


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()
lowerCamelCase_       :       Optional[int]      						=  logging.get_logger(__name__)

lowerCamelCase_       :       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 _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""

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

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

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







    """simple docstring"""
    a					=[]
    a					=fairseq_model.state_dict()

    a					=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        a					=False
        if "conv_layers" in name:
            load_conv_layer(
                a__	,   a__	,   a__	,   a__	,   hf_model.config.feat_extract_norm == '''group'''	,   )
            a					=True
        else:
            for key, mapped_key in MAPPING.items():
                a					='''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					=True
                    if "*" in mapped_key:
                        a					=name.split(a__		)[0].split('''.'''		)[-2]
                        a					=mapped_key.replace('''*'''	,   a__		)
                    if "weight_g" in name:
                        a					='''weight_g'''
                    elif "weight_v" in name:
                        a					='''weight_v'''
                    elif "weight" in name:
                        a					='''weight'''
                    elif "bias" in name:
                        a					='''bias'''
                    else:
                        a					=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 _A						(						lowercase	,   lowercase	,   lowercase	,   lowercase	,   lowercase		):







    """simple docstring"""
    a					=full_name.split('''conv_layers.'''		)[-1]
    a					=name.split('''.'''		)
    a					=int(items[0]		)
    a					=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					=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					=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					=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					=value
            logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
    else:
        unused_weights.append(a__		)
def _A						(						lowercase	,   lowercase		):







    """simple docstring"""
    a					=SEWConfig()
    if is_finetuned:
        a					=model.wav_encoder.wav_model.cfg
    else:
        a					=model.cfg

    a					=fs_config.conv_bias
    a					=eval(fs_config.conv_feature_layers		)
    a					=[x[0] for x in conv_layers]
    a					=[x[1] for x in conv_layers]
    a					=[x[2] for x in conv_layers]
    a					='''gelu'''
    a					='''layer''' if fs_config.extractor_mode == '''layer_norm''' else '''group'''
    a					=0.0
    a					=fs_config.activation_fn.name
    a					=fs_config.encoder_embed_dim
    a					=0.02
    a					=fs_config.encoder_ffn_embed_dim
    a					=1E-5
    a					=fs_config.encoder_layerdrop
    a					=fs_config.encoder_attention_heads
    a					=fs_config.conv_pos_groups
    a					=fs_config.conv_pos
    a					=len(a__		)
    a					=fs_config.encoder_layers
    a					=fs_config.squeeze_factor

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

    a					='''Wav2Vec2FeatureExtractor'''
    a					='''Wav2Vec2CTCTokenizer'''

    return config
@torch.no_grad()
def _A						(						lowercase	,   lowercase	,   lowercase=None	,   lowercase=None	,   lowercase=True		):







    """simple docstring"""

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

    if config_path is not None:
        a					=SEWConfig.from_pretrained(a__		)
    else:
        a					=convert_config(model[0]	,   a__		)
    a					=model[0].eval()

    a					=True if config.feat_extract_norm == '''layer''' else False
    a					=WavaVecaFeatureExtractor(
        feature_size=1	,   sampling_rate=1_60_00	,   padding_value=0	,   do_normalize=a__	,   return_attention_mask=a__	,   )

    if is_finetuned:
        if dict_path:
            a					=Dictionary.load(a__		)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            a					=target_dict.pad_index
            a					=target_dict.bos_index
            a					=target_dict.pad_index
            a					=target_dict.bos_index
            a					=target_dict.eos_index
            a					=len(target_dict.symbols		)
            a					=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					=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					=WavaVecaProcessor(feature_extractor=a__	,   tokenizer=a__		)
            processor.save_pretrained(a__		)

        a					=SEWForCTC(a__		)
    else:
        a					=SEWModel(a__		)
        feature_extractor.save_pretrained(a__		)

    recursively_load_weights(a__	,   a__	,   a__		)

    hf_model.save_pretrained(a__		)


if __name__ == "__main__":
       lowerCamelCase_       :       Optional[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"""
       )
       lowerCamelCase_       :       Union[str, Any]      						=  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"""







from ...processing_utils import ProcessorMixin






class 				__A    (	_SCREAMING_SNAKE_CASE      ):






 """simple docstring"""




 __lowerCAmelCase			 =  "WhisperFeatureExtractor"
 __lowerCAmelCase			 =  "WhisperTokenizer"


 def __init__(		self			,   __A			,   __A						) ->					Dict:
     super().__init__(__A			,   __A						)
     a					=self.feature_extractor
     a					=False


 def 				SCREAMING_SNAKE_CASE							(		self			,   __A=None			,   __A=None			,   __A=True						) ->					int:
     return self.tokenizer.get_decoder_prompt_ids(task=__A			,   language=__A			,   no_timestamps=__A						)


 def __call__(		self			,   *__A			,   **__A						) ->					Tuple:
     # For backward compatibility
     if self._in_target_context_manager:
         return self.current_processor(*__A			,   **__A						)

     a					=kwargs.pop('''audio'''			,   __A						)
     a					=kwargs.pop('''sampling_rate'''			,   __A						)
     a					=kwargs.pop('''text'''			,   __A						)
     if len(__A						) > 0:
         a					=args[0]
         a					=args[1:]

     if audio is None and text is None:
         raise ValueError('''You need to specify either an `audio` or `text` input to process.'''						)

     if audio is not None:
         a					=self.feature_extractor(__A			,   *__A			,   sampling_rate=__A			,   **__A						)
     if text is not None:
         a					=self.tokenizer(__A			,   **__A						)

     if text is None:
         return inputs

     elif audio is None:
         return encodings
     else:
         a					=encodings['''input_ids''']
         return inputs


 def 				SCREAMING_SNAKE_CASE							(		self			,   *__A			,   **__A						) ->					Optional[Any]:
     return self.tokenizer.batch_decode(*__A			,   **__A						)


 def 				SCREAMING_SNAKE_CASE							(		self			,   *__A			,   **__A						) ->					Union[str, Any]:
     return self.tokenizer.decode(*__A			,   **__A						)





 def 				SCREAMING_SNAKE_CASE							(		self			,   __A			,   __A="np"						) ->					Optional[Any]:
     return self.tokenizer.get_prompt_ids(__A			,   return_tensors=__A						)