



'''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 (
    SEWConfig,
    SEWForCTC,
    SEWModel,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaProcessor,
    logging,
)


logging.set_verbosity_info()
a							   =				logging.get_logger(__name__)

a							   =				{
    'post_extract_proj': 'feature_projection',
    'encoder.pos_conv.0': 'encoder.pos_conv_embed.conv',
    'self_attn.k_proj': 'encoder.layers.*.attention.k_proj',
    'self_attn.v_proj': 'encoder.layers.*.attention.v_proj',
    'self_attn.q_proj': 'encoder.layers.*.attention.q_proj',
    'self_attn.out_proj': 'encoder.layers.*.attention.out_proj',
    'self_attn_layer_norm': 'encoder.layers.*.layer_norm',
    'fc1': 'encoder.layers.*.feed_forward.intermediate_dense',
    'fc2': 'encoder.layers.*.feed_forward.output_dense',
    'final_layer_norm': 'encoder.layers.*.final_layer_norm',
    '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				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Optional[Any]:





			"""simple docstring"""







			for attribute in key.split('.'					):
						snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

			if weight_type is not None:
						snake_case:    Union[str, Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					).shape
			else:
						snake_case:    Tuple      							=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":
						snake_case:    str      							=value
			elif weight_type == "weight_g":
						snake_case:    int      							=value
			elif weight_type == "weight_v":
						snake_case:    Dict      							=value
			elif weight_type == "bias":
						snake_case:    int      							=value
			else:
						snake_case:    int      							=value

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







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					str:





			"""simple docstring"""







			snake_case:    List[str]      							=[]
			snake_case:    Any      							=fairseq_model.state_dict()

			snake_case:    Optional[Any]      							=hf_model.sew.feature_extractor if is_finetuned else hf_model.feature_extractor

			for name, value in fairseq_dict.items():
						snake_case:    Union[str, Any]      							=False
						if "conv_layers" in name:
									load_conv_layer(
									    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							hf_model.config.feat_extract_norm == 'group'				,							)
									snake_case:    List[Any]      							=True
						else:
									for key, mapped_key in MAPPING.items():
												snake_case:    Union[str, Any]      							='sew.' + 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]:
															snake_case:    Tuple      							=True
															if "*" in mapped_key:
																		snake_case:    List[str]      							=name.split(__UpperCAmelCase					)[0].split('.'					)[-2]
																		snake_case:    str      							=mapped_key.replace('*'				,							__UpperCAmelCase					)
															if "weight_g" in name:
																		snake_case:    List[str]      							='weight_g'
															elif "weight_v" in name:
																		snake_case:    int      							='weight_v'
															elif "weight" in name:
																		snake_case:    Optional[Any]      							='weight'
															elif "bias" in name:
																		snake_case:    Optional[int]      							='bias'
															else:
																		snake_case:    int      							=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				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)				->					Any:





			"""simple docstring"""







			snake_case:    Union[str, Any]      							=full_name.split('conv_layers.'					)[-1]
			snake_case:    Any      							=name.split('.'					)
			snake_case:    Union[str, Any]      							=int(items[0]					)
			snake_case:    Tuple      							=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.'''
									)
									snake_case:    int      							=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.'''
									)
									snake_case:    List[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."
									)
									snake_case:    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.'''
									)
									snake_case:    Tuple      							=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				,							__UpperCAmelCase					)				->					List[Any]:





			"""simple docstring"""







			snake_case:    str      							=SEWConfig()
			if is_finetuned:
						snake_case:    Dict      							=model.wav_encoder.wav_model.cfg
			else:
						snake_case:    Union[str, Any]      							=model.cfg

			snake_case:    Tuple      							=fs_config.conv_bias
			snake_case:    Optional[Any]      							=eval(fs_config.conv_feature_layers					)
			snake_case:    List[str]      							=[x[0] for x in conv_layers]
			snake_case:    int      							=[x[1] for x in conv_layers]
			snake_case:    List[str]      							=[x[2] for x in conv_layers]
			snake_case:    int      							='gelu'
			snake_case:    Optional[int]      							='layer' if fs_config.extractor_mode == 'layer_norm' else 'group'
			snake_case:    Tuple      							=0.0
			snake_case:    List[str]      							=fs_config.activation_fn.name
			snake_case:    int      							=fs_config.encoder_embed_dim
			snake_case:    str      							=0.02
			snake_case:    List[str]      							=fs_config.encoder_ffn_embed_dim
			snake_case:    Optional[int]      							=1e-5
			snake_case:    Dict      							=fs_config.encoder_layerdrop
			snake_case:    str      							=fs_config.encoder_attention_heads
			snake_case:    Dict      							=fs_config.conv_pos_groups
			snake_case:    Union[str, Any]      							=fs_config.conv_pos
			snake_case:    int      							=len(__UpperCAmelCase					)
			snake_case:    Optional[Any]      							=fs_config.encoder_layers
			snake_case:    int      							=fs_config.squeeze_factor

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

			snake_case:    Tuple      							='Wav2Vec2FeatureExtractor'
			snake_case:    Optional[int]      							='Wav2Vec2CTCTokenizer'

			return config







@torch.no_grad()
def a_     (    __UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase=None				,							__UpperCAmelCase=None				,							__UpperCAmelCase=True					)				->					str:





			"""simple docstring"""







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

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

			snake_case:    Tuple      							=True if config.feat_extract_norm == 'layer' else False
			snake_case:    List[str]      							=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:
									snake_case:    Optional[int]      							=Dictionary.load(__UpperCAmelCase					)

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

						snake_case:    Union[str, Any]      							=SEWForCTC(__UpperCAmelCase					)
			else:
						snake_case:    Union[str, Any]      							=SEWModel(__UpperCAmelCase					)
						feature_extractor.save_pretrained(__UpperCAmelCase					)

			recursively_load_weights(__UpperCAmelCase				,							__UpperCAmelCase				,							__UpperCAmelCase					)

			hf_model.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				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							   =				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'''

import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


a							   =				logging.get_logger(__name__)
logging.set_verbosity_info()







def a_     (    __UpperCAmelCase				,							__UpperCAmelCase					)				->					Dict:





			"""simple docstring"""







			if "xprophetnet" in prophetnet_checkpoint_path:
						snake_case:    Optional[Any]      							=XLMProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    str      							=XLMProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)
			else:
						snake_case:    Optional[int]      							=ProphetNetForConditionalGenerationOld.from_pretrained(__UpperCAmelCase					)
						snake_case							,  snake_case:    List[str]      							=ProphetNetForConditionalGeneration.from_pretrained(
						    __UpperCAmelCase				,							output_loading_info=__UpperCAmelCase					)

			snake_case:    str      							=['key_proj', 'value_proj', 'query_proj']

			snake_case:    str      							={
			    'self_attn': 'ngram_self_attn',
			    'cross_attn': 'encoder_attn',
			    'cross_attn_layer_norm': 'encoder_attn_layer_norm',
			    'feed_forward_layer_norm': 'final_layer_norm',
			    'feed_forward': '',
			    'intermediate': 'fc1',
			    'output': 'fc2',
			    'key_proj': 'k_proj',
			    'query_proj': 'q_proj',
			    'value_proj': 'v_proj',
			    'word_embeddings': 'embed_tokens',
			    'embeddings_layer_norm': 'emb_layer_norm',
			    'relative_pos_embeddings': 'relative_linear',
			    'ngram_embeddings': 'ngram_input_embed',
			    'position_embeddings': 'embed_positions',
			}

			for key in loading_info["missing_keys"]:
						snake_case:    List[str]      							=key.split('.'					)

						if attributes[0] == "lm_head":
									snake_case:    Dict      							=prophet
									snake_case:    List[str]      							=prophet_old
						else:
									snake_case:    Any      							=prophet.prophetnet
									snake_case:    int      							=prophet_old.model

						snake_case:    int      							=False
						for attribute in attributes:
									if attribute in mapping:
												snake_case:    Dict      							=mapping[attribute]
												if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					) and len(__UpperCAmelCase					) > 0:
															snake_case:    Optional[int]      							=attribute
									elif hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
												snake_case:    Optional[Any]      							=attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												snake_case:    Optional[Any]      							=old_model.weight
												logger.info(f'''{attribute} is initialized.'''					)
												snake_case:    List[str]      							=True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												snake_case:    List[Any]      							=old_model.bias
												logger.info(f'''{attribute} is initialized'''					)
												snake_case:    List[Any]      							=True
												break
									elif attribute in special_keys and hasattr(__UpperCAmelCase				,							'in_proj_weight'					):
												snake_case:    Optional[int]      							=old_model.in_proj_weight.shape[0] // 3
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)
												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
												if attribute == "query_proj":
															snake_case:    int      							=nn.Parameter(old_model.in_proj_weight[:embed_dim, :]					)
															snake_case:    Any      							=nn.Parameter(old_model.in_proj_bias[:embed_dim]					)

												elif attribute == "key_proj":
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]					)
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]					)
												elif attribute == "value_proj":
															snake_case:    List[str]      							=nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]					)
															snake_case:    Union[str, Any]      							=nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]					)
												snake_case:    Any      							=True
												break
									elif attribute == "position_embeddings":
												assert (
												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
												), "Hidden size has to match"
												assert model.position_embeddings.weight.shape[0] == 5_12, "We want 512 position_embeddings."
												snake_case:    Dict      							=nn.Parameter(old_model.embed_positions.weight[:5_12, :]					)
												snake_case:    Dict      							=True
												break

									if attribute.isdigit():
												snake_case:    int      							=model[int(__UpperCAmelCase					)]
												snake_case:    Optional[int]      							=old_model[int(__UpperCAmelCase					)]
									else:
												snake_case:    List[str]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

												if old_attribute == "":
															snake_case:    Union[str, Any]      							=old_model
												else:
															if not hasattr(__UpperCAmelCase				,							__UpperCAmelCase					):
																		raise ValueError(f'''{old_model} does not have {old_attribute}'''					)
															snake_case:    List[Any]      							=getattr(__UpperCAmelCase				,							__UpperCAmelCase					)

						if not is_key_init:
									raise ValueError(f'''{key} was not correctly initialized!'''					)

			print(f'''Saving model to {pytorch_dump_folder_path}'''					)
			prophet.save_pretrained(__UpperCAmelCase					)


if __name__ == "__main__":
	a							   =				argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
	)
	parser.add_argument(
	    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
	)
	a							   =				parser.parse_args()
	convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




