



"""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    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										super().__init__()
										snake_case_						:Any								=						mega_args
										snake_case_						:Tuple								=						nn.Embedding(a    ,       self.mega_args.encoder_embed_dim   )
										snake_case_						:List[str]								=						nn.ModuleList([MegaEncoderLayer(self.mega_args   ) for _ in range(a   )]   )
										snake_case_						:Union[str, Any]								=						depth







					def 		_a (      self    ,       a    ,       a    ,       a=True    ,       a=0   ):




										"""simple docstring"""





										if batch_first:
															snake_case_						: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:
															snake_case_						:str								=						1 - attention_mask

										# get token embeddings from IDs
										snake_case_						:Optional[int]								=						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:
															snake_case_						:Dict								=						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    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										super().__init__()
										snake_case_						:Dict								=						MegaLM(a    ,       a    ,       a   )
										snake_case_						:List[str]								=						nn.Linear(mega_args.encoder_embed_dim    ,       a   )
										snake_case_						:Dict								=						nn.Dropout(p=0.1   )







					def 		_a (      self    ,       a    ,       a    ,       a=True    ,       a=0   ):




										"""simple docstring"""





										snake_case_						:Any								=						self.mega(a    ,       a    ,       a    ,       a   )
										return self.mlm_head(self.dropout(a   )   )



def      A      (			_A,     _A,     _A							):




					"""simple docstring"""
					with open(os.path.join(_A,     "model_args.pkl"							),     "rb"							) as f:
										snake_case_						:Any								=						pkl.load(_A							)

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

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

					# run forward passes
					snake_case_						:Dict								=						original_mlm(_A,     _A,     batch_first=_A,     ignore_mask_value=0							)
					snake_case_						:Dict								=						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_						:Union[str, Any]								=						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_						:List[Any]								=						AutoTokenizer.from_pretrained(_A							)
										tokenizer.save_pretrained(_A							)


if __name__ == "__main__":
				__UpperCAmelCase      :      Optional[int]						=					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      :      Any						=					parser.parse_args()

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






"""simple docstring"""






from typing import Any

import numpy as np

class 	__lowerCAmelCase							:


					'''simple docstring'''


					def __init__(      self    ,       a    ,       a   ):




										"""simple docstring"""





										if np.any(tableau[:, -1]    ,       where=tableau[:, -1] < 0   ):
															raise ValueError("RHS must be > 0"   )

										snake_case_						:List[Any]								=						tableau
										snake_case_	,  snake_case_						:Dict								=						tableau.shape

										# Number of decision variables x1, x2, x3...
										snake_case_						:Union[str, Any]								=						n_vars

										# Number of artificial variables to be minimised
										snake_case_						:Tuple								=						len(np.where(tableau[self.n_vars : -1] == -1   )[0]   )

										# 2 if there are >= or == constraints (nonstandard), 1 otherwise (std)
										snake_case_						:List[str]								=						(self.n_art_vars > 0) + 1

										# Number of slack variables added to make inequalities into equalities
										snake_case_						:Optional[int]								=						self.n_rows - self.n_stages

										# Objectives for each stage
										snake_case_						:Union[str, Any]								=						["max"]

										# In two stage simplex, first minimise then maximise
										if self.n_art_vars:
															self.objectives.append("min"   )

										snake_case_						:Dict								=						[""]

										# Index of current pivot row and column
										snake_case_						:List[str]								=						None
										snake_case_						:List[Any]								=						None

										# Does objective row only contain (non)-negative values?
										snake_case_						:Union[str, Any]								=						False







					@staticmethod
					def 		_a (      *a   ):




										"""simple docstring"""





										if len(a   ) != 3:
															raise ValueError("Must provide n_vars, n_slack, and n_art_vars"   )

										if not all(x >= 0 and isinstance(a    ,       a   ) for x in args   ):
															raise ValueError("All arguments must be non-negative integers"   )

										# decision | slack | artificial
										snake_case_						:Tuple								=						["x", "s", "a"]
										snake_case_						:Dict								=						[]
										for i in range(3   ):
															for j in range(args[i]   ):
																				titles.append(string_starts[i] + str(j + 1   )   )
										titles.append("RHS"   )
										return titles







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										snake_case_						:str								=						self.objectives[-1]

										# Find entries of highest magnitude in objective rows
										snake_case_						:Dict								=						(objective == "min") - (objective == "max")
										snake_case_						:Optional[Any]								=						np.argmax(sign * tableau[0, : self.n_vars]   )

										# Choice is only valid if below 0 for maximise, and above for minimise
										if sign * self.tableau[0, col_idx] <= 0:
															snake_case_						:Dict								=						True
															return 0, 0

										# Pivot row is chosen as having the lowest quotient when elements of
										# the pivot column divide the right-hand side

										# Slice excluding the objective rows
										snake_case_						:str								=						slice(self.n_stages    ,       self.n_rows   )

										# RHS
										snake_case_						:Optional[Any]								=						tableau[s, -1]

										# Elements of pivot column within slice
										snake_case_						:Union[str, Any]								=						tableau[s, col_idx]

										# Array filled with nans
										snake_case_						:Optional[Any]								=						np.full(self.n_rows - self.n_stages    ,       np.nan   )

										# If element in pivot column is greater than zeron_stages, return
										# quotient or nan otherwise
										snake_case_						:Dict								=						np.divide(a    ,       a    ,       out=a    ,       where=divisor > 0   )

										# Arg of minimum quotient excluding the nan values. n_stages is added
										# to compensate for earlier exclusion of objective columns
										snake_case_						:List[Any]								=						np.nanargmin(a   ) + self.n_stages
										return row_idx, col_idx







					def 		_a (      self    ,       a    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:Tuple								=						tableau[row_idx].copy()

										snake_case_						:Any								=						piv_row[col_idx]

										# Entry becomes 1
										piv_row *= 1 / piv_val

										# Variable in pivot column becomes basic, ie the only non-zero entry
										for idx, coeff in enumerate(tableau[:, col_idx]   ):
															tableau[idx] += -coeff * piv_row
										snake_case_						:str								=						piv_row
										return tableau







					def 		_a (      self    ,       a   ):




										"""simple docstring"""





										self.objectives.pop()

										if not self.objectives:
															return tableau

										# Slice containing ids for artificial columns
										snake_case_						:str								=						slice(-self.n_art_vars - 1    ,       -1   )

										# Delete the artificial variable columns
										snake_case_						:Optional[int]								=						np.delete(a    ,       a    ,       axis=1   )

										# Delete the objective row of the first stage
										snake_case_						:List[str]								=						np.delete(a    ,       0    ,       axis=0   )

										snake_case_						:Optional[Any]								=						1
										self.n_rows -= 1
										snake_case_						:List[Any]								=						0
										snake_case_						:Dict								=						False
										return tableau







					def 		_a (      self   ):




										"""simple docstring"""





										for _ in range(1_00   ):
															# Completion of each stage removes an objective. If both stages
															# are complete, then no objectives are left
															if not self.objectives:
																				snake_case_						:str								=						self.generate_col_titles(
																				    self.n_vars    ,       self.n_slack    ,       self.n_art_vars   )

																				# Find the values of each variable at optimal solution
																				return self.interpret_tableau(self.tableau    ,       self.col_titles   )

															snake_case_	,  snake_case_						:Union[str, Any]								=						self.find_pivot(self.tableau   )

															# If there are no more negative values in objective row
															if self.stop_iter:
																				# Delete artificial variable columns and rows. Update attributes
																				snake_case_						:str								=						self.change_stage(self.tableau   )
															else:
																				snake_case_						:str								=						self.pivot(self.tableau    ,       a    ,       a   )
										return {}







					def 		_a (      self    ,       a    ,       a   ):




										"""simple docstring"""





										snake_case_						:Any								=						{"P": abs(tableau[0, -1]   )}

										for i in range(self.n_vars   ):
															# Gives ids of nonzero entries in the ith column
															snake_case_						:Dict								=						np.nonzero(tableau[:, i]   )
															snake_case_						:Tuple								=						len(nonzero[0]   )

															# First entry in the nonzero ids
															snake_case_						:Any								=						nonzero[0][0]
															snake_case_						:int								=						tableau[nonzero_rowidx, i]

															# If there is only one nonzero value in column, which is one
															if n_nonzero == nonzero_val == 1:
																				snake_case_						:Tuple								=						tableau[nonzero_rowidx, -1]
																				snake_case_						:Tuple								=						rhs_val

        # Check for basic variables
										for title in col_titles:
															# Don't add RHS or slack variables to output dict
															if title[0] not in "R-s-a":
																				output_dict.setdefault(a    ,       0   )
										return output_dict


if __name__ == "__main__":
				import doctest

				doctest.testmod()

