





'''simple docstring'''







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()
lowerCAmelCase_					:    str      	=			logging.get_logger(__name__)

lowerCAmelCase_					:    Tuple      	=			{
    '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',
}
lowerCAmelCase_					:    Optional[int]      	=			[
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]






def  _lowerCamelCase				( lowercase						:      Optional[int]    ,							lowercase						:      Union[str, Any]    ,							lowercase						:      Optional[int]    ,							lowercase						:      List[Any]    ,							lowercase						:      str						)	->	int:
				for attribute in key.split("."						):
								_a								= getattr(lowercase    ,							lowercase						)

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

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






def  _lowerCamelCase				( lowercase						:      int    ,							lowercase						:      str						)	->	Optional[Any]:
				_a								= []
				_a								= fairseq_model.state_dict()

				_a								= hf_model.feature_extractor

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

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

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






def  _lowerCamelCase				( lowercase						:      Union[str, Any]    ,							lowercase						:      Optional[Any]    ,							lowercase						:      Union[str, Any]    ,							lowercase						:      List[str]    ,							lowercase						:      Dict						)	->	Any:
				_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:
												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.'
												)
												_a								= 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.'
												)
												_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:
												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."
												)
												_a								= 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.'
												)
												_a								= value
												logger.info(F'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'						)
				else:
								unused_weights.append(lowercase						)






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

				if config_path is not None:
								_a								= WavLMConfig.from_pretrained(lowercase						)
				else:
								_a								= WavLMConfig()

				_a								= WavLMModel(lowercase						)

				recursively_load_weights(lowercase    ,							lowercase						)

				hf_wavlm.save_pretrained(lowercase						)


if __name__ == "__main__":
					lowerCAmelCase_					:    Tuple      	=			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')
					lowerCAmelCase_					:    List[Any]      	=			parser.parse_args()
					convert_wavlm_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path)




def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      _enforce_args(_A    ,					_A						)
      if n == 0:
            return 0
      lowerCamelCase__		:				Union[str, Any]    =  float("-inf"						)
      for i in range(1    ,					n + 1						):
            lowerCamelCase__		:				int    =  max(
                _A    ,					prices[i - 1] + naive_cut_rod_recursive(n - i    ,					_A						)						)

      return max_revue

def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      _enforce_args(_A    ,					_A						)
      lowerCamelCase__		:				int    =  [float("-inf"						) for _ in range(n + 1						)]
      return _top_down_cut_rod_recursive(_A    ,					_A    ,					_A						)

def       lowercase_		(		_A			:    int    ,					_A			:    list    ,					_A			:    list						):

      """simple docstring"""




      if max_rev[n] >= 0:
            return max_rev[n]
      elif n == 0:
            return 0
      else:
            lowerCamelCase__		:				Dict    =  float("-inf"						)
            for i in range(1    ,					n + 1						):
                  lowerCamelCase__		:				int    =  max(
                      _A    ,					prices[i - 1] + _top_down_cut_rod_recursive(n - i    ,					_A    ,					_A						)    ,					)

            lowerCamelCase__		:				List[Any]    =  max_revenue

      return max_rev[n]

def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      _enforce_args(_A    ,					_A						)

      # length(max_rev) = n + 1, to accommodate for the revenue obtainable from a rod of
      # length 0.
      lowerCamelCase__		:				int    =  [float("-inf"						) for _ in range(n + 1						)]
      lowerCamelCase__		:				Optional[int]    =  0

      for i in range(1    ,					n + 1						):
            lowerCamelCase__		:				Union[str, Any]    =  max_rev[i]
            for j in range(1    ,					i + 1						):
                  lowerCamelCase__		:				Any    =  max(_A    ,					prices[j - 1] + max_rev[i - j]						)

            lowerCamelCase__		:				Any    =  max_revenue_i

      return max_rev[n]

def       lowercase_		(		_A			:    int    ,					_A			:    list						):

      """simple docstring"""




      if n < 0:
            lowerCamelCase__		:				Optional[int]    =  F"n must be greater than or equal to 0. Got n = {n}"
            raise ValueError(_A						)

      if n > len(_A						):
            lowerCamelCase__		:				Optional[int]    =  (
                "Each integral piece of rod must have a corresponding price. "
                F"Got n = {n} but length of prices = {len(_A						)}"
            )
            raise ValueError(_A						)

def       lowercase_		(		):

      """simple docstring"""




      lowerCamelCase__		:				Optional[Any]    =  [6, 10, 12, 15, 20, 23]
      lowerCamelCase__		:				Dict    =  len(_A						)

      # the best revenue comes from cutting the rod into 6 pieces, each
      # of length 1 resulting in a revenue of 6 * 6 = 36.
      lowerCamelCase__		:				int    =  36

      lowerCamelCase__		:				List[Any]    =  top_down_cut_rod(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  bottom_up_cut_rod(_A    ,					_A						)
      lowerCamelCase__		:				List[Any]    =  naive_cut_rod_recursive(_A    ,					_A						)

      assert expected_max_revenue == max_rev_top_down
      assert max_rev_top_down == max_rev_bottom_up
      assert max_rev_bottom_up == max_rev_naive


if __name__ == "__main__":
    main()


