






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()
SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    '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        _lowerCamelCase		(  __A      :							List[Any]						,						__A      :							Dict						,						__A      :							Union[str, Any]						,						__A      :							Optional[Any]						,						__A      :							Dict	) ->   Dict:




    for attribute in key.split('''.'''	):
        _UpperCAmelCase			: Any     					=			getattr(__A						,						__A	)

    if weight_type is not None:
        _UpperCAmelCase			: List[Any]     					=			getattr(__A						,						__A	).shape
    else:
        _UpperCAmelCase			: 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":
        _UpperCAmelCase			: List[str]     					=			value
    elif weight_type == "weight_g":
        _UpperCAmelCase			: Dict     					=			value
    elif weight_type == "weight_v":
        _UpperCAmelCase			: Any     					=			value
    elif weight_type == "bias":
        _UpperCAmelCase			: List[Any]     					=			value
    else:
        _UpperCAmelCase			: List[Any]     					=			value

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

def        _lowerCamelCase		(  __A      :							List[str]						,						__A      :							Any						,						__A      :							Union[str, Any]	) ->   Optional[int]:
    _UpperCAmelCase			: List[Any]     					=			[]
    _UpperCAmelCase			: Tuple     					=			fairseq_model.state_dict()

    _UpperCAmelCase			: Union[str, Any]     					=			hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        _UpperCAmelCase			: Optional[int]     					=			False
        if "conv_layers" in name:
            load_conv_layer(
                __A						,						__A						,						__A						,						__A						,						hf_model.config.feat_extract_norm == '''group'''						,						)
            _UpperCAmelCase			: List[Any]     					=			True
        else:
            for key, mapped_key in MAPPING.items():
                _UpperCAmelCase			: int     					=			'''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			: Tuple     					=			True
                    if "*" in mapped_key:
                        _UpperCAmelCase			: List[Any]     					=			name.split(__A	)[0].split('''.'''	)[-2]
                        if not layer_index.isnumeric():
                            continue
                        _UpperCAmelCase			: List[str]     					=			mapped_key.replace('''*'''						,						__A	)
                    if "weight_g" in name:
                        _UpperCAmelCase			: Tuple     					=			'''weight_g'''
                    elif "weight_v" in name:
                        _UpperCAmelCase			: Any     					=			'''weight_v'''
                    elif "weight" in name:
                        _UpperCAmelCase			: Optional[int]     					=			'''weight'''
                    elif "bias" in name:
                        _UpperCAmelCase			: Tuple     					=			'''bias'''
                    else:
                        _UpperCAmelCase			: Tuple     					=			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        _lowerCamelCase		(  __A      :							int						,						__A      :							Optional[Any]						,						__A      :							List[Any]						,						__A      :							Dict						,						__A      :							Optional[Any]	) ->   List[str]:
    _UpperCAmelCase			: List[Any]     					=			full_name.split('''conv_layers.'''	)[-1]
    _UpperCAmelCase			: Union[str, Any]     					=			name.split('''.'''	)
    _UpperCAmelCase			: Dict     					=			int(items[0]	)
    _UpperCAmelCase			: 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.'''
            )
            _UpperCAmelCase			: str     					=			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[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."
            )
            _UpperCAmelCase			: 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.'''
            )
            _UpperCAmelCase			: 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        _lowerCamelCase		(  __A      :							List[Any]						,						__A      :							str	) ->   Optional[Any]:
    _UpperCAmelCase			: str     					=			SEWDConfig()
    if is_finetuned:
        _UpperCAmelCase			: Optional[Any]     					=			model.wav_encoder.wav_model.cfg
    else:
        _UpperCAmelCase			: Optional[Any]     					=			model.cfg

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

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

    _UpperCAmelCase			: str     					=			'''Wav2Vec2FeatureExtractor'''
    _UpperCAmelCase			: Any     					=			'''Wav2Vec2CTCTokenizer'''

    return config

@torch.no_grad()
def        _lowerCamelCase		(  __A      :							List[str]						,						__A      :							int						,						__A      :							List[str]=None						,						__A      :							int=None						,						__A      :							List[Any]=True	) ->   List[str]:




    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			: Any     					=			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]	)

    if config_path is not None:
        _UpperCAmelCase			: Optional[Any]     					=			SEWDConfig.from_pretrained(__A	)
    else:
        _UpperCAmelCase			: Optional[Any]     					=			convert_config(model[0]						,						__A	)
    _UpperCAmelCase			: int     					=			model[0].eval()

    _UpperCAmelCase			: List[str]     					=			True if config.feat_extract_norm == '''layer''' else False
    _UpperCAmelCase			: str     					=			WavaVecaFeatureExtractor(
        feature_size=1						,						sampling_rate=16_000						,						padding_value=0						,						do_normalize=__A						,						return_attention_mask=__A						,						)

    if is_finetuned:
        if dict_path:
            _UpperCAmelCase			: List[str]     					=			Dictionary.load(__A	)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            _UpperCAmelCase			: Optional[int]     					=			target_dict.pad_index
            _UpperCAmelCase			: Any     					=			target_dict.bos_index
            _UpperCAmelCase			: Optional[int]     					=			target_dict.pad_index
            _UpperCAmelCase			: List[str]     					=			target_dict.bos_index
            _UpperCAmelCase			: int     					=			target_dict.eos_index
            _UpperCAmelCase			: Dict     					=			len(target_dict.symbols	)
            _UpperCAmelCase			: int     					=			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	)
            _UpperCAmelCase			: Optional[int]     					=			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						,						)
            _UpperCAmelCase			: Optional[Any]     					=			WavaVecaProcessor(feature_extractor=__A						,						tokenizer=__A	)
            processor.save_pretrained(__A	)

        _UpperCAmelCase			: Optional[Any]     					=			SEWDForCTC(__A	)
    else:
        _UpperCAmelCase			: Optional[int]     					=			SEWDModel(__A	)
        feature_extractor.save_pretrained(__A	)

    recursively_load_weights(__A						,						__A						,						__A	)

    hf_model.save_pretrained(__A	)


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












from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


SCREAMING_SNAKE_CASE								=  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE								=  {
    'MBZUAI/swiftformer-xs': 'https://huggingface.co/MBZUAI/swiftformer-xs/resolve/main/config.json',
}
class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     Dict	=   "swiftformer"


 def __init__(		self							,					_A=3							,					_A=[3, 3, 6, 4]							,					_A=[48, 56, 112, 220]							,					_A=4							,					_A=[True, True, True, True]							,					_A="gelu"							,					_A=3							,					_A=2							,					_A=1							,					_A=0.0							,					_A=True							,					_A=1e-5							,					_A=1e-5							,					**_A							,					)	->						List[Any]:

     """simple docstring"""



     super().__init__(**_A)
     _UpperCAmelCase			: Tuple     					=			num_channels
     _UpperCAmelCase			: Optional[int]     					=			depths
     _UpperCAmelCase			: Union[str, Any]     					=			embed_dims
     _UpperCAmelCase			: Dict     					=			mlp_ratio
     _UpperCAmelCase			: Tuple     					=			downsamples
     _UpperCAmelCase			: str     					=			hidden_act
     _UpperCAmelCase			: int     					=			down_patch_size
     _UpperCAmelCase			: Dict     					=			down_stride
     _UpperCAmelCase			: Dict     					=			down_pad
     _UpperCAmelCase			: Tuple     					=			drop_path_rate
     _UpperCAmelCase			: Tuple     					=			use_layer_scale
     _UpperCAmelCase			: int     					=			layer_scale_init_value
     _UpperCAmelCase			: int     					=			batch_norm_eps



class 							A_      (  __lowercase     ):






 '''simple docstring'''




 _SCREAMING_SNAKE_CASE							:     List[Any]	=   version.parse("1.11"     )


 @property
 def 	snake_case__			(		self)	->						Mapping[str, Mapping[int, str]]:

     """simple docstring"""



     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ])


 @property
 def 	snake_case__			(		self)	->						float:

     """simple docstring"""



     return 1e-4




