





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:	List[str]					,  __lowerCamelCase:	Union[str, Any]					,  __lowerCamelCase:	Dict					,  __lowerCamelCase:	Optional[int]			):
          '''simple docstring'''


          super().__init__()
          UpperCamelCase__:					Any												=      mega_args
          UpperCamelCase__:					Optional[Any]												=      nn.Embedding(__lowerCamelCase					,  self.mega_args.encoder_embed_dim			)
          UpperCamelCase__:					Any												=      nn.ModuleList([MegaEncoderLayer(self.mega_args			) for _ in range(__lowerCamelCase			)]			)
          UpperCamelCase__:					Union[str, Any]												=      depth






      def 	UpperCAmelCase_   (						self:	Dict					,  __lowerCamelCase:	int					,  __lowerCamelCase:	List[Any]					,  __lowerCamelCase:	Dict=True					,  __lowerCamelCase:	Dict=0			):
          '''simple docstring'''


          if batch_first:
              UpperCamelCase__:					Optional[int]												=      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:
              UpperCamelCase__:					Union[str, Any]												=      1 - attention_mask

          # get token embeddings from IDs
          UpperCamelCase__:					List[str]												=      self.embedding_layer(__lowerCamelCase			)

          # pass through the Mega layers
          # input is (time, batch, encoder dim) and output is the same
          for encoder in self.encoders:
              UpperCamelCase__:					List[Any]												=      encoder(__lowerCamelCase					,  __lowerCamelCase			)

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




class    _a  (  nn.Module):

      """simple docstring"""


      def __init__(						self:	Dict					,  __lowerCamelCase:	Any					,  __lowerCamelCase:	Tuple					,  __lowerCamelCase:	Union[str, Any]			):
          '''simple docstring'''


          super().__init__()
          UpperCamelCase__:					int												=      MegaLM(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          UpperCamelCase__:					int												=      nn.Linear(mega_args.encoder_embed_dim					,  __lowerCamelCase			)
          UpperCamelCase__:					Optional[int]												=      nn.Dropout(p=0.1			)






      def 	UpperCAmelCase_   (						self:	str					,  __lowerCamelCase:	Dict					,  __lowerCamelCase:	List[str]					,  __lowerCamelCase:	Tuple=True					,  __lowerCamelCase:	Dict=0			):
          '''simple docstring'''


          UpperCamelCase__:					Union[str, Any]												=      self.mega(__lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase					,  __lowerCamelCase			)
          return self.mlm_head(self.dropout(__lowerCamelCase			)			)



def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    with open(os.path.join(A_  ,"model_args.pkl")  ,"rb") as f:
        UpperCamelCase__:					Dict												=      pkl.load(A_)

    # load the original encoder
    UpperCamelCase__:					Any												=      OriginalMegaForMaskedLM(**A_).eval()

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

    # create a new config from the old one
    UpperCamelCase__:					List[Any]												=      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=A_  ,norm_affine=A_  ,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=A_  ,add_lm_hidden_dense_layer=A_  ,)

    UpperCamelCase__:					str												=      MegaForMaskedLM(A_).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
    UpperCamelCase__:					Tuple												=      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
    UpperCamelCase__:					Any												=      original_mlm.mega.encoders.state_dict()
    UpperCamelCase__:					List[str]												=      {}
    for module_name in original_state_dict.keys():
        UpperCamelCase__:					List[str]												=      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:
                UpperCamelCase__:					str												=      module_name.replace("move.beta"  ,"ema_gate.ema_expansion_matrix")
            elif "mega_layer.beta" in module_name:
                UpperCamelCase__:					Union[str, Any]												=      module_name.replace("beta"  ,"qk_bias")
            else:
                UpperCamelCase__:					List[Any]												=      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:
                UpperCamelCase__:					Optional[int]												=      module_name.replace("move.gamma"  ,"ema_gate.kernel_projection_matrix")
            elif "mega_layer.gamma" in module_name:
                UpperCamelCase__:					Dict												=      module_name.replace("gamma"  ,"qk_weight")
            else:
                UpperCamelCase__:					Any												=      module_name.replace("gamma"  ,"g_param")
        # alpha is used in EMA and positional bias; renaming to improve readability
        elif "move.alpha" in module_name:
            UpperCamelCase__:					Tuple												=      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:
            UpperCamelCase__:					Any												=      module_name.replace("move.delta"  ,"ema_gate.damping_factor")
        # omega is only used in EMA; renaming to improve readability
        elif "omega" in module_name:
            UpperCamelCase__:					str												=      module_name.replace("move.omega"  ,"ema_gate.residual_weight")

        if new_module_name:
            UpperCamelCase__:					int												=      new_module_name

    if len(A_) != 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():
        UpperCamelCase__:					Tuple												=      original_state_dict.pop(A_)

    # 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(A_))

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

    # test on a randomly generated input sequence
    UpperCamelCase__:					Optional[Any]												=      torch.randint(0  ,hf_config.vocab_size  ,size=(4, 2_56))
    UpperCamelCase__:					Any												=      torch.ones_like(A_)
    # mask a few tokens to make sure masking is applied appropriately :)
    UpperCamelCase__:					Union[str, Any]												=      0

    # run forward passes
    UpperCamelCase__:					Any												=      original_mlm(A_  ,A_  ,batch_first=A_  ,ignore_mask_value=0)
    UpperCamelCase__:					int												=      hf_mlm(A_  ,A_)[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
    UpperCamelCase__:					str												=      torch.allclose(A_  ,A_  ,atol=1e-3)

    if success:
        print("Yay!")
        hf_mlm.save_pretrained(A_)
    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")
        UpperCamelCase__:					Union[str, Any]												=      AutoTokenizer.from_pretrained(A_)
        tokenizer.save_pretrained(A_)


if __name__ == "__main__":
   A__:  int       					=					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''',
   )

   A__:  Optional[Any]       					=					parser.parse_args()

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








def 							lowerCAmelCase_				(    A_  ,A_  ,A_):
    UpperCamelCase__:					Optional[int]												=      [1 / first_term]
    UpperCamelCase__:					int												=      1 / first_term
    for _ in range(number_of_terms - 1):
        first_term += common_difference
        arithmetic_progression.append(A_)
    UpperCamelCase__:					str												=      [1 / step for step in arithmetic_progression]
    return sum(A_)


if __name__ == "__main__":
   import doctest

   doctest.testmod()
   print(sum_of_harmonic_progression(1 / 2, 2, 2))

