





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
     from transformers import SpeechaTextFeatureExtractor

_snake_case  :    Optional[int]						    =  random.Random()

def   a_    ( lowerCAmelCase_      :			Optional[Any],	lowerCAmelCase_      :			str=1.0,	lowerCAmelCase_      :			Union[str, Any]=None,	lowerCAmelCase_      :			Union[str, Any]=None	):
   if rng is None:
      __lowerCAmelCase								=	global_rng

   __lowerCAmelCase								=	[]
   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 				_UpperCAmelCase    (    unittest.TestCase    ):

  """simple docstring"""


  def __init__(				self :		str					,  lowerCAmelCase_ :		Optional[int]					,  lowerCAmelCase_ :		Optional[Any]=7					,  lowerCAmelCase_ :		Optional[int]=4_0_0					,  lowerCAmelCase_ :		Any=2_0_0_0					,  lowerCAmelCase_ :		Dict=2_4					,  lowerCAmelCase_ :		Tuple=2_4					,  lowerCAmelCase_ :		Optional[Any]=0.0					,  lowerCAmelCase_ :		List[str]=1_6_0_0_0					,  lowerCAmelCase_ :		List[Any]=True					,  lowerCAmelCase_ :		Tuple=True					,  )							->						str:
     __lowerCAmelCase								=	parent
     __lowerCAmelCase								=	batch_size
     __lowerCAmelCase								=	min_seq_length
     __lowerCAmelCase								=	max_seq_length
     __lowerCAmelCase								=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
     __lowerCAmelCase								=	feature_size
     __lowerCAmelCase								=	num_mel_bins
     __lowerCAmelCase								=	padding_value
     __lowerCAmelCase								=	sampling_rate
     __lowerCAmelCase								=	return_attention_mask
     __lowerCAmelCase								=	do_normalize






  def 				lowercase		(				self :		str     )							->						Any:
     return {
         "feature_size": self.feature_size,
         "num_mel_bins": self.num_mel_bins,
         "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 :		List[Any]					,  lowerCAmelCase_ :		Dict=False					,  lowerCAmelCase_ :		List[Any]=False     )							->						List[Any]:
     def _flatten(lowerCAmelCase_ :		List[Any]     ):
        return list(itertools.chain(*lowerCAmelCase_     )     )

     if equal_length:
        __lowerCAmelCase								=	[floats_list((self.max_seq_length, self.feature_size)     ) for _ in range(self.batch_size     )]
     else:
        # make sure that inputs increase in size
        __lowerCAmelCase								=	[
            floats_list((x, self.feature_size)     )
            for x in range(self.min_seq_length					,  self.max_seq_length					,  self.seq_length_diff     )
        ]
     if numpify:
        __lowerCAmelCase								=	[np.asarray(lowerCAmelCase_     ) for x in speech_inputs]
     return speech_inputs





@require_torch
@require_torchaudio
class 				_UpperCAmelCase    (    _UpperCamelCase		,    unittest.TestCase    ):

  """simple docstring"""


  a_        = SpeechaTextFeatureExtractor if is_speech_available() else None






  def 				lowercase		(				self :		List[str]     )							->						List[Any]:
     __lowerCAmelCase								=	SpeechaTextFeatureExtractionTester(self     )






  def 				lowercase		(				self :		Union[str, Any]					,  lowerCAmelCase_ :		List[str]     )							->						Union[str, Any]:
     self.assertTrue(np.all(np.mean(lowerCAmelCase_					,  axis=0     ) < 1e-3     )     )
     self.assertTrue(np.all(np.abs(np.var(lowerCAmelCase_					,  axis=0     ) - 1     ) < 1e-3     )     )






  def 				lowercase		(				self :		Union[str, Any]     )							->						List[Any]:
     # Tests that all call wrap to encode_plus and batch_encode_plus
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     # create three inputs of length 800, 1000, and 1200
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	[np.asarray(lowerCAmelCase_     ) for speech_input in speech_inputs]

     # Test feature size
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  padding=lowerCAmelCase_					,  return_tensors='np'     ).input_features
     self.assertTrue(input_features.ndim == 3     )
     self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size     )

     # Test not batched input
     __lowerCAmelCase								=	feature_extractor(speech_inputs[0]					,  return_tensors='np'     ).input_features
     __lowerCAmelCase								=	feature_extractor(np_speech_inputs[0]					,  return_tensors='np'     ).input_features
     self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )

     # Test batched
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_features
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )

     # Test 2-D numpy arrays are batched.
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in (8_0_0, 8_0_0, 8_0_0)]
     __lowerCAmelCase								=	np.asarray(lowerCAmelCase_     )
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_features
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='np'     ).input_features
     for enc_seq_a, enc_seq_a in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        self.assertTrue(np.allclose(lowerCAmelCase_					,  lowerCAmelCase_					,  atol=1e-3     )     )






  def 				lowercase		(				self :		List[Any]     )							->						List[str]:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]

     __lowerCAmelCase								=	['longest', 'max_length', 'do_not_pad']
     __lowerCAmelCase								=	[None, 1_6, None]
     for max_length, padding in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	feature_extractor(
            lowerCAmelCase_					,  padding=lowerCAmelCase_					,  max_length=lowerCAmelCase_					,  return_attention_mask=lowerCAmelCase_     )
        __lowerCAmelCase								=	inputs.input_features
        __lowerCAmelCase								=	inputs.attention_mask
        __lowerCAmelCase								=	[np.sum(lowerCAmelCase_     ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )






  def 				lowercase		(				self :		int     )							->						Optional[Any]:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]

     __lowerCAmelCase								=	['longest', 'max_length', 'do_not_pad']
     __lowerCAmelCase								=	[None, 1_6, None]
     for max_length, padding in zip(lowerCAmelCase_					,  lowerCAmelCase_     ):
        __lowerCAmelCase								=	feature_extractor(
            lowerCAmelCase_					,  max_length=lowerCAmelCase_					,  padding=lowerCAmelCase_					,  return_tensors='np'					,  return_attention_mask=lowerCAmelCase_     )
        __lowerCAmelCase								=	inputs.input_features
        __lowerCAmelCase								=	inputs.attention_mask
        __lowerCAmelCase								=	[np.sum(lowerCAmelCase_     ) for x in attention_mask]

        self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]     )
        self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1e-6     )
        self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]     )
        self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1e-6     )
        self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]     )






  def 				lowercase		(				self :		Optional[int]     )							->						Any:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	feature_extractor(
         lowerCAmelCase_					,  padding='max_length'					,  max_length=4					,  truncation=lowerCAmelCase_					,  return_tensors='np'					,  return_attention_mask=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	inputs.input_features
     __lowerCAmelCase								=	inputs.attention_mask
     __lowerCAmelCase								=	np.sum(attention_mask == 1					,  axis=1     )

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
     self._check_zero_mean_unit_variance(input_features[1]     )
     self._check_zero_mean_unit_variance(input_features[2]     )






  def 				lowercase		(				self :		Optional[int]     )							->						Dict:
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	feature_extractor(
         lowerCAmelCase_					,  padding='longest'					,  max_length=4					,  truncation=lowerCAmelCase_					,  return_tensors='np'					,  return_attention_mask=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	inputs.input_features
     __lowerCAmelCase								=	inputs.attention_mask
     __lowerCAmelCase								=	np.sum(attention_mask == 1					,  axis=1     )

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
     self._check_zero_mean_unit_variance(input_features[2]     )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape					,  (3, 4, 2_4)     )

     __lowerCAmelCase								=	[floats_list((1, x)     )[0] for x in range(8_0_0					,  1_4_0_0					,  2_0_0     )]
     __lowerCAmelCase								=	feature_extractor(
         lowerCAmelCase_					,  padding='longest'					,  max_length=1_6					,  truncation=lowerCAmelCase_					,  return_tensors='np'					,  return_attention_mask=lowerCAmelCase_					,  )
     __lowerCAmelCase								=	inputs.input_features
     __lowerCAmelCase								=	inputs.attention_mask
     __lowerCAmelCase								=	np.sum(attention_mask == 1					,  axis=1     )

     self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]     )
     self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]     )
     self._check_zero_mean_unit_variance(input_features[2]     )

     # make sure that if max_length < longest -> then pad to max_length
     self.assertEqual(input_features.shape					,  (3, 6, 2_4)     )






  def 				lowercase		(				self :		List[Any]     )							->						int:
     import torch

     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	np.random.rand(1_0_0					,  3_2     ).astype(np.floataa     )
     __lowerCAmelCase								=	np_speech_inputs.tolist()

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






  def 				lowercase		(				self :		Optional[int]					,  lowerCAmelCase_ :		List[str]     )							->						str:
     from datasets import load_dataset

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

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






  def 				lowercase		(				self :		Any     )							->						Tuple:
     # fmt: off
     __lowerCAmelCase								=	np.array([
         -1.57_45, -1.77_13, -1.70_20, -1.60_69, -1.22_50, -1.11_05, -0.90_72, -0.82_41,
         -1.23_10, -0.80_98, -0.33_20, -0.41_01, -0.79_85, -0.49_96, -0.82_13, -0.91_28,
         -1.04_20, -1.12_86, -1.04_40, -0.79_99, -0.84_05, -1.22_75, -1.54_43, -1.46_25,
     ]     )
     # fmt: on

     __lowerCAmelCase								=	self._load_datasamples(1     )
     __lowerCAmelCase								=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()     )
     __lowerCAmelCase								=	feature_extractor(lowerCAmelCase_					,  return_tensors='pt'     ).input_features
     self.assertEquals(input_features.shape					,  (1, 5_8_4, 2_4)     )
     self.assertTrue(np.allclose(input_features[0, 0, :3_0]					,  lowerCAmelCase_					,  atol=1e-4     )     )








# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_snake_case  :    Dict						    =  {'configuration_mra': ['MRA_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MraConfig']}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _snake_case  :    Optional[int]						    =  [
         'MRA_PRETRAINED_MODEL_ARCHIVE_LIST',
         'MraForMaskedLM',
         'MraForMultipleChoice',
         'MraForQuestionAnswering',
         'MraForSequenceClassification',
         'MraForTokenClassification',
         'MraLayer',
         'MraModel',
         'MraPreTrainedModel',
     ]


if TYPE_CHECKING:
     from .configuration_mra import MRA_PRETRAINED_CONFIG_ARCHIVE_MAP, MraConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_mra import (
              MRA_PRETRAINED_MODEL_ARCHIVE_LIST,
              MraForMaskedLM,
              MraForMultipleChoice,
              MraForQuestionAnswering,
              MraForSequenceClassification,
              MraForTokenClassification,
              MraLayer,
              MraModel,
              MraPreTrainedModel,
          )
else:
     import sys

     _snake_case  :    Tuple						    =  _LazyModule(__name__, globals()['__file__'], _import_structure)

