


# 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 							a						:




							"""simple docstring"""


							__UpperCAmelCase       :				CommonSchedulerState

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

							@classmethod
							def        __snake_case   (				cls			:    str       ,      lowerCamelCase			:    CommonSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray							)      ->       int:
														return cls(common=lowerCamelCase       ,      init_noise_sigma=lowerCamelCase       ,      timesteps=lowerCamelCase							)

@dataclass
class 							a						(_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				DDPMSchedulerState

class 							a						(_lowerCAmelCase	,							_lowerCAmelCase ):




							"""simple docstring"""


							__UpperCAmelCase       :				Optional[Any]	    =    [e.name for e in FlaxKarrasDiffusionSchedulers]

							__UpperCAmelCase       :				jnp.dtype

							@property
							def        __snake_case   (				self			:    str							)      ->       Optional[int]:
														return True

							@register_to_config
							def __init__(				self			:    int       ,      lowerCamelCase			:    int = 1000       ,      lowerCamelCase			:    float = 0.00_01       ,      lowerCamelCase			:    float = 0.02       ,      lowerCamelCase			:    str = "linear"       ,      lowerCamelCase			:    Optional[jnp.ndarray] = None       ,      lowerCamelCase			:    str = "fixed_small"       ,      lowerCamelCase			:    bool = True       ,      lowerCamelCase			:    str = "epsilon"       ,      lowerCamelCase			:    jnp.dtype = jnp.floataa       ,      )      ->       Any:
														__snake_case						:     List[str]									=  dtype

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    Optional[CommonSchedulerState] = None							)      ->       DDPMSchedulerState:
														if common is None:
																					__snake_case						:     Optional[int]									=  CommonSchedulerState.create(self							)

														# standard deviation of the initial noise distribution
														__snake_case						:     int									=  jnp.array(1.0       ,      dtype=self.dtype							)

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

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

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    Optional[int] = None							)      ->       jnp.ndarray:
														return sample

							def        __snake_case   (				self			:    Dict       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    Tuple = ()							)      ->       DDPMSchedulerState:
														__snake_case						:     List[Any]									=  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						:     Tuple									=  (jnp.arange(0       ,      lowerCamelCase							) * step_ratio).round()[::-1]

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

							def        __snake_case   (				self			:    int       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    List[Any]       ,      lowerCamelCase			:    List[str]=None       ,      lowerCamelCase			:    Optional[int]=None							)      ->       Optional[int]:
														__snake_case						:     Union[str, Any]									=  state.common.alphas_cumprod[t]
														__snake_case						:     Union[str, 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
														__snake_case						:     List[Any]									=  (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

														if variance_type is None:
																					__snake_case						:     int									=  self.config.variance_type

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

														return variance

							def        __snake_case   (				self			:    Any       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    int       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    Optional[jax.random.KeyArray] = None       ,      lowerCamelCase			:    bool = True       ,      )      ->       Union[FlaxDDPMSchedulerOutput, Tuple]:
														__snake_case						:     int									=  timestep

														if key is None:
																					__snake_case						:     int									=  jax.random.PRNGKey(0							)

														if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
																					__snake_case					,     __snake_case						:     str									=  jnp.split(lowerCamelCase       ,      sample.shape[1]       ,      axis=1							)
														else:
																					__snake_case						:     List[Any]									=  None

														# 1. compute alphas, betas
														__snake_case						:     Optional[Any]									=  state.common.alphas_cumprod[t]
														__snake_case						:     List[Any]									=  jnp.where(t > 0       ,      state.common.alphas_cumprod[t - 1]       ,      jnp.array(1.0       ,      dtype=self.dtype							)							)
														__snake_case						:     List[Any]									=  1 - alpha_prod_t
														__snake_case						:     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":
																					__snake_case						:     int									=  (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
														elif self.config.prediction_type == "sample":
																					__snake_case						:     Optional[Any]									=  model_output
														elif self.config.prediction_type == "v_prediction":
																					__snake_case						:     List[str]									=  (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:
																					__snake_case						:     List[str]									=  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
														__snake_case						:     Tuple									=  (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
														__snake_case						:     str									=  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
														__snake_case						:     int									=  pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

														# 6. Add noise
														def random_variance():
																					__snake_case						:     List[Any]									=  jax.random.split(lowerCamelCase       ,      num=1							)
																					__snake_case						:     Dict									=  jax.random.normal(lowerCamelCase       ,      shape=model_output.shape       ,      dtype=self.dtype							)
																					return (self._get_variance(lowerCamelCase       ,      lowerCamelCase       ,      predicted_variance=lowerCamelCase							) ** 0.5) * noise

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

														__snake_case						:     int									=  pred_prev_sample + variance

														if not return_dict:
																					return (pred_prev_sample, state)

														return FlaxDDPMSchedulerOutput(prev_sample=lowerCamelCase       ,      state=lowerCamelCase							)

							def        __snake_case   (				self			:    Optional[Any]       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      )      ->       jnp.ndarray:
														return add_noise_common(state.common       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)

							def        __snake_case   (				self			:    Union[str, Any]       ,      lowerCamelCase			:    DDPMSchedulerState       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      lowerCamelCase			:    jnp.ndarray       ,      )      ->       jnp.ndarray:
														return get_velocity_common(state.common       ,      lowerCamelCase       ,      lowerCamelCase       ,      lowerCamelCase							)






							def __len__(				self			:    Dict							)      ->       Dict:
														return self.config.num_train_timesteps




import argparse

import torch

from transformers import (
    EncodecConfig,
    EncodecFeatureExtractor,
    EncodecModel,
    logging,
)


# checkpoints downloaded from:
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_24khz-d7cc33bc.th
# https://huggingface.co/facebook/musicgen-small/resolve/main/compression_state_dict.bin
# https://dl.fbaipublicfiles.com/encodec/v0/encodec_48khz-7e698e3e.th


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE		:List[str]						=     logging.get_logger("""transformers.models.encodec""")

SCREAMING_SNAKE_CASE		:Tuple						=     {
    """quantizer.vq.layers.*._codebook.inited""": """quantizer.layers.*.codebook.inited""",
    """quantizer.vq.layers.*._codebook.cluster_size""": """quantizer.layers.*.codebook.cluster_size""",
    """quantizer.vq.layers.*._codebook.embed""": """quantizer.layers.*.codebook.embed""",
    """quantizer.vq.layers.*._codebook.embed_avg""": """quantizer.layers.*.codebook.embed_avg""",
}
SCREAMING_SNAKE_CASE		:List[str]						=     {
    """encoder.model.0.conv.conv""": """encoder.layers.0.conv""",
    """encoder.model.1.block.1.conv.conv""": """encoder.layers.1.block.1.conv""",
    """encoder.model.1.block.3.conv.conv""": """encoder.layers.1.block.3.conv""",
    """encoder.model.1.shortcut.conv.conv""": """encoder.layers.1.shortcut.conv""",
    """encoder.model.3.conv.conv""": """encoder.layers.3.conv""",
    """encoder.model.4.block.1.conv.conv""": """encoder.layers.4.block.1.conv""",
    """encoder.model.4.block.3.conv.conv""": """encoder.layers.4.block.3.conv""",
    """encoder.model.4.shortcut.conv.conv""": """encoder.layers.4.shortcut.conv""",
    """encoder.model.6.conv.conv""": """encoder.layers.6.conv""",
    """encoder.model.7.block.1.conv.conv""": """encoder.layers.7.block.1.conv""",
    """encoder.model.7.block.3.conv.conv""": """encoder.layers.7.block.3.conv""",
    """encoder.model.7.shortcut.conv.conv""": """encoder.layers.7.shortcut.conv""",
    """encoder.model.9.conv.conv""": """encoder.layers.9.conv""",
    """encoder.model.10.block.1.conv.conv""": """encoder.layers.10.block.1.conv""",
    """encoder.model.10.block.3.conv.conv""": """encoder.layers.10.block.3.conv""",
    """encoder.model.10.shortcut.conv.conv""": """encoder.layers.10.shortcut.conv""",
    """encoder.model.12.conv.conv""": """encoder.layers.12.conv""",
    """encoder.model.13.lstm""": """encoder.layers.13.lstm""",
    """encoder.model.15.conv.conv""": """encoder.layers.15.conv""",
}
SCREAMING_SNAKE_CASE		:Union[str, Any]						=     {
    """encoder.model.0.conv.norm""": """encoder.layers.0.norm""",
    """encoder.model.1.block.1.conv.norm""": """encoder.layers.1.block.1.norm""",
    """encoder.model.1.block.3.conv.norm""": """encoder.layers.1.block.3.norm""",
    """encoder.model.1.shortcut.conv.norm""": """encoder.layers.1.shortcut.norm""",
    """encoder.model.3.conv.norm""": """encoder.layers.3.norm""",
    """encoder.model.4.block.1.conv.norm""": """encoder.layers.4.block.1.norm""",
    """encoder.model.4.block.3.conv.norm""": """encoder.layers.4.block.3.norm""",
    """encoder.model.4.shortcut.conv.norm""": """encoder.layers.4.shortcut.norm""",
    """encoder.model.6.conv.norm""": """encoder.layers.6.norm""",
    """encoder.model.7.block.1.conv.norm""": """encoder.layers.7.block.1.norm""",
    """encoder.model.7.block.3.conv.norm""": """encoder.layers.7.block.3.norm""",
    """encoder.model.7.shortcut.conv.norm""": """encoder.layers.7.shortcut.norm""",
    """encoder.model.9.conv.norm""": """encoder.layers.9.norm""",
    """encoder.model.10.block.1.conv.norm""": """encoder.layers.10.block.1.norm""",
    """encoder.model.10.block.3.conv.norm""": """encoder.layers.10.block.3.norm""",
    """encoder.model.10.shortcut.conv.norm""": """encoder.layers.10.shortcut.norm""",
    """encoder.model.12.conv.norm""": """encoder.layers.12.norm""",
    """encoder.model.15.conv.norm""": """encoder.layers.15.norm""",
}
SCREAMING_SNAKE_CASE		:Optional[Any]						=     {
    """decoder.model.0.conv.conv""": """decoder.layers.0.conv""",
    """decoder.model.1.lstm""": """decoder.layers.1.lstm""",
    """decoder.model.3.convtr.convtr""": """decoder.layers.3.conv""",
    """decoder.model.4.block.1.conv.conv""": """decoder.layers.4.block.1.conv""",
    """decoder.model.4.block.3.conv.conv""": """decoder.layers.4.block.3.conv""",
    """decoder.model.4.shortcut.conv.conv""": """decoder.layers.4.shortcut.conv""",
    """decoder.model.6.convtr.convtr""": """decoder.layers.6.conv""",
    """decoder.model.7.block.1.conv.conv""": """decoder.layers.7.block.1.conv""",
    """decoder.model.7.block.3.conv.conv""": """decoder.layers.7.block.3.conv""",
    """decoder.model.7.shortcut.conv.conv""": """decoder.layers.7.shortcut.conv""",
    """decoder.model.9.convtr.convtr""": """decoder.layers.9.conv""",
    """decoder.model.10.block.1.conv.conv""": """decoder.layers.10.block.1.conv""",
    """decoder.model.10.block.3.conv.conv""": """decoder.layers.10.block.3.conv""",
    """decoder.model.10.shortcut.conv.conv""": """decoder.layers.10.shortcut.conv""",
    """decoder.model.12.convtr.convtr""": """decoder.layers.12.conv""",
    """decoder.model.13.block.1.conv.conv""": """decoder.layers.13.block.1.conv""",
    """decoder.model.13.block.3.conv.conv""": """decoder.layers.13.block.3.conv""",
    """decoder.model.13.shortcut.conv.conv""": """decoder.layers.13.shortcut.conv""",
    """decoder.model.15.conv.conv""": """decoder.layers.15.conv""",
}
SCREAMING_SNAKE_CASE		:Optional[int]						=     {
    """decoder.model.0.conv.norm""": """decoder.layers.0.norm""",
    """decoder.model.3.convtr.norm""": """decoder.layers.3.norm""",
    """decoder.model.4.block.1.conv.norm""": """decoder.layers.4.block.1.norm""",
    """decoder.model.4.block.3.conv.norm""": """decoder.layers.4.block.3.norm""",
    """decoder.model.4.shortcut.conv.norm""": """decoder.layers.4.shortcut.norm""",
    """decoder.model.6.convtr.norm""": """decoder.layers.6.norm""",
    """decoder.model.7.block.1.conv.norm""": """decoder.layers.7.block.1.norm""",
    """decoder.model.7.block.3.conv.norm""": """decoder.layers.7.block.3.norm""",
    """decoder.model.7.shortcut.conv.norm""": """decoder.layers.7.shortcut.norm""",
    """decoder.model.9.convtr.norm""": """decoder.layers.9.norm""",
    """decoder.model.10.block.1.conv.norm""": """decoder.layers.10.block.1.norm""",
    """decoder.model.10.block.3.conv.norm""": """decoder.layers.10.block.3.norm""",
    """decoder.model.10.shortcut.conv.norm""": """decoder.layers.10.shortcut.norm""",
    """decoder.model.12.convtr.norm""": """decoder.layers.12.norm""",
    """decoder.model.13.block.1.conv.norm""": """decoder.layers.13.block.1.norm""",
    """decoder.model.13.block.3.conv.norm""": """decoder.layers.13.block.3.norm""",
    """decoder.model.13.shortcut.conv.norm""": """decoder.layers.13.shortcut.norm""",
    """decoder.model.15.conv.norm""": """decoder.layers.15.norm""",
}
SCREAMING_SNAKE_CASE		:List[Any]						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_DECODER,
}
SCREAMING_SNAKE_CASE		:int						=     {
    **MAPPING_QUANTIZER,
    **MAPPING_ENCODER,
    **MAPPING_ENCODER_48K,
    **MAPPING_DECODER,
    **MAPPING_DECODER_48K,
}
SCREAMING_SNAKE_CASE		:Dict						=     []
SCREAMING_SNAKE_CASE		:int						=     []




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						List[Any]:



    """simple docstring"""






    for attribute in key.split("."			):
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)

    if weight_type is not None:
        UpperCamelCase_			  =						getattr(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			).shape
    else:
        UpperCamelCase_			  =						hf_pointer.shape

    if hf_shape != value.shape:
        raise ValueError(
            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":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_g":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_v":
        UpperCamelCase_			  =						value
    elif weight_type == "bias":
        UpperCamelCase_			  =						value
    elif weight_type == "running_mean":
        UpperCamelCase_			  =						value
    elif weight_type == "running_var":
        UpperCamelCase_			  =						value
    elif weight_type == "num_batches_tracked":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l0":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "weight_hh_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_ih_l1":
        UpperCamelCase_			  =						value
    elif weight_type == "bias_hh_l1":
        UpperCamelCase_			  =						value
    else:
        UpperCamelCase_			  =						value

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




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    for key in ignore_keys:
        if key.endswith(".*"			):
            if name.startswith(key[:-1]			):
                return True
        elif ".*." in key:
            UpperCamelCase_  ,       UpperCamelCase_			  =						key.split(".*."			)
            if prefix in name and suffix in name:
                return True
        elif key in name:
            return True
    return False




def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)->						int:



    """simple docstring"""






    UpperCamelCase_			  =						[]

    if model_name == "encodec_24khz" or "encodec_32khz":
        UpperCamelCase_			  =						MAPPING_24K
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						MAPPING_48K
    else:
        raise ValueError(f"Unsupported model: {model_name}"			)

    for name, value in orig_dict.items():
        if should_ignore(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			):
            logger.info(f"{name} was ignored"			)
            continue

        UpperCamelCase_			  =						False
        for key, mapped_key in MAPPING.items():
            if "*" in key:
                UpperCamelCase_  ,       UpperCamelCase_			  =						key.split(".*."			)
                if prefix in name and suffix in name:
                    UpperCamelCase_			  =						suffix

            if key in name:
                # HACK otherwise .embed gets initialized with .embed_avg too
                if key.endswith("embed"			) and name.endswith("embed_avg"			):
                    continue

                UpperCamelCase_			  =						True
                if "*" in mapped_key:
                    UpperCamelCase_			  =						name.split(SCREAMING_SNAKE_CASE_			)[0].split("."			)[-2]
                    UpperCamelCase_			  =						mapped_key.replace("*"			,						SCREAMING_SNAKE_CASE_			)
                if "weight_g" in name:
                    UpperCamelCase_			  =						"weight_g"
                elif "weight_v" in name:
                    UpperCamelCase_			  =						"weight_v"
                elif "weight_ih_l0" in name:
                    UpperCamelCase_			  =						"weight_ih_l0"
                elif "weight_hh_l0" in name:
                    UpperCamelCase_			  =						"weight_hh_l0"
                elif "bias_ih_l0" in name:
                    UpperCamelCase_			  =						"bias_ih_l0"
                elif "bias_hh_l0" in name:
                    UpperCamelCase_			  =						"bias_hh_l0"
                elif "weight_ih_l1" in name:
                    UpperCamelCase_			  =						"weight_ih_l1"
                elif "weight_hh_l1" in name:
                    UpperCamelCase_			  =						"weight_hh_l1"
                elif "bias_ih_l1" in name:
                    UpperCamelCase_			  =						"bias_ih_l1"
                elif "bias_hh_l1" in name:
                    UpperCamelCase_			  =						"bias_hh_l1"
                elif "bias" in name:
                    UpperCamelCase_			  =						"bias"
                elif "weight" in name:
                    UpperCamelCase_			  =						"weight"
                elif "running_mean" in name:
                    UpperCamelCase_			  =						"running_mean"
                elif "running_var" in name:
                    UpperCamelCase_			  =						"running_var"
                elif "num_batches_tracked" in name:
                    UpperCamelCase_			  =						"num_batches_tracked"
                else:
                    UpperCamelCase_			  =						None
                set_recursively(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
            continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE_			)

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




@torch.no_grad()
def 			lowerCAmelCase(      SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_=None			,						SCREAMING_SNAKE_CASE_=None			,						)->						Union[str, Any]:



    """simple docstring"""






    if config_path is not None:
        UpperCamelCase_			  =						EncodecConfig.from_pretrained(SCREAMING_SNAKE_CASE_			)
    else:
        UpperCamelCase_			  =						EncodecConfig()

    if model_name == "encodec_24khz":
        pass  # config is already correct
    elif model_name == "encodec_32khz":
        UpperCamelCase_			  =						[8, 5, 4, 4]
        UpperCamelCase_			  =						[2.2]
        UpperCamelCase_			  =						6_4
        UpperCamelCase_			  =						3_2_0_0_0
        UpperCamelCase_			  =						2_0_4_8
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						False
    elif model_name == "encodec_48khz":
        UpperCamelCase_			  =						[8, 5, 4, 2]
        UpperCamelCase_			  =						[3.0, 6.0, 12.0, 24.0]
        UpperCamelCase_			  =						4_8_0_0_0
        UpperCamelCase_			  =						2
        UpperCamelCase_			  =						False
        UpperCamelCase_			  =						"time_group_norm"
        UpperCamelCase_			  =						True
        UpperCamelCase_			  =						1.0
        UpperCamelCase_			  =						0.01
    else:
        raise ValueError(f"Unknown model name: {model_name}"			)

    UpperCamelCase_			  =						EncodecModel(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						EncodecFeatureExtractor(
        feature_size=config.audio_channels			,						sampling_rate=config.sampling_rate			,						chunk_length_s=config.chunk_length_s			,						overlap=config.overlap			,						)
    feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE_			)

    UpperCamelCase_			  =						torch.load(SCREAMING_SNAKE_CASE_			)
    if "best_state" in original_checkpoint:
        # we might have a training state saved, in which case discard the yaml results and just retain the weights
        UpperCamelCase_			  =						original_checkpoint["best_state"]
    recursively_load_weights(SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			,						SCREAMING_SNAKE_CASE_			)
    model.save_pretrained(SCREAMING_SNAKE_CASE_			)

    if repo_id:
        print("Pushing to the hub..."			)
        feature_extractor.push_to_hub(SCREAMING_SNAKE_CASE_			)
        model.push_to_hub(SCREAMING_SNAKE_CASE_			)


if __name__ == "__main__":
 SCREAMING_SNAKE_CASE		:Tuple						=     argparse.ArgumentParser()
 parser.add_argument(
     """--model""",
     default="""encodec_24khz""",
     type=str,
     help="""The model to convert. Should be one of 'encodec_24khz', 'encodec_32khz', 'encodec_48khz'.""",
 )
 parser.add_argument("""--checkpoint_path""", required=True, default=None, type=str, help="""Path to original checkpoint""")
 parser.add_argument("""--config_path""", default=None, type=str, help="""Path to hf config.json of model to convert""")
 parser.add_argument(
     """--pytorch_dump_folder_path""", required=True, default=None, type=str, help="""Path to the output PyTorch model."""
 )
 parser.add_argument(
     """--push_to_hub""", default=None, type=str, help="""Where to upload the converted model on the 🤗 hub."""
 )

 SCREAMING_SNAKE_CASE		:Dict						=     parser.parse_args()
 convert_checkpoint(
     args.model,
     args.checkpoint_path,
     args.pytorch_dump_folder_path,
     args.config_path,
     args.push_to_hub,
 )
