'''simple docstring'''




import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


__UpperCAmelCase												='scheduler_config.json'




class    a__				(       UpperCamelCase__			):
     lowerCamelCase  :					Dict							=1
     lowerCamelCase  :					Optional[int]							=2
     lowerCamelCase  :					List[str]							=3
     lowerCamelCase  :					Any							=4
     lowerCamelCase  :					Optional[int]							=5




@dataclass
class    a__				(       UpperCamelCase__			):
     lowerCamelCase  :					jnp.ndarray




class    a__				:
     lowerCamelCase  :					List[Any]							=SCHEDULER_CONFIG_NAME
     lowerCamelCase  :					List[str]							=["""dtype"""]
     lowerCamelCase  :					List[Any]							=[]
     lowerCamelCase  :					Any							=True






     @classmethod
     def        SCREAMING_SNAKE_CASE__       (			cls		:		Tuple  ,    a		:		Dict[str, Any] = None  ,    a		:		Optional[str] = None  ,    a		:		Any=False  ,    **a		:		Dict  ,    ):





         """simple docstring"""



         __lowerCamelCase     ,  __lowerCamelCase          =  cls.load_config(
             pretrained_model_name_or_path=__a  ,    subfolder=__a  ,    return_unused_kwargs=__a  ,    **__a  ,    )
         __lowerCamelCase     ,  __lowerCamelCase          =  cls.from_config(__a  ,    return_unused_kwargs=__a  ,    **__a   )

         if hasattr(__a  ,    '''create_state'''   ) and getattr(__a  ,    '''has_state'''  ,    __a   ):
             __lowerCamelCase          =  scheduler.create_state()

         if return_unused_kwargs:
             return scheduler, state, unused_kwargs

         return scheduler, state






     def        SCREAMING_SNAKE_CASE__       (			self		:		Any  ,    a		:		Union[str, os.PathLike]  ,    a		:		bool = False  ,    **a		:		Dict   ):





         """simple docstring"""



         self.save_config(save_directory=__a  ,    push_to_hub=__a  ,    **__a   )






     @property
     def        SCREAMING_SNAKE_CASE__       (			self		:		List[str]   ):





         """simple docstring"""



         return self._get_compatibles()






     @classmethod
     def        SCREAMING_SNAKE_CASE__       (			cls		:		List[str]   ):





         """simple docstring"""



         __lowerCamelCase          =  list(set([cls.__name__] + cls._compatibles   )   )
         __lowerCamelCase          =  importlib.import_module(__name__.split('''.'''   )[0]   )
         __lowerCamelCase          =  [
             getattr(__a  ,    __a   ) for c in compatible_classes_str if hasattr(__a  ,    __a   )
         ]
         return compatible_classes






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   jnp.ndarray:






    """simple docstring"""




    assert len(snake_case_    ) >= x.ndim
    return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case_    ) - x.ndim)    )    ,  snake_case_    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__=0.9_9_9    ,  UpperCamelCase__=jnp.floataa    )     ->   jnp.ndarray:






    """simple docstring"""




    def alpha_bar(UpperCamelCase__    ):
        return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2    ) ** 2

    __lowerCamelCase          =  []
    for i in range(snake_case_    ):
        __lowerCamelCase          =  i / num_diffusion_timesteps
        __lowerCamelCase          =  (i + 1) / num_diffusion_timesteps
        betas.append(min(1 - alpha_bar(snake_case_    ) / alpha_bar(snake_case_    )    ,  snake_case_    )    )
    return jnp.array(snake_case_    ,  dtype=snake_case_    )




@flax.struct.dataclass
class    a__				:
     lowerCamelCase  :					jnp.ndarray
     lowerCamelCase  :					jnp.ndarray
     lowerCamelCase  :					jnp.ndarray






     @classmethod
     def        SCREAMING_SNAKE_CASE__       (			cls		:		Tuple  ,    a		:		Optional[Any]   ):





         """simple docstring"""



         __lowerCamelCase          =  scheduler.config

         if config.trained_betas is not None:
             __lowerCamelCase          =  jnp.asarray(config.trained_betas  ,    dtype=scheduler.dtype   )
         elif config.beta_schedule == "linear":
             __lowerCamelCase          =  jnp.linspace(config.beta_start  ,    config.beta_end  ,    config.num_train_timesteps  ,    dtype=scheduler.dtype   )
         elif config.beta_schedule == "scaled_linear":
             # this schedule is very specific to the latent diffusion model.
             __lowerCamelCase          =  (
                 jnp.linspace(
                     config.beta_start**0.5  ,    config.beta_end**0.5  ,    config.num_train_timesteps  ,    dtype=scheduler.dtype   )
                 ** 2
             )
         elif config.beta_schedule == "squaredcos_cap_v2":
             # Glide cosine schedule
             __lowerCamelCase          =  betas_for_alpha_bar(config.num_train_timesteps  ,    dtype=scheduler.dtype   )
         else:
             raise NotImplementedError(
                 f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""   )

         __lowerCamelCase          =  1.0 - betas

         __lowerCamelCase          =  jnp.cumprod(__a  ,    axis=0   )

         return cls(
             alphas=__a  ,    betas=__a  ,    alphas_cumprod=__a  ,    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[str]:






    """simple docstring"""




    __lowerCamelCase          =  state.alphas_cumprod

    __lowerCamelCase          =  alphas_cumprod[timesteps] ** 0.5
    __lowerCamelCase          =  sqrt_alpha_prod.flatten()
    __lowerCamelCase          =  broadcast_to_shape_from_left(snake_case_    ,  original_samples.shape    )

    __lowerCamelCase          =  (1 - alphas_cumprod[timesteps]) ** 0.5
    __lowerCamelCase          =  sqrt_one_minus_alpha_prod.flatten()
    __lowerCamelCase          =  broadcast_to_shape_from_left(snake_case_    ,  original_samples.shape    )

    return sqrt_alpha_prod, sqrt_one_minus_alpha_prod






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   int:






    """simple docstring"""




    __lowerCamelCase     ,  __lowerCamelCase          =  get_sqrt_alpha_prod(snake_case_    ,  snake_case_    ,  snake_case_    ,  snake_case_    )
    __lowerCamelCase          =  sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
    return noisy_samples






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:






    """simple docstring"""




    __lowerCamelCase     ,  __lowerCamelCase          =  get_sqrt_alpha_prod(snake_case_    ,  snake_case_    ,  snake_case_    ,  snake_case_    )
    __lowerCamelCase          =  sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
    return velocity

'''simple docstring'''




import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


logging.set_verbosity_info()
__UpperCAmelCase												=logging.get_logger(__name__)

__UpperCAmelCase												={
    "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_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",
    "encoder.layer_norm_for_extract": "layer_norm_for_extract",
    "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": "lm_head",
    "label_embs_concat": "label_embeddings_concat",
    "mask_emb": "masked_spec_embed",
    "spk_proj": "speaker_proj",
}
__UpperCAmelCase												=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "label_embeddings_concat",
    "speaker_proj",
    "layer_norm_for_extract",
]






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    for attribute in key.split('''.'''    ):
        __lowerCamelCase          =  getattr(UpperCamelCase__    ,  UpperCamelCase__    )

    if weight_type is not None:
        __lowerCamelCase          =  getattr(UpperCamelCase__    ,  UpperCamelCase__    ).shape
    else:
        __lowerCamelCase          =  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":
        __lowerCamelCase          =  value
    elif weight_type == "weight_g":
        __lowerCamelCase          =  value
    elif weight_type == "weight_v":
        __lowerCamelCase          =  value
    elif weight_type == "bias":
        __lowerCamelCase          =  value
    else:
        __lowerCamelCase          =  value

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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    )     ->   List[Any]:
    __lowerCamelCase          =  []
    __lowerCamelCase          =  fairseq_model.state_dict()

    __lowerCamelCase          =  hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        __lowerCamelCase          =  False
        if "conv_layers" in name:
            load_conv_layer(
                UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  hf_model.config.feat_extract_norm == '''group'''    ,  )
            __lowerCamelCase          =  True
        else:
            for key, mapped_key in MAPPING.items():
                __lowerCamelCase          =  '''unispeech_sat.''' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
                if key in name or key.split('''w2v_model.'''    )[-1] == name.split('''.'''    )[0]:
                    if "layer_norm_for_extract" in name and (".".join(name.split('''.'''    )[:-1]    ) != key):
                        # special case since naming is very similar
                        continue
                    __lowerCamelCase          =  True
                    if "*" in mapped_key:
                        __lowerCamelCase          =  name.split(UpperCamelCase__    )[0].split('''.'''    )[-2]
                        __lowerCamelCase          =  mapped_key.replace('''*'''    ,  UpperCamelCase__    )
                    if "weight_g" in name:
                        __lowerCamelCase          =  '''weight_g'''
                    elif "weight_v" in name:
                        __lowerCamelCase          =  '''weight_v'''
                    elif "bias" in name:
                        __lowerCamelCase          =  '''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        __lowerCamelCase          =  '''weight'''
                    else:
                        __lowerCamelCase          =  None
                    set_recursively(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )
                continue
        if not is_used:
            unused_weights.append(UpperCamelCase__    )

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






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )     ->   Any:
    __lowerCamelCase          =  full_name.split('''conv_layers.'''    )[-1]
    __lowerCamelCase          =  name.split('''.'''    )
    __lowerCamelCase          =  int(items[0]    )
    __lowerCamelCase          =  int(items[1]    )

    if type_id == 0:
        if "bias" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found."""    )
            __lowerCamelCase          =  value
            logger.info(f"""Feat extract conv layer {layer_id} was initialized from {full_name}."""    )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found."""    )
            __lowerCamelCase          =  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:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor[layer_id].layer_norm.bias.data.shape} was found."""    )
            __lowerCamelCase          =  value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
        elif "weight" in name:
            if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
                raise ValueError(
                    f"""{full_name} has size {value.shape}, but"""
                    f""" {feature_extractor[layer_id].layer_norm.weight.data.shape} was found."""    )
            __lowerCamelCase          =  value
            logger.info(f"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."""    )
    else:
        unused_weights.append(UpperCamelCase__    )






@torch.no_grad()
def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=None    ,  UpperCamelCase__=None    ,  UpperCamelCase__=True    )     ->   Optional[Any]:
    if config_path is not None:
        __lowerCamelCase          =  UniSpeechSatConfig.from_pretrained(UpperCamelCase__    )
    else:
        __lowerCamelCase          =  UniSpeechSatConfig()

    __lowerCamelCase          =  ''''''

    if is_finetuned:
        __lowerCamelCase          =  UniSpeechSatForCTC(UpperCamelCase__    )
    else:
        __lowerCamelCase          =  UniSpeechSatForPreTraining(UpperCamelCase__    )

    __lowerCamelCase     ,  __lowerCamelCase     ,  __lowerCamelCase          =  fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]    ,  arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''    )[:-1]    )}    )
    __lowerCamelCase          =  model[0].eval()

    recursively_load_weights(UpperCamelCase__    ,  UpperCamelCase__    )

    hf_wavavec.save_pretrained(UpperCamelCase__    )


if __name__ == "__main__":
  __UpperCAmelCase												=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("--dict_path", default=None, type=str, help="Path to dict of fine-tuned model")
  parser.add_argument("--config_path", default=None, type=str, help="Path to hf config.json of model to convert")
  parser.add_argument(
      "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
  )
  __UpperCAmelCase												=parser.parse_args()
  convert_unispeech_sat_checkpoint(
      args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
  )
