



"""simple docstring"""

import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConformerConfig,
    WavaVecaConformerForCTC,
    WavaVecaConformerForPreTraining,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
lowercase__					:						int												=			logging.get_logger(__name__)

lowercase__					:						int												=			{
    '''post_extract_proj''': '''feature_projection.projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''self_attn.linear_k''': '''encoder.layers.*.self_attn.linear_k''',
    '''self_attn.linear_v''': '''encoder.layers.*.self_attn.linear_v''',
    '''self_attn.linear_q''': '''encoder.layers.*.self_attn.linear_q''',
    '''self_attn.pos_bias_u''': '''encoder.layers.*.self_attn.pos_bias_u''',
    '''self_attn.pos_bias_v''': '''encoder.layers.*.self_attn.pos_bias_v''',
    '''self_attn.linear_out''': '''encoder.layers.*.self_attn.linear_out''',
    '''self_attn.linear_pos''': '''encoder.layers.*.self_attn.linear_pos''',
    '''self_attn.rotary_emb''': '''encoder.embed_positions''',
    '''self_attn_layer_norm''': '''encoder.layers.*.self_attn_layer_norm''',
    '''conv_module.pointwise_conv1''': '''encoder.layers.*.conv_module.pointwise_conv1''',
    '''conv_module.pointwise_conv2''': '''encoder.layers.*.conv_module.pointwise_conv2''',
    '''conv_module.depthwise_conv''': '''encoder.layers.*.conv_module.depthwise_conv''',
    '''conv_module.batch_norm''': '''encoder.layers.*.conv_module.batch_norm''',
    '''conv_module.layer_norm''': '''encoder.layers.*.conv_module.layer_norm''',
    '''ffn1.w_1''': '''encoder.layers.*.ffn1.intermediate_dense''',
    '''ffn1.w_2''': '''encoder.layers.*.ffn1.output_dense''',
    '''ffn1.layer_norm''': '''encoder.layers.*.ffn1_layer_norm''',
    '''ffn2.w_1''': '''encoder.layers.*.ffn2.intermediate_dense''',
    '''ffn2.w_2''': '''encoder.layers.*.ffn2.output_dense''',
    '''ffn2.layer_norm''': '''encoder.layers.*.ffn2_layer_norm''',
    '''final_layer_norm''': '''encoder.layers.*.final_layer_norm''',
    '''encoder.layer_norm''': '''encoder.layer_norm''',
    '''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''',
}
lowercase__					:						Union[str, Any]												=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
]




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    for attribute in key.split('''.'''					):
        snake_case_				:  Tuple       =			getattr(_a	,    _a					)

    if weight_type is not None:
        snake_case_				:  Tuple       =			getattr(_a	,    _a					).shape
    else:
        snake_case_				:  Optional[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":
        snake_case_				:  Any       =			value
    elif weight_type == "weight_g":
        snake_case_				:  Dict       =			value
    elif weight_type == "weight_v":
        snake_case_				:  List[str]       =			value
    elif weight_type == "bias":
        snake_case_				:  List[Any]       =			value
    elif weight_type == "running_mean":
        snake_case_				:  Dict       =			value
    elif weight_type == "running_var":
        snake_case_				:  Any       =			value
    elif weight_type == "num_batches_tracked":
        snake_case_				:  Tuple       =			value
    elif weight_type == "inv_freq":
        snake_case_				:  int       =			value
    else:
        snake_case_				:  Dict       =			value

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




def __lowercase    (							_a	,    _a	,    _a					):
    snake_case_				:  Tuple       =			[]
    snake_case_				:  Any       =			fairseq_model.state_dict()

    snake_case_				:  Optional[Any]       =			hf_model.wavaveca_conformer.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_				:  Optional[Any]       =			False
        if "conv_layers" in name:
            load_conv_layer(
                _a	,    _a	,    _a	,    _a	,    hf_model.config.feat_extract_norm == '''group'''	,    )
            snake_case_				:  Tuple       =			True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_				:  Dict       =			'''wav2vec2_conformer.''' + 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]:
                    snake_case_				:  Optional[int]       =			True
                    if "*" in mapped_key:
                        snake_case_				:  int       =			name.split(_a					)[0].split('''.'''					)[-2]
                        snake_case_				:  str       =			mapped_key.replace('''*'''	,    _a					)
                    if "pos_bias_u" in name:
                        snake_case_				:  str       =			None
                    elif "pos_bias_v" in name:
                        snake_case_				:  Optional[Any]       =			None
                    elif "weight_g" in name:
                        snake_case_				:  str       =			'''weight_g'''
                    elif "weight_v" in name:
                        snake_case_				:  Any       =			'''weight_v'''
                    elif "bias" in name:
                        snake_case_				:  int       =			'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case_				:  Optional[Any]       =			'''weight'''
                    elif "running_mean" in name:
                        snake_case_				:  Tuple       =			'''running_mean'''
                    elif "inv_freq" in name:
                        snake_case_				:  List[str]       =			'''inv_freq'''
                    elif "running_var" in name:
                        snake_case_				:  Tuple       =			'''running_var'''
                    elif "num_batches_tracked" in name:
                        snake_case_				:  int       =			'''num_batches_tracked'''
                    else:
                        snake_case_				:  Tuple       =			None
                    set_recursively(_a	,    _a	,    _a	,    _a	,    _a					)
                continue
        if not is_used:
            unused_weights.append(_a					)

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




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    snake_case_				:  int       =			full_name.split('''conv_layers.'''					)[-1]
    snake_case_				:  Tuple       =			name.split('''.'''					)
    snake_case_				:  str       =			int(items[0]					)
    snake_case_				:  List[Any]       =			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."					)
            snake_case_				:  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."					)
            snake_case_				:  Dict       =			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."					)
            snake_case_				:  Union[str, Any]       =			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."					)
            snake_case_				:  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 __lowercase    (							_a	,    _a	,    _a=None	,    _a=None	,    _a=True					):
    if config_path is not None:
        snake_case_				:  str       =			WavaVecaConformerConfig.from_pretrained(_a	,    hidden_act='''swish'''					)
    else:
        snake_case_				:  Tuple       =			WavaVecaConformerConfig()

    if "rope" in checkpoint_path:
        snake_case_				:  Union[str, Any]       =			'''rotary'''

    if is_finetuned:
        if dict_path:
            snake_case_				:  List[Any]       =			Dictionary.load(_a					)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            snake_case_				:  Optional[Any]       =			target_dict.pad_index
            snake_case_				:  Optional[int]       =			target_dict.bos_index
            snake_case_				:  List[str]       =			target_dict.eos_index
            snake_case_				:  Optional[Any]       =			len(target_dict.symbols					)
            snake_case_				:  str       =			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					)
            snake_case_				:  str       =			target_dict.indices

            # fairseq has the <pad> and <s> switched
            snake_case_				:  Union[str, Any]       =			0
            snake_case_				:  Optional[int]       =			1
            with open(_a	,    '''w'''	,    encoding='''utf-8'''					) as vocab_handle:
                json.dump(_a	,    _a					)
            snake_case_				:  Dict       =			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	,    )
            snake_case_				:  List[str]       =			True if config.feat_extract_norm == '''layer''' else False
            snake_case_				:  Dict       =			WavaVecaFeatureExtractor(
                feature_size=1	,    sampling_rate=16_000	,    padding_value=0	,    do_normalize=_a	,    return_attention_mask=_a	,    )
            snake_case_				:  Union[str, Any]       =			WavaVecaProcessor(feature_extractor=_a	,    tokenizer=_a					)
            processor.save_pretrained(_a					)

        snake_case_				:  int       =			WavaVecaConformerForCTC(_a					)
    else:
        snake_case_				:  Optional[int]       =			WavaVecaConformerForPreTraining(_a					)

    if is_finetuned:
        snake_case_,			snake_case_,			snake_case_				:  Any       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
            [checkpoint_path]	,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
    else:
        snake_case_				:  List[str]       =			argparse.Namespace(task='''audio_pretraining'''					)
        snake_case_				:  int       =			fairseq.tasks.setup_task(_a					)

        snake_case_,			snake_case_,			snake_case_				:  Union[str, Any]       =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	,    task=_a					)

    snake_case_				:  Any       =			model[0].eval()

    recursively_load_weights(_a	,    _a	,    not is_finetuned					)

    hf_wavavec.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						Optional[Any]												=			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'''
     )
     lowercase__					:						Optional[Any]												=			parser.parse_args()
     convert_wavaveca_conformer_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, not args.not_finetuned
     )










"""simple docstring"""

from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


lowercase__					:						Any												=			logging.get_logger(__name__)

lowercase__					:						Tuple												=			{
    '''roberta-base''': '''https://huggingface.co/roberta-base/resolve/main/config.json''',
    '''roberta-large''': '''https://huggingface.co/roberta-large/resolve/main/config.json''',
    '''roberta-large-mnli''': '''https://huggingface.co/roberta-large-mnli/resolve/main/config.json''',
    '''distilroberta-base''': '''https://huggingface.co/distilroberta-base/resolve/main/config.json''',
    '''roberta-base-openai-detector''': '''https://huggingface.co/roberta-base-openai-detector/resolve/main/config.json''',
    '''roberta-large-openai-detector''': '''https://huggingface.co/roberta-large-openai-detector/resolve/main/config.json''',
}




class  _UpperCAmelCase (    lowerCAmelCase__):
 _lowerCAmelCase     :    Dict	      =	"""roberta"""

 def __init__(  self	: int						,  lowercase_	: Any=50265						,  lowercase_	: Union[str, Any]=768						,  lowercase_	: Optional[int]=12						,  lowercase_	: List[str]=12						,  lowercase_	: Dict=3072						,  lowercase_	: Tuple="gelu"						,  lowercase_	: Optional[int]=0.1						,  lowercase_	: Tuple=0.1						,  lowercase_	: List[str]=512						,  lowercase_	: List[Any]=2						,  lowercase_	: Union[str, Any]=0.02						,  lowercase_	: List[Any]=1E-12						,  lowercase_	: Optional[int]=1						,  lowercase_	: Tuple=0						,  lowercase_	: str=2						,  lowercase_	: int="absolute"						,  lowercase_	: str=True						,  lowercase_	: Tuple=None						,  **lowercase_	: str						,  ):
     super().__init__(pad_token_id=lowercase_						,  bos_token_id=lowercase_						,  eos_token_id=lowercase_						,  **lowercase_       )

     snake_case_				:  str       =			vocab_size
     snake_case_				:  Optional[Any]       =			hidden_size
     snake_case_				:  Any       =			num_hidden_layers
     snake_case_				:  List[str]       =			num_attention_heads
     snake_case_				:  Dict       =			hidden_act
     snake_case_				:  List[Any]       =			intermediate_size
     snake_case_				:  Optional[int]       =			hidden_dropout_prob
     snake_case_				:  List[str]       =			attention_probs_dropout_prob
     snake_case_				:  Any       =			max_position_embeddings
     snake_case_				:  Optional[Any]       =			type_vocab_size
     snake_case_				:  Optional[int]       =			initializer_range
     snake_case_				:  Dict       =			layer_norm_eps
     snake_case_				:  Optional[int]       =			position_embedding_type
     snake_case_				:  str       =			use_cache
     snake_case_				:  Tuple       =			classifier_dropout





class  _UpperCAmelCase (    lowerCAmelCase__):

 @property
 def 		_snake_case      (  self	: Any       ):
     if self.task == "multiple-choice":
         snake_case_				:  List[Any]       =			{0: '''batch''', 1: '''choice''', 2: '''sequence'''}
     else:
         snake_case_				:  Union[str, Any]       =			{0: '''batch''', 1: '''sequence'''}
     return OrderedDict(
         [
             ('''input_ids''', dynamic_axis),
             ('''attention_mask''', dynamic_axis),
         ]       )





