



import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
				import jax.numpy as jnp

				from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
				    FlaxRobertaPreLayerNormForCausalLM,
				    FlaxRobertaPreLayerNormForMaskedLM,
				    FlaxRobertaPreLayerNormForMultipleChoice,
				    FlaxRobertaPreLayerNormForQuestionAnswering,
				    FlaxRobertaPreLayerNormForSequenceClassification,
				    FlaxRobertaPreLayerNormForTokenClassification,
				    FlaxRobertaPreLayerNormModel,
				)



class        lowerCAmelCase__					(  unittest.TestCase       ):


				def __init__(							self      :  List[Any]						, __UpperCamelCase      :  List[Any]						, __UpperCamelCase      :  str=13						, __UpperCamelCase      :  Optional[int]=7						, __UpperCamelCase      :  Union[str, Any]=True						, __UpperCamelCase      :  Optional[int]=True						, __UpperCamelCase      :  Any=True						, __UpperCamelCase      :  List[Any]=True						, __UpperCamelCase      :  List[str]=99						, __UpperCamelCase      :  Optional[int]=32						, __UpperCamelCase      :  List[Any]=5						, __UpperCamelCase      :  int=4						, __UpperCamelCase      :  Any=37						, __UpperCamelCase      :  Tuple="gelu"						, __UpperCamelCase      :  Union[str, Any]=0.1						, __UpperCamelCase      :  Optional[Any]=0.1						, __UpperCamelCase      :  int=512						, __UpperCamelCase      :  Union[str, Any]=16						, __UpperCamelCase      :  Union[str, Any]=2						, __UpperCamelCase      :  List[Any]=0.0_2						, __UpperCamelCase      :  int=4						, )		->     Union[str, Any]:
											A      						=    parent
											A      						=    batch_size
											A      						=    seq_length
											A      						=    is_training
											A      						=    use_attention_mask
											A      						=    use_token_type_ids
											A      						=    use_labels
											A      						=    vocab_size
											A      						=    hidden_size
											A      						=    num_hidden_layers
											A      						=    num_attention_heads
											A      						=    intermediate_size
											A      						=    hidden_act
											A      						=    hidden_dropout_prob
											A      						=    attention_probs_dropout_prob
											A      						=    max_position_embeddings
											A      						=    type_vocab_size
											A      						=    type_sequence_label_size
											A      						=    initializer_range
											A      						=    num_choices


				def __UpperCamelCase						(							self      :  List[str]       )		->     Optional[int]:
											A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.vocab_size       )

											A      						=    None
											if self.use_attention_mask:
																		A      						=    random_attention_mask([self.batch_size, self.seq_length]       )

											A      						=    None
											if self.use_token_type_ids:
																		A      						=    ids_tensor([self.batch_size, self.seq_length]						, self.type_vocab_size       )

											A      						=    RobertaPreLayerNormConfig(
											    vocab_size=self.vocab_size						, hidden_size=self.hidden_size						, num_hidden_layers=self.num_hidden_layers						, num_attention_heads=self.num_attention_heads						, intermediate_size=self.intermediate_size						, hidden_act=self.hidden_act						, hidden_dropout_prob=self.hidden_dropout_prob						, attention_probs_dropout_prob=self.attention_probs_dropout_prob						, max_position_embeddings=self.max_position_embeddings						, type_vocab_size=self.type_vocab_size						, is_decoder=__UpperCamelCase						, initializer_range=self.initializer_range						, )

											return config, input_ids, token_type_ids, attention_mask


				def __UpperCamelCase						(							self      :  Optional[Any]       )		->     Optional[Any]:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A						,   A      						=    config_and_inputs
											A      						=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
											return config, inputs_dict


				def __UpperCamelCase						(							self      :  Any       )		->     Tuple:
											A      						=    self.prepare_config_and_inputs()
											A						,   A						,   A						,   A      						=    config_and_inputs

											A      						=    True
											A      						=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size]       )
											A      						=    ids_tensor([self.batch_size, self.seq_length]						, vocab_size=2       )

											return (
											    config,
											    input_ids,
											    token_type_ids,
											    encoder_hidden_states,
											    encoder_attention_mask,
											)





@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        lowerCAmelCase__					(  _lowerCamelCase ,				unittest.TestCase       ):
				A_   :     Tuple								=     True

				A_   :     Union[str, Any]								=     (
				    (
				        FlaxRobertaPreLayerNormModel,
				        FlaxRobertaPreLayerNormForCausalLM,
				        FlaxRobertaPreLayerNormForMaskedLM,
				        FlaxRobertaPreLayerNormForSequenceClassification,
				        FlaxRobertaPreLayerNormForTokenClassification,
				        FlaxRobertaPreLayerNormForMultipleChoice,
				        FlaxRobertaPreLayerNormForQuestionAnswering,
				    )
				    if is_flax_available()
				    else ()
				)


				def __UpperCamelCase						(							self      :  List[str]       )		->     List[str]:
											A      						=    FlaxRobertaPreLayerNormModelTester(self       )


				@slow
				def __UpperCamelCase						(							self      :  Any       )		->     Optional[Any]:
											for model_class_name in self.all_model_classes:
																		A      						=    model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )
																		A      						=    model(np.ones((1, 1)       )       )
																		self.assertIsNotNone(__UpperCamelCase       )





@require_flax
class        lowerCAmelCase__					(  unittest.TestCase       ):


				@slow
				def __UpperCamelCase						(							self      :  Union[str, Any]       )		->     Tuple:
											A      						=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )

											A      						=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						, dtype=jnp.intaa       )
											A      						=    model(__UpperCamelCase       )[0]
											A      						=    [1, 11, 50_265]
											self.assertEqual(list(output.shape       )						, __UpperCamelCase       )
											# compare the actual values for a slice.
											A      						=    np.array(
											    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]						, dtype=np.floataa       )
											self.assertTrue(np.allclose(output[:, :3, :3]						, __UpperCamelCase						, atol=1e-4       )       )


				@slow
				def __UpperCamelCase						(							self      :  Dict       )		->     List[str]:
											A      						=    FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'						, from_pt=__UpperCamelCase       )

											A      						=    np.array([[0, 31_414, 232, 328, 740, 1_140, 12_695, 69, 46_078, 1_588, 2]]						, dtype=jnp.intaa       )
											A      						=    model(__UpperCamelCase       )[0]
											# compare the actual values for a slice.
											A      						=    np.array(
											    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]						, dtype=np.floataa       )
											self.assertTrue(np.allclose(output[:, :3, :3]						, __UpperCamelCase						, atol=1e-4       )       )



'''simple docstring'''






from typing import List, Optional, Union

import numpy as np
import torch
import torchaudio.compliance.kaldi as ta_kaldi

from ...feature_extraction_sequence_utils import SequenceFeatureExtractor
from ...feature_extraction_utils import BatchFeature
from ...utils import PaddingStrategy, TensorType, logging


lowercase				=logging.get_logger(__name__)






class    __magic_name__				(						lowerCAmelCase							):
       UpperCAmelCase		 =["input_features", "attention_mask"]






       def __init__(     self    ,				snake_case=8_0    ,				snake_case=1_6_0_0_0    ,				snake_case=8_0    ,				snake_case=0.0    ,				snake_case=True    ,				snake_case=True    ,				snake_case=True    ,				**snake_case    ,				)		->    str:




          '''simple docstring'''
          super().__init__(feature_size=snake_case    ,				sampling_rate=snake_case    ,				padding_value=snake_case    ,				**snake_case)
          _UpperCAmelCase  :  Optional[Any]				       =num_mel_bins
          _UpperCAmelCase  :  Optional[int]				       =do_ceptral_normalize
          _UpperCAmelCase  :  Optional[Any]				       =normalize_means
          _UpperCAmelCase  :  Optional[Any]				       =normalize_vars
          _UpperCAmelCase  :  Tuple				       =True






       def      lowerCAmelCase						(     self    ,				snake_case    ,				)		->    np.ndarray:




          '''simple docstring'''
          _UpperCAmelCase  :  List[Any]				       =waveform * (2**1_5)  # Kaldi compliance: 16-bit signed integers
          _UpperCAmelCase  :  Dict				       =torch.from_numpy(snake_case).unsqueeze(0)
          _UpperCAmelCase  :  Union[str, Any]				       =ta_kaldi.fbank(snake_case    ,				num_mel_bins=self.num_mel_bins    ,				sample_frequency=self.sampling_rate)
          return features.numpy()






       @staticmethod
       def      lowerCAmelCase						(     snake_case    ,				snake_case    ,				snake_case = True    ,				snake_case = True    ,				snake_case = 0.0    ,				)		->    np.ndarray:




          '''simple docstring'''
          # make sure we normalize float32 arrays
          if normalize_means:
             _UpperCAmelCase  :  int				       =x[:input_length].mean(axis=0)
             _UpperCAmelCase  :  List[Any]				       =np.subtract(snake_case    ,				snake_case)
          if normalize_vars:
             _UpperCAmelCase  :  List[Any]				       =x[:input_length].std(axis=0)
             _UpperCAmelCase  :  Optional[int]				       =np.divide(snake_case    ,				snake_case)

          if input_length < x.shape[0]:
             _UpperCAmelCase  :  Dict				       =padding_value

          # make sure array is in float32
          _UpperCAmelCase  :  str				       =x.astype(np.floataa)

          return x






       def      lowerCAmelCase						(     self    ,				snake_case    ,				snake_case = None)		->    List[np.ndarray]:




          '''simple docstring'''
          _UpperCAmelCase  :  str				       =attention_mask.sum(-1) if attention_mask is not None else [x.shape[0] for x in input_features]
          return [
              self.utterance_cmvn(snake_case    ,				snake_case    ,				self.normalize_means    ,				self.normalize_vars    ,				self.padding_value)
              for x, n in zip(snake_case    ,				snake_case)
          ]






       def __call__(     self    ,				snake_case    ,				snake_case = False    ,				snake_case = None    ,				snake_case = False    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				snake_case = None    ,				**snake_case    ,				)		->    BatchFeature:




          '''simple docstring'''

          if sampling_rate is not None:
             if sampling_rate != self.sampling_rate:
                raise ValueError(
                    f"The model corresponding to this feature extractor: {self} was trained using a sampling rate of"
                    f" {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with"
                    f" {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.')

          _UpperCAmelCase  :  Optional[int]				       =isinstance(snake_case    ,				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}")
          _UpperCAmelCase  :  List[Any]				       =is_batched_numpy or (
              isinstance(snake_case    ,				(list, tuple)) and (isinstance(raw_speech[0]    ,				(np.ndarray, tuple, list)))
          )

          if is_batched:
             _UpperCAmelCase  :  int				       =[np.asarray(snake_case    ,				dtype=np.floataa) for speech in raw_speech]
          elif not is_batched and not isinstance(snake_case    ,				np.ndarray):
             _UpperCAmelCase  :  Tuple				       =np.asarray(snake_case    ,				dtype=np.floataa)
          elif isinstance(snake_case    ,				np.ndarray) and raw_speech.dtype is np.dtype(np.floataa):
             _UpperCAmelCase  :  int				       =raw_speech.astype(np.floataa)

          # always return batch
          if not is_batched:
             _UpperCAmelCase  :  Dict				       =[raw_speech]

          # extract fbank features
          _UpperCAmelCase  :  Optional[Any]				       =[self._extract_fbank_features(snake_case) for waveform in raw_speech]

          # convert into correct format for padding
          _UpperCAmelCase  :  List[str]				       =BatchFeature({'input_features': features})

          _UpperCAmelCase  :  Any				       =self.pad(
              snake_case    ,				padding=snake_case    ,				max_length=snake_case    ,				truncation=snake_case    ,				pad_to_multiple_of=snake_case    ,				return_attention_mask=snake_case    ,				**snake_case    ,				)

          # make sure list is in array format
          _UpperCAmelCase  :  Dict				       =padded_inputs.get('input_features')
          if isinstance(input_features[0]    ,				snake_case):
             _UpperCAmelCase  :  Any				       =[np.asarray(snake_case    ,				dtype=np.floataa) for feature in input_features]

          _UpperCAmelCase  :  int				       =padded_inputs.get('attention_mask')
          if attention_mask is not None:
             _UpperCAmelCase  :  Tuple				       =[np.asarray(snake_case    ,				dtype=np.intaa) for array in attention_mask]

          # Utterance-level cepstral mean and variance normalization
          if self.do_ceptral_normalize:
             _UpperCAmelCase  :  Optional[Any]				       =(
                 np.array(snake_case    ,				dtype=np.intaa)
                 if self._get_padding_strategies(snake_case    ,				max_length=snake_case) is not PaddingStrategy.DO_NOT_PAD
                 else None
             )
             _UpperCAmelCase  :  Optional[int]				       =self.normalize(
                 padded_inputs['input_features']    ,				attention_mask=snake_case)

          if return_tensors is not None:
             _UpperCAmelCase  :  List[Any]				       =padded_inputs.convert_to_tensors(snake_case)

          return padded_inputs

