


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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A__     				=	logging.get_logger(__name__)

A__     				=	{
    """post_extract_proj""": """feature_projection""",
    """encoder.pos_conv.0""": """encoder.pos_conv_embed.conv""",
    """attention.self.query_proj""": """encoder.encoder.layer.*.attention.self.query_proj""",
    """attention.self.key_proj""": """encoder.encoder.layer.*.attention.self.key_proj""",
    """attention.self.value_proj""": """encoder.encoder.layer.*.attention.self.value_proj""",
    """attention.output.dense""": """encoder.encoder.layer.*.attention.output.dense""",
    """attention.output.LayerNorm""": """encoder.encoder.layer.*.attention.output.LayerNorm""",
    """intermediate.dense""": """encoder.encoder.layer.*.intermediate.dense""",
    """output.dense""": """encoder.encoder.layer.*.output.dense""",
    """output.LayerNorm""": """encoder.encoder.layer.*.output.LayerNorm""",
    """encoder.encoder.rel_embeddings""": """encoder.encoder.rel_embeddings""",
    """encoder.encoder.LayerNorm""": """encoder.encoder.LayerNorm""",
    """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 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					for attribute in key.split("""."""      ):
										_lowerCAmelCase      =   getattr(snake_case     ,						snake_case      )

					if weight_type is not None:
										_lowerCAmelCase      =   getattr(snake_case     ,						snake_case      ).shape
					else:
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   value
					elif weight_type == "weight_g":
										_lowerCAmelCase      =   value
					elif weight_type == "weight_v":
										_lowerCAmelCase      =   value
					elif weight_type == "bias":
										_lowerCAmelCase      =   value
					else:
										_lowerCAmelCase      =   value

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   []
					_lowerCAmelCase      =   fairseq_model.state_dict()

					_lowerCAmelCase      =   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										_lowerCAmelCase      =   False
										if "conv_layers" in name:
															load_conv_layer(
															    snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						hf_model.config.feat_extract_norm == """group"""     ,						)
															_lowerCAmelCase      =   True
										else:
															for key, mapped_key in MAPPING.items():
																				_lowerCAmelCase      =   """sew_d.""" + 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]:
																									_lowerCAmelCase      =   True
																									if "*" in mapped_key:
																														_lowerCAmelCase      =   name.split(snake_case      )[0].split("""."""      )[-2]
																														if not layer_index.isnumeric():
																																			continue
																														_lowerCAmelCase      =   mapped_key.replace("""*"""     ,						snake_case      )
																									if "weight_g" in name:
																														_lowerCAmelCase      =   """weight_g"""
																									elif "weight_v" in name:
																														_lowerCAmelCase      =   """weight_v"""
																									elif "weight" in name:
																														_lowerCAmelCase      =   """weight"""
																									elif "bias" in name:
																														_lowerCAmelCase      =   """bias"""
																									else:
																														_lowerCAmelCase      =   None
																									set_recursively(snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      )
																				continue
										if not is_used:
															unused_weights.append(snake_case      )

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


def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   full_name.split("""conv_layers."""      )[-1]
					_lowerCAmelCase      =   name.split("""."""      )
					_lowerCAmelCase      =   int(items[0]      )
					_lowerCAmelCase      =   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.'
															)
															_lowerCAmelCase      =   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.'
															)
															_lowerCAmelCase      =   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."
															)
															_lowerCAmelCase      =   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.'
															)
															_lowerCAmelCase      =   value
															logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'      )
					else:
										unused_weights.append(snake_case      )


def 		_UpperCAmelCase    (				snake_case     ,						snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   SEWDConfig()
					if is_finetuned:
										_lowerCAmelCase      =   model.wav_encoder.wav_model.cfg
					else:
										_lowerCAmelCase      =   model.cfg

					_lowerCAmelCase      =   fs_config.conv_bias
					_lowerCAmelCase      =   eval(fs_config.conv_feature_layers      )
					_lowerCAmelCase      =   [x[0] for x in conv_layers]
					_lowerCAmelCase      =   [x[1] for x in conv_layers]
					_lowerCAmelCase      =   [x[2] for x in conv_layers]
					_lowerCAmelCase      =   """gelu"""
					_lowerCAmelCase      =   """layer""" if fs_config.extractor_mode == """layer_norm""" else """group"""
					_lowerCAmelCase      =   0.0
					_lowerCAmelCase      =   fs_config.activation_fn.name
					_lowerCAmelCase      =   fs_config.encoder_embed_dim
					_lowerCAmelCase      =   0.02
					_lowerCAmelCase      =   fs_config.encoder_ffn_embed_dim
					_lowerCAmelCase      =   1E-5
					_lowerCAmelCase      =   fs_config.encoder_layerdrop
					_lowerCAmelCase      =   fs_config.encoder_attention_heads
					_lowerCAmelCase      =   fs_config.conv_pos_groups
					_lowerCAmelCase      =   fs_config.conv_pos
					_lowerCAmelCase      =   len(snake_case      )
					_lowerCAmelCase      =   fs_config.encoder_layers
					_lowerCAmelCase      =   fs_config.squeeze_factor
					# DeBERTa-specific parameters:
					_lowerCAmelCase      =   fs_config.max_position_embeddings
					_lowerCAmelCase      =   fs_config.position_buckets
					_lowerCAmelCase      =   fs_config.share_att_key
					_lowerCAmelCase      =   fs_config.relative_attention
					_lowerCAmelCase      =   fs_config.position_biased_input
					_lowerCAmelCase      =   tuple(fs_config.pos_att_type.split("""|"""      )      )
					_lowerCAmelCase      =   fs_config.norm_rel_ebd

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

					_lowerCAmelCase      =   """Wav2Vec2FeatureExtractor"""
					_lowerCAmelCase      =   """Wav2Vec2CTCTokenizer"""

					return config


@torch.no_grad()
def 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case=None     ,						snake_case=None     ,						snake_case=True      ):





					"""simple docstring"""

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

					if config_path is not None:
										_lowerCAmelCase      =   SEWDConfig.from_pretrained(snake_case      )
					else:
										_lowerCAmelCase      =   convert_config(model[0]     ,						snake_case      )
					_lowerCAmelCase      =   model[0].eval()

					_lowerCAmelCase      =   True if config.feat_extract_norm == """layer""" else False
					_lowerCAmelCase      =   WavaVecaFeatureExtractor(
					    feature_size=1     ,						sampling_rate=1_60_00     ,						padding_value=0     ,						do_normalize=snake_case     ,						return_attention_mask=snake_case     ,						)

					if is_finetuned:
										if dict_path:
															_lowerCAmelCase      =   Dictionary.load(snake_case      )

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

										_lowerCAmelCase      =   SEWDForCTC(snake_case      )
					else:
										_lowerCAmelCase      =   SEWDModel(snake_case      )
										feature_extractor.save_pretrained(snake_case      )

					recursively_load_weights(snake_case     ,						snake_case     ,						snake_case      )

					hf_model.save_pretrained(snake_case      )


if __name__ == "__main__":
	A__     				=	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"""
	)
	A__     				=	parser.parse_args()
	convert_sew_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
	)








from typing import List, Optional, Union

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

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


A__     				=	logging.get_logger(__name__)





class  __lowerCAmelCase			(					lowerCamelCase__	):
				__lowerCamelCase			=    ['''input_values''', '''attention_mask''']




				def __init__(							self						,    _snake_case=1						,    _snake_case=16000						,    _snake_case=128						,    _snake_case=1024						,    _snake_case=0.0						,    _snake_case=True						,    _snake_case=-4.267_7393						,    _snake_case=4.568_9974						,    _snake_case=False						,    **_snake_case						,    ):



									"""simple docstring"""





									super().__init__(feature_size=_snake_case						,    sampling_rate=_snake_case						,    padding_value=_snake_case						,    **_snake_case		)
									_lowerCAmelCase      =   num_mel_bins
									_lowerCAmelCase      =   max_length
									_lowerCAmelCase      =   do_normalize
									_lowerCAmelCase      =   mean
									_lowerCAmelCase      =   std
									_lowerCAmelCase      =   return_attention_mask




				def 							snake_case       (							self						,    _snake_case						,    _snake_case						,    ):



									"""simple docstring"""





									_lowerCAmelCase      =   torch.from_numpy(_snake_case		).unsqueeze(0		)
									_lowerCAmelCase      =   ta_kaldi.fbank(
									    _snake_case						,    htk_compat=_snake_case						,    sample_frequency=self.sampling_rate						,    use_energy=_snake_case						,    window_type="""hanning"""						,    num_mel_bins=self.num_mel_bins						,    dither=0.0						,    frame_shift=10						,    )

									_lowerCAmelCase      =   fbank.shape[0]
									_lowerCAmelCase      =   max_length - n_frames

									# pad or truncate, depending on difference
									if difference > 0:
														_lowerCAmelCase      =   torch.nn.ZeroPadad((0, 0, 0, difference)		)
														_lowerCAmelCase      =   pad_module(_snake_case		)
									elif difference < 0:
														_lowerCAmelCase      =   fbank[0:max_length, :]

									_lowerCAmelCase      =   fbank.numpy()

									return fbank




				def 							snake_case       (							self						,    _snake_case		):



									"""simple docstring"""





									return (input_values - (self.mean)) / (self.std * 2)




				def __call__(							self						,    _snake_case						,    _snake_case = None						,    _snake_case = None						,    **_snake_case						,    ):



									"""simple docstring"""





									if sampling_rate is not None:
														if sampling_rate != self.sampling_rate:
																			raise ValueError(
																			    F'The model corresponding to this feature extractor: {self} was trained using a sampling rate of'
																			    F' {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled with'
																			    F' {self.sampling_rate} and not {sampling_rate}.'		)
									else:
														logger.warning(
														    """It is strongly recommended to pass the `sampling_rate` argument to this function. """
														    """Failing to do so can result in silent errors that might be hard to debug."""		)

									_lowerCAmelCase      =   isinstance(_snake_case						,    np.ndarray		) and len(raw_speech.shape		) > 1
									if is_batched_numpy and len(raw_speech.shape		) > 2:
														raise ValueError(F'Only mono-channel audio is supported for input to {self}'		)
									_lowerCAmelCase      =   is_batched_numpy or (
									    isinstance(_snake_case						,    (list, tuple)		) and (isinstance(raw_speech[0]						,    (np.ndarray, tuple, list)		))
									)

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

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

									# extract fbank features and pad/truncate to max_length
									_lowerCAmelCase      =   [self._extract_fbank_features(_snake_case						,    max_length=self.max_length		) for waveform in raw_speech]

									# convert into BatchFeature
									_lowerCAmelCase      =   BatchFeature({"""input_values""": features}		)

									# make sure list is in array format
									_lowerCAmelCase      =   padded_inputs.get("""input_values"""		)
									if isinstance(input_values[0]						,    _snake_case		):
														_lowerCAmelCase      =   [np.asarray(_snake_case						,    dtype=np.floataa		) for feature in input_values]

									# normalization
									if self.do_normalize:
														_lowerCAmelCase      =   [self.normalize(_snake_case		) for feature in input_values]

									if return_tensors is not None:
														_lowerCAmelCase      =   padded_inputs.convert_to_tensors(_snake_case		)

									return padded_inputs



