


import math
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import SchedulerMixin, SchedulerOutput







class 	snake_case_       (lowerCamelCase_     ,						lowerCamelCase_				):
     UpperCAmelCase__ :   List[str]   				=      1
     @register_to_config
     def __init__(				self       :List[Any]      ,__snake_case       :int = 10_00      ,__snake_case       :Optional[Union[np.ndarray, List[float]]] = None				) ->   Union[str, Any]:
            # set `betas`, `alphas`, `timesteps`
            self.set_timesteps(__snake_case				)

            # standard deviation of the initial noise distribution
            a__					=					1.0

            # For now we only support F-PNDM, i.e. the runge-kutta method
            # For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
            # mainly at formula (9), (12), (13) and the Algorithm 2.
            a__					=					4

            # running values
            a__					=					[]
     def 			lowerCamelCase__(				self       :Optional[Any]      ,__snake_case       :int      ,__snake_case       :Union[str, torch.device] = None				) ->   List[Any]:
            a__					=					num_inference_steps
            a__					=					torch.linspace(1      ,0      ,num_inference_steps + 1				)[:-1]
            a__					=					torch.cat([steps, torch.tensor([0.0]				)]				)

            if self.config.trained_betas is not None:
                   a__					=					torch.tensor(self.config.trained_betas      ,dtype=torch.floataa				)
            else:
                   a__					=					torch.sin(steps * math.pi / 2				) ** 2

            a__					=					(1.0 - self.betas**2) ** 0.5

            a__					=					(torch.atana(self.betas      ,self.alphas				) / math.pi * 2)[:-1]
            a__					=					timesteps.to(__snake_case				)

            a__					=					[]
     def 			lowerCamelCase__(				self       :List[str]      ,__snake_case       :torch.FloatTensor      ,__snake_case       :int      ,__snake_case       :torch.FloatTensor      ,__snake_case       :bool = True      ,) ->   Union[SchedulerOutput, Tuple]:
            if self.num_inference_steps is None:
                   raise ValueError(
                       'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'				)

            a__					=					(self.timesteps == timestep).nonzero().item()
            a__					=					timestep_index + 1

            a__					=					sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
            self.ets.append(__snake_case				)

            if len(self.ets				) == 1:
                   a__					=					self.ets[-1]
            elif len(self.ets				) == 2:
                   a__					=					(3 * self.ets[-1] - self.ets[-2]) / 2
            elif len(self.ets				) == 3:
                   a__					=					(23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
            else:
                   a__					=					(1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

            a__					=					self._get_prev_sample(__snake_case      ,__snake_case      ,__snake_case      ,__snake_case				)

            if not return_dict:
                   return (prev_sample,)

            return SchedulerOutput(prev_sample=__snake_case				)
     def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :torch.FloatTensor      ,*__snake_case       :List[Any]      ,**__snake_case       :Optional[int]				) ->   torch.FloatTensor:
            return sample
     def 			lowerCamelCase__(				self       :Union[str, Any]      ,__snake_case       :int      ,__snake_case       :Dict      ,__snake_case       :Any      ,__snake_case       :Optional[int]				) ->   List[Any]:
            a__					=					self.alphas[timestep_index]
            a__					=					self.betas[timestep_index]

            a__					=					self.alphas[prev_timestep_index]
            a__					=					self.betas[prev_timestep_index]

            a__					=					(sample - sigma * ets) / max(__snake_case      ,1E-8				)
            a__					=					next_alpha * pred + ets * next_sigma

            return prev_sample



     def __len__(				self       :Optional[Any]				) ->   Tuple:
            return self.config.num_train_timesteps








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)




