
'''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 						__snake_case (						nn.Module  ):




		def __init__(     self				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:List[Any] ,						_SCREAMING_SNAKE_CASE				:Any ,						_SCREAMING_SNAKE_CASE				:int)				-> Optional[Any]:






								'''simple docstring'''






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





		def   _snake_case  (     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:Dict ,						_SCREAMING_SNAKE_CASE				:int ,						_SCREAMING_SNAKE_CASE				:Union[str, Any]=True ,						_SCREAMING_SNAKE_CASE				:Any=0)				-> Optional[int]:






								'''simple docstring'''






								if batch_first:
														UpperCamelCase_    :  Dict				    =    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_    :  int				    =    1 - attention_mask

								# get token embeddings from IDs
								UpperCamelCase_    :  Optional[int]				    =    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:
														UpperCamelCase_    :  Dict				    =    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 						__snake_case (						nn.Module  ):




		def __init__(     self				:Union[str, Any] ,						_SCREAMING_SNAKE_CASE				:List[Any] ,						_SCREAMING_SNAKE_CASE				:str ,						_SCREAMING_SNAKE_CASE				:List[Any])				-> Dict:






								'''simple docstring'''






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





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






								'''simple docstring'''






								UpperCamelCase_    :  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 				__magic_name__(					UpperCAmelCase__   :					Tuple       ,							UpperCAmelCase__   :					List[Any]       ,							UpperCAmelCase__   :					Optional[int]  ):
						with open(os.path.join(UpperCAmelCase__       ,							"""model_args.pkl"""  )       ,							"""rb"""  ) as f:
												UpperCamelCase_    :  List[Any]				    =    pkl.load(UpperCAmelCase__  )

						# load the original encoder
						UpperCamelCase_    :  Optional[int]				    =    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
						UpperCamelCase_    :  str				    =    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__       ,							)

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

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

						# run forward passes
						UpperCamelCase_    :  Union[str, Any]				    =    original_mlm(UpperCAmelCase__       ,							UpperCAmelCase__       ,							batch_first=UpperCAmelCase__       ,							ignore_mask_value=0  )
						UpperCamelCase_    :  int				    =    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
						UpperCamelCase_    :  List[str]				    =    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"""  )
												UpperCamelCase_    :  Union[str, Any]				    =    AutoTokenizer.from_pretrained(UpperCAmelCase__  )
												tokenizer.save_pretrained(UpperCAmelCase__  )


if __name__ == "__main__":
	__SCREAMING_SNAKE_CASE  =			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",
	)

	__SCREAMING_SNAKE_CASE  =			parser.parse_args()

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






'''simple docstring'''


# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import json
import os
from dataclasses import dataclass
from enum import Enum
from typing import List, Optional, Union

import yaml

from ...utils import ComputeEnvironment, DistributedType, SageMakerDistributedType
from ...utils.constants import SAGEMAKER_PYTHON_VERSION, SAGEMAKER_PYTORCH_VERSION, SAGEMAKER_TRANSFORMERS_VERSION


__SCREAMING_SNAKE_CASE  =			os.path.expanduser(
    os.getenv("HF_HOME", os.path.join(os.getenv("XDG_CACHE_HOME", "~/.cache"), "huggingface"))
)
__SCREAMING_SNAKE_CASE  =			os.path.join(hf_cache_home, "accelerate")
__SCREAMING_SNAKE_CASE  =			os.path.join(cache_dir, "default_config.yaml")
__SCREAMING_SNAKE_CASE  =			os.path.join(cache_dir, "default_config.yaml")

# For backward compatibility: the default config is the json one if it's the only existing file.
if os.path.isfile(default_yaml_config_file) or not os.path.isfile(default_json_config_file):
	__SCREAMING_SNAKE_CASE  =			default_yaml_config_file
else:
	__SCREAMING_SNAKE_CASE  =			default_json_config_file
def 				__magic_name__(					UpperCAmelCase__   :					int  ):
						if config_file is not None:
												if not os.path.isfile(UpperCAmelCase__  ):
																		raise FileNotFoundError(
																		    f"""The passed configuration file `{config_file}` does not exist. """
																		    """Please pass an existing file to `accelerate launch`, or use the the default one """
																		    """created through `accelerate config` and run `accelerate launch` """
																		    """without the `--config_file` argument."""  )
						else:
												UpperCamelCase_    :  List[Any]				    =    default_config_file
						with open(UpperCAmelCase__       ,							"""r"""       ,							encoding="""utf-8"""  ) as f:
												if config_file.endswith(""".json"""  ):
																		if (
																		    json.load(UpperCAmelCase__  ).get("""compute_environment"""       ,							ComputeEnvironment.LOCAL_MACHINE  )
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								UpperCamelCase_    :  int				    =    ClusterConfig
																		else:
																								UpperCamelCase_    :  Optional[Any]				    =    SageMakerConfig
																		return config_class.from_json_file(json_file=UpperCAmelCase__  )
												else:
																		if (
																		    yaml.safe_load(UpperCAmelCase__  ).get("""compute_environment"""       ,							ComputeEnvironment.LOCAL_MACHINE  )
																		    == ComputeEnvironment.LOCAL_MACHINE
																		):
																								UpperCamelCase_    :  Union[str, Any]				    =    ClusterConfig
																		else:
																								UpperCamelCase_    :  Optional[int]				    =    SageMakerConfig
																		return config_class.from_yaml_file(yaml_file=UpperCAmelCase__  )




@dataclass
class 						__snake_case :
		a_        =				42
		a_        =				42
		a_        =				42
		a_        =				42




		def   _snake_case  (     self				:List[Any])				-> Optional[Any]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    self.__dict__
								# For serialization, it's best to convert Enums to strings (or their underlying value type).
								for key, value in result.items():
														if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE):
																				UpperCamelCase_    :  str				    =    value.value
														if isinstance(_SCREAMING_SNAKE_CASE ,						_SCREAMING_SNAKE_CASE) and not bool(_SCREAMING_SNAKE_CASE):
																				UpperCamelCase_    :  Dict				    =    None
								UpperCamelCase_    :  Dict				    =    {k: v for k, v in result.items() if v is not None}
								return result




		@classmethod
		def   _snake_case  (     cls				:Dict ,						_SCREAMING_SNAKE_CASE				:Optional[int]=None)				-> int:






								'''simple docstring'''






								UpperCamelCase_    :  str				    =    default_json_config_file if json_file is None else json_file
								with open(_SCREAMING_SNAKE_CASE ,						"""r""" ,						encoding="""utf-8""") as f:
														UpperCamelCase_    :  List[str]				    =    json.load(_SCREAMING_SNAKE_CASE)
								if "compute_environment" not in config_dict:
														UpperCamelCase_    :  Union[str, Any]				    =    ComputeEnvironment.LOCAL_MACHINE
								if "mixed_precision" not in config_dict:
														UpperCamelCase_    :  List[Any]				    =    """fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
								if "fp16" in config_dict:  # Convert the config to the new format.
														del config_dict["fp16"]
								if "dynamo_backend" in config_dict:  # Convert the config to the new format.
														UpperCamelCase_    :  Optional[int]				    =    config_dict.pop("""dynamo_backend""")
														UpperCamelCase_    :  List[str]				    =    {} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
								if "use_cpu" not in config_dict:
														UpperCamelCase_    :  Dict				    =    False
								return cls(**_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:int ,						_SCREAMING_SNAKE_CASE				:List[Any])				-> List[str]:






								'''simple docstring'''






								with open(_SCREAMING_SNAKE_CASE ,						"""w""" ,						encoding="""utf-8""") as f:
														UpperCamelCase_    :  Union[str, Any]				    =    json.dumps(self.to_dict() ,						indent=2 ,						sort_keys=_SCREAMING_SNAKE_CASE) + """\n"""
														f.write(_SCREAMING_SNAKE_CASE)




		@classmethod
		def   _snake_case  (     cls				:Optional[Any] ,						_SCREAMING_SNAKE_CASE				:Any=None)				-> List[str]:






								'''simple docstring'''






								UpperCamelCase_    :  List[str]				    =    default_yaml_config_file if yaml_file is None else yaml_file
								with open(_SCREAMING_SNAKE_CASE ,						"""r""" ,						encoding="""utf-8""") as f:
														UpperCamelCase_    :  Any				    =    yaml.safe_load(_SCREAMING_SNAKE_CASE)
								if "compute_environment" not in config_dict:
														UpperCamelCase_    :  Union[str, Any]				    =    ComputeEnvironment.LOCAL_MACHINE

								if "mixed_precision" not in config_dict:
														UpperCamelCase_    :  int				    =    """fp16""" if ("""fp16""" in config_dict and config_dict["""fp16"""]) else None
								if isinstance(config_dict["""mixed_precision"""] ,						_SCREAMING_SNAKE_CASE) and not config_dict["mixed_precision"]:
														UpperCamelCase_    :  Union[str, Any]				    =    """no"""
								if "fp16" in config_dict:  # Convert the config to the new format.
														del config_dict["fp16"]
								if "dynamo_backend" in config_dict:  # Convert the config to the new format.
														UpperCamelCase_    :  Any				    =    config_dict.pop("""dynamo_backend""")
														UpperCamelCase_    :  Optional[Any]				    =    {} if dynamo_backend == """NO""" else {"""dynamo_backend""": dynamo_backend}
								if "use_cpu" not in config_dict:
														UpperCamelCase_    :  List[str]				    =    False
								return cls(**_SCREAMING_SNAKE_CASE)




		def   _snake_case  (     self				:List[Any] ,						_SCREAMING_SNAKE_CASE				:str)				-> int:






								'''simple docstring'''






								with open(_SCREAMING_SNAKE_CASE ,						"""w""" ,						encoding="""utf-8""") as f:
														yaml.safe_dump(self.to_dict() ,						_SCREAMING_SNAKE_CASE)





		def   _snake_case  (     self				:Any)				-> int:






								'''simple docstring'''






								if isinstance(self.compute_environment ,						_SCREAMING_SNAKE_CASE):
														UpperCamelCase_    :  Dict				    =    ComputeEnvironment(self.compute_environment)
								if isinstance(self.distributed_type ,						_SCREAMING_SNAKE_CASE):
														if self.compute_environment == ComputeEnvironment.AMAZON_SAGEMAKER:
																				UpperCamelCase_    :  Optional[Any]				    =    SageMakerDistributedType(self.distributed_type)
														else:
																				UpperCamelCase_    :  Optional[int]				    =    DistributedType(self.distributed_type)
								if self.dynamo_config is None:
														UpperCamelCase_    :  str				    =    {}







@dataclass
class 						__snake_case (						_a  ):
		a_        =				42
		a_        =				0
		a_        =				1
		a_        =				None
		a_        =				None
		a_        =				None
		a_        =				"static"
		a_        =				False
		a_        =				"main"

		# args for deepspeed_plugin
		a_        =				None
		# args for fsdp
		a_        =				None
		# args for megatron_lm
		a_        =				None
		# args for ipex
		a_        =				None
		# args for TPU
		a_        =				False

		# args for TPU pods
		a_        =				None
		a_        =				None
		a_        =				False
		a_        =				False
		a_        =				None
		a_        =				None
		a_        =				None
		a_        =				None

		# args for dynamo
		a_        =				None




		def   _snake_case  (     self				:List[str])				-> Tuple:






								'''simple docstring'''






								if self.deepspeed_config is None:
														UpperCamelCase_    :  Tuple				    =    {}
								if self.fsdp_config is None:
														UpperCamelCase_    :  Union[str, Any]				    =    {}
								if self.megatron_lm_config is None:
														UpperCamelCase_    :  int				    =    {}
								if self.ipex_config is None:
														UpperCamelCase_    :  Optional[int]				    =    {}
								return super().__post_init__()







@dataclass
class 						__snake_case (						_a  ):
		a_        =				42
		a_        =				42
		a_        =				None
		a_        =				None
		a_        =				"us-east-1"
		a_        =				1
		a_        =				"all"
		a_        =				F"""accelerate-sagemaker-{num_machines}"""
		a_        =				SAGEMAKER_PYTORCH_VERSION
		a_        =				SAGEMAKER_TRANSFORMERS_VERSION
		a_        =				SAGEMAKER_PYTHON_VERSION
		a_        =				None
		a_        =				None
		a_        =				None
		a_        =				None




