






"""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       :  Optional[int]    ,   UpperCAmelCase       :  List[Any]    ,   UpperCAmelCase       :  Dict    ,   UpperCAmelCase       :  List[Any]      )  ->					Union[str, Any]:
           super().__init__()
           lowerCAmelCase      :List[str]				  = mega_args
           lowerCAmelCase      :List[str]				  = nn.Embedding(UpperCAmelCase    ,   self.mega_args.encoder_embed_dim      )
           lowerCAmelCase      :List[Any]				  = nn.ModuleList([MegaEncoderLayer(self.mega_args      ) for _ in range(UpperCAmelCase      )]      )
           lowerCAmelCase      :int				  = depth
     def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  Optional[int]    ,   UpperCAmelCase       :  List[str]    ,   UpperCAmelCase       :  int=True    ,   UpperCAmelCase       :  Tuple=0      )  ->					Dict:

           # Mega expects embeddings to be (time, batch, embedding size), but
           # Hugging Face returns tokens as (batch, time)
           if batch_first:
                 lowerCAmelCase      :Any				  = 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:
                 lowerCAmelCase      :str				  = 1 - attention_mask

           # get token embeddings from IDs
           lowerCAmelCase      :Union[str, Any]				  = self.embedding_layer(UpperCAmelCase      )

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

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




class 	__UpperCamelCase    (    nn.Module	):
     def __init__(       self       :  List[Any]    ,   UpperCAmelCase       :  Tuple    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  Optional[int]      )  ->					List[str]:
           super().__init__()
           lowerCAmelCase      :str				  = MegaLM(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
           lowerCAmelCase      :str				  = nn.Linear(mega_args.encoder_embed_dim    ,   UpperCAmelCase      )
           lowerCAmelCase      :int				  = nn.Dropout(p=0.1      )
     def 					UpperCAmelCase__   (       self       :  str    ,   UpperCAmelCase       :  Any    ,   UpperCAmelCase       :  Optional[Any]    ,   UpperCAmelCase       :  List[str]=True    ,   UpperCAmelCase       :  Union[str, Any]=0      )  ->					Any:
           lowerCAmelCase      :Union[str, Any]				  = self.mega(UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase    ,   UpperCAmelCase      )
           return self.mlm_head(self.dropout(UpperCAmelCase      )      )


def 					UpperCAmelCase			(				a__    ,			a__    ,			a__   ):





      '''simple docstring'''






      with open(os.path.join(a__    ,			'model_args.pkl'   )    ,			'rb'   ) as f:
            lowerCAmelCase      :Tuple				  = pkl.load(a__   )

      # load the original encoder
      lowerCAmelCase      :Dict				  = 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
      lowerCAmelCase      :Optional[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=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__    ,			)

      lowerCAmelCase      :Dict				  = 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
      lowerCAmelCase      :Optional[int]				  = 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
      lowerCAmelCase      :List[str]				  = original_mlm.mega.encoders.state_dict()
      lowerCAmelCase      :str				  = {}
      for module_name in original_state_dict.keys():
            lowerCAmelCase      :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:
                        lowerCAmelCase      :Optional[Any]				  = module_name.replace('move.beta'    ,			'ema_gate.ema_expansion_matrix'   )
                  elif "mega_layer.beta" in module_name:
                        lowerCAmelCase      :List[str]				  = module_name.replace('beta'    ,			'qk_bias'   )
                  else:
                        lowerCAmelCase      :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:
                        lowerCAmelCase      :int				  = module_name.replace('move.gamma'    ,			'ema_gate.kernel_projection_matrix'   )
                  elif "mega_layer.gamma" in module_name:
                        lowerCAmelCase      :List[str]				  = module_name.replace('gamma'    ,			'qk_weight'   )
                  else:
                        lowerCAmelCase      :List[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:
                  lowerCAmelCase      :List[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:
                  lowerCAmelCase      :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:
                  lowerCAmelCase      :List[str]				  = module_name.replace('move.omega'    ,			'ema_gate.residual_weight'   )

            if new_module_name:
                  lowerCAmelCase      :Dict				  = 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():
            lowerCAmelCase      :Dict				  = 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
      lowerCAmelCase      :int				  = torch.randint(0    ,			hf_config.vocab_size    ,			size=(4, 2_56)   )
      lowerCAmelCase      :str				  = torch.ones_like(a__   )
      # mask a few tokens to make sure masking is applied appropriately :)
      lowerCAmelCase      :Optional[int]				  = 0

      # run forward passes
      lowerCAmelCase      :List[Any]				  = original_mlm(a__    ,			a__    ,			batch_first=a__    ,			ignore_mask_value=0   )
      lowerCAmelCase      :List[str]				  = 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
      lowerCAmelCase      :Optional[Any]				  = 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'   )
            lowerCAmelCase      :Any				  = AutoTokenizer.from_pretrained(a__   )
            tokenizer.save_pretrained(a__   )


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

  __SCREAMING_SNAKE_CASE      =      parser.parse_args()

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







"""simple docstring"""


import os
from argparse import ArgumentParser
from pathlib import Path
from shutil import copyfile, rmtree
from typing import Generator

import datasets.config
from datasets.builder import DatasetBuilder
from datasets.commands import BaseDatasetsCLICommand
from datasets.download.download_manager import DownloadMode
from datasets.load import dataset_module_factory, import_main_class
from datasets.utils.filelock import logger as fl_logger
from datasets.utils.info_utils import VerificationMode
from datasets.utils.logging import ERROR, get_logger


__SCREAMING_SNAKE_CASE      =      get_logger(__name__)


def 					UpperCAmelCase			(				a__   ):





						'''simple docstring'''






						return TestCommand(
						    args.dataset    ,			args.name    ,			args.cache_dir    ,			args.data_dir    ,			args.all_configs    ,			args.save_info or args.save_infos    ,			args.ignore_verifications    ,			args.force_redownload    ,			args.clear_cache    ,			)







class 	__UpperCamelCase    (    UpperCamelCase	):
					lowercase_						:							str			=     False  # to tell pytest it's not a test class
					@staticmethod
					def 					UpperCAmelCase__   (       UpperCAmelCase       :  ArgumentParser      )  ->					Optional[int]:
											lowerCAmelCase      :Union[str, Any]				  = parser.add_parser('test'    ,   help='Test dataset implementation.'      )
											test_parser.add_argument('--name'    ,   type=UpperCAmelCase    ,   default=UpperCAmelCase    ,   help='Dataset processing name'      )
											test_parser.add_argument(
											    '--cache_dir'    ,   type=UpperCAmelCase    ,   default=UpperCAmelCase    ,   help='Cache directory where the datasets are stored.'    ,   )
											test_parser.add_argument(
											    '--data_dir'    ,   type=UpperCAmelCase    ,   default=UpperCAmelCase    ,   help='Can be used to specify a manual directory to get the files from.'    ,   )
											test_parser.add_argument('--all_configs'    ,   action='store_true'    ,   help='Test all dataset configurations'      )
											test_parser.add_argument(
											    '--save_info'    ,   action='store_true'    ,   help='Save the dataset infos in the dataset card (README.md)'      )
											test_parser.add_argument(
											    '--ignore_verifications'    ,   action='store_true'    ,   help='Run the test without checksums and splits checks.'    ,   )
											test_parser.add_argument('--force_redownload'    ,   action='store_true'    ,   help='Force dataset redownload'      )
											test_parser.add_argument(
											    '--clear_cache'    ,   action='store_true'    ,   help='Remove downloaded files and cached datasets after each config test'    ,   )
											# aliases
											test_parser.add_argument('--save_infos'    ,   action='store_true'    ,   help='alias to save_info'      )
											test_parser.add_argument('dataset'    ,   type=UpperCAmelCase    ,   help='Name of the dataset to download'      )
											test_parser.set_defaults(func=_test_command_factory      )
					def __init__(       self       :  List[Any]    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  str    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   UpperCAmelCase       :  bool    ,   )  ->					Any:
											lowerCAmelCase      :int				  = dataset
											lowerCAmelCase      :Tuple				  = name
											lowerCAmelCase      :Tuple				  = cache_dir
											lowerCAmelCase      :List[Any]				  = data_dir
											lowerCAmelCase      :int				  = all_configs
											lowerCAmelCase      :Dict				  = save_infos
											lowerCAmelCase      :Union[str, Any]				  = ignore_verifications
											lowerCAmelCase      :int				  = force_redownload
											lowerCAmelCase      :Union[str, Any]				  = clear_cache
											if clear_cache and not cache_dir:
																	print(
																	    'When --clear_cache is used, specifying a cache directory is mandatory.\n'
																	    'The \'download\' folder of the cache directory and the dataset builder cache will be deleted after each configuration test.\n'
																	    'Please provide a --cache_dir that will be used to test the dataset script.'      )
																	exit(1      )
											if save_infos:
																	lowerCAmelCase      :List[str]				  = True
					def 					UpperCAmelCase__   (       self       :  Optional[Any]      )  ->					int:
											fl_logger().setLevel(UpperCAmelCase      )
											if self._name is not None and self._all_configs:
																	print('Both parameters `config` and `all_configs` can\'t be used at once.'      )
																	exit(1      )
											lowerCAmelCase  ,   lowerCAmelCase      :Dict				  = self._dataset, self._name
											lowerCAmelCase      :int				  = dataset_module_factory(UpperCAmelCase      )
											lowerCAmelCase      :int				  = import_main_class(module.module_path      )
											lowerCAmelCase      :Dict				  = len(builder_cls.BUILDER_CONFIGS      ) if self._all_configs and builder_cls.BUILDER_CONFIGS else 1

											def get_builders() -> Generator[DatasetBuilder, None, None]:
																	if self._all_configs and builder_cls.BUILDER_CONFIGS:
																							for i, config in enumerate(builder_cls.BUILDER_CONFIGS      ):
																													if "config_name" in module.builder_kwargs:
																																			yield builder_cls(
																																			    cache_dir=self._cache_dir    ,   data_dir=self._data_dir    ,   **module.builder_kwargs    ,   )
																													else:
																																			yield builder_cls(
																																			    config_name=config.name    ,   cache_dir=self._cache_dir    ,   data_dir=self._data_dir    ,   **module.builder_kwargs    ,   )
																	else:
																							if "config_name" in module.builder_kwargs:
																													yield builder_cls(cache_dir=self._cache_dir    ,   data_dir=self._data_dir    ,   **module.builder_kwargs      )
																							else:
																													yield builder_cls(
																													    config_name=UpperCAmelCase    ,   cache_dir=self._cache_dir    ,   data_dir=self._data_dir    ,   **module.builder_kwargs    ,   )

											for j, builder in enumerate(get_builders()      ):
																	print(f"""Testing builder '{builder.config.name}' ({j + 1}/{n_builders})"""      )
																	lowerCAmelCase      :int				  = os.path.exists(
																	    os.path.join(builder.get_imported_module_dir()    ,   datasets.config.DATASETDICT_INFOS_FILENAME      )      )  # record checksums only if we need to update a (deprecated) dataset_infos.json
																	builder.download_and_prepare(
																	    download_mode=DownloadMode.REUSE_CACHE_IF_EXISTS
																	    if not self._force_redownload
																	    else DownloadMode.FORCE_REDOWNLOAD    ,   verification_mode=VerificationMode.NO_CHECKS
																	    if self._ignore_verifications
																	    else VerificationMode.ALL_CHECKS    ,   try_from_hf_gcs=UpperCAmelCase    ,   )
																	builder.as_dataset()
																	if self._save_infos:
																							builder._save_infos()

																	# If save_infos=True, the dataset card (README.md) is created next to the loaded module file.
																	# The dataset_infos are saved in the YAML part of the README.md

																	# Let's move it to the original directory of the dataset script, to allow the user to
																	# upload them on S3 at the same time afterwards.
																	if self._save_infos:
																							lowerCAmelCase      :Optional[Any]				  = os.path.join(builder_cls.get_imported_module_dir()    ,   'README.md'      )
																							lowerCAmelCase      :List[str]				  = Path(UpperCAmelCase      ).name + '.py'
																							lowerCAmelCase      :Dict				  = os.path.join(UpperCAmelCase    ,   UpperCAmelCase      )
																							if os.path.isfile(UpperCAmelCase      ):
																													lowerCAmelCase      :Any				  = os.path.dirname(UpperCAmelCase      )
																							elif os.path.isfile(UpperCAmelCase      ):
																													lowerCAmelCase      :Any				  = path
																							elif os.path.isdir(UpperCAmelCase      ):  # for local directories containing only data files
																													lowerCAmelCase      :Dict				  = path
																							else:  # in case of a remote dataset
																													lowerCAmelCase      :int				  = None
																													print(f"""Dataset card saved at {dataset_readme_path}"""      )

																							# Move dataset_info back to the user
																							if dataset_dir is not None:
																													lowerCAmelCase      :Any				  = os.path.join(UpperCAmelCase    ,   'README.md'      )
																													copyfile(UpperCAmelCase    ,   UpperCAmelCase      )
																													print(f"""Dataset card saved at {user_dataset_readme_path}"""      )

            # If clear_cache=True, the download folder and the dataset builder cache directory are deleted
																	if self._clear_cache:
																							if os.path.isdir(builder._cache_dir      ):
																													logger.warning(f"""Clearing cache at {builder._cache_dir}"""      )
																													rmtree(builder._cache_dir      )
																							lowerCAmelCase      :str				  = os.path.join(self._cache_dir    ,   datasets.config.DOWNLOADED_DATASETS_DIR      )
																							if os.path.isdir(UpperCAmelCase      ):
																													logger.warning(f"""Clearing cache at {download_dir}"""      )
																													rmtree(UpperCAmelCase      )

											print('Test successful.'      )