





'''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()
snake_case_ :	Any             =   logging.get_logger(__name__)

snake_case_ :	Optional[Any]             =   {
    """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""",
}
snake_case_ :	int             =   [
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]



def 			lowercase__(		_UpperCamelCase	:			int			, _UpperCamelCase	:			List[str]			, _UpperCamelCase	:			Dict			, _UpperCamelCase	:			Dict			, _UpperCamelCase	:			List[str]					)-> Optional[Any]:






					"""simple docstring"""

					for attribute in key.split("."					):
										_UpperCamelCase									= getattr(lowercase_			, lowercase_					)

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

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



def 			lowercase__(		_UpperCamelCase	:			Dict			, _UpperCamelCase	:			Optional[int]					)-> Dict:






					"""simple docstring"""

					_UpperCamelCase									= []
					_UpperCamelCase									= fairseq_model.state_dict()

					_UpperCamelCase									= hf_model.feature_extractor

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






					"""simple docstring"""

					_UpperCamelCase									= full_name.split("conv_layers."					)[-1]
					_UpperCamelCase									= name.split("."					)
					_UpperCamelCase									= int(items[0]					)
					_UpperCamelCase									= 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."
															)
															_UpperCamelCase									= 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."
															)
															_UpperCamelCase									= 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."
															)
															_UpperCamelCase									= 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."
															)
															_UpperCamelCase									= 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 			lowercase__(		_UpperCamelCase	:			List[Any]			, _UpperCamelCase	:			Union[str, Any]			, _UpperCamelCase	:			List[str]=None					)-> List[Any]:






					"""simple docstring"""

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

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

					_UpperCamelCase									= WavLMModel(lowercase_					)

					recursively_load_weights(lowercase_			, lowercase_					)

					hf_wavlm.save_pretrained(lowercase_					)


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









def SCREAMING_SNAKE_CASE							(			lowercase_					,							lowercase_		)  ->			int:



       """simple docstring"""

       return int((input_a, input_a).count(1		) != 0		)







def SCREAMING_SNAKE_CASE							(			)  ->			None:



       """simple docstring"""

       assert or_gate(0					,							0		) == 0
       assert or_gate(0					,							1		) == 1
       assert or_gate(1					,							0		) == 1
       assert or_gate(1					,							1		) == 1


if __name__ == "__main__":
  print(or_gate(0, 1))
  print(or_gate(1, 0))
  print(or_gate(0, 0))
  print(or_gate(1, 1))


