

"""simple docstring"""


import pickle
import shutil
import tempfile
import unittest

from transformers import SPIECE_UNDERLINE, XGLMTokenizer, XGLMTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__A						       =      get_tests_dir("fixtures/test_sentencepiece.model")





@require_sentencepiece
@require_tokenizers
class    snake_case     (    __snake_case,							unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  XGLMTokenizer
				SCREAMING_SNAKE_CASE_       :				int       				=  XGLMTokenizerFast
				SCREAMING_SNAKE_CASE_       :				List[Any]       				=  True
				SCREAMING_SNAKE_CASE_       :				Optional[int]       				=  True

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



						'''simple docstring'''


						super().setUp()

						# We have a SentencePiece fixture for testing
						__lowerCAmelCase:	Union[str, Any]   				=      XGLMTokenizer(UpperCamelCase__					,  keep_accents=UpperCamelCase__)
						tokenizer.save_pretrained(self.tmpdirname)

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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      "<pad>"
						__lowerCAmelCase:	Union[str, Any]   				=      1

						self.assertEqual(self.get_tokenizer()._convert_token_to_id(UpperCamelCase__)					,  UpperCamelCase__)
						self.assertEqual(self.get_tokenizer()._convert_id_to_token(UpperCamelCase__)					,  UpperCamelCase__)

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



						'''simple docstring'''


						__lowerCAmelCase:	List[Any]   				=      list(self.get_tokenizer().get_vocab().keys())

						self.assertEqual(vocab_keys[0]					,  "<s>")
						self.assertEqual(vocab_keys[1]					,  "<pad>")
						self.assertEqual(len(UpperCamelCase__)					,  1_0_0_8)

				def 							lowercase_					(     self							:    List[str])->       Dict:



						'''simple docstring'''


						self.assertEqual(self.get_tokenizer().vocab_size					,  1_0_0_8)

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



						'''simple docstring'''


						__lowerCAmelCase:	List[str]   				=      XGLMTokenizer(UpperCamelCase__					,  keep_accents=UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      tokenizer.tokenize("This is a test")
						self.assertListEqual(UpperCamelCase__					,  ["▁This", "▁is", "▁a", "▁t", "est"])

						self.assertListEqual(
						    tokenizer.convert_tokens_to_ids(UpperCamelCase__)					,  [value + tokenizer.fairseq_offset for value in [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]]					,  )

						__lowerCAmelCase:	Optional[Any]   				=      tokenizer.tokenize("I was born in 92000, and this is falsé.")
						self.assertListEqual(
						    UpperCamelCase__					,  [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "9",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "é",
						        ".",
						    ]					,  )
						__lowerCAmelCase:	Optional[int]   				=      tokenizer.convert_tokens_to_ids(UpperCamelCase__)
						self.assertListEqual(
						    UpperCamelCase__					,  [
						        value + tokenizer.fairseq_offset
						        for value in [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 2, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 2, 4]
						    ]					,  )

						__lowerCAmelCase:	Tuple   				=      tokenizer.convert_ids_to_tokens(UpperCamelCase__)
						self.assertListEqual(
						    UpperCamelCase__					,  [
						        SPIECE_UNDERLINE + "I",
						        SPIECE_UNDERLINE + "was",
						        SPIECE_UNDERLINE + "b",
						        "or",
						        "n",
						        SPIECE_UNDERLINE + "in",
						        SPIECE_UNDERLINE + "",
						        "<unk>",
						        "2",
						        "0",
						        "0",
						        "0",
						        ",",
						        SPIECE_UNDERLINE + "and",
						        SPIECE_UNDERLINE + "this",
						        SPIECE_UNDERLINE + "is",
						        SPIECE_UNDERLINE + "f",
						        "al",
						        "s",
						        "<unk>",
						        ".",
						    ]					,  )

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



						'''simple docstring'''


						return XGLMTokenizer.from_pretrained("facebook/xglm-564M")

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



						'''simple docstring'''


						with tempfile.NamedTemporaryFile() as f:
								shutil.copyfile(UpperCamelCase__					,  f.name)
								__lowerCAmelCase:	List[Any]   				=      XGLMTokenizer(f.name					,  keep_accents=UpperCamelCase__)
								__lowerCAmelCase:	Union[str, Any]   				=      pickle.dumps(UpperCamelCase__)
						pickle.loads(UpperCamelCase__)

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



						'''simple docstring'''


						if not self.test_rust_tokenizer:
								return

						__lowerCAmelCase:	int   				=      self.get_tokenizer()
						__lowerCAmelCase:	Union[str, Any]   				=      self.get_rust_tokenizer()

						__lowerCAmelCase:	Tuple   				=      "I was born in 92000, and this is falsé."

						__lowerCAmelCase:	Any   				=      tokenizer.tokenize(UpperCamelCase__)
						__lowerCAmelCase:	List[Any]   				=      rust_tokenizer.tokenize(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Optional[Any]   				=      tokenizer.encode(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      rust_tokenizer.encode(UpperCamelCase__					,  add_special_tokens=UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

						__lowerCAmelCase:	Dict   				=      self.get_rust_tokenizer()
						__lowerCAmelCase:	int   				=      tokenizer.encode(UpperCamelCase__)
						__lowerCAmelCase:	Any   				=      rust_tokenizer.encode(UpperCamelCase__)
						self.assertListEqual(UpperCamelCase__					,  UpperCamelCase__)

				@slow
				def 							lowercase_					(     self							:    List[str])->       int:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      "Hello World!"
						__lowerCAmelCase:	Union[str, Any]   				=      [2, 3_1_2_2_7, 4_4_4_7, 3_5]

						self.assertListEqual(UpperCamelCase__					,  self.big_tokenizer.encode(UpperCamelCase__))

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



						'''simple docstring'''


						__lowerCAmelCase:	Dict   				=      (
						    "This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) \" [ ] ! : - . Also we will"
						    " add words that should not exsist and be tokenized to unk, such as saoneuhaoesuth"
						)
						# fmt: off
						__lowerCAmelCase:	int   				=      [2, 1_0_1_8, 6_7, 1_1, 1_9_8_8, 2_6_1_7, 5_6_3_1, 2_7_8, 1_1, 3_4_0_7, 4_8, 7_1_6_3_0, 2_8_0_8_5, 4, 3_2_3_4, 1_5_7, 1_3, 6, 5, 6, 4, 3_5_2_6, 7_6_8, 1_5, 6_5_9, 5_7, 2_9_8, 3_9_8_3, 8_6_4, 1_2_9, 2_1, 6, 5, 1_3_6_7_5, 3_7_7, 6_5_2, 7_5_8_0, 1_0_3_4_1, 1_5_5, 2_8_1_7, 4_2_2, 1_6_6_6, 7, 1_6_7_4, 5_3, 1_1_3, 2_0_2_2_7_7, 1_7_8_9_2, 3_3, 6_0, 8_7, 4, 3_2_3_4, 1_5_7, 6_1, 2_6_6_7, 5_2_3_7_6, 1_9, 8_8, 2_3, 7_3_5]
						# fmt: on

						self.assertListEqual(UpperCamelCase__					,  self.big_tokenizer.encode(UpperCamelCase__))




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



						'''simple docstring'''


						__lowerCAmelCase:	int   				=      {
						    "input_ids": [[2, 1_0_8_8_2_5, 1_1_6_3, 1_5, 8_8_0_1_0, 4_7_3, 1_5_8_9_8, 1_5_7, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 2_3_8_0_2_1, 1_1_6_3, 5_3, 1_3_6_7_2, 1_8_5_7, 3_1_2, 8, 5_3_2_8_3, 1_8_2_3_9_6, 8, 1_8_5_6_6, 1_6, 3_6_7_3_3, 4_1_0_1, 8, 2_3_0, 2_4_4_0_1_7, 1_2_2_5_5_3, 7, 1_5, 1_3_2_5_9_7, 4, 2_9_3, 1_2_5_1_1, 7_6_1_0, 4, 3_4_1_4, 1_3_2_5_9_7, 9, 4, 3_2_3_6_1, 3_6_2, 4, 7_3_4, 2_8_5_1_2, 3_2_5_6_9, 1_8, 4, 3_2_3_6_1, 2_6_0_9_6, 1_4_9_8_2, 7_3, 1_8_7_1_5, 2_1_4_3_3, 2_3_5_2_6_1, 1_5, 4_9_2, 1_2_4_2_7, 1_6, 5_3, 1_8_7_1_5, 2_1_4_3_3, 6_5_4_5_4, 1_5, 2_3_6_5_9, 5_6_3, 1_6, 2_7_8, 5_9_7, 2_8_4_3, 5_9_5, 7_9_3_1, 1_8_2_3_9_6, 6_4_1_8_6, 2_2, 8_8_6, 5_9_5, 1_3_2_9_8_1, 5_3, 2_5_5_4_0, 3_4_4_9, 4_3_9_8_2, 3_9_9_0_1, 5_9_5_1, 8_7_8, 3_3_0, 4, 2_7_6_9_4, 8_0_2_6_9, 3_1_2, 5_3, 6_5_1_7, 1_1_7_8_0, 6_1_1, 2_0_4_0_8, 5], [2, 6, 1_3_2_5_9_7, 6_7, 4_2_8_9_7, 3_3, 5_9_2, 8, 1_6_3_7_2_9, 2_5_5_4_0, 3_6_1, 1_3_6_9_9_7, 1_0_9_5_1_4, 1_7_3_2_3_0, 7, 5_0_1, 6_0, 1_0_2_9_1_3, 1_9_6, 5_6_3_1, 2_3_5, 6_3_2_4_3, 4_7_3, 6, 2_3_1_7_5_7, 7_4, 5_2_7_7, 7_9_0_5, 5_3, 3_0_9_5, 3_7_3_1_7, 2_2, 4_5_4, 1_8_3_8_7_4, 5], [2, 2_6_8, 3_1_2_9_8, 4_6_5_3_0, 6, 1_3_2_9_3_5, 4_3_8_3_1, 7, 5_9_7, 3_2, 2_4, 3_6_8_8, 9_8_6_5, 5]],
						    "attention_mask": [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]
						}  # noqa: E501
						# fmt: on

						self.tokenizer_integration_test_util(
						    expected_encoding=UpperCamelCase__					,  model_name="facebook/xglm-564M"					,  padding=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)


