

"""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


__A						       =      pytest.mark.integration





@require_faiss
class    snake_case     (    __snake_case  ):

				def 							lowercase_					(     self							:    List[Any])->       Any:



						'''simple docstring'''


						__lowerCAmelCase:	str   				=      Dataset.from_dict({"filename": ["my_name-train" + "_" + str(UpperCamelCase__) for x in np.arange(3_0).tolist()]})
						return dset

				def 							lowercase_					(     self							:    str)->       str:



						'''simple docstring'''


						import faiss

						__lowerCAmelCase:	Dataset   				=      self._create_dummy_dataset()
						__lowerCAmelCase:	Tuple   				=      dset.map(
						    lambda UpperCamelCase__					,  UpperCamelCase__: {"vecs": i * np.ones(5					,  dtype=np.floataa)}					,  with_indices=UpperCamelCase__					,  keep_in_memory=UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      dset.add_faiss_index("vecs"					,  batch_size=1_0_0					,  metric_type=faiss.METRIC_INNER_PRODUCT)
						__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      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 							lowercase_					(     self							:    str)->       Dict:



						'''simple docstring'''


						import faiss

						__lowerCAmelCase:	Dataset   				=      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1					,  1)					,  index_name="vecs"					,  batch_size=1_0_0					,  metric_type=faiss.METRIC_INNER_PRODUCT					,  )
						__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      dset.get_nearest_examples("vecs"					,  np.ones(5					,  dtype=np.floataa))
						self.assertEqual(examples["filename"][0]					,  "my_name-train_29")

				def 							lowercase_					(     self							:    Any)->       List[Any]:



						'''simple docstring'''


						import faiss

						__lowerCAmelCase:	Dataset   				=      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).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=UpperCamelCase__) 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:	Tuple   				=      dset.get_nearest_examples("vecs2"					,  np.ones(5					,  dtype=np.floataa))
						self.assertEqual(examples["filename"][0]					,  "my_name-train_29")

				def 							lowercase_					(     self							:    Optional[Any])->       Optional[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Dataset   				=      self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((3_0, 5)) * np.arange(3_0).reshape(-1					,  1)					,  index_name="vecs")
						dset.drop_index("vecs")
						self.assertRaises(UpperCamelCase__					,  partial(dset.get_nearest_examples					,  "vecs2"					,  np.ones(5					,  dtype=np.floataa)))




				def 							lowercase_					(     self							:    Dict)->       Any:



						'''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[int]   				=      {"acknowledged": True}
								mocked_bulk.return_value([(True, None)] * 3_0)
								__lowerCAmelCase:	Dict   				=      {"hits": {"hits": [{"_score": 1, "_id": 2_9}]}}
								__lowerCAmelCase:	Optional[Any]   				=      Elasticsearch()

								dset.add_elasticsearch_index("filename"					,  es_client=UpperCamelCase__)
								__lowerCAmelCase				,			__lowerCAmelCase:	int   				=      dset.get_nearest_examples("filename"					,  "my_name-train_29")
								self.assertEqual(examples["filename"][0]					,  "my_name-train_29")





@require_faiss
class    snake_case     (    __snake_case  ):

				def 							lowercase_					(     self							:    Any)->       int:



						'''simple docstring'''


						import faiss

						__lowerCAmelCase:	Union[str, Any]   				=      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					,  1_0)

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

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

				def 							lowercase_					(     self							:    Optional[int])->       Union[str, Any]:



						'''simple docstring'''


						import faiss

						__lowerCAmelCase:	Optional[Any]   				=      FaissIndex(string_factory="Flat")
						index.add_vectors(np.eye(5					,  dtype=np.floataa))
						self.assertIsInstance(index.faiss_index					,  faiss.IndexFlat)
						__lowerCAmelCase:	Optional[int]   				=      FaissIndex(string_factory="LSH")
						index.add_vectors(np.eye(5					,  dtype=np.floataa))
						self.assertIsInstance(index.faiss_index					,  faiss.IndexLSH)
						with self.assertRaises(UpperCamelCase__):
								__lowerCAmelCase:	int   				=      FaissIndex(string_factory="Flat"					,  custom_index=faiss.IndexFlat(5))

				def 							lowercase_					(     self							:    Union[str, Any])->       Tuple:



						'''simple docstring'''


						import faiss

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




				def 							lowercase_					(     self							:    List[str])->       Union[str, Any]:



						'''simple docstring'''


						import faiss

						__lowerCAmelCase:	Optional[int]   				=      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=UpperCamelCase__) as tmp_file:
								index.save(tmp_file.name)
								__lowerCAmelCase:	str   				=      FaissIndex.load(tmp_file.name)
						os.unlink(tmp_file.name)

						__lowerCAmelCase:	str   				=      np.zeros(5					,  dtype=np.floataa)
						__lowerCAmelCase:	List[Any]   				=      1
						__lowerCAmelCase				,			__lowerCAmelCase:	List[Any]   				=      index.search(UpperCamelCase__)
						self.assertGreater(scores[0]					,  0)
						self.assertEqual(indices[0]					,  1)






@require_faiss
def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Dict:
		import faiss

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

		__lowerCAmelCase:	Optional[Any]   				=      "index.faiss"
		__lowerCAmelCase:	str   				=      F"mock://{index_name}"
		index.save(__SCREAMING_SNAKE_CASE ,			storage_options=mockfs.storage_options			)
		__lowerCAmelCase:	Union[str, Any]   				=      FaissIndex.load(__SCREAMING_SNAKE_CASE ,			storage_options=mockfs.storage_options			)

		__lowerCAmelCase:	str   				=      np.zeros(5 ,			dtype=np.floataa			)
		__lowerCAmelCase:	List[Any]   				=      1
		__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      index.search(__SCREAMING_SNAKE_CASE			)
		assert scores[0] > 0
		assert indices[0] == 1





@require_elasticsearch
class    snake_case     (    __snake_case  ):

				def 							lowercase_					(     self							:    Optional[int])->       int:



						'''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:	int   				=      {"acknowledged": True}
								__lowerCAmelCase:	Union[str, Any]   				=      ElasticSearchIndex(es_client=UpperCamelCase__)
								mocked_bulk.return_value([(True, None)] * 3)
								index.add_documents(["foo", "bar", "foobar"])

								# single query
								__lowerCAmelCase:	Tuple   				=      "foo"
								__lowerCAmelCase:	Dict   				=      {"hits": {"hits": [{"_score": 1, "_id": 0}]}}
								__lowerCAmelCase				,			__lowerCAmelCase:	Union[str, Any]   				=      index.search(UpperCamelCase__)
								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:	Union[str, Any]   				=      index.search(UpperCamelCase__					,  request_timeout=3_0)
								self.assertEqual(scores[0]					,  1)
								self.assertEqual(indices[0]					,  0)

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

								# batched queries with timeout
								__lowerCAmelCase:	List[str]   				=      ["foo", "bar", "foobar"]
								__lowerCAmelCase:	List[str]   				=      {"hits": {"hits": [{"_score": 1, "_id": 1}]}}
								__lowerCAmelCase				,			__lowerCAmelCase:	Optional[Any]   				=      index.search_batch(UpperCamelCase__					,  request_timeout=3_0)
								__lowerCAmelCase:	str   				=      [scores[0] for scores in total_scores]
								__lowerCAmelCase:	Union[str, Any]   				=      [indices[0] for indices in total_indices]
								self.assertGreater(np.min(UpperCamelCase__)					,  0)
								self.assertListEqual([1, 1, 1]					,  UpperCamelCase__)





"""simple docstring"""


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__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	int   				=      args.log_outputs
		__lowerCAmelCase:	Tuple   				=      "_".join(args.dataset.split("/"			) + [args.config, args.split]			)

		# load metric
		__lowerCAmelCase:	str   				=      load_metric("wer"			)
		__lowerCAmelCase:	int   				=      load_metric("cer"			)

		# compute metrics
		__lowerCAmelCase:	Optional[Any]   				=      wer.compute(references=result["target"] ,			predictions=result["prediction"]			)
		__lowerCAmelCase:	Dict   				=      cer.compute(references=result["target"] ,			predictions=result["prediction"]			)

		# print & log results
		__lowerCAmelCase:	Optional[int]   				=      F"WER: {wer_result}\nCER: {cer_result}"
		print(__SCREAMING_SNAKE_CASE			)

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

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

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

						result.map(__SCREAMING_SNAKE_CASE ,			with_indices=__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     str:
		__lowerCAmelCase:	Any   				=      "[,?.!\-\;\:\"“%‘”�—’…–]"  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

		__lowerCAmelCase:	List[Any]   				=      re.sub(__SCREAMING_SNAKE_CASE ,			"" ,			text.lower()			)

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

		for t in token_sequences_to_ignore:
				__lowerCAmelCase:	str   				=      " ".join(text.split(__SCREAMING_SNAKE_CASE			)			)

		return text






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Dict:
		# load dataset
		__lowerCAmelCase:	List[str]   				=      load_dataset(args.dataset ,			args.config ,			split=args.split ,			use_auth_token=__SCREAMING_SNAKE_CASE			)

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

		# load processor
		__lowerCAmelCase:	Dict   				=      AutoFeatureExtractor.from_pretrained(args.model_id			)
		__lowerCAmelCase:	str   				=      feature_extractor.sampling_rate

		# resample audio
		__lowerCAmelCase:	Tuple   				=      dataset.cast_column("audio" ,			Audio(sampling_rate=__SCREAMING_SNAKE_CASE			)			)

		# load eval pipeline
		if args.device is None:
				__lowerCAmelCase:	List[Any]   				=      0 if torch.cuda.is_available() else -1
		__lowerCAmelCase:	Optional[int]   				=      pipeline("automatic-speech-recognition" ,			model=args.model_id ,			device=args.device			)

		# map function to decode audio
		def map_to_pred(__SCREAMING_SNAKE_CASE			):
				__lowerCAmelCase:	Union[str, Any]   				=      asr(
				    batch["audio"]["array"] ,			chunk_length_s=args.chunk_length_s ,			stride_length_s=args.stride_length_s			)

				__lowerCAmelCase:	Any   				=      prediction["text"]
				__lowerCAmelCase:	List[str]   				=      normalize_text(batch["sentence"]			)
				return batch

		# run inference on all examples
		__lowerCAmelCase:	str   				=      dataset.map(__SCREAMING_SNAKE_CASE ,			remove_columns=dataset.column_names			)

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


if __name__ == "__main__":
			__A						       =      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.",
			)
			__A						       =      parser.parse_args()

			main(args)


