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					:   Union[str, 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''',
}
_lowerCamelCase					:   List[Any]							=		[
    '''ctc_proj''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							Union[str, Any]      , UpperCAmelCase__     :							Tuple  ):
					for attribute in key.split("."  ):
										SCREAMING_SNAKE_CASE    			=       getattr(UpperCAmelCase__      , UpperCAmelCase__  )

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

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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Tuple      , UpperCAmelCase__     :							Union[str, Any]  ):
					SCREAMING_SNAKE_CASE    			=       []
					SCREAMING_SNAKE_CASE    			=       fairseq_model.state_dict()

					SCREAMING_SNAKE_CASE    			=       hf_model.feature_extractor

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

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

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





def 	__lowerCamelCase							(UpperCAmelCase__     :							Any      , UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							Dict      , UpperCAmelCase__     :							List[str]      , UpperCAmelCase__     :							List[str]  ):
					SCREAMING_SNAKE_CASE    			=       full_name.split("conv_layers."  )[-1]
					SCREAMING_SNAKE_CASE    			=       name.split("."  )
					SCREAMING_SNAKE_CASE    			=       int(items[0]  )
					SCREAMING_SNAKE_CASE    			=       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    			=       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    			=       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    			=       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    			=       value
															logger.info(F"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."  )
					else:
										unused_weights.append(UpperCAmelCase__  )





@torch.no_grad()
def 	__lowerCamelCase							(UpperCAmelCase__     :							Optional[int]      , UpperCAmelCase__     :							int      , UpperCAmelCase__     :							int=None  ):
					# load the pre-trained checkpoints
					SCREAMING_SNAKE_CASE    			=       torch.load(UpperCAmelCase__  )
					SCREAMING_SNAKE_CASE    			=       WavLMConfigOrig(checkpoint["cfg"]  )
					SCREAMING_SNAKE_CASE    			=       WavLMOrig(UpperCAmelCase__  )
					model.load_state_dict(checkpoint["model"]  )
					model.eval()

					if config_path is not None:
										SCREAMING_SNAKE_CASE    			=       WavLMConfig.from_pretrained(UpperCAmelCase__  )
					else:
										SCREAMING_SNAKE_CASE    			=       WavLMConfig()

					SCREAMING_SNAKE_CASE    			=       WavLMModel(UpperCAmelCase__  )

					recursively_load_weights(UpperCAmelCase__      , UpperCAmelCase__  )

					hf_wavlm.save_pretrained(UpperCAmelCase__  )


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





import torch

from diffusers import KDPMaDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest


class 						lowercase    (     a     ):
							lowercase__						:       Tuple								=							(KDPMaDiscreteScheduler,)
							lowercase__						:       Optional[int]								=							10




							def 	__snake_case(						self   :					Optional[Any]	,							**_UpperCamelCase   :					List[Any]      )					->				Tuple:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       {
												    "num_train_timesteps": 1_100,
												    "beta_start": 0.0_0_0_1,
												    "beta_end": 0.0_2,
												    "beta_schedule": "linear",
												}

												config.update(**_UpperCamelCase      )
												return config




							def 	__snake_case(						self   :					int      )					->				List[Any]:



												'''simple docstring'''

												for timesteps in [10, 50, 100, 1_000]:
																	self.check_over_configs(num_train_timesteps=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				Union[str, Any]:



												'''simple docstring'''

												for beta_start, beta_end in zip([0.0_0_0_0_1, 0.0_0_0_1, 0.0_0_1]	,							[0.0_0_0_2, 0.0_0_2, 0.0_2]      ):
																	self.check_over_configs(beta_start=_UpperCamelCase	,							beta_end=_UpperCamelCase      )




							def 	__snake_case(						self   :					Union[str, Any]      )					->				str:



												'''simple docstring'''

												for schedule in ["linear", "scaled_linear"]:
																	self.check_over_configs(beta_schedule=_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]      )					->				int:



												'''simple docstring'''

												for prediction_type in ["epsilon", "v_prediction"]:
																	self.check_over_configs(prediction_type=_UpperCamelCase      )




							def 	__snake_case(						self   :					Optional[int]      )					->				Any:



												'''simple docstring'''

												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config(prediction_type="v_prediction"      )
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 4.6_934e-07      ) < 1e-2
																	assert abs(result_mean.item() - 6.1_112e-10      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 4.693_428_650_170_972e-07      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_0_0_2      ) < 1e-3




							def 	__snake_case(						self   :					Dict      )					->				int:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter * scheduler.init_noise_sigma
												SCREAMING_SNAKE_CASE    			=       sample.to(_UpperCamelCase      )

												for i, t in enumerate(scheduler.timesteps      ):
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if torch_device in ["cpu", "mps"]:
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3





							def 	__snake_case(						self   :					Tuple      )					->				str:



												'''simple docstring'''

												if torch_device == "mps":
																	return
												SCREAMING_SNAKE_CASE    			=       self.scheduler_classes[0]
												SCREAMING_SNAKE_CASE    			=       self.get_scheduler_config()
												SCREAMING_SNAKE_CASE    			=       scheduler_class(**_UpperCamelCase      )

												scheduler.set_timesteps(self.num_inference_steps	,							device=_UpperCamelCase      )

												SCREAMING_SNAKE_CASE    			=       self.dummy_model()
												SCREAMING_SNAKE_CASE    			=       self.dummy_sample_deter.to(_UpperCamelCase      ) * scheduler.init_noise_sigma

												for t in scheduler.timesteps:
																	SCREAMING_SNAKE_CASE    			=       scheduler.scale_model_input(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       model(_UpperCamelCase	,							_UpperCamelCase      )

																	SCREAMING_SNAKE_CASE    			=       scheduler.step(_UpperCamelCase	,							_UpperCamelCase	,							_UpperCamelCase      )
																	SCREAMING_SNAKE_CASE    			=       output.prev_sample

												SCREAMING_SNAKE_CASE    			=       torch.sum(torch.abs(_UpperCamelCase      )      )
												SCREAMING_SNAKE_CASE    			=       torch.mean(torch.abs(_UpperCamelCase      )      )

												if str(_UpperCamelCase      ).startswith("cpu"      ):
																	# The following sum varies between 148 and 156 on mps. Why?
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3
												else:
																	# CUDA
																	assert abs(result_sum.item() - 2_0.4_1_2_5      ) < 1e-2
																	assert abs(result_mean.item() - 0.0_2_6_6      ) < 1e-3




