




import argparse

import torch

# Step 1. clone https://github.com/microsoft/unilm
# Step 2. git checkout to https://github.com/microsoft/unilm/commit/b94ec76c36f02fb2b0bf0dcb0b8554a2185173cd
# Step 3. cd unilm
# Step 4. ln -s $(realpath wavlm/modules.py) ./  # create simlink
# import classes
from unilm.wavlm.WavLM import WavLM as WavLMOrig
from unilm.wavlm.WavLM import WavLMConfig as WavLMConfigOrig

from transformers import WavLMConfig, WavLMModel, logging


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

UpperCamelCase__         =      {
    """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.grep_linear""": """encoder.layers.*.attention.gru_rel_pos_linear""",
    """self_attn.relative_attention_bias""": """encoder.layers.*.attention.rel_attn_embed""",
    """self_attn.grep_a""": """encoder.layers.*.attention.gru_rel_pos_const""",
    """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""",
    """quantizer.weight_proj""": """quantizer.weight_proj""",
    """quantizer.vars""": """quantizer.codevectors""",
    """project_q""": """project_q""",
    """final_proj""": """project_hid""",
    """w2v_encoder.proj""": """ctc_proj""",
    """mask_emb""": """masked_spec_embed""",
}
UpperCamelCase__         =      [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]







def      _a				(						SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    Tuple				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]				,				SCREAMING_SNAKE_CASE_							:    str					):
					for attribute in key.split("."					):
										__lowerCAmelCase           =	getattr(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					if weight_type is not None:
										__lowerCAmelCase           =	getattr(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					).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      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[int]				,				SCREAMING_SNAKE_CASE_							:    Dict					):
					__lowerCAmelCase           =	[]
					__lowerCAmelCase           =	fairseq_model.state_dict()

					__lowerCAmelCase           =	hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										__lowerCAmelCase           =	False
										if "conv_layers" in name:
															load_conv_layer(
															    SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				hf_model.config.feat_extract_norm == "group"				,				)
															__lowerCAmelCase           =	True
										else:
															for key, mapped_key in MAPPING.items():
																				if key in name or key.split("w2v_model."					)[-1] == name.split("."					)[0]:
																									__lowerCAmelCase           =	True
																									if "*" in mapped_key:
																														__lowerCAmelCase           =	name.split(SCREAMING_SNAKE_CASE_					)[0].split("."					)[-2]
																														__lowerCAmelCase           =	mapped_key.replace("*"				,				SCREAMING_SNAKE_CASE_					)
																									if "weight_g" in name:
																														__lowerCAmelCase           =	"weight_g"
																									elif "weight_v" in name:
																														__lowerCAmelCase           =	"weight_v"
																									elif "bias" in name and "relative_attention_bias" not in name:
																														__lowerCAmelCase           =	"bias"
																									elif "weight" in name:
																														# TODO: don't match quantizer.weight_proj
																														__lowerCAmelCase           =	"weight"
																									else:
																														__lowerCAmelCase           =	None

																									set_recursively(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
																				continue
										if not is_used:
															unused_weights.append(SCREAMING_SNAKE_CASE_					)

					logger.warning(F"""Unused weights: {unused_weights}"""					)







def      _a				(						SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    Any				,				SCREAMING_SNAKE_CASE_							:    List[Any]				,				SCREAMING_SNAKE_CASE_							:    List[str]					):
					__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(SCREAMING_SNAKE_CASE_					)







@torch.no_grad()
def      _a				(						SCREAMING_SNAKE_CASE_							:    str				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]=None					):
					# load the pre-trained checkpoints
					__lowerCAmelCase           =	torch.load(SCREAMING_SNAKE_CASE_					)
					__lowerCAmelCase           =	WavLMConfigOrig(checkpoint["cfg"]					)
					__lowerCAmelCase           =	WavLMOrig(SCREAMING_SNAKE_CASE_					)
					model.load_state_dict(checkpoint["model"]					)
					model.eval()

					if config_path is not None:
										__lowerCAmelCase           =	WavLMConfig.from_pretrained(SCREAMING_SNAKE_CASE_					)
					else:
										__lowerCAmelCase           =	WavLMConfig()

					__lowerCAmelCase           =	WavLMModel(SCREAMING_SNAKE_CASE_					)

					recursively_load_weights(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)

					hf_wavlm.save_pretrained(SCREAMING_SNAKE_CASE_					)


if __name__ == "__main__":
					UpperCamelCase__         =      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("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
					UpperCamelCase__         =      parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)










from pathlib import Path

import fire
from tqdm import tqdm







def      _a				(						SCREAMING_SNAKE_CASE_							:    Dict="ro"				,				SCREAMING_SNAKE_CASE_							:    Union[str, Any]="en"				,				SCREAMING_SNAKE_CASE_							:    Optional[Any]="wmt16"				,				SCREAMING_SNAKE_CASE_							:    List[str]=None					):
					try:
										import datasets
					except (ModuleNotFoundError, ImportError):
										raise ImportError("run pip install datasets"					)
					__lowerCAmelCase           =	F"""{src_lang}-{tgt_lang}"""
					print(F"""Converting {dataset}-{pair}"""					)
					__lowerCAmelCase           =	datasets.load_dataset(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_					)
					if save_dir is None:
										__lowerCAmelCase           =	F"""{dataset}-{pair}"""
					__lowerCAmelCase           =	Path(SCREAMING_SNAKE_CASE_					)
					save_dir.mkdir(exist_ok=SCREAMING_SNAKE_CASE_					)

					for split in ds.keys():
										print(F"""Splitting {split} with {ds[split].num_rows} records"""					)

										# to save to val.source, val.target like summary datasets
										__lowerCAmelCase           =	"val" if split == "validation" else split
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.source"""					)
										__lowerCAmelCase           =	save_dir.joinpath(F"""{fn}.target"""					)
										__lowerCAmelCase           =	src_path.open("w+"					)
										__lowerCAmelCase           =	tgt_path.open("w+"					)

										# reader is the bottleneck so writing one record at a time doesn't slow things down
										for x in tqdm(ds[split]					):
															__lowerCAmelCase           =	x["translation"]
															src_fp.write(ex[src_lang] + "\n"					)
															tgt_fp.write(ex[tgt_lang] + "\n"					)

					print(F"""Saved {dataset} dataset to {save_dir}"""					)


if __name__ == "__main__":
					fire.Fire(download_wmt_dataset)




