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


SCREAMING_SNAKE_CASE :	Union[str, Any]							     =		pytest.mark.integration
@require_faiss
class    A_ (       a_ ):

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   __a       =  Dataset.from_dict({"filename": ["my_name-train" + "_" + str(__SCREAMING_SNAKE_CASE       ) for x in np.arange(30       ).tolist()]}       )
   return dset

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   import faiss

   __a       =  self._create_dummy_dataset()
   __a       =  dset.map(
       lambda __SCREAMING_SNAKE_CASE     ,							__SCREAMING_SNAKE_CASE       : {"vecs": i * np.ones(5     ,							dtype=np.floataa       )}     ,							with_indices=__SCREAMING_SNAKE_CASE     ,							keep_in_memory=__SCREAMING_SNAKE_CASE       )
   __a       =  dset.add_faiss_index("vecs"     ,							batch_size=1_00     ,							metric_type=faiss.METRIC_INNER_PRODUCT       )
   __a       ,     __a       =  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 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   import faiss

   __a       =  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=1_00     ,							metric_type=faiss.METRIC_INNER_PRODUCT     ,							)
   __a       ,     __a       =  dset.get_nearest_examples("vecs"     ,							np.ones(5     ,							dtype=np.floataa       )       )
   self.assertEqual(examples["filename"][0]     ,							"my_name-train_29"       )

  def 		_UpperCAmelCase   (		self :      Optional[Any]       ):
   import faiss

   __a       =  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=__SCREAMING_SNAKE_CASE       ) as tmp_file:
    dset.save_faiss_index("vecs"     ,							tmp_file.name       )
    dset.load_faiss_index("vecs2"     ,							tmp_file.name       )
   os.unlink(tmp_file.name       )

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

  def 		_UpperCAmelCase   (		self :      Optional[int]       ):
   __a       =  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(__SCREAMING_SNAKE_CASE     ,							partial(dset.get_nearest_examples     ,							"vecs2"     ,							np.ones(5     ,							dtype=np.floataa       )       )       )

  def 		_UpperCAmelCase   (		self :      Dict       ):
   from elasticsearch import Elasticsearch

   __a       =  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:
    __a       =  {"acknowledged": True}
    mocked_bulk.return_value([(True, None)] * 30       )
    __a       =  {"hits": {"hits": [{"_score": 1, "_id": 29}]}}
    __a       =  Elasticsearch()

    dset.add_elasticsearch_index("filename"     ,							es_client=__SCREAMING_SNAKE_CASE       )
    __a       ,     __a       =  dset.get_nearest_examples("filename"     ,							"my_name-train_29"       )
    self.assertEqual(examples["filename"][0]     ,							"my_name-train_29"       )
@require_faiss
class    A_ (       a_ ):

  def 		_UpperCAmelCase   (		self :      List[Any]       ):
   import faiss

   __a       =  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
   __a       =  np.zeros(5     ,							dtype=np.floataa       )
   __a       =  1
   __a       ,     __a       =  index.search(__SCREAMING_SNAKE_CASE       )
   self.assertRaises(__SCREAMING_SNAKE_CASE     ,							index.search     ,							query.reshape(-1     ,							1       )       )
   self.assertGreater(scores[0]     ,							0       )
   self.assertEqual(indices[0]     ,							1       )

   # batched queries
   __a       =  np.eye(5     ,							dtype=np.floataa       )[::-1]
   __a       ,     __a       =  index.search_batch(__SCREAMING_SNAKE_CASE       )
   self.assertRaises(__SCREAMING_SNAKE_CASE     ,							index.search_batch     ,							queries[0]       )
   __a       =  [scores[0] for scores in total_scores]
   __a       =  [indices[0] for indices in total_indices]
   self.assertGreater(np.min(__SCREAMING_SNAKE_CASE       )     ,							0       )
   self.assertListEqual([4, 3, 2, 1, 0]     ,							__SCREAMING_SNAKE_CASE       )

  def 		_UpperCAmelCase   (		self :      List[str]       ):
   import faiss

   __a       =  FaissIndex(string_factory="Flat"       )
   index.add_vectors(np.eye(5     ,							dtype=np.floataa       )       )
   self.assertIsInstance(index.faiss_index     ,							faiss.IndexFlat       )
   __a       =  FaissIndex(string_factory="LSH"       )
   index.add_vectors(np.eye(5     ,							dtype=np.floataa       )       )
   self.assertIsInstance(index.faiss_index     ,							faiss.IndexLSH       )
   with self.assertRaises(__SCREAMING_SNAKE_CASE       ):
    __a       =  FaissIndex(string_factory="Flat"     ,							custom_index=faiss.IndexFlat(5       )       )

  def 		_UpperCAmelCase   (		self :      Tuple       ):
   import faiss

   __a       =  faiss.IndexFlat(5       )
   __a       =  FaissIndex(custom_index=__SCREAMING_SNAKE_CASE       )
   index.add_vectors(np.eye(5     ,							dtype=np.floataa       )       )
   self.assertIsInstance(index.faiss_index     ,							faiss.IndexFlat       )

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   import faiss

   __a       =  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=__SCREAMING_SNAKE_CASE       ) as tmp_file:
    index.save(tmp_file.name       )
    __a       =  FaissIndex.load(tmp_file.name       )
   os.unlink(tmp_file.name       )

   __a       =  np.zeros(5     ,							dtype=np.floataa       )
   __a       =  1
   __a       ,     __a       =  index.search(__SCREAMING_SNAKE_CASE       )
   self.assertGreater(scores[0]     ,							0       )
   self.assertEqual(indices[0]     ,							1       )



@require_faiss
def   __A		(			_A					):



 """simple docstring"""


 import faiss

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

 __a       =  "index.faiss"
 __a       =  f"""mock://{index_name}"""
 index.save(_A							,				storage_options=mockfs.storage_options					)
 __a       =  FaissIndex.load(_A							,				storage_options=mockfs.storage_options					)

 __a       =  np.zeros(5							,				dtype=np.floataa					)
 __a       =  1
 __a       ,     __a       =  index.search(_A					)
 assert scores[0] > 0
 assert indices[0] == 1






@require_elasticsearch
class    A_ (       a_ ):

  def 		_UpperCAmelCase   (		self :      Union[str, Any]       ):
   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:
    __a       =  Elasticsearch()
    __a       =  {"acknowledged": True}
    __a       =  ElasticSearchIndex(es_client=__SCREAMING_SNAKE_CASE       )
    mocked_bulk.return_value([(True, None)] * 3       )
    index.add_documents(["foo", "bar", "foobar"]       )

    # single query
    __a       =  "foo"
    __a       =  {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
    __a       ,     __a       =  index.search(__SCREAMING_SNAKE_CASE       )
    self.assertEqual(scores[0]     ,							1       )
    self.assertEqual(indices[0]     ,							0       )

    # single query with timeout
    __a       =  "foo"
    __a       =  {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
    __a       ,     __a       =  index.search(__SCREAMING_SNAKE_CASE     ,							request_timeout=30       )
    self.assertEqual(scores[0]     ,							1       )
    self.assertEqual(indices[0]     ,							0       )

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

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



import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline



def   __A		(			_A							,				_A					):



 """simple docstring"""


 __a       =  args.log_outputs
 __a       =  "_".join(args.dataset.split("/"					) + [args.config, args.split]					)

 # load metric
 __a       =  load_metric("wer"					)
 __a       =  load_metric("cer"					)

 # compute metrics
 __a       =  wer.compute(references=result["target"]							,				predictions=result["prediction"]					)
 __a       =  cer.compute(references=result["target"]							,				predictions=result["prediction"]					)

 # print & log results
 __a       =  f"""WER: {wer_result}\nCER: {cer_result}"""
 print(_A					)

 with open(f"""{dataset_id}_eval_results.txt"""							,				"w"					) as f:
  f.write(_A					)

 # log all results in text file. Possibly interesting for analysis
 if log_outputs is not None:
  __a       =  f"""log_{dataset_id}_predictions.txt"""
  __a       =  f"""log_{dataset_id}_targets.txt"""

  with open(_A							,				"w"					) as p, open(_A							,				"w"					) as t:
   # mapping function to write output
   def write_to_file(_A							,				_A					):
    p.write(f"""{i}""" + "\n"					)
    p.write(batch["prediction"] + "\n"					)
    t.write(f"""{i}""" + "\n"					)
    t.write(batch["target"] + "\n"					)

   result.map(_A							,				with_indices=_A					)



def   __A		(			_A					):



 """simple docstring"""


 __a       =  "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

 __a       =  re.sub(_A							,				""							,				text.lower()					)

 # In addition, we can normalize the target text, e.g. removing new lines characters etc...
 # note that order is important here!
 __a       =  ["\n\n", "\n", "   ", "  "]

 for t in token_sequences_to_ignore:
  __a       =  " ".join(text.split(_A					)					)

 return text



def   __A		(			_A					):



 """simple docstring"""


 __a       =  load_dataset(args.dataset							,				args.config							,				split=args.split							,				use_auth_token=_A					)

 # for testing: only process the first two examples as a test
 # dataset = dataset.select(range(10))

 # load processor
 __a       =  AutoFeatureExtractor.from_pretrained(args.model_id					)
 __a       =  feature_extractor.sampling_rate

 # resample audio
 __a       =  dataset.cast_column("audio"							,				Audio(sampling_rate=_A					)					)

 # load eval pipeline
 if args.device is None:
  __a       =  0 if torch.cuda.is_available() else -1
 __a       =  pipeline("automatic-speech-recognition"							,				model=args.model_id							,				device=args.device					)

 # map function to decode audio
 def map_to_pred(_A					):
  __a       =  asr(
      batch["audio"]["array"]							,				chunk_length_s=args.chunk_length_s							,				stride_length_s=args.stride_length_s					)

  __a       =  prediction["text"]
  __a       =  normalize_text(batch["sentence"]					)
  return batch

 # run inference on all examples
 __a       =  dataset.map(_A							,				remove_columns=dataset.column_names					)

 # compute and log_results
 # do not change function below
 log_results(_A							,				_A					)


if __name__ == "__main__":
  SCREAMING_SNAKE_CASE :	Any							     =		argparse.ArgumentParser()

  parser.add_argument(
      """--model_id""", type=str, required=True, help="""Model identifier. Should be loadable with 🤗 Transformers"""
  )
  parser.add_argument(
      """--dataset""",
      type=str,
      required=True,
      help="""Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets""",
  )
  parser.add_argument(
      """--config""", type=str, required=True, help="""Config of the dataset. *E.g.* `'en'`  for Common Voice"""
  )
  parser.add_argument("""--split""", type=str, required=True, help="""Split of the dataset. *E.g.* `'test'`""")
  parser.add_argument(
      """--chunk_length_s""", type=float, default=None, help="""Chunk length in seconds. Defaults to 5 seconds."""
  )
  parser.add_argument(
      """--stride_length_s""", type=float, default=None, help="""Stride of the audio chunks. Defaults to 1 second."""
  )
  parser.add_argument(
      """--log_outputs""", action="""store_true""", help="""If defined, write outputs to log file for analysis."""
  )
  parser.add_argument(
      """--device""",
      type=int,
      default=None,
      help="""The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.""",
  )
  SCREAMING_SNAKE_CASE :	Optional[Any]							     =		parser.parse_args()

  main(args)


