


"""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()
__UpperCamelCase		= logging.get_logger(__name__)

__UpperCamelCase		= {
    '''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        lowercase     (SCREAMING_SNAKE_CASE_						:							Union[str, Any]					,     SCREAMING_SNAKE_CASE_						:							Optional[int]					,     SCREAMING_SNAKE_CASE_						:							Optional[int]					,     SCREAMING_SNAKE_CASE_						:							Tuple					,     SCREAMING_SNAKE_CASE_						:							Optional[Any]    )  ->		int:


						for attribute in key.split('.'    ):
												SCREAMING_SNAKE_CASE						=   getattr(__lowerCAmelCase					,     __lowerCAmelCase    )

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

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


def        lowercase     (SCREAMING_SNAKE_CASE_						:							Union[str, Any]					,     SCREAMING_SNAKE_CASE_						:							List[str]					,     SCREAMING_SNAKE_CASE_						:							int    )  ->		int:
						SCREAMING_SNAKE_CASE						=   []
						SCREAMING_SNAKE_CASE						=   fairseq_model.state_dict()

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

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE						=   False
												if "conv_layers" in name:
																		load_conv_layer(
																		    __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase					,     hf_model.config.feat_extract_norm == 'group'					,     )
																		SCREAMING_SNAKE_CASE						=   True
												else:
																		for key, mapped_key in MAPPING.items():
																								SCREAMING_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]:
																														SCREAMING_SNAKE_CASE						=   True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE						=   name.split(__lowerCAmelCase    )[0].split('.'    )[-2]
																																				SCREAMING_SNAKE_CASE						=   mapped_key.replace('*'					,     __lowerCAmelCase    )
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE						=   'weight_g'
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE						=   'weight_v'
																														elif "weight" in name:
																																				SCREAMING_SNAKE_CASE						=   'weight'
																														elif "bias" in name:
																																				SCREAMING_SNAKE_CASE						=   'bias'
																														else:
																																				SCREAMING_SNAKE_CASE						=   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        lowercase     (SCREAMING_SNAKE_CASE_						:							Dict					,     SCREAMING_SNAKE_CASE_						:							List[Any]					,     SCREAMING_SNAKE_CASE_						:							Any					,     SCREAMING_SNAKE_CASE_						:							int					,     SCREAMING_SNAKE_CASE_						:							Tuple    )  ->		List[str]:
						SCREAMING_SNAKE_CASE						=   full_name.split('conv_layers.'    )[-1]
						SCREAMING_SNAKE_CASE						=   name.split('.'    )
						SCREAMING_SNAKE_CASE						=   int(items[0]    )
						SCREAMING_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.'
																		)
																		SCREAMING_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.'
																		)
																		SCREAMING_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."
																		)
																		SCREAMING_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.'
																		)
																		SCREAMING_SNAKE_CASE						=   value
																		logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'    )
						else:
												unused_weights.append(__lowerCAmelCase    )


def        lowercase     (SCREAMING_SNAKE_CASE_						:							List[str]					,     SCREAMING_SNAKE_CASE_						:							Dict    )  ->		List[Any]:
						SCREAMING_SNAKE_CASE						=   SEWConfig()
						if is_finetuned:
												SCREAMING_SNAKE_CASE						=   model.wav_encoder.wav_model.cfg
						else:
												SCREAMING_SNAKE_CASE						=   model.cfg

						SCREAMING_SNAKE_CASE						=   fs_config.conv_bias
						SCREAMING_SNAKE_CASE						=   eval(fs_config.conv_feature_layers    )
						SCREAMING_SNAKE_CASE						=   [x[0] for x in conv_layers]
						SCREAMING_SNAKE_CASE						=   [x[1] for x in conv_layers]
						SCREAMING_SNAKE_CASE						=   [x[2] for x in conv_layers]
						SCREAMING_SNAKE_CASE						=   'gelu'
						SCREAMING_SNAKE_CASE						=   'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
						SCREAMING_SNAKE_CASE						=   0.0
						SCREAMING_SNAKE_CASE						=   fs_config.activation_fn.name
						SCREAMING_SNAKE_CASE						=   fs_config.encoder_embed_dim
						SCREAMING_SNAKE_CASE						=   0.02
						SCREAMING_SNAKE_CASE						=   fs_config.encoder_ffn_embed_dim
						SCREAMING_SNAKE_CASE						=   1E-5
						SCREAMING_SNAKE_CASE						=   fs_config.encoder_layerdrop
						SCREAMING_SNAKE_CASE						=   fs_config.encoder_attention_heads
						SCREAMING_SNAKE_CASE						=   fs_config.conv_pos_groups
						SCREAMING_SNAKE_CASE						=   fs_config.conv_pos
						SCREAMING_SNAKE_CASE						=   len(__lowerCAmelCase    )
						SCREAMING_SNAKE_CASE						=   fs_config.encoder_layers
						SCREAMING_SNAKE_CASE						=   fs_config.squeeze_factor

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

						SCREAMING_SNAKE_CASE						=   'Wav2Vec2FeatureExtractor'
						SCREAMING_SNAKE_CASE						=   'Wav2Vec2CTCTokenizer'

						return config


@torch.no_grad()
def        lowercase     (SCREAMING_SNAKE_CASE_						:							str					,     SCREAMING_SNAKE_CASE_						:							str					,     SCREAMING_SNAKE_CASE_						:							Dict=None					,     SCREAMING_SNAKE_CASE_						:							Union[str, Any]=None					,     SCREAMING_SNAKE_CASE_						:							int=True    )  ->		Any:


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

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

						SCREAMING_SNAKE_CASE						=   True if config.feat_extract_norm == 'layer' else False
						SCREAMING_SNAKE_CASE						=   WavaVecaFeatureExtractor(
						    feature_size=1					,     sampling_rate=1_60_00					,     padding_value=0					,     do_normalize=__lowerCAmelCase					,     return_attention_mask=__lowerCAmelCase					,     )

						if is_finetuned:
												if dict_path:
																		SCREAMING_SNAKE_CASE						=   Dictionary.load(__lowerCAmelCase    )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		SCREAMING_SNAKE_CASE						=   target_dict.pad_index
																		SCREAMING_SNAKE_CASE						=   target_dict.bos_index
																		SCREAMING_SNAKE_CASE						=   target_dict.pad_index
																		SCREAMING_SNAKE_CASE						=   target_dict.bos_index
																		SCREAMING_SNAKE_CASE						=   target_dict.eos_index
																		SCREAMING_SNAKE_CASE						=   len(target_dict.symbols    )
																		SCREAMING_SNAKE_CASE						=   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    )
																		with open(__lowerCAmelCase					,     'w'					,     encoding='utf-8'    ) as vocab_handle:
																								json.dump(target_dict.indices					,     __lowerCAmelCase    )
																		SCREAMING_SNAKE_CASE						=   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					,     )
																		SCREAMING_SNAKE_CASE						=   WavaVecaProcessor(feature_extractor=__lowerCAmelCase					,     tokenizer=__lowerCAmelCase    )
																		processor.save_pretrained(__lowerCAmelCase    )

												SCREAMING_SNAKE_CASE						=   SEWForCTC(__lowerCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE						=   SEWModel(__lowerCAmelCase    )
												feature_extractor.save_pretrained(__lowerCAmelCase    )

						recursively_load_weights(__lowerCAmelCase					,     __lowerCAmelCase					,     __lowerCAmelCase    )

						hf_model.save_pretrained(__lowerCAmelCase    )


if __name__ == "__main__":
	__UpperCamelCase		= 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'''
	)
	__UpperCamelCase		= 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

import datasets
from datasets.tasks import AudioClassification

from ..folder_based_builder import folder_based_builder


_A  =	datasets.utils.logging.get_logger(__name__)





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilderConfig			):
				_lowercase     						=None
				_lowercase     						=None





class       _lowerCAmelCase   (      folder_based_builder.FolderBasedBuilder			):
				_lowercase     						=datasets.Audio()
				_lowercase     						='''audio'''
				_lowercase     						=AudioFolderConfig
				_lowercase     						=42  # definition at the bottom of the script
				_lowercase     						=AudioClassification(audio_column='''audio'''   , label_column='''label'''			)


_A  =	[
    ".aiff",
    ".au",
    ".avr",
    ".caf",
    ".flac",
    ".htk",
    ".svx",
    ".mat4",
    ".mat5",
    ".mpc2k",
    ".ogg",
    ".paf",
    ".pvf",
    ".raw",
    ".rf64",
    ".sd2",
    ".sds",
    ".ircam",
    ".voc",
    ".w64",
    ".wav",
    ".nist",
    ".wavex",
    ".wve",
    ".xi",
    ".mp3",
    ".opus",
]
_A  =	AUDIO_EXTENSIONS



