotal			,							5    )
  index.add_vectors(np.zeros((5, 5)			,							dtype=np.floataa    )    )
  self.assertEqual(index.faiss_index.ntotal			,							10    )

  # single query
  lowerCAmelCase__     :   Tuple     		=	np.zeros(5			,							dtype=np.floataa    )
  lowerCAmelCase__     :   Any     		=	1
  lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	index.search(a__    )
  self.assertRaises(a__			,							index.search			,							query.reshape(-1			,							1    )    )
  self.assertGreater(scores[0]			,							0    )
  self.assertEqual(indices[0]			,							1    )

  # batched queries
  lowerCAmelCase__     :   int     		=	np.eye(5			,							dtype=np.floataa    )[::-1]
  lowerCAmelCase__		,     lowerCAmelCase__     :   Dict     		=	index.search_batch(a__    )
  self.assertRaises(a__			,							index.search_batch			,							queries[0]    )
  lowerCAmelCase__     :   Optional[Any]     		=	[scores[0] for scores in total_scores]
  lowerCAmelCase__     :   Union[str, Any]     		=	[indices[0] for indices in total_indices]
  self.assertGreater(np.min(a__    )			,							0    )
  self.assertListEqual([4, 3, 2, 1, 0]			,							a__    )


 def    _A	(    self  : Tuple    ):
  '''simple docstring'''

  import faiss

  lowerCAmelCase__     :   Optional[int]     		=	FaissIndex(string_factory="Flat"    )
  index.add_vectors(np.eye(5			,							dtype=np.floataa    )    )
  self.assertIsInstance(index.faiss_index			,							faiss.IndexFlat    )
  lowerCAmelCase__     :   List[str]     		=	FaissIndex(string_factory="LSH"    )
  index.add_vectors(np.eye(5			,							dtype=np.floataa    )    )
  self.assertIsInstance(index.faiss_index			,							faiss.IndexLSH    )
  with self.assertRaises(a__    ):
   lowerCAmelCase__     :   Tuple     		=	FaissIndex(string_factory="Flat"			,							custom_index=faiss.IndexFlat(5    )    )


 def    _A	(    self  : Dict    ):
  '''simple docstring'''

  import faiss

  lowerCAmelCase__     :   List[str]     		=	faiss.IndexFlat(5    )
  lowerCAmelCase__     :   Any     		=	FaissIndex(custom_index=a__    )
  index.add_vectors(np.eye(5			,							dtype=np.floataa    )    )
  self.assertIsInstance(index.faiss_index			,							faiss.IndexFlat    )


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  import faiss

  lowerCAmelCase__     :   Any     		=	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT    )
  index.add_vectors(np.eye(5			,							dtype=np.floataa    )    )

  # Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
  # ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
  # see https://bugs.python.org/issue14243 and
  # https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
  with tempfile.NamedTemporaryFile(delete=a__    ) as tmp_file:
   index.save(tmp_file.name    )
   lowerCAmelCase__     :   str     		=	FaissIndex.load(tmp_file.name    )
  os.unlink(tmp_file.name    )

  lowerCAmelCase__     :   Union[str, Any]     		=	np.zeros(5			,							dtype=np.floataa    )
  lowerCAmelCase__     :   int     		=	1
  lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	index.search(a__    )
  self.assertGreater(scores[0]			,							0    )
  self.assertEqual(indices[0]			,							1    )



@require_faiss
def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 import faiss

 lowerCAmelCase__     :   Tuple     		=	FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT			)
 index.add_vectors(np.eye(5      ,			dtype=np.floataa			)			)

 lowerCAmelCase__     :   int     		=	"index.faiss"
 lowerCAmelCase__     :   Optional[Any]     		=	f'''mock://{index_name}'''
 index.save(lowerCamelCase_      ,			storage_options=mockfs.storage_options			)
 lowerCAmelCase__     :   Union[str, Any]     		=	FaissIndex.load(lowerCamelCase_      ,			storage_options=mockfs.storage_options			)

 lowerCAmelCase__     :   str     		=	np.zeros(5      ,			dtype=np.floataa			)
 lowerCAmelCase__     :   Optional[int]     		=	1
 lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	index.search(lowerCamelCase_			)
 assert scores[0] > 0
 assert indices[0] == 1







@require_elasticsearch
class    lowerCAmelCase       (     UpperCamelCase_  ):


 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  from elasticsearch import Elasticsearch

  with patch("elasticsearch.Elasticsearch.search"    ) as mocked_search, patch(
      "elasticsearch.client.IndicesClient.create"    ) as mocked_index_create, patch("elasticsearch.helpers.streaming_bulk"    ) as mocked_bulk:
   lowerCAmelCase__     :   Optional[Any]     		=	Elasticsearch()
   lowerCAmelCase__     :   Union[str, Any]     		=	{"acknowledged": True}
   lowerCAmelCase__     :   int     		=	ElasticSearchIndex(es_client=a__    )
   mocked_bulk.return_value([(True, None)] * 3    )
   index.add_documents(["foo", "bar", "foobar"]    )

   # single query
   lowerCAmelCase__     :   Union[str, Any]     		=	"foo"
   lowerCAmelCase__     :   int     		=	{"hits": {"hits": [{"_score": 1, "_id": 0}]}}
   lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	index.search(a__    )
   self.assertEqual(scores[0]			,							1    )
   self.assertEqual(indices[0]			,							0    )

   # single query with timeout
   lowerCAmelCase__     :   Union[str, Any]     		=	"foo"
   lowerCAmelCase__     :   Any     		=	{"hits": {"hits": [{"_score": 1, "_id": 0}]}}
   lowerCAmelCase__		,     lowerCAmelCase__     :   List[str]     		=	index.search(a__			,							request_timeout=30    )
   self.assertEqual(scores[0]			,							1    )
   self.assertEqual(indices[0]			,							0    )

   # batched queries
   lowerCAmelCase__     :   List[Any]     		=	["foo", "bar", "foobar"]
   lowerCAmelCase__     :   Tuple     		=	{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
   lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	index.search_batch(a__    )
   lowerCAmelCase__     :   int     		=	[scores[0] for scores in total_scores]
   lowerCAmelCase__     :   Any     		=	[indices[0] for indices in total_indices]
   self.assertGreater(np.min(a__    )			,							0    )
   self.assertListEqual([1, 1, 1]			,							a__    )

   # batched queries with timeout
   lowerCAmelCase__     :   str     		=	["foo", "bar", "foobar"]
   lowerCAmelCase__     :   Dict     		=	{"hits": {"hits": [{"_score": 1, "_id": 1}]}}
   lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[Any]     		=	index.search_batch(a__			,							request_timeout=30    )
   lowerCAmelCase__     :   int     		=	[scores[0] for scores in total_scores]
   lowerCAmelCase__     :   str     		=	[indices[0] for indices in total_indices]
   self.assertGreater(np.min(a__    )			,							0    )
   self.assertListEqual([1, 1, 1]			,							a__    )







'''simple docstring'''

import argparse
import os
from pathlib import Path

import fairseq
import torch
from packaging import version
from torch import nn

from transformers import (
    BartConfig,
    BartForConditionalGeneration,
    BartForSequenceClassification,
    BartModel,
    BartTokenizer,
)
from transformers.utils import logging


snake_case									=    ["""bart.large""", """bart.large.mnli""", """bart.large.cnn""", """bart_xsum/model.pt"""]
snake_case									=    {"""bart.large""": BartModel, """bart.large.mnli""": BartForSequenceClassification}
if version.parse(fairseq.__version__) < version.parse("""0.9.0"""):
     raise Exception("""requires fairseq >= 0.9.0""")


logging.set_verbosity_info()
snake_case									=    logging.get_logger(__name__)

snake_case									=    """ Hello world! cécé herlolip"""

snake_case									=    [
    ("""model.classification_heads.mnli.dense.weight""", """classification_head.dense.weight"""),
    ("""model.classification_heads.mnli.dense.bias""", """classification_head.dense.bias"""),
    ("""model.classification_heads.mnli.out_proj.weight""", """classification_head.out_proj.weight"""),
    ("""model.classification_heads.mnli.out_proj.bias""", """classification_head.out_proj.bias"""),
]


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Dict     		=	[
     "encoder.version",
     "decoder.version",
     "model.encoder.version",
     "model.decoder.version",
     "_float_tensor",
 ]
 for k in ignore_keys:
  state_dict.pop(lowerCamelCase_      ,			lowerCamelCase_			)


def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[Any]     		=	dct.pop(lowerCamelCase_			)
 lowerCAmelCase__     :   List[str]     		=	val


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   Optional[int]     		=	torch.load(lowerCamelCase_      ,			map_location="cpu"			)
 lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			"bart.large.cnn"			).eval()
 hub_interface.model.load_state_dict(sd["model"]			)
 return hub_interface


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__		,     lowerCAmelCase__     :   Optional[int]     		=	emb.weight.shape
 lowerCAmelCase__     :   List[Any]     		=	nn.Linear(lowerCamelCase_      ,			lowerCamelCase_      ,			bias=lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[int]     		=	emb.weight.data
 return lin_layer


@torch.no_grad()
def        UpperCAmelCase_ (  lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_=None			):







 """simple docstring"""


 if not os.path.exists(lowerCamelCase_			):
  lowerCAmelCase__     :   Union[str, Any]     		=	torch.hub.load("pytorch/fairseq"      ,			lowerCamelCase_			).eval()
 else:
  lowerCAmelCase__     :   Tuple     		=	load_xsum_checkpoint(lowerCamelCase_			)

 bart.model.upgrade_state_dict(bart.model.state_dict()			)
 if hf_checkpoint_name is None:
  lowerCAmelCase__     :   Union[str, Any]     		=	checkpoint_path.replace("."      ,			"-"			)
 lowerCAmelCase__     :   Optional[int]     		=	BartConfig.from_pretrained(lowerCamelCase_			)
 lowerCAmelCase__     :   Optional[Any]     		=	bart.encode(lowerCamelCase_			).unsqueeze(0			)
 lowerCAmelCase__     :   str     		=	BartTokenizer.from_pretrained(lowerCamelCase_			).encode(lowerCamelCase_      ,			return_tensors="pt"			).unsqueeze(0			)
 if not torch.eq(lowerCamelCase_      ,			lowerCamelCase_			).all():
  raise ValueError(
      f'''converted tokenizer and pretrained tokenizer returned different output: {tokens} != {tokensa}'''			)

 if checkpoint_path == "bart.large.mnli":
  lowerCAmelCase__     :   List[str]     		=	bart.state_dict()
  remove_ignore_keys_(lowerCamelCase_			)
  lowerCAmelCase__     :   int     		=	state_dict["model.decoder.embed_tokens.weight"]
  for src, dest in mnli_rename_keys:
   rename_key(lowerCamelCase_      ,			lowerCamelCase_      ,			lowerCamelCase_			)
  lowerCAmelCase__     :   Optional[Any]     		=	BartForSequenceClassification(lowerCamelCase_			).eval()
  model.load_state_dict(lowerCamelCase_			)
  lowerCAmelCase__     :   Dict     		=	bart.predict("mnli"      ,			lowerCamelCase_      ,			return_logits=lowerCamelCase_			)
  lowerCAmelCase__     :   Any     		=	model(lowerCamelCase_			)[0]  # logits
 else:  # no classification heads to worry about
  lowerCAmelCase__     :   Tuple     		=	bart.model.state_dict()
  remove_ignore_keys_(lowerCamelCase_			)
  lowerCAmelCase__     :   str     		=	state_dict["decoder.embed_tokens.weight"]
  lowerCAmelCase__     :   Optional[Any]     		=	bart.extract_features(lowerCamelCase_			)
  if hf_checkpoint_name == "facebook/bart-large":
   lowerCAmelCase__     :   Optional[Any]     		=	BartModel(lowerCamelCase_			).eval()
   model.load_state_dict(lowerCamelCase_			)
   lowerCAmelCase__     :   int     		=	model(lowerCamelCase_			).model[0]
  else:
   lowerCAmelCase__     :   Optional[Any]     		=	BartForConditionalGeneration(lowerCamelCase_			).eval()  # an existing summarization ckpt
   model.model.load_state_dict(lowerCamelCase_			)
   if hasattr(lowerCamelCase_      ,			"lm_head"			):
    lowerCAmelCase__     :   Tuple     		=	make_linear_from_emb(model.model.shared			)
   lowerCAmelCase__     :   Tuple     		=	model.model(lowerCamelCase_			)[0]

    # Check results
 if fairseq_output.shape != new_model_outputs.shape:
  raise ValueError(
      f'''`fairseq_output` shape and `new_model_output` shape are different: {fairseq_output.shape=}, {new_model_outputs.shape}'''			)
 if (fairseq_output != new_model_outputs).any().item():
  raise ValueError("Some values in `fairseq_output` are different from `new_model_outputs`"			)
 Path(lowerCamelCase_			).mkdir(exist_ok=lowerCamelCase_			)
 model.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
     snake_case									=    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """fairseq_path""", type=str, help="""bart.large, bart.large.cnn or a path to a model.pt on local filesystem."""
     )
     parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
     parser.add_argument(
         """--hf_config""", default=None, type=str, help="""Which huggingface architecture to use: bart-large-xsum"""
     )
     snake_case									=    parser.parse_args()
     convert_bart_checkpoint(args.fairseq_path, args.pytorch_dump_folder_path, hf_checkpoint_name=args.hf_config)





