


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							:	str					      =				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							:	Dict					      =				[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]






def 							__lowercase       (			__lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    str ,   __lowerCAmelCase							:    Union[str, Any] ,   __lowerCAmelCase							:    Dict       ):
							for attribute in key.split('.'       ):
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       )

							if weight_type is not None:
														a__					=					getattr(__lowerCAmelCase ,   __lowerCAmelCase       ).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 							__lowercase       (			__lowerCAmelCase							:    List[str] ,   __lowerCAmelCase							:    List[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(
																					    __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   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(__lowerCAmelCase       )[0].split('.'       )[-2]
																																										a__					=					mapped_key.replace('*' ,   __lowerCAmelCase       )
																																			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(__lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase ,   __lowerCAmelCase       )
																												continue
														if not is_used:
																					unused_weights.append(__lowerCAmelCase       )

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






def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Dict ,   __lowerCAmelCase							:    int ,   __lowerCAmelCase							:    Optional[int]       ):
							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(__lowerCAmelCase       )






@torch.no_grad()
def 							__lowercase       (			__lowerCAmelCase							:    Optional[int] ,   __lowerCAmelCase							:    Any ,   __lowerCAmelCase							:    List[Any]=None       ):
							# load the pre-trained checkpoints
							a__					=					torch.load(__lowerCAmelCase       )
							a__					=					WavLMConfigOrig(checkpoint['cfg']       )
							a__					=					WavLMOrig(__lowerCAmelCase       )
							model.load_state_dict(checkpoint['model']       )
							model.eval()

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

							a__					=					WavLMModel(__lowerCAmelCase       )

							recursively_load_weights(__lowerCAmelCase ,   __lowerCAmelCase       )

							hf_wavlm.save_pretrained(__lowerCAmelCase       )


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








import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


snake_case							:	Tuple					      =				logging.get_logger(__name__)

enable_full_determinism()







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   str   				=      UNetaDModel
					UpperCAmelCase__ :   str   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Optional[int]				) ->   List[str]:
												a__					=					4
												a__					=					3
												a__					=					(32, 32)

												a__					=					floats_tensor((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor([10]				).to(__snake_case				)

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Tuple:
												return (3, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :List[str]				) ->   Optional[Any]:
												return (3, 32, 32)



					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												a__					=					{
												    'block_out_channels': (32, 64),
												    'down_block_types': ('DownBlock2D', 'AttnDownBlock2D'),
												    'up_block_types': ('AttnUpBlock2D', 'UpBlock2D'),
												    'attention_head_dim': 3,
												    'out_channels': 3,
												    'in_channels': 3,
												    'layers_per_block': 2,
												    'sample_size': 32,
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   int   				=      UNetaDModel
					UpperCAmelCase__ :   Any   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Dict				) ->   List[str]:
												a__					=					4
												a__					=					4
												a__					=					(32, 32)

												a__					=					floats_tensor((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor([10]				).to(__snake_case				)

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   str:
												return (4, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :Any				) ->   Dict:
												return (4, 32, 32)
					def 			lowerCamelCase__(				self       :int				) ->   int:
												a__					=					{
												    'sample_size': 32,
												    'in_channels': 4,
												    'out_channels': 4,
												    'layers_per_block': 2,
												    'block_out_channels': (32, 64),
												    'attention_head_dim': 32,
												    'down_block_types': ('DownBlock2D', 'DownBlock2D'),
												    'up_block_types': ('UpBlock2D', 'UpBlock2D'),
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict
					def 			lowerCamelCase__(				self       :str				) ->   Any:
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)

												self.assertIsNotNone(__snake_case				)
												self.assertEqual(len(loading_info['missing_keys']				)      ,0				)

												model.to(__snake_case				)
												a__					=					model(**self.dummy_input				).sample

												assert image is not None, "Make sure output is not None"
					@unittest.skipIf(torch_device != 'cuda'      ,'This test is supposed to run on GPU'				)
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)
												model.to(__snake_case				)
												a__					=					model(**self.dummy_input				).sample

												assert image is not None, "Make sure output is not None"
					@unittest.skipIf(torch_device != 'cuda'      ,'This test is supposed to run on GPU'				)
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   int:
												# by defautl model loading will use accelerate as `low_cpu_mem_usage=True`
												a__				, a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case				)
												model_accelerate.to(__snake_case				)
												model_accelerate.eval()

												a__					=					torch.randn(
												    1      ,model_accelerate.config.in_channels      ,model_accelerate.config.sample_size      ,model_accelerate.config.sample_size      ,generator=torch.manual_seed(0				)      ,)
												a__					=					noise.to(__snake_case				)
												a__					=					torch.tensor([10] * noise.shape[0]				).to(__snake_case				)

												a__					=					model_accelerate(__snake_case      ,__snake_case				)['sample']

												# two models don't need to stay in the device at the same time
												del model_accelerate
												torch.cuda.empty_cache()
												gc.collect()

												a__				, a__					=					UNetaDModel.from_pretrained(
												    'fusing/unet-ldm-dummy-update'      ,output_loading_info=__snake_case      ,low_cpu_mem_usage=__snake_case				)
												model_normal_load.to(__snake_case				)
												model_normal_load.eval()
												a__					=					model_normal_load(__snake_case      ,__snake_case				)['sample']

												assert torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-3				)



					def 			lowerCamelCase__(				self       :str				) ->   Union[str, Any]:
												a__					=					UNetaDModel.from_pretrained('fusing/unet-ldm-dummy-update'				)
												model.eval()
												model.to(__snake_case				)

												a__					=					torch.randn(
												    1      ,model.config.in_channels      ,model.config.sample_size      ,model.config.sample_size      ,generator=torch.manual_seed(0				)      ,)
												a__					=					noise.to(__snake_case				)
												a__					=					torch.tensor([10] * noise.shape[0]				).to(__snake_case				)

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -1, -3:, -3:].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-13.32_58, -20.11_00, -15.98_73, -17.66_17, -23.05_96, -17.94_19, -13.36_75, -16.18_89, -12.38_00]				)
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-3				)				)







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_     ,						unittest.TestCase				):
					UpperCAmelCase__ :   Dict   				=      UNetaDModel
					UpperCAmelCase__ :   Optional[Any]   				=      '''sample'''
					@property
					def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :List[Any]=(32, 32)				) ->   Optional[int]:
												a__					=					4
												a__					=					3

												a__					=					floats_tensor((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor(batch_size * [10]				).to(dtype=torch.intaa      ,device=__snake_case				)

												return {"sample": noise, "timestep": time_step}
					@property
					def 			lowerCamelCase__(				self       :Tuple				) ->   Optional[int]:
												return (3, 32, 32)
					@property
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   Optional[int]:
												return (3, 32, 32)
					def 			lowerCamelCase__(				self       :Optional[Any]				) ->   List[str]:
												a__					=					{
												    'block_out_channels': [32, 64, 64, 64],
												    'in_channels': 3,
												    'layers_per_block': 1,
												    'out_channels': 3,
												    'time_embedding_type': 'fourier',
												    'norm_eps': 1E-6,
												    'mid_block_scale_factor': math.sqrt(2.0				),
												    'norm_num_groups': None,
												    'down_block_types': [
												        'SkipDownBlock2D',
												        'AttnSkipDownBlock2D',
												        'SkipDownBlock2D',
												        'SkipDownBlock2D',
												    ],
												    'up_block_types': [
												        'SkipUpBlock2D',
												        'SkipUpBlock2D',
												        'AttnSkipUpBlock2D',
												        'SkipUpBlock2D',
												    ],
												}
												a__					=					self.dummy_input
												return init_dict, inputs_dict
					@slow
					def 			lowerCamelCase__(				self       :str				) ->   Tuple:
												a__				, a__					=					UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'      ,output_loading_info=__snake_case				)
												self.assertIsNotNone(__snake_case				)
												self.assertEqual(len(loading_info['missing_keys']				)      ,0				)

												model.to(__snake_case				)
												a__					=					self.dummy_input
												a__					=					floats_tensor((4, 3) + (2_56, 2_56)				).to(__snake_case				)
												a__					=					noise
												a__					=					model(**__snake_case				)

												assert image is not None, "Make sure output is not None"
					@slow
					def 			lowerCamelCase__(				self       :Union[str, Any]				) ->   Dict:
												a__					=					UNetaDModel.from_pretrained('google/ncsnpp-celebahq-256'				)
												model.to(__snake_case				)

												a__					=					4
												a__					=					3
												a__					=					(2_56, 2_56)

												a__					=					torch.ones((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor(batch_size * [1E-4]				).to(__snake_case				)

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-48_42.86_91, -64_99.66_31, -38_00.19_53, -79_78.26_86, -1_09_80.71_29, -2_00_28.85_35, 81_48.28_22, 23_42.29_05, 5_67.76_08]				)
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-2				)				)
					def 			lowerCamelCase__(				self       :Dict				) ->   int:
												a__					=					UNetaDModel.from_pretrained('fusing/ncsnpp-ffhq-ve-dummy-update'				)
												model.to(__snake_case				)

												a__					=					4
												a__					=					3
												a__					=					(32, 32)

												a__					=					torch.ones((batch_size, num_channels) + sizes				).to(__snake_case				)
												a__					=					torch.tensor(batch_size * [1E-4]				).to(__snake_case				)

												with torch.no_grad():
																			a__					=					model(__snake_case      ,__snake_case				).sample

												a__					=					output[0, -3:, -3:, -1].flatten().cpu()
												# fmt: off
												a__					=					torch.tensor([-0.03_25, -0.09_00, -0.08_69, -0.03_32, -0.07_25, -0.02_70, -0.01_01, 0.02_27, 0.02_56]				)
												# fmt: on

												self.assertTrue(torch_all_close(__snake_case      ,__snake_case      ,rtol=1E-2				)				)



					def 			lowerCamelCase__(				self       :int				) ->   str:
												# not required for this model
												pass




