



"""simple docstring"""

def __lowercase    (							_a	,    _a					):
    snake_case_				:  int       =			len(_a					)
    snake_case_				:  int       =			len(_a					)
    snake_case_				:  int       =			(
        first_str_length if first_str_length > second_str_length else second_str_length
    )
    snake_case_				:  list       =			[]
    for char_count in range(_a					):
        if char_count < first_str_length:
            output_list.append(first_str[char_count]					)
        if char_count < second_str_length:
            output_list.append(second_str[char_count]					)
    return "".join(_a					)


if __name__ == "__main__":
     print(alternative_string_arrange('''AB''', '''XYZ'''), end=''' ''')










"""simple docstring"""

import argparse

import fairseq
import torch

from transformers import UniSpeechSatConfig, UniSpeechSatForCTC, UniSpeechSatForPreTraining, logging


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

lowercase__					:						List[str]												=			{
    '''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''',
}
lowercase__					:						Dict												=			[
    '''lm_head''',
    '''quantizer.weight_proj''',
    '''quantizer.codevectors''',
    '''project_q''',
    '''project_hid''',
    '''label_embeddings_concat''',
    '''speaker_proj''',
    '''layer_norm_for_extract''',
]




def __lowercase    (							_a	,    _a	,    _a	,    _a	,    _a					):
    for attribute in key.split('''.'''					):
        snake_case_				:  Optional[Any]       =			getattr(_a	,    _a					)

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

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




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

    snake_case_				:  Optional[int]       =			hf_model.unispeech_sat.feature_extractor

    for name, value in fairseq_dict.items():
        snake_case_				:  int       =			False
        if "conv_layers" in name:
            load_conv_layer(
                _a	,    _a	,    _a	,    _a	,    hf_model.config.feat_extract_norm == '''group'''	,    )
            snake_case_				:  Any       =			True
        else:
            for key, mapped_key in MAPPING.items():
                snake_case_				:  Tuple       =			'''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
                    snake_case_				:  List[str]       =			True
                    if "*" in mapped_key:
                        snake_case_				:  List[Any]       =			name.split(_a					)[0].split('''.'''					)[-2]
                        snake_case_				:  Dict       =			mapped_key.replace('''*'''	,    _a					)
                    if "weight_g" in name:
                        snake_case_				:  Any       =			'''weight_g'''
                    elif "weight_v" in name:
                        snake_case_				:  List[str]       =			'''weight_v'''
                    elif "bias" in name:
                        snake_case_				:  List[str]       =			'''bias'''
                    elif "weight" in name:
                        # TODO: don't match quantizer.weight_proj
                        snake_case_				:  Optional[int]       =			'''weight'''
                    else:
                        snake_case_				:  List[Any]       =			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_				:  Tuple       =			full_name.split('''conv_layers.'''					)[-1]
    snake_case_				:  int       =			name.split('''.'''					)
    snake_case_				:  int       =			int(items[0]					)
    snake_case_				:  int       =			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_				:  int       =			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_				:  str       =			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."					)
            snake_case_				:  Dict       =			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."					)
            snake_case_				:  Optional[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_				:  int       =			UniSpeechSatConfig.from_pretrained(_a					)
    else:
        snake_case_				:  Tuple       =			UniSpeechSatConfig()

    snake_case_				:  List[Any]       =			''''''

    if is_finetuned:
        snake_case_				:  str       =			UniSpeechSatForCTC(_a					)
    else:
        snake_case_				:  Dict       =			UniSpeechSatForPreTraining(_a					)

    snake_case_,			snake_case_,			snake_case_				:  str       =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
        [checkpoint_path]	,    arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''					)[:-1]					)}					)
    snake_case_				:  Dict       =			model[0].eval()

    recursively_load_weights(_a	,    _a					)

    hf_wavavec.save_pretrained(_a					)


if __name__ == "__main__":
     lowercase__					:						List[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[int]												=			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
     )





