
"""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()
UpperCAmelCase__      :    List[str]    							= logging.get_logger(__name__)

UpperCAmelCase__      :    str    							= {
    '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__      :    Union[str, Any]    							= [
    'ctc_proj',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
]

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							for attribute in key.split("""."""						):
														SCREAMING_SNAKE_CASE__						:       Dict           =   getattr(_snake_case   ,_snake_case						)

							if weight_type is not None:
														SCREAMING_SNAKE_CASE__						:       int           =   getattr(_snake_case   ,_snake_case						).shape
							else:
														SCREAMING_SNAKE_CASE__						:       Any           =   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":
														SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   value
							elif weight_type == "weight_g":
														SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   value
							elif weight_type == "weight_v":
														SCREAMING_SNAKE_CASE__						:       List[Any]           =   value
							elif weight_type == "bias":
														SCREAMING_SNAKE_CASE__						:       Dict           =   value
							else:
														SCREAMING_SNAKE_CASE__						:       int           =   value

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

def lowercase_  (   _snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       int           =   []
							SCREAMING_SNAKE_CASE__						:       List[str]           =   fairseq_model.state_dict()

							SCREAMING_SNAKE_CASE__						:       str           =   hf_model.feature_extractor

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

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

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

def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case   ,_snake_case   ,_snake_case						):
							SCREAMING_SNAKE_CASE__						:       List[str]           =   full_name.split("""conv_layers."""						)[-1]
							SCREAMING_SNAKE_CASE__						:       List[str]           =   name.split("""."""						)
							SCREAMING_SNAKE_CASE__						:       Optional[int]           =   int(items[0]						)
							SCREAMING_SNAKE_CASE__						:       Union[str, Any]           =   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.'''
																					)
																					SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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.'''
																					)
																					SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   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."
																					)
																					SCREAMING_SNAKE_CASE__						:       Optional[int]           =   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.'''
																					)
																					SCREAMING_SNAKE_CASE__						:       int           =   value
																					logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''						)
							else:
														unused_weights.append(_snake_case						)

@torch.no_grad()
def lowercase_  (   _snake_case   ,_snake_case   ,_snake_case=None						):
							# load the pre-trained checkpoints
							SCREAMING_SNAKE_CASE__						:       Any           =   torch.load(_snake_case						)
							SCREAMING_SNAKE_CASE__						:       List[str]           =   WavLMConfigOrig(checkpoint["""cfg"""]						)
							SCREAMING_SNAKE_CASE__						:       Optional[Any]           =   WavLMOrig(_snake_case						)
							model.load_state_dict(checkpoint["""model"""]						)
							model.eval()

							if config_path is not None:
														SCREAMING_SNAKE_CASE__						:       str           =   WavLMConfig.from_pretrained(_snake_case						)
							else:
														SCREAMING_SNAKE_CASE__						:       Any           =   WavLMConfig()

							SCREAMING_SNAKE_CASE__						:       Tuple           =   WavLMModel(_snake_case						)

							recursively_load_weights(_snake_case   ,_snake_case						)

							hf_wavlm.save_pretrained(_snake_case						)


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





'''simple docstring'''

import unittest

from knapsack import knapsack as k



class 			UpperCAmelCase  (						unittest.TestCase					):





       '''simple docstring'''



       def _lowerCAmelCase(						self )  ->  Tuple:
        lowercase__				:    Optional[Any]  =					0
        lowercase__				:    Any  =					[0]
        lowercase__				:    List[Any]  =					[0]
        lowercase__				:    Optional[Any]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   0 )

        lowercase__				:    List[str]  =					[60]
        lowercase__				:    List[str]  =					[10]
        lowercase__				:    List[str]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   0 )

       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    Optional[Any]  =					3
        lowercase__				:    List[Any]  =					[1, 2, 3]
        lowercase__				:    Union[str, Any]  =					[3, 2, 1]
        lowercase__				:    List[Any]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   5 )


       def _lowerCAmelCase(						self )  ->  Optional[Any]:
        lowercase__				:    int  =					50
        lowercase__				:    Any  =					[60, 100, 120]
        lowercase__				:    int  =					[10, 20, 30]
        lowercase__				:    Optional[Any]  =					len(__lowerCAmelCase )
        self.assertEqual(k.knapsack(__lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase	,   __lowerCAmelCase )	,   220 )


if __name__ == "__main__":
  unittest.main()

