
import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


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

__A				={
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''feature_projection.layer_norm''',
    '''quantizer.weight_proj''': '''quantizer.weight_proj''',
    '''quantizer.vars''': '''quantizer.codevectors''',
    '''project_q''': '''project_q''',
    '''final_proj''': '''project_hid''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}
__A				=[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				for attribute in key.split("."    ):
								lowerCamelCase_						=			getattr(lowerCamelCase__	, lowerCamelCase__    )

				if weight_type is not None:
								lowerCamelCase_						=			getattr(lowerCamelCase__	, lowerCamelCase__    ).shape
				else:
								lowerCamelCase_						=			hf_pointer.shape

				if hf_shape != value.shape:
								raise ValueError(
								    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
				elif weight_type == "running_mean":
								lowerCamelCase_						=			value
				elif weight_type == "running_var":
								lowerCamelCase_						=			value
				elif weight_type == "num_batches_tracked":
								lowerCamelCase_						=			value
				elif weight_type == "inv_freq":
								lowerCamelCase_						=			value
				else:
								lowerCamelCase_						=			value

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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				lowerCamelCase_						=			[]
				lowerCamelCase_						=			fairseq_model.state_dict()

				lowerCamelCase_						=			hf_model.wavaveca_conformer.feature_extractor

				for name, value in fairseq_dict.items():
								lowerCamelCase_						=			False
								if "conv_layers" in name:
												load_conv_layer(
												    lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, hf_model.config.feat_extract_norm == "group"	, )
												lowerCamelCase_						=			True
								else:
												for key, mapped_key in MAPPING.items():
																lowerCamelCase_						=			"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS 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(lowerCamelCase__    )[0].split("."    )[-2]
																								lowerCamelCase_						=			mapped_key.replace("*"	, lowerCamelCase__    )
																				if "pos_bias_u" in name:
																								lowerCamelCase_						=			None
																				elif "pos_bias_v" in name:
																								lowerCamelCase_						=			None
																				elif "weight_g" in name:
																								lowerCamelCase_						=			"weight_g"
																				elif "weight_v" in name:
																								lowerCamelCase_						=			"weight_v"
																				elif "bias" in name:
																								lowerCamelCase_						=			"bias"
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								lowerCamelCase_						=			"weight"
																				elif "running_mean" in name:
																								lowerCamelCase_						=			"running_mean"
																				elif "inv_freq" in name:
																								lowerCamelCase_						=			"inv_freq"
																				elif "running_var" in name:
																								lowerCamelCase_						=			"running_var"
																				elif "num_batches_tracked" in name:
																								lowerCamelCase_						=			"num_batches_tracked"
																				else:
																								lowerCamelCase_						=			None
																				set_recursively(lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    )
																continue
								if not is_used:
												unused_weights.append(lowerCamelCase__    )

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





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__    ):
				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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																raise ValueError(
																    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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																raise ValueError(
																    F'{full_name} has size {value.shape}, but'
																    F' {feature_extractor.conv_layers[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:
												if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																raise ValueError(
																    F'{full_name} has size {value.shape}, but'
																    F' {feature_extractor.conv_layers[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(lowerCamelCase__    )





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__=None	, lowerCamelCase__=None	, lowerCamelCase__=True    ):
				if config_path is not None:
								lowerCamelCase_						=			WavaVecaConformerConfig.from_pretrained(lowerCamelCase__	, hidden_act="swish"    )
				else:
								lowerCamelCase_						=			WavaVecaConformerConfig()

				if "rope" in checkpoint_path:
								lowerCamelCase_						=			"rotary"

				if is_finetuned:
								if dict_path:
												lowerCamelCase_						=			Dictionary.load(lowerCamelCase__    )

												# 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.eos_index
												lowerCamelCase_						=			len(target_dict.symbols    )
												lowerCamelCase_						=			os.path.join(lowerCamelCase__	, "vocab.json"    )
												if not os.path.isdir(lowerCamelCase__    ):
																logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(lowerCamelCase__    )    )
																return
												os.makedirs(lowerCamelCase__	, exist_ok=lowerCamelCase__    )
												lowerCamelCase_						=			target_dict.indices

												# fairseq has the <pad> and <s> switched
												lowerCamelCase_						=			0
												lowerCamelCase_						=			1
												with open(lowerCamelCase__	, "w"	, encoding="utf-8"    ) as vocab_handle:
																json.dump(lowerCamelCase__	, lowerCamelCase__    )
												lowerCamelCase_						=			WavaVecaCTCTokenizer(
												    lowerCamelCase__	, 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=lowerCamelCase__	, )
												lowerCamelCase_						=			True if config.feat_extract_norm == "layer" else False
												lowerCamelCase_						=			WavaVecaFeatureExtractor(
												    feature_size=1	, sampling_rate=1_6_0_0_0	, padding_value=0	, do_normalize=lowerCamelCase__	, return_attention_mask=lowerCamelCase__	, )
												lowerCamelCase_						=			WavaVecaProcessor(feature_extractor=lowerCamelCase__	, tokenizer=lowerCamelCase__    )
												processor.save_pretrained(lowerCamelCase__    )

								lowerCamelCase_						=			WavaVecaConformerForCTC(lowerCamelCase__    )
				else:
								lowerCamelCase_						=			WavaVecaConformerForPreTraining(lowerCamelCase__    )

				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_						=			argparse.Namespace(task="audio_pretraining"    )
								lowerCamelCase_						=			fairseq.tasks.setup_task(lowerCamelCase__    )

								lowerCamelCase_	,    lowerCamelCase_	,    lowerCamelCase_						=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	, task=lowerCamelCase__    )

				lowerCamelCase_						=			model[0].eval()

				recursively_load_weights(lowerCamelCase__	, lowerCamelCase__	, not is_finetuned    )

				hf_wavavec.save_pretrained(lowerCamelCase__    )


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(
			    '''--not_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
			)
			__A				=parser.parse_args()
			convert_wavaveca_conformer_checkpoint(
			    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
			)








"""simple docstring"""




from math import ceil
from typing import List, Optional, Union

import numpy as np

from ...audio_utils import mel_filter_bank, spectrogram, window_function
from ...feature_extraction_sequence_utils import BatchFeature, SequenceFeatureExtractor
from ...utils import TensorType, logging


lowerCAmelCase							:			List[str]     							=							logging.get_logger(__name__)






class     __magic_name__							(       UpperCAmelCase__				):






				'''simple docstring'''
				__UpperCamelCase			  =						["audio_values", "audio_mask"]





				def __init__(       self		, _a=2_048		, _a=1		, _a=[16, 16]		, _a=128		, _a=44_100		, _a=86		, _a=2_048		, _a=0.0		, **_a		, ):





							"""simple docstring"""
							super().__init__(
							    feature_size=_a		, sampling_rate=_a		, padding_value=_a		, **_a		, )

							lowerCamelCase			      =					spectrogram_length
							lowerCamelCase			      =					num_channels
							lowerCamelCase			      =					patch_size
							lowerCamelCase			      =					feature_size // self.patch_size[1]
							lowerCamelCase			      =					n_fft
							lowerCamelCase			      =					sampling_rate // hop_length_to_sampling_rate
							lowerCamelCase			      =					sampling_rate
							lowerCamelCase			      =					padding_value
							lowerCamelCase			      =					mel_filter_bank(
							    num_frequency_bins=1 + n_fft // 2		, num_mel_filters=_a		, min_frequency=0.0		, max_frequency=22_050.0		, sampling_rate=_a		, norm="""slaney"""		, mel_scale="""slaney"""		, ).T





				def    _lowerCAmelCase				(       self		, _a ):





							"""simple docstring"""
							lowerCamelCase			      =					spectrogram(
							    _a		, window_function(self.n_fft		, """hann""" )		, frame_length=self.n_fft		, hop_length=self.hop_length		, power=2.0		, mel_filters=self.mel_filters.T		, log_mel="""dB"""		, db_range=80.0		, )
							lowerCamelCase			      =					log_spec[:, :-1]
							lowerCamelCase			      =					log_spec - 20.0
							lowerCamelCase			      =					np.clip(log_spec / 40.0		, -2.0		, 0.0 ) + 1.0
							return log_spec





				def __call__(       self		, _a		, _a = None		, _a = True		, _a = None		, _a = False		, _a = False		, **_a		, ):





							"""simple docstring"""

							if sampling_rate is not None:
										if sampling_rate != self.sampling_rate:
													raise ValueError(
													    """This feature extractor is set to support sampling rate"""
													    f' of {self.sampling_rate}. Please make sure that the provided `raw_speech` input was sampled'
													    f' with {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(_a		, 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(_a		, (list, tuple) ) and (isinstance(raw_speech[0]		, (np.ndarray, tuple, list) ))
							)
							if is_batched:
										lowerCamelCase			      =					[np.asarray([speech]		, dtype=np.floataa ).T for speech in raw_speech]
							elif not is_batched and not isinstance(_a		, np.ndarray ):
										lowerCamelCase			      =					np.asarray(_a		, dtype=np.floataa )
							elif isinstance(_a		, 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			      =					[np.asarray([raw_speech] ).T]

							# Convert audio signals to log mel spectrograms, truncate by time axis
							lowerCamelCase			      =					[
							    self._np_extract_fbank_features(waveform.squeeze() ).T[: self.spectrogram_length] for waveform in raw_speech
							]
							if isinstance(audio_features[0]		, _a ):
										lowerCamelCase			      =					[np.asarray(_a		, dtype=np.floataa ) for feature in audio_features]

							# Create audio attention mask
							lowerCamelCase			      =					max(
							    [ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len for feature in audio_features] )  # The maximum number of audio patches in a batch
							if return_attention_mask:
										lowerCamelCase			      =					[
										    (ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [1]
										    + (max_patch_len - ceil(feature.shape[0] / self.patch_size[0] ) * self.freq_len) * [0]
										    for feature in audio_features
										]
										lowerCamelCase			      =					np.array(_a ).astype(np.floataa )

							# convert into correct format for padding
							lowerCamelCase			      =					max_patch_len // self.freq_len * self.patch_size[0]  # The maximum audio size in a batch
							lowerCamelCase			      =					np.ones([len(_a ), 1, max_time_len, self.feature_size] ).astype(np.floataa )
							lowerCamelCase			      =					padded_audio_features * self.padding_value
							for i in range(len(_a ) ):
										lowerCamelCase			      =					audio_features[i]
										lowerCamelCase			      =					feature

							# return as BatchFeature
							if return_attention_mask:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features, """audio_mask""": audio_mask}
							else:
										lowerCamelCase			      =					{"""audio_values""": padded_audio_features}

							lowerCamelCase			      =					BatchFeature(data=_a		, tensor_type=_a )
							return encoded_inputs



