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()
__a  :     Union[str, Any]       =  logging.get_logger(__name__)

__a  :     Dict       =  {
    "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",
}
__a  :     Dict       =  [
    "ctc_proj",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
]





def 				UpperCAmelCase						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				for attribute in key.split('''.'''							):
								__lowercase 			=							getattr(_UpperCamelCase							,			_UpperCamelCase							)

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

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





def 				UpperCAmelCase						(    lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							[]
				__lowercase 			=							fairseq_model.state_dict()

				__lowercase 			=							hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__lowercase 			=							False
								if "conv_layers" in name:
												load_conv_layer(
												    _UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			_UpperCamelCase							,			hf_model.config.feat_extract_norm == '''group'''							,			)
												__lowercase 			=							True
								else:
												for key, mapped_key in MAPPING.items():
																if key in name or key.split('''w2v_model.'''							)[-1] == name.split('''.'''							)[0]:
																				__lowercase 			=							True
																				if "*" in mapped_key:
																								__lowercase 			=							name.split(_UpperCamelCase							)[0].split('''.'''							)[-2]
																								__lowercase 			=							mapped_key.replace('''*'''							,			_UpperCamelCase							)
																				if "weight_g" in name:
																								__lowercase 			=							'''weight_g'''
																				elif "weight_v" in name:
																								__lowercase 			=							'''weight_v'''
																				elif "bias" in name and "relative_attention_bias" not in name:
																								__lowercase 			=							'''bias'''
																				elif "weight" in name:
																								# TODO: don't match quantizer.weight_proj
																								__lowercase 			=							'''weight'''
																				else:
																								__lowercase 			=							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						(    lowercase							,			lowercase							,			lowercase							,			lowercase							,			lowercase							):







				"""simple docstring"""



				__lowercase 			=							full_name.split('''conv_layers.'''							)[-1]
				__lowercase 			=							name.split('''.'''							)
				__lowercase 			=							int(items[0]							)
				__lowercase 			=							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."
												)
												__lowercase 			=							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."
												)
												__lowercase 			=							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."
												)
												__lowercase 			=							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."
												)
												__lowercase 			=							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						(    lowercase							,			lowercase							,			lowercase=None							):







				"""simple docstring"""



				__lowercase 			=							torch.load(_UpperCamelCase							)
				__lowercase 			=							WavLMConfigOrig(checkpoint['''cfg''']							)
				__lowercase 			=							WavLMOrig(_UpperCamelCase							)
				model.load_state_dict(checkpoint['''model''']							)
				model.eval()

				if config_path is not None:
								__lowercase 			=							WavLMConfig.from_pretrained(_UpperCamelCase							)
				else:
								__lowercase 			=							WavLMConfig()

				__lowercase 			=							WavLMModel(_UpperCamelCase							)

				recursively_load_weights(_UpperCamelCase							,			_UpperCamelCase							)

				hf_wavlm.save_pretrained(_UpperCamelCase							)


if __name__ == "__main__":
		__a  :     List[str]       =  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""")
		__a  :     Optional[Any]       =  parser.parse_args()
		convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)



"""simple docstring"""


from __future__ import annotations


def _lowerCamelCase	(		_UpperCamelCase = 4      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			abs(_UpperCamelCase      ) or 4
			return [[1 + x + y * row_size for x in range(_UpperCamelCase      )] for y in range(_UpperCamelCase      )]


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_row(transpose(_UpperCamelCase      )      )
			# OR.. transpose(reverse_column(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_row(reverse_column(_UpperCamelCase      )      )
			# OR.. reverse_column(reverse_row(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			return reverse_column(transpose(_UpperCamelCase      )      )
			# OR.. transpose(reverse_row(matrix))


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[list(_UpperCamelCase      ) for x in zip(*_UpperCamelCase      )]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			matrix[::-1]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			__lowerCAmelCase				    =			[x[::-1] for x in matrix]
			return matrix


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			for i in matrix:
						print(*_UpperCamelCase      )


if __name__ == "__main__":
	A     :   Dict			    =			make_matrix()
	print("\norigin:\n")
	print_matrix(matrix)
	print("\nrotate 90 counterclockwise:\n")
	print_matrix(rotate_aa(matrix))

	A     :   List[str]			    =			make_matrix()
	print("\norigin:\n")
	print_matrix(matrix)
	print("\nrotate 180:\n")
	print_matrix(rotate_aaa(matrix))

	A     :   str			    =			make_matrix()
	print("\norigin:\n")
	print_matrix(matrix)
	print("\nrotate 270 counterclockwise:\n")
	print_matrix(rotate_aaa(matrix))




