






"""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()
a :				str      					=		logging.get_logger(__name__)

a :				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 						lowercase__(A					,		A					,		A					,		A					,		A						)				->str:




						"""simple docstring"""



						for attribute in key.split("."						):
												lowercase__				:      Union[str, Any]=       getattr(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						)

						if weight_type is not None:
												lowercase__				:      Union[str, Any]=       getattr(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						).shape
						else:
												lowercase__				:      List[Any]=       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":
												lowercase__				:      Tuple=       value
						elif weight_type == "weight_g":
												lowercase__				:      str=       value
						elif weight_type == "weight_v":
												lowercase__				:      List[Any]=       value
						elif weight_type == "bias":
												lowercase__				:      List[str]=       value
						else:
												lowercase__				:      int=       value

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





def 						lowercase__(A					,		A					,		A						)				->Any:




						"""simple docstring"""



						lowercase__				:      Union[str, Any]=       []
						lowercase__				:      Optional[int]=       fairseq_model.state_dict()

						lowercase__				:      int=       hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

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





def 						lowercase__(A					,		A					,		A					,		A					,		A						)				->Dict:




						"""simple docstring"""



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





def 						lowercase__(A					,		A						)				->Dict:




						"""simple docstring"""



						lowercase__				:      int=       SEWDConfig()
						if is_finetuned:
												lowercase__				:      Optional[Any]=       model.wav_encoder.wav_model.cfg
						else:
												lowercase__				:      Dict=       model.cfg

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

						# take care of any params that are overridden by the Wav2VecCtc model
						if is_finetuned:
												lowercase__				:      Any=       model.cfg
												lowercase__				:      Any=       fs_config.final_dropout
												lowercase__				:      Dict=       fs_config.layerdrop
						lowercase__				:      str=       fs_config.activation_dropout
						lowercase__				:      List[Any]=       fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
						lowercase__				:      List[str]=       fs_config.attention_dropout
						lowercase__				:      Tuple=       fs_config.dropout_input
						lowercase__				:      int=       fs_config.dropout
						lowercase__				:      Tuple=       fs_config.mask_channel_length
						lowercase__				:      int=       fs_config.mask_channel_prob
						lowercase__				:      List[str]=       fs_config.mask_length
						lowercase__				:      Tuple=       fs_config.mask_prob

						lowercase__				:      Tuple=       "Wav2Vec2FeatureExtractor"
						lowercase__				:      int=       "Wav2Vec2CTCTokenizer"

						return config





@torch.no_grad()
def 						lowercase__(A					,		A					,		A=None					,		A=None					,		A=True						)				->Optional[Any]:




						"""simple docstring"""



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

						if config_path is not None:
												lowercase__				:      str=       SEWDConfig.from_pretrained(_SCREAMING_SNAKE_CASE						)
						else:
												lowercase__				:      List[str]=       convert_config(model[0]					,		_SCREAMING_SNAKE_CASE						)
						lowercase__				:      Any=       model[0].eval()

						lowercase__				:      int=       True if config.feat_extract_norm == "layer" else False
						lowercase__				:      Tuple=       WavaVecaFeatureExtractor(
						    feature_size=1					,		sampling_rate=16_000					,		padding_value=0					,		do_normalize=_SCREAMING_SNAKE_CASE					,		return_attention_mask=_SCREAMING_SNAKE_CASE					,		)

						if is_finetuned:
												if dict_path:
																		lowercase__				:      List[Any]=       Dictionary.load(_SCREAMING_SNAKE_CASE						)

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

												lowercase__				:      Optional[Any]=       SEWDForCTC(_SCREAMING_SNAKE_CASE						)
						else:
												lowercase__				:      Optional[Any]=       SEWDModel(_SCREAMING_SNAKE_CASE						)
												feature_extractor.save_pretrained(_SCREAMING_SNAKE_CASE						)

						recursively_load_weights(_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE					,		_SCREAMING_SNAKE_CASE						)

						hf_model.save_pretrained(_SCREAMING_SNAKE_CASE						)


if __name__ == "__main__":
			a :				Optional[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"""
			)
			a :				Tuple      					=		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 __future__ import annotations

import random
from collections.abc import Iterable
from typing import Any, Generic, TypeVar

UpperCAmelCase			=    TypeVar('T', bound=bool)

class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_      )	->	None:
							lowerCAmelCase    =	value
							lowerCAmelCase    =	None
							lowerCAmelCase    =	None


						@property
						def 				__snake_case    (					self      )	->	T:
							return self._value




						@staticmethod
						def 				__snake_case    (					A_	,       A_      )	->	RandomizedHeapNode[T] | None:
							if not roota:
								return roota

							if not roota:
								return roota

							if roota.value > roota.value:
								lowerCAmelCase,   lowerCAmelCase    =	roota, roota

							if random.choice([True, False]      ):
								lowerCAmelCase,   lowerCAmelCase    =	roota.right, roota.left

							lowerCAmelCase    =	RandomizedHeapNode.merge(roota.left	,       A_      )

							return roota







class __snake_case(      Generic[T]					):


						'''simple docstring'''



						def __init__(					self	,       A_ = ()      )	->	None:
							lowerCAmelCase    =	None

							if data:
								for item in data:
									self.insert(A_      )


						def 				__snake_case    (					self	,       A_      )	->	None:
							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root	,       RandomizedHeapNode(A_      )      )


						def 				__snake_case    (					self      )	->	T | None:
							lowerCAmelCase    =	self.top()

							if self._root is None:
								return None

							lowerCAmelCase    =	RandomizedHeapNode.merge(self._root.left	,       self._root.right      )

							return result


						def 				__snake_case    (					self      )	->	T:
							if not self._root:
								raise IndexError("""Can't get top element for the empty heap."""      )
							return self._root.value


						def 				__snake_case    (					self      )	->	None:
							lowerCAmelCase    =	None


						def 				__snake_case    (					self      )	->	list[Any]:
							lowerCAmelCase    =	[]
							while self:
								result.append(self.pop()      )

							return result




						def __bool__(					self      )	->	bool:
							return self._root is not None


if __name__ == "__main__":
				import doctest

				doctest.testmod()