
'''simple docstring'''







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


lowercase    :							str      =					random.Random()







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A=1.0			,				__A=None			,				__A=None	)       ->	str:
	if rng is None:
		_snake_case   						=		global_rng

	_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 						__UpperCAmelCase      (					unittest.TestCase						):


							def __init__(       self    ,   lowerCAmelCase_    ,   lowerCAmelCase_=7    ,   lowerCAmelCase_=4_00    ,   lowerCAmelCase_=20_00    ,   lowerCAmelCase_=1    ,   lowerCAmelCase_=0.0    ,   lowerCAmelCase_=1_60_00    ,   lowerCAmelCase_=True    ,   lowerCAmelCase_=80    ,   lowerCAmelCase_=16    ,   lowerCAmelCase_=64    ,   lowerCAmelCase_="hann_window"    ,   lowerCAmelCase_=80    ,   lowerCAmelCase_=76_00    ,   lowerCAmelCase_=1E-10    ,   lowerCAmelCase_=True    ,   ):





								"""simple docstring"""






								_snake_case   						=		parent
								_snake_case   						=		batch_size
								_snake_case   						=		min_seq_length
								_snake_case   						=		max_seq_length
								_snake_case   						=		(self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
								_snake_case   						=		feature_size
								_snake_case   						=		padding_value
								_snake_case   						=		sampling_rate
								_snake_case   						=		do_normalize
								_snake_case   						=		num_mel_bins
								_snake_case   						=		hop_length
								_snake_case   						=		win_length
								_snake_case   						=		win_function
								_snake_case   						=		fmin
								_snake_case   						=		fmax
								_snake_case   						=		mel_floor
								_snake_case   						=		return_attention_mask


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								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    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






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

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

								return speech_inputs




							def  lowerCamelCase				(       self    ,   lowerCAmelCase_=False    ,   lowerCAmelCase_=False  ):





								"""simple docstring"""






								if equal_length:
									_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
									_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:
									_snake_case   						=		[np.asarray(lowerCAmelCase_  ) for x in speech_inputs]

								return speech_inputs


@require_torch
class 						__UpperCAmelCase      (					_lowerCamelCase						,   unittest.TestCase						):
							__lowercase      		=       SpeechTaFeatureExtractor


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		SpeechTaFeatureExtractionTester(self  )


							def  lowerCamelCase				(       self    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								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  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								# create three inputs of length 800, 1000, and 1200
								_snake_case   						=		[floats_list((1, x)  )[0] for x in range(8_00    ,   14_00    ,   2_00  )]
								_snake_case   						=		[np.asarray(lowerCAmelCase_  ) for speech_input in speech_inputs]

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

								# Test batched
								_snake_case   						=		feat_extract(lowerCAmelCase_    ,   return_tensors='np'  ).input_values
								_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  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								_snake_case   						=		[floats_list((1, x)  )[0] for x in range(8_00    ,   14_00    ,   2_00  )]

								_snake_case   						=		['longest', 'max_length', 'do_not_pad']
								_snake_case   						=		[None, 16_00, None]
								for max_length, padding in zip(lowerCAmelCase_    ,   lowerCAmelCase_  ):
									_snake_case   						=		feat_extract(lowerCAmelCase_    ,   padding=lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   return_tensors='np'  )
									_snake_case   						=		processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00]  )
									self.assertTrue(input_values[0][8_00:].sum() < 1E-6  )
									self._check_zero_mean_unit_variance(input_values[1][:10_00]  )
									self.assertTrue(input_values[0][10_00:].sum() < 1E-6  )
									self._check_zero_mean_unit_variance(input_values[2][:12_00]  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								_snake_case   						=		range(8_00    ,   14_00    ,   2_00  )
								_snake_case   						=		[floats_list((1, x)  )[0] for x in lengths]

								_snake_case   						=		['longest', 'max_length', 'do_not_pad']
								_snake_case   						=		[None, 16_00, None]

								for max_length, padding in zip(lowerCAmelCase_    ,   lowerCAmelCase_  ):
									_snake_case   						=		feat_extract(lowerCAmelCase_    ,   max_length=lowerCAmelCase_    ,   padding=lowerCAmelCase_  )
									_snake_case   						=		processed.input_values

									self._check_zero_mean_unit_variance(input_values[0][:8_00]  )
									self._check_zero_mean_unit_variance(input_values[1][:10_00]  )
									self._check_zero_mean_unit_variance(input_values[2][:12_00]  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								_snake_case   						=		[floats_list((1, x)  )[0] for x in range(8_00    ,   14_00    ,   2_00  )]
								_snake_case   						=		feat_extract(
								    lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   max_length=10_00    ,   padding='max_length'    ,   return_tensors='np'  )
								_snake_case   						=		processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_00]  )
								self._check_zero_mean_unit_variance(input_values[1]  )
								self._check_zero_mean_unit_variance(input_values[2]  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								_snake_case   						=		[floats_list((1, x)  )[0] for x in range(8_00    ,   14_00    ,   2_00  )]
								_snake_case   						=		feat_extract(
								    lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   max_length=10_00    ,   padding='longest'    ,   return_tensors='np'  )
								_snake_case   						=		processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_00]  )
								self._check_zero_mean_unit_variance(input_values[1, :10_00]  )
								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, 10_00)  )

								_snake_case   						=		[floats_list((1, x)  )[0] for x in range(8_00    ,   14_00    ,   2_00  )]
								_snake_case   						=		feat_extract(
								    lowerCAmelCase_    ,   truncation=lowerCAmelCase_    ,   max_length=20_00    ,   padding='longest'    ,   return_tensors='np'  )
								_snake_case   						=		processed.input_values

								self._check_zero_mean_unit_variance(input_values[0, :8_00]  )
								self._check_zero_mean_unit_variance(input_values[1, :10_00]  )
								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, 12_00)  )


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								_snake_case   						=		np.random.rand(1_00  ).astype(np.floataa  )
								_snake_case   						=		np_speech_inputs.tolist()

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()  )
								# create three inputs of length 800, 1000, and 1200
								_snake_case   						=		[floats_list((1, x)  )[0] for x in range(8_00    ,   14_00    ,   2_00  )]
								_snake_case   						=		[np.asarray(lowerCAmelCase_  ) for speech_input in speech_inputs]

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

								# Test batched
								_snake_case   						=		feature_extractor(lowerCAmelCase_    ,   return_tensors='np'  ).input_values
								_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.
								_snake_case   						=		[floats_list((1, x)  )[0] for x in (8_00, 8_00, 8_00)]
								_snake_case   						=		np.asarray(lowerCAmelCase_  )
								_snake_case   						=		feature_extractor(lowerCAmelCase_    ,   return_tensors='np'  ).input_values
								_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  ):





								"""simple docstring"""






								_snake_case   						=		self.feat_extract_tester.prepare_inputs_for_target()
								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_dict  )
								_snake_case   						=		feat_extract.model_input_names[0]

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

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

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

								_snake_case   						=		processed_features[input_name]

								if len(batch_features_input.shape  ) < 3:
									_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  ):





								"""simple docstring"""






								_snake_case   						=		self.feat_extract_tester.prepare_inputs_for_target(equal_length=lowerCAmelCase_  )
								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_dict  )
								_snake_case   						=		feat_extract.model_input_names[0]

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

								_snake_case   						=		processed_features[input_name]

								if len(batch_features_input.shape  ) < 3:
									_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  ):





								"""simple docstring"""






								_snake_case   						=		self.feature_extraction_class(**self.feat_extract_dict  )
								_snake_case   						=		self.feat_extract_tester.prepare_inputs_for_target()
								_snake_case   						=		feat_extract.model_input_names[0]

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

								_snake_case   						=		feat_extract.num_mel_bins  # hack!

								_snake_case   						=		feat_extract.pad(lowerCAmelCase_    ,   padding='longest'    ,   return_tensors='np'  )[input_name]
								_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  ):





								"""simple docstring"""






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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )

								_snake_case   						=		feat_extract.num_mel_bins  # hack!

								_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  ):





								"""simple docstring"""






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

								_snake_case   						=		BatchFeature({input_name: speech_inputs}  )
								_snake_case   						=		min(lowerCAmelCase_  )

								_snake_case   						=		feat_extract.num_mel_bins  # hack!

								_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    ,   lowerCAmelCase_  ):





								"""simple docstring"""






								from datasets import load_dataset

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

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


							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_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

								_snake_case   						=		self._load_datasamples(1  )
								_snake_case   						=		SpeechTaFeatureExtractor()
								_snake_case   						=		feature_extractor(lowerCAmelCase_    ,   return_tensors='pt'  ).input_values
								self.assertEquals(input_values.shape    ,   (1, 9_36_80)  )
								self.assertTrue(torch.allclose(input_values[0, :30]    ,   lowerCAmelCase_    ,   atol=1E-6  )  )




							def  lowerCamelCase				(       self  ):





								"""simple docstring"""






								_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

								_snake_case   						=		self._load_datasamples(1  )
								_snake_case   						=		SpeechTaFeatureExtractor()
								_snake_case   						=		feature_extractor(audio_target=lowerCAmelCase_    ,   return_tensors='pt'  ).input_values
								self.assertEquals(input_values.shape    ,   (1, 3_66, 80)  )
								self.assertTrue(torch.allclose(input_values[0, 0, :30]    ,   lowerCAmelCase_    ,   atol=1E-4  )  )



'''simple docstring'''







import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase    :							str      =					logging.get_logger(__name__)

lowercase    :							Union[str, Any]      =					{
    "post_extract_proj": "feature_projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.k_proj": "encoder.layers.*.attention.k_proj",
    "self_attn.v_proj": "encoder.layers.*.attention.v_proj",
    "self_attn.q_proj": "encoder.layers.*.attention.q_proj",
    "self_attn.out_proj": "encoder.layers.*.attention.out_proj",
    "self_attn_layer_norm": "encoder.layers.*.layer_norm",
    "fc1": "encoder.layers.*.feed_forward.intermediate_dense",
    "fc2": "encoder.layers.*.feed_forward.output_dense",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.upsample.0": "encoder.upsample.projection",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	Dict:
	for attribute in key.split('.'	):
		_snake_case   						=		getattr(__A			,				__A	)

	if weight_type is not None:
		_snake_case   						=		getattr(__A			,				__A	).shape
	else:
		_snake_case   						=		hf_pointer.shape

	assert hf_shape == value.shape, (
	    F'Shape of hf {key + "." + weight_type if weight_type is not None else ""} is {hf_shape}, but should be'
	    F' {value.shape} for {full_name}'
	)

	if weight_type == "weight":
		_snake_case   						=		value
	elif weight_type == "weight_g":
		_snake_case   						=		value
	elif weight_type == "weight_v":
		_snake_case   						=		value
	elif weight_type == "bias":
		_snake_case   						=		value
	else:
		_snake_case   						=		value

	logger.info(F'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A	)       ->	Any:
	_snake_case   						=		[]
	_snake_case   						=		fairseq_model.state_dict()

	_snake_case   						=		hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

	for name, value in fairseq_dict.items():
		_snake_case   						=		False
		if "conv_layers" in name:
			load_conv_layer(
			    __A			,				__A			,				__A			,				__A			,				hf_model.config.feat_extract_norm == 'group'			,				)
			_snake_case   						=		True
		else:
			for key, mapped_key in MAPPING.items():
				_snake_case   						=		'sew.' + mapped_key if (is_finetuned and mapped_key != 'lm_head') else mapped_key

				if key in name or key.split('w2v_model.'	)[-1] == name.split('.'	)[0]:
					_snake_case   						=		True
					if "*" in mapped_key:
						_snake_case   						=		name.split(__A	)[0].split('.'	)[-2]
						_snake_case   						=		mapped_key.replace('*'			,				__A	)
					if "weight_g" in name:
						_snake_case   						=		'weight_g'
					elif "weight_v" in name:
						_snake_case   						=		'weight_v'
					elif "weight" in name:
						_snake_case   						=		'weight'
					elif "bias" in name:
						_snake_case   						=		'bias'
					else:
						_snake_case   						=		None
					set_recursively(__A			,				__A			,				__A			,				__A			,				__A	)
				continue
		if not is_used:
			unused_weights.append(__A	)

	logger.warning(F'Unused weights: {unused_weights}'	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A			,				__A			,				__A	)       ->	int:
	_snake_case   						=		full_name.split('conv_layers.'	)[-1]
	_snake_case   						=		name.split('.'	)
	_snake_case   						=		int(items[0]	)
	_snake_case   						=		int(items[1]	)

	if type_id == 0:
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'
			)
			_snake_case   						=		value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'	)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].conv.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'
			)
			_snake_case   						=		value
			logger.info(F'Feat extract conv layer {layer_id} was initialized from {full_name}.'	)
	elif (type_id == 2 and not use_group_norm) or (type_id == 2 and layer_id == 0 and use_group_norm):
		if "bias" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape, (
			    F'{full_name} has size {value.shape}, but {feature_extractor[layer_id].layer_norm.bias.data.shape} was'
			    " found."
			)
			_snake_case   						=		value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
		elif "weight" in name:
			assert value.shape == feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape, (
			    F'{full_name} has size {value.shape}, but'
			    F' {feature_extractor[layer_id].layer_norm.weight.data.shape} was found.'
			)
			_snake_case   						=		value
			logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'	)
	else:
		unused_weights.append(__A	)







def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A	)       ->	str:
	_snake_case   						=		SEWConfig()
	if is_finetuned:
		_snake_case   						=		model.wav_encoder.wav_model.cfg
	else:
		_snake_case   						=		model.cfg

	_snake_case   						=		fs_config.conv_bias
	_snake_case   						=		eval(fs_config.conv_feature_layers	)
	_snake_case   						=		[x[0] for x in conv_layers]
	_snake_case   						=		[x[1] for x in conv_layers]
	_snake_case   						=		[x[2] for x in conv_layers]
	_snake_case   						=		'gelu'
	_snake_case   						=		'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
	_snake_case   						=		0.0
	_snake_case   						=		fs_config.activation_fn.name
	_snake_case   						=		fs_config.encoder_embed_dim
	_snake_case   						=		0.0_2
	_snake_case   						=		fs_config.encoder_ffn_embed_dim
	_snake_case   						=		1e-5
	_snake_case   						=		fs_config.encoder_layerdrop
	_snake_case   						=		fs_config.encoder_attention_heads
	_snake_case   						=		fs_config.conv_pos_groups
	_snake_case   						=		fs_config.conv_pos
	_snake_case   						=		len(__A	)
	_snake_case   						=		fs_config.encoder_layers
	_snake_case   						=		fs_config.squeeze_factor

	# take care of any params that are overridden by the Wav2VecCtc model
	if is_finetuned:
		_snake_case   						=		model.cfg
		_snake_case   						=		fs_config.final_dropout
		_snake_case   						=		fs_config.layerdrop
	_snake_case   						=		fs_config.activation_dropout
	_snake_case   						=		fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
	_snake_case   						=		fs_config.attention_dropout
	_snake_case   						=		fs_config.dropout_input
	_snake_case   						=		fs_config.dropout
	_snake_case   						=		fs_config.mask_channel_length
	_snake_case   						=		fs_config.mask_channel_prob
	_snake_case   						=		fs_config.mask_length
	_snake_case   						=		fs_config.mask_prob

	_snake_case   						=		'Wav2Vec2FeatureExtractor'
	_snake_case   						=		'Wav2Vec2CTCTokenizer'

	return config







@torch.no_grad()
def 				SCREAMING_SNAKE_CASE__	(   __A			,				__A			,				__A=None			,				__A=None			,				__A=True	)       ->	List[str]:

	if is_finetuned:
		_snake_case						, _snake_case						, _snake_case   						=		fairseq.checkpoint_utils.load_model_ensemble_and_task(
		    [checkpoint_path]			,				arg_overrides={'data': '/'.join(dict_path.split('/'	)[:-1]	)}	)
	else:
		_snake_case						, _snake_case						, _snake_case   						=		fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

	if config_path is not None:
		_snake_case   						=		SEWConfig.from_pretrained(__A	)
	else:
		_snake_case   						=		convert_config(model[0]			,				__A	)
	_snake_case   						=		model[0].eval()

	_snake_case   						=		True if config.feat_extract_norm == 'layer' else False
	_snake_case   						=		WavaVecaFeatureExtractor(
	    feature_size=1			,				sampling_rate=16_000			,				padding_value=0			,				do_normalize=__A			,				return_attention_mask=__A			,				)

	if is_finetuned:
		if dict_path:
			_snake_case   						=		Dictionary.load(__A	)

			# important change bos & pad token id since CTC symbol is <pad> and
			# not <s> as in fairseq
			_snake_case   						=		target_dict.pad_index
			_snake_case   						=		target_dict.bos_index
			_snake_case   						=		target_dict.pad_index
			_snake_case   						=		target_dict.bos_index
			_snake_case   						=		target_dict.eos_index
			_snake_case   						=		len(target_dict.symbols	)
			_snake_case   						=		os.path.join(__A			,				'vocab.json'	)
			if not os.path.isdir(__A	):
				logger.error('--pytorch_dump_folder_path ({}) should be a directory'.format(__A	)	)
				return
			os.makedirs(__A			,				exist_ok=__A	)
			with open(__A			,				'w'			,				encoding='utf-8'	) as vocab_handle:
				json.dump(target_dict.indices			,				__A	)
			_snake_case   						=		WavaVecaCTCTokenizer(
			    __A			,				unk_token=target_dict.unk_word			,				pad_token=target_dict.pad_word			,				bos_token=target_dict.bos_word			,				eos_token=target_dict.eos_word			,				word_delimiter_token='|'			,				do_lower_case=__A			,				)
			_snake_case   						=		WavaVecaProcessor(feature_extractor=__A			,				tokenizer=__A	)
			processor.save_pretrained(__A	)

		_snake_case   						=		SEWForCTC(__A	)
	else:
		_snake_case   						=		SEWModel(__A	)
		feature_extractor.save_pretrained(__A	)

	recursively_load_weights(__A			,				__A			,				__A	)

	hf_model.save_pretrained(__A	)


if __name__ == "__main__":
					lowercase    :							int      =					argparse.ArgumentParser()
					parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
					parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to fairseq checkpoint")
					parser.add_argument("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
					parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
					parser.add_argument(
					    "--is_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
					)
					lowercase    :							Union[str, Any]      =					parser.parse_args()
					convert_sew_checkpoint(
					    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
					)

