


"""simple docstring"""






# DISCLAIMER: This code is strongly influenced by https://github.com/pesser/pytorch_diffusion
# and https://github.com/hojonathanho/diffusion

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

import flax
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 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							__magic_name__					:     CommonSchedulerState
							__magic_name__					:     jnp.ndarray

							# setable values
							__magic_name__					:     jnp.ndarray
							__magic_name__					:     jnp.ndarray
							__magic_name__					:     Optional[int]				  =  None




							@classmethod
							def        UpperCAmelCase (						cls	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	Tuple:







								'''simple docstring'''




								return cls(
								    common=lowerCamelCase__	,					final_alpha_cumprod=lowerCamelCase__	,					init_noise_sigma=lowerCamelCase__	,					timesteps=lowerCamelCase__	,					)


@dataclass
class 			SCREAMING_SNAKE_CASE_  (   _lowercase):




							'''simple docstring'''





							__magic_name__					:     DDIMSchedulerState


class 			SCREAMING_SNAKE_CASE_  (   _lowercase	,      _lowercase):




							'''simple docstring'''





							__magic_name__					:     Optional[int]				  =  [e.name for e in FlaxKarrasDiffusionSchedulers]

							__magic_name__					:     jnp.dtype




							@property
							def        UpperCAmelCase (						self)   ->	Tuple:







								'''simple docstring'''




								return True




							@register_to_config
							def __init__(						self	,					lowerCamelCase__ = 1_000	,					lowerCamelCase__ = 0.00_01	,					lowerCamelCase__ = 0.02	,					lowerCamelCase__ = "linear"	,					lowerCamelCase__ = None	,					lowerCamelCase__ = True	,					lowerCamelCase__ = 0	,					lowerCamelCase__ = "epsilon"	,					lowerCamelCase__ = jnp.floataa	,					)   ->	int:







								'''simple docstring'''




								snake_case__  :						Optional[int]		=      dtype




							def        UpperCAmelCase (						self	,					lowerCamelCase__ = None)   ->	DDIMSchedulerState:







								'''simple docstring'''




								if common is None:
									snake_case__  :						int		=      CommonSchedulerState.create(self)

								# At every step in ddim, we are looking into the previous alphas_cumprod
								# For the final step, there is no previous alphas_cumprod because we are already at 0
								# `set_alpha_to_one` decides whether we set this parameter simply to one or
								# whether we use the final alpha of the "non-previous" one.
								snake_case__  :						Tuple		=      (
								    jnp.array(1.0	,					dtype=self.dtype) if self.config.set_alpha_to_one else common.alphas_cumprod[0]
								)

								# standard deviation of the initial noise distribution
								snake_case__  :						List[str]		=      jnp.array(1.0	,					dtype=self.dtype)

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

								return DDIMSchedulerState.create(
								    common=lowerCamelCase__	,					final_alpha_cumprod=lowerCamelCase__	,					init_noise_sigma=lowerCamelCase__	,					timesteps=lowerCamelCase__	,					)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = None)   ->	jnp.ndarray:







								'''simple docstring'''




								return sample




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = ())   ->	DDIMSchedulerState:







								'''simple docstring'''




								snake_case__  :						List[str]		=      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
								snake_case__  :						str		=      (jnp.arange(0	,					lowerCamelCase__) * step_ratio).round()[::-1] + self.config.steps_offset

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




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__  :						Dict		=      state.common.alphas_cumprod[timestep]
								snake_case__  :						Any		=      jnp.where(
								    prev_timestep >= 0	,					state.common.alphas_cumprod[prev_timestep]	,					state.final_alpha_cumprod)
								snake_case__  :						Any		=      1 - alpha_prod_t
								snake_case__  :						str		=      1 - alpha_prod_t_prev

								snake_case__  :						Union[str, Any]		=      (beta_prod_t_prev / beta_prod_t) * (1 - alpha_prod_t / alpha_prod_t_prev)

								return variance




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__ = 0.0	,					lowerCamelCase__ = True	,					)   ->	Union[FlaxDDIMSchedulerOutput, Tuple]:







								'''simple docstring'''




								if state.num_inference_steps is None:
									raise ValueError(
									    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler")

								# See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
								# Ideally, read DDIM paper in-detail understanding

								# Notation (<variable name> -> <name in paper>
								# - pred_noise_t -> e_theta(x_t, t)
								# - pred_original_sample -> f_theta(x_t, t) or x_0
								# - std_dev_t -> sigma_t
								# - eta -> η
								# - pred_sample_direction -> "direction pointing to x_t"
								# - pred_prev_sample -> "x_t-1"

								# 1. get previous step value (=t-1)
								snake_case__  :						Optional[Any]		=      timestep - self.config.num_train_timesteps // state.num_inference_steps

								snake_case__  :						List[str]		=      state.common.alphas_cumprod
								snake_case__  :						Any		=      state.final_alpha_cumprod

								# 2. compute alphas, betas
								snake_case__  :						Optional[int]		=      alphas_cumprod[timestep]
								snake_case__  :						Dict		=      jnp.where(prev_timestep >= 0	,					alphas_cumprod[prev_timestep]	,					lowerCamelCase__)

								snake_case__  :						List[str]		=      1 - alpha_prod_t

								# 3. compute predicted original sample from predicted noise also called
								# "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								if self.config.prediction_type == "epsilon":
									snake_case__  :						List[Any]		=      (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
									snake_case__  :						Union[str, Any]		=      model_output
								elif self.config.prediction_type == "sample":
									snake_case__  :						Dict		=      model_output
									snake_case__  :						Tuple		=      (sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5
								elif self.config.prediction_type == "v_prediction":
									snake_case__  :						str		=      (alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
									snake_case__  :						Optional[int]		=      (alpha_prod_t**0.5) * model_output + (beta_prod_t**0.5) * sample
								else:
									raise ValueError(
									    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample`, or"""
									    " `v_prediction`")

								# 4. compute variance: "sigma_t(η)" -> see formula (16)
								# σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
								snake_case__  :						List[str]		=      self._get_variance(lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)
								snake_case__  :						Optional[int]		=      eta * variance ** 0.5

								# 5. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								snake_case__  :						List[Any]		=      (1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * pred_epsilon

								# 6. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
								snake_case__  :						str		=      alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

								if not return_dict:
									return (prev_sample, state)

								return FlaxDDIMSchedulerOutput(prev_sample=lowerCamelCase__	,					state=lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	jnp.ndarray:







								'''simple docstring'''




								return add_noise_common(state.common	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)




							def        UpperCAmelCase (						self	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__	,					)   ->	jnp.ndarray:







								'''simple docstring'''




								return get_velocity_common(state.common	,					lowerCamelCase__	,					lowerCamelCase__	,					lowerCamelCase__)







							def __len__(						self)   ->	List[Any]:







								'''simple docstring'''




								return self.config.num_train_timesteps






"""simple docstring"""







import argparse

import torch

from transformers import (
    WavaVecaFeatureExtractor,
    WavLMConfig,
    WavLMForAudioFrameClassification,
    WavLMForSequenceClassification,
    WavLMForXVector,
    logging,
)


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE	:		Optional[Any]   =   logging.get_logger(__name__)



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Any      , lowercase_     :	Optional[int]						)  ->							Union[str, Any]:
   __lowerCamelCase    		=					WavLMForSequenceClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['projector.weight']
   __lowerCamelCase    		=					downstream_dict['projector.bias']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.post_net.linear.bias']
   return model



def       A__    (	lowercase_     :	Dict      , lowercase_     :	str      , lowercase_     :	int						)  ->							List[Any]:
   __lowerCamelCase    		=					WavLMForAudioFrameClassification.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['model.linear.weight']
   __lowerCamelCase    		=					downstream_dict['model.linear.bias']
   return model



def       A__    (	lowercase_     :	List[str]      , lowercase_     :	Dict      , lowercase_     :	List[str]						)  ->							Tuple:
   __lowerCamelCase    		=					WavLMForXVector.from_pretrained(lowercase_      , config=lowercase_						)
   __lowerCamelCase    		=					downstream_dict['connector.weight']
   __lowerCamelCase    		=					downstream_dict['connector.bias']
   for i, kernel_size in enumerate(hf_config.tdnn_kernel						):
      __lowerCamelCase    		=					downstream_dict[
          F'''model.framelevel_feature_extractor.module.{i}.kernel.weight'''
      ]
      __lowerCamelCase    		=					downstream_dict[F'''model.framelevel_feature_extractor.module.{i}.kernel.bias''']

   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear1.bias']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.weight']
   __lowerCamelCase    		=					downstream_dict['model.utterancelevel_feature_extractor.linear2.bias']
   __lowerCamelCase    		=					downstream_dict['objective.W']
   return model



@torch.no_grad()
def       A__    (	lowercase_     :	str      , lowercase_     :	Optional[int]      , lowercase_     :	List[str]      , lowercase_     :	Union[str, Any]						)  ->							Optional[Any]:
   __lowerCamelCase    		=					torch.load(lowercase_      , map_location='cpu'						)

   __lowerCamelCase    		=					checkpoint['Downstream']

   __lowerCamelCase    		=					WavLMConfig.from_pretrained(lowercase_						)
   __lowerCamelCase    		=					WavaVecaFeatureExtractor.from_pretrained(
       lowercase_      , return_attention_mask=lowercase_      , do_normalize=lowercase_						)

   __lowerCamelCase    		=					hf_config.architectures[0]
   if arch.endswith('ForSequenceClassification'						):
      __lowerCamelCase    		=					convert_classification(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForAudioFrameClassification'						):
      __lowerCamelCase    		=					convert_diarization(lowercase_      , lowercase_      , lowercase_						)
   elif arch.endswith('ForXVector'						):
      __lowerCamelCase    		=					convert_xvector(lowercase_      , lowercase_      , lowercase_						)
   else:
      raise NotImplementedError(F'''S3PRL weights conversion is not supported for {arch}'''						)

   if hf_config.use_weighted_layer_sum:
      __lowerCamelCase    		=					checkpoint['Featurizer']['weights']

   hf_feature_extractor.save_pretrained(lowercase_						)
   hf_model.save_pretrained(lowercase_						)


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE	:		Optional[Any]   =   argparse.ArgumentParser()
     parser.add_argument(
         '''--base_model_name''', default=None, type=str, help='''Name of the huggingface pretrained base model.'''
     )
     parser.add_argument('''--config_path''', default=None, type=str, help='''Path to the huggingface classifier config.''')
     parser.add_argument('''--checkpoint_path''', default=None, type=str, help='''Path to the s3prl checkpoint.''')
     parser.add_argument('''--model_dump_path''', default=None, type=str, help='''Path to the final converted model.''')
     _SCREAMING_SNAKE_CASE	:		Dict   =   parser.parse_args()
     convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)






