

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_						:     str			  =				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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       List[Any]:






				'''simple docstring'''
				for attribute in key.split('''.''' ):
								__UpperCAmelCase			   =     getattr(_lowerCAmelCase    ,				_lowerCAmelCase )

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

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


def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       str:






				'''simple docstring'''
				__UpperCAmelCase			   =     []
				__UpperCAmelCase			   =     fairseq_model.state_dict()

				__UpperCAmelCase			   =     hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

				for name, value in fairseq_dict.items():
								__UpperCAmelCase			   =     False
								if "conv_layers" in name:
												load_conv_layer(
												    _lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase    ,				hf_model.config.feat_extract_norm == '''group'''    ,				)
												__UpperCAmelCase			   =     True
								else:
												for key, mapped_key in MAPPING.items():
																__UpperCAmelCase			   =     '''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			   =     True
																				if "*" in mapped_key:
																								__UpperCAmelCase			   =     name.split(_lowerCAmelCase )[0].split('''.''' )[-2]
																								if not layer_index.isnumeric():
																												continue
																								__UpperCAmelCase			   =     mapped_key.replace('''*'''    ,				_lowerCAmelCase )
																				if "weight_g" in name:
																								__UpperCAmelCase			   =     '''weight_g'''
																				elif "weight_v" in name:
																								__UpperCAmelCase			   =     '''weight_v'''
																				elif "weight" in name:
																								__UpperCAmelCase			   =     '''weight'''
																				elif "bias" in name:
																								__UpperCAmelCase			   =     '''bias'''
																				else:
																								__UpperCAmelCase			   =     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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Optional[int]:






				'''simple docstring'''
				__UpperCAmelCase			   =     full_name.split('''conv_layers.''' )[-1]
				__UpperCAmelCase			   =     name.split('''.''' )
				__UpperCAmelCase			   =     int(items[0] )
				__UpperCAmelCase			   =     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			   =     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			   =     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			   =     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			   =     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    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE ) ->       Optional[Any]:






				'''simple docstring'''
				__UpperCAmelCase			   =     SEWDConfig()
				if is_finetuned:
								__UpperCAmelCase			   =     model.wav_encoder.wav_model.cfg
				else:
								__UpperCAmelCase			   =     model.cfg

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

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

				__UpperCAmelCase			   =     '''Wav2Vec2FeatureExtractor'''
				__UpperCAmelCase			   =     '''Wav2Vec2CTCTokenizer'''

				return config


@torch.no_grad()
def  __a    (				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE    ,				SCREAMING_SNAKE_CASE=None    ,				SCREAMING_SNAKE_CASE=None    ,				SCREAMING_SNAKE_CASE=True ) ->       Tuple:






				'''simple docstring'''

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

				if config_path is not None:
								__UpperCAmelCase			   =     SEWDConfig.from_pretrained(_lowerCAmelCase )
				else:
								__UpperCAmelCase			   =     convert_config(model[0]    ,				_lowerCAmelCase )
				__UpperCAmelCase			   =     model[0].eval()

				__UpperCAmelCase			   =     True if config.feat_extract_norm == '''layer''' else False
				__UpperCAmelCase			   =     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			   =     Dictionary.load(_lowerCAmelCase )

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

								__UpperCAmelCase			   =     SEWDForCTC(_lowerCAmelCase )
				else:
								__UpperCAmelCase			   =     SEWDModel(_lowerCAmelCase )
								feature_extractor.save_pretrained(_lowerCAmelCase )

				recursively_load_weights(_lowerCAmelCase    ,				_lowerCAmelCase    ,				_lowerCAmelCase )

				hf_model.save_pretrained(_lowerCAmelCase )


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







from dataclasses import dataclass
from typing import Callable, Optional

import datasets
@dataclass
class    A_				( datasets.BuilderConfig  ):


    '''simple docstring'''






    a__       							=    None
    a__       							=    None
    a__       							=    None


    def        lowerCAmelCase_  (self					)       -> Dict:
        assert self.generator is not None, "generator must be specified"

        if self.gen_kwargs is None:
            __UpperCAmelCase			   =     {}






class    A_				( datasets.GeneratorBasedBuilder  ):


    '''simple docstring'''






    a__       							=    GeneratorConfig


    def        lowerCAmelCase_  (self					)       -> Dict:
        return datasets.DatasetInfo(features=self.config.features					)


    def        lowerCAmelCase_  (self       , lowercase__					)       -> List[str]:
        return [datasets.SplitGenerator(name=datasets.Split.TRAIN       , gen_kwargs=self.config.gen_kwargs					)]






    def        lowerCAmelCase_  (self       , **lowercase__					)       -> str:
        for idx, ex in enumerate(self.config.generator(**lowercase__					)					):
            yield idx, ex




