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


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

UpperCamelCase_       :       List[str]							   =		{
    """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   a__      (		UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Optional[int]			)->	int:
						for attribute in key.split("""."""			):
												UpperCAmelCase__						=		getattr(UpperCAmelCase__		,			UpperCAmelCase__			)

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

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


def   a__      (		UpperCAmelCase__       :			Optional[Any]		,			UpperCAmelCase__       :			Any		,			UpperCAmelCase__       :			int			)->	Tuple:
						UpperCAmelCase__						=		[]
						UpperCAmelCase__						=		fairseq_model.state_dict()

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

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

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


def   a__      (		UpperCAmelCase__       :			Union[str, Any]		,			UpperCAmelCase__       :			int		,			UpperCAmelCase__       :			List[Any]		,			UpperCAmelCase__       :			Dict		,			UpperCAmelCase__       :			List[Any]			)->	Union[str, Any]:
						UpperCAmelCase__						=		full_name.split("""conv_layers."""			)[-1]
						UpperCAmelCase__						=		name.split("""."""			)
						UpperCAmelCase__						=		int(items[0]			)
						UpperCAmelCase__						=		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.'
																		)
																		UpperCAmelCase__						=		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.'
																		)
																		UpperCAmelCase__						=		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."
																		)
																		UpperCAmelCase__						=		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.'
																		)
																		UpperCAmelCase__						=		value
																		logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
						else:
												unused_weights.append(UpperCAmelCase__			)


def   a__      (		UpperCAmelCase__       :			Optional[int]		,			UpperCAmelCase__       :			str			)->	List[Any]:
						UpperCAmelCase__						=		SEWDConfig()
						if is_finetuned:
												UpperCAmelCase__						=		model.wav_encoder.wav_model.cfg
						else:
												UpperCAmelCase__						=		model.cfg

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

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

						UpperCAmelCase__						=		"""Wav2Vec2FeatureExtractor"""
						UpperCAmelCase__						=		"""Wav2Vec2CTCTokenizer"""

						return config


@torch.no_grad()
def   a__      (		UpperCAmelCase__       :			Tuple		,			UpperCAmelCase__       :			str		,			UpperCAmelCase__       :			Tuple=None		,			UpperCAmelCase__       :			int=None		,			UpperCAmelCase__       :			Union[str, Any]=True			)->	Optional[Any]:

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

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

						UpperCAmelCase__						=		True if config.feat_extract_norm == """layer""" else False
						UpperCAmelCase__						=		WavaVecaFeatureExtractor(
						    feature_size=1		,			sampling_rate=1_6_0_0_0		,			padding_value=0		,			do_normalize=UpperCAmelCase__		,			return_attention_mask=UpperCAmelCase__		,			)

						if is_finetuned:
												if dict_path:
																		UpperCAmelCase__						=		Dictionary.load(UpperCAmelCase__			)

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

												UpperCAmelCase__						=		SEWDForCTC(UpperCAmelCase__			)
						else:
												UpperCAmelCase__						=		SEWDModel(UpperCAmelCase__			)
												feature_extractor.save_pretrained(UpperCAmelCase__			)

						recursively_load_weights(UpperCAmelCase__		,			UpperCAmelCase__		,			UpperCAmelCase__			)

						hf_model.save_pretrained(UpperCAmelCase__			)


if __name__ == "__main__":
						UpperCamelCase_       :       Tuple							   =		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_       :       Dict							   =		parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)


"""simple docstring"""







from ..utils import DummyObject, requires_backends


class 			_lowercase		(   metaclass=__lowerCamelCase  ):
				lowerCamelCase__						=       ['torch', 'transformers', 'invisible_watermark']

				def __init__(     self       :    Tuple       ,    *_lowercase       :    int       ,    **_lowercase       :    List[Any]     ):


										'''simple docstring'''
										requires_backends(self       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    Tuple       ,    *_lowercase       :    Dict       ,    **_lowercase       :    Union[str, Any]     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    Tuple       ,    *_lowercase       :    Tuple       ,    **_lowercase       :    Any     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )


class 			_lowercase		(   metaclass=__lowerCamelCase  ):
				lowerCamelCase__						=       ['torch', 'transformers', 'invisible_watermark']

				def __init__(     self       :    Union[str, Any]       ,    *_lowercase       :    List[Any]       ,    **_lowercase       :    List[Any]     ):


										'''simple docstring'''
										requires_backends(self       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    List[str]       ,    *_lowercase       :    Any       ,    **_lowercase       :    Tuple     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

				@classmethod
				def        A (     cls       :    List[Any]       ,    *_lowercase       :    Optional[Any]       ,    **_lowercase       :    Union[str, Any]     ):


										'''simple docstring'''
										requires_backends(cls       ,    ["""torch""", """transformers""", """invisible_watermark"""]     )

