


"""simple docstring"""




import argparse
import json
import os

import fairseq
import torch
from fairseq.data import Dictionary

from transformers import (
    WavaVecaConfig,
    WavaVecaCTCTokenizer,
    WavaVecaFeatureExtractor,
    WavaVecaForCTC,
    WavaVecaForPreTraining,
    WavaVecaProcessor,
    logging,
)
from transformers.models.wavaveca.modeling_wavaveca import WavaVecaForSequenceClassification


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

lowercase_			     =  {
    "post_extract_proj": "feature_projection.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.layer_norm": "encoder.layer_norm",
    "adapter_layer": "encoder.layers.*.adapter_layer",
    "w2v_model.layer_norm": "feature_projection.layer_norm",
    "quantizer.weight_proj": "quantizer.weight_proj",
    "quantizer.vars": "quantizer.codevectors",
    "project_q": "project_q",
    "final_proj": "project_hid",
    "w2v_encoder.proj": "lm_head",
    "mask_emb": "masked_spec_embed",
    "pooling_layer.linear": "projector",
    "pooling_layer.projection": "classifier",
}
lowercase_			     =  [
    "lm_head",
    "quantizer.weight_proj",
    "quantizer.codevectors",
    "project_q",
    "project_hid",
    "projector",
    "classifier",
]

def        lowercase							(		lowerCAmelCase__ :     Dict			) ->      Optional[Any]:
		__a           =			{}
		with open(lowerCAmelCase__    ,					'''r'''			) as file:
				for line_number, line in enumerate(lowerCAmelCase__			):
						__a           =			line.strip()
						if line:
								__a           =			line.split()
								__a           =			line_number
								__a           =			words[0]
								__a           =			value
		return result

def        lowercase							(		lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     Optional[int]    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Tuple			) ->      Tuple:
		for attribute in key.split('''.'''			):
				__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			)

		__a           =			None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(lowerCAmelCase__			):
						__a           =			PARAM_MAPPING[full_name.split('''.'''			)[-1]]
						__a           =			'param'

		if weight_type is not None and weight_type != "param":
				__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			).shape
		elif weight_type is not None and weight_type == "param":
				__a           =			hf_pointer
				for attribute in hf_param_name.split('''.'''			):
						__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			)
				__a           =			shape_pointer.shape

				# let's reduce dimension
				__a           =			value[0]
		else:
				__a           =			hf_pointer.shape

		if hf_shape != value.shape:
				raise ValueError(
				    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           =			value
		elif weight_type == "weight_g":
				__a           =			value
		elif weight_type == "weight_v":
				__a           =			value
		elif weight_type == "bias":
				__a           =			value
		elif weight_type == "param":
				for attribute in hf_param_name.split('''.'''			):
						__a           =			getattr(lowerCAmelCase__    ,					lowerCAmelCase__			)
				__a           =			value
		else:
				__a           =			value

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

def        lowercase							(		lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     List[Any]    ,					lowerCAmelCase__ :     int    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Tuple			) ->      int:
		__a           =			None
		for param_key in PARAM_MAPPING.keys():
				if full_name.endswith(lowerCAmelCase__			):
						__a           =			PARAM_MAPPING[full_name.split('''.'''			)[-1]]
						__a           =			'param'

		if weight_type is not None and weight_type != "param":
				__a           =			'.'.join([key, weight_type]			)
		elif weight_type is not None and weight_type == "param":
				__a           =			'.'.join([key, hf_param_name]			)
		else:
				__a           =			key

		__a           =			value if 'lm_head' in full_key else value[0]


lowercase_			     =  {
    "W_a": "linear_1.weight",
    "W_b": "linear_2.weight",
    "b_a": "linear_1.bias",
    "b_b": "linear_2.bias",
    "ln_W": "norm.weight",
    "ln_b": "norm.bias",
}

def        lowercase							(		lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Optional[Any]=None    ,					lowerCAmelCase__ :     List[str]=None			) ->      Any:
		__a           =			False
		for key, mapped_key in MAPPING.items():
				__a           =			'wav2vec2.' + mapped_key if mapped_key not in TOP_LEVEL_KEYS else mapped_key
				if key in name or key.split('''w2v_model.'''			)[-1] == name.split('''.'''			)[0]:
						__a           =			True
						if "*" in mapped_key:
								__a           =			name.split(lowerCAmelCase__			)[0].split('''.'''			)[-2]
								__a           =			mapped_key.replace('''*'''    ,					lowerCAmelCase__			)
						if "weight_g" in name:
								__a           =			'weight_g'
						elif "weight_v" in name:
								__a           =			'weight_v'
						elif "bias" in name:
								__a           =			'bias'
						elif "weight" in name:
								# TODO: don't match quantizer.weight_proj
								__a           =			'weight'
						else:
								__a           =			None
						if hf_dict is not None:
								rename_dict(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
						else:
								set_recursively(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
						return is_used
		return is_used

def        lowercase							(		lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Optional[int]			) ->      str:
		__a           =			[]
		__a           =			fairseq_model.state_dict()

		__a           =			hf_model.wavaveca.feature_extractor

		for name, value in fairseq_dict.items():
				__a           =			False
				if "conv_layers" in name:
						load_conv_layer(
						    lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__    ,					hf_model.config.feat_extract_norm == '''group'''    ,					)
						__a           =			True
				else:
						__a           =			load_wavaveca_layer(lowerCAmelCase__    ,					lowerCAmelCase__    ,					lowerCAmelCase__			)
				if not is_used:
						unused_weights.append(lowerCAmelCase__			)

		logger.warning(f'''Unused weights: {unused_weights}'''			)

def        lowercase							(		lowerCAmelCase__ :     Dict    ,					lowerCAmelCase__ :     Optional[Any]    ,					lowerCAmelCase__ :     Tuple    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     List[str]			) ->      List[str]:
		__a           =			full_name.split('''conv_layers.'''			)[-1]
		__a           =			name.split('''.'''			)
		__a           =			int(items[0]			)
		__a           =			int(items[1]			)

		if type_id == 0:
				if "bias" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.bias.data.shape} was found.'''			)
						__a           =			value
						logger.info(f'''Feat extract conv layer {layer_id} was initialized from {full_name}.'''			)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].conv.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].conv.weight.data.shape} was found.'''			)
						__a           =			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:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.bias.data.shape} was found.'''			)
						__a           =			value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
				elif "weight" in name:
						if value.shape != feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape:
								raise ValueError(
								    f'''{full_name} has size {value.shape}, but'''
								    f''' {feature_extractor.conv_layers[layer_id].layer_norm.weight.data.shape} was found.'''			)
						__a           =			value
						logger.info(f'''Feat extract layer norm weight of layer {layer_id} was initialized from {full_name}.'''			)
		else:
				unused_weights.append(lowerCAmelCase__			)

@torch.no_grad()
def        lowercase							(		lowerCAmelCase__ :     Union[str, Any]    ,					lowerCAmelCase__ :     str    ,					lowerCAmelCase__ :     Tuple=None    ,					lowerCAmelCase__ :     List[str]=None    ,					lowerCAmelCase__ :     Any=True    ,					lowerCAmelCase__ :     Optional[int]=False			) ->      Dict:
		if config_path is not None:
				__a           =			WavaVecaConfig.from_pretrained(lowerCAmelCase__			)
		else:
				__a           =			WavaVecaConfig()

		if is_seq_class:
				__a           =			read_txt_into_dict(lowerCAmelCase__			)
				__a           =			idalabel
				__a           =			WavaVecaForSequenceClassification(lowerCAmelCase__			)
				__a           =			WavaVecaFeatureExtractor(
				    feature_size=1    ,					sampling_rate=16000    ,					padding_value=0    ,					do_normalize=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					)
				feature_extractor.save_pretrained(lowerCAmelCase__			)

		elif is_finetuned:
				if dict_path:
						__a           =			Dictionary.load(lowerCAmelCase__			)

						# important change bos & pad token id since CTC symbol is <pad> and
						# not <s> as in fairseq
						__a           =			target_dict.pad_index
						__a           =			target_dict.bos_index
						__a           =			target_dict.eos_index
						__a           =			len(target_dict.symbols			)
						__a           =			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__			)
						__a           =			target_dict.indices

						# fairseq has the <pad> and <s> switched
						__a           =			0
						__a           =			1
						with open(lowerCAmelCase__    ,					'''w'''    ,					encoding='''utf-8'''			) as vocab_handle:
								json.dump(lowerCAmelCase__    ,					lowerCAmelCase__			)
						__a           =			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__    ,					)
						__a           =			True if config.feat_extract_norm == 'layer' else False
						__a           =			WavaVecaFeatureExtractor(
						    feature_size=1    ,					sampling_rate=16000    ,					padding_value=0    ,					do_normalize=lowerCAmelCase__    ,					return_attention_mask=lowerCAmelCase__    ,					)
						__a           =			WavaVecaProcessor(feature_extractor=lowerCAmelCase__    ,					tokenizer=lowerCAmelCase__			)
						processor.save_pretrained(lowerCAmelCase__			)

				__a           =			WavaVecaForCTC(lowerCAmelCase__			)
		else:
				__a           =			WavaVecaForPreTraining(lowerCAmelCase__			)

		if is_finetuned or is_seq_class:
				__a           =			fairseq.checkpoint_utils.load_model_ensemble_and_task(
				    [checkpoint_path]    ,					arg_overrides={'''data''': '''/'''.join(dict_path.split('''/'''			)[:-1]			)}			)
		else:
				__a           =			argparse.Namespace(task='''audio_pretraining'''			)
				__a           =			fairseq.tasks.setup_task(lowerCAmelCase__			)

				__a           =			fairseq.checkpoint_utils.load_model_ensemble_and_task([checkpoint_path]    ,					task=lowerCAmelCase__			)

		__a           =			model[0].eval()

		recursively_load_weights(lowerCAmelCase__    ,					lowerCAmelCase__    ,					not is_finetuned			)

		hf_wavavec.save_pretrained(lowerCAmelCase__			)


if __name__ == "__main__":
				lowercase_			     =  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(
				    "--not_finetuned", action="store_true", help="Whether the model to convert is a fine-tuned model or not"
				)
				parser.add_argument(
				    "--is_seq_class",
				    action="store_true",
				    help="Whether the model to convert is a fine-tuned sequence classification model or not",
				)
				lowercase_			     =  parser.parse_args()

				lowercase_			     =  not args.not_finetuned and not args.is_seq_class
				convert_wavaveca_checkpoint(
				    args.checkpoint_path,
				    args.pytorch_dump_folder_path,
				    args.config_path,
				    args.dict_path,
				    is_finetuned,
				    args.is_seq_class,
				)











import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME
def        lowercase_							(A : List[str]      ):
       snake_case__							:	Tuple   	=	[
           'encoder.version',
           'decoder.version',
           'model.encoder.version',
           'model.decoder.version',
           'decoder.output_projection.weight',
           '_float_tensor',
           'encoder.embed_positions._float_tensor',
           'decoder.embed_positions._float_tensor',
       ]
       for k in ignore_keys:
              state_dict.pop(A		,					A      )
def        lowercase_							(A : str      ):
       snake_case__	,				snake_case__							:	Union[str, Any]   	=	emb.weight.shape
       snake_case__							:	str   	=	nn.Linear(A		,					A		,					bias=A      )
       snake_case__							:	str   	=	emb.weight.data
       return lin_layer
def        lowercase_							(A : Optional[int]		,					A : Union[str, Any]=None      ):
       snake_case__							:	Any   	=	{}
       for old_key in state_dict.keys():
              snake_case__							:	Tuple   	=	old_key
              if "moe_layer.experts." in key:
                     if expert_idx is not None:
                            snake_case__							:	int   	=	key.replace('moe_layer.experts.0'		,					F'''ffn.experts.expert_{expert_idx}'''      )
                     else:
                            snake_case__							:	Any   	=	key.replace('moe_layer.experts.'		,					'ffn.experts.expert_'      )
              if "gate" in key:
                     snake_case__							:	Dict   	=	key.replace('.moe_layer.gate.wg'		,					'.ffn.router.classifier'      )
              if "fc2" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc2.'		,					'.ffn.fc2.'      )
              if "fc1" and "experts" not in key:
                     snake_case__							:	str   	=	key.replace('.fc1.'		,					'.ffn.fc1.'      )
              if ".encoder_attn." in key:
                     snake_case__							:	Tuple   	=	key.replace('.encoder_attn.'		,					'.cross_attention.'      )
              if "encoder_attn_layer_norm" in key:
                     snake_case__							:	Tuple   	=	key.replace('encoder_attn_layer_norm'		,					'cross_attention_layer_norm'      )
              if "final_layer_norm" in key:
                     snake_case__							:	Optional[int]   	=	key.replace('final_layer_norm'		,					'ff_layer_norm'      )
              snake_case__							:	Dict   	=	state_dict[old_key]
       return new_dict
def        lowercase_							(A : List[Any]		,					A : Tuple		,					A : List[Any]		,					A : List[str]		,					A : str = WEIGHTS_NAME      ):
       snake_case__							:	Dict   	=	[]
       snake_case__							:	str   	=	0
       os.makedirs(A		,					exist_ok=A      )

       for expert in range(A      ):
              snake_case__							:	Tuple   	=	switch_checkpoint_path + F'''-rank-{expert}.pt'''
              if os.path.isfile(A      ):
                     snake_case__							:	Optional[Any]   	=	torch.load(A      )['model']
                     remove_ignore_keys_(A      )
                     snake_case__							:	Optional[Any]   	=	rename_fairseq_keys(A		,					A      )
                     snake_case__							:	Dict   	=	os.path.join(
                         A		,					weights_name.replace('.bin'		,					F'''-{len(A      )+1:05d}-of-???.bin'''      )      )
                     torch.save(A		,					A      )
                     sharded_state_dicts.append(expert_state.keys()      )
                     total_size += sum([value.numel() for key, value in expert_state.items()]      ) * dtype_byte_size(
                         expert_state[list(A      )[0]].dtype      )

    # Add the last block
       snake_case__							:	Tuple   	=	os.path.join(A		,					weights_name.replace('.bin'		,					F'''-{len(A      )+1:05d}-of-???.bin'''      )      )
       snake_case__							:	Union[str, Any]   	=	torch.load(switch_checkpoint_path + '-shared.pt'      )['model']
       remove_ignore_keys_(A      )
       snake_case__							:	str   	=	rename_fairseq_keys(A		,					A      )
       snake_case__							:	Any   	=	shared_weights['decoder.embed_tokens.weight']
       sharded_state_dicts.append(shared_weights.keys()      )

       # If we only have the shared weights (dummy model/experts saved on the same file)
       if len(A      ) == 1:
              snake_case__							:	Any   	=	os.path.join(A		,					A      )
              torch.save(A		,					A      )
              return {weights_name: sharded_state_dicts[0]}, None
       else:
              torch.save(A		,					A      )
       # Otherwise, let's build the index
       snake_case__							:	Tuple   	=	{}
       for idx, shard in enumerate(A      ):
              snake_case__							:	Optional[int]   	=	weights_name.replace('.bin'		,					F'''-{idx+1:05d}-of-{len(A      ):05d}.bin'''      )
              snake_case__							:	List[Any]   	=	os.path.join(A		,					weights_name.replace('.bin'		,					F'''-{idx+1:05d}-of-???.bin'''      )      )
              os.rename(A		,					os.path.join(A		,					A      )      )
              for key in shard:
                     snake_case__							:	Any   	=	shard_file

    # Add the metadata
       snake_case__							:	int   	=	{'total_size': total_size}
       snake_case__							:	Dict   	=	{'metadata': metadata, 'weight_map': weight_map}

       with open(os.path.join(A		,					A      )		,					'w'		,					encoding='utf-8'      ) as f:
              snake_case__							:	Any   	=	json.dumps(A		,					indent=2		,					sort_keys=A      ) + '\n'
              f.write(A      )

       return metadata, index


if __name__ == "__main__":
 a_   :int        =      argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     "--nllb_moe_checkpoint_path",
     default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000",
     type=str,
     required=False,
     help="Path to a directory containing a folder per layer. Follows the original Google format.",
 )
 parser.add_argument("--dtype", default="float32", type=str, required=False, help="dtype of the saved model")
 parser.add_argument(
     "--pytorch_dump_folder_path",
     default="/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b",
     type=str,
     required=False,
     help="Path to the output pytorch model.",
 )
 a_   :Optional[Any]        =      parser.parse_args()
 a_  ,						a_   :Optional[Any]        =      shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 a_   :List[str]        =      NllbMoeConfig.from_pretrained(
     "facebook/nllb-200-3.3B", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
 )
 config.save_pretrained(args.pytorch_dump_folder_path)
 a_   :int        =      NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("Done")
 model.save_pretrained(args.pytorch_dump_folder_path)






