

"""simple docstring"""


import asyncio
import os
import shutil
import subprocess
import sys
import tempfile
import unittest
from distutils.util import strtobool
from functools import partial
from pathlib import Path
from typing import List, Union
from unittest import mock

import torch

from ..state import AcceleratorState, PartialState
from ..utils import (
    gather,
    is_bnb_available,
    is_comet_ml_available,
    is_datasets_available,
    is_deepspeed_available,
    is_mps_available,
    is_safetensors_available,
    is_tensorboard_available,
    is_torch_version,
    is_tpu_available,
    is_transformers_available,
    is_wandb_available,
    is_xpu_available,
)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=False			)     ->     str:
		try:
				__lowerCAmelCase:	Optional[int]   				=      os.environ[key]
		except KeyError:
				# KEY isn't set, default to `default`.
				__lowerCAmelCase:	Optional[int]   				=      default
		else:
				# KEY is set, convert it to True or False.
				try:
						__lowerCAmelCase:	str   				=      strtobool(__SCREAMING_SNAKE_CASE			)
				except ValueError:
						# More values are supported, but let's keep the message simple.
						raise ValueError(F"If set, {key} must be yes or no."			)
		return _value


__A						       =      parse_flag_from_env("RUN_SLOW", default=False)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Dict:
		return unittest.skip("Test was skipped"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		return unittest.skipUnless(_run_slow_tests ,			"test is slow"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		return unittest.skipUnless(not torch.cuda.is_available() ,			"test requires only a CPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		return unittest.skipUnless(torch.cuda.is_available() ,			"test requires a GPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		return unittest.skipUnless(is_xpu_available() ,			"test requires a XPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Union[str, Any]:
		return unittest.skipUnless(is_mps_available() ,			"test requires a `mps` backend support in `torch`"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Optional[Any]:
		return unittest.skipUnless(
		    is_transformers_available() and is_datasets_available() ,			"test requires the Hugging Face suite"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		return unittest.skipUnless(is_bnb_available() ,			"test requires the bitsandbytes library"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		return unittest.skipUnless(is_tpu_available() ,			"test requires TPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Union[str, Any]:
		return unittest.skipUnless(torch.cuda.device_count() == 1 ,			"test requires a GPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		return unittest.skipUnless(torch.xpu.device_count() == 1 ,			"test requires a XPU"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		return unittest.skipUnless(torch.cuda.device_count() > 1 ,			"test requires multiple GPUs"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		return unittest.skipUnless(torch.xpu.device_count() > 1 ,			"test requires multiple XPUs"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		return unittest.skipUnless(is_safetensors_available() ,			"test requires safetensors"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		return unittest.skipUnless(is_deepspeed_available() ,			"test requires DeepSpeed"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     List[Any]:
		return unittest.skipUnless(is_torch_version(">=" ,			"1.12.0"			) ,			"test requires torch version >= 1.12.0"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None			)     ->     Tuple:
		if test_case is None:
				return partial(__SCREAMING_SNAKE_CASE ,			version=__SCREAMING_SNAKE_CASE			)
		return unittest.skipUnless(is_torch_version(">=" ,			__SCREAMING_SNAKE_CASE			) ,			F"test requires torch version >= {version}"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Union[str, Any]:
		return unittest.skipUnless(is_tensorboard_available() ,			"test requires Tensorboard"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		return unittest.skipUnless(is_wandb_available() ,			"test requires wandb"			)(__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Tuple:
		return unittest.skipUnless(is_comet_ml_available() ,			"test requires comet_ml"			)(__SCREAMING_SNAKE_CASE			)


__A						       =      (
    any([is_wandb_available(), is_tensorboard_available()]) and not is_comet_ml_available()
)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     int:
		return unittest.skipUnless(
		    _atleast_one_tracker_available ,			"test requires at least one tracker to be available and for `comet_ml` to not be installed" ,			)(__SCREAMING_SNAKE_CASE			)





class    snake_case     (    unittest.TestCase  ):
				SCREAMING_SNAKE_CASE_       :				List[str]       				=  True

				@classmethod
				def 							lowercase_					(     cls							:    Tuple)->       List[Any]:



						'''simple docstring'''


						__lowerCAmelCase:	Tuple   				=      tempfile.mkdtemp()

				@classmethod
				def 							lowercase_					(     cls							:    List[Any])->       List[str]:



						'''simple docstring'''


						if os.path.exists(cls.tmpdir):
								shutil.rmtree(cls.tmpdir)




				def 							lowercase_					(     self							:    int)->       Dict:



						'''simple docstring'''


						if self.clear_on_setup:
								for path in Path(self.tmpdir).glob("**/*"):
										if path.is_file():
												path.unlink()
										elif path.is_dir():
												shutil.rmtree(UpperCamelCase__)





class    snake_case     (    unittest.TestCase  ):

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



						'''simple docstring'''


						super().tearDown()
						# Reset the state of the AcceleratorState singleton.
						AcceleratorState._reset_state()
						PartialState._reset_state()





class    snake_case     (    unittest.TestCase  ):

				def 							lowercase_					(     self							:    List[Any]					,  UpperCamelCase__							:    Union[mock.Mock, List[mock.Mock]])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[Any]   				=      mocks if isinstance(UpperCamelCase__					,  (tuple, list)) else [mocks]
						for m in self.mocks:
								m.start()
								self.addCleanup(m.stop)






def    a__   (   __SCREAMING_SNAKE_CASE			)     ->     Any:
		__lowerCAmelCase:	Any   				=      AcceleratorState()
		__lowerCAmelCase:	Optional[int]   				=      tensor[None].clone().to(state.device			)
		__lowerCAmelCase:	int   				=      gather(__SCREAMING_SNAKE_CASE			).cpu()
		__lowerCAmelCase:	Union[str, Any]   				=      tensor[0].cpu()
		for i in range(tensors.shape[0]			):
				if not torch.equal(tensors[i] ,			__SCREAMING_SNAKE_CASE			):
						return False
		return True





class    snake_case     :

				def __init__(     self							:    Any					,  UpperCamelCase__							:    List[str]					,  UpperCamelCase__							:    Union[str, Any]					,  UpperCamelCase__							:    List[str])->       Dict:



						'''simple docstring'''


						__lowerCAmelCase:	Optional[int]   				=      returncode
						__lowerCAmelCase:	List[str]   				=      stdout
						__lowerCAmelCase:	Tuple   				=      stderr






async def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)     ->     List[str]:
		while True:
				__lowerCAmelCase:	Dict   				=      await stream.readline()
				if line:
						callback(__SCREAMING_SNAKE_CASE			)
				else:
						break






async def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=False ,			__SCREAMING_SNAKE_CASE=False			)     ->     _RunOutput:
		if echo:
				print("\nRunning: " ,			" ".join(__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Union[str, Any]   				=      await asyncio.create_subprocess_exec(
		    cmd[0] ,			*cmd[1:] ,			stdin=__SCREAMING_SNAKE_CASE ,			stdout=asyncio.subprocess.PIPE ,			stderr=asyncio.subprocess.PIPE ,			env=__SCREAMING_SNAKE_CASE ,			)

		# note: there is a warning for a possible deadlock when using `wait` with huge amounts of data in the pipe
		# https://docs.python.org/3/library/asyncio-subprocess.html#asyncio.asyncio.subprocess.Process.wait
		#
		# If it starts hanging, will need to switch to the following code. The problem is that no data
		# will be seen until it's done and if it hangs for example there will be no debug info.
		# out, err = await p.communicate()
		# return _RunOutput(p.returncode, out, err)

		__lowerCAmelCase:	int   				=      []
		__lowerCAmelCase:	List[str]   				=      []

		def tee(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=""			):
				__lowerCAmelCase:	int   				=      line.decode("utf-8"			).rstrip()
				sink.append(__SCREAMING_SNAKE_CASE			)
				if not quiet:
						print(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			file=__SCREAMING_SNAKE_CASE			)

    # XXX: the timeout doesn't seem to make any difference here
		await asyncio.wait(
		    [
		        asyncio.create_task(_read_stream(p.stdout ,			lambda __SCREAMING_SNAKE_CASE			: tee(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			sys.stdout ,			label="stdout:"			)			)			),
		        asyncio.create_task(_read_stream(p.stderr ,			lambda __SCREAMING_SNAKE_CASE			: tee(__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE ,			sys.stderr ,			label="stderr:"			)			)			),
		    ] ,			timeout=__SCREAMING_SNAKE_CASE ,			)
		return _RunOutput(await p.wait() ,			__SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE			)






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=None ,			__SCREAMING_SNAKE_CASE=1_8_0 ,			__SCREAMING_SNAKE_CASE=False ,			__SCREAMING_SNAKE_CASE=True			)     ->     _RunOutput:
		__lowerCAmelCase:	Optional[int]   				=      asyncio.get_event_loop()
		__lowerCAmelCase:	int   				=      loop.run_until_complete(
		    _stream_subprocess(__SCREAMING_SNAKE_CASE ,			env=__SCREAMING_SNAKE_CASE ,			stdin=__SCREAMING_SNAKE_CASE ,			timeout=__SCREAMING_SNAKE_CASE ,			quiet=__SCREAMING_SNAKE_CASE ,			echo=__SCREAMING_SNAKE_CASE			)			)

		__lowerCAmelCase:	Union[str, Any]   				=      " ".join(__SCREAMING_SNAKE_CASE			)
		if result.returncode > 0:
				__lowerCAmelCase:	Dict   				=      "\n".join(result.stderr			)
				raise RuntimeError(
				    F"'{cmd_str}' failed with returncode {result.returncode}\n\n"
				    F"The combined stderr from workers follows:\n{stderr}"			)

		return result





class    snake_case     (    __snake_case  ):
				pass






def    a__   (   __SCREAMING_SNAKE_CASE ,			__SCREAMING_SNAKE_CASE=False			)     ->     Optional[int]:
		try:
				__lowerCAmelCase:	Union[str, Any]   				=      subprocess.check_output(__SCREAMING_SNAKE_CASE ,			stderr=subprocess.STDOUT			)
				if return_stdout:
						if hasattr(__SCREAMING_SNAKE_CASE ,			"decode"			):
								__lowerCAmelCase:	List[str]   				=      output.decode("utf-8"			)
						return output
		except subprocess.CalledProcessError as e:
				raise SubprocessCallException(
				    F"Command `{' '.join(__SCREAMING_SNAKE_CASE			)}` failed with the following error:\n\n{e.output.decode()}"			) from e





"""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)


