

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()
A_						:     str			  =				logging.get_logger(__name__)

A_						:     Any			  =				{
    '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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Any:






    '''simple docstring'''
    for attribute in key.split('''.''' ):
        __UpperCAmelCase			   =     getattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )

    if weight_type is not None:
        __UpperCAmelCase			   =     getattr(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ).shape
    else:
        __UpperCAmelCase			   =     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":
        __UpperCAmelCase			   =     value
    elif weight_type == "weight_g":
        __UpperCAmelCase			   =     value
    elif weight_type == "weight_v":
        __UpperCAmelCase			   =     value
    elif weight_type == "bias":
        __UpperCAmelCase			   =     value
    else:
        __UpperCAmelCase			   =     value

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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Tuple:






    '''simple docstring'''
    __UpperCAmelCase			   =     []
    __UpperCAmelCase			   =     fairseq_model.state_dict()

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

    for name, value in fairseq_dict.items():
        __UpperCAmelCase			   =     False
        if "conv_layers" in name:
            load_conv_layer(
                SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				hf_model.config.feat_extract_norm == '''group'''    ,				)
            __UpperCAmelCase			   =     True
        else:
            for key, mapped_key in MAPPING.items():
                __UpperCAmelCase			   =     '''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]:
                    __UpperCAmelCase			   =     True
                    if "*" in mapped_key:
                        __UpperCAmelCase			   =     name.split(SCREAMING_SNAKE_CASE )[0].split('''.''' )[-2]
                        if not layer_index.isnumeric():
                            continue
                        __UpperCAmelCase			   =     mapped_key.replace('''*'''    ,				SCREAMING_SNAKE_CASE )
                    if "weight_g" in name:
                        __UpperCAmelCase			   =     '''weight_g'''
                    elif "weight_v" in name:
                        __UpperCAmelCase			   =     '''weight_v'''
                    elif "weight" in name:
                        __UpperCAmelCase			   =     '''weight'''
                    elif "bias" in name:
                        __UpperCAmelCase			   =     '''bias'''
                    else:
                        __UpperCAmelCase			   =     None
                    set_recursively(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )
                continue
        if not is_used:
            unused_weights.append(SCREAMING_SNAKE_CASE )

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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Optional[int]:






    '''simple docstring'''
    __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:
            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.'''
            )
            __UpperCAmelCase			   =     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.'''
            )
            __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:
            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."
            )
            __UpperCAmelCase			   =     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.'''
            )
            __UpperCAmelCase			   =     value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.''' )
    else:
        unused_weights.append(SCREAMING_SNAKE_CASE )


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[Any]:






    '''simple docstring'''
    __UpperCAmelCase			   =     SEWDConfig()
    if is_finetuned:
        __UpperCAmelCase			   =     model.wav_encoder.wav_model.cfg
    else:
        __UpperCAmelCase			   =     model.cfg

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

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

    __UpperCAmelCase			   =     '''Wav2Vec2FeatureExtractor'''
    __UpperCAmelCase			   =     '''Wav2Vec2CTCTokenizer'''

    return config


@torch.no_grad()
def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE=None    ,				SCREAMING_SNAKE_CASE=None    ,				SCREAMING_SNAKE_CASE=True ) ->       Optional[int]:






    '''simple docstring'''

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

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

    __UpperCAmelCase			   =     True if config.feat_extract_norm == '''layer''' else False
    __UpperCAmelCase			   =     WavaVecaFeatureExtractor(
        feature_size=1    ,				sampling_rate=1_6_0_0_0    ,				padding_value=0    ,				do_normalize=SCREAMING_SNAKE_CASE    ,				return_attention_mask=SCREAMING_SNAKE_CASE    ,				)

    if is_finetuned:
        if dict_path:
            __UpperCAmelCase			   =     Dictionary.load(SCREAMING_SNAKE_CASE )

            # 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.pad_index
            __UpperCAmelCase			   =     target_dict.bos_index
            __UpperCAmelCase			   =     target_dict.eos_index
            __UpperCAmelCase			   =     len(target_dict.symbols )
            __UpperCAmelCase			   =     os.path.join(SCREAMING_SNAKE_CASE    ,				'''vocab.json''' )
            if not os.path.isdir(SCREAMING_SNAKE_CASE ):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(SCREAMING_SNAKE_CASE ) )
                return
            os.makedirs(SCREAMING_SNAKE_CASE    ,				exist_ok=SCREAMING_SNAKE_CASE )
            with open(SCREAMING_SNAKE_CASE    ,				'''w'''    ,				encoding='''utf-8''' ) as vocab_handle:
                json.dump(target_dict.indices    ,				SCREAMING_SNAKE_CASE )
            __UpperCAmelCase			   =     WavaVecaCTCTokenizer(
                SCREAMING_SNAKE_CASE    ,				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=SCREAMING_SNAKE_CASE    ,				)
            __UpperCAmelCase			   =     WavaVecaProcessor(feature_extractor=SCREAMING_SNAKE_CASE    ,				tokenizer=SCREAMING_SNAKE_CASE )
            processor.save_pretrained(SCREAMING_SNAKE_CASE )

        __UpperCAmelCase			   =     SEWDForCTC(SCREAMING_SNAKE_CASE )
    else:
        __UpperCAmelCase			   =     SEWDModel(SCREAMING_SNAKE_CASE )
        feature_extractor.save_pretrained(SCREAMING_SNAKE_CASE )

    recursively_load_weights(SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE )

    hf_model.save_pretrained(SCREAMING_SNAKE_CASE )


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







# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    '--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000'
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/




