



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()
UpperCAmelCase_				:     Dict              =    logging.get_logger(__name__)

UpperCAmelCase_				:     List[str]              =    {
    "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",
}
UpperCAmelCase_				:     str              =    [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]
def    UpperCamelCase		(  _A					: Union[str, Any]					,				_A					: Optional[int]					,				_A					: str					,				_A					: List[str]					,				_A					: Union[str, Any]							)->  Tuple:





						"""simple docstring"""





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

						if weight_type is not None:
												A__					=  getattr(_lowerCAmelCase					,				_lowerCAmelCase							).shape
						else:
												A__					=  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":
												A__					=  value
						elif weight_type == "weight_g":
												A__					=  value
						elif weight_type == "weight_v":
												A__					=  value
						elif weight_type == "bias":
												A__					=  value
						elif weight_type == "running_mean":
												A__					=  value
						elif weight_type == "running_var":
												A__					=  value
						elif weight_type == "num_batches_tracked":
												A__					=  value
						elif weight_type == "inv_freq":
												A__					=  value
						else:
												A__					=  value

						logger.info(f"""{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}."""							)
def    UpperCamelCase		(  _A					: int					,				_A					: Optional[int]					,				_A					: List[str]							)->  Tuple:





						"""simple docstring"""





						A__					=  []
						A__					=  fairseq_model.state_dict()

						A__					=  hf_model.wavaveca_conformer.feature_extractor

						for name, value in fairseq_dict.items():
												A__					=  False
												if "conv_layers" in name:
																		load_conv_layer(
																		    _lowerCAmelCase					,				_lowerCAmelCase					,				_lowerCAmelCase					,				_lowerCAmelCase					,				hf_model.config.feat_extract_norm == "group"					,				)
																		A__					=  True
												else:
																		for key, mapped_key in MAPPING.items():
																								A__					=  """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]:
																														A__					=  True
																														if "*" in mapped_key:
																																				A__					=  name.split(_lowerCAmelCase							)[0].split("."							)[-2]
																																				A__					=  mapped_key.replace("*"					,				_lowerCAmelCase							)
																														if "pos_bias_u" in name:
																																				A__					=  None
																														elif "pos_bias_v" in name:
																																				A__					=  None
																														elif "weight_g" in name:
																																				A__					=  """weight_g"""
																														elif "weight_v" in name:
																																				A__					=  """weight_v"""
																														elif "bias" in name:
																																				A__					=  """bias"""
																														elif "weight" in name:
																																				# TODO: don't match quantizer.weight_proj
																																				A__					=  """weight"""
																														elif "running_mean" in name:
																																				A__					=  """running_mean"""
																														elif "inv_freq" in name:
																																				A__					=  """inv_freq"""
																														elif "running_var" in name:
																																				A__					=  """running_var"""
																														elif "num_batches_tracked" in name:
																																				A__					=  """num_batches_tracked"""
																														else:
																																				A__					=  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    UpperCamelCase		(  _A					: int					,				_A					: Dict					,				_A					: Tuple					,				_A					: Optional[Any]					,				_A					: int							)->  List[Any]:





						"""simple docstring"""





						A__					=  full_name.split("conv_layers."							)[-1]
						A__					=  name.split("."							)
						A__					=  int(items[0]							)
						A__					=  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."""							)
																		A__					=  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."""							)
																		A__					=  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."""							)
																		A__					=  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."""							)
																		A__					=  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    UpperCamelCase		(  _A					: Union[str, Any]					,				_A					: Union[str, Any]					,				_A					: Any=None					,				_A					: List[str]=None					,				_A					: Any=True							)->  List[str]:





						"""simple docstring"""





						if config_path is not None:
												A__					=  WavaVecaConformerConfig.from_pretrained(_lowerCAmelCase					,				hidden_act="swish"							)
						else:
												A__					=  WavaVecaConformerConfig()

						if "rope" in checkpoint_path:
												A__					=  """rotary"""

						if is_finetuned:
												if dict_path:
																		A__					=  Dictionary.load(_lowerCAmelCase							)

																		# important change bos & pad token id since CTC symbol is <pad> and
																		# not <s> as in fairseq
																		A__					=  target_dict.pad_index
																		A__					=  target_dict.bos_index
																		A__					=  target_dict.eos_index
																		A__					=  len(target_dict.symbols							)
																		A__					=  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							)
																		A__					=  target_dict.indices

																		# fairseq has the <pad> and <s> switched
																		A__					=  0
																		A__					=  1
																		with open(_lowerCAmelCase					,				"w"					,				encoding="utf-8"							) as vocab_handle:
																								json.dump(_lowerCAmelCase					,				_lowerCAmelCase							)
																		A__					=  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					,				)
																		A__					=  True if config.feat_extract_norm == """layer""" else False
																		A__					=  WavaVecaFeatureExtractor(
																		    feature_size=1					,				sampling_rate=16000					,				padding_value=0					,				do_normalize=_lowerCAmelCase					,				return_attention_mask=_lowerCAmelCase					,				)
																		A__					=  WavaVecaProcessor(feature_extractor=_lowerCAmelCase					,				tokenizer=_lowerCAmelCase							)
																		processor.save_pretrained(_lowerCAmelCase							)

												A__					=  WavaVecaConformerForCTC(_lowerCAmelCase							)
						else:
												A__					=  WavaVecaConformerForPreTraining(_lowerCAmelCase							)

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

												A__					=  fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,				task=_lowerCAmelCase							)

						A__					=  model[0].eval()

						recursively_load_weights(_lowerCAmelCase					,				_lowerCAmelCase					,				not is_finetuned							)

						hf_wavavec.save_pretrained(_lowerCAmelCase							)


if __name__ == "__main__":
						UpperCAmelCase_				:     Optional[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(
						    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
						)
						UpperCAmelCase_				:     str              =    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
						)










'''simple docstring'''





# flake8: noqa
# Lint as: python3
__a  			=						[
    "VerificationMode",
    "Version",
    "disable_progress_bar",
    "enable_progress_bar",
    "is_progress_bar_enabled",
    "experimental",
]

from .info_utils import VerificationMode
from .logging import disable_progress_bar, enable_progress_bar, is_progress_bar_enabled
from .version import Version
from .experimental import experimental
