

"""simple docstring"""


import json
import os
import shutil
import tempfile
from unittest import TestCase

from transformers import BartTokenizer, BartTokenizerFast, DPRQuestionEncoderTokenizer, DPRQuestionEncoderTokenizerFast
from transformers.models.bart.configuration_bart import BartConfig
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.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_tokenizers, require_torch, slow
from transformers.utils import is_datasets_available, is_faiss_available, is_torch_available


if is_torch_available() and is_datasets_available() and is_faiss_available():
			from transformers.models.rag.configuration_rag import RagConfig
			from transformers.models.rag.tokenization_rag import RagTokenizer





@require_faiss
@require_torch
class    snake_case     (    __snake_case  ):

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



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      tempfile.mkdtemp()
						__lowerCAmelCase:	Tuple   				=      8

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

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

				def 							lowercase_					(     self							:    int)->       DPRQuestionEncoderTokenizer:



						'''simple docstring'''


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

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



						'''simple docstring'''


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

				def 							lowercase_					(     self							:    Tuple)->       str:



						'''simple docstring'''


						shutil.rmtree(self.tmpdirname)

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



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      os.path.join(self.tmpdirname					,  "rag_tokenizer")
						__lowerCAmelCase:	str   				=      RagConfig(question_encoder=DPRConfig().to_dict()					,  generator=BartConfig().to_dict())
						__lowerCAmelCase:	Optional[Any]   				=      RagTokenizer(question_encoder=self.get_dpr_tokenizer()					,  generator=self.get_bart_tokenizer())
						rag_config.save_pretrained(UpperCamelCase__)
						rag_tokenizer.save_pretrained(UpperCamelCase__)
						__lowerCAmelCase:	Optional[Any]   				=      RagTokenizer.from_pretrained(UpperCamelCase__					,  config=UpperCamelCase__)
						self.assertIsInstance(new_rag_tokenizer.question_encoder					,  UpperCamelCase__)
						self.assertEqual(new_rag_tokenizer.question_encoder.get_vocab()					,  rag_tokenizer.question_encoder.get_vocab())
						self.assertIsInstance(new_rag_tokenizer.generator					,  UpperCamelCase__)
						self.assertEqual(new_rag_tokenizer.generator.get_vocab()					,  rag_tokenizer.generator.get_vocab())

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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      RagTokenizer.from_pretrained("facebook/rag-token-nq")
						__lowerCAmelCase:	List[Any]   				=      [
						    "who got the first nobel prize in physics",
						    "when is the next deadpool movie being released",
						    "which mode is used for short wave broadcast service",
						    "who is the owner of reading football club",
						    "when is the next scandal episode coming out",
						    "when is the last time the philadelphia won the superbowl",
						    "what is the most current adobe flash player version",
						    "how many episodes are there in dragon ball z",
						    "what is the first step in the evolution of the eye",
						    "where is gall bladder situated in human body",
						    "what is the main mineral in lithium batteries",
						    "who is the president of usa right now",
						    "where do the greasers live in the outsiders",
						    "panda is a national animal of which country",
						    "what is the name of manchester united stadium",
						]
						__lowerCAmelCase:	List[Any]   				=      tokenizer(UpperCamelCase__)
						self.assertIsNotNone(UpperCamelCase__)




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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
						__lowerCAmelCase:	List[str]   				=      [
						    "who got the first nobel prize in physics",
						    "when is the next deadpool movie being released",
						    "which mode is used for short wave broadcast service",
						    "who is the owner of reading football club",
						    "when is the next scandal episode coming out",
						    "when is the last time the philadelphia won the superbowl",
						    "what is the most current adobe flash player version",
						    "how many episodes are there in dragon ball z",
						    "what is the first step in the evolution of the eye",
						    "where is gall bladder situated in human body",
						    "what is the main mineral in lithium batteries",
						    "who is the president of usa right now",
						    "where do the greasers live in the outsiders",
						    "panda is a national animal of which country",
						    "what is the name of manchester united stadium",
						]
						__lowerCAmelCase:	Tuple   				=      tokenizer(UpperCamelCase__)
						self.assertIsNotNone(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)


