






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()
SCREAMING_SNAKE_CASE__      :			Union[str, Any]							=		logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__      :			Optional[int]							=		{
    """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""",
}
SCREAMING_SNAKE_CASE__      :			List[str]							=		[
    """ctc_proj""",
    """quantizer.weight_proj""",
    """quantizer.codevectors""",
    """project_q""",
    """project_hid""",
]

def      __lowercase					( snake_case,  snake_case,  snake_case,  snake_case,  snake_case ):







				"""simple docstring"""






				for attribute in key.split('''.''' ):
								__magic_name__		:Any        =      getattr(snake_case,  snake_case )

				if weight_type is not None:
								__magic_name__		:str        =      getattr(snake_case,  snake_case ).shape
				else:
								__magic_name__		:Tuple        =      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":
								__magic_name__		:Optional[int]        =      value
				elif weight_type == "weight_g":
								__magic_name__		:Optional[Any]        =      value
				elif weight_type == "weight_v":
								__magic_name__		:Optional[Any]        =      value
				elif weight_type == "bias":
								__magic_name__		:Any        =      value
				else:
								__magic_name__		:Dict        =      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 ):







				"""simple docstring"""






				__magic_name__		:Tuple        =      []
				__magic_name__		:Any        =      fairseq_model.state_dict()

				__magic_name__		:Tuple        =      hf_model.feature_extractor

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







				"""simple docstring"""






				__magic_name__		:List[str]        =      full_name.split('''conv_layers.''' )[-1]
				__magic_name__		:int        =      name.split('''.''' )
				__magic_name__		:Tuple        =      int(items[0] )
				__magic_name__		:int        =      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.'''
												)
												__magic_name__		:Dict        =      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.'''
												)
												__magic_name__		:int        =      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."
												)
												__magic_name__		:Optional[Any]        =      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.'''
												)
												__magic_name__		:Any        =      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 ):







				"""simple docstring"""






				__magic_name__		:int        =      torch.load(snake_case )
				__magic_name__		:Any        =      WavLMConfigOrig(checkpoint['''cfg'''] )
				__magic_name__		:str        =      WavLMOrig(snake_case )
				model.load_state_dict(checkpoint['''model'''] )
				model.eval()

				if config_path is not None:
								__magic_name__		:List[Any]        =      WavLMConfig.from_pretrained(snake_case )
				else:
								__magic_name__		:int        =      WavLMConfig()

				__magic_name__		:Optional[Any]        =      WavLMModel(snake_case )

				recursively_load_weights(snake_case,  snake_case )

				hf_wavlm.save_pretrained(snake_case )


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














# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)
@flax.struct.dataclass
class 						lowerCamelCase_				:
				a__					       =  42

				# setable values
				a__					       =  42
				a__					       =  42
				a__					       =  None







				@classmethod
				def 			A	(						cls       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								return cls(common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase     )







@dataclass
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  42







class 						lowerCamelCase_				(      lowerCamelCase			,				lowerCamelCase ):
				a__					       =  [e.name for e in FlaxKarrasDiffusionSchedulers]

				a__					       =  42







				@property
				def 			A	(						self     ):
								"""simple docstring"""
								return True







				@register_to_config
				def __init__(						self       ,					__lowerCAmelCase = 1_0_0_0       ,					__lowerCAmelCase = 0.0001       ,					__lowerCAmelCase = 0.02       ,					__lowerCAmelCase = "linear"       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = "fixed_small"       ,					__lowerCAmelCase = True       ,					__lowerCAmelCase = "epsilon"       ,					__lowerCAmelCase = jnp.floataa       ,					):
								"""simple docstring"""
								__magic_name__		:Optional[int]        =      dtype







				def 			A	(						self       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								if common is None:
												__magic_name__		:Dict        =      CommonSchedulerState.create(self     )

								# standard deviation of the initial noise distribution
								__magic_name__		:Optional[Any]        =      jnp.array(1.0       ,					dtype=self.dtype     )

								__magic_name__		:str        =      jnp.arange(0       ,					self.config.num_train_timesteps     ).round()[::-1]

								return DDPMSchedulerState.create(
								    common=__lowerCAmelCase       ,					init_noise_sigma=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None     ):
								"""simple docstring"""
								return sample







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = ()     ):
								"""simple docstring"""
								__magic_name__		:int        =      self.config.num_train_timesteps // num_inference_steps
								# creates integer timesteps by multiplying by ratio
								# rounding to avoid issues when num_inference_step is power of 3
								__magic_name__		:List[Any]        =      (jnp.arange(0       ,					__lowerCAmelCase     ) * step_ratio).round()[::-1]

								return state.replace(
								    num_inference_steps=__lowerCAmelCase       ,					timesteps=__lowerCAmelCase       ,					)







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None     ):
								"""simple docstring"""
								__magic_name__		:Optional[Any]        =      state.common.alphas_cumprod[t]
								__magic_name__		:Optional[Any]        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )

								# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
								# and sample from it to get previous sample
								# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
								__magic_name__		:Tuple        =      (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

								if variance_type is None:
												__magic_name__		:Optional[Any]        =      self.config.variance_type

								# hacks - were probably added for training stability
								if variance_type == "fixed_small":
												__magic_name__		:Optional[Any]        =      jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )
								# for rl-diffuser https://arxiv.org/abs/2205.09991
								elif variance_type == "fixed_small_log":
												__magic_name__		:Dict        =      jnp.log(jnp.clip(__lowerCAmelCase       ,					a_min=1E-20     )     )
								elif variance_type == "fixed_large":
												__magic_name__		:Tuple        =      state.common.betas[t]
								elif variance_type == "fixed_large_log":
												# Glide max_log
												__magic_name__		:Optional[Any]        =      jnp.log(state.common.betas[t]     )
								elif variance_type == "learned":
												return predicted_variance
								elif variance_type == "learned_range":
												__magic_name__		:Union[str, Any]        =      variance
												__magic_name__		:List[str]        =      state.common.betas[t]
												__magic_name__		:Any        =      (predicted_variance + 1) / 2
												__magic_name__		:str        =      frac * max_log + (1 - frac) * min_log

								return variance







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase = None       ,					__lowerCAmelCase = True       ,					):
								"""simple docstring"""
								__magic_name__		:List[str]        =      timestep

								if key is None:
												__magic_name__		:Union[str, Any]        =      jax.random.PRNGKey(0     )

								if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
												__magic_name__     ,				__magic_name__		:Dict        =      jnp.split(__lowerCAmelCase       ,					sample.shape[1]       ,					axis=1     )
								else:
												__magic_name__		:Optional[int]        =      None

								# 1. compute alphas, betas
								__magic_name__		:Any        =      state.common.alphas_cumprod[t]
								__magic_name__		:int        =      jnp.where(t > 0       ,					state.common.alphas_cumprod[t - 1]       ,					jnp.array(1.0       ,					dtype=self.dtype     )     )
								__magic_name__		:Optional[int]        =      1 - alpha_prod_t
								__magic_name__		:Union[str, Any]        =      1 - alpha_prod_t_prev

								# 2. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
								if self.config.prediction_type == "epsilon":
												__magic_name__		:List[Any]        =      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
								elif self.config.prediction_type == "sample":
												__magic_name__		:Tuple        =      model_output
								elif self.config.prediction_type == "v_prediction":
												__magic_name__		:Dict        =      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
								else:
												raise ValueError(
												    F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` '''
												    ''' for the FlaxDDPMScheduler.'''     )

								# 3. Clip "predicted x_0"
								if self.config.clip_sample:
												__magic_name__		:Union[str, Any]        =      jnp.clip(__lowerCAmelCase       ,					-1       ,					1     )

								# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__magic_name__		:Union[str, Any]        =      (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
								__magic_name__		:Union[str, Any]        =      state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

								# 5. Compute predicted previous sample µ_t
								# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
								__magic_name__		:Union[str, Any]        =      pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

								# 6. Add noise
								def random_variance():
												__magic_name__		:Tuple        =      jax.random.split(__lowerCAmelCase       ,					num=1     )
												__magic_name__		:Dict        =      jax.random.normal(__lowerCAmelCase       ,					shape=model_output.shape       ,					dtype=self.dtype     )
												return (self._get_variance(__lowerCAmelCase       ,					__lowerCAmelCase       ,					predicted_variance=__lowerCAmelCase     ) ** 0.5) * noise

								__magic_name__		:List[str]        =      jnp.where(t > 0       ,					random_variance()       ,					jnp.zeros(model_output.shape       ,					dtype=self.dtype     )     )

								__magic_name__		:int        =      pred_prev_sample + variance

								if not return_dict:
												return (pred_prev_sample, state)

								return FlaxDDPMSchedulerOutput(prev_sample=__lowerCAmelCase       ,					state=__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return add_noise_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					):
								"""simple docstring"""
								return get_velocity_common(state.common       ,					__lowerCAmelCase       ,					__lowerCAmelCase       ,					__lowerCAmelCase     )







				def __len__(						self     ):
								"""simple docstring"""
								return self.config.num_train_timesteps






