

'''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 	SCREAMING_SNAKE_CASE__			(				nn.Module      ):




    def __init__(					self				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






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







    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__=True				,     lowercase__=0			):






          """simple docstring"""






          if batch_first:
                SCREAMING_SNAKE_CASE_      :			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:
                SCREAMING_SNAKE_CASE_      :			Tuple     =       1 - attention_mask

          # get token embeddings from IDs
          SCREAMING_SNAKE_CASE_      :			List[Any]     =       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:
                SCREAMING_SNAKE_CASE_      :			List[Any]     =       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 	SCREAMING_SNAKE_CASE__			(				nn.Module      ):




    def __init__(					self				,     lowercase__				,     lowercase__				,     lowercase__			):






          """simple docstring"""






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







    def   __lowerCamelCase      (					self				,     lowercase__				,     lowercase__				,     lowercase__=True				,     lowercase__=0			):






          """simple docstring"""






          SCREAMING_SNAKE_CASE_      :			Optional[Any]     =       self.mega(__A				,     __A				,     __A				,     __A			)
          return self.mlm_head(self.dropout(__A			)			)






def 					__lowerCamelCase			(      SCREAMING_SNAKE_CASE_     :	List[Any]  ,  SCREAMING_SNAKE_CASE_     :	List[Any]  ,  SCREAMING_SNAKE_CASE_     :	Union[str, Any]    )							->   Union[str, Any]:
      """simple docstring"""

      with open(os.path.join(_lowercase  ,  "model_args.pkl"    )  ,  "rb"    ) as f:
            SCREAMING_SNAKE_CASE_      :			int     =       pkl.load(_lowercase    )

      # load the original encoder
      SCREAMING_SNAKE_CASE_      :			List[str]     =       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
      SCREAMING_SNAKE_CASE_      :			int     =       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  ,  )

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

            if new_module_name:
                  SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       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():
            SCREAMING_SNAKE_CASE_      :			Dict     =       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
      SCREAMING_SNAKE_CASE_      :			Union[str, Any]     =       torch.randint(0  ,  hf_config.vocab_size  ,  size=(4, 2_5_6)    )
      SCREAMING_SNAKE_CASE_      :			int     =       torch.ones_like(_lowercase    )
      # mask a few tokens to make sure masking is applied appropriately :)
      SCREAMING_SNAKE_CASE_      :			List[str]     =       0

      # run forward passes
      SCREAMING_SNAKE_CASE_      :			Dict     =       original_mlm(_lowercase  ,  _lowercase  ,  batch_first=_lowercase  ,  ignore_mask_value=0    )
      SCREAMING_SNAKE_CASE_      :			Dict     =       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
      SCREAMING_SNAKE_CASE_      :			Any     =       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"    )
            SCREAMING_SNAKE_CASE_      :			str     =       AutoTokenizer.from_pretrained(_lowercase    )
            tokenizer.save_pretrained(_lowercase    )


if __name__ == "__main__":
      snake_case_       =       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',
      )

      snake_case_       =       parser.parse_args()

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









from math import factorial




def    __A    (   _lowercase    ,	_lowercase       ):

					'''simple docstring'''







					return factorial(_lowercase       ) / (factorial(_lowercase       ) * factorial(n - r       ))




def    __A    (   ):

					'''simple docstring'''







					_A									=       0

					for i in range(1    ,	1_01       ):
										for j in range(1    ,	i + 1       ):
															if combinations(_lowercase    ,	_lowercase       ) > 1e6:
																				total += 1
					return total


if __name__ == "__main__":
				print(solution())



