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






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

					# Mega expects embeddings to be (time, batch, embedding size), but
					# Hugging Face returns tokens as (batch, time)
					if batch_first:
						__snake_case     :				Union[str, 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:
						__snake_case     :				Optional[int] 						=					1 - attention_mask

					# get token embeddings from IDs
					__snake_case     :				List[str] 						=					self.embedding_layer(lowercase__)

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

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







class 						UpperCamelCase     (       nn.Module		):



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






				def    _lowercase	(self							: Optional[int]      ,    _A							: Dict      ,    _A							: Union[str, Any]      ,    _A							: Optional[int]=True      ,    _A							: List[str]=0)       ->      Any:
					__snake_case     :				Dict 						=					self.mega(lowercase__      ,    lowercase__      ,    lowercase__      ,    lowercase__)
					return self.mlm_head(self.dropout(lowercase__))







def     __UpperCAmelCase							(       UpperCAmelCase_       :							Dict     ,				UpperCAmelCase_       :							Optional[int]     ,				UpperCAmelCase_       :							Dict    )				->     Optional[Any]:

	'''simple docstring'''





	with open(os.path.join(_A     ,				'model_args.pkl'    )     ,				'rb'    ) as f:
		__snake_case     :				int 						=					pkl.load(_A    )

	# load the original encoder
	__snake_case     :				str 						=					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
	__snake_case     :				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     ,				)

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

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

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


if __name__ == "__main__":
	_a	:	Tuple=					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	:	Optional[Any]=					parser.parse_args()

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











import io
import json
import subprocess


UpperCamelCase__       				=	[
    ['''en''', '''ru'''],
    ['''ru''', '''en'''],
    ['''en''', '''de'''],
    ['''de''', '''en'''],
]

UpperCamelCase__       				=	8






def 				UpperCAmelCase__  (							_A      ,      _A			):






  """simple docstring"""


  a_		    =  {}
  for src, tgt in pairs:
    a_		    =  f"{src}-{tgt}"
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo src".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  f"sacrebleu -t wmt19 -l {pair} --echo ref".split()
    a_		    =  subprocess.run(_A      ,      stdout=subprocess.PIPE			).stdout.decode('''utf-8'''			).splitlines()
    a_		    =  {'''src''': src_lines[:n_objs], '''tgt''': tgt_lines[:n_objs]}
  return text


UpperCamelCase__       				=	get_all_data(pairs, n_objs)
UpperCamelCase__       				=	'''./fsmt_val_data.json'''
with io.open(filename, '''w''', encoding='''utf-8''') as f:
  UpperCamelCase__       				=	json.dump(text, f, indent=2, ensure_ascii=False)
