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 	_lowercase		(      nn.Module ):

 def __init__(				self			:							str     ,   snake_case			:							Tuple     ,   snake_case			:							int     ,   snake_case			:							Optional[int]    )     ->		List[str]:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_      :		int				=      mega_args
  UpperCamelCase_      :		Optional[Any]				=      nn.Embedding(snake_case     ,   self.mega_args.encoder_embed_dim    )
  UpperCamelCase_      :		Dict				=      nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(snake_case    )]    )
  UpperCamelCase_      :		List[Any]				=      depth


 def 		SCREAMING_SNAKE_CASE__     (				self			:							Dict     ,   snake_case			:							List[str]     ,   snake_case			:							List[str]     ,   snake_case			:							Optional[Any]=True     ,   snake_case			:							Optional[int]=0    )     ->		Dict:

  """simple docstring"""





  if batch_first:
   UpperCamelCase_      :		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:
   UpperCamelCase_      :		Any				=      1 - attention_mask

  # get token embeddings from IDs
  UpperCamelCase_      :		Any				=      self.embedding_layer(snake_case    )

  # pass through the Mega layers
  # input is (time, batch, encoder dim) and output is the same
  for encoder in self.encoders:
   UpperCamelCase_      :		Any				=      encoder(snake_case     ,   snake_case    )

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





class 	_lowercase		(      nn.Module ):

 def __init__(				self			:							Optional[Any]     ,   snake_case			:							Union[str, Any]     ,   snake_case			:							Dict     ,   snake_case			:							str    )     ->		Union[str, Any]:

  """simple docstring"""





  super().__init__()
  UpperCamelCase_      :		Any				=      MegaLM(snake_case     ,   snake_case     ,   snake_case    )
  UpperCamelCase_      :		Any				=      nn.Linear(mega_args.encoder_embed_dim     ,   snake_case    )
  UpperCamelCase_      :		Optional[Any]				=      nn.Dropout(p=0.1    )


 def 		SCREAMING_SNAKE_CASE__     (				self			:							List[str]     ,   snake_case			:							Tuple     ,   snake_case			:							str     ,   snake_case			:							Any=True     ,   snake_case			:							Optional[Any]=0    )     ->		List[Any]:

  """simple docstring"""





  UpperCamelCase_      :		List[str]				=      self.mega(snake_case     ,   snake_case     ,   snake_case     ,   snake_case    )
  return self.mlm_head(self.dropout(snake_case    )    )





def      __lowercase  (		lowerCamelCase      :  List[str]						,						lowerCamelCase      :  Optional[int]						,						lowerCamelCase      :  str			):
 with open(os.path.join(lowerCamelCase						,						'model_args.pkl'			)						,						'rb'			) as f:
  UpperCamelCase_      :		int				=      pkl.load(lowerCamelCase			)

 # load the original encoder
 UpperCamelCase_      :		int				=      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
 UpperCamelCase_      :		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						,						)

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

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

 # run forward passes
 UpperCamelCase_      :		Dict				=      original_mlm(lowerCamelCase						,						lowerCamelCase						,						batch_first=lowerCamelCase						,						ignore_mask_value=0			)
 UpperCamelCase_      :		Optional[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
 UpperCamelCase_      :		Optional[int]				=      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'			)
  UpperCamelCase_      :		Any				=      AutoTokenizer.from_pretrained(lowerCamelCase			)
  tokenizer.save_pretrained(lowerCamelCase			)


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)




from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


a_					      = {
    'configuration_marian': ['MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP', 'MarianConfig', 'MarianOnnxConfig'],
}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['MarianTokenizer']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = [
       'MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST',
       'MarianForCausalLM',
       'MarianModel',
       'MarianMTModel',
       'MarianPreTrainedModel',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['TFMarianModel', 'TFMarianMTModel', 'TFMarianPreTrainedModel']

try:
   if not is_flax_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   a_					      = ['FlaxMarianModel', 'FlaxMarianMTModel', 'FlaxMarianPreTrainedModel']

if TYPE_CHECKING:
   from .configuration_marian import MARIAN_PRETRAINED_CONFIG_ARCHIVE_MAP, MarianConfig, MarianOnnxConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_marian import MarianTokenizer

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_marian import (
          MARIAN_PRETRAINED_MODEL_ARCHIVE_LIST,
          MarianForCausalLM,
          MarianModel,
          MarianMTModel,
          MarianPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_marian import TFMarianModel, TFMarianMTModel, TFMarianPreTrainedModel

   try:
      if not is_flax_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_flax_marian import FlaxMarianModel, FlaxMarianMTModel, FlaxMarianPreTrainedModel

else:
   import sys

   a_					      = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)



