




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       lowercase_		(nn.Module   ):



      def __init__( self  :      List[Any]				,       snake_case__  :      Any				,       snake_case__  :      Optional[Any]				,       snake_case__  :      Optional[Any]     ):

        """simple docstring"""

        super().__init__()
        SCREAMING_SNAKE_CASE_							=       mega_args
        SCREAMING_SNAKE_CASE_							=       nn.Embedding(snake_case__				,       self.mega_args.encoder_embed_dim     )
        SCREAMING_SNAKE_CASE_							=       nn.ModuleList([MegaEncoderLayer(self.mega_args     ) for _ in range(snake_case__     )]     )
        SCREAMING_SNAKE_CASE_							=       depth






      def   __a				( self  :      Optional[int]				,       snake_case__  :      List[str]				,       snake_case__  :      str				,       snake_case__  :      Optional[Any]=True				,       snake_case__  :      Tuple=0     ):

        """simple docstring"""

        if batch_first:
          SCREAMING_SNAKE_CASE_							=       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_							=       1 - attention_mask

        # get token embeddings from IDs
        SCREAMING_SNAKE_CASE_							=       self.embedding_layer(snake_case__     )

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

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



class       lowercase_		(nn.Module   ):



      def __init__( self  :      str				,       snake_case__  :      int				,       snake_case__  :      Any				,       snake_case__  :      List[Any]     ):

        """simple docstring"""

        super().__init__()
        SCREAMING_SNAKE_CASE_							=       MegaLM(snake_case__				,       snake_case__				,       snake_case__     )
        SCREAMING_SNAKE_CASE_							=       nn.Linear(mega_args.encoder_embed_dim				,       snake_case__     )
        SCREAMING_SNAKE_CASE_							=       nn.Dropout(p=0.1     )






      def   __a				( self  :      Any				,       snake_case__  :      str				,       snake_case__  :      Any				,       snake_case__  :      Tuple=True				,       snake_case__  :      Union[str, Any]=0     ):

        """simple docstring"""

        SCREAMING_SNAKE_CASE_							=       self.mega(snake_case__				,       snake_case__				,       snake_case__				,       snake_case__     )
        return self.mlm_head(self.dropout(snake_case__     )     )


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase						,	lowerCAmelCase   )->  List[str]:
  with open(os.path.join(lowerCAmelCase						,	'model_args.pkl'   )						,	'rb'   ) as f:
    SCREAMING_SNAKE_CASE_							=       pkl.load(lowerCAmelCase   )

  # load the original encoder
  SCREAMING_SNAKE_CASE_							=       OriginalMegaForMaskedLM(**lowerCAmelCase   ).eval()

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

  # create a new config from the old one
  SCREAMING_SNAKE_CASE_							=       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=lowerCAmelCase						,	norm_affine=lowerCAmelCase						,	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=lowerCAmelCase						,	add_lm_hidden_dense_layer=lowerCAmelCase						,	)

  SCREAMING_SNAKE_CASE_							=       MegaForMaskedLM(lowerCAmelCase   ).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_							=       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_							=       original_mlm.mega.encoders.state_dict()
  SCREAMING_SNAKE_CASE_							=       {}
  for module_name in original_state_dict.keys():
    SCREAMING_SNAKE_CASE_							=       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_							=       module_name.replace('move.beta'						,	'ema_gate.ema_expansion_matrix'   )
      elif "mega_layer.beta" in module_name:
        SCREAMING_SNAKE_CASE_							=       module_name.replace('beta'						,	'qk_bias'   )
      else:
        SCREAMING_SNAKE_CASE_							=       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_							=       module_name.replace('move.gamma'						,	'ema_gate.kernel_projection_matrix'   )
      elif "mega_layer.gamma" in module_name:
        SCREAMING_SNAKE_CASE_							=       module_name.replace('gamma'						,	'qk_weight'   )
      else:
        SCREAMING_SNAKE_CASE_							=       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_							=       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_							=       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_							=       module_name.replace('move.omega'						,	'ema_gate.residual_weight'   )

    if new_module_name:
      SCREAMING_SNAKE_CASE_							=       new_module_name

  if len(lowerCAmelCase   ) != 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_							=       original_state_dict.pop(lowerCAmelCase   )

  # 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(lowerCAmelCase   )   )

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

  # test on a randomly generated input sequence
  SCREAMING_SNAKE_CASE_							=       torch.randint(0						,	hf_config.vocab_size						,	size=(4, 256)   )
  SCREAMING_SNAKE_CASE_							=       torch.ones_like(lowerCAmelCase   )
  # mask a few tokens to make sure masking is applied appropriately :)
  SCREAMING_SNAKE_CASE_							=       0

  # run forward passes
  SCREAMING_SNAKE_CASE_							=       original_mlm(lowerCAmelCase						,	lowerCAmelCase						,	batch_first=lowerCAmelCase						,	ignore_mask_value=0   )
  SCREAMING_SNAKE_CASE_							=       hf_mlm(lowerCAmelCase						,	lowerCAmelCase   )[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_							=       torch.allclose(lowerCAmelCase						,	lowerCAmelCase						,	atol=1E-3   )

  if success:
    print('Yay!'   )
    hf_mlm.save_pretrained(lowerCAmelCase   )
  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_							=       AutoTokenizer.from_pretrained(lowerCAmelCase   )
    tokenizer.save_pretrained(lowerCAmelCase   )


if __name__ == "__main__":
      SCREAMING_SNAKE_CASE:    Union[str, Any]											=   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''',
      )

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

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





from __future__ import annotations


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Union[str, Any]:
		SCREAMING_SNAKE_CASE_							=       0
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   ) - 1
		while left <= right:
				SCREAMING_SNAKE_CASE_							=       (left + right) // 2
				if left == right:
						if sorted_list[middle] < item:
								SCREAMING_SNAKE_CASE_							=       middle + 1
						break
				elif sorted_list[middle] < item:
						SCREAMING_SNAKE_CASE_							=       middle + 1
				else:
						SCREAMING_SNAKE_CASE_							=       middle - 1
		sorted_list.insert(lowerCAmelCase						,	lowerCAmelCase   )
		return sorted_list


def 				_a       (				lowerCAmelCase						,	lowerCAmelCase   )->  Any:
		SCREAMING_SNAKE_CASE_							=       []
		while left and right:
				if left[0][0] < right[0][0]:
						result.append(left.pop(0   )   )
				else:
						result.append(right.pop(0   )   )
		return result + left + right


def 				_a       (				lowerCAmelCase   )->  List[str]:
		SCREAMING_SNAKE_CASE_							=       len(lowerCAmelCase   )
		if length <= 1:
				return list_ad
		SCREAMING_SNAKE_CASE_							=       length // 2
		return merge(sortlist_ad(list_ad[:middle]   )						,	sortlist_ad(list_ad[middle:]   )   )


def 				_a       (				lowerCAmelCase   )->  list[int]:

		if len(lowerCAmelCase   ) <= 1:
				return collection
		SCREAMING_SNAKE_CASE_							=       []
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(0						,	len(lowerCAmelCase   )						,	2   ):
				if i == len(lowerCAmelCase   ) - 1:
						SCREAMING_SNAKE_CASE_							=       True
				else:
						if collection[i] < collection[i + 1]:
								two_paired_list.append([collection[i], collection[i + 1]]   )
						else:
								two_paired_list.append([collection[i + 1], collection[i]]   )
		SCREAMING_SNAKE_CASE_							=       sortlist_ad(lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       [i[0] for i in sorted_list_ad]
		result.append(sorted_list_ad[-1][1]   )
		if has_last_odd_item:
				SCREAMING_SNAKE_CASE_							=       collection[-1]
				SCREAMING_SNAKE_CASE_							=       binary_search_insertion(lowerCAmelCase						,	lowerCAmelCase   )
		SCREAMING_SNAKE_CASE_							=       False
		for i in range(len(lowerCAmelCase   ) - 1   ):
				if result[i] == collection[-1] and has_last_odd_item:
						SCREAMING_SNAKE_CASE_							=       True
				SCREAMING_SNAKE_CASE_							=       sorted_list_ad[i][1]
				# If last_odd_item is inserted before the item's index,
				# you should forward index one more.
				if is_last_odd_item_inserted_before_this_index:
						SCREAMING_SNAKE_CASE_							=       result[: i + 2] + binary_search_insertion(result[i + 2 :]						,	lowerCAmelCase   )
				else:
						SCREAMING_SNAKE_CASE_							=       result[: i + 1] + binary_search_insertion(result[i + 1 :]						,	lowerCAmelCase   )

		return result


if __name__ == "__main__":
						SCREAMING_SNAKE_CASE:    List[str]											=   input('''Enter numbers separated by a comma:\n''').strip()
						SCREAMING_SNAKE_CASE:    Any											=   [int(item) for item in user_input.split(''',''')]
						print(merge_insertion_sort(unsorted))