'''simple docstring'''




import math
from enum import Enum
from typing import Optional, Union

from torch.optim import Optimizer
from torch.optim.lr_scheduler import LambdaLR

from .utils import logging


__UpperCAmelCase												=logging.get_logger(__name__)




class    a__				(       UpperCAmelCase__			):
     lowerCamelCase  :					str							="linear"
     lowerCamelCase  :					int							="cosine"
     lowerCamelCase  :					Union[str, Any]							="cosine_with_restarts"
     lowerCamelCase  :					Tuple							="polynomial"
     lowerCamelCase  :					Dict							="constant"
     lowerCamelCase  :					Dict							="constant_with_warmup"
     lowerCamelCase  :					Union[str, Any]							="piecewise_constant"






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__ = -1    )     ->   Dict:






    """simple docstring"""




    return LambdaLR(UpperCamelCase__    ,  lambda UpperCamelCase__    : 1    ,  last_epoch=UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = -1    )     ->   Any:






    """simple docstring"""




    def lr_lambda(UpperCamelCase__    ):
        if current_step < num_warmup_steps:
            return float(UpperCamelCase__    ) / float(max(1.0    ,  UpperCamelCase__    )    )
        return 1.0

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = -1    )     ->   Any:






    """simple docstring"""




    __lowerCamelCase          =  {}
    __lowerCamelCase          =  step_rules.split(''','''    )
    for rule_str in rule_list[:-1]:
        __lowerCamelCase     ,  __lowerCamelCase          =  rule_str.split(''':'''    )
        __lowerCamelCase          =  int(UpperCamelCase__    )
        __lowerCamelCase          =  float(UpperCamelCase__    )
        __lowerCamelCase          =  value
    __lowerCamelCase          =  float(rule_list[-1]    )

    def create_rules_function(UpperCamelCase__    ,  UpperCamelCase__    ):
        def rule_func(UpperCamelCase__    ) -> float:
            __lowerCamelCase          =  sorted(rules_dict.keys()    )
            for i, sorted_step in enumerate(UpperCamelCase__    ):
                if steps < sorted_step:
                    return rules_dict[sorted_steps[i]]
            return last_lr_multiple

        return rule_func

    __lowerCamelCase          =  create_rules_function(UpperCamelCase__    ,  UpperCamelCase__    )

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=-1    )     ->   Optional[int]:






    """simple docstring"""




    def lr_lambda(UpperCamelCase__    ):
        if current_step < num_warmup_steps:
            return float(UpperCamelCase__    ) / float(max(1    ,  UpperCamelCase__    )    )
        return max(
            0.0    ,  float(num_training_steps - current_step    ) / float(max(1    ,  num_training_steps - num_warmup_steps    )    )    )

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = 0.5    ,  UpperCamelCase__ = -1    )     ->   int:






    """simple docstring"""




    def lr_lambda(UpperCamelCase__    ):
        if current_step < num_warmup_steps:
            return float(UpperCamelCase__    ) / float(max(1    ,  UpperCamelCase__    )    )
        __lowerCamelCase          =  float(current_step - num_warmup_steps    ) / float(max(1    ,  num_training_steps - num_warmup_steps    )    )
        return max(0.0    ,  0.5 * (1.0 + math.cos(math.pi * float(UpperCamelCase__    ) * 2.0 * progress    ))    )

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = 1    ,  UpperCamelCase__ = -1    )     ->   Any:






    """simple docstring"""




    def lr_lambda(UpperCamelCase__    ):
        if current_step < num_warmup_steps:
            return float(UpperCamelCase__    ) / float(max(1    ,  UpperCamelCase__    )    )
        __lowerCamelCase          =  float(current_step - num_warmup_steps    ) / float(max(1    ,  num_training_steps - num_warmup_steps    )    )
        if progress >= 1.0:
            return 0.0
        return max(0.0    ,  0.5 * (1.0 + math.cos(math.pi * ((float(UpperCamelCase__    ) * progress) % 1.0)    ))    )

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__=1E-7    ,  UpperCamelCase__=1.0    ,  UpperCamelCase__=-1    )     ->   Dict:






    """simple docstring"""




    __lowerCamelCase          =  optimizer.defaults['''lr''']
    if not (lr_init > lr_end):
        raise ValueError(f"""lr_end ({lr_end}) must be be smaller than initial lr ({lr_init})"""    )

    def lr_lambda(UpperCamelCase__    ):
        if current_step < num_warmup_steps:
            return float(UpperCamelCase__    ) / float(max(1    ,  UpperCamelCase__    )    )
        elif current_step > num_training_steps:
            return lr_end / lr_init  # as LambdaLR multiplies by lr_init
        else:
            __lowerCamelCase          =  lr_init - lr_end
            __lowerCamelCase          =  num_training_steps - num_warmup_steps
            __lowerCamelCase          =  1 - (current_step - num_warmup_steps) / decay_steps
            __lowerCamelCase          =  lr_range * pct_remaining**power + lr_end
            return decay / lr_init  # as LambdaLR multiplies by lr_init

    return LambdaLR(UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__    )


__UpperCAmelCase												={
    SchedulerType.LINEAR: get_linear_schedule_with_warmup,
    SchedulerType.COSINE: get_cosine_schedule_with_warmup,
    SchedulerType.COSINE_WITH_RESTARTS: get_cosine_with_hard_restarts_schedule_with_warmup,
    SchedulerType.POLYNOMIAL: get_polynomial_decay_schedule_with_warmup,
    SchedulerType.CONSTANT: get_constant_schedule,
    SchedulerType.CONSTANT_WITH_WARMUP: get_constant_schedule_with_warmup,
    SchedulerType.PIECEWISE_CONSTANT: get_piecewise_constant_schedule,
}






def __lowerCAmelCase			(							UpperCamelCase__    ,  UpperCamelCase__    ,  UpperCamelCase__ = None    ,  UpperCamelCase__ = None    ,  UpperCamelCase__ = None    ,  UpperCamelCase__ = 1    ,  UpperCamelCase__ = 1.0    ,  UpperCamelCase__ = -1    ,  )     ->   int:






    """simple docstring"""




    __lowerCamelCase          =  SchedulerType(UpperCamelCase__    )
    __lowerCamelCase          =  TYPE_TO_SCHEDULER_FUNCTION[name]
    if name == SchedulerType.CONSTANT:
        return schedule_func(UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )

    if name == SchedulerType.PIECEWISE_CONSTANT:
        return schedule_func(UpperCamelCase__    ,  step_rules=UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )

    # All other schedulers require `num_warmup_steps`
    if num_warmup_steps is None:
        raise ValueError(f"""{name} requires `num_warmup_steps`, please provide that argument."""    )

    if name == SchedulerType.CONSTANT_WITH_WARMUP:
        return schedule_func(UpperCamelCase__    ,  num_warmup_steps=UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )

    # All other schedulers require `num_training_steps`
    if num_training_steps is None:
        raise ValueError(f"""{name} requires `num_training_steps`, please provide that argument."""    )

    if name == SchedulerType.COSINE_WITH_RESTARTS:
        return schedule_func(
            UpperCamelCase__    ,  num_warmup_steps=UpperCamelCase__    ,  num_training_steps=UpperCamelCase__    ,  num_cycles=UpperCamelCase__    ,  last_epoch=UpperCamelCase__    ,  )

    if name == SchedulerType.POLYNOMIAL:
        return schedule_func(
            UpperCamelCase__    ,  num_warmup_steps=UpperCamelCase__    ,  num_training_steps=UpperCamelCase__    ,  power=UpperCamelCase__    ,  last_epoch=UpperCamelCase__    ,  )

    return schedule_func(
        UpperCamelCase__    ,  num_warmup_steps=UpperCamelCase__    ,  num_training_steps=UpperCamelCase__    ,  last_epoch=UpperCamelCase__    )

'''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
  )
