code: 
  '''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  lowerCAmelCase__   (       nn.Module			):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : int	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : Union[str, Any]	, lowerCAmelCase_  : Optional[int]    )   ->       str:
          super().__init__()
          snake_case_						       =							mega_args
          snake_case_						       =							nn.Embedding(lowerCAmelCase_	, self.mega_args.encoder_embed_dim    )
          snake_case_						       =							nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(lowerCAmelCase_    )]    )
          snake_case_						       =							depth
  
  
    def 		_lowerCamelCase	(self  : str	, lowerCAmelCase_  : str	, lowerCAmelCase_  : Any	, lowerCAmelCase_  : List[str]=True	, lowerCAmelCase_  : Union[str, Any]=0    )   ->       int:
  
  
  
  
  
  
          if batch_first:
                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:
                snake_case_						       =							1 - attention_mask
  
          # get token embeddings from IDs
          snake_case_						       =							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:
                snake_case_						       =							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  lowerCAmelCase__   (       nn.Module			):
  
  
  
    '''simple docstring'''
  
  
  
  
  
  
    def __init__(self  : Union[str, Any]	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : Optional[Any]	, lowerCAmelCase_  : Dict    )   ->       Optional[Any]:
          super().__init__()
          snake_case_						       =							MegaLM(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )
          snake_case_						       =							nn.Linear(mega_args.encoder_embed_dim	, lowerCAmelCase_    )
          snake_case_						       =							nn.Dropout(p=0.1    )
  
  
    def 		_lowerCamelCase	(self  : List[str]	, lowerCAmelCase_  : Tuple	, lowerCAmelCase_  : int	, lowerCAmelCase_  : Union[str, Any]=True	, lowerCAmelCase_  : List[str]=0    )   ->       Any:
          snake_case_						       =							self.mega(lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_	, lowerCAmelCase_    )
          return self.mlm_head(self.dropout(lowerCAmelCase_    )    )
  
  def   snake_case_       (							_a     :							Optional[Any] ,					_a     :							Any ,					_a     :							List[Any]			):
  
  
        with open(os.path.join(_a ,					"model_args.pkl"			) ,					"rb"			) as f:
              snake_case_						       =							pkl.load(_a			)
  
        # load the original encoder
        snake_case_						       =							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
        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=_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 ,					)
  
        snake_case_						       =							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
        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
        snake_case_						       =							original_mlm.mega.encoders.state_dict()
        snake_case_						       =							{}
        for module_name in original_state_dict.keys():
              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:
                          snake_case_						       =							module_name.replace("move.beta" ,					"ema_gate.ema_expansion_matrix"			)
                    elif "mega_layer.beta" in module_name:
                          snake_case_						       =							module_name.replace("beta" ,					"qk_bias"			)
                    else:
                          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:
                          snake_case_						       =							module_name.replace("move.gamma" ,					"ema_gate.kernel_projection_matrix"			)
                    elif "mega_layer.gamma" in module_name:
                          snake_case_						       =							module_name.replace("gamma" ,					"qk_weight"			)
                    else:
                          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:
                    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:
                    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:
                    snake_case_						       =							module_name.replace("move.omega" ,					"ema_gate.residual_weight"			)
  
              if new_module_name:
                    snake_case_						       =							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():
              snake_case_						       =							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
        snake_case_						       =							torch.randint(0 ,					hf_config.vocab_size ,					size=(4, 256)			)
        snake_case_						       =							torch.ones_like(_a			)
        # mask a few tokens to make sure masking is applied appropriately :)
        snake_case_						       =							0
  
        # run forward passes
        snake_case_						       =							original_mlm(_a ,					_a ,					batch_first=_a ,					ignore_mask_value=0			)
        snake_case_						       =							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
        snake_case_						       =							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"			)
              snake_case_						       =							AutoTokenizer.from_pretrained(_a			)
              tokenizer.save_pretrained(_a			)
  
  
  if __name__ == "__main__":
       A           =   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           =   parser.parse_args()
  
       convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
  
  
style_context: 
  '''simple docstring'''
  
  
  
  
  
  from typing import TYPE_CHECKING
  
  from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available
  
  
  A           =   {
      "configuration_deta": ["DETA_PRETRAINED_CONFIG_ARCHIVE_MAP", "DetaConfig"],
  }
  
  try:
       if not is_vision_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   ["DetaImageProcessor"]
  
  try:
       if not is_torch_available():
            raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
       pass
  else:
       A           =   [
           "DETA_PRETRAINED_MODEL_ARCHIVE_LIST",
           "DetaForObjectDetection",
           "DetaModel",
           "DetaPreTrainedModel",
       ]
  
  
  if TYPE_CHECKING:
       from .configuration_deta import DETA_PRETRAINED_CONFIG_ARCHIVE_MAP, DetaConfig
  
       try:
            if not is_vision_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .image_processing_deta import DetaImageProcessor
  
       try:
            if not is_torch_available():
                 raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
            pass
       else:
            from .modeling_deta import (
                DETA_PRETRAINED_MODEL_ARCHIVE_LIST,
                DetaForObjectDetection,
                DetaModel,
                DetaPreTrainedModel,
            )
  
  else:
       import sys
  
       A           =   _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)
  
  