
import inspect
import os
import unittest
from pathlib import Path

import torch

import accelerate
from accelerate.test_utils import execute_subprocess_async
from accelerate.test_utils.testing import run_command






class   UpperCAmelCase		(						unittest.TestCase ):
   A__					:		List[str]						     =     inspect.getfile(accelerate.test_utils )
   A__					:		List[str]						     =     os.path.sep.join(mod_file.split(os.path.sep )[:-1] + ["scripts", "test_cli.py"] )

   A__					:		Tuple						     =     ["accelerate", "launch"]
   A__					:		int						     =     Path.home() / ".cache/huggingface/accelerate"
   A__					:		Tuple						     =     "default_config.yaml"
   A__					:		List[Any]						     =     config_folder / config_file
   A__					:		List[Any]						     =     config_folder / "_default_config.yaml"

   A__					:		List[str]						     =     Path("tests/test_configs" )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Union[str, Any] )		->	Optional[int]:


          '''simple docstring'''


          if cls.config_path.is_file():
                 cls.config_path.rename(cls.changed_path )



   @classmethod
   def 				_SCREAMING_SNAKE_CASE   (cls		:					Optional[Any] )		->	Optional[int]:


          '''simple docstring'''


          if cls.changed_path.is_file():
                 cls.changed_path.rename(cls.config_path )



   def 				_SCREAMING_SNAKE_CASE   (self		:					str )		->	Dict:


          '''simple docstring'''


          snake_case       :   int							=      self.base_cmd
          if torch.cuda.is_available() and (torch.cuda.device_count() > 1):
                 cmd += ["--multi_gpu"]
          execute_subprocess_async(cmd + [self.test_file_path]    ,						env=os.environ.copy() )



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Tuple:


          '''simple docstring'''


          for config in sorted(self.test_config_path.glob("**/*.yaml" ) ):
                 with self.subTest(config_file=snake_case__ ):
                        execute_subprocess_async(
                            self.base_cmd + ["--config_file", str(snake_case__ ), self.test_file_path]    ,						env=os.environ.copy() )



   def 				_SCREAMING_SNAKE_CASE   (self		:					Union[str, Any] )		->	Dict:


          '''simple docstring'''


          execute_subprocess_async(["accelerate", "test"]    ,						env=os.environ.copy() )






class   UpperCAmelCase		(						unittest.TestCase ):
   A__					:		List[Any]						     =     "test-tpu"
   A__					:		int						     =     "us-central1-a"
   A__					:		Any						     =     "ls"
   A__					:		int						     =     ["accelerate", "tpu-config"]
   A__					:		Dict						     =     "cd /usr/share"
   A__					:		List[Any]						     =     "tests/test_samples/test_command_file.sh"
   A__					:		Optional[int]						     =     "Running gcloud compute tpus tpu-vm ssh"



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	int:


          '''simple docstring'''


          snake_case       :   List[str]							=      run_command(
              self.cmd
              + ["--command", self.command, "--tpu_zone", self.tpu_zone, "--tpu_name", self.tpu_name, "--debug"]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""    ,						snake_case__    ,						)



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


          '''simple docstring'''


          snake_case       :   Tuple							=      run_command(
              self.cmd
              + [
                  "--config_file",
                  "tests/test_configs/0_12_0.yaml",
                  "--command",
                  self.command,
                  "--tpu_zone",
                  self.tpu_zone,
                  "--tpu_name",
                  self.tpu_name,
                  "--debug",
              ]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""    ,						snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Any )		->	int:


          '''simple docstring'''


          snake_case       :   Any							=      run_command(
              self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--debug"]    ,						return_stdout=snake_case__ )
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""    ,						snake_case__    ,						)



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


          '''simple docstring'''


          snake_case       :   str							=      run_command(
              self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--command", self.command, "--debug"]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls --worker all"""    ,						snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					int )		->	Dict:


          '''simple docstring'''


          snake_case       :   Union[str, Any]							=      run_command(
              self.cmd
              + [
                  "--config_file",
                  "tests/test_configs/latest.yaml",
                  "--command",
                  self.command,
                  "--command",
                  "echo \"Hello World\"",
                  "--debug",
              ]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; ls; echo \"Hello World\" --worker all"""    ,						snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[int] )		->	List[str]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      run_command(
              self.cmd
              + ["--config_file", "tests/test_configs/latest.yaml", "--command_file", self.command_file, "--debug"]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""    ,						snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					Optional[Any] )		->	Tuple:


          '''simple docstring'''


          snake_case       :   Any							=      run_command(
              self.cmd
              + [
                  "--config_file",
                  "tests/test_configs/0_12_0.yaml",
                  "--command_file",
                  self.command_file,
                  "--tpu_zone",
                  self.tpu_zone,
                  "--tpu_name",
                  self.tpu_name,
                  "--debug",
              ]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; echo \"hello world\"; echo \"this is a second command\" --worker all"""    ,						snake_case__    ,						)



   def 				_SCREAMING_SNAKE_CASE   (self		:					List[str] )		->	Optional[int]:


          '''simple docstring'''


          snake_case       :   List[Any]							=      run_command(
              self.cmd + ["--config_file", "tests/test_configs/latest.yaml", "--install_accelerate", "--debug"]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate -U; echo \"hello world\"; echo \"this is a second command\" --worker all"""    ,						snake_case__    ,						)



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


          '''simple docstring'''


          snake_case       :   Optional[Any]							=      run_command(
              self.cmd
              + [
                  "--config_file",
                  "tests/test_configs/latest.yaml",
                  "--install_accelerate",
                  "--accelerate_version",
                  "12.0.0",
                  "--debug",
              ]    ,						return_stdout=snake_case__    ,						)
          self.assertIn(
              f"""{self.gcloud} test-tpu --zone us-central1-a --command {self.base_output}; pip install accelerate==12.0.0; echo \"hello world\"; echo \"this is a second command\" --worker all"""    ,						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 			UpperCamelCase					(    __lowerCamelCase     : Dataset				,					__lowerCamelCase     : Dict[str, str]    ):
       snake_case       :   int							=      args.log_outputs
       snake_case       :   Dict							=      "_".join(args.dataset.split("/"    ) + [args.config, args.split]    )

       # load metric
       snake_case       :   List[str]							=      load_metric("wer"    )
       snake_case       :   Tuple							=      load_metric("cer"    )

       # compute metrics
       snake_case       :   List[Any]							=      wer.compute(references=result["target"]				,					predictions=result["prediction"]    )
       snake_case       :   int							=      cer.compute(references=result["target"]				,					predictions=result["prediction"]    )

       # print & log results
       snake_case       :   int							=      f"""WER: {wer_result}\nCER: {cer_result}"""
       print(__lowerCamelCase    )

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

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

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

                     result.map(__lowerCamelCase				,					with_indices=__lowerCamelCase    )


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

       snake_case       :   List[Any]							=      re.sub(__lowerCamelCase				,					""				,					text.lower()    )

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

       for t in token_sequences_to_ignore:
              snake_case       :   Dict							=      " ".join(text.split(__lowerCamelCase    )    )

       return text


def 			UpperCamelCase					(    __lowerCamelCase     : int    ):
       # load dataset
       snake_case       :   str							=      load_dataset(args.dataset				,					args.config				,					split=args.split				,					use_auth_token=__lowerCamelCase    )

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

       # load processor
       snake_case       :   List[Any]							=      AutoFeatureExtractor.from_pretrained(args.model_id    )
       snake_case       :   Union[str, Any]							=      feature_extractor.sampling_rate

       # resample audio
       snake_case       :   Union[str, Any]							=      dataset.cast_column("audio"				,					Audio(sampling_rate=__lowerCamelCase    )    )

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

       # map function to decode audio
       def map_to_pred(__lowerCamelCase     : int    ):
              snake_case       :   Dict							=      asr(
                  batch["audio"]["array"]				,					chunk_length_s=args.chunk_length_s				,					stride_length_s=args.stride_length_s    )

              snake_case       :   str							=      prediction["text"]
              snake_case       :   Tuple							=      normalize_text(batch["sentence"]    )
              return batch

       # run inference on all examples
       snake_case       :   Dict							=      dataset.map(__lowerCamelCase				,					remove_columns=dataset.column_names    )

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


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

   main(args)





