


'''simple docstring'''







import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM





def 		_UpperCAmelCase   (							__A							:   List[str]      ):
				a_		:  Optional[int]											=      [
				    '''decoder.version''',
				    '''decoder.output_projection.weight''',
				    '''_float_tensor''',
				    '''decoder.embed_positions._float_tensor''',
				]
				for k in ignore_keys:
								state_dict.pop(__A						,		__A      )





def 		_UpperCAmelCase   (							__A							:   str      ):
				a_		:  Dict											=      emb.weight.shape
				a_		:  Optional[Any]											=      nn.Linear(__A						,		__A						,		bias=__A      )
				a_		:  Dict											=      emb.weight.data
				return lin_layer





def 		_UpperCAmelCase   (							__A							:   Any      ):
				a_		:  List[str]											=      torch.load(__A						,		map_location='''cpu'''      )
				a_		:  Optional[int]											=      Namespace(**checkpoint['''cfg''']['''model''']      )
				a_		:  Tuple											=      checkpoint['''model''']
				remove_ignore_keys_(__A      )
				a_		:  str											=      state_dict['''decoder.embed_tokens.weight'''].shape[0]

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

				a_		:  List[str]											=      XGLMConfig(
				    vocab_size=__A						,		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_		:  Tuple											=      XGLMForCausalLM(__A      )
				a_		:  Any											=      model.load_state_dict(__A						,		strict=__A      )
				print(__A      )
				a_		:  List[str]											=      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 dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import ClassLabel, Features, Image
from .base import TaskTemplate





@dataclass(frozen=SCREAMING_SNAKE_CASE_       )
class 	SCREAMING_SNAKE_CASE					(					SCREAMING_SNAKE_CASE_       ):
			snake_case__  =				field(default="image-classification"	,   metadata={"include_in_asdict_even_if_is_default": True}       )
			snake_case__  =				Features({"image": Image()}       )
			snake_case__  =				Features({"labels": ClassLabel}       )
			snake_case__  =				"image"
			snake_case__  =				"labels"






			def 			SCREAMING_SNAKE_CASE      ( self      :			List[str]  ,						__SCREAMING_SNAKE_CASE      :			List[Any]					)			->			Any:
							if self.label_column not in features:
											raise ValueError(f'Column {self.label_column} is not present in features.'					)
							if not isinstance(features[self.label_column]  ,						__SCREAMING_SNAKE_CASE					):
											raise ValueError(f'Column {self.label_column} is not a ClassLabel.'					)
							a_		:  Optional[int]											=      copy.deepcopy(self					)
							a_		:  int											=      self.label_schema.copy()
							a_		:  Tuple											=      features[self.label_column]
							a_		:  str											=      label_schema
							return task_template







			@property
			def 			SCREAMING_SNAKE_CASE      ( self      :			Tuple					)			->			Dict[str, str]:
							return {
							    self.image_column: "image",
							    self.label_column: "labels",
							}




