


'''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__			(   lowerCAmelCase__    ,   lowerCAmelCase__		)       ->      Union[str, Any]:
    UpperCAmelCase__     :						Dict						=							args.log_outputs
    UpperCAmelCase__     :						int						=							'''_'''.join(args.dataset.split('''/'''		) + [args.config, args.split]		)

    # load metric
    UpperCAmelCase__     :						int						=							load_metric('''wer'''		)
    UpperCAmelCase__     :						Optional[int]						=							load_metric('''cer'''		)

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

    # print & log results
    UpperCAmelCase__     :						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:
        UpperCAmelCase__     :						Tuple						=							F"""log_{dataset_id}_predictions.txt"""
        UpperCAmelCase__     :						Dict						=							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__    ,   lowerCAmelCase__		):
                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    a__			(   lowerCAmelCase__		)       ->      str:
    UpperCAmelCase__     :						Optional[Any]						=							'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

    UpperCAmelCase__     :						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!
    UpperCAmelCase__     :						Any						=							['''\n\n''', '''\n''', '''   ''', '''  ''']

    for t in token_sequences_to_ignore:
        UpperCAmelCase__     :						Optional[Any]						=							''' '''.join(text.split(lowerCAmelCase__		)		)

    return text





def    a__			(   lowerCAmelCase__		)       ->      str:
    # load dataset
    UpperCAmelCase__     :						Any						=							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
    UpperCAmelCase__     :						Union[str, Any]						=							AutoFeatureExtractor.from_pretrained(args.model_id		)
    UpperCAmelCase__     :						Any						=							feature_extractor.sampling_rate

    # resample audio
    UpperCAmelCase__     :						List[str]						=							dataset.cast_column('''audio'''    ,   Audio(sampling_rate=lowerCAmelCase__		)		)

    # load eval pipeline
    if args.device is None:
        UpperCAmelCase__     :						Any						=							0 if torch.cuda.is_available() else -1
    UpperCAmelCase__     :						int						=							pipeline('''automatic-speech-recognition'''    ,   model=args.model_id    ,   device=args.device		)

    # map function to decode audio
    def map_to_pred(lowerCAmelCase__		):
        UpperCAmelCase__     :						Optional[int]						=							asr(
            batch['''audio''']['''array''']    ,   chunk_length_s=args.chunk_length_s    ,   stride_length_s=args.stride_length_s		)

        UpperCAmelCase__     :						List[Any]						=							prediction['''text''']
        UpperCAmelCase__     :						Union[str, Any]						=							normalize_text(batch['''sentence''']		)
        return batch

    # run inference on all examples
    UpperCAmelCase__     :						Optional[int]						=							dataset.map(lowerCAmelCase__    ,   remove_columns=dataset.column_names		)

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


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

  main(args)

'''simple docstring'''

import json
import os
import tempfile
from unittest.mock import patch

import torch
from torch.utils.data import DataLoader, TensorDataset

from accelerate import DistributedType, infer_auto_device_map, init_empty_weights
from accelerate.accelerator import Accelerator
from accelerate.state import GradientState, PartialState
from accelerate.test_utils import require_bnb, require_multi_gpu, slow
from accelerate.test_utils.testing import AccelerateTestCase, require_cuda
from accelerate.utils import patch_environment



def snake_case_      (					)			->					str:
   lowerCAmelCase_			=      torch.nn.Linear(2	,   4)
   lowerCAmelCase_			=      torch.optim.AdamW(model.parameters()	,   lr=1.0)
   lowerCAmelCase_			=      torch.optim.lr_scheduler.OneCycleLR(__snake_case	,   max_lr=0.0_1	,   steps_per_epoch=2	,   epochs=1)
   lowerCAmelCase_			=      DataLoader(TensorDataset(torch.tensor([1, 2, 3])))
   lowerCAmelCase_			=      DataLoader(TensorDataset(torch.tensor([4, 5, 6])))

   return model, optimizer, scheduler, train_dl, valid_dl



def snake_case_      (					__snake_case  :							Any)			->					List[Any]:
   return (model.weight.abs().sum() + model.bias.abs().sum()).item()



def snake_case_      (					__snake_case  :							Optional[int])			->					Any:
   lowerCAmelCase_			=      torch.nn.Linear(*tuple(model.weight.T.shape)).state_dict()
   model.load_state_dict(__snake_case)






class     __UpperCAmelCase       ( __a		):





  @require_cuda
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     assert PartialState._shared_state["_cpu"] is False
     assert PartialState._shared_state["device"].type == "cuda"
     with self.assertRaises(_lowerCamelCase ):
        lowerCAmelCase_			=      Accelerator(cpu=_lowerCamelCase )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_			=      GradientState()
     assert state.num_steps == 1
     lowerCAmelCase_			=      4
     assert state.num_steps == 4

     assert state.sync_gradients is True
     lowerCAmelCase_			=      False
     assert state.sync_gradients is False
     GradientState._reset_state()





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()

     (
         (
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,(
         lowerCAmelCase_
     )	,
     )			=      accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     self.assertTrue(prepared_model in accelerator._models )
     self.assertTrue(prepared_optimizer in accelerator._optimizers )
     self.assertTrue(prepared_scheduler in accelerator._schedulers )
     self.assertTrue(prepared_train_dl in accelerator._dataloaders )
     self.assertTrue(prepared_valid_dl in accelerator._dataloaders )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     accelerator.free_memory()

     self.assertTrue(len(accelerator._models ) == 0 )
     self.assertTrue(len(accelerator._optimizers ) == 0 )
     self.assertTrue(len(accelerator._schedulers ) == 0 )
     self.assertTrue(len(accelerator._dataloaders ) == 0 )





  def 						UpperCAmelCase_	(	self ):
     PartialState._reset_state()

     # Mock torch.cuda.set_device to avoid an exception as the device doesn't exist
     def noop(*_lowerCamelCase    ,     **_lowerCamelCase ):
        pass

     with patch('''torch.cuda.set_device'''    ,     _lowerCamelCase ), patch_environment(ACCELERATE_TORCH_DEVICE='''cuda:64''' ):
        lowerCAmelCase_			=      Accelerator()
        self.assertEqual(str(accelerator.state.device )    ,     '''cuda:64''' )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      get_signature(_lowerCamelCase )

     with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(_lowerCamelCase )

        # make sure random weights don't match
        load_random_weights(_lowerCamelCase )
        self.assertTrue(abs(model_signature - get_signature(_lowerCamelCase ) ) > 1E-3 )

        # make sure loaded weights match
        accelerator.load_state(_lowerCamelCase )
        self.assertTrue(abs(model_signature - get_signature(_lowerCamelCase ) ) < 1E-3 )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     accelerator.prepare(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )

     lowerCAmelCase_			=      get_signature(_lowerCamelCase )

     # saving hook
     def save_config(_lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase ):
        lowerCAmelCase_			=      {'''class_name''': models[0].__class__.__name__}

        with open(os.path.join(_lowerCamelCase    ,     '''data.json''' )    ,     '''w''' ) as f:
           json.dump(_lowerCamelCase    ,     _lowerCamelCase )

        # loading hook
     def load_config(_lowerCamelCase    ,     _lowerCamelCase ):
        with open(os.path.join(_lowerCamelCase    ,     '''data.json''' )    ,     '''r''' ) as f:
           lowerCAmelCase_			=      json.load(_lowerCamelCase )

        lowerCAmelCase_			=      config['''class_name''']

     lowerCAmelCase_			=      accelerator.register_save_state_pre_hook(_lowerCamelCase )
     lowerCAmelCase_			=      accelerator.register_load_state_pre_hook(_lowerCamelCase )

     with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(_lowerCamelCase )

        # make sure random weights don't match with hooks
        load_random_weights(_lowerCamelCase )
        self.assertTrue(abs(model_signature - get_signature(_lowerCamelCase ) ) > 1E-3 )

        # random class name to verify correct one is loaded
        lowerCAmelCase_			=      '''random'''

        # make sure loaded weights match with hooks
        accelerator.load_state(_lowerCamelCase )
        self.assertTrue(abs(model_signature - get_signature(_lowerCamelCase ) ) < 1E-3 )

        # mode.class_name is loaded from config
        self.assertTrue(model.class_name == model.__class__.__name__ )

     # remove hooks
     save_hook.remove()
     load_hook.remove()

     with tempfile.TemporaryDirectory() as tmpdirname:
        accelerator.save_state(_lowerCamelCase )

        # make sure random weights don't match with hooks removed
        load_random_weights(_lowerCamelCase )
        self.assertTrue(abs(model_signature - get_signature(_lowerCamelCase ) ) > 1E-3 )

        # random class name to verify correct one is loaded
        lowerCAmelCase_			=      '''random'''

        # make sure loaded weights match with hooks removed
        accelerator.load_state(_lowerCamelCase )
        self.assertTrue(abs(model_signature - get_signature(_lowerCamelCase ) ) < 1E-3 )

        # mode.class_name is NOT loaded from config
        self.assertTrue(model.class_name != model.__class__.__name__ )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     lowerCAmelCase_			=      None

     # This should work
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.prepare(
         _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     self.assertTrue(dummy_obj is None )





  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      Accelerator()
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      create_components()
     lowerCAmelCase_			=      [1, 2, 3]

     # This should work
     lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_	,lowerCAmelCase_			=      accelerator.prepare(
         _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase    ,     _lowerCamelCase )
     self.assertEqual(
         getattr(_lowerCamelCase    ,     '''_is_accelerate_prepared'''    ,     _lowerCamelCase )    ,     _lowerCamelCase    ,     '''Dummy object should have `_is_accelerate_prepared` set to `True`'''    ,     )
     self.assertEqual(
         getattr(_lowerCamelCase    ,     '''_is_accelerate_prepared'''    ,     _lowerCamelCase )    ,     _lowerCamelCase    ,     '''Model is missing `_is_accelerator_prepared` or is set to `False`'''    ,     )
     self.assertEqual(
         getattr(_lowerCamelCase    ,     '''_is_accelerate_prepared'''    ,     _lowerCamelCase )    ,     _lowerCamelCase    ,     '''Optimizer is missing `_is_accelerator_prepared` or is set to `False`'''    ,     )
     self.assertEqual(
         getattr(_lowerCamelCase    ,     '''_is_accelerate_prepared'''    ,     _lowerCamelCase )    ,     _lowerCamelCase    ,     '''Scheduler is missing `_is_accelerator_prepared` or is set to `False`'''    ,     )
     self.assertEqual(
         getattr(_lowerCamelCase    ,     '''_is_accelerate_prepared'''    ,     _lowerCamelCase )    ,     _lowerCamelCase    ,     '''Train Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''    ,     )
     self.assertEqual(
         getattr(_lowerCamelCase    ,     '''_is_accelerate_prepared'''    ,     _lowerCamelCase )    ,     _lowerCamelCase    ,     '''Valid Dataloader is missing `_is_accelerator_prepared` or is set to `False`'''    ,     )





  @slow
  @require_bnb
  def 						UpperCAmelCase_	(	self ):
     from transformers import AutoModelForCausalLM

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     load_in_abit=_lowerCamelCase    ,     device_map={'''''': 0}    ,     )
     lowerCAmelCase_			=      Accelerator()

     # This should work
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





  @slow
  @require_bnb
  def 						UpperCAmelCase_	(	self ):
     from transformers import AutoModelForCausalLM

     lowerCAmelCase_			=      Accelerator()

     with init_empty_weights():
        lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''    ,     )
        model.tie_weights()
        lowerCAmelCase_			=      infer_auto_device_map(_lowerCamelCase )
        lowerCAmelCase_			=      '''cpu'''

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     device_map=_lowerCamelCase    ,     load_in_abit=_lowerCamelCase    ,     llm_inta_enable_fpaa_cpu_offload=_lowerCamelCase )

     # This should not work and get value error
     with self.assertRaises(_lowerCamelCase ):
        lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





  @slow
  @require_bnb
  @require_multi_gpu
  def 						UpperCAmelCase_	(	self ):
     from transformers import AutoModelForCausalLM

     lowerCAmelCase_			=      {'''distributed_type''': DistributedType.MULTI_GPU}

     with init_empty_weights():
        lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''    ,     )
        model.tie_weights()
        lowerCAmelCase_			=      infer_auto_device_map(_lowerCamelCase )
        lowerCAmelCase_			=      1

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     load_in_abit=_lowerCamelCase    ,     device_map=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      Accelerator()

     # This should not work and get value error
     with self.assertRaises(_lowerCamelCase ):
        lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )

     PartialState._reset_state()





  @slow
  @require_bnb
  @require_multi_gpu
  def 						UpperCAmelCase_	(	self ):
     from transformers import AutoModelForCausalLM

     with init_empty_weights():
        lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
            '''EleutherAI/gpt-neo-125m'''    ,     )
        lowerCAmelCase_			=      infer_auto_device_map(_lowerCamelCase )
        lowerCAmelCase_			=      1

     lowerCAmelCase_			=      AutoModelForCausalLM.from_pretrained(
         '''EleutherAI/gpt-neo-125m'''    ,     load_in_abit=_lowerCamelCase    ,     device_map=_lowerCamelCase    ,     )
     lowerCAmelCase_			=      Accelerator()

     # This should work
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )







  @require_cuda
  def 						UpperCAmelCase_	(	self ):
     lowerCAmelCase_			=      torch.nn.Linear(10    ,     10 )
     lowerCAmelCase_			=      torch.optim.SGD(model.parameters()    ,     lr=0.01 )
     lowerCAmelCase_			=      Accelerator(cpu=_lowerCamelCase )
     lowerCAmelCase_			=      accelerator.prepare(_lowerCamelCase )





