



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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE_:Optional[Any]             =						logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:Optional[int]             =						{
    """post_extract_proj""": """feature_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.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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Union[str, Any]:



    """simple docstring"""






    for attribute in key.split("""."""		):
        A :       str          =   getattr(__a      ,     __a		)

    if weight_type is not None:
        A :       Dict          =   getattr(__a      ,     __a		).shape
    else:
        A :       List[str]          =   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":
        A :       Dict          =   value
    elif weight_type == "weight_g":
        A :       Union[str, Any]          =   value
    elif weight_type == "weight_v":
        A :       Tuple          =   value
    elif weight_type == "bias":
        A :       Dict          =   value
    else:
        A :       Optional[Any]          =   value

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




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Optional[int]:



    """simple docstring"""






    A :       List[str]          =   []
    A :       List[Any]          =   fairseq_model.state_dict()

    A :       Any          =   hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        A :       Dict          =   False
        if "conv_layers" in name:
            load_conv_layer(
                __a      ,     __a      ,     __a      ,     __a      ,     hf_model.config.feat_extract_norm == """group"""      ,     )
            A :       Any          =   True
        else:
            for key, mapped_key in MAPPING.items():
                A :       Union[str, Any]          =   '''sew.''' + 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]:
                    A :       Optional[int]          =   True
                    if "*" in mapped_key:
                        A :       int          =   name.split(__a		)[0].split("""."""		)[-2]
                        A :       Optional[int]          =   mapped_key.replace("""*"""      ,     __a		)
                    if "weight_g" in name:
                        A :       Dict          =   '''weight_g'''
                    elif "weight_v" in name:
                        A :       Union[str, Any]          =   '''weight_v'''
                    elif "weight" in name:
                        A :       Tuple          =   '''weight'''
                    elif "bias" in name:
                        A :       Dict          =   '''bias'''
                    else:
                        A :       List[str]          =   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 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  str:



    """simple docstring"""






    A :       Optional[int]          =   full_name.split("""conv_layers."""		)[-1]
    A :       str          =   name.split("""."""		)
    A :       Optional[int]          =   int(items[0]		)
    A :       List[str]          =   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.'''
            )
            A :       Optional[int]          =   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.'''
            )
            A :       Optional[int]          =   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."
            )
            A :       Tuple          =   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.'''
            )
            A :       Optional[int]          =   value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
    else:
        unused_weights.append(__a		)




def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase		)       ->  Any:



    """simple docstring"""






    A :       Union[str, Any]          =   SEWConfig()
    if is_finetuned:
        A :       Optional[int]          =   model.wav_encoder.wav_model.cfg
    else:
        A :       Optional[int]          =   model.cfg

    A :       Tuple          =   fs_config.conv_bias
    A :       Tuple          =   eval(fs_config.conv_feature_layers		)
    A :       Any          =   [x[0] for x in conv_layers]
    A :       int          =   [x[1] for x in conv_layers]
    A :       int          =   [x[2] for x in conv_layers]
    A :       List[Any]          =   '''gelu'''
    A :       List[Any]          =   '''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    A :       List[str]          =   0.0
    A :       Dict          =   fs_config.activation_fn.name
    A :       Optional[Any]          =   fs_config.encoder_embed_dim
    A :       List[str]          =   0.02
    A :       Tuple          =   fs_config.encoder_ffn_embed_dim
    A :       List[Any]          =   1e-5
    A :       Tuple          =   fs_config.encoder_layerdrop
    A :       Optional[int]          =   fs_config.encoder_attention_heads
    A :       Any          =   fs_config.conv_pos_groups
    A :       Tuple          =   fs_config.conv_pos
    A :       Dict          =   len(__a		)
    A :       Tuple          =   fs_config.encoder_layers
    A :       Optional[Any]          =   fs_config.squeeze_factor

    # take care of any params that are overridden by the Wav2VecCtc model
    if is_finetuned:
        A :       Optional[int]          =   model.cfg
        A :       Optional[Any]          =   fs_config.final_dropout
        A :       Dict          =   fs_config.layerdrop
    A :       Tuple          =   fs_config.activation_dropout
    A :       str          =   fs_config.mask_prob > 0 or fs_config.mask_channel_prob > 0
    A :       int          =   fs_config.attention_dropout
    A :       Dict          =   fs_config.dropout_input
    A :       List[str]          =   fs_config.dropout
    A :       Any          =   fs_config.mask_channel_length
    A :       Optional[int]          =   fs_config.mask_channel_prob
    A :       str          =   fs_config.mask_length
    A :       Tuple          =   fs_config.mask_prob

    A :       Union[str, Any]          =   '''Wav2Vec2FeatureExtractor'''
    A :       Any          =   '''Wav2Vec2CTCTokenizer'''

    return config




@torch.no_grad()
def 	__UpperCamelCase	(  _lowerCAmelCase      ,     _lowerCAmelCase      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=None      ,     _lowerCAmelCase=True		)       ->  str:



    """simple docstring"""






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

    if config_path is not None:
        A :       Tuple          =   SEWConfig.from_pretrained(__a		)
    else:
        A :       Dict          =   convert_config(model[0]      ,     __a		)
    A :       Union[str, Any]          =   model[0].eval()

    A :       List[str]          =   True if config.feat_extract_norm == '''layer''' else False
    A :       Optional[int]          =   WavaVecaFeatureExtractor(
        feature_size=1      ,     sampling_rate=1_6000      ,     padding_value=0      ,     do_normalize=__a      ,     return_attention_mask=__a      ,     )

    if is_finetuned:
        if dict_path:
            A :       str          =   Dictionary.load(__a		)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            A :       List[str]          =   target_dict.pad_index
            A :       Tuple          =   target_dict.bos_index
            A :       Any          =   target_dict.pad_index
            A :       Dict          =   target_dict.bos_index
            A :       str          =   target_dict.eos_index
            A :       str          =   len(target_dict.symbols		)
            A :       Optional[Any]          =   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		)
            with open(__a      ,     """w"""      ,     encoding="""utf-8"""		) as vocab_handle:
                json.dump(target_dict.indices      ,     __a		)
            A :       Tuple          =   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      ,     )
            A :       Tuple          =   WavaVecaProcessor(feature_extractor=__a      ,     tokenizer=__a		)
            processor.save_pretrained(__a		)

        A :       Union[str, Any]          =   SEWForCTC(__a		)
    else:
        A :       List[Any]          =   SEWModel(__a		)
        feature_extractor.save_pretrained(__a		)

    recursively_load_weights(__a      ,     __a      ,     __a		)

    hf_model.save_pretrained(__a		)


if __name__ == "__main__":
     SCREAMING_SNAKE_CASE_:int             =						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"""
     )
     SCREAMING_SNAKE_CASE_:int             =						parser.parse_args()
     convert_sew_checkpoint(
         args.checkpoint_path, args.pytorch_dump_folder_path, args.config_path, args.dict_path, args.is_finetuned
     )










"""simple docstring"""






import random
from typing import Any



def 					_A			(__a							) ->       list[Any]:
    """simple docstring"""






    for _ in range(len(__a							)							):
        SCREAMING_SNAKE_CASE_      :  Optional[int]    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_      :  Tuple    						=		random.randint(0    ,				len(__a							) - 1							)
        SCREAMING_SNAKE_CASE_						,			SCREAMING_SNAKE_CASE_      :  Union[str, Any]    						=		data[b], data[a]
    return data


if __name__ == "__main__":
  UpperCAmelCase_   : Dict   			= [0, 1, 2, 3, 4, 5, 6, 7]
  UpperCAmelCase_   : Dict   			= ["""python""", """says""", """hello""", """!"""]
  print("""Fisher-Yates Shuffle:""")
  print("""List""", integers, strings)
  print("""FY Shuffle""", fisher_yates_shuffle(integers), fisher_yates_shuffle(strings))


