



"""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   a__		(						nn.Module     ):


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






   def        lowercase				(							self      :							Optional[Any],     lowerCAmelCase      :							List[str],     lowerCAmelCase      :							Any,     lowerCAmelCase      :							Any=True,     lowerCAmelCase      :							int=0			)    ->       Any:



         if batch_first:
               lowercase  :      List[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:
               lowercase  :      List[Any]					=   1 - attention_mask

         # get token embeddings from IDs
         lowercase  :      Union[str, 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:
               lowercase  :      str					=   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   a__		(						nn.Module     ):


   def __init__(							self      :							str,     lowerCAmelCase      :							List[Any],     lowerCAmelCase      :							Any,     lowerCAmelCase      :							str			)    ->       Optional[int]:
         super().__init__()
         lowercase  :      Union[str, Any]					=   MegaLM(a_,     a_,     a_			)
         lowercase  :      Union[str, Any]					=   nn.Linear(mega_args.encoder_embed_dim,     a_			)
         lowercase  :      Tuple					=   nn.Dropout(p=0.1			)






   def        lowercase				(							self      :							Dict,     lowerCAmelCase      :							Tuple,     lowerCAmelCase      :							str,     lowerCAmelCase      :							int=True,     lowerCAmelCase      :							int=0			)    ->       Tuple:
         lowercase  :      Dict					=   self.mega(a_,     a_,     a_,     a_			)
         return self.mlm_head(self.dropout(a_			)			)




def   lowercase__     (			_UpperCAmelCase	,      _UpperCAmelCase	,      _UpperCAmelCase				)     ->			int:






      '''simple docstring'''


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

      # load the original encoder
      lowercase  :      Union[str, Any]					=   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
      lowercase  :      List[Any]					=   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	,      )

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

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

      # run forward passes
      lowercase  :      Any					=   original_mlm(_UpperCamelCase	,      _UpperCamelCase	,      batch_first=_UpperCamelCase	,      ignore_mask_value=0				)
      lowercase  :      Dict					=   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
      lowercase  :      Tuple					=   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'				)
            lowercase  :      Union[str, Any]					=   AutoTokenizer.from_pretrained(_UpperCamelCase				)
            tokenizer.save_pretrained(_UpperCamelCase				)


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

     _UpperCamelCase:				List[Any]    							=  parser.parse_args()

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





"""simple docstring"""




import re
import tempfile
import unittest
from pathlib import Path

from datasets.utils.metadata import DatasetMetadata


def   lowercase__     (			_UpperCAmelCase				)     ->			str:






      '''simple docstring'''


      lowercase  :      List[str]					=   min(re.search('^ +'	,      _UpperCAmelCase				).end() if t.startswith(' '				) else 0 for t in string.splitlines()				)
      return "\n".join([line[indent_level:] for line in string.splitlines() if indent_level < len(_UpperCAmelCase				)]				)


_UpperCamelCase:				Any    							=  '\\n---\nlanguage:\n- zh\n- en\ntask_ids:\n- sentiment-classification\n---\n# Begin of markdown\n\nSome cool dataset card\n'

_UpperCamelCase:				Dict    							=  '\\n---\n---\n# Begin of markdown\n\nSome cool dataset card\n'


_UpperCamelCase:				Dict    							=  '\\n# Begin of markdown\n\nSome cool dataset card\n'





class   a__		(						unittest.TestCase     ):


   def        lowercase				(							self      :							int			)    ->       Dict:
         with tempfile.TemporaryDirectory() as tmp_dir:
               lowercase  :      Tuple					=   Path(lowerCAmelCase			) / 'README.md'
               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      List[str]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {'language': ['zh', 'en'], 'task_ids': ['sentiment-classification']}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      str					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertDictEqual(lowerCAmelCase,     {}			)

               with open(lowerCAmelCase,     'w+'			) as readme_file:
                     readme_file.write(lowerCAmelCase			)
               lowercase  :      Optional[Any]					=   DatasetMetadata.from_readme(lowerCAmelCase			)
               self.assertEqual(lowerCAmelCase,     {}			)






   def        lowercase				(							self      :							Optional[Any]			)    ->       List[Any]:
         lowercase  :      Union[str, Any]					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      Any					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - question-answering\n            task_ids:\n            - open-domain-qa\n            task_ids:\n            - open-domain-qa\n            '			)
         with self.assertRaises(lowerCAmelCase			):
               DatasetMetadata.from_yaml_string(lowerCAmelCase			)

         lowercase  :      int					=   _dedent(
             '\\n            annotations_creators:\n            - found\n            language_creators:\n            - found\n            language:\n            - en\n            license:\n            - unknown\n            multilinguality:\n            - monolingual\n            pretty_name: Test Dataset\n            size_categories:\n            - 10K<n<100K\n            source_datasets:\n            - extended|other-yahoo-webscope-l6\n            task_categories:\n            - text-classification\n            task_ids:\n            - multi-class-classification\n            paperswithcode_id:\n            - squad\n            configs:\n            - en\n            train-eval-index:\n            - config: en\n              task: text-classification\n              task_id: multi_class_classification\n              splits:\n                train_split: train\n                eval_split: test\n              col_mapping:\n                text: text\n                label: target\n              metrics:\n                - type: accuracy\n                  name: Accuracy\n            extra_gated_prompt: |\n              By clicking on “Access repository” below, you also agree to ImageNet Terms of Access:\n              [RESEARCHER_FULLNAME] (the "Researcher") has requested permission to use the ImageNet database (the "Database") at Princeton University and Stanford University. In exchange for such permission, Researcher hereby agrees to the following terms and conditions:\n              1. Researcher shall use the Database only for non-commercial research and educational purposes.\n            extra_gated_fields:\n              Company: text\n              Country: text\n              I agree to use this model for non-commerical use ONLY: checkbox\n            '			)
         assert DatasetMetadata.from_yaml_string(lowerCAmelCase			)
