




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


lowerCamelCase__					:  Dict      							=				random.Random()

def    UpperCAmelCase_				( __UpperCAmelCase		:      int     ,     __UpperCAmelCase		:      Tuple=1.0     ,     __UpperCAmelCase		:      Dict=None     ,     __UpperCAmelCase		:      Dict=None				)   ->					Tuple:
      if rng is None:
            SCREAMING_SNAKE_CASE_      		=     global_rng

      SCREAMING_SNAKE_CASE_      		=     []
      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   ):


 '''simple docstring'''







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



 def  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       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  lowerCAmelCase_    (				self    :						Dict							,							_lowerCAmelCase    :						Optional[int]=False							,							_lowerCAmelCase    :						str=False		):
       def _flatten(_lowerCAmelCase    :						Dict		):
             return list(itertools.chain(*_lowerCAmelCase		)		)

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

       return speech_inputs




 def  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Union[str, Any]=False							,							_lowerCAmelCase    :						Optional[int]=False		):
       if equal_length:
             SCREAMING_SNAKE_CASE_      		=     [floats_list((self.max_seq_length, self.num_mel_bins)		) for _ in range(self.batch_size		)]
       else:
             # make sure that inputs increase in size
             SCREAMING_SNAKE_CASE_      		=     [
                 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:
             SCREAMING_SNAKE_CASE_      		=     [np.asarray(_lowerCAmelCase		) for x in speech_inputs]

       return speech_inputs





@require_torch
class 			lowerCamelCase_				( _SCREAMING_SNAKE_CASE			,  unittest.TestCase   ):


 '''simple docstring'''







 lowercase_							       =       SpeechTaFeatureExtractor



 def  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     SpeechTaFeatureExtractionTester(self		)



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

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

       # Test batched
       SCREAMING_SNAKE_CASE_      		=     feat_extract(_lowerCAmelCase							,							return_tensors='np'		).input_values
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Union[str, Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
       SCREAMING_SNAKE_CASE_      		=     [floats_list((1, x)		)[0] for x in range(800							,							1_400							,							200		)]

       SCREAMING_SNAKE_CASE_      		=     ['longest', 'max_length', 'do_not_pad']
       SCREAMING_SNAKE_CASE_      		=     [None, 1_600, None]
       for max_length, padding in zip(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     feat_extract(_lowerCAmelCase							,							padding=_lowerCAmelCase							,							max_length=_lowerCAmelCase							,							return_tensors='np'		)
             SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Optional[Any]		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
       SCREAMING_SNAKE_CASE_      		=     range(800							,							1_400							,							200		)
       SCREAMING_SNAKE_CASE_      		=     [floats_list((1, x)		)[0] for x in lengths]

       SCREAMING_SNAKE_CASE_      		=     ['longest', 'max_length', 'do_not_pad']
       SCREAMING_SNAKE_CASE_      		=     [None, 1_600, None]

       for max_length, padding in zip(_lowerCAmelCase							,							_lowerCAmelCase		):
             SCREAMING_SNAKE_CASE_      		=     feat_extract(_lowerCAmelCase							,							max_length=_lowerCAmelCase							,							padding=_lowerCAmelCase		)
             SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
       SCREAMING_SNAKE_CASE_      		=     [floats_list((1, x)		)[0] for x in range(800							,							1_400							,							200		)]
       SCREAMING_SNAKE_CASE_      		=     feat_extract(
           _lowerCAmelCase							,							truncation=_lowerCAmelCase							,							max_length=1_000							,							padding='max_length'							,							return_tensors='np'		)
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
       SCREAMING_SNAKE_CASE_      		=     [floats_list((1, x)		)[0] for x in range(800							,							1_400							,							200		)]
       SCREAMING_SNAKE_CASE_      		=     feat_extract(
           _lowerCAmelCase							,							truncation=_lowerCAmelCase							,							max_length=1_000							,							padding='longest'							,							return_tensors='np'		)
       SCREAMING_SNAKE_CASE_      		=     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)		)

       SCREAMING_SNAKE_CASE_      		=     [floats_list((1, x)		)[0] for x in range(800							,							1_400							,							200		)]
       SCREAMING_SNAKE_CASE_      		=     feat_extract(
           _lowerCAmelCase							,							truncation=_lowerCAmelCase							,							max_length=2_000							,							padding='longest'							,							return_tensors='np'		)
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Optional[int]		):
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()		)
       SCREAMING_SNAKE_CASE_      		=     np.random.rand(100		).astype(np.floataa		)
       SCREAMING_SNAKE_CASE_      		=     np_speech_inputs.tolist()

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



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

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

       # Test batched
       SCREAMING_SNAKE_CASE_      		=     feature_extractor(_lowerCAmelCase							,							return_tensors='np'		).input_values
       SCREAMING_SNAKE_CASE_      		=     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.
       SCREAMING_SNAKE_CASE_      		=     [floats_list((1, x)		)[0] for x in (800, 800, 800)]
       SCREAMING_SNAKE_CASE_      		=     np.asarray(_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     feature_extractor(_lowerCAmelCase							,							return_tensors='np'		).input_values
       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						str		):
       SCREAMING_SNAKE_CASE_      		=     self.feat_extract_tester.prepare_inputs_for_target()
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_dict		)
       SCREAMING_SNAKE_CASE_      		=     feat_extract.model_input_names[0]

       SCREAMING_SNAKE_CASE_      		=     BatchFeature({input_name: speech_inputs}		)

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

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

       SCREAMING_SNAKE_CASE_      		=     processed_features[input_name]

       if len(batch_features_input.shape		) < 3:
             SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Dict		):
       SCREAMING_SNAKE_CASE_      		=     self.feat_extract_tester.prepare_inputs_for_target(equal_length=_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**self.feat_extract_dict		)
       SCREAMING_SNAKE_CASE_      		=     feat_extract.model_input_names[0]

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

       SCREAMING_SNAKE_CASE_      		=     processed_features[input_name]

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

       SCREAMING_SNAKE_CASE_      		=     BatchFeature({input_name: speech_inputs}		)

       SCREAMING_SNAKE_CASE_      		=     feat_extract.num_mel_bins  # hack!

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

       SCREAMING_SNAKE_CASE_      		=     BatchFeature({input_name: speech_inputs}		)

       SCREAMING_SNAKE_CASE_      		=     feat_extract.num_mel_bins  # hack!

       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						Any		):
       SCREAMING_SNAKE_CASE_      		=     self.feat_extract_dict
       SCREAMING_SNAKE_CASE_      		=     True
       SCREAMING_SNAKE_CASE_      		=     self.feature_extraction_class(**_lowerCAmelCase		)
       SCREAMING_SNAKE_CASE_      		=     self.feat_extract_tester.prepare_inputs_for_target()
       SCREAMING_SNAKE_CASE_      		=     [len(_lowerCAmelCase		) for x in speech_inputs]
       SCREAMING_SNAKE_CASE_      		=     feat_extract.model_input_names[0]

       SCREAMING_SNAKE_CASE_      		=     BatchFeature({input_name: speech_inputs}		)
       SCREAMING_SNAKE_CASE_      		=     min(_lowerCAmelCase		)

       SCREAMING_SNAKE_CASE_      		=     feat_extract.num_mel_bins  # hack!

       SCREAMING_SNAKE_CASE_      		=     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  lowerCAmelCase_    (				self    :						List[Any]							,							_lowerCAmelCase    :						Tuple		):
       from datasets import load_dataset

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

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



 def  lowerCAmelCase_    (				self    :						Any		):
       # fmt: off
       SCREAMING_SNAKE_CASE_      		=     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

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




 def  lowerCAmelCase_    (				self    :						Optional[int]		):
       # fmt: off
       SCREAMING_SNAKE_CASE_      		=     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

       SCREAMING_SNAKE_CASE_      		=     self._load_datasamples(1		)
       SCREAMING_SNAKE_CASE_      		=     SpeechTaFeatureExtractor()
       SCREAMING_SNAKE_CASE_      		=     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		)		)



'''simple docstring'''

import argparse
import json
import os
from collections import OrderedDict

import numpy as np
import tensorflow as tf
import torch

def _SCREAMING_SNAKE_CASE (A			)			->			Dict:






       """simple docstring"""







       lowercase__      =					os.path.join(args.tf_model_dir    ,					'''parameters.json'''			)
       lowercase__      =					json.loads(open(A			).read()			)
       if not params:
              raise ValueError(
                  f"It seems that the json file at {parameter_file} is empty. Make sure you have a correct json file."			)
       if not args.output.endswith('''.pt'''			):
              lowercase__      =					args.output + '''.pt'''
       lowercase__      =					OrderedDict()
       with tf.device('''/CPU:0'''			):
              lowercase__      =					tf.train.load_checkpoint(args.tf_model_dir			)
              lowercase__      =					reader.get_variable_to_shape_map()
              for key_name in shapes.keys():
                     lowercase__      =					reader.get_tensor(A			).astype(np.floataa			)
                     if key_name.endswith('''/adam_m'''			) or key_name.endswith('''/adam_v'''			):
                            continue
                     if key_name.startswith('''pasts/'''			):
                            if key_name.startswith('''pasts/mlp'''			):
                                   lowercase__      =					int(key_name[9]			)
                            elif key_name.startswith('''pasts/out'''			):
                                   lowercase__      =					8
                            lowercase__      =					'''model.sqout.%d.weight''' % (player * 2)  # enter to nn.Sequencial with Tanh, so 2 at a time
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/moe'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/switch_gating/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.router.classifier.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/softmlp/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.soft_bypass_mlp.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/wo/kernel'''			) or key_name.endswith('''/wi/kernel'''			):
                                   lowercase__      =					key_name[-9:-7]
                                   for i in range(16			):
                                          lowercase__      =					'''model.blocks.%d.feed_forward.mlp.experts.expert_%d.%s.weight''' % (player, i, nlayer)
                                          lowercase__      =					(
                                              vnp[i].transpose([1, 0]			).copy()
                                          )  # In Mesh-Tensorflow, it is one array, so it is divided
                                          lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/mlp'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/p1/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p1/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wi.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.weight''' % player
                                   lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/p2/bias'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.mlp.wo.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/ln'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.feed_forward.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/att'''			):
                            lowercase__      =					int(key_name[9:].split('''/'''			)[0]			)
                            if key_name.endswith('''/qkv/kernel'''			):
                                   lowercase__      =					vnp.copy()  # Compute same dimension as Mesh-tensorflow using einsum
                                   lowercase__      =					state[:, 0, :, :]
                                   lowercase__      =					state[:, 1, :, :]
                                   lowercase__      =					state[:, 2, :, :]
                                   lowercase__      =					(
                                       state_q.reshape([state_q.shape[0], state_q.shape[1] * state_q.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					(
                                       state_k.reshape([state_k.shape[0], state_k.shape[1] * state_k.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					(
                                       state_v.reshape([state_v.shape[0], state_v.shape[1] * state_v.shape[2]]			)
                                       .transpose([1, 0]			)
                                       .copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.q_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.k_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.v_proj.weight''' % player
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/o/kernel'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.self_attn.out_proj.weight''' % player
                                   lowercase__      =					(
                                       vnp.reshape([vnp.shape[0] * vnp.shape[1], vnp.shape[2]]			).transpose([1, 0]			).copy()
                                   )  # Mesh-Tensorflow is a diagonal matrix
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/an'''			):
                            lowercase__      =					int(key_name[8:].split('''/'''			)[0]			)
                            if key_name.endswith('''/b'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.bias''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                            elif key_name.endswith('''/g'''			):
                                   lowercase__      =					'''model.blocks.%d.self_attn.norm.weight''' % player
                                   lowercase__      =					vnp.copy()  # same because it is one dimensional
                                   lowercase__      =					torch.tensor(A			)
                     elif (
                         key_name.startswith('''model/wte'''			)
                         or key_name.startswith('''model/wpe'''			)
                         or key_name.startswith('''model/ete'''			)
                     ):
                            lowercase__      =					{'''wte''': '''embed_tokens''', '''wpe''': '''position_embeddings''', '''ete''': '''extra_position_embeddings'''}[
                                key_name[-3:]
                            ]
                            lowercase__      =					'''model.%s.weight''' % nlayer
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					torch.tensor(A			)
                            if key_name.startswith('''model/wte'''			):
                                   lowercase__      =					'''lm_head.weight'''
                                   lowercase__      =					vnp.copy()  # same in embedded
                                   lowercase__      =					torch.tensor(A			)
                     elif key_name.startswith('''model/wob'''			):
                            lowercase__      =					'''final_logits_bias'''
                            lowercase__      =					vnp.copy()  # same in embedded
                            lowercase__      =					state.reshape((1, -1)			)
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense/kernel":
                            lowercase__      =					'''model.last_project.weight'''
                            lowercase__      =					vnp.transpose([1, 0]			).copy()  # Mesh-Tensorflow is a diagonal matrix
                            lowercase__      =					torch.tensor(A			)
                     elif key_name == "model/dense_1/bias":
                            lowercase__      =					'''model.last_project.bias'''
                            lowercase__      =					vnp.copy()  # same because it is one dimensional
                            lowercase__      =					torch.tensor(A			)
       torch.save(A    ,					args.output			)


if __name__ == "__main__":
 lowerCamelCase				:  Optional[int]    		=      argparse.ArgumentParser(
     description='model converter.', formatter_class=argparse.ArgumentDefaultsHelpFormatter
 )
 parser.add_argument('--tf_model_dir', metavar='PATH', type=str, required=True, help='import model')
 parser.add_argument('--output', metavar='PATH', type=str, required=True, help='output model')
 lowerCamelCase				:  Dict    		=      parser.parse_args()
 convert_tf_gptsan_to_pt(args)




