




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()
_lowerCamelCase     					=		logging.get_logger(__name__)

_lowerCamelCase     					=		{
    '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',
}
_lowerCamelCase     					=		[
    'lm_head',
    'quantizer.weight_proj',
    'quantizer.codevectors',
    'project_q',
    'project_hid',
    'projector',
    'classifier',
]




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		int )    ->				Optional[Any]:
    UpperCAmelCase_									=	{}
    with open(a__			,	'''r''' ) as file:
        for line_number, line in enumerate(a__ ):
            UpperCAmelCase_									=	line.strip()
            if line:
                UpperCAmelCase_									=	line.split()
                UpperCAmelCase_									=	line_number
                UpperCAmelCase_									=	words[0]
                UpperCAmelCase_									=	value
    return result




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


    for attribute in key.split('''.''' ):
        UpperCAmelCase_									=	getattr(a__			,	a__ )

    UpperCAmelCase_									=	None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(a__ ):
            UpperCAmelCase_									=	PARAM_MAPPING[full_name.split('''.''' )[-1]]
            UpperCAmelCase_									=	'''param'''

    if weight_type is not None and weight_type != "param":
        UpperCAmelCase_									=	getattr(a__			,	a__ ).shape
    elif weight_type is not None and weight_type == "param":
        UpperCAmelCase_									=	hf_pointer
        for attribute in hf_param_name.split('''.''' ):
            UpperCAmelCase_									=	getattr(a__			,	a__ )
        UpperCAmelCase_									=	shape_pointer.shape

        # let's reduce dimension
        UpperCAmelCase_									=	value[0]
    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 == "param":
        for attribute in hf_param_name.split('''.''' ):
            UpperCAmelCase_									=	getattr(a__			,	a__ )
        UpperCAmelCase_									=	value
    else:
        UpperCAmelCase_									=	value

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




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Dict			,	__UpperCamelCase     :		List[Any]			,	__UpperCamelCase     :		Optional[int]			,	__UpperCamelCase     :		Dict			,	__UpperCamelCase     :		List[str] )    ->				Optional[int]:
    UpperCAmelCase_									=	None
    for param_key in PARAM_MAPPING.keys():
        if full_name.endswith(a__ ):
            UpperCAmelCase_									=	PARAM_MAPPING[full_name.split('''.''' )[-1]]
            UpperCAmelCase_									=	'''param'''

    if weight_type is not None and weight_type != "param":
        UpperCAmelCase_									=	'''.'''.join([key, weight_type] )
    elif weight_type is not None and weight_type == "param":
        UpperCAmelCase_									=	'''.'''.join([key, hf_param_name] )
    else:
        UpperCAmelCase_									=	key

    UpperCAmelCase_									=	value if '''lm_head''' in full_key else value[0]


_lowerCamelCase     					=		{
    '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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Tuple			,	__UpperCamelCase     :		Tuple			,	__UpperCamelCase     :		Union[str, Any]=None			,	__UpperCamelCase     :		Optional[int]=None )    ->				List[str]:
    UpperCAmelCase_									=	False
    for key, mapped_key in MAPPING.items():
        UpperCAmelCase_									=	'''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_									=	True
            if "*" in mapped_key:
                UpperCAmelCase_									=	name.split(a__ )[0].split('''.''' )[-2]
                UpperCAmelCase_									=	mapped_key.replace('''*'''			,	a__ )
            if "weight_g" in name:
                UpperCAmelCase_									=	'''weight_g'''
            elif "weight_v" in name:
                UpperCAmelCase_									=	'''weight_v'''
            elif "bias" in name:
                UpperCAmelCase_									=	'''bias'''
            elif "weight" in name:
                # TODO: don't match quantizer.weight_proj
                UpperCAmelCase_									=	'''weight'''
            else:
                UpperCAmelCase_									=	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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		Union[str, Any]			,	__UpperCamelCase     :		List[str]			,	__UpperCamelCase     :		Optional[int] )    ->				Tuple:
    UpperCAmelCase_									=	[]
    UpperCAmelCase_									=	fairseq_model.state_dict()

    UpperCAmelCase_									=	hf_model.wavaveca.feature_extractor

    for name, value in fairseq_dict.items():
        UpperCAmelCase_									=	False
        if "conv_layers" in name:
            load_conv_layer(
                a__			,	a__			,	a__			,	a__			,	hf_model.config.feat_extract_norm == '''group'''			,	)
            UpperCAmelCase_									=	True
        else:
            UpperCAmelCase_									=	load_wavaveca_layer(a__			,	a__			,	a__ )
        if not is_used:
            unused_weights.append(a__ )

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




def    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		int			,	__UpperCamelCase     :		List[str]			,	__UpperCamelCase     :		Tuple			,	__UpperCamelCase     :		Any			,	__UpperCamelCase     :		Any )    ->				Tuple:
    UpperCAmelCase_									=	full_name.split('''conv_layers.''' )[-1]
    UpperCAmelCase_									=	name.split('''.''' )
    UpperCAmelCase_									=	int(items[0] )
    UpperCAmelCase_									=	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_									=	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_									=	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_									=	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_									=	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    SCREAMING_SNAKE_CASE      (			__UpperCamelCase     :		List[Any]			,	__UpperCamelCase     :		Optional[int]			,	__UpperCamelCase     :		Optional[Any]=None			,	__UpperCamelCase     :		Optional[int]=None			,	__UpperCamelCase     :		Dict=True			,	__UpperCamelCase     :		Any=False )    ->				List[Any]:


    if config_path is not None:
        UpperCAmelCase_									=	WavaVecaConfig.from_pretrained(a__ )
    else:
        UpperCAmelCase_									=	WavaVecaConfig()

    if is_seq_class:
        UpperCAmelCase_									=	read_txt_into_dict(a__ )
        UpperCAmelCase_									=	idalabel
        UpperCAmelCase_									=	WavaVecaForSequenceClassification(a__ )
        UpperCAmelCase_									=	WavaVecaFeatureExtractor(
            feature_size=1			,	sampling_rate=1_6000			,	padding_value=0			,	do_normalize=a__			,	return_attention_mask=a__			,	)
        feature_extractor.save_pretrained(a__ )

    elif is_finetuned:
        if dict_path:
            UpperCAmelCase_									=	Dictionary.load(a__ )

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            UpperCAmelCase_									=	target_dict.pad_index
            UpperCAmelCase_									=	target_dict.bos_index
            UpperCAmelCase_									=	target_dict.eos_index
            UpperCAmelCase_									=	len(target_dict.symbols )
            UpperCAmelCase_									=	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_									=	target_dict.indices

            # fairseq has the <pad> and <s> switched
            UpperCAmelCase_									=	0
            UpperCAmelCase_									=	1
            with open(a__			,	'''w'''			,	encoding='''utf-8''' ) as vocab_handle:
                json.dump(a__			,	a__ )
            UpperCAmelCase_									=	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_									=	True if config.feat_extract_norm == '''layer''' else False
            UpperCAmelCase_									=	WavaVecaFeatureExtractor(
                feature_size=1			,	sampling_rate=1_6000			,	padding_value=0			,	do_normalize=a__			,	return_attention_mask=a__			,	)
            UpperCAmelCase_									=	WavaVecaProcessor(feature_extractor=a__			,	tokenizer=a__ )
            processor.save_pretrained(a__ )

        UpperCAmelCase_									=	WavaVecaForCTC(a__ )
    else:
        UpperCAmelCase_									=	WavaVecaForPreTraining(a__ )

    if is_finetuned or is_seq_class:
        UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_									=	fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]			,	arg_overrides={'''data''': '''/'''.join(dict_path.split('''/''' )[:-1] )} )
    else:
        UpperCAmelCase_									=	argparse.Namespace(task='''audio_pretraining''' )
        UpperCAmelCase_									=	fairseq.tasks.setup_task(a__ )

        UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_									=	fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]			,	task=a__ )

    UpperCAmelCase_									=	model[0].eval()

    recursively_load_weights(a__			,	a__			,	not is_finetuned )

    hf_wavavec.save_pretrained(a__ )


if __name__ == "__main__":
 _lowerCamelCase     					=		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',
 )
 _lowerCamelCase     					=		parser.parse_args()

 _lowerCamelCase     					=		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'''



from typing import Optional, Union

import torch
from torch import nn

from ...configuration_utils import ConfigMixin, register_to_config
from ...models.modeling_utils import ModelMixin







class 		lowerCAmelCase__  ( a					,   a   ):



 """simple docstring"""



 @register_to_config
 def __init__(						self   :			int   ,				__SCREAMING_SNAKE_CASE   :			int = 768   ,				)    ->					int:


      """simple docstring"""




      super().__init__()

      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.zeros(1   ,				__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(torch.ones(1   ,				__SCREAMING_SNAKE_CASE     )     )
 def  UpperCAmelCase__						(						self   :			Any   ,				__SCREAMING_SNAKE_CASE   :			Optional[Union[str, torch.device]] = None   ,				__SCREAMING_SNAKE_CASE   :			Optional[torch.dtype] = None   ,				)    ->					List[str]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.mean.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      __SCREAMING_SNAKE_CASE      = nn.Parameter(self.std.to(__SCREAMING_SNAKE_CASE     ).to(__SCREAMING_SNAKE_CASE     )     )
      return self
 def  UpperCAmelCase__						(						self   :			Optional[Any]   ,				__SCREAMING_SNAKE_CASE   :			Optional[int]     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds - self.mean) * 1.0 / self.std
      return embeds


 def  UpperCAmelCase__						(						self   :			Tuple   ,				__SCREAMING_SNAKE_CASE   :			Any     )    ->					Optional[int]:


      """simple docstring"""




      __SCREAMING_SNAKE_CASE      = (embeds * self.std) + self.mean
      return embeds






