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  UpperCAmelCase (       nn.Module):
  
   def __init__( self :	str,					a_ :	Optional[Any],					a_ :	Tuple,					a_ :	Optional[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().__init__()
      UpperCamelCase__ 						=						mega_args
      UpperCamelCase__ 						=						nn.Embedding(a_,					self.mega_args.encoder_embed_dim					)
      UpperCamelCase__ 						=						nn.ModuleList([MegaEncoderLayer(self.mega_args					) for _ in range(a_					)]					)
      UpperCamelCase__ 						=						depth
  
   def 		lowercase_						( self :	Optional[Any],					a_ :	List[str],					a_ :	Any,					a_ :	Any=True,					a_ :	int=0					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      if batch_first:
         UpperCamelCase__ 						=						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__ 						=						1 - attention_mask
  
      # get token embeddings from IDs
      UpperCamelCase__ 						=						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:
         UpperCamelCase__ 						=						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  UpperCAmelCase (       nn.Module):
  
   def __init__( self :	str,					a_ :	List[Any],					a_ :	Any,					a_ :	str					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      super().__init__()
      UpperCamelCase__ 						=						MegaLM(a_,					a_,					a_					)
      UpperCamelCase__ 						=						nn.Linear(mega_args.encoder_embed_dim,					a_					)
      UpperCamelCase__ 						=						nn.Dropout(p=0.1					)
  
   def 		lowercase_						( self :	Dict,					a_ :	Tuple,					a_ :	str,					a_ :	int=True,					a_ :	int=0					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						self.mega(a_,					a_,					a_,					a_					)
      return self.mlm_head(self.dropout(a_					)					)
  
  
  
  
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[int]    ,		_UpperCamelCase      :				List[str]    ,		_UpperCamelCase      :				List[Any]   )		->					int:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     with open(os.path.join(_UpperCamelCase    ,		"model_args.pkl"   )    ,		"rb"   ) as f:
        UpperCamelCase__ 						=						pkl.load(_UpperCamelCase   )
  
     # load the original encoder
     UpperCamelCase__ 						=						OriginalMegaForMaskedLM(**_UpperCamelCase   ).eval()
  
     # load its weights
     print(
         "Original Mega encoder:"    ,		original_mlm.mega.load_state_dict(
             torch.load(os.path.join(_UpperCamelCase    ,		"encoder_weights.pt"   )    ,		map_location="cpu"   )   )    ,		)
     print(
         "Original Mega MLM layer:"    ,		original_mlm.mlm_head.load_state_dict(
             torch.load(os.path.join(_UpperCamelCase    ,		"mlm_head_weights.pt"   )    ,		map_location="cpu"   )   )    ,		)
  
     # create a new config from the old one
     UpperCamelCase__ 						=						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=_UpperCamelCase    ,		norm_affine=_UpperCamelCase    ,		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=_UpperCamelCase    ,		add_lm_hidden_dense_layer=_UpperCamelCase    ,		)
  
     UpperCamelCase__ 						=						MegaForMaskedLM(_UpperCamelCase   ).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__ 						=						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__ 						=						original_mlm.mega.encoders.state_dict()
     UpperCamelCase__ 						=						{}
     for module_name in original_state_dict.keys():
        UpperCamelCase__ 						=						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__ 						=						module_name.replace("move.beta"    ,		"ema_gate.ema_expansion_matrix"   )
           elif "mega_layer.beta" in module_name:
              UpperCamelCase__ 						=						module_name.replace("beta"    ,		"qk_bias"   )
           else:
              UpperCamelCase__ 						=						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__ 						=						module_name.replace("move.gamma"    ,		"ema_gate.kernel_projection_matrix"   )
           elif "mega_layer.gamma" in module_name:
              UpperCamelCase__ 						=						module_name.replace("gamma"    ,		"qk_weight"   )
           else:
              UpperCamelCase__ 						=						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__ 						=						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__ 						=						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__ 						=						module_name.replace("move.omega"    ,		"ema_gate.residual_weight"   )
  
        if new_module_name:
           UpperCamelCase__ 						=						new_module_name
  
     if len(_UpperCamelCase   ) != 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__ 						=						original_state_dict.pop(_UpperCamelCase   )
  
     # 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(_UpperCamelCase   )   )
  
     # load the MLM head weights directly
     print(
         "HF Mega MLM layer:"    ,		hf_mlm.mlm_head.load_state_dict(
             torch.load(os.path.join(_UpperCamelCase    ,		"mlm_head_weights.pt"   )    ,		map_location="cpu"   )   )    ,		)
  
     # test on a randomly generated input sequence
     UpperCamelCase__ 						=						torch.randint(0    ,		hf_config.vocab_size    ,		size=(4, 2_56)   )
     UpperCamelCase__ 						=						torch.ones_like(_UpperCamelCase   )
     # mask a few tokens to make sure masking is applied appropriately :)
     UpperCamelCase__ 						=						0
  
     # run forward passes
     UpperCamelCase__ 						=						original_mlm(_UpperCamelCase    ,		_UpperCamelCase    ,		batch_first=_UpperCamelCase    ,		ignore_mask_value=0   )
     UpperCamelCase__ 						=						hf_mlm(_UpperCamelCase    ,		_UpperCamelCase   )[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__ 						=						torch.allclose(_UpperCamelCase    ,		_UpperCamelCase    ,		atol=1e-3   )
  
     if success:
        print("Yay!"   )
        hf_mlm.save_pretrained(_UpperCamelCase   )
     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__ 						=						AutoTokenizer.from_pretrained(_UpperCamelCase   )
        tokenizer.save_pretrained(_UpperCamelCase   )
  
  
  if __name__ == "__main__":
         __lowercase:		Dict							=     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",
         )
  
         __lowercase:		List[Any]							=     parser.parse_args()
  
         convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)
style_context: 
  
  
  '''simple docstring'''
  
  import os
  import sys
  from pathlib import Path
  
  import torch
  
  from transformers.testing_utils import TestCasePlus, execute_subprocess_async, require_torch_multi_gpu
  from utils import load_json
  
  
  __lowercase:		Union[str, Any]							=     torch.cuda.is_available()
  __lowercase:		str							=     [" Sam ate lunch today.", "Sams lunch ingredients."]
  __lowercase:		Optional[Any]							=     ["A very interesting story about what I ate for lunch.", "Avocado, celery, turkey, coffee"]
  __lowercase:		Optional[int]							=     {
      "max_tokens_per_batch": None,
      "supervise_forward": True,
      "normalize_hidden": True,
      "label_smoothing": 0.2,
      "eval_max_gen_length": None,
      "eval_beams": 1,
      "val_metric": "loss",
      "save_top_k": 1,
      "adafactor": True,
      "early_stopping_patience": 2,
      "logger_name": "default",
      "length_penalty": 0.5,
      "cache_dir": "",
      "task": "summarization",
      "num_workers": 2,
      "alpha_hid": 0,
      "freeze_embeds": True,
      "enc_only": False,
      "tgt_suffix": "",
      "resume_from_checkpoint": None,
      "sortish_sampler": True,
      "student_decoder_layers": 1,
      "val_check_interval": 1.0,
      "output_dir": "",
      "fp16": False,  # TODO(SS): set this to CUDA_AVAILABLE if ci installs apex or start using native amp
      "no_teacher": False,
      "fp16_opt_level": "O1",
      "gpus": 1 if CUDA_AVAILABLE else 0,
      "n_tpu_cores": 0,
      "max_grad_norm": 1.0,
      "do_train": True,
      "do_predict": True,
      "accumulate_grad_batches": 1,
      "server_ip": "",
      "server_port": "",
      "seed": 42,
      "model_name_or_path": "sshleifer/bart-tiny-random",
      "config_name": "",
      "tokenizer_name": "facebook/bart-large",
      "do_lower_case": False,
      "learning_rate": 0.3,
      "lr_scheduler": "linear",
      "weight_decay": 0.0,
      "adam_epsilon": 1e-0_8,
      "warmup_steps": 0,
      "max_epochs": 1,
      "train_batch_size": 2,
      "eval_batch_size": 2,
      "max_source_length": 12,
      "max_target_length": 12,
      "val_max_target_length": 12,
      "test_max_target_length": 12,
      "fast_dev_run": False,
      "no_cache": False,
      "n_train": -1,
      "n_val": -1,
      "n_test": -1,
      "student_encoder_layers": 1,
      "freeze_encoder": False,
      "auto_scale_batch_size": False,
      "overwrite_output_dir": False,
      "student": None,
  }
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Path    ,		_UpperCamelCase      :				list   )		->					Optional[Any]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     UpperCamelCase__ 						=						"\n".join(_UpperCamelCase   )
     Path(_UpperCamelCase   ).open("w"   ).writelines(_UpperCamelCase   )
  
  
  
  def        SCREAMING_SNAKE_CASE__(		_UpperCamelCase      :				Optional[Any]   )		->					List[str]:
  
  
  
  
  
     '''simple docstring'''
  
  
  
  
  
  
  
     for split in ["train", "val", "test"]:
        _dump_articles(os.path.join(_UpperCamelCase    ,		F'{split}.source'   )    ,		_UpperCamelCase   )
        _dump_articles(os.path.join(_UpperCamelCase    ,		F'{split}.target'   )    ,		_UpperCamelCase   )
     return tmp_dir
  
  
  
  
  
  
  
  class  UpperCAmelCase (       SCREAMING_SNAKE_CASE__):
  
   @classmethod
   def 		lowercase_						( cls :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      return cls
  
   @require_torch_multi_gpu
   def 		lowercase_						( self :	List[Any]					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						{
          "no_teacher": True,
          "freeze_encoder": True,
          "gpus": 2,
          "overwrite_output_dir": True,
          "sortish_sampler": True,
      }
      self._test_distiller_cli_fork(a_,					check_contents=a_					)
  
   def 		lowercase_						( self :	Tuple,					a_ :	Optional[Any],					a_ :	Optional[int]=True					):
  
  
  
  
  
      """simple docstring"""
  
  
  
      UpperCamelCase__ 						=						{
          "label_smoothing": 0.0,
          "early_stopping_patience": -1,
          "train_batch_size": 1,
          "eval_batch_size": 2,
          "max_epochs": 2,
          "alpha_mlm": 0.2,
          "alpha_ce": 0.8,
          "do_predict": True,
          "model_name_or_path": "sshleifer/tinier_bart",
          "teacher": CHEAP_ARGS["model_name_or_path"],
          "val_check_interval": 0.5,
      }
      default_updates.update(a_					)
      UpperCamelCase__ 						=						CHEAP_ARGS.copy()
      UpperCamelCase__ 						=						make_test_data_dir(tmp_dir=self.get_auto_remove_tmp_dir()					)
      UpperCamelCase__ 						=						self.get_auto_remove_tmp_dir()
      args_d.update(data_dir=a_,					output_dir=a_,					**a_					)
  
      def convert(a_ :	str,					a_ :	Any					):
         if k in ["tgt_suffix", "server_ip", "server_port", "out", "n_tpu_cores"]:
            return ""
         if v is False or v is None:
            return ""
         if v is True:  # or len(str(v))==0:
            return f'--{k}'
         return f'--{k}={v}'
  
      UpperCamelCase__ 						=						[x for x in (convert(a_,					a_					) for k, v in args_d.items()) if len(a_					)]
      UpperCamelCase__ 						=						[sys.executable, f'{self.test_file_dir}/distillation.py'] + cli_args
      execute_subprocess_async(a_,					env=self.get_env()					)
  
      UpperCamelCase__ 						=						os.listdir(a_					)
      UpperCamelCase__ 						=						{os.path.basename(a_					) for p in contents}
      UpperCamelCase__ 						=						[p for p in contents if p.endswith("ckpt"					)]
      assert len(a_					) > 0
  
      self.assertIn("test_generations.txt",					a_					)
      self.assertIn("test_results.txt",					a_					)
  
      # get the following from the module, (we don't have access to `model` here)
      UpperCamelCase__ 						=						os.path.join(a_,					"metrics.json"					)
      UpperCamelCase__ 						=						"rouge2"
  
      UpperCamelCase__ 						=						load_json(a_					)
      # {'test': [{'test_avg_loss': 10.63731575012207, 'test_avg_rouge1': 0.0, 'test_avg_rouge2': 0.0, 'test_avg_rougeL': 0.0, 'test_avg_gen_time': 0.1822289228439331, 'test_avg_gen_len': 142.0, 'step_count': 1}]}
      print(a_					)
      UpperCamelCase__ 						=						metrics["val"][-1]
      self.assertGreaterEqual(last_step_stats["val_avg_gen_time"],					0.01					)
      self.assertIsInstance(last_step_stats[f'val_avg_{val_metric}'],					a_					)
      self.assertEqual(len(metrics["test"]					),					1					)
      UpperCamelCase__ 						=						int(args_d["max_epochs"] * (1 / args_d["val_check_interval"]) / 2 + 1					)
      self.assertEqual(len(metrics["val"]					),					a_					)