






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

__a   :							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       _SCREAMING_SNAKE_CASE    (			__lowercase :      str      ,					__lowercase :      Optional[int]      ,					__lowercase :      List[Any]      ,					__lowercase :      Tuple      ,					__lowercase :      List[Any]     )    ->   int:





    """simple docstring"""

    for attribute in key.split("""."""     ):
        __A       =						getattr(__lowercase      ,					__lowercase     )

    if weight_type is not None:
        __A       =						getattr(__lowercase      ,					__lowercase     ).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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Tuple      ,					__lowercase :      Dict      ,					__lowercase :      List[Any]     )    ->   Optional[int]:





    """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(
                __lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase      ,					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(__lowercase     )[0].split("""."""     )[-2]
                        __A       =						mapped_key.replace("""*"""      ,					__lowercase     )
                    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(__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase      ,					__lowercase     )
                continue
        if not is_used:
            unused_weights.append(__lowercase     )

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



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      Any      ,					__lowercase :      str      ,					__lowercase :      str      ,					__lowercase :      Any      ,					__lowercase :      str     )    ->   Dict:





    """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(__lowercase     )



def       _SCREAMING_SNAKE_CASE    (			__lowercase :      List[Any]      ,					__lowercase :      Optional[int]     )    ->   Tuple:





    """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(__lowercase     )
    __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       _SCREAMING_SNAKE_CASE    (			__lowercase :      Union[str, Any]      ,					__lowercase :      List[Any]      ,					__lowercase :      Dict=None      ,					__lowercase :      List[str]=None      ,					__lowercase :      List[Any]=True     )    ->   Tuple:





    """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(__lowercase     )
    else:
        __A       =						convert_config(model[0]      ,					__lowercase     )
    __A       =						model[0].eval()

    __A       =						True if config.feat_extract_norm == """layer""" else False
    __A       =						WavaVecaFeatureExtractor(
        feature_size=1      ,					sampling_rate=1_6_0_0_0      ,					padding_value=0      ,					do_normalize=__lowercase      ,					return_attention_mask=__lowercase      ,					)

    if is_finetuned:
        if dict_path:
            __A       =						Dictionary.load(__lowercase     )

            # 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(__lowercase      ,					"""vocab.json"""     )
            if not os.path.isdir(__lowercase     ):
                logger.error("""--pytorch_dump_folder_path ({}) should be a directory""".format(__lowercase     )     )
                return
            os.makedirs(__lowercase      ,					exist_ok=__lowercase     )
            with open(__lowercase      ,					"""w"""      ,					encoding="""utf-8"""     ) as vocab_handle:
                json.dump(target_dict.indices      ,					__lowercase     )
            __A       =						WavaVecaCTCTokenizer(
                __lowercase      ,					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=__lowercase      ,					)
            __A       =						WavaVecaProcessor(feature_extractor=__lowercase      ,					tokenizer=__lowercase     )
            processor.save_pretrained(__lowercase     )

        __A       =						SEWForCTC(__lowercase     )
    else:
        __A       =						SEWModel(__lowercase     )
        feature_extractor.save_pretrained(__lowercase     )

    recursively_load_weights(__lowercase      ,					__lowercase      ,					__lowercase     )

    hf_model.save_pretrained(__lowercase     )


if __name__ == "__main__":
  __a   :							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(
      "--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
  )












from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__a   :							Any						= {
    "configuration_time_series_transformer": [
        "TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "TimeSeriesTransformerConfig",
    ],
}

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __a   :							Dict						= [
      "TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
      "TimeSeriesTransformerForPrediction",
      "TimeSeriesTransformerModel",
      "TimeSeriesTransformerPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_time_series_transformer import (
      TIME_SERIES_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
      TimeSeriesTransformerConfig,
  )

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_time_series_transformer import (
        TIME_SERIES_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
        TimeSeriesTransformerForPrediction,
        TimeSeriesTransformerModel,
        TimeSeriesTransformerPreTrainedModel,
    )

else:
  import sys

  __a   :							str						= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




