




"""simple docstring"""





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()
A				=							logging.get_logger(__name__)

A				=							{
    """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""",
}
A				=							[
    """lm_head""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      Union[str, Any]:


			"""simple docstring"""
			for attribute in key.split("."				):
						__UpperCAmelCase						:			List[str]												=       getattr(UpperCamelCase					,							UpperCamelCase				)

			if weight_type is not None:
						__UpperCAmelCase						:			List[str]												=       getattr(UpperCamelCase					,							UpperCamelCase				).shape
			else:
						__UpperCAmelCase						:			Optional[int]												=       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":
						__UpperCAmelCase						:			Union[str, Any]												=       value
			elif weight_type == "weight_g":
						__UpperCAmelCase						:			List[Any]												=       value
			elif weight_type == "weight_v":
						__UpperCAmelCase						:			Optional[int]												=       value
			elif weight_type == "bias":
						__UpperCAmelCase						:			int												=       value
			elif weight_type == "running_mean":
						__UpperCAmelCase						:			int												=       value
			elif weight_type == "running_var":
						__UpperCAmelCase						:			Optional[int]												=       value
			elif weight_type == "num_batches_tracked":
						__UpperCAmelCase						:			Any												=       value
			elif weight_type == "inv_freq":
						__UpperCAmelCase						:			List[str]												=       value
			else:
						__UpperCAmelCase						:			Union[str, Any]												=       value

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

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)      ->      List[Any]:


			"""simple docstring"""
			__UpperCAmelCase						:			Union[str, Any]												=       []
			__UpperCAmelCase						:			Optional[int]												=       fairseq_model.state_dict()

			__UpperCAmelCase						:			str												=       hf_model.wavaveca_conformer.feature_extractor

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


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[Any]												=       full_name.split("conv_layers."				)[-1]
			__UpperCAmelCase						:			List[Any]												=       name.split("."				)
			__UpperCAmelCase						:			List[Any]												=       int(items[0]				)
			__UpperCAmelCase						:			List[Any]												=       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."				)
									__UpperCAmelCase						:			Union[str, 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."				)
									__UpperCAmelCase						:			Dict												=       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."				)
									__UpperCAmelCase						:			int												=       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."				)
									__UpperCAmelCase						:			Any												=       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        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase=None					,							UpperCamelCase=None					,							UpperCamelCase=True				)      ->      Optional[int]:


			"""simple docstring"""
			if config_path is not None:
						__UpperCAmelCase						:			int												=       WavaVecaConformerConfig.from_pretrained(UpperCamelCase					,							hidden_act="swish"				)
			else:
						__UpperCAmelCase						:			List[Any]												=       WavaVecaConformerConfig()

			if "rope" in checkpoint_path:
						__UpperCAmelCase						:			str												=       "rotary"

			if is_finetuned:
						if dict_path:
									__UpperCAmelCase						:			int												=       Dictionary.load(UpperCamelCase				)

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

									# fairseq has the <pad> and <s> switched
									__UpperCAmelCase						:			Any												=       0
									__UpperCAmelCase						:			Tuple												=       1
									with open(UpperCamelCase					,							"w"					,							encoding="utf-8"				) as vocab_handle:
												json.dump(UpperCamelCase					,							UpperCamelCase				)
									__UpperCAmelCase						:			Any												=       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					,							)
									__UpperCAmelCase						:			List[str]												=       True if config.feat_extract_norm == "layer" else False
									__UpperCAmelCase						:			str												=       WavaVecaFeatureExtractor(
									    feature_size=1					,							sampling_rate=1_6000					,							padding_value=0					,							do_normalize=UpperCamelCase					,							return_attention_mask=UpperCamelCase					,							)
									__UpperCAmelCase						:			List[Any]												=       WavaVecaProcessor(feature_extractor=UpperCamelCase					,							tokenizer=UpperCamelCase				)
									processor.save_pretrained(UpperCamelCase				)

						__UpperCAmelCase						:			Any												=       WavaVecaConformerForCTC(UpperCamelCase				)
			else:
						__UpperCAmelCase						:			int												=       WavaVecaConformerForPreTraining(UpperCamelCase				)

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

						__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			Union[str, Any]												=       fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					,							task=UpperCamelCase				)

			__UpperCAmelCase						:			List[str]												=       model[0].eval()

			recursively_load_weights(UpperCamelCase					,							UpperCamelCase					,							not is_finetuned				)

			hf_wavavec.save_pretrained(UpperCamelCase				)


if __name__ == "__main__":
					A				=							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				=							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"""

from typing import TYPE_CHECKING

from ..utils import _LazyModule


A_						:    str				=      {
    'config': [
        'EXTERNAL_DATA_FORMAT_SIZE_LIMIT',
        'OnnxConfig',
        'OnnxConfigWithPast',
        'OnnxSeq2SeqConfigWithPast',
        'PatchingSpec',
    ],
    'convert': ['export', 'validate_model_outputs'],
    'features': ['FeaturesManager'],
    'utils': ['ParameterFormat', 'compute_serialized_parameters_size'],
}


if TYPE_CHECKING:
							from .config import (
							    EXTERNAL_DATA_FORMAT_SIZE_LIMIT,
							    OnnxConfig,
							    OnnxConfigWithPast,
							    OnnxSeqaSeqConfigWithPast,
							    PatchingSpec,
							)
							from .convert import export, validate_model_outputs
							from .features import FeaturesManager
							from .utils import ParameterFormat, compute_serialized_parameters_size

else:
							import sys

							A_						:    Optional[Any]				=      _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





