
import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[Any])      ->					List[str]:






							'''simple docstring'''





							__UpperCamelCase			:     Union[str, Any]         =      [
							    "decoder.version",
							    "decoder.output_projection.weight",
							    "_float_tensor",
							    "decoder.embed_positions._float_tensor",
							]
							for k in ignore_keys:
														state_dict.pop(_lowerCamelCase		,						_lowerCamelCase)




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    str)      ->					List[Any]:






							'''simple docstring'''





							__UpperCamelCase     ,		__UpperCamelCase			:     List[str]         =      emb.weight.shape
							__UpperCamelCase			:     Optional[int]         =      nn.Linear(_lowerCamelCase		,						_lowerCamelCase		,						bias=_lowerCamelCase)
							__UpperCamelCase			:     List[Any]         =      emb.weight.data
							return lin_layer




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Optional[int])      ->					Any:






							'''simple docstring'''





							__UpperCamelCase			:     List[Any]         =      torch.load(_lowerCamelCase		,						map_location="cpu")
							__UpperCamelCase			:     Dict         =      Namespace(**checkpoint["cfg"]["model"])
							__UpperCamelCase			:     List[Any]         =      checkpoint["model"]
							remove_ignore_keys_(_lowerCamelCase)
							__UpperCamelCase			:     Optional[Any]         =      state_dict["decoder.embed_tokens.weight"].shape[0]

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

							__UpperCamelCase			:     str         =      XGLMConfig(
							    vocab_size=_lowerCamelCase		,						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			:     Any         =      XGLMForCausalLM(_lowerCamelCase)
							__UpperCamelCase			:     Any         =      model.load_state_dict(_lowerCamelCase		,						strict=_lowerCamelCase)
							print(_lowerCamelCase)
							__UpperCamelCase			:     Union[str, Any]         =      make_linear_from_emb(model.model.embed_tokens)

							return model


if __name__ == "__main__":
		lowercase						:      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.')
		lowercase						:      List[str]									= parser.parse_args()
		lowercase						:      Optional[Any]									= convert_fairseq_xglm_checkpoint_from_disk(args.fairseq_path)
		model.save_pretrained(args.pytorch_dump_folder_path)

from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowercase						:      Optional[Any]									= logging.get_logger(__name__)

lowercase						:      List[str]									= {
    'shi-labs/nat-mini-in1k-224': 'https://huggingface.co/shi-labs/nat-mini-in1k-224/resolve/main/config.json',
    # See all Nat models at https://huggingface.co/models?filter=nat
}




class 				lowerCamelCase__							(       __lowercase  ,		__lowercase):




	'''simple docstring'''




	_A			=  'nat'

	_A			=  {
	    'num_attention_heads': 'num_heads',
	    'num_hidden_layers': 'num_layers',
	}







	def __init__(      self	:Optional[Any]  ,     a	:Any=4  ,     a	:Any=3  ,     a	:int=6_4  ,     a	:Dict=[3, 4, 6, 5]  ,     a	:Dict=[2, 4, 8, 1_6]  ,     a	:Optional[Any]=7  ,     a	:Any=3.0  ,     a	:Optional[int]=True  ,     a	:int=0.0  ,     a	:Union[str, Any]=0.0  ,     a	:List[Any]=0.1  ,     a	:str="gelu"  ,     a	:Union[str, Any]=0.02  ,     a	:Tuple=1E-5  ,     a	:str=0.0  ,     a	:Optional[int]=None  ,     a	:Dict=None  ,     **a	:Optional[Any]  ,     )	->				int:
								super().__init__(**a		)

								__UpperCamelCase			:     Any         =      patch_size
								__UpperCamelCase			:     str         =      num_channels
								__UpperCamelCase			:     List[Any]         =      embed_dim
								__UpperCamelCase			:     str         =      depths
								__UpperCamelCase			:     str         =      len(a		)
								__UpperCamelCase			:     Optional[Any]         =      num_heads
								__UpperCamelCase			:     Dict         =      kernel_size
								__UpperCamelCase			:     Union[str, Any]         =      mlp_ratio
								__UpperCamelCase			:     Union[str, Any]         =      qkv_bias
								__UpperCamelCase			:     List[str]         =      hidden_dropout_prob
								__UpperCamelCase			:     Any         =      attention_probs_dropout_prob
								__UpperCamelCase			:     Any         =      drop_path_rate
								__UpperCamelCase			:     Any         =      hidden_act
								__UpperCamelCase			:     Tuple         =      layer_norm_eps
								__UpperCamelCase			:     Dict         =      initializer_range
								# we set the hidden_size attribute in order to make Nat work with VisionEncoderDecoderModel
								# this indicates the channel dimension after the last stage of the model
								__UpperCamelCase			:     int         =      int(embed_dim * 2 ** (len(a		) - 1)		)
								__UpperCamelCase			:     List[Any]         =      layer_scale_init_value
								__UpperCamelCase			:     Optional[Any]         =      ["stem"] + [f'stage{idx}' for idx in range(1  ,     len(a		) + 1		)]
								__UpperCamelCase     ,		__UpperCamelCase			:     Any         =      get_aligned_output_features_output_indices(
								    out_features=a  ,     out_indices=a  ,     stage_names=self.stage_names		)