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


UpperCAmelCase_								=							random.Random()

def 							UpperCamelCase				(    lowerCAmelCase_       ,					lowerCAmelCase_=1.0       ,					lowerCAmelCase_=None       ,					lowerCAmelCase_=None   )		->						Any:




       '''simple docstring'''






       if rng is None:
              _A=	global_rng

       _A=	[]
       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      lowerCAmelCase						(					unittest.TestCase			):



     def __init__(					self				,      lowerCAmelCase__				,      lowerCAmelCase__=7				,      lowerCAmelCase__=400				,      lowerCAmelCase__=2000				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0.0				,      lowerCAmelCase__=16000				,      lowerCAmelCase__=True				,      lowerCAmelCase__=80				,      lowerCAmelCase__=16				,      lowerCAmelCase__=64				,      lowerCAmelCase__="hann_window"				,      lowerCAmelCase__=80				,      lowerCAmelCase__=7600				,      lowerCAmelCase__=1E-10				,      lowerCAmelCase__=True				,      ):
            _A=	parent
            _A=	batch_size
            _A=	min_seq_length
            _A=	max_seq_length
            _A=	(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
            _A=	feature_size
            _A=	padding_value
            _A=	sampling_rate
            _A=	do_normalize
            _A=	num_mel_bins
            _A=	hop_length
            _A=	win_length
            _A=	win_function
            _A=	fmin
            _A=	fmax
            _A=	mel_floor
            _A=	return_attention_mask



     def 			a__  (					self       ):
            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 			a__  (					self				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False       ):
            def _flatten(lowerCAmelCase__       ):
                   return list(itertools.chain(*lowerCAmelCase__       )       )

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

            return speech_inputs





     def 			a__  (					self				,      lowerCAmelCase__=False				,      lowerCAmelCase__=False       ):
            if equal_length:
                   _A=	[floats_list((self.max_seq_length, self.num_mel_bins)       ) for _ in range(self.batch_size       )]
            else:
                   # make sure that inputs increase in size
                   _A=	[
                       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:
                   _A=	[np.asarray(lowerCAmelCase__       ) for x in speech_inputs]

            return speech_inputs





@require_torch
class      lowerCAmelCase						(					_a			,		unittest.TestCase			):
     _SCREAMING_SNAKE_CASE     :     Optional[Any]		=SpeechTaFeatureExtractor



     def 			a__  (					self       ):
            _A=	SpeechTaFeatureExtractionTester(self       )



     def 			a__  (					self				,      lowerCAmelCase__       ):
            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 			a__  (					self       ):
            # Tests that all call wrap to encode_plus and batch_encode_plus
            _A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
            # create three inputs of length 800, 1000, and 1200
            _A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
            _A=	[np.asarray(lowerCAmelCase__       ) for speech_input in speech_inputs]

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

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



     def 			a__  (					self       ):
            _A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
            _A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]

            _A=	['longest', 'max_length', 'do_not_pad']
            _A=	[None, 1600, None]
            for max_length, padding in zip(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	feat_extract(lowerCAmelCase__				,      padding=lowerCAmelCase__				,      max_length=lowerCAmelCase__				,      return_tensors='np'       )
                   _A=	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][:1000]       )
                   self.assertTrue(input_values[0][1000:].sum() < 1E-6       )
                   self._check_zero_mean_unit_variance(input_values[2][:1200]       )



     def 			a__  (					self       ):
            _A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
            _A=	range(800				,      1400				,      200       )
            _A=	[floats_list((1, x)       )[0] for x in lengths]

            _A=	['longest', 'max_length', 'do_not_pad']
            _A=	[None, 1600, None]

            for max_length, padding in zip(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   _A=	feat_extract(lowerCAmelCase__				,      max_length=lowerCAmelCase__				,      padding=lowerCAmelCase__       )
                   _A=	processed.input_values

                   self._check_zero_mean_unit_variance(input_values[0][:800]       )
                   self._check_zero_mean_unit_variance(input_values[1][:1000]       )
                   self._check_zero_mean_unit_variance(input_values[2][:1200]       )



     def 			a__  (					self       ):
            _A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
            _A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
            _A=	feat_extract(
                lowerCAmelCase__				,      truncation=lowerCAmelCase__				,      max_length=1000				,      padding='max_length'				,      return_tensors='np'       )
            _A=	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 			a__  (					self       ):
            _A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
            _A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
            _A=	feat_extract(
                lowerCAmelCase__				,      truncation=lowerCAmelCase__				,      max_length=1000				,      padding='longest'				,      return_tensors='np'       )
            _A=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :800]       )
            self._check_zero_mean_unit_variance(input_values[1, :1000]       )
            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, 1000)       )

            _A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
            _A=	feat_extract(
                lowerCAmelCase__				,      truncation=lowerCAmelCase__				,      max_length=2000				,      padding='longest'				,      return_tensors='np'       )
            _A=	processed.input_values

            self._check_zero_mean_unit_variance(input_values[0, :800]       )
            self._check_zero_mean_unit_variance(input_values[1, :1000]       )
            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, 1200)       )



     def 			a__  (					self       ):
            _A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
            _A=	np.random.rand(100       ).astype(np.floataa       )
            _A=	np_speech_inputs.tolist()

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



     def 			a__  (					self       ):
            # Tests that all call wrap to encode_plus and batch_encode_plus
            _A=	self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()       )
            # create three inputs of length 800, 1000, and 1200
            _A=	[floats_list((1, x)       )[0] for x in range(800				,      1400				,      200       )]
            _A=	[np.asarray(lowerCAmelCase__       ) for speech_input in speech_inputs]

            # Test feature size
            _A=	feature_extractor(audio_target=lowerCAmelCase__				,      padding=lowerCAmelCase__				,      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
            _A=	feature_extractor(speech_inputs[0]				,      return_tensors='np'       ).input_values
            _A=	feature_extractor(np_speech_inputs[0]				,      return_tensors='np'       ).input_values
            self.assertTrue(np.allclose(lowerCAmelCase__				,      lowerCAmelCase__				,      atol=1E-3       )       )

            # Test batched
            _A=	feature_extractor(lowerCAmelCase__				,      return_tensors='np'       ).input_values
            _A=	feature_extractor(lowerCAmelCase__				,      return_tensors='np'       ).input_values
            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.
            _A=	[floats_list((1, x)       )[0] for x in (800, 800, 800)]
            _A=	np.asarray(lowerCAmelCase__       )
            _A=	feature_extractor(lowerCAmelCase__				,      return_tensors='np'       ).input_values
            _A=	feature_extractor(lowerCAmelCase__				,      return_tensors='np'       ).input_values
            for enc_seq_a, enc_seq_a in zip(lowerCAmelCase__				,      lowerCAmelCase__       ):
                   self.assertTrue(np.allclose(lowerCAmelCase__				,      lowerCAmelCase__				,      atol=1E-3       )       )



     def 			a__  (					self       ):
            _A=	self.feat_extract_tester.prepare_inputs_for_target()
            _A=	self.feature_extraction_class(**self.feat_extract_dict       )
            _A=	feat_extract.model_input_names[0]

            _A=	BatchFeature({input_name: speech_inputs}       )

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

            _A=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__       )
            _A=	BatchFeature({input_name: speech_inputs}				,      tensor_type='np'       )

            _A=	processed_features[input_name]

            if len(batch_features_input.shape       ) < 3:
                   _A=	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 			a__  (					self       ):
            _A=	self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase__       )
            _A=	self.feature_extraction_class(**self.feat_extract_dict       )
            _A=	feat_extract.model_input_names[0]

            _A=	BatchFeature({input_name: speech_inputs}				,      tensor_type='pt'       )

            _A=	processed_features[input_name]

            if len(batch_features_input.shape       ) < 3:
                   _A=	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 			a__  (					self       ):
            _A=	self.feature_extraction_class(**self.feat_extract_dict       )
            _A=	self.feat_extract_tester.prepare_inputs_for_target()
            _A=	feat_extract.model_input_names[0]

            _A=	BatchFeature({input_name: speech_inputs}       )

            _A=	feat_extract.num_mel_bins  # hack!

            _A=	feat_extract.pad(lowerCAmelCase__				,      padding='longest'				,      return_tensors='np'       )[input_name]
            _A=	feat_extract.pad(lowerCAmelCase__				,      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 			a__  (					self       ):
            _A=	self.feat_extract_dict
            _A=	True
            _A=	self.feature_extraction_class(**lowerCAmelCase__       )
            _A=	self.feat_extract_tester.prepare_inputs_for_target()
            _A=	[len(lowerCAmelCase__       ) for x in speech_inputs]
            _A=	feat_extract.model_input_names[0]

            _A=	BatchFeature({input_name: speech_inputs}       )

            _A=	feat_extract.num_mel_bins  # hack!

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



     def 			a__  (					self       ):
            _A=	self.feat_extract_dict
            _A=	True
            _A=	self.feature_extraction_class(**lowerCAmelCase__       )
            _A=	self.feat_extract_tester.prepare_inputs_for_target()
            _A=	[len(lowerCAmelCase__       ) for x in speech_inputs]
            _A=	feat_extract.model_input_names[0]

            _A=	BatchFeature({input_name: speech_inputs}       )
            _A=	min(lowerCAmelCase__       )

            _A=	feat_extract.num_mel_bins  # hack!

            _A=	feat_extract.pad(
                lowerCAmelCase__				,      padding='max_length'				,      max_length=lowerCAmelCase__				,      truncation=lowerCAmelCase__				,      return_tensors='np'       )
            self.assertIn('attention_mask'				,      lowerCAmelCase__       )
            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 			a__  (					self				,      lowerCAmelCase__       ):
            from datasets import load_dataset

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

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



     def 			a__  (					self       ):
            # fmt: off
            _A=	torch.tensor(
                [2.3804E-03, 2.0752E-03, 1.9836E-03, 2.1057E-03, 1.6174E-03,
                 3.0518E-04, 9.1553E-05, 3.3569E-04, 9.7656E-04, 1.8311E-03,
                 2.0142E-03, 2.1057E-03, 1.7395E-03, 4.5776E-04, -3.9673E-04,
                 4.5776E-04, 1.0071E-03, 9.1553E-05, 4.8828E-04, 1.1597E-03,
                 7.3242E-04, 9.4604E-04, 1.8005E-03, 1.8311E-03, 8.8501E-04,
                 4.2725E-04, 4.8828E-04, 7.3242E-04, 1.0986E-03, 2.1057E-03]       )
            # fmt: on

            _A=	self._load_datasamples(1       )
            _A=	SpeechTaFeatureExtractor()
            _A=	feature_extractor(lowerCAmelCase__				,      return_tensors='pt'       ).input_values
            self.assertEquals(input_values.shape				,      (1, 93680)       )
            self.assertTrue(torch.allclose(input_values[0, :30]				,      lowerCAmelCase__				,      atol=1E-6       )       )





     def 			a__  (					self       ):
            # fmt: off
            _A=	torch.tensor(
                [-2.6870, -3.0104, -3.1356, -3.5352, -3.0044, -3.0353, -3.4719, -3.6777,
                 -3.1520, -2.9435, -2.6553, -2.8795, -2.9944, -2.5921, -3.0279, -3.0386,
                 -3.0864, -3.1291, -3.2353, -2.7444, -2.6831, -2.7287, -3.1761, -3.1571,
                 -3.2726, -3.0582, -3.1007, -3.4533, -3.4695, -3.0998]       )
            # fmt: on

            _A=	self._load_datasamples(1       )
            _A=	SpeechTaFeatureExtractor()
            _A=	feature_extractor(audio_target=lowerCAmelCase__				,      return_tensors='pt'       ).input_values
            self.assertEquals(input_values.shape				,      (1, 366, 80)       )
            self.assertTrue(torch.allclose(input_values[0, 0, :30]				,      lowerCAmelCase__				,      atol=1E-4       )       )
from collections import OrderedDict
from typing import Mapping

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


UpperCAmelCase_								=							logging.get_logger(__name__)

UpperCAmelCase_								=							{
    '''YituTech/conv-bert-base''': '''https://huggingface.co/YituTech/conv-bert-base/resolve/main/config.json''',
    '''YituTech/conv-bert-medium-small''': (
        '''https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/config.json'''
    ),
    '''YituTech/conv-bert-small''': '''https://huggingface.co/YituTech/conv-bert-small/resolve/main/config.json''',
    # See all ConvBERT models at https://huggingface.co/models?filter=convbert
}





class      lowerCAmelCase						(					_a			):
     _SCREAMING_SNAKE_CASE     :     List[str]		="""convbert"""



     def __init__(					self				,      lowerCAmelCase__=30522				,      lowerCAmelCase__=768				,      lowerCAmelCase__=12				,      lowerCAmelCase__=12				,      lowerCAmelCase__=3072				,      lowerCAmelCase__="gelu"				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=0.1				,      lowerCAmelCase__=512				,      lowerCAmelCase__=2				,      lowerCAmelCase__=0.02				,      lowerCAmelCase__=1E-12				,      lowerCAmelCase__=1				,      lowerCAmelCase__=0				,      lowerCAmelCase__=2				,      lowerCAmelCase__=768				,      lowerCAmelCase__=2				,      lowerCAmelCase__=9				,      lowerCAmelCase__=1				,      lowerCAmelCase__=None				,      **lowerCAmelCase__				,      ):
            super().__init__(
                pad_token_id=lowerCAmelCase__				,      bos_token_id=lowerCAmelCase__				,      eos_token_id=lowerCAmelCase__				,      **lowerCAmelCase__				,      )

            _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=	initializer_range
            _A=	layer_norm_eps
            _A=	embedding_size
            _A=	head_ratio
            _A=	conv_kernel_size
            _A=	num_groups
            _A=	classifier_dropout





class      lowerCAmelCase						(					_a			):



     @property
     def 			a__  (					self       ):
            if self.task == "multiple-choice":
                   _A=	{0: 'batch', 1: 'choice', 2: 'sequence'}
            else:
                   _A=	{0: 'batch', 1: 'sequence'}
            return OrderedDict(
                [
                    ('input_ids', dynamic_axis),
                    ('attention_mask', dynamic_axis),
                    ('token_type_ids', dynamic_axis),
                ]       )