

'''simple docstring'''




from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowerCAmelCase_							=	logging.get_logger(__name__)

lowerCAmelCase_							=	{
    'andreasmadsen/efficient_mlm_m0.40': (
        'https://huggingface.co/andreasmadsen/efficient_mlm_m0.40/resolve/main/config.json'
    ),
}

class     UpperCAmelCase_ (	SCREAMING_SNAKE_CASE__    ):



	"""simple docstring"""





	__SCREAMING_SNAKE_CASE					=       '''roberta-prelayernorm'''

	def __init__(    self				,			lowerCamelCase=5_02_65				,			lowerCamelCase=7_68				,			lowerCamelCase=12				,			lowerCamelCase=12				,			lowerCamelCase=30_72				,			lowerCamelCase="gelu"				,			lowerCamelCase=0.1				,			lowerCamelCase=0.1				,			lowerCamelCase=5_12				,			lowerCamelCase=2				,			lowerCamelCase=0.02				,			lowerCamelCase=1e-12				,			lowerCamelCase=1				,			lowerCamelCase=0				,			lowerCamelCase=2				,			lowerCamelCase="absolute"				,			lowerCamelCase=True				,			lowerCamelCase=None				,			**lowerCamelCase				,			)							->     List[str]:




				'''simple docstring'''






				super().__init__(pad_token_id=A_				,			bos_token_id=A_				,			eos_token_id=A_				,			**A_   )

				UpperCamelCase				:	Tuple    						=					vocab_size
				UpperCamelCase				:	List[Any]    						=					hidden_size
				UpperCamelCase				:	Optional[int]    						=					num_hidden_layers
				UpperCamelCase				:	Optional[int]    						=					num_attention_heads
				UpperCamelCase				:	str    						=					hidden_act
				UpperCamelCase				:	str    						=					intermediate_size
				UpperCamelCase				:	Dict    						=					hidden_dropout_prob
				UpperCamelCase				:	List[str]    						=					attention_probs_dropout_prob
				UpperCamelCase				:	str    						=					max_position_embeddings
				UpperCamelCase				:	Optional[Any]    						=					type_vocab_size
				UpperCamelCase				:	Tuple    						=					initializer_range
				UpperCamelCase				:	Union[str, Any]    						=					layer_norm_eps
				UpperCamelCase				:	int    						=					position_embedding_type
				UpperCamelCase				:	List[str]    						=					use_cache
				UpperCamelCase				:	str    						=					classifier_dropout

class     UpperCAmelCase_ (	SCREAMING_SNAKE_CASE__    ):



	"""simple docstring"""





	@property
	def 	SCREAMING_SNAKE_CASE__							(    self   )							->     Mapping[str, Mapping[int, str]]:




				'''simple docstring'''






				if self.task == "multiple-choice":
							UpperCamelCase				:	Dict    						=					{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
				else:
							UpperCamelCase				:	Optional[Any]    						=					{0: '''batch''', 1: '''sequence'''}
				return OrderedDict(
				    [
				        ("input_ids", dynamic_axis),
				        ("attention_mask", dynamic_axis),
				    ]   )


"""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

A__ :       List[Any]				= random.Random()




def 			_lowerCAmelCase		(		_UpperCamelCase			,					_UpperCamelCase=1.0			,					_UpperCamelCase=None			,					_UpperCamelCase=None    ):






  """simple docstring"""



  if rng is None:
    _lowercase:      Any   			=  global_rng

  _lowercase:      List[str]   			=  []
  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       __magic_name__							(							unittest.TestCase			):
       def __init__(  self			,					A_			,					A_=7			,					A_=400			,					A_=2000			,					A_=10			,					A_=160			,					A_=8			,					A_=0.0			,					A_=4000			,					A_=False			,					A_=True			,					)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      str   			=  parent
         _lowercase:      int   			=  batch_size
         _lowercase:      Tuple   			=  min_seq_length
         _lowercase:      Any   			=  max_seq_length
         _lowercase:      List[str]   			=  (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
         _lowercase:      Any   			=  padding_value
         _lowercase:      List[str]   			=  sampling_rate
         _lowercase:      Union[str, Any]   			=  return_attention_mask
         _lowercase:      Optional[Any]   			=  do_normalize
         _lowercase:      List[str]   			=  feature_size
         _lowercase:      Optional[Any]   			=  chunk_length
         _lowercase:      str   			=  hop_length
       def 				lowercase_				(  self							)  ->						Optional[Any]:





         """simple docstring"""




         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 				lowercase_				(  self			,					A_=False			,					A_=False							)  ->						List[Any]:





         """simple docstring"""




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

         if equal_length:
           _lowercase:      List[str]   			=  [floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
         else:
           # make sure that inputs increase in size
           _lowercase:      Union[str, Any]   			=  [
               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:      str   			=  [np.asarray(A_							) for x in speech_inputs]
         return speech_inputs


@require_torch
@require_torchaudio
class       __magic_name__							(							SCREAMING_SNAKE_CASE__ ,       unittest.TestCase			):
       UpperCamelCase_ =		WhisperFeatureExtractor if is_speech_available() else None
       def 				lowercase_				(  self							)  ->						Dict:





         """simple docstring"""




         _lowercase:      int   			=  WhisperFeatureExtractionTester(self							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
           _lowercase:      Any   			=  feat_extract_first.save_pretrained(A_							)[0]
           check_json_file_has_correct_format(A_							)
           _lowercase:      str   			=  self.feature_extraction_class.from_pretrained(A_							)

         _lowercase:      Dict   			=  feat_extract_first.to_dict()
         _lowercase:      List[str]   			=  feat_extract_second.to_dict()
         _lowercase:      List[str]   			=  feat_extract_first.mel_filters
         _lowercase:      str   			=  feat_extract_second.mel_filters
         self.assertTrue(np.allclose(A_			,					A_							)							)
         self.assertEqual(A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      Dict   			=  self.feature_extraction_class(**self.feat_extract_dict							)

         with tempfile.TemporaryDirectory() as tmpdirname:
           _lowercase:      Union[str, Any]   			=  os.path.join(A_			,					'''feat_extract.json'''							)
           feat_extract_first.to_json_file(A_							)
           _lowercase:      Tuple   			=  self.feature_extraction_class.from_json_file(A_							)

         _lowercase:      int   			=  feat_extract_first.to_dict()
         _lowercase:      Optional[int]   			=  feat_extract_second.to_dict()
         _lowercase:      Tuple   			=  feat_extract_first.mel_filters
         _lowercase:      Optional[int]   			=  feat_extract_second.mel_filters
         self.assertTrue(np.allclose(A_			,					A_							)							)
         self.assertEqual(A_			,					A_							)
       def 				lowercase_				(  self							)  ->						Tuple:





         """simple docstring"""




         _lowercase:      Any   			=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         # create three inputs of length 800, 1000, and 1200
         _lowercase:      Tuple   			=  [floats_list((1, x)							)[0] for x in range(800			,					1400			,					200							)]
         _lowercase:      List[str]   			=  [np.asarray(A_							) for speech_input in speech_inputs]

         # Test feature size
         _lowercase:      Dict   			=  feature_extractor(A_			,					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:      Dict   			=  feature_extractor(speech_inputs[0]			,					return_tensors='''np'''							).input_features
         _lowercase:      Dict   			=  feature_extractor(np_speech_inputs[0]			,					return_tensors='''np'''							).input_features
         self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)

         # Test batched
         _lowercase:      Union[str, Any]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         _lowercase:      List[Any]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         for enc_seq_a, enc_seq_a in zip(A_			,					A_							):
           self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)

         # Test 2-D numpy arrays are batched.
         _lowercase:      Optional[Any]   			=  [floats_list((1, x)							)[0] for x in (800, 800, 800)]
         _lowercase:      Any   			=  np.asarray(A_							)
         _lowercase:      Tuple   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         _lowercase:      Dict   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         for enc_seq_a, enc_seq_a in zip(A_			,					A_							):
           self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)

         # Test truncation required
         _lowercase:      List[str]   			=  [floats_list((1, x)							)[0] for x in range(200			,					(feature_extractor.n_samples + 500)			,					200							)]
         _lowercase:      List[Any]   			=  [np.asarray(A_							) for speech_input in speech_inputs]

         _lowercase:      Any   			=  [x[: feature_extractor.n_samples] for x in speech_inputs]
         _lowercase:      str   			=  [np.asarray(A_							) for speech_input in speech_inputs_truncated]

         _lowercase:      Optional[Any]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         _lowercase:      List[str]   			=  feature_extractor(A_			,					return_tensors='''np'''							).input_features
         for enc_seq_a, enc_seq_a in zip(A_			,					A_							):
           self.assertTrue(np.allclose(A_			,					A_			,					atol=1E-3							)							)
       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         import torch

         _lowercase:      List[str]   			=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _lowercase:      List[Any]   			=  np.random.rand(100			,					32							).astype(np.floataa							)
         _lowercase:      Tuple   			=  np_speech_inputs.tolist()

         for inputs in [py_speech_inputs, np_speech_inputs]:
           _lowercase:      Optional[Any]   			=  feature_extractor.pad([{'''input_features''': inputs}]			,					return_tensors='''np'''							)
           self.assertTrue(np_processed.input_features.dtype == np.floataa							)
           _lowercase:      int   			=  feature_extractor.pad([{'''input_features''': inputs}]			,					return_tensors='''pt'''							)
           self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)
       def 				lowercase_				(  self			,					A_							)  ->						List[Any]:





         """simple docstring"""




         _lowercase:      Optional[Any]   			=  load_dataset('''hf-internal-testing/librispeech_asr_dummy'''			,					'''clean'''			,					split='''validation'''							)
         # automatic decoding with librispeech
         _lowercase:      List[str]   			=  ds.sort('''id'''							).select(range(A_							)							)[:num_samples]['''audio''']

         return [x["array"] for x in speech_samples]
       def 				lowercase_				(  self							)  ->						Optional[int]:





         """simple docstring"""




         _lowercase:      List[str]   			=  torch.tensor(
             [
                 0.11_93, -0.09_46, -0.10_98, -0.01_96, 0.02_25, -0.06_90, -0.17_36, 0.09_51,
                 0.09_71, -0.08_17, -0.07_02, 0.01_62, 0.02_60, 0.00_17, -0.01_92, -0.16_78,
                 0.07_09, -0.18_67, -0.06_55, -0.02_74, -0.02_34, -0.18_84, -0.05_16, -0.05_54,
                 -0.02_74, -0.14_25, -0.14_23, 0.08_37, 0.03_77, -0.08_54
             ]							)
         # fmt: on

         _lowercase:      Optional[Any]   			=  self._load_datasamples(1							)
         _lowercase:      Optional[int]   			=  WhisperFeatureExtractor()
         _lowercase:      Dict   			=  feature_extractor(A_			,					return_tensors='''pt'''							).input_features
         self.assertEqual(input_features.shape			,					(1, 80, 3000)							)
         self.assertTrue(torch.allclose(input_features[0, 0, :30]			,					A_			,					atol=1E-4							)							)



       def 				lowercase_				(  self							)  ->						Union[str, Any]:





         """simple docstring"""




         _lowercase:      Union[str, Any]   			=  self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
         _lowercase:      List[str]   			=  self._load_datasamples(1							)[0]
         _lowercase:      Union[str, Any]   			=  ((audio - audio.min()) / (audio.max() - audio.min())) * 6_5535  # Rescale to [0, 65535] to show issue
         _lowercase:      Any   			=  feat_extract.zero_mean_unit_var_norm([audio]			,					attention_mask=A_							)[0]

         self.assertTrue(np.all(np.mean(A_							) < 1E-3							)							)
         self.assertTrue(np.all(np.abs(np.var(A_							) - 1							) < 1E-3							)							)
