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

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class    lowercase_    :
							_lowerCamelCase						=		42

							# setable values
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		42
							_lowerCamelCase						=		None

							# running values
							_lowerCamelCase						=		None



							@classmethod
							def        UpperCamelCase       (						cls      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													return cls(common=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				sigmas=lowercase_				)




@dataclass
class    lowercase_    (     __snake_case		):
							_lowerCamelCase						=		42




class    lowercase_    (     __snake_case						, __snake_case		):
							_lowerCamelCase						=		[e.name for e in FlaxKarrasDiffusionSchedulers]

							_lowerCamelCase						=		42



							@property
							def        UpperCamelCase       (						self				):
													return True



							@register_to_config
							def __init__(						self      ,				lowercase_ = 1_000      ,				lowercase_ = 0.0_001      ,				lowercase_ = 0.02      ,				lowercase_ = "linear"      ,				lowercase_ = None      ,				lowercase_ = "epsilon"      ,				lowercase_ = jnp.floataa      ,				):
													_snake_case						:     List[Any]				=    dtype



							def        UpperCamelCase       (						self      ,				lowercase_ = None				):
													if common is None:
																			_snake_case						:     List[str]				=    CommonSchedulerState.create(self				)

													_snake_case						:     int				=    jnp.arange(0      ,				self.config.num_train_timesteps				).round()[::-1]
													_snake_case						:     Union[str, Any]				=    ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

													# standard deviation of the initial noise distribution
													_snake_case						:     int				=    sigmas.max()

													return LMSDiscreteSchedulerState.create(
													    common=lowercase_      ,				init_noise_sigma=lowercase_      ,				timesteps=lowercase_      ,				sigmas=lowercase_      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_				):
													((_snake_case)				,)						:     str				=    jnp.where(state.timesteps == timestep      ,				size=1				)
													_snake_case						:     Optional[Any]				=    step_index[0]

													_snake_case						:     Tuple				=    state.sigmas[step_index]
													_snake_case						:     Union[str, Any]				=    sample / ((sigma**2 + 1) ** 0.5)
													return sample



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				):

													def lms_derivative(lowercase_				):
																			_snake_case						:     Union[str, Any]				=    1.0
																			for k in range(lowercase_				):
																									if current_order == k:
																															continue
																									prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
																			return prod

													_snake_case						:     Tuple				=    integrate.quad(lowercase_      ,				state.sigmas[t]      ,				state.sigmas[t + 1]      ,				epsrel=1e-4				)[0]

													return integrated_coeff



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_ = ()				):
													_snake_case						:     Tuple				=    jnp.linspace(self.config.num_train_timesteps - 1      ,				0      ,				lowercase_      ,				dtype=self.dtype				)

													_snake_case						:     str				=    jnp.floor(lowercase_				).astype(jnp.intaa				)
													_snake_case						:     Any				=    jnp.ceil(lowercase_				).astype(jnp.intaa				)

													_snake_case						:     str				=    jnp.mod(lowercase_      ,				1.0				)

													_snake_case						:     int				=    ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
													_snake_case						:     List[str]				=    (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
													_snake_case						:     Union[str, Any]				=    jnp.concatenate([sigmas, jnp.array([0.0]      ,				dtype=self.dtype				)]				)

													_snake_case						:     str				=    timesteps.astype(jnp.intaa				)

													# initial running values
													_snake_case						:     List[str]				=    jnp.zeros((0,) + shape      ,				dtype=self.dtype				)

													return state.replace(
													    timesteps=lowercase_      ,				sigmas=lowercase_      ,				num_inference_steps=lowercase_      ,				derivatives=lowercase_      ,				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_ = 4      ,				lowercase_ = True      ,				):
													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"				)

													_snake_case						:     List[str]				=    state.sigmas[timestep]

													# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
													if self.config.prediction_type == "epsilon":
																			_snake_case						:     Any				=    sample - sigma * model_output
													elif self.config.prediction_type == "v_prediction":
																			# * c_out + input * c_skip
																			_snake_case						:     Dict				=    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
													else:
																			raise ValueError(
																			    f"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

													# 2. Convert to an ODE derivative
													_snake_case						:     Tuple				=    (sample - pred_original_sample) / sigma
													_snake_case						:     List[str]				=    state.replace(derivatives=jnp.append(state.derivatives      ,				lowercase_				)				)
													if len(state.derivatives				) > order:
																			_snake_case						:     Optional[Any]				=    state.replace(derivatives=jnp.delete(state.derivatives      ,				0				)				)

													# 3. Compute linear multistep coefficients
													_snake_case						:     Any				=    min(timestep + 1      ,				lowercase_				)
													_snake_case						:     Optional[Any]				=    [self.get_lms_coefficient(lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_				) for curr_order in range(lowercase_				)]

													# 4. Compute previous sample based on the derivatives path
													_snake_case						:     Optional[int]				=    sample + sum(
													    coeff * derivative for coeff, derivative in zip(lowercase_      ,				reversed(state.derivatives				)				)				)

													if not return_dict:
																			return (prev_sample, state)

													return FlaxLMSSchedulerOutput(prev_sample=lowercase_      ,				state=lowercase_				)



							def        UpperCamelCase       (						self      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				lowercase_      ,				):
													_snake_case						:     Optional[int]				=    state.sigmas[timesteps].flatten()
													_snake_case						:     int				=    broadcast_to_shape_from_left(lowercase_      ,				noise.shape				)

													_snake_case						:     List[str]				=    original_samples + noise * sigma

													return noisy_samples



							def __len__(						self				):
													return self.config.num_train_timesteps
import argparse

import torch

from transformers import HubertConfig, HubertForSequenceClassification, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__SCREAMING_SNAKE_CASE						:    Dict     = logging.get_logger(__name__)

__SCREAMING_SNAKE_CASE						:    List[Any]     = ['UtteranceLevel']
@torch.no_grad()
def 					snake_case						(__lowercase					,			__lowercase					,			__lowercase					,			__lowercase						)       ->       Tuple:

						'''simple docstring'''



						_snake_case						:     Optional[int]				=    torch.load(__lowercase					,			map_location="cpu"						)
						if checkpoint["Config"]["downstream_expert"]["modelrc"]["select"] not in SUPPORTED_MODELS:
												raise NotImplementedError(F"""The supported s3prl models are {SUPPORTED_MODELS}"""						)

						_snake_case						:     List[str]				=    checkpoint["Downstream"]

						_snake_case						:     List[str]				=    HubertConfig.from_pretrained(__lowercase						)
						_snake_case						:     Union[str, Any]				=    HubertForSequenceClassification.from_pretrained(__lowercase					,			config=__lowercase						)
						_snake_case						:     Union[str, Any]				=    WavaVecaFeatureExtractor.from_pretrained(
						    __lowercase					,			return_attention_mask=__lowercase					,			do_normalize=__lowercase						)

						if hf_congfig.use_weighted_layer_sum:
												_snake_case						:     Optional[int]				=    checkpoint["Featurizer"]["weights"]

						_snake_case						:     str				=    downstream_dict["projector.weight"]
						_snake_case						:     Tuple				=    downstream_dict["projector.bias"]
						_snake_case						:     List[str]				=    downstream_dict["model.post_net.linear.weight"]
						_snake_case						:     str				=    downstream_dict["model.post_net.linear.bias"]

						hf_feature_extractor.save_pretrained(__lowercase						)
						hf_model.save_pretrained(__lowercase						)


if __name__ == "__main__":
				__SCREAMING_SNAKE_CASE						:    Optional[int]     = 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						:    str     = parser.parse_args()
				convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)