




'''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()
snake_case__          =						logging.get_logger(__name__)

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 						snake_case__    ( lowerCamelCase__				:   Optional[Any]			,		lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Union[str, Any]			,		lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Dict			)   ->   str:
					for attribute in key.split('''.'''			):
										A_ :     Union[str, Any] =							getattr(UpperCamelCase__			,		UpperCamelCase__			)

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

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




def 						snake_case__    ( lowerCamelCase__				:   Any			,		lowerCamelCase__				:   Any			,		lowerCamelCase__				:   Any			)   ->   Tuple:
					A_ :     Optional[Any] =							[]
					A_ :     Optional[int] =							fairseq_model.state_dict()

					A_ :     Any =							hf_model.sew_d.feature_extractor if is_finetuned else hf_model.feature_extractor

					for name, value in fairseq_dict.items():
										A_ :     Optional[int] =							False
										if "conv_layers" in name:
															load_conv_layer(
															    UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			,		hf_model.config.feat_extract_norm == '''group'''			,		)
															A_ :     Optional[Any] =							True
										else:
															for key, mapped_key in MAPPING.items():
																				A_ :     str =							"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]:
																									A_ :     Dict =							True
																									if "*" in mapped_key:
																														A_ :     Tuple =							name.split(UpperCamelCase__			)[0].split('''.'''			)[-2]
																														if not layer_index.isnumeric():
																																			continue
																														A_ :     List[Any] =							mapped_key.replace('''*'''			,		UpperCamelCase__			)
																									if "weight_g" in name:
																														A_ :     Dict =							"weight_g"
																									elif "weight_v" in name:
																														A_ :     Dict =							"weight_v"
																									elif "weight" in name:
																														A_ :     Optional[int] =							"weight"
																									elif "bias" in name:
																														A_ :     Any =							"bias"
																									else:
																														A_ :     List[str] =							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 						snake_case__    ( lowerCamelCase__				:   List[str]			,		lowerCamelCase__				:   str			,		lowerCamelCase__				:   List[Any]			,		lowerCamelCase__				:   Optional[int]			,		lowerCamelCase__				:   List[str]			)   ->   Optional[Any]:
					A_ :     Any =							full_name.split('''conv_layers.'''			)[-1]
					A_ :     Union[str, Any] =							name.split('''.'''			)
					A_ :     Union[str, Any] =							int(items[0]			)
					A_ :     int =							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_ :     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.'
															)
															A_ :     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."
															)
															A_ :     Union[str, 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.'
															)
															A_ :     int =							value
															logger.info(f'Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'			)
					else:
										unused_weights.append(UpperCamelCase__			)




def 						snake_case__    ( lowerCamelCase__				:   Tuple			,		lowerCamelCase__				:   Any			)   ->   Dict:
					A_ :     Any =							SEWDConfig()
					if is_finetuned:
										A_ :     List[str] =							model.wav_encoder.wav_model.cfg
					else:
										A_ :     Tuple =							model.cfg

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

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

					A_ :     Dict =							"Wav2Vec2FeatureExtractor"
					A_ :     List[str] =							"Wav2Vec2CTCTokenizer"

					return config




@torch.no_grad()
def 						snake_case__    ( lowerCamelCase__				:   Any			,		lowerCamelCase__				:   List[Any]			,		lowerCamelCase__				:   Tuple=None			,		lowerCamelCase__				:   Union[str, Any]=None			,		lowerCamelCase__				:   List[Any]=True			)   ->   List[str]:

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

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

					A_ :     Tuple =							True if config.feat_extract_norm == "layer" else False
					A_ :     List[str] =							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:
															A_ :     Tuple =							Dictionary.load(UpperCamelCase__			)

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

										A_ :     Dict =							SEWDForCTC(UpperCamelCase__			)
					else:
										A_ :     Any =							SEWDModel(UpperCamelCase__			)
										feature_extractor.save_pretrained(UpperCamelCase__			)

					recursively_load_weights(UpperCamelCase__			,		UpperCamelCase__			,		UpperCamelCase__			)

					hf_model.save_pretrained(UpperCamelCase__			)


if __name__ == "__main__":
					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"""
					)
					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
					)









'''simple docstring'''




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__          =						{"""configuration_nat""": ["""NAT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """NatConfig"""]}


try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							snake_case__          =						[
							    """NAT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """NatForImageClassification""",
							    """NatModel""",
							    """NatPreTrainedModel""",
							    """NatBackbone""",
							]

if TYPE_CHECKING:
							from .configuration_nat import NAT_PRETRAINED_CONFIG_ARCHIVE_MAP, NatConfig

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_nat import (
														    NAT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    NatBackbone,
														    NatForImageClassification,
														    NatModel,
														    NatPreTrainedModel,
														)

else:
							import sys

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



