




"""simple docstring"""




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 (
    SEWDConfig,
    SEWDForCTC,
    SEWDModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
__A       :						Tuple												=			logging.get_logger(__name__)

__A       :						Tuple												=			{
    '''post_extract_proj''': '''feature_projection''',
    '''encoder.pos_conv.0''': '''encoder.pos_conv_embed.conv''',
    '''attention.self.query_proj''': '''encoder.encoder.layer.*.attention.self.query_proj''',
    '''attention.self.key_proj''': '''encoder.encoder.layer.*.attention.self.key_proj''',
    '''attention.self.value_proj''': '''encoder.encoder.layer.*.attention.self.value_proj''',
    '''attention.output.dense''': '''encoder.encoder.layer.*.attention.output.dense''',
    '''attention.output.LayerNorm''': '''encoder.encoder.layer.*.attention.output.LayerNorm''',
    '''intermediate.dense''': '''encoder.encoder.layer.*.intermediate.dense''',
    '''output.dense''': '''encoder.encoder.layer.*.output.dense''',
    '''output.LayerNorm''': '''encoder.encoder.layer.*.output.LayerNorm''',
    '''encoder.encoder.rel_embeddings''': '''encoder.encoder.rel_embeddings''',
    '''encoder.encoder.LayerNorm''': '''encoder.encoder.LayerNorm''',
    '''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_	(						snake_case_   :				Optional[int]  ,snake_case_   :				Union[str, Any]  ,snake_case_   :				str  ,snake_case_   :				Any  ,snake_case_   :				Any			):







    '''simple docstring'''





    for attribute in key.split("""."""			):
        UpperCamelCase :      List[Any]      				=							getattr(lowerCamelCase_  ,lowerCamelCase_			)

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

    logger.info(f'{key + "." + weight_type if weight_type is not None else ""} was initialized from {full_name}.'			)
def     A_	(						snake_case_   :				Any  ,snake_case_   :				str  ,snake_case_   :				int			):







    '''simple docstring'''
    UpperCamelCase :      str      				=							[]
    UpperCamelCase :      Union[str, Any]      				=							fairseq_model.state_dict()

    UpperCamelCase :      Optional[Any]      				=							hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

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

    logger.warning(f'Unused weights: {unused_weights}'			)
def     A_	(						snake_case_   :				Tuple  ,snake_case_   :				Tuple  ,snake_case_   :				Tuple  ,snake_case_   :				Optional[int]  ,snake_case_   :				int			):







    '''simple docstring'''
    UpperCamelCase :      int      				=							full_name.split("""conv_layers."""			)[-1]
    UpperCamelCase :      int      				=							name.split("""."""			)
    UpperCamelCase :      int      				=							int(items[0]			)
    UpperCamelCase :      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.'
            )
            UpperCamelCase :      Optional[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 :      Optional[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 :      List[Any]      				=							value
            logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
    else:
        unused_weights.append(lowerCamelCase_			)
def     A_	(						snake_case_   :				Dict  ,snake_case_   :				str			):







    '''simple docstring'''
    UpperCamelCase :      str      				=							SEWDConfig()
    if is_finetuned:
        UpperCamelCase :      List[Any]      				=							model.wav_encoder.wav_model.cfg
    else:
        UpperCamelCase :      str      				=							model.cfg

    UpperCamelCase :      Dict      				=							fs_config.conv_bias
    UpperCamelCase :      Tuple      				=							eval(fs_config.conv_feature_layers			)
    UpperCamelCase :      str      				=							[x[0] for x in conv_layers]
    UpperCamelCase :      Optional[Any]      				=							[x[1] for x in conv_layers]
    UpperCamelCase :      Any      				=							[x[2] for x in conv_layers]
    UpperCamelCase :      str      				=							'gelu'
    UpperCamelCase :      Dict      				=							'layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
    UpperCamelCase :      List[Any]      				=							0.0
    UpperCamelCase :      List[str]      				=							fs_config.activation_fn.name
    UpperCamelCase :      Any      				=							fs_config.encoder_embed_dim
    UpperCamelCase :      str      				=							0.02
    UpperCamelCase :      List[str]      				=							fs_config.encoder_ffn_embed_dim
    UpperCamelCase :      List[Any]      				=							1e-5
    UpperCamelCase :      Union[str, Any]      				=							fs_config.encoder_layerdrop
    UpperCamelCase :      Optional[Any]      				=							fs_config.encoder_attention_heads
    UpperCamelCase :      List[Any]      				=							fs_config.conv_pos_groups
    UpperCamelCase :      Tuple      				=							fs_config.conv_pos
    UpperCamelCase :      Optional[Any]      				=							len(lowerCamelCase_			)
    UpperCamelCase :      int      				=							fs_config.encoder_layers
    UpperCamelCase :      Any      				=							fs_config.squeeze_factor
    # DeBERTa-specific parameters:
    UpperCamelCase :      Tuple      				=							fs_config.max_position_embeddings
    UpperCamelCase :      List[str]      				=							fs_config.position_buckets
    UpperCamelCase :      str      				=							fs_config.share_att_key
    UpperCamelCase :      Any      				=							fs_config.relative_attention
    UpperCamelCase :      Optional[Any]      				=							fs_config.position_biased_input
    UpperCamelCase :      Union[str, Any]      				=							tuple(fs_config.pos_att_type.split("""|"""			)			)
    UpperCamelCase :      str      				=							fs_config.norm_rel_ebd

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

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

    return config
@torch.no_grad()
def     A_	(						snake_case_   :				Dict  ,snake_case_   :				Tuple  ,snake_case_   :				int=None  ,snake_case_   :				int=None  ,snake_case_   :				Dict=True			):







    '''simple docstring'''





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

    if config_path is not None:
        UpperCamelCase :      str      				=							SEWDConfig.from_pretrained(lowerCamelCase_			)
    else:
        UpperCamelCase :      Optional[Any]      				=							convert_config(model[0]  ,lowerCamelCase_			)
    UpperCamelCase :      List[Any]      				=							model[0].eval()

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

    if is_finetuned:
        if dict_path:
            UpperCamelCase :      Union[str, Any]      				=							Dictionary.load(lowerCamelCase_			)

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

        UpperCamelCase :      Optional[int]      				=							SEWDForCTC(lowerCamelCase_			)
    else:
        UpperCamelCase :      List[Any]      				=							SEWDModel(lowerCamelCase_			)
        feature_extractor.save_pretrained(lowerCamelCase_			)

    recursively_load_weights(lowerCamelCase_  ,lowerCamelCase_  ,lowerCamelCase_			)

    hf_model.save_pretrained(lowerCamelCase_			)


if __name__ == "__main__":
  __A       :						List[str]												=			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       :						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 typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


snake_case__     :			int      			=  {
    '''configuration_markuplm''': ['''MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MarkupLMConfig'''],
    '''feature_extraction_markuplm''': ['''MarkupLMFeatureExtractor'''],
    '''processing_markuplm''': ['''MarkupLMProcessor'''],
    '''tokenization_markuplm''': ['''MarkupLMTokenizer'''],
}

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			List[str]      			=  ['''MarkupLMTokenizerFast''']

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 snake_case__     :			Dict      			=  [
     '''MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST''',
     '''MarkupLMForQuestionAnswering''',
     '''MarkupLMForSequenceClassification''',
     '''MarkupLMForTokenClassification''',
     '''MarkupLMModel''',
     '''MarkupLMPreTrainedModel''',
 ]


if TYPE_CHECKING:
 from .configuration_markuplm import MARKUPLM_PRETRAINED_CONFIG_ARCHIVE_MAP, MarkupLMConfig
 from .feature_extraction_markuplm import MarkupLMFeatureExtractor
 from .processing_markuplm import MarkupLMProcessor
 from .tokenization_markuplm import MarkupLMTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_markuplm_fast import MarkupLMTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_markuplm import (
      MARKUPLM_PRETRAINED_MODEL_ARCHIVE_LIST,
      MarkupLMForQuestionAnswering,
      MarkupLMForSequenceClassification,
      MarkupLMForTokenClassification,
      MarkupLMModel,
      MarkupLMPreTrainedModel,
  )


else:
 import sys

 snake_case__     :			Optional[Any]      			=  _LazyModule(__name__, globals()['''__file__'''], _import_structure)






