

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

lowerCAmelCase							:  Optional[Any]   	=						{
    """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 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						for attribute in key.split("."    ):
												SCREAMING_SNAKE_CASE_: Optional[Any]  		=       getattr(_UpperCAmelCase				,	_UpperCAmelCase    )

						if weight_type is not None:
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       getattr(_UpperCAmelCase				,	_UpperCAmelCase    ).shape
						else:
												SCREAMING_SNAKE_CASE_: List[str]  		=       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_: Optional[Any]  		=       value
						elif weight_type == "weight_g":
												SCREAMING_SNAKE_CASE_: int  		=       value
						elif weight_type == "weight_v":
												SCREAMING_SNAKE_CASE_: Any  		=       value
						elif weight_type == "bias":
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       value
						else:
												SCREAMING_SNAKE_CASE_: List[Any]  		=       value

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







def 			A_      (    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       []
						SCREAMING_SNAKE_CASE_: int  		=       fairseq_model.state_dict()

						SCREAMING_SNAKE_CASE_: Tuple  		=       hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

						for name, value in fairseq_dict.items():
												SCREAMING_SNAKE_CASE_: Optional[int]  		=       False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	hf_model.config.feat_extract_norm == "group"				,	)
																		SCREAMING_SNAKE_CASE_: Any  		=       True
												else:
																		for key, mapped_key in MAPPING.items():
																								SCREAMING_SNAKE_CASE_: Dict  		=       "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_: List[Any]  		=       True
																														if "*" in mapped_key:
																																				SCREAMING_SNAKE_CASE_: Any  		=       name.split(_UpperCAmelCase    )[0].split("."    )[-2]
																																				SCREAMING_SNAKE_CASE_: Tuple  		=       mapped_key.replace("*"				,	_UpperCAmelCase    )
																														if "weight_g" in name:
																																				SCREAMING_SNAKE_CASE_: Tuple  		=       "weight_g"
																														elif "weight_v" in name:
																																				SCREAMING_SNAKE_CASE_: Optional[int]  		=       "weight_v"
																														elif "weight" in name:
																																				SCREAMING_SNAKE_CASE_: Optional[int]  		=       "weight"
																														elif "bias" in name:
																																				SCREAMING_SNAKE_CASE_: Any  		=       "bias"
																														else:
																																				SCREAMING_SNAKE_CASE_: Tuple  		=       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				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       full_name.split("conv_layers."    )[-1]
						SCREAMING_SNAKE_CASE_: List[Any]  		=       name.split("."    )
						SCREAMING_SNAKE_CASE_: int  		=       int(items[0]    )
						SCREAMING_SNAKE_CASE_: str  		=       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_: Tuple  		=       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_: 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."
																		)
																		SCREAMING_SNAKE_CASE_: int  		=       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_: Tuple  		=       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				,	_UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       SEWConfig()
						if is_finetuned:
												SCREAMING_SNAKE_CASE_: str  		=       model.wav_encoder.wav_model.cfg
						else:
												SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       model.cfg

						SCREAMING_SNAKE_CASE_: int  		=       fs_config.conv_bias
						SCREAMING_SNAKE_CASE_: List[str]  		=       eval(fs_config.conv_feature_layers    )
						SCREAMING_SNAKE_CASE_: int  		=       [x[0] for x in conv_layers]
						SCREAMING_SNAKE_CASE_: str  		=       [x[1] for x in conv_layers]
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       [x[2] for x in conv_layers]
						SCREAMING_SNAKE_CASE_: int  		=       "gelu"
						SCREAMING_SNAKE_CASE_: Dict  		=       "layer" if fs_config.extractor_mode == "layer_norm" else "group"
						SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       0.0
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       fs_config.activation_fn.name
						SCREAMING_SNAKE_CASE_: Tuple  		=       fs_config.encoder_embed_dim
						SCREAMING_SNAKE_CASE_: List[str]  		=       0.0_2
						SCREAMING_SNAKE_CASE_: List[Any]  		=       fs_config.encoder_ffn_embed_dim
						SCREAMING_SNAKE_CASE_: List[str]  		=       1e-5
						SCREAMING_SNAKE_CASE_: Tuple  		=       fs_config.encoder_layerdrop
						SCREAMING_SNAKE_CASE_: Dict  		=       fs_config.encoder_attention_heads
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       fs_config.conv_pos_groups
						SCREAMING_SNAKE_CASE_: Any  		=       fs_config.conv_pos
						SCREAMING_SNAKE_CASE_: Tuple  		=       len(_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       fs_config.encoder_layers
						SCREAMING_SNAKE_CASE_: List[str]  		=       fs_config.squeeze_factor

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

						SCREAMING_SNAKE_CASE_: List[str]  		=       "Wav2Vec2FeatureExtractor"
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       "Wav2Vec2CTCTokenizer"

						return config







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

						if is_finetuned:
												SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       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_: Optional[Any]  		=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    )

						if config_path is not None:
												SCREAMING_SNAKE_CASE_: int  		=       SEWConfig.from_pretrained(_UpperCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE_: int  		=       convert_config(model[0]				,	_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: List[str]  		=       model[0].eval()

						SCREAMING_SNAKE_CASE_: str  		=       True if config.feat_extract_norm == "layer" else False
						SCREAMING_SNAKE_CASE_: Tuple  		=       WavaVecaFeatureExtractor(
						    feature_size=1				,	sampling_rate=1_60_00				,	padding_value=0				,	do_normalize=_UpperCAmelCase				,	return_attention_mask=_UpperCAmelCase				,	)

						if is_finetuned:
												if dict_path:
																		SCREAMING_SNAKE_CASE_: Any  		=       Dictionary.load(_UpperCAmelCase    )

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       target_dict.pad_index
																		SCREAMING_SNAKE_CASE_: Any  		=       target_dict.bos_index
																		SCREAMING_SNAKE_CASE_: Any  		=       target_dict.pad_index
																		SCREAMING_SNAKE_CASE_: str  		=       target_dict.bos_index
																		SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       target_dict.eos_index
																		SCREAMING_SNAKE_CASE_: str  		=       len(target_dict.symbols    )
																		SCREAMING_SNAKE_CASE_: List[Any]  		=       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    )
																		SCREAMING_SNAKE_CASE_: int  		=       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				,	)
																		SCREAMING_SNAKE_CASE_: Dict  		=       WavaVecaProcessor(feature_extractor=_UpperCAmelCase				,	tokenizer=_UpperCAmelCase    )
																		processor.save_pretrained(_UpperCAmelCase    )

												SCREAMING_SNAKE_CASE_: Any  		=       SEWForCTC(_UpperCAmelCase    )
						else:
												SCREAMING_SNAKE_CASE_: Tuple  		=       SEWModel(_UpperCAmelCase    )
												feature_extractor.save_pretrained(_UpperCAmelCase    )

						recursively_load_weights(_UpperCAmelCase				,	_UpperCAmelCase				,	_UpperCAmelCase    )

						hf_model.save_pretrained(_UpperCAmelCase    )


if __name__ == "__main__":
						lowerCAmelCase							:  List[Any]   	=						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"""
						)
						lowerCAmelCase							:  Any   	=						parser.parse_args()
						convert_sew_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
						)









class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     List[Any]   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[Any]):
									SCREAMING_SNAKE_CASE_: List[str]  		=       name
									SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       val



			def __str__( self  :     Dict):
									return F"{self.__class__.__name__}({self.name}, {self.val})"




			def __lt__( self  :     List[str]   ,						lowerCAmelCase__  :     Any):
									return self.val < other.val







class   __lowercase				:




			"""simple docstring"""







			def __init__( self  :     Tuple   ,						lowerCAmelCase__  :     Dict):
									SCREAMING_SNAKE_CASE_: str  		=       {}
									SCREAMING_SNAKE_CASE_: int  		=       {}
									SCREAMING_SNAKE_CASE_: Any  		=       self.build_heap(lowerCAmelCase__)



			def __getitem__( self  :     List[Any]   ,						lowerCAmelCase__  :     Dict):
									return self.get_value(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Dict):
									return (idx - 1) // 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Optional[Any]):
									return idx * 2 + 1



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Tuple):
									return idx * 2 + 2



			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[int]   ,						lowerCAmelCase__  :     Optional[int]):
									return self.heap_dict[key]



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]   ,						lowerCAmelCase__  :     Union[str, Any]):
									SCREAMING_SNAKE_CASE_: Tuple  		=       len(lowerCAmelCase__) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       self.get_parent_idx(lowerCAmelCase__)

									for idx, i in enumerate(lowerCAmelCase__):
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       idx
															SCREAMING_SNAKE_CASE_: str  		=       i.val

									for i in range(lowerCAmelCase__   ,						-1   ,						-1):
															self.sift_down(lowerCAmelCase__   ,						lowerCAmelCase__)
									return array



			def        _SCREAMING_SNAKE_CASE			( self  :     str   ,						lowerCAmelCase__  :     Optional[int]   ,						lowerCAmelCase__  :     List[str]):
									while True:
															SCREAMING_SNAKE_CASE_: Optional[Any]  		=       self.get_left_child_idx(lowerCAmelCase__)  # noqa: E741
															SCREAMING_SNAKE_CASE_: Dict  		=       self.get_right_child_idx(lowerCAmelCase__)

															SCREAMING_SNAKE_CASE_: int  		=       idx
															if l < len(lowerCAmelCase__) and array[l] < array[idx]:
																					SCREAMING_SNAKE_CASE_: List[str]  		=       l
															if r < len(lowerCAmelCase__) and array[r] < array[smallest]:
																					SCREAMING_SNAKE_CASE_: str  		=       r

															if smallest != idx:
																					SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Any  		=       array[smallest], array[idx]
																					(
																					    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    (
																					    SCREAMING_SNAKE_CASE_
																					)		,    
																					): Optional[Any]  		=       (
																					    self.idx_of_element[array[smallest]],
																					    self.idx_of_element[array[idx]],
																					)
																					SCREAMING_SNAKE_CASE_: Optional[int]  		=       smallest
															else:
																					break



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict   ,						lowerCAmelCase__  :     str):
									SCREAMING_SNAKE_CASE_: Any  		=       self.get_parent_idx(lowerCAmelCase__)
									while p >= 0 and self.heap[p] > self.heap[idx]:
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[Any]  		=       self.heap[idx], self.heap[p]
															SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       (
															    self.idx_of_element[self.heap[idx]],
															    self.idx_of_element[self.heap[p]],
															)
															SCREAMING_SNAKE_CASE_: Union[str, Any]  		=       p
															SCREAMING_SNAKE_CASE_: Optional[int]  		=       self.get_parent_idx(lowerCAmelCase__)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return self.heap[0]



			def        _SCREAMING_SNAKE_CASE			( self  :     Dict):
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: Tuple  		=       self.heap[-1], self.heap[0]
									SCREAMING_SNAKE_CASE_		,    SCREAMING_SNAKE_CASE_: List[str]  		=       (
									    self.idx_of_element[self.heap[-1]],
									    self.idx_of_element[self.heap[0]],
									)

									SCREAMING_SNAKE_CASE_: int  		=       self.heap.pop()
									del self.idx_of_element[x]
									self.sift_down(0   ,						self.heap)
									return x



			def        _SCREAMING_SNAKE_CASE			( self  :     Union[str, Any]   ,						lowerCAmelCase__  :     Tuple):
									self.heap.append(lowerCAmelCase__)
									SCREAMING_SNAKE_CASE_: List[str]  		=       len(self.heap) - 1
									SCREAMING_SNAKE_CASE_: List[str]  		=       node.val
									self.sift_up(len(self.heap) - 1)



			def        _SCREAMING_SNAKE_CASE			( self  :     List[Any]):
									return len(self.heap) == 0




			def        _SCREAMING_SNAKE_CASE			( self  :     Optional[Any]   ,						lowerCAmelCase__  :     Any   ,						lowerCAmelCase__  :     Optional[int]):
									assert (
									    self.heap[self.idx_of_element[node]].val > new_value
									), "newValue must be less that current value"
									SCREAMING_SNAKE_CASE_: Any  		=       new_value
									SCREAMING_SNAKE_CASE_: Tuple  		=       new_value
									self.sift_up(self.idx_of_element[node])







lowerCAmelCase							:  int   	=						Node("""R""", -1)
lowerCAmelCase							:  str   	=						Node("""B""", 6)
lowerCAmelCase							:  str   	=						Node("""A""", 3)
lowerCAmelCase							:  List[str]   	=						Node("""X""", 1)
lowerCAmelCase							:  Union[str, Any]   	=						Node("""E""", 4)

# Use one of these two ways to generate Min-Heap

# Generating Min-Heap from array
lowerCAmelCase							:  Optional[Any]   	=						MinHeap([r, b, a, x, e])

# Generating Min-Heap by Insert method
# myMinHeap.insert(a)
# myMinHeap.insert(b)
# myMinHeap.insert(x)
# myMinHeap.insert(r)
# myMinHeap.insert(e)

# Before
print("""Min Heap - before decrease key""")
for i in my_min_heap.heap:
						print(i)

print("""Min Heap - After decrease key of node [B -> -17]""")
my_min_heap.decrease_key(b, -17)

# After
for i in my_min_heap.heap:
						print(i)

if __name__ == "__main__":
						import doctest

						doctest.testmod()






