


import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    HubertConfig,
    HubertForCTC,
    HubertModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
A  :     Optional[Any]    					=    logging.get_logger(__name__)

A  :     Tuple    					=    {
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}


def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			for attribute in key.split("."   ):
						__lowerCAmelCase       					=    getattr(_UpperCamelCase					,				_UpperCamelCase   )

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

			'''simple docstring'''




			__lowerCAmelCase       					=    []
			__lowerCAmelCase       					=    fairseq_model.state_dict()

			__lowerCAmelCase       					=    hf_model.hubert.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						__lowerCAmelCase       					=    False
						if "conv_layers" in name:
									load_conv_layer(
									    _UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				hf_model.config.feat_extract_norm == "group"					,				)
									__lowerCAmelCase       					=    True
						else:
									for key, mapped_key in MAPPING.items():
												__lowerCAmelCase       					=    "hubert." + 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] and not is_finetuned):
															__lowerCAmelCase       					=    True
															if "*" in mapped_key:
																		__lowerCAmelCase       					=    name.split(_UpperCamelCase   )[0].split("."   )[-2]
																		__lowerCAmelCase       					=    mapped_key.replace("*"					,				_UpperCamelCase   )
															if "weight_g" in name:
																		__lowerCAmelCase       					=    "weight_g"
															elif "weight_v" in name:
																		__lowerCAmelCase       					=    "weight_v"
															elif "weight" in name:
																		__lowerCAmelCase       					=    "weight"
															elif "bias" in name:
																		__lowerCAmelCase       					=    "bias"
															else:
																		__lowerCAmelCase       					=    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 _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   ):

			'''simple docstring'''




			__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:
									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."
									)
									__lowerCAmelCase       					=    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."
									)
									__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:
									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."
									)
									__lowerCAmelCase       					=    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."
									)
									__lowerCAmelCase       					=    value
									logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."   )
			else:
						unused_weights.append(_UpperCamelCase   )


@torch.no_grad()
def _lowerCamelCase			(	_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase=None					,				_UpperCamelCase=None					,				_UpperCamelCase=True   ):

			'''simple docstring'''




			if config_path is not None:
						__lowerCAmelCase       					=    HubertConfig.from_pretrained(_UpperCamelCase   )
			else:
						__lowerCAmelCase       					=    HubertConfig()

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

									# 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(_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   )
									__lowerCAmelCase       					=    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					,				)
									__lowerCAmelCase       					=    True if config.feat_extract_norm == "layer" else False
									__lowerCAmelCase       					=    WavaVecaFeatureExtractor(
									    feature_size=1					,				sampling_rate=1_6000					,				padding_value=0					,				do_normalize=_UpperCamelCase					,				return_attention_mask=_UpperCamelCase					,				)
									__lowerCAmelCase       					=    WavaVecaProcessor(feature_extractor=_UpperCamelCase					,				tokenizer=_UpperCamelCase   )
									processor.save_pretrained(_UpperCamelCase   )

						__lowerCAmelCase       					=    HubertForCTC(_UpperCamelCase   )
			else:
						__lowerCAmelCase       					=    HubertModel(_UpperCamelCase   )

			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					,			__lowerCAmelCase					,			__lowerCAmelCase       					=    fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]   )

			__lowerCAmelCase       					=    model[0].eval()

			recursively_load_weights(_UpperCamelCase					,				_UpperCamelCase					,				_UpperCamelCase   )

			hf_wavavec.save_pretrained(_UpperCamelCase   )


if __name__ == "__main__":
	A  :     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(
	    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
	)
	A  :     List[Any]    					=    parser.parse_args()
	convert_hubert_checkpoint(
	    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
	)








"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


A  :     Optional[int]    					=    logging.get_logger(__name__)

A  :     List[str]    					=    {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}
class 						_UpperCamelCase						(     lowerCAmelCase__ ,lowerCAmelCase__	):
							'''simple docstring'''







							__UpperCAmelCase		:					Any          ="""bit"""
							__UpperCAmelCase		:					Optional[int]          =["""preactivation""", """bottleneck"""]
							__UpperCAmelCase		:					List[str]          =["""SAME""", """VALID"""]

							def __init__(  self					,     __a=3					,     __a=64					,     __a=[2_56, 5_12, 10_24, 20_48]					,     __a=[3, 4, 6, 3]					,     __a="preactivation"					,     __a="relu"					,     __a=None					,     __a=32					,     __a=0.0					,     __a=False					,     __a=32					,     __a=1					,     __a=None					,     __a=None					,     **__a					,     ):
										super().__init__(**__a			)
										if layer_type not in self.layer_types:
													raise ValueError(f"layer_type={layer_type} is not one of {','.join(self.layer_types			)}"			)
										if global_padding is not None:
													if global_padding.upper() in self.supported_padding:
																__lowerCAmelCase       					=    global_padding.upper()
													else:
																raise ValueError(f"Padding strategy {global_padding} not supported"			)
										__lowerCAmelCase       					=    num_channels
										__lowerCAmelCase       					=    embedding_size
										__lowerCAmelCase       					=    hidden_sizes
										__lowerCAmelCase       					=    depths
										__lowerCAmelCase       					=    layer_type
										__lowerCAmelCase       					=    hidden_act
										__lowerCAmelCase       					=    global_padding
										__lowerCAmelCase       					=    num_groups
										__lowerCAmelCase       					=    drop_path_rate
										__lowerCAmelCase       					=    embedding_dynamic_padding
										__lowerCAmelCase       					=    output_stride
										__lowerCAmelCase       					=    width_factor

										__lowerCAmelCase       					=    ["stem"] + [f"stage{idx}" for idx in range(1					,     len(__a			) + 1			)]
										__lowerCAmelCase					,			__lowerCAmelCase       					=    get_aligned_output_features_output_indices(
										    out_features=__a					,     out_indices=__a					,     stage_names=self.stage_names			)




