




'''simple docstring'''





import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


if is_tpu_available(check_device=False):
   import torch_xla.core.xla_model as xm





def 			__a			(     _UpperCamelCase:					int      ,		_UpperCamelCase:					bool = False					)		->				Dict:

     """simple docstring"""




     if device_specific:
          seed += AcceleratorState().process_index
     random.seed(_UpperCamelCase					)
     np.random.seed(_UpperCamelCase					)
     torch.manual_seed(_UpperCamelCase					)
     if is_xpu_available():
          torch.xpu.manual_seed_all(_UpperCamelCase					)
     elif is_npu_available():
          torch.npu.manual_seed_all(_UpperCamelCase					)
     else:
          torch.cuda.manual_seed_all(_UpperCamelCase					)
     # ^^ safe to call this function even if cuda is not available
     if is_tpu_available():
          xm.set_rng_state(_UpperCamelCase					)





def 			__a			(     _UpperCamelCase:					Optional[RNGType] = None      ,		_UpperCamelCase:					Optional[torch.Generator] = None					)		->				Any:

     """simple docstring"""




     if rng_type == RNGType.TORCH:
          _snake_case							=   torch.get_rng_state()
     elif rng_type == RNGType.CUDA:
          _snake_case							=   torch.cuda.get_rng_state()
     elif rng_type == RNGType.XLA:
          assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
          _snake_case							=   torch.tensor(xm.get_rng_state()					)
     elif rng_type == RNGType.NPU:
          assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
          _snake_case							=   torch.npu.get_rng_state()
     elif rng_type == RNGType.XPU:
          assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
          _snake_case							=   torch.xpu.get_rng_state()
     elif rng_type == RNGType.GENERATOR:
          assert generator is not None, "Need a generator to synchronize its seed."
          _snake_case							=   generator.get_state()

     # Broadcast the rng state from device 0 to other devices
     _snake_case							=   AcceleratorState()
     if state.distributed_type == DistributedType.TPU:
          _snake_case							=   rng_state.to(xm.xla_device()					)
          xm.collective_broadcast([rng_state]					)
          xm.mark_step()
          _snake_case							=   rng_state.cpu()
     elif (
         state.distributed_type in CUDA_DISTRIBUTED_TYPES
         or state.distributed_type == DistributedType.MULTI_NPU
         or state.distributed_type == DistributedType.MULTI_XPU
     ):
          _snake_case							=   rng_state.to(state.device					)
          torch.distributed.broadcast(_UpperCamelCase      ,		0					)
          _snake_case							=   rng_state.cpu()
     elif state.distributed_type == DistributedType.MULTI_CPU:
          torch.distributed.broadcast(_UpperCamelCase      ,		0					)

     # Set the broadcast rng state
     if rng_type == RNGType.TORCH:
          torch.set_rng_state(_UpperCamelCase					)
     elif rng_type == RNGType.CUDA:
          torch.cuda.set_rng_state(_UpperCamelCase					)
     elif rng_type == RNGType.NPU:
          torch.npu.set_rng_state(_UpperCamelCase					)
     elif rng_type == RNGType.XPU:
          torch.xpu.set_rng_state(_UpperCamelCase					)
     elif rng_type == RNGType.XLA:
          xm.set_rng_state(rng_state.item()					)
     elif rng_type == RNGType.GENERATOR:
          generator.set_state(_UpperCamelCase					)





def 			__a			(     _UpperCamelCase:					List[Union[str, RNGType]]      ,		_UpperCamelCase:					Optional[torch.Generator] = None					)		->				Optional[int]:

     """simple docstring"""




     for rng_type in rng_types:
          synchronize_rng_state(RNGType(_UpperCamelCase					)      ,		generator=_UpperCamelCase					)








'''simple docstring'''





import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

# Register SEW's fairseq modules
from sew_asapp import tasks  # noqa: F401

from transformers import (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
UpperCamelCase_		:    Optional[int]									=		logging.get_logger(__name__)

UpperCamelCase_		:    Dict									=		{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''encoder.upsample.0''': '''encoder.upsample.projection''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''w2v_model.layer_norm''': '''layer_norm''',
    '''w2v_encoder.proj''': '''lm_head''',
    '''mask_emb''': '''masked_spec_embed''',
}





def 			__a			(     _UpperCamelCase:					Optional[Any]      ,		_UpperCamelCase:					Any      ,		_UpperCamelCase:					str      ,		_UpperCamelCase:					List[Any]      ,		_UpperCamelCase:					Optional[int]					)		->				List[Any]:

     """simple docstring"""




     for attribute in key.split("."					):
          _snake_case							=   getattr(_UpperCamelCase      ,		_UpperCamelCase					)

     if weight_type is not None:
          _snake_case							=   getattr(_UpperCamelCase      ,		_UpperCamelCase					).shape
     else:
          _snake_case							=   hf_pointer.shape

     assert hf_shape == value.shape, (
         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":
          _snake_case							=   value
     elif weight_type == "weight_g":
          _snake_case							=   value
     elif weight_type == "weight_v":
          _snake_case							=   value
     elif weight_type == "bias":
          _snake_case							=   value
     else:
          _snake_case							=   value

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





def 			__a			(     _UpperCamelCase:					Tuple      ,		_UpperCamelCase:					List[str]      ,		_UpperCamelCase:					Optional[int]					)		->				Dict:

     """simple docstring"""




     _snake_case							=   []
     _snake_case							=   fairseq_model.state_dict()

     _snake_case							=   hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

     for name, value in fairseq_dict.items():
          _snake_case							=   False
          if "conv_layers" in name:
               load_conv_layer(
                   _UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase      ,		hf_model.config.feat_extract_norm == "group"      ,		)
               _snake_case							=   True
          else:
               for key, mapped_key in MAPPING.items():
                    _snake_case							=   "sew_d." + mapped_key if (is_finetuned and mapped_key != "lm_head") else mapped_key

                    if key in name or key.split("w2v_model."					)[-1] == name.split("."					)[0]:
                         _snake_case							=   True
                         if "*" in mapped_key:
                              _snake_case							=   name.split(_UpperCamelCase					)[0].split("."					)[-2]
                              if not layer_index.isnumeric():
                                   continue
                              _snake_case							=   mapped_key.replace("*"      ,		_UpperCamelCase					)
                         if "weight_g" in name:
                              _snake_case							=   "weight_g"
                         elif "weight_v" in name:
                              _snake_case							=   "weight_v"
                         elif "weight" in name:
                              _snake_case							=   "weight"
                         elif "bias" in name:
                              _snake_case							=   "bias"
                         else:
                              _snake_case							=   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 			__a			(     _UpperCamelCase:					List[str]      ,		_UpperCamelCase:					Any      ,		_UpperCamelCase:					Tuple      ,		_UpperCamelCase:					Union[str, Any]      ,		_UpperCamelCase:					Tuple					)		->				Optional[Any]:

     """simple docstring"""




     _snake_case							=   full_name.split("conv_layers."					)[-1]
     _snake_case							=   name.split("."					)
     _snake_case							=   int(items[0]					)
     _snake_case							=   int(items[1]					)

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





def 			__a			(     _UpperCamelCase:					List[Any]      ,		_UpperCamelCase:					str					)		->				Dict:

     """simple docstring"""




     _snake_case							=   SEWDConfig()
     if is_finetuned:
          _snake_case							=   model.wav_encoder.wav_model.cfg
     else:
          _snake_case							=   model.cfg

     _snake_case							=   fs_config.conv_bias
     _snake_case							=   eval(fs_config.conv_feature_layers					)
     _snake_case							=   [x[0] for x in conv_layers]
     _snake_case							=   [x[1] for x in conv_layers]
     _snake_case							=   [x[2] for x in conv_layers]
     _snake_case							=   "gelu"
     _snake_case							=   "layer" if fs_config.extractor_mode == "layer_norm" else "group"
     _snake_case							=   0.0
     _snake_case							=   fs_config.activation_fn.name
     _snake_case							=   fs_config.encoder_embed_dim
     _snake_case							=   0.02
     _snake_case							=   fs_config.encoder_ffn_embed_dim
     _snake_case							=   1E-5
     _snake_case							=   fs_config.encoder_layerdrop
     _snake_case							=   fs_config.encoder_attention_heads
     _snake_case							=   fs_config.conv_pos_groups
     _snake_case							=   fs_config.conv_pos
     _snake_case							=   len(_UpperCamelCase					)
     _snake_case							=   fs_config.encoder_layers
     _snake_case							=   fs_config.squeeze_factor
     # DeBERTa-specific parameters:
     _snake_case							=   fs_config.max_position_embeddings
     _snake_case							=   fs_config.position_buckets
     _snake_case							=   fs_config.share_att_key
     _snake_case							=   fs_config.relative_attention
     _snake_case							=   fs_config.position_biased_input
     _snake_case							=   tuple(fs_config.pos_att_type.split("|"					)					)
     _snake_case							=   fs_config.norm_rel_ebd

     # take care of any params that are overridden by the Wav2VecCtc model
     if is_finetuned:
          _snake_case							=   model.cfg
          _snake_case							=   fs_config.final_dropout
          _snake_case							=   fs_config.layerdrop
     _snake_case							=   fs_config.activation_dropout
     _snake_case							=   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
     _snake_case							=   fs_config.attention_dropout
     _snake_case							=   fs_config.dropout_input
     _snake_case							=   fs_config.dropout
     _snake_case							=   fs_config.mask_channel_length
     _snake_case							=   fs_config.mask_channel_prob
     _snake_case							=   fs_config.mask_length
     _snake_case							=   fs_config.mask_prob

     _snake_case							=   "Wav2Vec2FeatureExtractor"
     _snake_case							=   "Wav2Vec2CTCTokenizer"

     return config





@torch.no_grad()
def 			__a			(     _UpperCamelCase:					Optional[int]      ,		_UpperCamelCase:					Any      ,		_UpperCamelCase:					Any=None      ,		_UpperCamelCase:					Union[str, Any]=None      ,		_UpperCamelCase:					Optional[Any]=True					)		->				int:

     """simple docstring"""




     if is_finetuned:
          _snake_case							,      _snake_case							,      _snake_case							=   fairseq.checkpoint_utils.load_model_ensemble_and_task(
              [checkpoint_path]      ,		arg_overrides={"data": "/".join(dict_path.split("/"					)[:-1]					)}					)
     else:
          _snake_case							,      _snake_case							,      _snake_case							=   fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]					)

     if config_path is not None:
          _snake_case							=   SEWDConfig.from_pretrained(_UpperCamelCase					)
     else:
          _snake_case							=   convert_config(model[0]      ,		_UpperCamelCase					)
     _snake_case							=   model[0].eval()

     _snake_case							=   True if config.feat_extract_norm == "layer" else False
     _snake_case							=   WavaVecaFeatureExtractor(
         feature_size=1      ,		sampling_rate=16_000      ,		padding_value=0      ,		do_normalize=_UpperCamelCase      ,		return_attention_mask=_UpperCamelCase      ,		)

     if is_finetuned:
          if dict_path:
               _snake_case							=   Dictionary.load(_UpperCamelCase					)

               # important change bos & pad token id since CTC symbol is <pad> and
               # not <s> as in fairseq
               _snake_case							=   target_dict.pad_index
               _snake_case							=   target_dict.bos_index
               _snake_case							=   target_dict.pad_index
               _snake_case							=   target_dict.bos_index
               _snake_case							=   target_dict.eos_index
               _snake_case							=   len(target_dict.symbols					)
               _snake_case							=   os.path.join(_UpperCamelCase      ,		"vocab.json"					)
               if not os.path.isdir(_UpperCamelCase					):
                    logger.error("--pytorch_dump_folder_path ({}) should be a directory".format(_UpperCamelCase					)					)
                    return
               os.makedirs(_UpperCamelCase      ,		exist_ok=_UpperCamelCase					)
               with open(_UpperCamelCase      ,		"w"      ,		encoding="utf-8"					) as vocab_handle:
                    json.dump(target_dict.indices      ,		_UpperCamelCase					)
               _snake_case							=   WavaVecaCTCTokenizer(
                   _UpperCamelCase      ,		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=_UpperCamelCase      ,		)
               _snake_case							=   WavaVecaProcessor(feature_extractor=_UpperCamelCase      ,		tokenizer=_UpperCamelCase					)
               processor.save_pretrained(_UpperCamelCase					)

          _snake_case							=   SEWDForCTC(_UpperCamelCase					)
     else:
          _snake_case							=   SEWDModel(_UpperCamelCase					)
          feature_extractor.save_pretrained(_UpperCamelCase					)

     recursively_load_weights(_UpperCamelCase      ,		_UpperCamelCase      ,		_UpperCamelCase					)

     hf_model.save_pretrained(_UpperCamelCase					)


if __name__ == "__main__":
   UpperCamelCase_		:    str									=		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(
       '''--is_finetuned''', action='''store_true''', help='''Whether the model to convert is a fine-tuned model or not'''
   )
   UpperCamelCase_		:    Union[str, Any]									=		parser.parse_args()
   convert_sew_checkpoint(
       args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
   )


