





"""simple docstring"""





import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[
		    "decoder.version",
		    "decoder.output_projection.weight",
		    "_float_tensor",
		    "decoder.embed_positions._float_tensor",
		]
		for k in ignore_keys:
				state_dict.pop(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   ,				UpperCamelCase   =	emb.weight.shape
		UpperCamelCase   =	nn.Linear(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	bias=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	emb.weight.data
		return lin_layer


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )
		UpperCamelCase   =	Namespace(**checkpoint["cfg"]["model"]    )
		UpperCamelCase   =	checkpoint["model"]
		remove_ignore_keys_(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	state_dict["decoder.embed_tokens.weight"].shape[0]

		UpperCamelCase   =	{key.replace("decoder"  ,	"model"    ): val for key, val in state_dict.items()}

		UpperCamelCase   =	XGLMConfig(
		    vocab_size=_SCREAMING_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  ,	)

		UpperCamelCase   =	XGLMForCausalLM(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		print(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	make_linear_from_emb(model.model.embed_tokens    )

		return model


if __name__ == "__main__":
						lowerCAmelCase__     			=			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.''')
						lowerCAmelCase__     			=			parser.parse_args()
						lowerCAmelCase__     			=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)













"""simple docstring"""





import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..auto.configuration_auto import CONFIG_MAPPING


lowerCAmelCase__     			=			logging.get_logger(__name__)





class        _lowerCamelCase      (       _lowercase ):
						UpperCAmelCase_ =			"upernet"


						def __init__(self   ,							__a=None   ,							__a=5_12   ,							__a=0.02   ,							__a=[1, 2, 3, 6]   ,							__a=True   ,							__a=0.4   ,							__a=3_84   ,							__a=2_56   ,							__a=1   ,							__a=False   ,							__a=2_55   ,							**__a   ,							) ->   Tuple:
								super().__init__(**__a		)

								if backbone_config is None:
										logger.info("`backbone_config` is `None`. Initializing the config with the default `ResNet` backbone."		)
										UpperCamelCase   =	CONFIG_MAPPING["resnet"](out_features=["stage1", "stage2", "stage3", "stage4"]		)
								elif isinstance(__a   ,							__a		):
										UpperCamelCase   =	backbone_config.get("model_type"		)
										UpperCamelCase   =	CONFIG_MAPPING[backbone_model_type]
										UpperCamelCase   =	config_class.from_dict(__a		)

								UpperCamelCase   =	backbone_config
								UpperCamelCase   =	hidden_size
								UpperCamelCase   =	initializer_range
								UpperCamelCase   =	pool_scales
								UpperCamelCase   =	use_auxiliary_head
								UpperCamelCase   =	auxiliary_loss_weight
								UpperCamelCase   =	auxiliary_in_channels
								UpperCamelCase   =	auxiliary_channels
								UpperCamelCase   =	auxiliary_num_convs
								UpperCamelCase   =	auxiliary_concat_input
								UpperCamelCase   =	loss_ignore_index




						def     snake_case_   (self		) ->   Optional[int]:
								UpperCamelCase   =	copy.deepcopy(self.__dict__		)
								UpperCamelCase   =	self.backbone_config.to_dict()
								UpperCamelCase   =	self.__class__.model_type
								return output






