

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ..bit import BitConfig


_lowerCAmelCase					:			str  				=			logging.get_logger(__name__)

_lowerCAmelCase					:			int  				=			{
    "Intel/dpt-large": "https://huggingface.co/Intel/dpt-large/resolve/main/config.json",
    # See all DPT models at https://huggingface.co/models?filter=dpt
}






class 	__magic_name__    (			lowerCAmelCase_      ):
			SCREAMING_SNAKE_CASE							   =						'dpt'





			def __init__(		self					,  __snake_case=768					,  __snake_case=12					,  __snake_case=12					,  __snake_case=3072					,  __snake_case="gelu"					,  __snake_case=0.0					,  __snake_case=0.0					,  __snake_case=0.02					,  __snake_case=1e-12					,  __snake_case=384					,  __snake_case=16					,  __snake_case=3					,  __snake_case=False					,  __snake_case=True					,  __snake_case=[2, 5, 8, 11]					,  __snake_case="project"					,  __snake_case=[4, 2, 1, 0.5]					,  __snake_case=[96, 192, 384, 768]					,  __snake_case=256					,  __snake_case=-1					,  __snake_case=False					,  __snake_case=True					,  __snake_case=0.4					,  __snake_case=255					,  __snake_case=0.1					,  __snake_case=[1, 1024, 24, 24]					,  __snake_case=[0, 1]					,  __snake_case=None					,  **__snake_case					,  )   -> Dict:




						'''simple docstring'''
						super().__init__(**__snake_case					)

						__a        =hidden_size
						__a        =is_hybrid

						if self.is_hybrid:
									if backbone_config is None:
												logger.info('Initializing the config with a `BiT` backbone.'					)
												__a        ={
												    'global_padding': 'same',
												    'layer_type': 'bottleneck',
												    'depths': [3, 4, 9],
												    'out_features': ['stage1', 'stage2', 'stage3'],
												    'embedding_dynamic_padding': True,
												}
												__a        =BitConfig(**__snake_case					)
									elif isinstance(__snake_case					,  __snake_case					):
												logger.info('Initializing the config with a `BiT` backbone.'					)
												__a        =BitConfig(**__snake_case					)
									elif isinstance(__snake_case					,  __snake_case					):
												__a        =backbone_config
									else:
												raise ValueError(
												    f'backbone_config must be a dictionary or a `PretrainedConfig`, got {backbone_config.__class__}.'					)

									__a        =backbone_featmap_shape
									__a        =neck_ignore_stages

									if readout_type != "project":
												raise ValueError('Readout type must be \'project\' when using `DPT-hybrid` mode.'					)
						else:
									__a        =None
									__a        =None
									__a        =[]

						__a        =num_hidden_layers
						__a        =num_attention_heads
						__a        =intermediate_size
						__a        =hidden_act
						__a        =hidden_dropout_prob
						__a        =attention_probs_dropout_prob
						__a        =initializer_range
						__a        =layer_norm_eps
						__a        =image_size
						__a        =patch_size
						__a        =num_channels
						__a        =qkv_bias
						__a        =backbone_out_indices
						if readout_type not in ["ignore", "add", "project"]:
									raise ValueError('Readout_type must be one of [\'ignore\', \'add\', \'project\']'					)
						__a        =readout_type
						__a        =reassemble_factors
						__a        =neck_hidden_sizes
						__a        =fusion_hidden_size
						__a        =head_in_index
						__a        =use_batch_norm_in_fusion_residual
						# auxiliary head attributes (semantic segmentation)
						__a        =use_auxiliary_head
						__a        =auxiliary_loss_weight
						__a        =semantic_loss_ignore_index
						__a        =semantic_classifier_dropout





			def 		__magic_name__    (		self					)   -> Any:




						'''simple docstring'''
						__a        =copy.deepcopy(self.__dict__					)

						if output["backbone_config"] is not None:
									__a        =self.backbone_config.to_dict()

						__a        =self.__class__.model_type
						return output









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM






def        UpperCamelCase_(							_snake_case   :  Union[str, Any]	):




			"""simple docstring"""






			__a        =[
			    '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        UpperCamelCase_(							_snake_case   :  Any	):




			"""simple docstring"""






			__a      ,    __a        =emb.weight.shape
			__a        =nn.Linear(_snake_case       ,     _snake_case       ,     bias=_snake_case	)
			__a        =emb.weight.data
			return lin_layer






def        UpperCamelCase_(							_snake_case   :  str	):




			"""simple docstring"""






			__a        =torch.load(_snake_case       ,     map_location='cpu'	)
			__a        =Namespace(**checkpoint['cfg']['model']	)
			__a        =checkpoint['model']
			remove_ignore_keys_(_snake_case	)
			__a        =state_dict['decoder.embed_tokens.weight'].shape[0]

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

			__a        =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       ,     )

			__a        =XGLMForCausalLM(_snake_case	)
			__a        =model.load_state_dict(_snake_case       ,     strict=_snake_case	)
			print(_snake_case	)
			__a        =make_linear_from_emb(model.model.embed_tokens	)

			return model


if __name__ == "__main__":
						_lowerCAmelCase					:			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.")
						_lowerCAmelCase					:			str  				=			parser.parse_args()
						_lowerCAmelCase					:			Union[str, Any]  				=			convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
						model.save_pretrained(args.pytorch_dump_folder_path)






