






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   SCREAMING_SNAKE_CASE (       nn.Module    ):





							"""simple docstring"""

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



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

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

												# get token embeddings from IDs
												_A									=       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:
																	_A									=       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   SCREAMING_SNAKE_CASE (       nn.Module    ):





							"""simple docstring"""

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



							def 					__A       (     self:      List[str]						,   __A:      str						,   __A:      Any						,   __A:      Optional[Any]=True						,   __A:      int=0      )			-> Tuple:
												_A									=       self.mega(__A						,   __A						,   __A						,   __A      )
												return self.mlm_head(self.dropout(__A      )      )




def    __A    (   _lowercase    ,	_lowercase    ,	_lowercase       ):

					'''simple docstring'''







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

					# load the original encoder
					_A									=       OriginalMegaForMaskedLM(**_lowercase       ).eval()

					# load its weights
					print(
					    '''Original Mega encoder:'''    ,	original_mlm.mega.load_state_dict(
					        torch.load(os.path.join(_lowercase    ,	'''encoder_weights.pt'''       )    ,	map_location='''cpu'''       )       )    ,	)
					print(
					    '''Original Mega MLM layer:'''    ,	original_mlm.mlm_head.load_state_dict(
					        torch.load(os.path.join(_lowercase    ,	'''mlm_head_weights.pt'''       )    ,	map_location='''cpu'''       )       )    ,	)

					# create a new config from the old one
					_A									=       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=_lowercase    ,	norm_affine=_lowercase    ,	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=_lowercase    ,	add_lm_hidden_dense_layer=_lowercase    ,	)

					_A									=       MegaForMaskedLM(_lowercase       ).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
					_A									=       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
					_A									=       original_mlm.mega.encoders.state_dict()
					_A									=       {}
					for module_name in original_state_dict.keys():
										_A									=       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:
																				_A									=       module_name.replace('''move.beta'''    ,	'''ema_gate.ema_expansion_matrix'''       )
															elif "mega_layer.beta" in module_name:
																				_A									=       module_name.replace('''beta'''    ,	'''qk_bias'''       )
															else:
																				_A									=       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:
																				_A									=       module_name.replace('''move.gamma'''    ,	'''ema_gate.kernel_projection_matrix'''       )
															elif "mega_layer.gamma" in module_name:
																				_A									=       module_name.replace('''gamma'''    ,	'''qk_weight'''       )
															else:
																				_A									=       module_name.replace('''gamma'''    ,	'''g_param'''       )
        # alpha is used in EMA and positional bias; renaming to improve readability
										elif "move.alpha" in module_name:
															_A									=       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:
															_A									=       module_name.replace('''move.delta'''    ,	'''ema_gate.damping_factor'''       )
										# omega is only used in EMA; renaming to improve readability
										elif "omega" in module_name:
															_A									=       module_name.replace('''move.omega'''    ,	'''ema_gate.residual_weight'''       )

										if new_module_name:
															_A									=       new_module_name

					if len(_lowercase       ) != 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():
										_A									=       original_state_dict.pop(_lowercase       )

					# 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(_lowercase       )       )

					# load the MLM head weights directly
					print(
					    '''HF Mega MLM layer:'''    ,	hf_mlm.mlm_head.load_state_dict(
					        torch.load(os.path.join(_lowercase    ,	'''mlm_head_weights.pt'''       )    ,	map_location='''cpu'''       )       )    ,	)

					# test on a randomly generated input sequence
					_A									=       torch.randint(0    ,	hf_config.vocab_size    ,	size=(4, 2_56)       )
					_A									=       torch.ones_like(_lowercase       )
					# mask a few tokens to make sure masking is applied appropriately :)
					_A									=       0

					# run forward passes
					_A									=       original_mlm(_lowercase    ,	_lowercase    ,	batch_first=_lowercase    ,	ignore_mask_value=0       )
					_A									=       hf_mlm(_lowercase    ,	_lowercase       )[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
					_A									=       torch.allclose(_lowercase    ,	_lowercase    ,	atol=1e-3       )

					if success:
										print('''Yay!'''       )
										hf_mlm.save_pretrained(_lowercase       )
					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'''       )
										_A									=       AutoTokenizer.from_pretrained(_lowercase       )
										tokenizer.save_pretrained(_lowercase       )


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)











import tempfile
import unittest

import torch

from diffusers import UNetaDConditionModel
from diffusers.training_utils import EMAModel
from diffusers.utils.testing_utils import enable_full_determinism, skip_mps, torch_device


enable_full_determinism()





class   SCREAMING_SNAKE_CASE (       unittest.TestCase    ):





							"""simple docstring"""

							A_       =    "hf-internal-testing/tiny-stable-diffusion-pipe"
							A_       =    1
							A_       =    77
							A_       =    32
							A_       =    4
							A_       =    A_       =    64
							A_       =    torch.manual_seed(0    )

							def 					__A       (     self:      List[Any]						,   __A:      List[Any]=0.9_999      )			-> Dict:
												_A									=       UNetaDConditionModel.from_pretrained(self.model_id						,   subfolder='''unet'''      )
												_A									=       unet.to(__A      )
												_A									=       EMAModel(unet.parameters()						,   decay=__A						,   model_cls=__A						,   model_config=unet.config      )
												return unet, ema_unet

							def 					__A       (     self:      Tuple      )			-> Union[str, Any]:
												_A									=       torch.randn(
												    self.batch_size						,   self.num_in_channels						,   self.latent_height						,   self.latent_width						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randint(0						,   10_00						,   size=(self.batch_size,)						,   generator=self.generator      ).to(__A      )
												_A									=       torch.randn(
												    self.batch_size						,   self.prompt_length						,   self.text_encoder_hidden_dim						,   generator=self.generator      ).to(__A      )
												return noisy_latents, timesteps, encoder_hidden_states

							def 					__A       (     self:      Tuple						,   __A:      Optional[int]      )			-> Optional[Any]:
												_A									=       {}
												for k, param in unet.state_dict().items():
																	_A									=       torch.randn_like(__A      ) + (param * torch.randn_like(__A      ))
																	updated_state_dict.update({k: updated_param}      )
												unet.load_state_dict(__A      )
												return unet

							def 					__A       (     self:      Any      )			-> Any:
												_A							,_A									=       self.get_models()
												# Take the first (hypothetical) EMA step.
												ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 1

												# Take two more.
												for _ in range(2      ):
																	ema_unet.step(unet.parameters()      )
												assert ema_unet.optimization_step == 3

							def 					__A       (     self:      Union[str, Any]      )			-> str:
												_A							,_A									=       self.get_models()
												# Since the `unet` is not being updated (i.e., backprop'd)
												# there won't be any difference between the `params` of `unet`
												# and `ema_unet` even if we call `ema_unet.step(unet.parameters())`.
												ema_unet.step(unet.parameters()      )
												_A									=       list(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

												# The above holds true even if we call `ema.step()` multiple times since
												# `unet` params are still not being updated.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Tuple      )			-> List[str]:
												_A							,_A									=       self.get_models()
												# Here we simulate the parameter updates for `unet`. Since there might
												# be some parameters which are initialized to zero we take extra care to
												# initialize their values to something non-zero before the multiplication.
												_A									=       self.simulate_backprop(__A      )

												# Take the EMA step.
												ema_unet.step(unet_pseudo_updated_step_one.parameters()      )

												# Now the EMA'd parameters won't be equal to the original model parameters.
												_A									=       list(unet_pseudo_updated_step_one.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

												# Ensure this is the case when we take multiple EMA steps.
												for _ in range(4      ):
																	ema_unet.step(unet.parameters()      )
												for s_param, param in zip(ema_unet.shadow_params						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      Optional[int]      )			-> Optional[int]:
												# If we call EMA step after a backpropagation consecutively for two times,
												# the shadow params from those two steps should be different.
												_A							,_A									=       self.get_models()

												# First backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												# Second backprop + EMA
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert ~torch.allclose(__A						,   __A      )

							def 					__A       (     self:      int      )			-> Any:
												# If there's no decay even if there are backprops, EMA steps
												# won't take any effect i.e., the shadow params would remain the
												# same.
												_A							,_A									=       self.get_models(decay=0.0      )
												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_one.parameters()      )
												_A									=       ema_unet.shadow_params

												_A									=       self.simulate_backprop(__A      )
												ema_unet.step(unet_step_two.parameters()      )
												_A									=       ema_unet.shadow_params

												for step_one, step_two in zip(__A						,   __A      ):
																	assert torch.allclose(__A						,   __A      )



							@skip_mps
							def 					__A       (     self:      Optional[Any]      )			-> List[str]:
												_A							,_A									=       self.get_models()
												_A							,_A							,_A									=       self.get_dummy_inputs()

												with tempfile.TemporaryDirectory() as tmpdir:
																	ema_unet.save_pretrained(__A      )
																	_A									=       UNetaDConditionModel.from_pretrained(__A						,   model_cls=__A      )
																	_A									=       loaded_unet.to(unet.device      )

												# Since no EMA step has been performed the outputs should match.
												_A									=       unet(__A						,   __A						,   __A      ).sample
												_A									=       loaded_unet(__A						,   __A						,   __A      ).sample

												assert torch.allclose(__A						,   __A						,   atol=1e-4      )



