



"""simple docstring"""






import copy
import re



class     A__     :




	'''simple docstring'''





	SCREAMING_SNAKE_CASE					   =			'hp'
	SCREAMING_SNAKE_CASE					   =			{}
	SCREAMING_SNAKE_CASE					   =			None

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	List[Any] ,    _SCREAMING_SNAKE_CASE:	List[str] ,    _SCREAMING_SNAKE_CASE:	Dict)       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						List[str]				= prefix
				__lowerCAmelCase   :						Optional[Any]				= defaults
				cls.build_naming_info()

	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	Any ,    _SCREAMING_SNAKE_CASE:	Any)       -> Dict:

				"""simple docstring"""

				if len(_SCREAMING_SNAKE_CASE) == 0:
							return ""
				__lowerCAmelCase   :						int				= None
				if any(char.isdigit() for char in word):
							raise Exception(F"""Parameters should not contain numbers: '{word}' contains a number""")
				if word in info["short_word"]:
							return info["short_word"][word]
				for prefix_len in range(1 ,    len(_SCREAMING_SNAKE_CASE) + 1):
							__lowerCAmelCase   :						Any				= word[:prefix_len]
							if prefix in info["reverse_short_word"]:
										continue
							else:
										__lowerCAmelCase   :						Optional[int]				= prefix
										break

				if short_word is None:
							# Paranoid fallback
							def int_to_alphabetic(_SCREAMING_SNAKE_CASE:	Union[str, Any]):
										__lowerCAmelCase   :						Union[str, Any]				= ""
										while integer != 0:
													__lowerCAmelCase   :						List[str]				= chr(ord("A") + integer % 10) + s
													integer //= 10
										return s

							__lowerCAmelCase   :						List[Any]				= 0
							while True:
										__lowerCAmelCase   :						Union[str, Any]				= word + "#" + int_to_alphabetic(_SCREAMING_SNAKE_CASE)
										if sword in info["reverse_short_word"]:
													continue
										else:
													__lowerCAmelCase   :						List[Any]				= sword
													break

				__lowerCAmelCase   :						int				= short_word
				__lowerCAmelCase   :						Union[str, Any]				= word
				return short_word

	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	Union[str, Any] ,    _SCREAMING_SNAKE_CASE:	str)       -> Dict:

				"""simple docstring"""

				__lowerCAmelCase   :						Optional[int]				= param_name.split("_")

				__lowerCAmelCase   :						Dict				= [TrialShortNamer.shortname_for_word(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE) for word in words]

				# We try to create a separatorless short name, but if there is a collision we have to fallback
				# to a separated short name
				__lowerCAmelCase   :						str				= ["", "_"]

				for separator in separators:
							__lowerCAmelCase   :						Optional[int]				= separator.join(_SCREAMING_SNAKE_CASE)
							if shortname not in info["reverse_short_param"]:
										__lowerCAmelCase   :						Any				= shortname
										__lowerCAmelCase   :						int				= param_name
										return shortname

				return param_name

	@staticmethod
	def 							_SCREAMING_SNAKE_CASE     (  _SCREAMING_SNAKE_CASE:	int ,    _SCREAMING_SNAKE_CASE:	List[str])       -> str:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= TrialShortNamer.shortname_for_key(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)
				__lowerCAmelCase   :						Dict				= short_name
				__lowerCAmelCase   :						Any				= param_name

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	List[str])       -> List[Any]:

				"""simple docstring"""

				if cls.NAMING_INFO is not None:
							return

				__lowerCAmelCase   :						Optional[Any]				= {
				    "short_word": {},
				    "reverse_short_word": {},
				    "short_param": {},
				    "reverse_short_param": {},
				}

				__lowerCAmelCase   :						Optional[Any]				= list(cls.DEFAULTS.keys())

				for k in field_keys:
							cls.add_new_param_name(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE)

				__lowerCAmelCase   :						List[Any]				= info

	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	List[str] ,    _SCREAMING_SNAKE_CASE:	Union[str, Any])       -> Union[str, Any]:

				"""simple docstring"""

				cls.build_naming_info()
				assert cls.PREFIX is not None
				__lowerCAmelCase   :						Optional[int]				= [copy.copy(cls.PREFIX)]

				for k, v in params.items():
							if k not in cls.DEFAULTS:
										raise Exception(F"""You should provide a default value for the param name {k} with value {v}""")
							if v == cls.DEFAULTS[k]:
										# The default value is not added to the name
										continue

							__lowerCAmelCase   :						Any				= cls.NAMING_INFO["short_param"][k]

							if isinstance(_SCREAMING_SNAKE_CASE ,    _SCREAMING_SNAKE_CASE):
										__lowerCAmelCase   :						Any				= 1 if v else 0

							__lowerCAmelCase   :						Dict				= "" if isinstance(_SCREAMING_SNAKE_CASE ,    (int, float)) else "-"
							__lowerCAmelCase   :						Tuple				= F"""{key}{sep}{v}"""
							name.append(_SCREAMING_SNAKE_CASE)

				return "_".join(_SCREAMING_SNAKE_CASE)







	@classmethod
	def 							_SCREAMING_SNAKE_CASE     (  cls:	Optional[Any] ,    _SCREAMING_SNAKE_CASE:	Dict)       -> Tuple:

				"""simple docstring"""

				__lowerCAmelCase   :						List[Any]				= repr[len(cls.PREFIX) + 1 :]
				if repr == "":
							__lowerCAmelCase   :						Dict				= []
				else:
							__lowerCAmelCase   :						Dict				= repr.split("_")

				__lowerCAmelCase   :						List[Any]				= {}

				for value in values:
							if "-" in value:
										__lowerCAmelCase		,			__lowerCAmelCase   :						Optional[int]				= value.split("-")
							else:
										__lowerCAmelCase   :						Any				= re.sub("[0-9.]" ,    "" ,    _SCREAMING_SNAKE_CASE)
										__lowerCAmelCase   :						Union[str, Any]				= float(re.sub("[^0-9.]" ,    "" ,    _SCREAMING_SNAKE_CASE))

							__lowerCAmelCase   :						Dict				= cls.NAMING_INFO["reverse_short_param"][p_k]

							__lowerCAmelCase   :						List[Any]				= p_v

				for k in cls.DEFAULTS:
							if k not in parameters:
										__lowerCAmelCase   :						Dict				= cls.DEFAULTS[k]

				return parameters




"""simple docstring"""






import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def     _lowercase       (						__snake_case			)		->     Tuple:
			__lowerCAmelCase   :						Dict				= [
			    "decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__snake_case      ,__snake_case			)


def     _lowercase       (						__snake_case			)		->     List[str]:
			__lowerCAmelCase		,			__lowerCAmelCase   :						List[Any]				= emb.weight.shape
			__lowerCAmelCase   :						str				= nn.Linear(__snake_case      ,__snake_case      ,bias=__snake_case			)
			__lowerCAmelCase   :						Optional[int]				= emb.weight.data
			return lin_layer


def     _lowercase       (						__snake_case			)		->     Any:
			__lowerCAmelCase   :						List[str]				= torch.load(__snake_case      ,map_location="cpu"			)
			__lowerCAmelCase   :						Dict				= Namespace(**checkpoint["cfg"]["model"]			)
			__lowerCAmelCase   :						Dict				= checkpoint["model"]
			remove_ignore_keys_(__snake_case			)
			__lowerCAmelCase   :						Dict				= state_dict["decoder.embed_tokens.weight"].shape[0]

			__lowerCAmelCase   :						Optional[int]				= {key.replace("decoder"      ,"model"			): val for key, val in state_dict.items()}

			__lowerCAmelCase   :						List[Any]				= XGLMConfig(
			    vocab_size=__snake_case      ,max_position_embeddings=args.max_target_positions      ,num_layers=args.decoder_layers      ,attention_heads=args.decoder_attention_heads      ,ffn_dim=args.decoder_ffn_embed_dim      ,d_model=args.decoder_embed_dim      ,layerdrop=args.decoder_layerdrop      ,dropout=args.dropout      ,attention_dropout=args.attention_dropout      ,activation_dropout=args.activation_dropout      ,activation_function="gelu"      ,scale_embedding=not args.no_scale_embedding      ,tie_word_embeddings=args.share_decoder_input_output_embed      ,)

			__lowerCAmelCase   :						Union[str, Any]				= XGLMForCausalLM(__snake_case			)
			__lowerCAmelCase   :						Tuple				= model.load_state_dict(__snake_case      ,strict=__snake_case			)
			print(__snake_case			)
			__lowerCAmelCase   :						List[str]				= make_linear_from_emb(model.model.embed_tokens			)

			return model


if __name__ == "__main__":
	__snake_case							:     str   =					argparse.ArgumentParser()
	# Required parameters
	parser.add_argument('fairseq_path', type=str, help='path to a model.pt on local filesystem.')
	parser.add_argument('pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model.')
	__snake_case							:     Optional[Any]   =					parser.parse_args()
	__snake_case							:     Optional[Any]   =					convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
	model.save_pretrained(args.pytorch_dump_folder_path)