
import argparse

import torch
from saprl.hub import distilhubert

from transformers import HubertConfig, HubertModel, WavaVecaFeatureExtractor, logging


logging.set_verbosity_info()
__magic_name__  :  Optional[Any]     			=   logging.get_logger(__name__)

__magic_name__  :  List[Any]     			=   {
    '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',
    'mask_emb': 'masked_spec_embed',
}





def        lowerCAmelCase       (				snake_case__   :      str				,					snake_case__   :      Optional[Any]				,					snake_case__   :      Any				,					snake_case__   :      Tuple				,					snake_case__   :      Union[str, Any] )->			Optional[Any]:
    for attribute in key.split("." ):
        A_								=					getattr(snake_case__				,					snake_case__ )

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

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





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

    A_								=					hf_model.feature_extractor

    for name, value in fairseq_dict.items():
        A_								=					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_								=					True
        else:
            for key, mapped_key in MAPPING.items():
                A_								=					mapped_key

                if key in name:
                    A_								=					True
                    if "*" in mapped_key:
                        A_								=					name.split(snake_case__ )[0].split("." )[-2]
                        A_								=					mapped_key.replace("*"				,					snake_case__ )
                    if "weight_g" in name:
                        A_								=					"weight_g"
                    elif "weight_v" in name:
                        A_								=					"weight_v"
                    elif "weight" in name:
                        A_								=					"weight"
                    elif "bias" in name:
                        A_								=					"bias"
                    else:
                        A_								=					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__   :      List[Any]				,					snake_case__   :      Tuple				,					snake_case__   :      str				,					snake_case__   :      Tuple				,					snake_case__   :      List[Any] )->			Any:
    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:
            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_								=					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_								=					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_								=					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_								=					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__   :      List[Any] )->			Any:
    A_								=					HubertConfig()
    A_								=					model.config

    A_								=					fs_config.activation_dropout
    A_								=					False
    A_								=					fs_config.attention_dropout
    A_								=					False
    A_								=					eval(fs_config.extractor_conv_feature_layers )
    A_								=					[x[0] for x in conv_layers]
    A_								=					[x[1] for x in conv_layers]
    A_								=					[x[2] for x in conv_layers]
    A_								=					"gelu"
    A_								=					"layer" if fs_config.extractor_mode == "layer_norm" else "group"
    A_								=					False
    A_								=					0.0
    A_								=					0.0
    A_								=					fs_config.activation_fn
    A_								=					fs_config.dropout
    A_								=					fs_config.encoder_embed_dim
    A_								=					0.0_2
    A_								=					fs_config.encoder_ffn_embed_dim
    A_								=					1e-5
    A_								=					0.0
    A_								=					fs_config.encoder_attention_heads
    A_								=					fs_config.conv_pos_groups
    A_								=					fs_config.conv_pos
    A_								=					len(snake_case__ )
    A_								=					fs_config.encoder_layers

    return config





@torch.no_grad()
def        lowerCAmelCase       (				snake_case__   :      Tuple				,					snake_case__   :      Dict=None )->			Optional[int]:
    A_								=					distilhubert().model.model

    if config_path is not None:
        A_								=					HubertConfig.from_pretrained(snake_case__ )
    else:
        A_								=					convert_config(snake_case__ )
    A_								=					model.eval()

    A_								=					WavaVecaFeatureExtractor(
        feature_size=1				,					sampling_rate=16000				,					padding_value=0				,					do_normalize=snake_case__				,					return_attention_mask=snake_case__				,					)
    A_								=					HubertModel(snake_case__ )

    recursively_load_weights(snake_case__				,					snake_case__ )

    feature_extractor.save_pretrained(snake_case__ )
    hf_model.save_pretrained(snake_case__ )


if __name__ == "__main__":
     __magic_name__  :  int     			=   argparse.ArgumentParser()
     parser.add_argument('--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
     parser.add_argument('--config_path', default=None, type=str, help='Path to hf config.json of model to convert')
     __magic_name__  :  List[str]     			=   parser.parse_args()
     convert_hubert_checkpoint(args.pytorch_dump_folder_path, args.config_path)








import argparse
import os
from os.path import isfile

import torch

from transformers import GPTaConfig





def        lowerCAmelCase       (				snake_case__   :      List[str]				,					snake_case__   :      List[str]				,					snake_case__   :      Optional[int]=0 )->			Union[str, Any]:
    # Format the message.
    if name is None:
        A_								=					None
    else:
        A_								=					"." * max(0				,					spaces - 2 ) + "# {:" + str(50 - spaces ) + "s}"
        A_								=					fmt.format(snake_case__ )

    # Print and recurse (if needed).
    if isinstance(snake_case__				,					snake_case__ ):
        if msg is not None:
            print(snake_case__ )
        for k in val.keys():
            recursive_print(snake_case__				,					val[k]				,					spaces + 2 )
    elif isinstance(snake_case__				,					torch.Tensor ):
        print(snake_case__				,					":"				,					val.size() )
    else:
        print(snake_case__				,					":"				,					snake_case__ )





def        lowerCAmelCase       (				snake_case__   :      Any				,					snake_case__   :      List[Any]				,					snake_case__   :      str				,					snake_case__   :      str )->			Union[str, Any]:
    # Permutes layout of param tensor to [num_splits * num_heads * hidden_size, :]
    # for compatibility with later versions of NVIDIA Megatron-LM.
    # The inverse operation is performed inside Megatron-LM to read checkpoints:
    # https://github.com/NVIDIA/Megatron-LM/blob/v2.4/megatron/checkpointing.py#L209
    # If param is the weight tensor of the self-attention block, the returned tensor
    # will have to be transposed one more time to be read by HuggingFace GPT2.
    A_								=					param.size()
    # other versions store [num_heads * num_splits * hidden_size, :]
    A_								=					(num_heads, num_splits, hidden_size) + input_shape[1:]
    A_								=					param.view(*snake_case__ )
    A_								=					param.transpose(0				,					1 ).contiguous()
    A_								=					param.view(*snake_case__ )
    return param





def        lowerCAmelCase       (				snake_case__   :      Dict				,					snake_case__   :      str )->			Optional[int]:
    A_								=					config.n_positions
    A_								=					config.n_layer
    A_								=					config.vocab_size
    A_								=					config.n_head
    A_								=					config.n_embd // config.n_head

    A_								=					sd_megatron["model.language_model.embedding.word_embeddings.weight"][:vocab_size, :]
    A_								=					{
        "transformer.wte.weight": word_embeddings,
        "transformer.wpe.weight": sd_megatron["model.language_model.embedding.position_embeddings.weight"],
        "transformer.ln_f.weight": sd_megatron["model.language_model.encoder.final_layernorm.weight"],
        "transformer.ln_f.bias": sd_megatron["model.language_model.encoder.final_layernorm.bias"],
    }

    A_								=					"model.language_model.encoder.layers."
    for i in range(snake_case__ ):
        A_								=					torch.tril(torch.ones((n_positions, n_positions)				,					dtype=torch.bool ) )
        A_								=					causal_mask.view(1				,					1				,					snake_case__				,					snake_case__ )
        A_								=					causal_mask
        A_								=					torch.tensor(-1e4				,					dtype=torch.bfloataa )

        A_								=					sd_megatron[f'{pf}{i}.input_layernorm.weight']
        A_								=					sd_megatron[f'{pf}{i}.input_layernorm.bias']

        A_								=					sd_megatron[f'{pf}{i}.self_attention.query_key_value.weight']
        A_								=					fix_query_key_value_ordering(snake_case__				,					3				,					snake_case__				,					snake_case__ )
        A_								=					vala.transpose(0				,					1 ).contiguous()

        A_								=					sd_megatron[f'{pf}{i}.self_attention.query_key_value.bias']
        A_								=					fix_query_key_value_ordering(snake_case__				,					3				,					snake_case__				,					snake_case__ )
        A_								=					vala

        A_								=					sd_megatron[f'{pf}{i}.self_attention.dense.weight'].transpose(
            0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.self_attention.dense.bias']
        A_								=					sd_megatron[f'{pf}{i}.post_attention_layernorm.weight']
        A_								=					sd_megatron[f'{pf}{i}.post_attention_layernorm.bias']
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.weight'].transpose(0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_h_to_4h.bias']
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.weight'].transpose(
            0				,					1 )
        A_								=					sd_megatron[f'{pf}{i}.mlp.dense_4h_to_h.bias']

    # For LM head, transformers' wants the matrix to weight embeddings.
    A_								=					word_embeddings

    return sd_hf





def        lowerCAmelCase       (				snake_case__   :      Optional[int]				,					snake_case__   :      List[Any] )->			Dict:
    A_								=					64000
    A_								=					config_megatron["encoder_seq_length"]
    A_								=					config_megatron["hidden_size"]
    A_								=					config_megatron["num_layers"]
    A_								=					config_megatron["num_attention_heads"]
    A_								=					config_megatron["ffn_hidden_size"]
    A_								=					"gelu"
    A_								=					0.1
    A_								=					0.1
    A_								=					0.1
    A_								=					config_megatron["layernorm_epsilon"]  # 1e-5
    A_								=					config_megatron["init_method_std"]  # 0.02
    A_								=					config_megatron["apply_query_key_layer_scaling"]  # True
    A_								=					True
    A_								=					True

    # This identifies the 6.7B (7B) model which uses a different tokenizer
    if config_megatron["hidden_size"] == 4096:
        A_								=					1  # <|endoftext|>
        A_								=					1  # <|endoftext|>
        A_								=					0  # <unk>
    else:
        A_								=					2  # <s>
        A_								=					3  # <|endoftext|>
        A_								=					0  # <pad>

    return config_hf





def        lowerCAmelCase       (				snake_case__   :      Tuple )->			Dict:
    print(snake_case__ )

    A_								=					args.checkpoint_path
    A_								=					args.save_path
    if isfile(snake_case__ ):
        raise FileNotFoundError(f'ERROR! could not find file {checkpoint_path}' )

    # Load the model.
    A_								=					torch.load(snake_case__				,					map_location="cpu" )

    # Load the config.
    A_								=					checkpoint["hyper_parameters"]["cfg"]
    A_								=					GPTaConfig()
    A_								=					copy_config(config_hf=snake_case__				,					config_megatron=snake_case__ )
    A_								=					["GPT2LMHeadModel"]

    A_								=					checkpoint["state_dict"]

    # Convert.
    print("Converting" )
    A_								=					convert_megatron_checkpoint(snake_case__				,					snake_case__ )

    # Print the structure of converted state dict.
    if args.print_checkpoint_structure:
        recursive_print(snake_case__				,					snake_case__ )

    A_								=					"GPTSw3Tokenizer"

    # Store the config to file.
    print("Saving config" )
    config_hf.save_pretrained(snake_case__ )

    # Store the state_dict to file.
    A_								=					os.path.join(snake_case__				,					"pytorch_model.bin" )
    print(f'Saving checkpoint to "{output_checkpoint_file}"' )
    torch.save(snake_case__				,					snake_case__ )


if __name__ == "__main__":
     __magic_name__  :  Union[str, Any]     			=   argparse.ArgumentParser()
     parser.add_argument(
         '--checkpoint_path',
         type=str,
         required=True,
         help='e.g. megatron_gpt--val_loss=2.42-step=38000-consumed_samples=54720000',
     )
     parser.add_argument('--save_path', type=str, required=True, help='e.g. /home/user/gpt-sw3/hf')
     parser.add_argument('--print-checkpoint-structure', action='store_true')
     __magic_name__  :  str     			=   parser.parse_args()
     main(_args)






