
'''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()
UpperCAmelCase__				:	List[str]					=						logging.get_logger(__name__)

UpperCAmelCase__				:	Dict					=						{
    "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       ( UpperCamelCase_   :       Dict  ,		UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       str  ,		UpperCamelCase_   :       Optional[int]  ,		UpperCamelCase_   :       int )   ->    Any:







			'''simple docstring'''



			for attribute in key.split("." ):
						lowerCAmelCase__									=							getattr(UpperCamelCase_  ,		UpperCamelCase_ )

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

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







def 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       Union[str, Any]  ,		UpperCamelCase_   :       Optional[Any] )   ->    int:







			'''simple docstring'''



			lowerCAmelCase__									=							[]
			lowerCAmelCase__									=							fairseq_model.state_dict()

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

			for name, value in fairseq_dict.items():
						lowerCAmelCase__									=							False
						if "conv_layers" in name:
									load_conv_layer(
									    UpperCamelCase_  ,		UpperCamelCase_  ,		UpperCamelCase_  ,		UpperCamelCase_  ,		hf_model.config.feat_extract_norm == "group"  ,		)
									lowerCAmelCase__									=							True
						else:
									for key, mapped_key in MAPPING.items():
												lowerCAmelCase__									=							"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]:
															lowerCAmelCase__									=							True
															if "*" in mapped_key:
																		lowerCAmelCase__									=							name.split(UpperCamelCase_ )[0].split("." )[-2]
																		if not layer_index.isnumeric():
																					continue
																		lowerCAmelCase__									=							mapped_key.replace("*"  ,		UpperCamelCase_ )
															if "weight_g" in name:
																		lowerCAmelCase__									=							"weight_g"
															elif "weight_v" in name:
																		lowerCAmelCase__									=							"weight_v"
															elif "weight" in name:
																		lowerCAmelCase__									=							"weight"
															elif "bias" in name:
																		lowerCAmelCase__									=							"bias"
															else:
																		lowerCAmelCase__									=							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 					A       ( UpperCamelCase_   :       str  ,		UpperCamelCase_   :       Union[str, Any]  ,		UpperCamelCase_   :       Dict  ,		UpperCamelCase_   :       Tuple  ,		UpperCamelCase_   :       int )   ->    Optional[int]:







			'''simple docstring'''



			lowerCAmelCase__									=							full_name.split("conv_layers." )[-1]
			lowerCAmelCase__									=							name.split("." )
			lowerCAmelCase__									=							int(items[0] )
			lowerCAmelCase__									=							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."""
									)
									lowerCAmelCase__									=							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."""
									)
									lowerCAmelCase__									=							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."
									)
									lowerCAmelCase__									=							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."""
									)
									lowerCAmelCase__									=							value
									logger.info(F"""Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.""" )
			else:
						unused_weights.append(UpperCamelCase_ )







def 					A       ( UpperCamelCase_   :       Tuple  ,		UpperCamelCase_   :       Union[str, Any] )   ->    Any:







			'''simple docstring'''



			lowerCAmelCase__									=							SEWDConfig()
			if is_finetuned:
						lowerCAmelCase__									=							model.wav_encoder.wav_model.cfg
			else:
						lowerCAmelCase__									=							model.cfg

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

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

			lowerCAmelCase__									=							"Wav2Vec2FeatureExtractor"
			lowerCAmelCase__									=							"Wav2Vec2CTCTokenizer"

			return config







@torch.no_grad()
def 					A       ( UpperCamelCase_   :       Any  ,		UpperCamelCase_   :       Optional[Any]  ,		UpperCamelCase_   :       Optional[Any]=None  ,		UpperCamelCase_   :       List[str]=None  ,		UpperCamelCase_   :       List[Any]=True )   ->    str:







			'''simple docstring'''



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

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

			lowerCAmelCase__									=							True if config.feat_extract_norm == "layer" else False
			lowerCAmelCase__									=							WavaVecaFeatureExtractor(
			    feature_size=1  ,		sampling_rate=1_60_00  ,		padding_value=0  ,		do_normalize=UpperCamelCase_  ,		return_attention_mask=UpperCamelCase_  ,		)

			if is_finetuned:
						if dict_path:
									lowerCAmelCase__									=							Dictionary.load(UpperCamelCase_ )

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

						lowerCAmelCase__									=							SEWDForCTC(UpperCamelCase_ )
			else:
						lowerCAmelCase__									=							SEWDModel(UpperCamelCase_ )
						feature_extractor.save_pretrained(UpperCamelCase_ )

			recursively_load_weights(UpperCamelCase_  ,		UpperCamelCase_  ,		UpperCamelCase_ )

			hf_model.save_pretrained(UpperCamelCase_ )


if __name__ == "__main__":
		UpperCAmelCase__				:	Union[str, Any]					=						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"
		)
		UpperCAmelCase__				:	Optional[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'''

# this script builds a small sample spm file tests/fixtures/test_sentencepiece_no_bos.model, with features needed by pegasus

# 1. pip install sentencepiece
#
# 2. wget https://raw.githubusercontent.com/google/sentencepiece/master/data/botchan.txt

# 3. build
import sentencepiece as spm

# pegasus:
# 1. no bos
# 2. eos_id is 1
# 3. unk_id is 2
# build a sample spm file accordingly
spm.SentencePieceTrainer.train(
    "--input=botchan.txt --model_prefix=test_sentencepiece_no_bos --bos_id=-1 --unk_id=2  --eos_id=1  --vocab_size=1000"
)

# 4. now update the fixture
# mv test_sentencepiece_no_bos.model ../../tests/fixtures/





