
'''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()
lowercase			:				int    		=			logging.get_logger(__name__)

lowercase			:				Tuple    		=			{
    '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 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    for attribute in key.split('''.'''						):
        A       :   int					=						getattr(snake_case__		,				snake_case__						)

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

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   List[Any]					=						[]
    A       :   Optional[int]					=						fairseq_model.state_dict()

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

    for name, value in fairseq_dict.items():
        A       :   Tuple					=						False
        if "conv_layers" in name:
            load_conv_layer(
                snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				hf_model.config.feat_extract_norm == '''group'''		,				)
            A       :   str					=						True
        else:
            for key, mapped_key in MAPPING.items():
                A       :   str					=						'''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]:
                    A       :   Union[str, Any]					=						True
                    if "*" in mapped_key:
                        A       :   List[Any]					=						name.split(snake_case__						)[0].split('''.'''						)[-2]
                        A       :   Optional[int]					=						mapped_key.replace('''*'''		,				snake_case__						)
                    if "weight_g" in name:
                        A       :   List[str]					=						'''weight_g'''
                    elif "weight_v" in name:
                        A       :   Union[str, Any]					=						'''weight_v'''
                    elif "weight" in name:
                        A       :   Dict					=						'''weight'''
                    elif "bias" in name:
                        A       :   Dict					=						'''bias'''
                    else:
                        A       :   Optional[Any]					=						None
                    set_recursively(snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						)
                continue
        if not is_used:
            unused_weights.append(snake_case__						)

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







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__		,				snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Union[str, Any]					=						full_name.split('''conv_layers.'''						)[-1]
    A       :   List[Any]					=						name.split('''.'''						)
    A       :   Union[str, Any]					=						int(items[0]						)
    A       :   Any					=						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       :   int					=						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(snake_case__						)







def 		lowerCAmelCase_				(   snake_case__		,				snake_case__						):




    '''simple docstring'''



    A       :   Dict					=						SEWConfig()
    if is_finetuned:
        A       :   List[Any]					=						model.wav_encoder.wav_model.cfg
    else:
        A       :   str					=						model.cfg

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

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

    A       :   Optional[int]					=						'''Wav2Vec2FeatureExtractor'''
    A       :   Union[str, Any]					=						'''Wav2Vec2CTCTokenizer'''

    return config







@torch.no_grad()
def 		lowerCAmelCase_				(   snake_case__		,				snake_case__		,				snake_case__=None		,				snake_case__=None		,				snake_case__=True						):




    '''simple docstring'''



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

    if config_path is not None:
        A       :   List[str]					=						SEWConfig.from_pretrained(snake_case__						)
    else:
        A       :   Dict					=						convert_config(model[0]		,				snake_case__						)
    A       :   str					=						model[0].eval()

    A       :   Union[str, Any]					=						True if config.feat_extract_norm == '''layer''' else False
    A       :   Optional[int]					=						WavaVecaFeatureExtractor(
        feature_size=1		,				sampling_rate=1_6000		,				padding_value=0		,				do_normalize=snake_case__		,				return_attention_mask=snake_case__		,				)

    if is_finetuned:
        if dict_path:
            A       :   Optional[Any]					=						Dictionary.load(snake_case__						)

            # important change bos & pad token id since CTC symbol is <pad> and
            # not <s> as in fairseq
            A       :   Optional[Any]					=						target_dict.pad_index
            A       :   Optional[Any]					=						target_dict.bos_index
            A       :   Union[str, Any]					=						target_dict.pad_index
            A       :   Union[str, Any]					=						target_dict.bos_index
            A       :   int					=						target_dict.eos_index
            A       :   Tuple					=						len(target_dict.symbols						)
            A       :   List[str]					=						os.path.join(snake_case__		,				'''vocab.json'''						)
            if not os.path.isdir(snake_case__						):
                logger.error('''--pytorch_dump_folder_path ({}) should be a directory'''.format(snake_case__						)						)
                return
            os.makedirs(snake_case__		,				exist_ok=snake_case__						)
            with open(snake_case__		,				'''w'''		,				encoding='''utf-8'''						) as vocab_handle:
                json.dump(target_dict.indices		,				snake_case__						)
            A       :   str					=						WavaVecaCTCTokenizer(
                snake_case__		,				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=snake_case__		,				)
            A       :   List[str]					=						WavaVecaProcessor(feature_extractor=snake_case__		,				tokenizer=snake_case__						)
            processor.save_pretrained(snake_case__						)

        A       :   Tuple					=						SEWForCTC(snake_case__						)
    else:
        A       :   Dict					=						SEWModel(snake_case__						)
        feature_extractor.save_pretrained(snake_case__						)

    recursively_load_weights(snake_case__		,				snake_case__		,				snake_case__						)

    hf_model.save_pretrained(snake_case__						)


if __name__ == "__main__":
      lowercase			:				Dict    		=			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'
      )
      lowercase			:				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'''




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       __lowerCamelCase  (					_lowercase		)      ->			Union[str, Any]:
							UpperCAmelCase     :		Dict							     =		[
							    """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(_lowercase	,				_lowercase		)



def       __lowerCamelCase  (					_lowercase		)      ->			Tuple:
							UpperCAmelCase     ,     UpperCAmelCase     :		List[str]							     =		emb.weight.shape
							UpperCAmelCase     :		Dict							     =		nn.Linear(_lowercase	,				_lowercase	,				bias=_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		emb.weight.data
							return lin_layer



def       __lowerCamelCase  (					_lowercase	,				_lowercase=None		)      ->			List[Any]:
							UpperCAmelCase     :		Dict							     =		{}
							for old_key in state_dict.keys():
														UpperCAmelCase     :		str							     =		old_key
														if "moe_layer.experts." in key:
																					if expert_idx is not None:
																												UpperCAmelCase     :		int							     =		key.replace("""moe_layer.experts.0"""	,				F'''ffn.experts.expert_{expert_idx}'''		)
																					else:
																												UpperCAmelCase     :		str							     =		key.replace("""moe_layer.experts."""	,				"""ffn.experts.expert_"""		)
														if "gate" in key:
																					UpperCAmelCase     :		str							     =		key.replace(""".moe_layer.gate.wg"""	,				""".ffn.router.classifier"""		)
														if "fc2" and "experts" not in key:
																					UpperCAmelCase     :		Tuple							     =		key.replace(""".fc2."""	,				""".ffn.fc2."""		)
														if "fc1" and "experts" not in key:
																					UpperCAmelCase     :		List[Any]							     =		key.replace(""".fc1."""	,				""".ffn.fc1."""		)
														if ".encoder_attn." in key:
																					UpperCAmelCase     :		Union[str, Any]							     =		key.replace(""".encoder_attn."""	,				""".cross_attention."""		)
														if "encoder_attn_layer_norm" in key:
																					UpperCAmelCase     :		str							     =		key.replace("""encoder_attn_layer_norm"""	,				"""cross_attention_layer_norm"""		)
														if "final_layer_norm" in key:
																					UpperCAmelCase     :		Optional[int]							     =		key.replace("""final_layer_norm"""	,				"""ff_layer_norm"""		)
														UpperCAmelCase     :		str							     =		state_dict[old_key]
							return new_dict



def       __lowerCamelCase  (					_lowercase	,				_lowercase	,				_lowercase	,				_lowercase	,				_lowercase = WEIGHTS_NAME		)      ->			Tuple:
							UpperCAmelCase     :		Any							     =		[]
							UpperCAmelCase     :		Dict							     =		0
							os.makedirs(_lowercase	,				exist_ok=_lowercase		)

							for expert in range(_lowercase		):
														UpperCAmelCase     :		str							     =		switch_checkpoint_path + F'''-rank-{expert}.pt'''
														if os.path.isfile(_lowercase		):
																					UpperCAmelCase     :		Optional[Any]							     =		torch.load(_lowercase		)["""model"""]
																					remove_ignore_keys_(_lowercase		)
																					UpperCAmelCase     :		List[str]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
																					UpperCAmelCase     :		Optional[Any]							     =		os.path.join(
																					    _lowercase	,				weights_name.replace(""".bin"""	,				F'''-{len(_lowercase		)+1:05d}-of-???.bin'''		)		)
																					torch.save(_lowercase	,				_lowercase		)
																					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(_lowercase		)[0]].dtype		)

    # Add the last block
							UpperCAmelCase     :		Optional[Any]							     =		os.path.join(_lowercase	,				weights_name.replace(""".bin"""	,				F'''-{len(_lowercase		)+1:05d}-of-???.bin'''		)		)
							UpperCAmelCase     :		Optional[int]							     =		torch.load(switch_checkpoint_path + """-shared.pt"""		)["""model"""]
							remove_ignore_keys_(_lowercase		)
							UpperCAmelCase     :		Optional[Any]							     =		rename_fairseq_keys(_lowercase	,				_lowercase		)
							UpperCAmelCase     :		int							     =		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(_lowercase		) == 1:
														UpperCAmelCase     :		Dict							     =		os.path.join(_lowercase	,				_lowercase		)
														torch.save(_lowercase	,				_lowercase		)
														return {weights_name: sharded_state_dicts[0]}, None
							else:
														torch.save(_lowercase	,				_lowercase		)
							# Otherwise, let's build the index
							UpperCAmelCase     :		Optional[int]							     =		{}
							for idx, shard in enumerate(_lowercase		):
														UpperCAmelCase     :		List[str]							     =		weights_name.replace(""".bin"""	,				F'''-{idx+1:05d}-of-{len(_lowercase		):05d}.bin'''		)
														UpperCAmelCase     :		List[str]							     =		os.path.join(_lowercase	,				weights_name.replace(""".bin"""	,				F'''-{idx+1:05d}-of-???.bin'''		)		)
														os.rename(_lowercase	,				os.path.join(_lowercase	,				_lowercase		)		)
														for key in shard:
																					UpperCAmelCase     :		Tuple							     =		shard_file

    # Add the metadata
							UpperCAmelCase     :		Any							     =		{"""total_size""": total_size}
							UpperCAmelCase     :		Optional[Any]							     =		{"""metadata""": metadata, """weight_map""": weight_map}

							with open(os.path.join(_lowercase	,				_lowercase		)	,				"""w"""	,				encoding="""utf-8"""		) as f:
														UpperCAmelCase     :		str							     =		json.dumps(_lowercase	,				indent=2	,				sort_keys=_lowercase		) + """\n"""
														f.write(_lowercase		)

							return metadata, index


if __name__ == "__main__":
							a   :			List[Any]					  =    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   :			int					  =    parser.parse_args()
							a  ,      a   :			Any					  =    shard_on_the_fly(
							    args.nllb_moe_checkpoint_path,
							    args.pytorch_dump_folder_path,
							    1_2_8,
							    args.dtype,
							)

							a   :			str					  =    NllbMoeConfig.from_pretrained(
							    """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=1_2_8
							)
							config.save_pretrained(args.pytorch_dump_folder_path)
							a   :			Optional[int]					  =    NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
							print("""Done""")
							model.save_pretrained(args.pytorch_dump_folder_path)

