



'''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				:     Union[str, Any]      ,     _a				:     Any      ,     _a				:     Optional[Any]      ,     _a				:     Union[str, Any]       ) -> Optional[Any]:
    super().__init__()
    __lowerCamelCase	:	int   	=			mega_args
    __lowerCamelCase	:	Tuple   	=			nn.Embedding(_a      ,     self.mega_args.encoder_embed_dim       )
    __lowerCamelCase	:	List[str]   	=			nn.ModuleList([MegaEncoderLayer(self.mega_args       ) for _ in range(_a       )]       )
    __lowerCamelCase	:	Optional[int]   	=			depth




  def 						_lowercase					(			self				:     Tuple      ,     _a				:     Tuple      ,     _a				:     Union[str, Any]      ,     _a				:     int=True      ,     _a				:     Optional[Any]=0       ) -> Union[str, Any]:

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

    # get token embeddings from IDs
    __lowerCamelCase	:	Dict   	=			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:
      __lowerCamelCase	:	Union[str, Any]   	=			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  lowerCamelCase_   (    nn.Module					):
  """simple docstring"""




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




  def 						_lowercase					(			self				:     Any      ,     _a				:     Tuple      ,     _a				:     Union[str, Any]      ,     _a				:     str=True      ,     _a				:     Optional[int]=0       ) -> Optional[Any]:
    __lowerCamelCase	:	Any   	=			self.mega(_a      ,     _a      ,     _a      ,     _a       )
    return self.mlm_head(self.dropout(_a       )       )




def 	a_					(							_lowerCAmelCase	,_lowerCAmelCase	,_lowerCAmelCase	)				->				Optional[Any]:
  with open(os.path.join(_lowerCAmelCase	,'model_args.pkl'	)	,'rb'	) as f:
    __lowerCamelCase	:	Tuple   	=			pkl.load(_lowerCAmelCase	)

  # load the original encoder
  __lowerCamelCase	:	List[str]   	=			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
  __lowerCamelCase	:	Union[str, 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=_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	,)

  __lowerCamelCase	:	Union[str, Any]   	=			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
  __lowerCamelCase	:	List[Any]   	=			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	:	Dict   	=			original_mlm.mega.encoders.state_dict()
  __lowerCamelCase	:	str   	=			{}
  for module_name in original_state_dict.keys():
    __lowerCamelCase	:	List[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	:	Union[str, 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	:	Dict   	=			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	:	Optional[int]   	=			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	:	Optional[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	:	Dict   	=			module_name.replace('move.omega'	,'ema_gate.residual_weight'	)

    if new_module_name:
      __lowerCamelCase	:	List[str]   	=			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():
    __lowerCamelCase	:	Any   	=			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
  __lowerCamelCase	:	str   	=			torch.randint(0	,hf_config.vocab_size	,size=(4, 256)	)
  __lowerCamelCase	:	Union[str, Any]   	=			torch.ones_like(_lowerCAmelCase	)
  # mask a few tokens to make sure masking is applied appropriately :)
  __lowerCamelCase	:	List[Any]   	=			0

  # run forward passes
  __lowerCamelCase	:	Optional[int]   	=			original_mlm(_lowerCAmelCase	,_lowerCAmelCase	,batch_first=_lowerCAmelCase	,ignore_mask_value=0	)
  __lowerCamelCase	:	int   	=			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
  __lowerCamelCase	:	Tuple   	=			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'	)
    __lowerCamelCase	:	Tuple   	=			AutoTokenizer.from_pretrained(_lowerCAmelCase	)
    tokenizer.save_pretrained(_lowerCAmelCase	)


if __name__ == "__main__":
   _UpperCamelCase							    =						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							    =						parser.parse_args()

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











'''simple docstring'''





import unittest

from diffusers import __version__
from diffusers.utils import deprecate






class  lowerCamelCase_   (    unittest.TestCase					):
  """simple docstring"""




  a_							 =""".""".join([str(int(__version__.split("""."""					)[0]					) + 1					)] + __version__.split("""."""					)[1:]					)
  a_							 ="""0.0.1"""




  def 						_lowercase					(			self				:     Optional[Any]       ) -> int:
    __lowerCamelCase	:	Any   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[int]   	=			deprecate('deprecated_arg'      ,     self.higher_version      ,     'message'      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Optional[int]:
    __lowerCamelCase	:	int   	=			{'deprecated_arg': 4}

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	Optional[Any]   	=			deprecate(('deprecated_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert output == 4
    assert (
        str(warning.warning       )
        == f'The `deprecated_arg` argument is deprecated and will be removed in version {self.higher_version}.'
        " message"
    )




  def 						_lowercase					(			self				:     int       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg_1': 4, 'deprecated_arg_2': 8}
    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	Any   	=			deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     take_from=_a      ,     )
    assert output_a == 4
    assert output_a == 8
    assert (
        str(warning.warnings[0].message       )
        == "The `deprecated_arg_1` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == "The `deprecated_arg_2` argument is deprecated and will be removed in version"
        f' {self.higher_version}. Hey'
    )




  def 						_lowercase					(			self				:     str       ) -> Optional[Any]:
    __lowerCamelCase	:	Optional[int]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.higher_version, 'message')      ,     take_from=_a       )

    assert "test_deprecate_function_incorrect_arg in" in str(error.exception       )
    assert "line" in str(error.exception       )
    assert "got an unexpected keyword argument `deprecated_arg`" in str(error.exception       )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'message')       )

    assert (
        str(warning.warning       )
        == f'`deprecated_arg` is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Optional[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(
          ('deprecated_arg_1', self.higher_version, 'Hey')      ,     ('deprecated_arg_2', self.higher_version, 'Hey')      ,     )
    assert (
        str(warning.warnings[0].message       )
        == f'`deprecated_arg_1` is deprecated and will be removed in version {self.higher_version}. Hey'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'`deprecated_arg_2` is deprecated and will be removed in version {self.higher_version}. Hey'
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> Dict:






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	:	int   	=			deprecate(('arg', self.higher_version, 'message')      ,     take_from=Args()       )

    assert arg == 5
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Dict       ) -> Union[str, Any]:






    class  lowerCamelCase_   :
      """simple docstring"""




      a_							 =5
      a_							 =7

    with self.assertWarns(_a       ) as warning:
      __lowerCamelCase	,__lowerCamelCase	:	List[str]   	=			deprecate(
          ('arg', self.higher_version, 'message')      ,     ('foo', self.higher_version, 'message')      ,     ('does not exist', self.higher_version, 'message')      ,     take_from=Args()      ,     )

    assert arg_a == 5
    assert arg_a == 7
    assert (
        str(warning.warning       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[0].message       )
        == f'The `arg` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )
    assert (
        str(warning.warnings[1].message       )
        == f'The `foo` attribute is deprecated and will be removed in version {self.higher_version}. message'
    )




  def 						_lowercase					(			self				:     Union[str, Any]       ) -> List[str]:
    __lowerCamelCase	:	Union[str, Any]   	=			{'deprecated_arg': 4}

    with self.assertRaises(_a       ) as error:
      deprecate(('wrong_arg', self.lower_version, 'message')      ,     take_from=_a       )

    assert (
        str(error.exception       )
        == "The deprecation tuple ('wrong_arg', '0.0.1', 'message') should be removed since diffusers' version"
        f' {__version__} is >= {self.lower_version}'
    )




  def 						_lowercase					(			self				:     Optional[int]       ) -> str:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )

    assert str(warning.warning       ) == "This message is better!!!"




  def 						_lowercase					(			self				:     List[Any]       ) -> Union[str, Any]:
    with self.assertWarns(_a       ) as warning:
      deprecate(('deprecated_arg', self.higher_version, 'This message is better!!!')      ,     standard_warn=_a       )
    assert str(warning.warning       ) == "This message is better!!!"
    assert "diffusers/tests/others/test_utils.py" in warning.filename






