






'''simple docstring'''

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "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",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
UpperCamelCase							=[
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]



def 							snake_case					(     a_							: Union[str, Any]	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   str					    =							{}
      with open(a_    ,   """r"""	) as file:
            for line_number, line in enumerate(a_	):
                  UpperCamelCase_				:   List[str]					    =							line.strip()
                  if line:
                        UpperCamelCase_				:   List[str]					    =							line.split()
                        UpperCamelCase_				:   List[Any]					    =							line_number
                        UpperCamelCase_				:   Union[str, Any]					    =							words[0]
                        UpperCamelCase_				:   Optional[int]					    =							value
      return result



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: List[Any]    ,   a_							: Union[str, Any]    ,   a_							: Optional[int]    ,   a_							: str	)       ->				Tuple:





      """simple docstring"""

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

      UpperCamelCase_				:   Union[str, Any]					    =							None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(a_	):
                  UpperCamelCase_				:   Dict					    =							PARAM_MAPPING[full_name.split("""."""	)[-1]]
                  UpperCamelCase_				:   List[str]					    =							"""param"""

      if weight_type is not None and weight_type != "param":
            UpperCamelCase_				:   Tuple					    =							getattr(a_    ,   a_	).shape
      elif weight_type is not None and weight_type == "param":
            UpperCamelCase_				:   Dict					    =							hf_pointer
            for attribute in hf_param_name.split("""."""	):
                  UpperCamelCase_				:   List[str]					    =							getattr(a_    ,   a_	)
            UpperCamelCase_				:   Any					    =							shape_pointer.shape

            # let's reduce dimension
            UpperCamelCase_				:   Optional[Any]					    =							value[0]
      else:
            UpperCamelCase_				:   Any					    =							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_				:   str					    =							value
      elif weight_type == "weight_g":
            UpperCamelCase_				:   Optional[Any]					    =							value
      elif weight_type == "weight_v":
            UpperCamelCase_				:   List[str]					    =							value
      elif weight_type == "bias":
            UpperCamelCase_				:   int					    =							value
      elif weight_type == "param":
            for attribute in hf_param_name.split("""."""	):
                  UpperCamelCase_				:   Union[str, Any]					    =							getattr(a_    ,   a_	)
            UpperCamelCase_				:   Union[str, Any]					    =							value
      else:
            UpperCamelCase_				:   List[str]					    =							value

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



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: List[Any]    ,   a_							: Tuple    ,   a_							: Dict    ,   a_							: Tuple	)       ->				Union[str, Any]:





      """simple docstring"""

      UpperCamelCase_				:   Dict					    =							None
      for param_key in PARAM_MAPPING.keys():
            if full_name.endswith(a_	):
                  UpperCamelCase_				:   Any					    =							PARAM_MAPPING[full_name.split("""."""	)[-1]]
                  UpperCamelCase_				:   List[Any]					    =							"""param"""

      if weight_type is not None and weight_type != "param":
            UpperCamelCase_				:   List[Any]					    =							""".""".join([key, weight_type]	)
      elif weight_type is not None and weight_type == "param":
            UpperCamelCase_				:   Optional[int]					    =							""".""".join([key, hf_param_name]	)
      else:
            UpperCamelCase_				:   Any					    =							key

      UpperCamelCase_				:   List[Any]					    =							value if """lm_head""" in full_key else value[0]


UpperCamelCase							={
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}



def 							snake_case					(     a_							: List[Any]    ,   a_							: Tuple    ,   a_							: Union[str, Any]=None    ,   a_							: Union[str, Any]=None	)       ->				List[Any]:





      """simple docstring"""

      UpperCamelCase_				:   List[str]					    =							False
      for key, mapped_key in MAPPING.items():
            UpperCamelCase_				:   List[Any]					    =							"""wav2vec2.""" + 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]:
                  UpperCamelCase_				:   List[str]					    =							True
                  if "*" in mapped_key:
                        UpperCamelCase_				:   Optional[int]					    =							name.split(a_	)[0].split("""."""	)[-2]
                        UpperCamelCase_				:   Tuple					    =							mapped_key.replace("""*"""    ,   a_	)
                  if "weight_g" in name:
                        UpperCamelCase_				:   List[str]					    =							"""weight_g"""
                  elif "weight_v" in name:
                        UpperCamelCase_				:   int					    =							"""weight_v"""
                  elif "bias" in name:
                        UpperCamelCase_				:   Union[str, Any]					    =							"""bias"""
                  elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        UpperCamelCase_				:   Optional[Any]					    =							"""weight"""
                  else:
                        UpperCamelCase_				:   Any					    =							None
                  if hf_dict is not None:
                        rename_dict(a_    ,   a_    ,   a_    ,   a_    ,   a_	)
                  else:
                        set_recursively(a_    ,   a_    ,   a_    ,   a_    ,   a_	)
                  return is_used
      return is_used



def 							snake_case					(     a_							: Any    ,   a_							: int    ,   a_							: Optional[Any]	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   Any					    =							[]
      UpperCamelCase_				:   int					    =							fairseq_model.state_dict()

      UpperCamelCase_				:   Optional[Any]					    =							hf_model.wavaveca.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCamelCase_				:   List[Any]					    =							False
            if "conv_layers" in name:
                  load_conv_layer(
                      a_    ,   a_    ,   a_    ,   a_    ,   hf_model.config.feat_extract_norm == """group"""    ,   )
                  UpperCamelCase_				:   Dict					    =							True
            else:
                  UpperCamelCase_				:   List[Any]					    =							load_wavaveca_layer(a_    ,   a_    ,   a_	)
            if not is_used:
                  unused_weights.append(a_	)

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



def 							snake_case					(     a_							: Union[str, Any]    ,   a_							: Dict    ,   a_							: List[str]    ,   a_							: List[str]    ,   a_							: Optional[int]	)       ->				int:





      """simple docstring"""

      UpperCamelCase_				:   Optional[int]					    =							full_name.split("""conv_layers."""	)[-1]
      UpperCamelCase_				:   int					    =							name.split("""."""	)
      UpperCamelCase_				:   List[Any]					    =							int(items[0]	)
      UpperCamelCase_				:   List[str]					    =							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."	)
                  UpperCamelCase_				:   List[str]					    =							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."	)
                  UpperCamelCase_				:   int					    =							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.conv_layers[layer_id].layer_norm.bias.data.shape} was found."	)
                  UpperCamelCase_				:   Dict					    =							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.conv_layers[layer_id].layer_norm.weight.data.shape} was found."	)
                  UpperCamelCase_				:   List[Any]					    =							value
                  logger.info(f"Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}."	)
      else:
            unused_weights.append(a_	)



@torch.no_grad()
def 							snake_case					(     a_							: List[str]    ,   a_							: Optional[int]    ,   a_							: Any=None    ,   a_							: Optional[int]=None    ,   a_							: List[str]=True    ,   a_							: Optional[Any]=False	)       ->				Optional[Any]:





      """simple docstring"""

      if config_path is not None:
            UpperCamelCase_				:   Dict					    =							WavaVecaConfig.from_pretrained(a_	)
      else:
            UpperCamelCase_				:   Optional[Any]					    =							WavaVecaConfig()

      if is_seq_class:
            UpperCamelCase_				:   Tuple					    =							read_txt_into_dict(a_	)
            UpperCamelCase_				:   int					    =							idalabel
            UpperCamelCase_				:   str					    =							WavaVecaForSequenceClassification(a_	)
            UpperCamelCase_				:   Optional[int]					    =							WavaVecaFeatureExtractor(
                feature_size=1    ,   sampling_rate=16_000    ,   padding_value=0    ,   do_normalize=a_    ,   return_attention_mask=a_    ,   )
            feature_extractor.save_pretrained(a_	)

      elif is_finetuned:
            if dict_path:
                  UpperCamelCase_				:   Optional[Any]					    =							Dictionary.load(a_	)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCamelCase_				:   List[str]					    =							target_dict.pad_index
                  UpperCamelCase_				:   List[Any]					    =							target_dict.bos_index
                  UpperCamelCase_				:   str					    =							target_dict.eos_index
                  UpperCamelCase_				:   Optional[int]					    =							len(target_dict.symbols	)
                  UpperCamelCase_				:   Union[str, Any]					    =							os.path.join(a_    ,   """vocab.json"""	)
                  if not os.path.isdir(a_	):
                        logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(a_	)	)
                        return
                  os.makedirs(a_    ,   exist_ok=a_	)
                  UpperCamelCase_				:   List[Any]					    =							target_dict.indices

                  # fairseq has the <pad> and <s> switched
                  UpperCamelCase_				:   Any					    =							0
                  UpperCamelCase_				:   int					    =							1
                  with open(a_    ,   """w"""    ,   encoding="""utf-8"""	) as vocab_handle:
                        json.dump(a_    ,   a_	)
                  UpperCamelCase_				:   int					    =							WavaVecaCTCTokenizer(
                      a_    ,   unk_token=target_dict.unk_word    ,   pad_token=target_dict.pad_word    ,   bos_token=target_dict.bos_word    ,   eos_token=target_dict.eos_word    ,   word_delimiter_token="""|"""    ,   do_lower_case=a_    ,   )
                  UpperCamelCase_				:   List[Any]					    =							True if config.feat_extract_norm == """layer""" else False
                  UpperCamelCase_				:   str					    =							WavaVecaFeatureExtractor(
                      feature_size=1    ,   sampling_rate=16_000    ,   padding_value=0    ,   do_normalize=a_    ,   return_attention_mask=a_    ,   )
                  UpperCamelCase_				:   Dict					    =							WavaVecaProcessor(feature_extractor=a_    ,   tokenizer=a_	)
                  processor.save_pretrained(a_	)

            UpperCamelCase_				:   List[Any]					    =							WavaVecaForCTC(a_	)
      else:
            UpperCamelCase_				:   Union[str, Any]					    =							WavaVecaForPreTraining(a_	)

      if is_finetuned or is_seq_class:
            UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Tuple					    =							fairseq.checkpoint_utils.load_model_ensemble_and_task(
                [checkpoint_path]    ,   arg_overrides={"""data""": """/""".join(dict_path.split("""/"""	)[:-1]	)}	)
      else:
            UpperCamelCase_				:   str					    =							argparse.Namespace(task="""audio_pretraining"""	)
            UpperCamelCase_				:   Optional[Any]					    =							fairseq.tasks.setup_task(a_	)

            UpperCamelCase_    ,			UpperCamelCase_    ,			UpperCamelCase_				:   Optional[Any]					    =							fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,   task=a_	)

      UpperCamelCase_				:   str					    =							model[0].eval()

      recursively_load_weights(a_    ,   a_    ,   not is_finetuned	)

      hf_wavavec.save_pretrained(a_	)


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"
      )
      parser.add_argument(
          "--is_seq_class",
          action="store_true",
          help="Whether the model to convert is a fine-tuned sequence classification model or not",
      )
      UpperCamelCase							=parser.parse_args()

      UpperCamelCase							=not args.not_finetuned and not args.is_seq_class
      convert_wavaveca_checkpoint(
          args.checkpoint_path,
          args.pytorch_dump_folder_path,
          args.config_path,
          args.dict_path,
          is_finetuned,
          args.is_seq_class,
      )














'''simple docstring'''

import contextlib
import copy
import random
from typing import Any, Dict, Iterable, Optional, Union

import numpy as np
import torch

from .utils import deprecate, is_transformers_available


if is_transformers_available():
      import transformers



def 							snake_case					(     a_							: int	)       ->				Union[str, Any]:





      """simple docstring"""

      random.seed(a_	)
      np.random.seed(a_	)
      torch.manual_seed(a_	)
      torch.cuda.manual_seed_all(a_	)
      # ^^ safe to call this function even if cuda is not available




class     A    :






 """simple docstring"""





 def __init__(							self			,     __lowerCAmelCase			,     __lowerCAmelCase = 0.99_99			,     __lowerCAmelCase = 0.0			,     __lowerCAmelCase = 0			,     __lowerCAmelCase = False			,     __lowerCAmelCase = 1.0			,     __lowerCAmelCase = 2 / 3			,     __lowerCAmelCase = None			,     __lowerCAmelCase = None			,     **__lowerCAmelCase			,     ):

       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   Dict					    =							(
                 """Passing a `torch.nn.Module` to `ExponentialMovingAverage` is deprecated. """
                 """Please pass the parameters of the module instead."""
             )
             deprecate(
                 """passing a `torch.nn.Module` to `ExponentialMovingAverage`"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase			,     )
             UpperCamelCase_				:   str					    =							parameters.parameters()

             # set use_ema_warmup to True if a torch.nn.Module is passed for backwards compatibility
             UpperCamelCase_				:   Optional[int]					    =							True

       if kwargs.get("""max_value"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   Tuple					    =							"""The `max_value` argument is deprecated. Please use `decay` instead."""
             deprecate("""max_value"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             UpperCamelCase_				:   str					    =							kwargs["""max_value"""]

       if kwargs.get("""min_value"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   Dict					    =							"""The `min_value` argument is deprecated. Please use `min_decay` instead."""
             deprecate("""min_value"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             UpperCamelCase_				:   Optional[Any]					    =							kwargs["""min_value"""]

       UpperCamelCase_				:   Optional[Any]					    =							list(__lowerCAmelCase )
       UpperCamelCase_				:   Any					    =							[p.clone().detach() for p in parameters]

       if kwargs.get("""device"""			,     __lowerCAmelCase ) is not None:
             UpperCamelCase_				:   str					    =							"""The `device` argument is deprecated. Please use `to` instead."""
             deprecate("""device"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase )
             self.to(device=kwargs["""device"""] )

       UpperCamelCase_				:   List[Any]					    =							None

       UpperCamelCase_				:   Optional[Any]					    =							decay
       UpperCamelCase_				:   List[str]					    =							min_decay
       UpperCamelCase_				:   int					    =							update_after_step
       UpperCamelCase_				:   Optional[int]					    =							use_ema_warmup
       UpperCamelCase_				:   Optional[int]					    =							inv_gamma
       UpperCamelCase_				:   Any					    =							power
       UpperCamelCase_				:   str					    =							0
       UpperCamelCase_				:   List[str]					    =							None  # set in `step()`

       UpperCamelCase_				:   Union[str, Any]					    =							model_cls
       UpperCamelCase_				:   Any					    =							model_config
 @classmethod
 def   _UpperCAmelCase       (							cls			,     __lowerCAmelCase			,     __lowerCAmelCase ):
       UpperCamelCase_    ,			UpperCamelCase_				:   int					    =							model_cls.load_config(__lowerCAmelCase			,     return_unused_kwargs=__lowerCAmelCase )
       UpperCamelCase_				:   str					    =							model_cls.from_pretrained(__lowerCAmelCase )

       UpperCamelCase_				:   Union[str, Any]					    =							cls(model.parameters()			,     model_cls=__lowerCAmelCase			,     model_config=model.config )

       ema_model.load_state_dict(__lowerCAmelCase )
       return ema_model
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.model_cls is None:
             raise ValueError("""`save_pretrained` can only be used if `model_cls` was defined at __init__.""" )

       if self.model_config is None:
             raise ValueError("""`save_pretrained` can only be used if `model_config` was defined at __init__.""" )

       UpperCamelCase_				:   int					    =							self.model_cls.from_config(self.model_config )
       UpperCamelCase_				:   List[Any]					    =							self.state_dict()
       state_dict.pop("""shadow_params"""			,     __lowerCAmelCase )

       model.register_to_config(**__lowerCAmelCase )
       self.copy_to(model.parameters() )
       model.save_pretrained(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   Any					    =							max(0			,     optimization_step - self.update_after_step - 1 )

       if step <= 0:
             return 0.0

       if self.use_ema_warmup:
             UpperCamelCase_				:   List[Any]					    =							1 - (1 + step / self.inv_gamma) ** -self.power
       else:
             UpperCamelCase_				:   List[Any]					    =							(1 + step) / (10 + step)

       UpperCamelCase_				:   Optional[Any]					    =							min(__lowerCAmelCase			,     self.decay )
       # make sure decay is not smaller than min_decay
       UpperCamelCase_				:   Optional[Any]					    =							max(__lowerCAmelCase			,     self.min_decay )
       return cur_decay_value
 @torch.no_grad()
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if isinstance(__lowerCAmelCase			,     torch.nn.Module ):
             UpperCamelCase_				:   str					    =							(
                 """Passing a `torch.nn.Module` to `ExponentialMovingAverage.step` is deprecated. """
                 """Please pass the parameters of the module instead."""
             )
             deprecate(
                 """passing a `torch.nn.Module` to `ExponentialMovingAverage.step`"""			,     """1.0.0"""			,     __lowerCAmelCase			,     standard_warn=__lowerCAmelCase			,     )
             UpperCamelCase_				:   int					    =							parameters.parameters()

       UpperCamelCase_				:   Optional[int]					    =							list(__lowerCAmelCase )

       self.optimization_step += 1

       # Compute the decay factor for the exponential moving average.
       UpperCamelCase_				:   Any					    =							self.get_decay(self.optimization_step )
       UpperCamelCase_				:   List[str]					    =							decay
       UpperCamelCase_				:   Any					    =							1 - decay

       UpperCamelCase_				:   Optional[int]					    =							contextlib.nullcontext
       if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
             import deepspeed

       for s_param, param in zip(self.shadow_params			,     __lowerCAmelCase ):
             if is_transformers_available() and transformers.deepspeed.is_deepspeed_zeroa_enabled():
                   UpperCamelCase_				:   Optional[Any]					    =							deepspeed.zero.GatheredParameters(__lowerCAmelCase			,     modifier_rank=__lowerCAmelCase )

             with context_manager():
                   if param.requires_grad:
                         s_param.sub_(one_minus_decay * (s_param - param) )
                   else:
                         s_param.copy_(__lowerCAmelCase )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   str					    =							list(__lowerCAmelCase )
       for s_param, param in zip(self.shadow_params			,     __lowerCAmelCase ):
             param.data.copy_(s_param.to(param.device ).data )
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase=None			,     __lowerCAmelCase=None ):
       UpperCamelCase_				:   Union[str, Any]					    =							[
           p.to(device=__lowerCAmelCase			,     dtype=__lowerCAmelCase ) if p.is_floating_point() else p.to(device=__lowerCAmelCase )
           for p in self.shadow_params
       ]
 def   _UpperCAmelCase       (							self ):
       return {
           "decay": self.decay,
           "min_decay": self.min_decay,
           "optimization_step": self.optimization_step,
           "update_after_step": self.update_after_step,
           "use_ema_warmup": self.use_ema_warmup,
           "inv_gamma": self.inv_gamma,
           "power": self.power,
           "shadow_params": self.shadow_params,
       }
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[str]					    =							[param.detach().cpu().clone() for param in parameters]
 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       if self.temp_stored_params is None:
             raise RuntimeError("""This ExponentialMovingAverage has no `store()`ed weights """ """to `restore()`""" )
       for c_param, param in zip(self.temp_stored_params			,     __lowerCAmelCase ):
             param.data.copy_(c_param.data )

       # Better memory-wise.
       UpperCamelCase_				:   List[Any]					    =							None





 def   _UpperCAmelCase       (							self			,     __lowerCAmelCase ):
       UpperCamelCase_				:   List[Any]					    =							copy.deepcopy(__lowerCAmelCase )

       UpperCamelCase_				:   int					    =							state_dict.get("""decay"""			,     self.decay )
       if self.decay < 0.0 or self.decay > 1.0:
             raise ValueError("""Decay must be between 0 and 1""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""min_decay"""			,     self.min_decay )
       if not isinstance(self.min_decay			,     __lowerCAmelCase ):
             raise ValueError("""Invalid min_decay""" )

       UpperCamelCase_				:   str					    =							state_dict.get("""optimization_step"""			,     self.optimization_step )
       if not isinstance(self.optimization_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid optimization_step""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""update_after_step"""			,     self.update_after_step )
       if not isinstance(self.update_after_step			,     __lowerCAmelCase ):
             raise ValueError("""Invalid update_after_step""" )

       UpperCamelCase_				:   List[str]					    =							state_dict.get("""use_ema_warmup"""			,     self.use_ema_warmup )
       if not isinstance(self.use_ema_warmup			,     __lowerCAmelCase ):
             raise ValueError("""Invalid use_ema_warmup""" )

       UpperCamelCase_				:   Any					    =							state_dict.get("""inv_gamma"""			,     self.inv_gamma )
       if not isinstance(self.inv_gamma			,     (float, int) ):
             raise ValueError("""Invalid inv_gamma""" )

       UpperCamelCase_				:   str					    =							state_dict.get("""power"""			,     self.power )
       if not isinstance(self.power			,     (float, int) ):
             raise ValueError("""Invalid power""" )

       UpperCamelCase_				:   Dict					    =							state_dict.get("""shadow_params"""			,     __lowerCAmelCase )
       if shadow_params is not None:
             UpperCamelCase_				:   Any					    =							shadow_params
             if not isinstance(self.shadow_params			,     __lowerCAmelCase ):
                   raise ValueError("""shadow_params must be a list""" )
             if not all(isinstance(__lowerCAmelCase			,     torch.Tensor ) for p in self.shadow_params ):
                   raise ValueError("""shadow_params must all be Tensors""" )






