





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      _UpperCamelCase  (a__ :Any ,      a__ :List[str] ,      a__ :str ,      a__ :Optional[int] ,      a__ :List[Any]							):






							"""simple docstring"""





							for attribute in key.split("""."""							):
														UpperCamelCase__					  =      getattr(a__ ,      a__							)

							if weight_type is not None:
														UpperCamelCase__					  =      getattr(a__ ,      a__							).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      _UpperCamelCase  (a__ :str ,      a__ :List[str]							):






							"""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(
																					    a__ ,      a__ ,      a__ ,      a__ ,      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(a__							)[0].split("""."""							)[-2]
																																										UpperCamelCase__					  =      mapped_key.replace("""*""" ,      a__							)
																																			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(a__ ,      a__ ,      a__ ,      a__ ,      a__							)
																												continue
														if not is_used:
																					unused_weights.append(a__							)

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





def      _UpperCamelCase  (a__ :List[Any] ,      a__ :Optional[int] ,      a__ :List[Any] ,      a__ :Any ,      a__ :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(a__							)





@torch.no_grad()
def      _UpperCamelCase  (a__ :List[str] ,      a__ :Tuple ,      a__ :Optional[int]=None							):






							"""simple docstring"""





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

							if config_path is not None:
														UpperCamelCase__					  =      WavLMConfig.from_pretrained(a__							)
							else:
														UpperCamelCase__					  =      WavLMConfig()

							UpperCamelCase__					  =      WavLMModel(a__							)

							recursively_load_weights(a__ ,      a__							)

							hf_wavlm.save_pretrained(a__							)


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)











import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()

class 							__SCREAMING_SNAKE_CASE       (  _a ,		_a ,		unittest.TestCase							):
							snake_case  :   List[str]      =							VQModel
							snake_case  :   Dict      =							"""sample"""






							@property
							def 					_lowerCamelCase		(    self	,			__lowerCAmelCase=(32, 32)	):
														UpperCamelCase__					  =      4
														UpperCamelCase__					  =      3

														UpperCamelCase__					  =      floats_tensor((batch_size, num_channels) + sizes	).to(__lowerCAmelCase	)

														return {"sample": image}






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							@property
							def 					_lowerCamelCase		(    self	):
														return (3, 32, 32)






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      {
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														UpperCamelCase__					  =      self.dummy_input
														return init_dict, inputs_dict






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														pass






							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__  ,  UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	,			output_loading_info=__lowerCAmelCase	)
														self.assertIsNotNone(__lowerCAmelCase	)
														self.assertEqual(len(loading_info["""missing_keys"""]	)	,			0	)

														model.to(__lowerCAmelCase	)
														UpperCamelCase__					  =      model(**self.dummy_input	)

														assert image is not None, "Make sure output is not None"







							def 					_lowerCamelCase		(    self	):
														UpperCamelCase__					  =      VQModel.from_pretrained("""fusing/vqgan-dummy"""	)
														model.to(__lowerCAmelCase	).eval()

														torch.manual_seed(0	)
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0	)

														UpperCamelCase__					  =      torch.randn(1	,			model.config.in_channels	,			model.config.sample_size	,			model.config.sample_size	)
														UpperCamelCase__					  =      image.to(__lowerCAmelCase	)
														with torch.no_grad():
																					UpperCamelCase__					  =      model(__lowerCAmelCase	).sample

														UpperCamelCase__					  =      output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														UpperCamelCase__					  =      torch.tensor([-0.0153, -0.4044, -0.1880, -0.5161, -0.2418, -0.4072, -0.1612, -0.0633, -0.0143]	)
														# fmt: on
														self.assertTrue(torch.allclose(__lowerCAmelCase	,			__lowerCAmelCase	,			atol=1E-3	)	)




