"""simple docstring"""
import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


lowercase__					       =	logging.get_logger(__name__)







def 						__a   (	_SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None   )	->Union[str, Any]:
  return field(default_factory=lambda: default		,     metadata=_SCREAMING_SNAKE_CASE   )




@dataclass
class    __snake_case       :
      a__        =		list_field(
          default=[]       ,      metadata={
              """help""": (
                  """Model checkpoints to be provided to the AutoModel classes. Leave blank to benchmark the base version"""
                  """ of all available models"""
              )
          }       ,      )

      a__        =		list_field(
          default=[8]       ,      metadata={"""help""": """List of batch sizes for which memory and time performance will be evaluated"""}				)

      a__        =		list_field(
          default=[8, 32, 128, 512]       ,      metadata={"""help""": """List of sequence lengths for which memory and time performance will be evaluated"""}       ,      )

      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """Whether to benchmark inference of model. Inference can be disabled via --no-inference."""}       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """Whether to run on available cuda devices. Cuda can be disabled via --no-cuda."""}       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """Whether to run on available tpu devices. TPU can be disabled via --no-tpu."""}				)
      a__        =		field(default=__lowerCAmelCase       ,      metadata={"""help""": """Use FP16 to accelerate inference."""}				)
      a__        =		field(default=__lowerCAmelCase       ,      metadata={"""help""": """Benchmark training of model"""}				)
      a__        =		field(default=__lowerCAmelCase       ,      metadata={"""help""": """Verbose memory tracing"""}				)
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={"""help""": """Whether to perform speed measurements. Speed measurements can be disabled via --no-speed."""}       ,      )
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={
              """help""": """Whether to perform memory measurements. Memory measurements can be disabled via --no-memory"""
          }       ,      )
      a__        =		field(default=__lowerCAmelCase       ,      metadata={"""help""": """Trace memory line by line"""}				)
      a__        =		field(default=__lowerCAmelCase       ,      metadata={"""help""": """Save result to a CSV file"""}				)
      a__        =		field(default=__lowerCAmelCase       ,      metadata={"""help""": """Save all print statements in a log file"""}				)
      a__        =		field(default=__lowerCAmelCase       ,      metadata={"""help""": """Whether to print environment information"""}				)
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={
              """help""": (
                  """Whether to use multiprocessing for memory and speed measurement. It is highly recommended to use"""
                  """ multiprocessing for accurate CPU and GPU memory measurements. This option should only be disabled"""
                  """ for debugging / testing and on TPU."""
              )
          }       ,      )
      a__        =		field(
          default=F"""inference_time_{round(time()				)}.csv"""       ,      metadata={"""help""": """CSV filename used if saving time results to csv."""}       ,      )
      a__        =		field(
          default=F"""inference_memory_{round(time()				)}.csv"""       ,      metadata={"""help""": """CSV filename used if saving memory results to csv."""}       ,      )
      a__        =		field(
          default=F"""train_time_{round(time()				)}.csv"""       ,      metadata={"""help""": """CSV filename used if saving time results to csv for training."""}       ,      )
      a__        =		field(
          default=F"""train_memory_{round(time()				)}.csv"""       ,      metadata={"""help""": """CSV filename used if saving memory results to csv for training."""}       ,      )
      a__        =		field(
          default=F"""env_info_{round(time()				)}.csv"""       ,      metadata={"""help""": """CSV filename used if saving environment information."""}       ,      )
      a__        =		field(
          default=F"""log_{round(time()				)}.csv"""       ,      metadata={"""help""": """Log filename used if print statements are saved in log."""}       ,      )
      a__        =		field(default=3       ,      metadata={"""help""": """Times an experiment will be run."""}				)
      a__        =		field(
          default=__lowerCAmelCase       ,      metadata={
              """help""": (
                  """Instead of loading the model as defined in `config.architectures` if exists, just load the pretrain"""
                  """ model weights."""
              )
          }       ,      )



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        warnings.warn(
            f'The class {self.__class__} is deprecated. Hugging Face Benchmarking utils'
            ' are deprecated in general and it is advised to use external Benchmarking libraries '
            ' to benchmark Transformer models.'    ,			lowercase    ,			)



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        return json.dumps(dataclasses.asdict(self)    ,			indent=2)



      @property
      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        if len(self.models) <= 0:
          raise ValueError(
              'Please make sure you provide at least one model name / model identifier, *e.g.* `--models'
              ' bert-base-cased` or `args.models = [\'bert-base-cased\'].')
        return self.models



      @property
      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        if not self.multi_process:
          return False
        elif self.is_tpu:
          logger.info('Multiprocessing is currently not possible on TPU.')
          return False
        else:
          return True



"""simple docstring"""
import itertools
import random
import unittest

import numpy as np

from transformers import WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST, WavaVecaConfig, WavaVecaFeatureExtractor
from transformers.testing_utils import require_torch, slow

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


lowercase__					       =	random.Random()







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=1.0		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE=None   )	->Optional[int]:
  if rng is None:
    a__:     Any										=	global_rng

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

  return values




class    __snake_case       (     unittest.TestCase				):



      def __init__(						self    ,			lowercase    ,			lowercase=7    ,			lowercase=4_00    ,			lowercase=20_00    ,			lowercase=1    ,			lowercase=0.0    ,			lowercase=1_60_00    ,			lowercase=True    ,			lowercase=True    ,			)	->			Union[str, Any]:





        '''simple docstring'''





        a__:     Tuple										=	parent
        a__:     Optional[int]										=	batch_size
        a__:     Optional[Any]										=	min_seq_length
        a__:     Optional[int]										=	max_seq_length
        a__:     Tuple										=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
        a__:     Dict										=	feature_size
        a__:     Any										=	padding_value
        a__:     Optional[Any]										=	sampling_rate
        a__:     Optional[Any]										=	return_attention_mask
        a__:     str										=	do_normalize



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        return {
            "feature_size": self.feature_size,
            "padding_value": self.padding_value,
            "sampling_rate": self.sampling_rate,
            "return_attention_mask": self.return_attention_mask,
            "do_normalize": self.do_normalize,
        }



      def   lowerCamelCase_   (						self    ,			lowercase=False    ,			lowercase=False)	->			Tuple:





        '''simple docstring'''





        def _flatten(lowercase):
          return list(itertools.chain(*lowercase))

        if equal_length:
          a__:     Dict										=	floats_list((self.batch_size, self.max_seq_length))
        else:
          # make sure that inputs increase in size
          a__:     List[Any]										=	[
              _flatten(floats_list((x, self.feature_size)))
              for x in range(self.min_seq_length    ,			self.max_seq_length    ,			self.seq_length_diff)
          ]

        if numpify:
          a__:     str										=	[np.asarray(lowercase) for x in speech_inputs]

        return speech_inputs







class    __snake_case       (     __lowerCAmelCase       ,      unittest.TestCase				):
      a__        =		WavaVecaFeatureExtractor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[int]										=	WavaVecaFeatureExtractionTester(self)



      def   lowerCamelCase_   (						self    ,			lowercase)	->			List[Any]:





        '''simple docstring'''





        self.assertTrue(np.all(np.mean(lowercase    ,			axis=0) < 1e-3))
        self.assertTrue(np.all(np.abs(np.var(lowercase    ,			axis=0) - 1) < 1e-3))



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     List[Any]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        # create three inputs of length 800, 1000, and 1200
        a__:     Optional[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     List[str]										=	[np.asarray(lowercase) for speech_input in speech_inputs]

        # Test not batched input
        a__:     Optional[Any]										=	feat_extract(speech_inputs[0]    ,			return_tensors='np').input_values
        a__:     Dict										=	feat_extract(np_speech_inputs[0]    ,			return_tensors='np').input_values
        self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))

        # Test batched
        a__:     Dict										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     int										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        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.
        a__:     int										=	[floats_list((1, x))[0] for x in (8_00, 8_00, 8_00)]
        a__:     Union[str, Any]										=	np.asarray(lowercase)
        a__:     int										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        a__:     Any										=	feat_extract(lowercase    ,			return_tensors='np').input_values
        for enc_seq_a, enc_seq_a in zip(lowercase    ,			lowercase):
          self.assertTrue(np.allclose(lowercase    ,			lowercase    ,			atol=1e-3))



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     List[Any]										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]

        a__:     Optional[int]										=	['longest', 'max_length', 'do_not_pad']
        a__:     List[Any]										=	[None, 16_00, None]
        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     Dict										=	feat_extract(lowercase    ,			padding=lowercase    ,			max_length=lowercase    ,			return_tensors='np')
          a__:     Union[str, Any]										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self.assertTrue(input_values[0][8_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self.assertTrue(input_values[0][10_00:].sum() < 1e-6)
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



      def   lowerCamelCase_   (						self)	->			Optional[int]:





        '''simple docstring'''





        a__:     str										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Optional[int]										=	range(8_00    ,			14_00    ,			2_00)
        a__:     List[str]										=	[floats_list((1, x))[0] for x in lengths]

        a__:     Tuple										=	['longest', 'max_length', 'do_not_pad']
        a__:     Dict										=	[None, 16_00, None]

        for max_length, padding in zip(lowercase    ,			lowercase):
          a__:     int										=	feat_extract(lowercase    ,			max_length=lowercase    ,			padding=lowercase)
          a__:     Any										=	processed.input_values

          self._check_zero_mean_unit_variance(input_values[0][:8_00])
          self._check_zero_mean_unit_variance(input_values[1][:10_00])
          self._check_zero_mean_unit_variance(input_values[2][:12_00])



      def   lowerCamelCase_   (						self)	->			List[str]:





        '''simple docstring'''





        a__:     Any										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Any										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Dict										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='max_length'    ,			return_tensors='np')
        a__:     int										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1])
        self._check_zero_mean_unit_variance(input_values[2])



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     Tuple										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     int										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     str										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=10_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     Any										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length < longest -> then pad to max_length
        self.assertTrue(input_values.shape == (3, 10_00))

        a__:     Dict										=	[floats_list((1, x))[0] for x in range(8_00    ,			14_00    ,			2_00)]
        a__:     Tuple										=	feat_extract(
            lowercase    ,			truncation=lowercase    ,			max_length=20_00    ,			padding='longest'    ,			return_tensors='np')
        a__:     str										=	processed.input_values

        self._check_zero_mean_unit_variance(input_values[0, :8_00])
        self._check_zero_mean_unit_variance(input_values[1, :10_00])
        self._check_zero_mean_unit_variance(input_values[2])

        # make sure that if max_length > longest -> then pad to longest
        self.assertTrue(input_values.shape == (3, 12_00))



      @require_torch
      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        import torch

        a__:     Optional[int]										=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict())
        a__:     Tuple										=	np.random.rand(1_00).astype(np.floataa)
        a__:     Tuple										=	np_speech_inputs.tolist()

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



      @slow
      @require_torch
      def   lowerCamelCase_   (						self)	->			Optional[Any]:





        '''simple docstring'''





        for model_id in WAV_2_VEC_2_PRETRAINED_MODEL_ARCHIVE_LIST:
          a__:     str										=	WavaVecaConfig.from_pretrained(lowercase)
          a__:     str										=	WavaVecaFeatureExtractor.from_pretrained(lowercase)

          # only "layer" feature extraction norm should make use of
          # attention_mask
          self.assertEqual(feat_extract.return_attention_mask    ,			config.feat_extract_norm == 'layer')


