

'''simple docstring'''






import logging
import os
from dataclasses import dataclass, field
from typing import Dict, Optional

import numpy as np
from utils_multiple_choice import MultipleChoiceDataset, Split, processors

import transformers
from transformers import (
    AutoConfig,
    AutoModelForMultipleChoice,
    AutoTokenizer,
    DataCollatorWithPadding,
    EvalPrediction,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import is_main_process


lowerCAmelCase__											=	logging.getLogger(__name__)


def 		_A     (    A__     ,				A__      ):






 """simple docstring"""



 return (preds == labels).mean()






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			field(
      metadata={'help': 'Path to pretrained model or model identifier from huggingface.co/models'}      )
  SCREAMING_SNAKE_CASE    :  Optional[str]							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'Pretrained config name or path if not the same as model_name'}      )
  SCREAMING_SNAKE_CASE    :  Optional[str]							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'Pretrained tokenizer name or path if not the same as model_name'}      )
  SCREAMING_SNAKE_CASE    :  Optional[str]							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'Where do you want to store the pretrained models downloaded from huggingface.co'}				,       )






@dataclass
class 			lowercase_	:




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  str							=			field(metadata={'help': 'The name of the task to train on: ' + ', '.join(processors.keys()      )}      )
  SCREAMING_SNAKE_CASE    :  str							=			field(metadata={'help': 'Should contain the data files for the task.'}      )
  SCREAMING_SNAKE_CASE    :  int							=			field(
      default=1_2_8				,       metadata={
          'help': (
              'The maximum total input sequence length after tokenization. Sequences longer '
              'than this will be truncated, sequences shorter will be padded.'
          )
      }				,       )
  SCREAMING_SNAKE_CASE    :  bool							=			field(
      default=lowerCamelCase__				,       metadata={'help': 'Overwrite the cached training and evaluation sets'}      )


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)      )
 __lowercase  ,	__lowercase  ,	__lowercase							  = parser.parse_args_into_dataclasses()

 if (
     os.path.exists(training_args.output_dir      )
     and os.listdir(training_args.output_dir      )
     and training_args.do_train
     and not training_args.overwrite_output_dir
 ):
  raise ValueError(
      F"Output directory ({training_args.output_dir}) already exists and is not empty. Use"
      ''' --overwrite_output_dir to overcome.'''      )

 # Setup logging
 logging.basicConfig(
     format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''     ,				datefmt='''%m/%d/%Y %H:%M:%S'''     ,				level=logging.INFO if training_args.local_rank in [-1, 0] else logging.WARN     ,				)
 logger.warning(
     '''Process rank: %s, device: %s, n_gpu: %s, distributed training: %s, 16-bits training: %s'''     ,				training_args.local_rank     ,				training_args.device     ,				training_args.n_gpu     ,				bool(training_args.local_rank != -1      )     ,				training_args.fpaa     ,				)
 # Set the verbosity to info of the Transformers logger (on main process only):
 if is_main_process(training_args.local_rank      ):
  transformers.utils.logging.set_verbosity_info()
  transformers.utils.logging.enable_default_handler()
  transformers.utils.logging.enable_explicit_format()
 logger.info('''Training/evaluation parameters %s'''     ,				A__      )

 # Set seed
 set_seed(training_args.seed      )

 try:
  __lowercase							  = processors[data_args.task_name]()
  __lowercase							  = processor.get_labels()
  __lowercase							  = len(A__      )
 except KeyError:
  raise ValueError('''Task not found: %s''' % (data_args.task_name)      )

 # Load pretrained model and tokenizer
 #
 # Distributed training:
 # The .from_pretrained methods guarantee that only one local process can concurrently
 # download model & vocab.

 __lowercase							  = AutoConfig.from_pretrained(
     model_args.config_name if model_args.config_name else model_args.model_name_or_path     ,				num_labels=A__     ,				finetuning_task=data_args.task_name     ,				cache_dir=model_args.cache_dir     ,				)
 __lowercase							  = AutoTokenizer.from_pretrained(
     model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path     ,				cache_dir=model_args.cache_dir     ,				)
 __lowercase							  = AutoModelForMultipleChoice.from_pretrained(
     model_args.model_name_or_path     ,				from_tf=bool('''.ckpt''' in model_args.model_name_or_path      )     ,				config=A__     ,				cache_dir=model_args.cache_dir     ,				)

 # Get datasets
 __lowercase							  = (
     MultipleChoiceDataset(
         data_dir=data_args.data_dir     ,				tokenizer=A__     ,				task=data_args.task_name     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.train     ,				)
     if training_args.do_train
     else None
 )
 __lowercase							  = (
     MultipleChoiceDataset(
         data_dir=data_args.data_dir     ,				tokenizer=A__     ,				task=data_args.task_name     ,				max_seq_length=data_args.max_seq_length     ,				overwrite_cache=data_args.overwrite_cache     ,				mode=Split.dev     ,				)
     if training_args.do_eval
     else None
 )

 def compute_metrics(A__      ) -> Dict:
  __lowercase							  = np.argmax(p.predictions     ,				axis=1      )
  return {"acc": simple_accuracy(A__     ,				p.label_ids      )}

 # Data collator
 __lowercase							  = DataCollatorWithPadding(A__     ,				pad_to_multiple_of=8      ) if training_args.fpaa else None

 # Initialize our Trainer
 __lowercase							  = Trainer(
     model=A__     ,				args=A__     ,				train_dataset=A__     ,				eval_dataset=A__     ,				compute_metrics=A__     ,				data_collator=A__     ,				)

 # Training
 if training_args.do_train:
  trainer.train(
      model_path=model_args.model_name_or_path if os.path.isdir(model_args.model_name_or_path      ) else None      )
  trainer.save_model()
  # For convenience, we also re-save the tokenizer to the same directory,
  # so that you can share your model easily on huggingface.co/models =)
  if trainer.is_world_master():
   tokenizer.save_pretrained(training_args.output_dir      )

    # Evaluation
 __lowercase							  = {}
 if training_args.do_eval:
  logger.info('''*** Evaluate ***'''      )

  __lowercase							  = trainer.evaluate()

  __lowercase							  = os.path.join(training_args.output_dir     ,				'''eval_results.txt'''      )
  if trainer.is_world_master():
   with open(A__     ,				'''w'''      ) as writer:
    logger.info('''***** Eval results *****'''      )
    for key, value in result.items():
     logger.info('''  %s = %s'''     ,				A__     ,				A__      )
     writer.write('''%s = %s\n''' % (key, value)      )

    results.update(A__      )

 return results


def 		_A     (    A__      ):






 """simple docstring"""



 main()


if __name__ == "__main__":
 main()




'''simple docstring'''






import itertools
import os
import random
import tempfile
import unittest

import numpy as np
from datasets import load_dataset

from transformers import is_speech_available
from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_torchaudio
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
 from transformers import WhisperFeatureExtractor

if is_torch_available():
 import torch

lowerCAmelCase__											=	random.Random()


def 		_A     (    A__     ,				A__=1.0     ,				A__=None     ,				A__=None      ):






 """simple docstring"""



 if rng is None:
  __lowercase							  = global_rng

 __lowercase							  = []
 for batch_idx in range(shape[0]      ):
  values.append([]      )
  for _ in range(shape[1]      ):
   values[-1].append(rng.random() * scale      )

 return values






@require_torch
@require_torchaudio
class 			lowercase_	(unittest.TestCase      ):




  """simple docstring"""




  def __init__(      self   :     List[str]					,lowercase__   :     List[str]					,lowercase__   :     List[Any]=7					,lowercase__   :     Dict=4_0_0					,lowercase__   :     Tuple=2_0_0_0					,lowercase__   :     Optional[int]=1_0					,lowercase__   :     Optional[int]=1_6_0					,lowercase__   :     Dict=8					,lowercase__   :     str=0.0					,lowercase__   :     Union[str, Any]=4_0_0_0					,lowercase__   :     Optional[int]=False					,lowercase__   :     List[str]=True					,):
   __lowercase							  = parent
   __lowercase							  = batch_size
   __lowercase							  = min_seq_length
   __lowercase							  = max_seq_length
   __lowercase							  = (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
   __lowercase							  = padding_value
   __lowercase							  = sampling_rate
   __lowercase							  = return_attention_mask
   __lowercase							  = do_normalize
   __lowercase							  = feature_size
   __lowercase							  = chunk_length
   __lowercase							  = hop_length






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   return {
       "feature_size": self.feature_size,
       "hop_length": self.hop_length,
       "chunk_length": self.chunk_length,
       "padding_value": self.padding_value,
       "sampling_rate": self.sampling_rate,
       "return_attention_mask": self.return_attention_mask,
       "do_normalize": self.do_normalize,
   }






  def 					SCREAMING_SNAKE_CASE      (      self   :     int					,lowercase__   :     List[str]=False					,lowercase__   :     Dict=False			):
   def _flatten(lowercase__   :     Optional[Any]			):
    return list(itertools.chain(*lowercase__			)			)

   if equal_length:
    __lowercase							  = [floats_list((self.max_seq_length, self.feature_size)			) for _ in range(self.batch_size			)]
   else:
    # make sure that inputs increase in size
    __lowercase							  = [
        floats_list((x, self.feature_size)			)
        for x in range(self.min_seq_length					,self.max_seq_length					,self.seq_length_diff			)
    ]
   if numpify:
    __lowercase							  = [np.asarray(lowercase__			) for x in speech_inputs]
   return speech_inputs






@require_torch
@require_torchaudio
class 			lowercase_	(lowerCamelCase__				,       unittest.TestCase      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Optional[int]							=			WhisperFeatureExtractor if is_speech_available() else None






  def 					SCREAMING_SNAKE_CASE      (      self   :     int			):
   __lowercase							  = WhisperFeatureExtractionTester(self			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = feat_extract_first.save_pretrained(lowercase__			)[0]
    check_json_file_has_correct_format(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_pretrained(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_dict			)

   with tempfile.TemporaryDirectory() as tmpdirname:
    __lowercase							  = os.path.join(lowercase__					,'''feat_extract.json'''			)
    feat_extract_first.to_json_file(lowercase__			)
    __lowercase							  = self.feature_extraction_class.from_json_file(lowercase__			)

   __lowercase							  = feat_extract_first.to_dict()
   __lowercase							  = feat_extract_second.to_dict()
   __lowercase							  = feat_extract_first.mel_filters
   __lowercase							  = feat_extract_second.mel_filters
   self.assertTrue(np.allclose(lowercase__					,lowercase__			)			)
   self.assertEqual(lowercase__					,lowercase__			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   # Tests that all call wrap to encode_plus and batch_encode_plus
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   # create three inputs of length 800, 1000, and 1200
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(8_0_0					,1_4_0_0					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   # Test feature size
   __lowercase							  = feature_extractor(lowercase__					,padding='''max_length'''					,return_tensors='''np'''			).input_features
   self.assertTrue(input_features.ndim == 3			)
   self.assertTrue(input_features.shape[-1] == feature_extractor.nb_max_frames			)
   self.assertTrue(input_features.shape[-2] == feature_extractor.feature_size			)

   # Test not batched input
   __lowercase							  = feature_extractor(speech_inputs[0]					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(np_speech_inputs[0]					,return_tensors='''np'''			).input_features
   self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test batched
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test 2-D numpy arrays are batched.
   __lowercase							  = [floats_list((1, x)			)[0] for x in (8_0_0, 8_0_0, 8_0_0)]
   __lowercase							  = np.asarray(lowercase__			)
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)

   # Test truncation required
   __lowercase							  = [floats_list((1, x)			)[0] for x in range(2_0_0					,(feature_extractor.n_samples + 5_0_0)					,2_0_0			)]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs]

   __lowercase							  = [x[: feature_extractor.n_samples] for x in speech_inputs]
   __lowercase							  = [np.asarray(lowercase__			) for speech_input in speech_inputs_truncated]

   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''np'''			).input_features
   for enc_seq_a, enc_seq_a in zip(lowercase__					,lowercase__			):
    self.assertTrue(np.allclose(lowercase__					,lowercase__					,atol=1e-3			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   import torch

   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = np.random.rand(1_0_0					,3_2			).astype(np.floataa			)
   __lowercase							  = np_speech_inputs.tolist()

   for inputs in [py_speech_inputs, np_speech_inputs]:
    __lowercase							  = feature_extractor.pad([{'''input_features''': inputs}]					,return_tensors='''np'''			)
    self.assertTrue(np_processed.input_features.dtype == np.floataa			)
    __lowercase							  = feature_extractor.pad([{'''input_features''': inputs}]					,return_tensors='''pt'''			)
    self.assertTrue(pt_processed.input_features.dtype == torch.floataa			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Any					,lowercase__   :     int			):
   __lowercase							  = load_dataset('''hf-internal-testing/librispeech_asr_dummy'''					,'''clean'''					,split='''validation'''			)
   # automatic decoding with librispeech
   __lowercase							  = ds.sort('''id'''			).select(range(lowercase__			)			)[:num_samples]['''audio''']

   return [x["array"] for x in speech_samples]






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   # fmt: off
   __lowercase							  = torch.tensor(
       [
           0.1_1_9_3, -0.0_9_4_6, -0.1_0_9_8, -0.0_1_9_6, 0.0_2_2_5, -0.0_6_9_0, -0.1_7_3_6, 0.0_9_5_1,
           0.0_9_7_1, -0.0_8_1_7, -0.0_7_0_2, 0.0_1_6_2, 0.0_2_6_0, 0.0_0_1_7, -0.0_1_9_2, -0.1_6_7_8,
           0.0_7_0_9, -0.1_8_6_7, -0.0_6_5_5, -0.0_2_7_4, -0.0_2_3_4, -0.1_8_8_4, -0.0_5_1_6, -0.0_5_5_4,
           -0.0_2_7_4, -0.1_4_2_5, -0.1_4_2_3, 0.0_8_3_7, 0.0_3_7_7, -0.0_8_5_4
       ]			)
   # fmt: on

   __lowercase							  = self._load_datasamples(1			)
   __lowercase							  = WhisperFeatureExtractor()
   __lowercase							  = feature_extractor(lowercase__					,return_tensors='''pt'''			).input_features
   self.assertEqual(input_features.shape					,(1, 8_0, 3_0_0_0)			)
   self.assertTrue(torch.allclose(input_features[0, 0, :3_0]					,lowercase__					,atol=1e-4			)			)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()			)
   __lowercase							  = self._load_datasamples(1			)[0]
   __lowercase							  = ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5_5_3_5  # Rescale to [0, 65535] to show issue
   __lowercase							  = feat_extract.zero_mean_unit_var_norm([audio]					,attention_mask=lowercase__			)[0]

   self.assertTrue(np.all(np.mean(lowercase__			) < 1e-3			)			)
   self.assertTrue(np.all(np.abs(np.var(lowercase__			) - 1			) < 1e-3			)			)

