

'''simple docstring'''






import copy
from typing import Any, Dict, List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import TensorType, logging


lowerCAmelCase__											=	logging.get_logger(__name__)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  SCREAMING_SNAKE_CASE    :  Any							=			['input_features']






  def __init__(      self   :     List[Any]					,lowercase__   :     Tuple=8_0					,lowercase__   :     List[Any]=1_6_0_0_0					,lowercase__   :     Optional[int]=1_6_0					,lowercase__   :     Dict=3_0					,lowercase__   :     Optional[Any]=4_0_0					,lowercase__   :     List[str]=0.0					,lowercase__   :     Optional[Any]=False					,**lowercase__   :     Optional[Any]					,):
   super().__init__(
       feature_size=lowercase__					,sampling_rate=lowercase__					,padding_value=lowercase__					,return_attention_mask=lowercase__					,**lowercase__					,)
   __lowercase							  = n_fft
   __lowercase							  = hop_length
   __lowercase							  = chunk_length
   __lowercase							  = chunk_length * sampling_rate
   __lowercase							  = self.n_samples // hop_length
   __lowercase							  = sampling_rate
   __lowercase							  = mel_filter_bank(
       num_frequency_bins=1 + n_fft // 2					,num_mel_filters=lowercase__					,min_frequency=0.0					,max_frequency=8_0_0_0.0					,sampling_rate=lowercase__					,norm='''slaney'''					,mel_scale='''slaney'''					,)






  def 					SCREAMING_SNAKE_CASE      (      self   :     Union[str, Any]					,lowercase__   :     np.array			):
   __lowercase							  = spectrogram(
       lowercase__					,window_function(self.n_fft					,'''hann'''			)					,frame_length=self.n_fft					,hop_length=self.hop_length					,power=2.0					,mel_filters=self.mel_filters					,log_mel='''log10'''					,)
   __lowercase							  = log_spec[:, :-1]
   __lowercase							  = np.maximum(lowercase__					,log_spec.max() - 8.0			)
   __lowercase							  = (log_spec + 4.0) / 4.0
   return log_spec






  @staticmethod
  # Copied from transformers.models.wav2vec2.feature_extraction_wav2vec2.Wav2Vec2FeatureExtractor.zero_mean_unit_var_norm
  def 					SCREAMING_SNAKE_CASE      (      lowercase__   :     List[np.ndarray]					,lowercase__   :     List[np.ndarray]					,lowercase__   :     float = 0.0			):
   if attention_mask is not None:
    __lowercase							  = np.array(lowercase__					,np.intaa			)
    __lowercase							  = []

    for vector, length in zip(lowercase__					,attention_mask.sum(-1			)			):
     __lowercase							  = (vector - vector[:length].mean()) / np.sqrt(vector[:length].var() + 1e-7			)
     if length < normed_slice.shape[0]:
      __lowercase							  = padding_value

     normed_input_values.append(lowercase__			)
   else:
    __lowercase							  = [(x - x.mean()) / np.sqrt(x.var() + 1e-7			) for x in input_values]

   return normed_input_values






  def __call__(      self   :     Optional[Any]					,lowercase__   :     Union[np.ndarray, List[float], List[np.ndarray], List[List[float]]]					,lowercase__   :     bool = True					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[Union[str, TensorType]] = None					,lowercase__   :     Optional[bool] = None					,lowercase__   :     Optional[str] = "max_length"					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[int] = None					,lowercase__   :     Optional[bool] = None					,**lowercase__   :     Optional[int]					,):

   if sampling_rate is not None:
    if sampling_rate != self.sampling_rate:
     raise ValueError(
         F"The model corresponding to this feature extractor: {self.__class__.__name__} was trained using a"
         F" sampling rate of {self.sampling_rate}. Please make sure that the provided `raw_speech` input"
         F" was sampled with {self.sampling_rate} and not {sampling_rate}."			)
   else:
    logger.warning(
        '''It is strongly recommended to pass the `sampling_rate` argument to this function. '''
        '''Failing to do so can result in silent errors that might be hard to debug.'''			)

   __lowercase							  = isinstance(lowercase__					,np.ndarray			) and len(raw_speech.shape			) > 1
   if is_batched_numpy and len(raw_speech.shape			) > 2:
    raise ValueError(F"Only mono-channel audio is supported for input to {self}"			)
   __lowercase							  = is_batched_numpy or (
       isinstance(lowercase__					,(list, tuple)			) and (isinstance(raw_speech[0]					,(np.ndarray, tuple, list)			))
   )

   if is_batched:
    __lowercase							  = [np.asarray([speech]					,dtype=np.floataa			).T for speech in raw_speech]
   elif not is_batched and not isinstance(lowercase__					,np.ndarray			):
    __lowercase							  = np.asarray(lowercase__					,dtype=np.floataa			)
   elif isinstance(lowercase__					,np.ndarray			) and raw_speech.dtype is np.dtype(np.floataa			):
    __lowercase							  = raw_speech.astype(np.floataa			)

   # always return batch
   if not is_batched:
    __lowercase							  = [np.asarray([raw_speech]			).T]

   __lowercase							  = BatchFeature({'''input_features''': raw_speech}			)

   # convert into correct format for padding

   __lowercase							  = self.pad(
       lowercase__					,padding=lowercase__					,max_length=max_length if max_length else self.n_samples					,truncation=lowercase__					,pad_to_multiple_of=lowercase__					,return_attention_mask=return_attention_mask or do_normalize					,)

   # zero-mean and unit-variance normalization
   if do_normalize:
    __lowercase							  = self.zero_mean_unit_var_norm(
        padded_inputs['''input_features''']					,attention_mask=padded_inputs['''attention_mask''']					,padding_value=self.padding_value					,)
    __lowercase							  = np.stack(padded_inputs['''input_features''']					,axis=0			)

   # make sure list is in array format
   __lowercase							  = padded_inputs.get('''input_features'''			).transpose(2					,0					,1			)

   __lowercase							  = [self._np_extract_fbank_features(lowercase__			) for waveform in input_features[0]]

   if isinstance(input_features[0]					,lowercase__			):
    __lowercase							  = [np.asarray(lowercase__					,dtype=np.floataa			) for feature in input_features]
   else:
    __lowercase							  = input_features

   if return_attention_mask:
    # rescale from sample (48000) to feature (3000)
    __lowercase							  = padded_inputs['''attention_mask'''][:, :: self.hop_length]

   if return_tensors is not None:
    __lowercase							  = padded_inputs.convert_to_tensors(lowercase__			)

   return padded_inputs






  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = copy.deepcopy(self.__dict__			)
   __lowercase							  = self.__class__.__name__
   if "mel_filters" in output:
    del output["mel_filters"]
   return output




'''simple docstring'''






import argparse
import json
import logging
import os
import shutil
import sys
import tempfile
import unittest
from unittest import mock

import torch
from accelerate.utils import write_basic_config

from transformers.testing_utils import TestCasePlus, get_gpu_count, run_command, slow, torch_device
from transformers.utils import is_apex_available


logging.basicConfig(level=logging.DEBUG)

lowerCAmelCase__											=	logging.getLogger()


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = argparse.ArgumentParser()
 parser.add_argument('''-f'''      )
 __lowercase							  = parser.parse_args()
 return args.f


def 		_A     (    A__      ):






 """simple docstring"""



 __lowercase							  = {}
 __lowercase							  = os.path.join(A__     ,				'''all_results.json'''      )
 if os.path.exists(A__      ):
  with open(A__     ,				'''r'''      ) as f:
   __lowercase							  = json.load(A__      )
 else:
  raise ValueError(F"can't find {path}"      )
 return results


def 		_A     (    ):






 """simple docstring"""



 __lowercase							  = torch.cuda.is_available() and torch_device == '''cuda'''
 return is_using_cuda and is_apex_available()


lowerCAmelCase__											=	logging.StreamHandler(sys.stdout)
logger.addHandler(stream_handler)






class 			lowercase_	(lowerCamelCase__      ):




  """simple docstring"""




  @classmethod
  def 					SCREAMING_SNAKE_CASE      (      cls   :     List[str]			):
   # Write Accelerate config, will pick up on CPU, GPU, and multi-GPU
   __lowercase							  = tempfile.mkdtemp()
   __lowercase							  = os.path.join(cls.tmpdir					,'''default_config.yml'''			)
   write_basic_config(save_location=cls.configPath			)
   __lowercase							  = ['''accelerate''', '''launch''', '''--config_file''', cls.configPath]






  @classmethod
  def 					SCREAMING_SNAKE_CASE      (      cls   :     str			):
   shutil.rmtree(cls.tmpdir			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Any			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/text-classification/run_glue_no_trainer.py\n            --model_name_or_path distilbert-base-uncased\n            --output_dir {tmp_dir}\n            --train_file ./tests/fixtures/tests_samples/MRPC/train.csv\n            --validation_file ./tests/fixtures/tests_samples/MRPC/dev.csv\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --learning_rate=1e-4\n            --seed=42\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   if is_cuda_and_apex_available():
    testargs.append('''--fp16'''			)

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''glue_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/language-modeling/run_clm_no_trainer.py\n            --model_name_or_path distilgpt2\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --block_size 128\n            --per_device_train_batch_size 5\n            --per_device_eval_batch_size 5\n            --num_train_epochs 2\n            --output_dir {tmp_dir}\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   if torch.cuda.device_count() > 1:
    # Skipping because there are not enough batches to train the model + would need a drop_last to work.
    return

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertLess(result['''perplexity''']					,1_0_0			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''clm_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[int]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/language-modeling/run_mlm_no_trainer.py\n            --model_name_or_path distilroberta-base\n            --train_file ./tests/fixtures/sample_text.txt\n            --validation_file ./tests/fixtures/sample_text.txt\n            --output_dir {tmp_dir}\n            --num_train_epochs=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertLess(result['''perplexity''']					,4_2			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''mlm_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   # with so little data distributed training needs more epochs to get the score on par with 0/1 gpu
   __lowercase							  = 7 if get_gpu_count() > 1 else 2

   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/token-classification/run_ner_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/conll/sample.json\n            --validation_file tests/fixtures/tests_samples/conll/sample.json\n            --output_dir {tmp_dir}\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=2\n            --num_train_epochs={epochs}\n            --seed 7\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.7_5			)
   self.assertLess(result['''train_loss''']					,0.5			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''ner_no_trainer'''			)			)			)






  @unittest.skip(reason='''Fix me @muellerzr'''			)
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Optional[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/question-answering/run_qa_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --version_2_with_negative\n            --train_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --validation_file tests/fixtures/tests_samples/SQUAD/sample.json\n            --output_dir {tmp_dir}\n            --seed=42\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   # Because we use --version_2_with_negative the testing script uses SQuAD v2 metrics.
   self.assertGreaterEqual(result['''eval_f1''']					,2_8			)
   self.assertGreaterEqual(result['''eval_exact''']					,2_8			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''qa_no_trainer'''			)			)			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Dict			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/multiple-choice/run_swag_no_trainer.py\n            --model_name_or_path bert-base-uncased\n            --train_file tests/fixtures/tests_samples/swag/sample.json\n            --validation_file tests/fixtures/tests_samples/swag/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=20\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.8			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''swag_no_trainer'''			)			)			)






  @slow
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[str]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/summarization/run_summarization_no_trainer.py\n            --model_name_or_path t5-small\n            --train_file tests/fixtures/tests_samples/xsum/sample.json\n            --validation_file tests/fixtures/tests_samples/xsum/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_rouge1''']					,1_0			)
   self.assertGreaterEqual(result['''eval_rouge2''']					,2			)
   self.assertGreaterEqual(result['''eval_rougeL''']					,7			)
   self.assertGreaterEqual(result['''eval_rougeLsum''']					,7			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''summarization_no_trainer'''			)			)			)






  @slow
  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     List[Any]			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/translation/run_translation_no_trainer.py\n            --model_name_or_path sshleifer/student_marian_en_ro_6_1\n            --source_lang en\n            --target_lang ro\n            --train_file tests/fixtures/tests_samples/wmt16/sample.json\n            --validation_file tests/fixtures/tests_samples/wmt16/sample.json\n            --output_dir {tmp_dir}\n            --max_train_steps=50\n            --num_warmup_steps=8\n            --num_beams=6\n            --learning_rate=3e-3\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --source_lang en_XX\n            --target_lang ro_RO\n            --checkpointing_steps epoch\n            --with_tracking\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_bleu''']					,3_0			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''epoch_0'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''translation_no_trainer'''			)			)			)






  @slow
  def 					SCREAMING_SNAKE_CASE      (      self   :     str			):
   __lowercase							  = logging.StreamHandler(sys.stdout			)
   logger.addHandler(lowercase__			)

   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/semantic-segmentation/run_semantic_segmentation_no_trainer.py\n            --dataset_name huggingface/semantic-segmentation-test-sample\n            --output_dir {tmp_dir}\n            --max_train_steps=10\n            --num_warmup_steps=2\n            --learning_rate=2e-4\n            --per_device_train_batch_size=2\n            --per_device_eval_batch_size=1\n            --checkpointing_steps epoch\n        ".split()

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   self.assertGreaterEqual(result['''eval_overall_accuracy''']					,0.1_0			)






  @mock.patch.dict(os.environ					,{'''WANDB_MODE''': '''offline'''}			)
  def 					SCREAMING_SNAKE_CASE      (      self   :     Tuple			):
   __lowercase							  = self.get_auto_remove_tmp_dir()
   __lowercase							  = F"\n            {self.examples_dir}/pytorch/image-classification/run_image_classification_no_trainer.py\n            --model_name_or_path google/vit-base-patch16-224-in21k\n            --dataset_name hf-internal-testing/cats_vs_dogs_sample\n            --learning_rate 1e-4\n            --per_device_train_batch_size 2\n            --per_device_eval_batch_size 1\n            --max_train_steps 2\n            --train_val_split 0.1\n            --seed 42\n            --output_dir {tmp_dir}\n            --with_tracking\n            --checkpointing_steps 1\n        ".split()

   if is_cuda_and_apex_available():
    testargs.append('''--fp16'''			)

   run_command(self._launch_args + testargs			)
   __lowercase							  = get_results(lowercase__			)
   # The base model scores a 25%
   self.assertGreaterEqual(result['''eval_accuracy''']					,0.6			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''step_1'''			)			)			)
   self.assertTrue(os.path.exists(os.path.join(lowercase__					,'''image_classification_no_trainer'''			)			)			)

