


"""simple docstring"""







import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
       from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
       raise ImportError("""You need to install the version of fairseq from the Mega repo!""")






class  __A							(			nn.Module     ):






 """simple docstring"""




 def __init__(						self					,       __A					,       __A					,       __A		)   ->	Union[str, Any]:
     super().__init__()
     a											=mega_args
     a											=nn.Embedding(__A					,       self.mega_args.encoder_embed_dim		)
     a											=nn.ModuleList([MegaEncoderLayer(self.mega_args		) for _ in range(__A		)]		)
     a											=depth





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A					,       __A=True					,       __A=0		)   ->	int:

     # Mega expects embeddings to be (time, batch, embedding size), but
     # Hugging Face returns tokens as (batch, time)
     if batch_first:
         a											=input_ids.T

     # to make things more confusing, Mega expects the attention mask to
     # be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
     # which is the opposite of what HF returns
     if ignore_mask_value == 0:
         a											=1 - attention_mask

     # get token embeddings from IDs
     a											=self.embedding_layer(__A		)

     # pass through the Mega layers
     # input is (time, batch, encoder dim) and output is the same
     for encoder in self.encoders:
         a											=encoder(__A					,       __A		)

     # return according to the shape specified
     if batch_first:
         # (T, B, H) --> (B, T, H)
         return torch.transpose(__A					,       0					,       1		)
     else:
         return embeds






class  __A							(			nn.Module     ):






 """simple docstring"""




 def __init__(						self					,       __A					,       __A					,       __A		)   ->	Union[str, Any]:
     super().__init__()
     a											=MegaLM(__A					,       __A					,       __A		)
     a											=nn.Linear(mega_args.encoder_embed_dim					,       __A		)
     a											=nn.Dropout(p=0.1		)





 def  SCREAMING_SNAKE_CASE				(						self					,       __A					,       __A					,       __A=True					,       __A=0		)   ->	int:
     a											=self.mega(__A					,       __A					,       __A					,       __A		)
     return self.mlm_head(self.dropout(__A		)		)

def _A		(		lowercase			,       lowercase			,       lowercase						):







    """simple docstring"""
    with open(os.path.join(lowercase			,       '''model_args.pkl'''						)			,       '''rb'''						) as f:
        a											=pkl.load(lowercase						)

    # load the original encoder
    a											=OriginalMegaForMaskedLM(**lowercase						).eval()

    # load its weights
    print(
        '''Original Mega encoder:'''			,       original_mlm.mega.load_state_dict(
            torch.load(os.path.join(lowercase			,       '''encoder_weights.pt'''						)			,       map_location='''cpu'''						)						)			,       )
    print(
        '''Original Mega MLM layer:'''			,       original_mlm.mlm_head.load_state_dict(
            torch.load(os.path.join(lowercase			,       '''mlm_head_weights.pt'''						)			,       map_location='''cpu'''						)						)			,       )

    # create a new config from the old one
    a											=MegaConfig(
        num_hidden_layers=mega_original_args['''depth''']			,       vocab_size=mega_original_args['''vocab_size''']			,       hidden_size=mega_original_args['''mega_args'''].encoder_embed_dim			,       shared_representation_size=mega_original_args['''mega_args'''].encoder_z_dim			,       intermediate_size=mega_original_args['''mega_args'''].encoder_hidden_dim			,       ema_projection_size=mega_original_args['''mega_args'''].encoder_n_dim			,       dropout_prob=mega_original_args['''mega_args'''].dropout			,       attention_probs_dropout_prob=mega_original_args['''mega_args'''].attention_dropout			,       hidden_dropout_prob=mega_original_args['''mega_args'''].hidden_dropout			,       activation=mega_original_args['''mega_args'''].activation_fn			,       attention_activation=mega_original_args['''mega_args'''].attention_activation_fn			,       bidirectional=mega_original_args['''mega_args'''].bidirectional			,       use_chunking=mega_original_args['''mega_args'''].encoder_chunk_size > 0			,       chunk_size=mega_original_args['''mega_args'''].encoder_chunk_size			,       truncation=mega_original_args['''mega_args'''].truncation_length			,       normalization_type=mega_original_args['''mega_args'''].normalization_type			,       normalize_before_mega=lowercase			,       norm_affine=lowercase			,       use_feature_dropout=mega_original_args['''mega_args'''].feature_dropout			,       relative_positional_bias=mega_original_args['''mega_args'''].rel_pos_bias			,       max_positions=mega_original_args['''mega_args'''].max_source_positions			,       nffn_hidden_size=mega_original_args['''mega_args'''].encoder_ffn_embed_dim			,       normalize_before_ffn=mega_original_args['''mega_args'''].normalize_before			,       nffn_activation_dropout_prob=0.0			,       add_token_type_embeddings=lowercase			,       add_lm_hidden_dense_layer=lowercase			,       )

    a											=MegaForMaskedLM(lowercase						).eval()

    # the originl checkpoint just uses nn.Embedding for the word embeddings
    # we use a wrapper module for embeddings to add support for positional embeddings
    a											=original_mlm.mega.embedding_layer.weight

    # modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
    # ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
    # also renaming previously confusing parameter names
    a											=original_mlm.mega.encoders.state_dict()
    a											={}
    for module_name in original_state_dict.keys():
        a											=None
        # have to handle gamma, beta, and alpha differently due to their use
        # in multiple modules within the original repository;
        # beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
        # the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
        if "beta" in module_name:
            # EMA sub-layers were always called "move" in the original repo
            if "move.beta" in module_name:
                a											=module_name.replace('''move.beta'''			,       '''ema_gate.ema_expansion_matrix'''						)
            elif "mega_layer.beta" in module_name:
                a											=module_name.replace('''beta'''			,       '''qk_bias'''						)
            else:
                a											=module_name.replace('''beta'''			,       '''b_param'''						)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
        elif "gamma" in module_name:
            if "move.gamma" in module_name:
                a											=module_name.replace('''move.gamma'''			,       '''ema_gate.kernel_projection_matrix'''						)
            elif "mega_layer.gamma" in module_name:
                a											=module_name.replace('''gamma'''			,       '''qk_weight'''						)
            else:
                a											=module_name.replace('''gamma'''			,       '''g_param'''						)
        # alpha is used in EMA and positional bias; renaming to improve readability
        elif "move.alpha" in module_name:
            a											=module_name.replace('''move.alpha'''			,       '''ema_gate.decay_factor'''						)
        # delta is only used in EMA; renaming to improve readability
        elif "move.delta" in module_name:
            a											=module_name.replace('''move.delta'''			,       '''ema_gate.damping_factor'''						)
        # omega is only used in EMA; renaming to improve readability
        elif "omega" in module_name:
            a											=module_name.replace('''move.omega'''			,       '''ema_gate.residual_weight'''						)

        if new_module_name:
            a											=new_module_name

    if len(lowercase						) != 0:
        print(f'''Renaming these keys: {updated_keys.keys()}'''						)
    else:
        print('''No need to rename state dict entries'''						)
    for old, new in updated_keys.items():
        a											=original_state_dict.pop(lowercase						)

    # now attempt to load the state dictionary with updated names
    # note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
    print('''HF Mega encoder:'''			,       hf_mlm.mega.layers.load_state_dict(lowercase						)						)

    # load the MLM head weights directly
    print(
        '''HF Mega MLM layer:'''			,       hf_mlm.mlm_head.load_state_dict(
            torch.load(os.path.join(lowercase			,       '''mlm_head_weights.pt'''						)			,       map_location='''cpu'''						)						)			,       )

    # test on a randomly generated input sequence
    a											=torch.randint(0			,       hf_config.vocab_size			,       size=(4, 2_56)						)
    a											=torch.ones_like(lowercase						)
    # mask a few tokens to make sure masking is applied appropriately :)
    a											=0

    # run forward passes
    a											=original_mlm(lowercase			,       lowercase			,       batch_first=lowercase			,       ignore_mask_value=0						)
    a											=hf_mlm(lowercase			,       lowercase						)[0]

    # print shapes and diff
    print(f'''original output {original_output.shape}'''						)
    print(f'''hf output {hf_output.shape}'''						)
    print(f'''max diff: {(original_output - hf_output).max()}'''						)  # 0.0
    a											=torch.allclose(lowercase			,       lowercase			,       atol=1E-3						)

    if success:
        print('''Yay!'''						)
        hf_mlm.save_pretrained(lowercase						)
    else:
        raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''						)

    if includes_tokenizer:
        print('''Transferring tokenizer'''						)
        a											=AutoTokenizer.from_pretrained(lowercase						)
        tokenizer.save_pretrained(lowercase						)


if __name__ == "__main__":
       lowerCamelCase_  :  List[str]     		=    argparse.ArgumentParser()

       parser.add_argument(
           """--pretrained_checkpoint_path""",
           default=None,
           type=str,
           required=True,
           help="""Point to the directory containing your model weights using the official Mega repo""",
       )

       parser.add_argument(
           """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
       )

       parser.add_argument(
           """--includes_tokenizer""",
           action="""store_true""",
           help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
       )

       lowerCamelCase_  :  List[Any]     		=    parser.parse_args()

       convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)



"""simple docstring"""







import pytest

from datasets.utils.version import Version
@pytest.mark.parametrize(
    '''other, expected_equality'''			,       [
        (Version('''1.0.0'''						), True),
        ('''1.0.0''', True),
        (Version('''2.0.0'''						), False),
        ('''2.0.0''', False),
        ('''1''', False),
        ('''a''', False),
        (1, False),
        (None, False),
    ]			,       )
def _A		(		lowercase			,       lowercase						):







    """simple docstring"""
    a											=Version('''1.0.0'''						)
    assert (version == other) is expected_equality
    assert (version != other) is not expected_equality
    assert (hash(lowercase						) == hash(lowercase						)) is expected_equality