



import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
_snake_case	=						logging.get_logger(__name__)

_snake_case	=						{
    "post_extract_proj": "feature_projection.projection",
    "encoder.pos_conv.0": "encoder.pos_conv_embed.conv",
    "self_attn.linear_k": "encoder.layers.*.self_attn.linear_k",
    "self_attn.linear_v": "encoder.layers.*.self_attn.linear_v",
    "self_attn.linear_q": "encoder.layers.*.self_attn.linear_q",
    "self_attn.pos_bias_u": "encoder.layers.*.self_attn.pos_bias_u",
    "self_attn.pos_bias_v": "encoder.layers.*.self_attn.pos_bias_v",
    "self_attn.linear_out": "encoder.layers.*.self_attn.linear_out",
    "self_attn.linear_pos": "encoder.layers.*.self_attn.linear_pos",
    "self_attn.rotary_emb": "encoder.embed_positions",
    "self_attn_layer_norm": "encoder.layers.*.self_attn_layer_norm",
    "conv_module.pointwise_conv1": "encoder.layers.*.conv_module.pointwise_conv1",
    "conv_module.pointwise_conv2": "encoder.layers.*.conv_module.pointwise_conv2",
    "conv_module.depthwise_conv": "encoder.layers.*.conv_module.depthwise_conv",
    "conv_module.batch_norm": "encoder.layers.*.conv_module.batch_norm",
    "conv_module.layer_norm": "encoder.layers.*.conv_module.layer_norm",
    "ffn1.w_1": "encoder.layers.*.ffn1.intermediate_dense",
    "ffn1.w_2": "encoder.layers.*.ffn1.output_dense",
    "ffn1.layer_norm": "encoder.layers.*.ffn1_layer_norm",
    "ffn2.w_1": "encoder.layers.*.ffn2.intermediate_dense",
    "ffn2.w_2": "encoder.layers.*.ffn2.output_dense",
    "ffn2.layer_norm": "encoder.layers.*.ffn2_layer_norm",
    "final_layer_norm": "encoder.layers.*.final_layer_norm",
    "encoder.layer_norm": "encoder.layer_norm",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
}
_snake_case	=						[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							for attribute in key.split("."						):
														_lowerCAmelCase  :							int				     =					getattr(_lowerCamelCase       ,				_lowerCamelCase						)

							if weight_type is not None:
														_lowerCAmelCase  :							Dict				     =					getattr(_lowerCamelCase       ,				_lowerCamelCase						).shape
							else:
														_lowerCAmelCase  :							str				     =					hf_pointer.shape

							if hf_shape != value.shape:
														raise ValueError(
														    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  :							Optional[Any]				     =					value
							elif weight_type == "weight_g":
														_lowerCAmelCase  :							List[str]				     =					value
							elif weight_type == "weight_v":
														_lowerCAmelCase  :							Dict				     =					value
							elif weight_type == "bias":
														_lowerCAmelCase  :							int				     =					value
							elif weight_type == "running_mean":
														_lowerCAmelCase  :							Optional[Any]				     =					value
							elif weight_type == "running_var":
														_lowerCAmelCase  :							Tuple				     =					value
							elif weight_type == "num_batches_tracked":
														_lowerCAmelCase  :							Tuple				     =					value
							elif weight_type == "inv_freq":
														_lowerCAmelCase  :							List[Any]				     =					value
							else:
														_lowerCAmelCase  :							Tuple				     =					value

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






def        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							List[Any]				     =					[]
							_lowerCAmelCase  :							Optional[int]				     =					fairseq_model.state_dict()

							_lowerCAmelCase  :							Dict				     =					hf_model.wavaveca_conformer.feature_extractor

							for name, value in fairseq_dict.items():
														_lowerCAmelCase  :							Tuple				     =					False
														if "conv_layers" in name:
																					load_conv_layer(
																					    _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				hf_model.config.feat_extract_norm == "group"       ,				)
																					_lowerCAmelCase  :							Tuple				     =					True
														else:
																					for key, mapped_key in MAPPING.items():
																												_lowerCAmelCase  :							Union[str, Any]				     =					"wav2vec2_conformer." + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
																												if key in name or key.split("w2v_model."						)[-1] == name.split("."						)[0]:
																																			_lowerCAmelCase  :							str				     =					True
																																			if "*" in mapped_key:
																																										_lowerCAmelCase  :							Union[str, Any]				     =					name.split(_lowerCamelCase						)[0].split("."						)[-2]
																																										_lowerCAmelCase  :							Tuple				     =					mapped_key.replace("*"       ,				_lowerCamelCase						)
																																			if "pos_bias_u" in name:
																																										_lowerCAmelCase  :							Optional[Any]				     =					None
																																			elif "pos_bias_v" in name:
																																										_lowerCAmelCase  :							List[Any]				     =					None
																																			elif "weight_g" in name:
																																										_lowerCAmelCase  :							List[str]				     =					"weight_g"
																																			elif "weight_v" in name:
																																										_lowerCAmelCase  :							Union[str, Any]				     =					"weight_v"
																																			elif "bias" in name:
																																										_lowerCAmelCase  :							int				     =					"bias"
																																			elif "weight" in name:
																																										# TODO: don't match quantizer.weight_proj
																																										_lowerCAmelCase  :							Any				     =					"weight"
																																			elif "running_mean" in name:
																																										_lowerCAmelCase  :							List[str]				     =					"running_mean"
																																			elif "inv_freq" in name:
																																										_lowerCAmelCase  :							Any				     =					"inv_freq"
																																			elif "running_var" in name:
																																										_lowerCAmelCase  :							List[str]				     =					"running_var"
																																			elif "num_batches_tracked" in name:
																																										_lowerCAmelCase  :							Optional[int]				     =					"num_batches_tracked"
																																			else:
																																										_lowerCAmelCase  :							int				     =					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        A       (      _lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase       ,				_lowerCamelCase						):





							'''simple docstring'''




							_lowerCAmelCase  :							Optional[Any]				     =					full_name.split("conv_layers."						)[-1]
							_lowerCAmelCase  :							Any				     =					name.split("."						)
							_lowerCAmelCase  :							Optional[int]				     =					int(items[0]						)
							_lowerCAmelCase  :							List[str]				     =					int(items[1]						)

							if type_id == 0:
														if "bias" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
																												raise ValueError(
																												    F"{full_name} has size {value.shape}, but"
																												    F" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."						)
																					_lowerCAmelCase  :							Any				     =					value
																					logger.info(F"Feat extract conv layer {layer_id} was initialized from {full_name}."						)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
																												raise ValueError(
																												    F"{full_name} has size {value.shape}, but"
																												    F" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."						)
																					_lowerCAmelCase  :							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:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
																												raise ValueError(
																												    F"{full_name} has size {value.shape}, but"
																												    F" {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found."						)
																					_lowerCAmelCase  :							Dict				     =					value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
														elif "weight" in name:
																					if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
																												raise ValueError(
																												    F"{full_name} has size {value.shape}, but"
																												    F" {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found."						)
																					_lowerCAmelCase  :							int				     =					value
																					logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."						)
							else:
														unused_weights.append(_lowerCamelCase						)






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





							'''simple docstring'''




							if config_path is not None:
														_lowerCAmelCase  :							int				     =					WavaVecaConformerConfig.from_pretrained(_lowerCamelCase       ,				hidden_act="swish"						)
							else:
														_lowerCAmelCase  :							Union[str, Any]				     =					WavaVecaConformerConfig()

							if "rope" in checkpoint_path:
														_lowerCAmelCase  :							List[Any]				     =					"rotary"

							if is_finetuned:
														if dict_path:
																					_lowerCAmelCase  :							Dict				     =					Dictionary.load(_lowerCamelCase						)

																					# important change bos & pad token id since CTC symbol is <pad> and
																					# not <s> as in fairseq
																					_lowerCAmelCase  :							Dict				     =					target_dict.pad_index
																					_lowerCAmelCase  :							Optional[Any]				     =					target_dict.bos_index
																					_lowerCAmelCase  :							List[Any]				     =					target_dict.eos_index
																					_lowerCAmelCase  :							List[str]				     =					len(target_dict.symbols						)
																					_lowerCAmelCase  :							Dict				     =					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						)
																					_lowerCAmelCase  :							List[str]				     =					target_dict.indices

																					# fairseq has the <pad> and <s> switched
																					_lowerCAmelCase  :							Union[str, Any]				     =					0
																					_lowerCAmelCase  :							str				     =					1
																					with open(_lowerCamelCase       ,				"w"       ,				encoding="utf-8"						) as vocab_handle:
																												json.dump(_lowerCamelCase       ,				_lowerCamelCase						)
																					_lowerCAmelCase  :							Optional[Any]				     =					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       ,				)
																					_lowerCAmelCase  :							Dict				     =					True if config.feat_extract_norm == "layer" else False
																					_lowerCAmelCase  :							Tuple				     =					WavaVecaFeatureExtractor(
																					    feature_size=1       ,				sampling_rate=16_000       ,				padding_value=0       ,				do_normalize=_lowerCamelCase       ,				return_attention_mask=_lowerCamelCase       ,				)
																					_lowerCAmelCase  :							str				     =					WavaVecaProcessor(feature_extractor=_lowerCamelCase       ,				tokenizer=_lowerCamelCase						)
																					processor.save_pretrained(_lowerCamelCase						)

														_lowerCAmelCase  :							str				     =					WavaVecaConformerForCTC(_lowerCamelCase						)
							else:
														_lowerCAmelCase  :							Dict				     =					WavaVecaConformerForPreTraining(_lowerCamelCase						)

							if is_finetuned:
														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							Dict				     =					fairseq.checkpoint_utils.load_model_ensemble_and_task(
														    [checkpoint_path]       ,				arg_overrides={"data": "/".join(dict_path.split("/"						)[:-1]						)}						)
							else:
														_lowerCAmelCase  :							int				     =					argparse.Namespace(task="audio_pretraining"						)
														_lowerCAmelCase  :							Dict				     =					fairseq.tasks.setup_task(_lowerCamelCase						)

														_lowerCAmelCase     ,					_lowerCAmelCase     ,					_lowerCAmelCase  :							List[str]				     =					fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]       ,				task=_lowerCamelCase						)

							_lowerCAmelCase  :							Optional[int]				     =					model[0].eval()

							recursively_load_weights(_lowerCamelCase       ,				_lowerCamelCase       ,				not is_finetuned						)

							hf_wavavec.save_pretrained(_lowerCamelCase						)


if __name__ == "__main__":
						_snake_case	=						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"
						)
						_snake_case	=						parser.parse_args()
						convert_wavaveca_conformer_checkpoint(
						    args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
						)





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


_snake_case	=						logging.get_logger(__name__)



class 						UpperCAmelCase_					( a):
							lowerCamelCase__         =	'upernet'







							def __init__(     self,    __a=None,    __a=512,    __a=0.02,    __a=[1, 2, 3, 6],    __a=True,    __a=0.4,    __a=384,    __a=256,    __a=1,    __a=False,    __a=255,    **__a,    ):

														'''simple docstring'''

														super().__init__(**__a)

														if backbone_config is None:
																					logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone.")
																					_lowerCAmelCase  :							List[str]				     =					CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"])
														elif isinstance(__a,    __a):
																					_lowerCAmelCase  :							List[Any]				     =					backbone_config.get("model_type")
																					_lowerCAmelCase  :							Dict				     =					CONFIG_MAPPING[backbone_model_type]
																					_lowerCAmelCase  :							Optional[Any]				     =					config_class.from_dict(__a)

														_lowerCAmelCase  :							Tuple				     =					backbone_config
														_lowerCAmelCase  :							List[Any]				     =					hidden_size
														_lowerCAmelCase  :							Union[str, Any]				     =					initializer_range
														_lowerCAmelCase  :							str				     =					pool_scales
														_lowerCAmelCase  :							List[str]				     =					use_auxiliary_head
														_lowerCAmelCase  :							Dict				     =					auxiliary_loss_weight
														_lowerCAmelCase  :							Tuple				     =					auxiliary_in_channels
														_lowerCAmelCase  :							Optional[Any]				     =					auxiliary_channels
														_lowerCAmelCase  :							str				     =					auxiliary_num_convs
														_lowerCAmelCase  :							Union[str, Any]				     =					auxiliary_concat_input
														_lowerCAmelCase  :							Dict				     =					loss_ignore_index







							def      snake_case__   (     self):

														'''simple docstring'''

														_lowerCAmelCase  :							Any				     =					copy.deepcopy(self.__dict__)
														_lowerCAmelCase  :							List[Any]				     =					self.backbone_config.to_dict()
														_lowerCAmelCase  :							Optional[Any]				     =					self.__class__.model_type
														return output
