
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      =logging.get_logger(__name__)

lowerCamelCase      ={
    "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_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      for attribute in key.split('''.'''		):
            UpperCamelCase__    :   Optional[Any]					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		)

      if weight_type is not None:
            UpperCamelCase__    :   int					=				getattr(UpperCamelCase__ ,		UpperCamelCase__		).shape
      else:
            UpperCamelCase__    :   Any					=				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__    :   str					=				value
      elif weight_type == "weight_g":
            UpperCamelCase__    :   Optional[int]					=				value
      elif weight_type == "weight_v":
            UpperCamelCase__    :   Any					=				value
      elif weight_type == "bias":
            UpperCamelCase__    :   List[str]					=				value
      else:
            UpperCamelCase__    :   Any					=				value

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Optional[Any]					=				[]
      UpperCamelCase__    :   int					=				fairseq_model.state_dict()

      UpperCamelCase__    :   Union[str, Any]					=				hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

      for name, value in fairseq_dict.items():
            UpperCamelCase__    :   Dict					=				False
            if "conv_layers" in name:
                  load_conv_layer(
                      UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		hf_model.config.feat_extract_norm == '''group''' ,		)
                  UpperCamelCase__    :   Dict					=				True
            else:
                  for key, mapped_key in MAPPING.items():
                        UpperCamelCase__    :   str					=				'''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]:
                              UpperCamelCase__    :   Optional[int]					=				True
                              if "*" in mapped_key:
                                    UpperCamelCase__    :   Any					=				name.split(UpperCamelCase__		)[0].split('''.'''		)[-2]
                                    UpperCamelCase__    :   Optional[int]					=				mapped_key.replace('''*''' ,		UpperCamelCase__		)
                              if "weight_g" in name:
                                    UpperCamelCase__    :   Union[str, Any]					=				'''weight_g'''
                              elif "weight_v" in name:
                                    UpperCamelCase__    :   List[Any]					=				'''weight_v'''
                              elif "weight" in name:
                                    UpperCamelCase__    :   Optional[Any]					=				'''weight'''
                              elif "bias" in name:
                                    UpperCamelCase__    :   Dict					=				'''bias'''
                              else:
                                    UpperCamelCase__    :   Dict					=				None
                              set_recursively(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)
                        continue
            if not is_used:
                  unused_weights.append(UpperCamelCase__		)

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


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Optional[int]					=				full_name.split('''conv_layers.'''		)[-1]
      UpperCamelCase__    :   List[Any]					=				name.split('''.'''		)
      UpperCamelCase__    :   List[Any]					=				int(items[0]		)
      UpperCamelCase__    :   Dict					=				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__    :   Union[str, Any]					=				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__    :   Union[str, Any]					=				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__    :   Any					=				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__    :   Union[str, Any]					=				value
                  logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''		)
      else:
            unused_weights.append(UpperCamelCase__		)


def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__		):
      UpperCamelCase__    :   Union[str, Any]					=				SEWConfig()
      if is_finetuned:
            UpperCamelCase__    :   Any					=				model.wav_encoder.wav_model.cfg
      else:
            UpperCamelCase__    :   List[str]					=				model.cfg

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

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

      UpperCamelCase__    :   Optional[int]					=				'''Wav2Vec2FeatureExtractor'''
      UpperCamelCase__    :   Optional[int]					=				'''Wav2Vec2CTCTokenizer'''

      return config


@torch.no_grad()
def 							SCREAMING_SNAKE_CASE_      (			UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__=None ,		UpperCamelCase__=None ,		UpperCamelCase__=True		):

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

      if config_path is not None:
            UpperCamelCase__    :   Tuple					=				SEWConfig.from_pretrained(UpperCamelCase__		)
      else:
            UpperCamelCase__    :   Optional[int]					=				convert_config(model[0] ,		UpperCamelCase__		)
      UpperCamelCase__    :   Any					=				model[0].eval()

      UpperCamelCase__    :   Optional[int]					=				True if config.feat_extract_norm == '''layer''' else False
      UpperCamelCase__    :   Optional[int]					=				WavaVecaFeatureExtractor(
          feature_size=1 ,		sampling_rate=1_6_0_0_0 ,		padding_value=0 ,		do_normalize=UpperCamelCase__ ,		return_attention_mask=UpperCamelCase__ ,		)

      if is_finetuned:
            if dict_path:
                  UpperCamelCase__    :   Any					=				Dictionary.load(UpperCamelCase__		)

                  # important change bos & pad token id since CTC symbol is <pad> and
                  # not <s> as in fairseq
                  UpperCamelCase__    :   Optional[Any]					=				target_dict.pad_index
                  UpperCamelCase__    :   Any					=				target_dict.bos_index
                  UpperCamelCase__    :   List[str]					=				target_dict.pad_index
                  UpperCamelCase__    :   Dict					=				target_dict.bos_index
                  UpperCamelCase__    :   Tuple					=				target_dict.eos_index
                  UpperCamelCase__    :   Any					=				len(target_dict.symbols		)
                  UpperCamelCase__    :   List[str]					=				os.path.join(UpperCamelCase__ ,		'''vocab.json'''		)
                  if not os.path.isdir(UpperCamelCase__		):
                        logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(UpperCamelCase__		)		)
                        return
                  os.makedirs(UpperCamelCase__ ,		exist_ok=UpperCamelCase__		)
                  with open(UpperCamelCase__ ,		'''w''' ,		encoding='''utf-8'''		) as vocab_handle:
                        json.dump(target_dict.indices ,		UpperCamelCase__		)
                  UpperCamelCase__    :   Any					=				WavaVecaCTCTokenizer(
                      UpperCamelCase__ ,		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=UpperCamelCase__ ,		)
                  UpperCamelCase__    :   Union[str, Any]					=				WavaVecaProcessor(feature_extractor=UpperCamelCase__ ,		tokenizer=UpperCamelCase__		)
                  processor.save_pretrained(UpperCamelCase__		)

            UpperCamelCase__    :   Optional[Any]					=				SEWForCTC(UpperCamelCase__		)
      else:
            UpperCamelCase__    :   List[str]					=				SEWModel(UpperCamelCase__		)
            feature_extractor.save_pretrained(UpperCamelCase__		)

      recursively_load_weights(UpperCamelCase__ ,		UpperCamelCase__ ,		UpperCamelCase__		)

      hf_model.save_pretrained(UpperCamelCase__		)


if __name__ == "__main__":
    lowerCamelCase      =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      =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_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase      ={
    "configuration_mobilebert": [
        "MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "MobileBertConfig",
        "MobileBertOnnxConfig",
    ],
    "tokenization_mobilebert": ["MobileBertTokenizer"],
}

try:
    if not is_tokenizers_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =["MobileBertTokenizerFast"]

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "MobileBertForMaskedLM",
        "MobileBertForMultipleChoice",
        "MobileBertForNextSentencePrediction",
        "MobileBertForPreTraining",
        "MobileBertForQuestionAnswering",
        "MobileBertForSequenceClassification",
        "MobileBertForTokenClassification",
        "MobileBertLayer",
        "MobileBertModel",
        "MobileBertPreTrainedModel",
        "load_tf_weights_in_mobilebert",
    ]

try:
    if not is_tf_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    lowerCamelCase      =[
        "TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST",
        "TFMobileBertForMaskedLM",
        "TFMobileBertForMultipleChoice",
        "TFMobileBertForNextSentencePrediction",
        "TFMobileBertForPreTraining",
        "TFMobileBertForQuestionAnswering",
        "TFMobileBertForSequenceClassification",
        "TFMobileBertForTokenClassification",
        "TFMobileBertMainLayer",
        "TFMobileBertModel",
        "TFMobileBertPreTrainedModel",
    ]


if TYPE_CHECKING:
    from .configuration_mobilebert import (
        MOBILEBERT_PRETRAINED_CONFIG_ARCHIVE_MAP,
        MobileBertConfig,
        MobileBertOnnxConfig,
    )
    from .tokenization_mobilebert import MobileBertTokenizer

    try:
        if not is_tokenizers_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .tokenization_mobilebert_fast import MobileBertTokenizerFast

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_mobilebert import (
            MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            MobileBertForMaskedLM,
            MobileBertForMultipleChoice,
            MobileBertForNextSentencePrediction,
            MobileBertForPreTraining,
            MobileBertForQuestionAnswering,
            MobileBertForSequenceClassification,
            MobileBertForTokenClassification,
            MobileBertLayer,
            MobileBertModel,
            MobileBertPreTrainedModel,
            load_tf_weights_in_mobilebert,
        )

    try:
        if not is_tf_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_tf_mobilebert import (
            TF_MOBILEBERT_PRETRAINED_MODEL_ARCHIVE_LIST,
            TFMobileBertForMaskedLM,
            TFMobileBertForMultipleChoice,
            TFMobileBertForNextSentencePrediction,
            TFMobileBertForPreTraining,
            TFMobileBertForQuestionAnswering,
            TFMobileBertForSequenceClassification,
            TFMobileBertForTokenClassification,
            TFMobileBertMainLayer,
            TFMobileBertModel,
            TFMobileBertPreTrainedModel,
        )

else:
    import sys

    lowerCamelCase      =_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)


