



import itertools
import random
import unittest

import numpy as np

from transformers import BatchFeature, SpeechTaFeatureExtractor
from transformers.testing_utils import require_torch
from transformers.utils.import_utils import is_torch_available

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_torch_available():
	import torch


__A       =						random.Random()



def 	__a					(   lowerCAmelCase_       : List[Any]      ,lowerCAmelCase_       : Tuple=1.0      ,lowerCAmelCase_       : Optional[int]=None      ,lowerCAmelCase_       : List[Any]=None    )				->			Union[str, Any]:






						'''simple docstring'''



						if rng is None:
												UpperCAmelCase_=       global_rng

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

						return values






@require_torch
class 		lowercase						(			unittest.TestCase):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						Dict						,  __UpperCAmelCase     :						int=7						,  __UpperCAmelCase     :						Union[str, Any]=400						,  __UpperCAmelCase     :						Optional[int]=2_000						,  __UpperCAmelCase     :						List[str]=1						,  __UpperCAmelCase     :						Union[str, Any]=0.0						,  __UpperCAmelCase     :						Optional[Any]=16_000						,  __UpperCAmelCase     :						List[Any]=True						,  __UpperCAmelCase     :						Dict=80						,  __UpperCAmelCase     :						Union[str, Any]=16						,  __UpperCAmelCase     :						Tuple=64						,  __UpperCAmelCase     :						Any="hann_window"						,  __UpperCAmelCase     :						int=80						,  __UpperCAmelCase     :						List[str]=7_600						,  __UpperCAmelCase     :						Optional[Any]=1E-10						,  __UpperCAmelCase     :						List[Any]=True						,  )      ->			Union[str, Any]:
							UpperCAmelCase_=       parent
							UpperCAmelCase_=       batch_size
							UpperCAmelCase_=       min_seq_length
							UpperCAmelCase_=       max_seq_length
							UpperCAmelCase_=       (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							UpperCAmelCase_=       feature_size
							UpperCAmelCase_=       padding_value
							UpperCAmelCase_=       sampling_rate
							UpperCAmelCase_=       do_normalize
							UpperCAmelCase_=       num_mel_bins
							UpperCAmelCase_=       hop_length
							UpperCAmelCase_=       win_length
							UpperCAmelCase_=       win_function
							UpperCAmelCase_=       fmin
							UpperCAmelCase_=       fmax
							UpperCAmelCase_=       mel_floor
							UpperCAmelCase_=       return_attention_mask




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			int:
							return {
							    "feature_size": self.feature_size,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "do_normalize": self.do_normalize,
							    "num_mel_bins": self.num_mel_bins,
							    "hop_length": self.hop_length,
							    "win_length": self.win_length,
							    "win_function": self.win_function,
							    "fmin": self.fmin,
							    "fmax": self.fmax,
							    "mel_floor": self.mel_floor,
							    "return_attention_mask": self.return_attention_mask,
							}




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						List[str]=False						,  __UpperCAmelCase     :						Dict=False					)      ->			str:
							def _flatten(__UpperCAmelCase     :						Optional[int]					):
													return list(itertools.chain(*__UpperCAmelCase					)					)

							if equal_length:
													UpperCAmelCase_=       floats_list((self.batch_size, self.max_seq_length)					)
							else:
													# make sure that inputs increase in size
													UpperCAmelCase_=       [
													    _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:
													UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for x in speech_inputs]

							return speech_inputs




	def   _SCREAMING_SNAKE_CASE		(     self     :						Any						,  __UpperCAmelCase     :						Union[str, Any]=False						,  __UpperCAmelCase     :						int=False					)      ->			Optional[Any]:
							if equal_length:
													UpperCAmelCase_=       [floats_list((self.max_seq_length, self.num_mel_bins)					) for _ in range(self.batch_size					)]
							else:
													# make sure that inputs increase in size
													UpperCAmelCase_=       [
													    floats_list((x, self.num_mel_bins)					)
													    for x in range(self.min_seq_length						,  self.max_seq_length						,  self.seq_length_diff					)
													]

							if numpify:
													UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for x in speech_inputs]

							return speech_inputs






@require_torch
class 		lowercase						(			snake_case__			,   unittest.TestCase):






	"""simple docstring"""


	a__				: int		       =							SpeechTaFeatureExtractor




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			Dict:
							UpperCAmelCase_=       SpeechTaFeatureExtractionTester(self					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			Optional[Any]:
							self.assertTrue(np.all(np.mean(__UpperCAmelCase						,  axis=0					) < 1E-3					)					)
							self.assertTrue(np.all(np.abs(np.var(__UpperCAmelCase						,  axis=0					) - 1					) < 1E-3					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[Any]					)      ->			int:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							# create three inputs of length 800, 1000, and 1200
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for speech_input in speech_inputs]

							# Test not batched input
							UpperCAmelCase_=       feat_extract(speech_inputs[0]						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feat_extract(np_speech_inputs[0]						,  return_tensors="""np"""					).input_values
							self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)

							# Test batched
							UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Tuple:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]

							UpperCAmelCase_=       ["""longest""", """max_length""", """do_not_pad"""]
							UpperCAmelCase_=       [None, 1_600, None]
							for max_length, padding in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  padding=__UpperCAmelCase						,  max_length=__UpperCAmelCase						,  return_tensors="""np"""					)
													UpperCAmelCase_=       processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]					)
													self.assertTrue(input_values[0][800:].sum() < 1E-6					)
													self._check_zero_mean_unit_variance(input_values[1][:1_000]					)
													self.assertTrue(input_values[0][1_000:].sum() < 1E-6					)
													self._check_zero_mean_unit_variance(input_values[2][:1_200]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			str:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       range(800						,  1_400						,  200					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in lengths]

							UpperCAmelCase_=       ["""longest""", """max_length""", """do_not_pad"""]
							UpperCAmelCase_=       [None, 1_600, None]

							for max_length, padding in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													UpperCAmelCase_=       feat_extract(__UpperCAmelCase						,  max_length=__UpperCAmelCase						,  padding=__UpperCAmelCase					)
													UpperCAmelCase_=       processed.input_values

													self._check_zero_mean_unit_variance(input_values[0][:800]					)
													self._check_zero_mean_unit_variance(input_values[1][:1_000]					)
													self._check_zero_mean_unit_variance(input_values[2][:1_200]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Dict:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       feat_extract(
							    __UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=1_000						,  padding="""max_length"""						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]					)
							self._check_zero_mean_unit_variance(input_values[1]					)
							self._check_zero_mean_unit_variance(input_values[2]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			Tuple:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       feat_extract(
							    __UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=1_000						,  padding="""longest"""						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]					)
							self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
							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, 1_000)					)

							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       feat_extract(
							    __UpperCAmelCase						,  truncation=__UpperCAmelCase						,  max_length=2_000						,  padding="""longest"""						,  return_tensors="""np"""					)
							UpperCAmelCase_=       processed.input_values

							self._check_zero_mean_unit_variance(input_values[0, :800]					)
							self._check_zero_mean_unit_variance(input_values[1, :1_000]					)
							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, 1_200)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int					)      ->			Any:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							UpperCAmelCase_=       np.random.rand(100					).astype(np.floataa					)
							UpperCAmelCase_=       np_speech_inputs.tolist()

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						str					)      ->			str:
							# Tests that all call wrap to encode_plus and batch_encode_plus
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()					)
							# create three inputs of length 800, 1000, and 1200
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in range(800						,  1_400						,  200					)]
							UpperCAmelCase_=       [np.asarray(__UpperCAmelCase					) for speech_input in speech_inputs]

							# Test feature size
							UpperCAmelCase_=       feature_extractor(audio_target=__UpperCAmelCase						,  padding=__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							self.assertTrue(input_values.ndim == 3					)
							self.assertTrue(input_values.shape[-1] == feature_extractor.num_mel_bins					)

							# Test not batched input
							UpperCAmelCase_=       feature_extractor(speech_inputs[0]						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feature_extractor(np_speech_inputs[0]						,  return_tensors="""np"""					).input_values
							self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)

							# Test batched
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)

							# Test 2-D numpy arrays are batched.
							UpperCAmelCase_=       [floats_list((1, x)					)[0] for x in (800, 800, 800)]
							UpperCAmelCase_=       np.asarray(__UpperCAmelCase					)
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""np"""					).input_values
							for enc_seq_a, enc_seq_a in zip(__UpperCAmelCase						,  __UpperCAmelCase					):
													self.assertTrue(np.allclose(__UpperCAmelCase						,  __UpperCAmelCase						,  atol=1E-3					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Tuple:
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							self.assertTrue(all(len(__UpperCAmelCase					) == len(__UpperCAmelCase					) for x, y in zip(__UpperCAmelCase						,  processed_features[input_name]					)					)					)

							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase					)
							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}						,  tensor_type="""np"""					)

							UpperCAmelCase_=       processed_features[input_name]

							if len(batch_features_input.shape					) < 3:
													UpperCAmelCase_=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)




	@require_torch
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target(equal_length=__UpperCAmelCase					)
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}						,  tensor_type="""pt"""					)

							UpperCAmelCase_=       processed_features[input_name]

							if len(batch_features_input.shape					) < 3:
													UpperCAmelCase_=       batch_features_input[:, :, None]

							self.assertTrue(
							    batch_features_input.shape
							    == (self.feat_extract_tester.batch_size, len(speech_inputs[0]					), self.feat_extract_tester.num_mel_bins)					)




	@require_torch
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			str:
							UpperCAmelCase_=       self.feature_extraction_class(**self.feat_extract_dict					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       feat_extract.num_mel_bins  # hack!

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)[input_name]
							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""pt"""					)[input_name]

							self.assertTrue(abs(input_np.astype(np.floataa					).sum() - input_pt.numpy().astype(np.floataa					).sum()					) < 1E-2					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			List[Any]:
							UpperCAmelCase_=       self.feat_extract_dict
							UpperCAmelCase_=       True
							UpperCAmelCase_=       self.feature_extraction_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       [len(__UpperCAmelCase					) for x in speech_inputs]
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)

							UpperCAmelCase_=       feat_extract.num_mel_bins  # hack!

							UpperCAmelCase_=       feat_extract.pad(__UpperCAmelCase						,  padding="""longest"""						,  return_tensors="""np"""					)
							self.assertIn("""attention_mask"""						,  __UpperCAmelCase					)
							self.assertListEqual(list(processed.attention_mask.shape					)						,  list(processed[input_name].shape[:2]					)					)
							self.assertListEqual(processed.attention_mask.sum(-1					).tolist()						,  __UpperCAmelCase					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			List[Any]:
							UpperCAmelCase_=       self.feat_extract_dict
							UpperCAmelCase_=       True
							UpperCAmelCase_=       self.feature_extraction_class(**__UpperCAmelCase					)
							UpperCAmelCase_=       self.feat_extract_tester.prepare_inputs_for_target()
							UpperCAmelCase_=       [len(__UpperCAmelCase					) for x in speech_inputs]
							UpperCAmelCase_=       feat_extract.model_input_names[0]

							UpperCAmelCase_=       BatchFeature({input_name: speech_inputs}					)
							UpperCAmelCase_=       min(__UpperCAmelCase					)

							UpperCAmelCase_=       feat_extract.num_mel_bins  # hack!

							UpperCAmelCase_=       feat_extract.pad(
							    __UpperCAmelCase						,  padding="""max_length"""						,  max_length=__UpperCAmelCase						,  truncation=__UpperCAmelCase						,  return_tensors="""np"""					)
							self.assertIn("""attention_mask"""						,  __UpperCAmelCase					)
							self.assertListEqual(
							    list(processed_pad.attention_mask.shape					)						,  [processed_pad[input_name].shape[0], max_length]					)
							self.assertListEqual(
							    processed_pad.attention_mask[:, :max_length].sum(-1					).tolist()						,  [max_length for x in speech_inputs]					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						int						,  __UpperCAmelCase     :						Union[str, Any]					)      ->			List[str]:
							from datasets import load_dataset

							UpperCAmelCase_=       load_dataset("""hf-internal-testing/librispeech_asr_dummy"""						,  """clean"""						,  split="""validation"""					)
							# automatic decoding with librispeech
							UpperCAmelCase_=       ds.sort("""id"""					).select(range(__UpperCAmelCase					)					)[:num_samples]["""audio"""]

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			Optional[Any]:
							# fmt: off
							UpperCAmelCase_=       torch.tensor(
							    [2.3_804E-03, 2.0_752E-03, 1.9_836E-03, 2.1_057E-03, 1.6_174E-03,
							     3.0_518E-04, 9.1_553E-05, 3.3_569E-04, 9.7_656E-04, 1.8_311E-03,
							     2.0_142E-03, 2.1_057E-03, 1.7_395E-03, 4.5_776E-04, -3.9_673E-04,
							     4.5_776E-04, 1.0_071E-03, 9.1_553E-05, 4.8_828E-04, 1.1_597E-03,
							     7.3_242E-04, 9.4_604E-04, 1.8_005E-03, 1.8_311E-03, 8.8_501E-04,
							     4.2_725E-04, 4.8_828E-04, 7.3_242E-04, 1.0_986E-03, 2.1_057E-03]					)
							# fmt: on

							UpperCAmelCase_=       self._load_datasamples(1					)
							UpperCAmelCase_=       SpeechTaFeatureExtractor()
							UpperCAmelCase_=       feature_extractor(__UpperCAmelCase						,  return_tensors="""pt"""					).input_values
							self.assertEquals(input_values.shape						,  (1, 93_680)					)
							self.assertTrue(torch.allclose(input_values[0, :30]						,  __UpperCAmelCase						,  atol=1E-6					)					)




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			str:
							# fmt: off
							UpperCAmelCase_=       torch.tensor(
							    [-2.6_870, -3.0_104, -3.1_356, -3.5_352, -3.0_044, -3.0_353, -3.4_719, -3.6_777,
							     -3.1_520, -2.9_435, -2.6_553, -2.8_795, -2.9_944, -2.5_921, -3.0_279, -3.0_386,
							     -3.0_864, -3.1_291, -3.2_353, -2.7_444, -2.6_831, -2.7_287, -3.1_761, -3.1_571,
							     -3.2_726, -3.0_582, -3.1_007, -3.4_533, -3.4_695, -3.0_998]					)
							# fmt: on

							UpperCAmelCase_=       self._load_datasamples(1					)
							UpperCAmelCase_=       SpeechTaFeatureExtractor()
							UpperCAmelCase_=       feature_extractor(audio_target=__UpperCAmelCase						,  return_tensors="""pt"""					).input_values
							self.assertEquals(input_values.shape						,  (1, 366, 80)					)
							self.assertTrue(torch.allclose(input_values[0, 0, :30]						,  __UpperCAmelCase						,  atol=1E-4					)					)









import dataclasses
import json
import warnings
from dataclasses import dataclass, field
from time import time
from typing import List

from ..utils import logging


__A       =						logging.get_logger(__name__)



def 	__a					(   lowerCAmelCase_       : Tuple=None      ,lowerCAmelCase_       : Optional[Any]=None    )				->			Tuple:






						'''simple docstring'''



						return field(default_factory=lambda: default      ,metadata=lowerCAmelCase_    )






@dataclass
class 		lowercase						:






	"""simple docstring"""


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

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

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




	def   _SCREAMING_SNAKE_CASE		(     self     :						Optional[int]					)      ->			str:
							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."""						,  __UpperCAmelCase						,  )




	def   _SCREAMING_SNAKE_CASE		(     self     :						Dict					)      ->			List[str]:
							return json.dumps(dataclasses.asdict(self					)						,  indent=2					)




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[Any]					)      ->			List[str]:
							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   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Tuple:
							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




