
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 ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    Optional[Any]				      =			mega_args
								lowerCAmelCase__ :    Optional[int]				      =			nn.Embedding(SCREAMING_SNAKE_CASE__ ,	self.mega_args.encoder_embed_dim    )
								lowerCAmelCase__ :    Any				      =			nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(SCREAMING_SNAKE_CASE__    )]    )
								lowerCAmelCase__ :    str				      =			depth
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0    ):
								"""simple docstring"""







								if batch_first:
											lowerCAmelCase__ :    int				      =			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__ :    List[Any]				      =			1 - attention_mask

								# get token embeddings from IDs
								lowerCAmelCase__ :    Optional[Any]				      =			self.embedding_layer(SCREAMING_SNAKE_CASE__    )

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

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





class 						lowercase       (  nn.Module					):
					def __init__(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								super().__init__()
								lowerCAmelCase__ :    Any				      =			MegaLM(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    str				      =			nn.Linear(mega_args.encoder_embed_dim ,	SCREAMING_SNAKE_CASE__    )
								lowerCAmelCase__ :    Any				      =			nn.Dropout(p=0.1    )
					def lowercase_			(		self ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__=True ,	SCREAMING_SNAKE_CASE__=0    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.mega(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
								return self.mlm_head(self.dropout(SCREAMING_SNAKE_CASE__    )    )





def      _a     (   __UpperCamelCase       :  Union[str, Any]	,__UpperCamelCase       :  List[Any]	,__UpperCamelCase       :  Optional[int]    ):
			with open(os.path.join(__UpperCamelCase	,'''model_args.pkl'''    )	,'''rb'''    ) as f:
						lowerCAmelCase__ :    int				      =			pkl.load(__UpperCamelCase    )

			# load the original encoder
			lowerCAmelCase__ :    Dict				      =			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
			lowerCAmelCase__ :    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	,)

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

						if new_module_name:
									lowerCAmelCase__ :    int				      =			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():
						lowerCAmelCase__ :    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
			lowerCAmelCase__ :    Dict				      =			torch.randint(0	,hf_config.vocab_size	,size=(4, 256)    )
			lowerCAmelCase__ :    Tuple				      =			torch.ones_like(__UpperCamelCase    )
			# mask a few tokens to make sure masking is applied appropriately :)
			lowerCAmelCase__ :    int				      =			0

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


if __name__ == "__main__":
							A__       :   List[Any]   						=					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 torch

from diffusers import HeunDiscreteScheduler
from diffusers.utils import torch_device

from .test_schedulers import SchedulerCommonTest





class 						lowercase       (  __UpperCamelCase					):
					__a					=    (HeunDiscreteScheduler,)
					__a					=    10
					def lowercase_			(		self ,	**SCREAMING_SNAKE_CASE__    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			{
								    '''num_train_timesteps''': 1100,
								    '''beta_start''': 0.0_001,
								    '''beta_end''': 0.02,
								    '''beta_schedule''': '''linear''',
								}

								config.update(**SCREAMING_SNAKE_CASE__    )
								return config
					def lowercase_			(		self    ):
								"""simple docstring"""







								for timesteps in [10, 50, 100, 1000]:
											self.check_over_configs(num_train_timesteps=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for beta_start, beta_end in zip([0.00_001, 0.0_001, 0.001] ,	[0.0_002, 0.002, 0.02]    ):
											self.check_over_configs(beta_start=SCREAMING_SNAKE_CASE__ ,	beta_end=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for schedule in ["linear", "scaled_linear", "exp"]:
											self.check_over_configs(beta_schedule=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for clip_sample_range in [1.0, 2.0, 3.0]:
											self.check_over_configs(clip_sample_range=SCREAMING_SNAKE_CASE__ ,	clip_sample=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								for prediction_type in ["epsilon", "v_prediction", "sample"]:
											self.check_over_configs(prediction_type=SCREAMING_SNAKE_CASE__    )
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    List[str]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Optional[Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Optional[int]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    Optional[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    List[Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Tuple				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Union[str, Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Any				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Union[str, Any]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[Any]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    str				      =			self.get_scheduler_config(prediction_type='''v_prediction'''    )
								lowerCAmelCase__ :    Any				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps    )

								lowerCAmelCase__ :    int				      =			self.dummy_model()
								lowerCAmelCase__ :    List[str]				      =			self.dummy_sample_deter * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for i, t in enumerate(scheduler.timesteps    ):
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[Any]				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Optional[int]				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    Tuple				      =			output.prev_sample

								lowerCAmelCase__ :    List[Any]				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if torch_device in ["cpu", "mps"]:
											assert abs(result_sum.item() - 4.6934E-07    ) < 1E-2
											assert abs(result_mean.item() - 6.1112E-10    ) < 1E-3
								else:
											# CUDA
											assert abs(result_sum.item() - 4.693_4286_5017_0972E-07    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Optional[int]				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    Union[str, Any]				      =			scheduler_class(**SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps ,	device=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    Optional[Any]				      =			self.dummy_model()
								lowerCAmelCase__ :    Dict				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    int				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Dict				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    Dict				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    List[str]				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								if str(SCREAMING_SNAKE_CASE__    ).startswith('''cpu'''    ):
											# The following sum varies between 148 and 156 on mps. Why?
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
								elif str(SCREAMING_SNAKE_CASE__    ).startswith('''mps'''    ):
											# Larger tolerance on mps
											assert abs(result_mean.item() - 0.0_002    ) < 1E-2
								else:
											# CUDA
											assert abs(result_sum.item() - 0.1_233    ) < 1E-2
											assert abs(result_mean.item() - 0.0_002    ) < 1E-3
					def lowercase_			(		self    ):
								"""simple docstring"""







								lowerCAmelCase__ :    Any				      =			self.scheduler_classes[0]
								lowerCAmelCase__ :    Optional[Any]				      =			self.get_scheduler_config()
								lowerCAmelCase__ :    str				      =			scheduler_class(**SCREAMING_SNAKE_CASE__ ,	use_karras_sigmas=SCREAMING_SNAKE_CASE__    )

								scheduler.set_timesteps(self.num_inference_steps ,	device=SCREAMING_SNAKE_CASE__    )

								lowerCAmelCase__ :    str				      =			self.dummy_model()
								lowerCAmelCase__ :    Any				      =			self.dummy_sample_deter.to(SCREAMING_SNAKE_CASE__    ) * scheduler.init_noise_sigma
								lowerCAmelCase__ :    List[Any]				      =			sample.to(SCREAMING_SNAKE_CASE__    )

								for t in scheduler.timesteps:
											lowerCAmelCase__ :    Union[str, Any]				      =			scheduler.scale_model_input(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    Any				      =			model(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )

											lowerCAmelCase__ :    str				      =			scheduler.step(SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__ ,	SCREAMING_SNAKE_CASE__    )
											lowerCAmelCase__ :    List[Any]				      =			output.prev_sample

								lowerCAmelCase__ :    int				      =			torch.sum(torch.abs(SCREAMING_SNAKE_CASE__    )    )
								lowerCAmelCase__ :    Dict				      =			torch.mean(torch.abs(SCREAMING_SNAKE_CASE__    )    )

								assert abs(result_sum.item() - 0.00_015    ) < 1E-2
								assert abs(result_mean.item() - 1.9869_5545_3503_4695E-07    ) < 1E-2



