



from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PreTrainedTokenizer, TensorType, is_torch_available
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfigWithPast, PatchingSpec
from ...utils import logging


__A       =						logging.get_logger(__name__)

__A       =						{
    '''EleutherAI/gpt-j-6B''': '''https://huggingface.co/EleutherAI/gpt-j-6B/resolve/main/config.json''',
    # See all GPT-J models at https://huggingface.co/models?filter=gpt_j
}






class 		lowercase						(			__UpperCamelCase):






	"""simple docstring"""


	a__				: Union[str, Any]		       =							"gptj"
	a__				: Optional[Any]		       =							{
	    "max_position_embeddings": "n_positions",
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}




	def __init__(     self     :						List[str]						,  __UpperCAmelCase     :						str=50_400						,  __UpperCAmelCase     :						List[str]=2_048						,  __UpperCAmelCase     :						str=4_096						,  __UpperCAmelCase     :						Optional[Any]=28						,  __UpperCAmelCase     :						Any=16						,  __UpperCAmelCase     :						Dict=64						,  __UpperCAmelCase     :						List[str]=None						,  __UpperCAmelCase     :						Optional[Any]="gelu_new"						,  __UpperCAmelCase     :						List[str]=0.0						,  __UpperCAmelCase     :						Optional[int]=0.0						,  __UpperCAmelCase     :						Optional[int]=0.0						,  __UpperCAmelCase     :						Optional[int]=1E-5						,  __UpperCAmelCase     :						List[Any]=0.02						,  __UpperCAmelCase     :						Tuple=True						,  __UpperCAmelCase     :						Optional[Any]=50_256						,  __UpperCAmelCase     :						Optional[Any]=50_256						,  __UpperCAmelCase     :						Dict=False						,  **__UpperCAmelCase     :						Tuple						,  )      ->			int:
							UpperCAmelCase_=       vocab_size
							UpperCAmelCase_=       n_positions
							UpperCAmelCase_=       n_embd
							UpperCAmelCase_=       n_layer
							UpperCAmelCase_=       n_head
							UpperCAmelCase_=       n_inner
							UpperCAmelCase_=       rotary_dim
							UpperCAmelCase_=       activation_function
							UpperCAmelCase_=       resid_pdrop
							UpperCAmelCase_=       embd_pdrop
							UpperCAmelCase_=       attn_pdrop
							UpperCAmelCase_=       layer_norm_epsilon
							UpperCAmelCase_=       initializer_range
							UpperCAmelCase_=       use_cache

							UpperCAmelCase_=       bos_token_id
							UpperCAmelCase_=       eos_token_id

							super().__init__(
							    bos_token_id=_lowerCAmelCase						,  eos_token_id=_lowerCAmelCase						,  tie_word_embeddings=_lowerCAmelCase						,  **_lowerCAmelCase					)






class 		lowercase						(			__UpperCamelCase):






	"""simple docstring"""


	def __init__(     self     :						int						,  __UpperCAmelCase     :						PretrainedConfig						,  __UpperCAmelCase     :						str = "default"						,  __UpperCAmelCase     :						List[PatchingSpec] = None						,  __UpperCAmelCase     :						bool = False						,  )      ->			Union[str, Any]:
							super().__init__(_lowerCAmelCase						,  task=_lowerCAmelCase						,  patching_specs=_lowerCAmelCase						,  use_past=_lowerCAmelCase					)
							if not getattr(self._config						,  """pad_token_id"""						,  _lowerCAmelCase					):
													# TODO: how to do that better?
													UpperCAmelCase_=       0




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Union[str, Any]					)      ->			Dict:
							UpperCAmelCase_=       OrderedDict({"""input_ids""": {0: """batch""", 1: """sequence"""}}					)
							if self.use_past:
													self.fill_with_past_key_values_(_lowerCAmelCase						,  direction="""inputs"""					)
													UpperCAmelCase_=       {0: """batch""", 1: """past_sequence + sequence"""}
							else:
													UpperCAmelCase_=       {0: """batch""", 1: """sequence"""}

							return common_inputs




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						Tuple					)      ->			Union[str, Any]:
							return self._config.n_layer




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			Optional[Any]:
							return self._config.n_head




	def   _SCREAMING_SNAKE_CASE		(     self     :						str						,  __UpperCAmelCase     :						PreTrainedTokenizer						,  __UpperCAmelCase     :						int = -1						,  __UpperCAmelCase     :						int = -1						,  __UpperCAmelCase     :						bool = False						,  __UpperCAmelCase     :						Optional[TensorType] = None						,  )      ->			Tuple:
							UpperCAmelCase_=       super(_lowerCAmelCase						,  self					).generate_dummy_inputs(
							    _lowerCAmelCase						,  batch_size=_lowerCAmelCase						,  seq_length=_lowerCAmelCase						,  is_pair=_lowerCAmelCase						,  framework=_lowerCAmelCase					)

							# We need to order the input in the way they appears in the forward()
							UpperCAmelCase_=       OrderedDict({"""input_ids""": common_inputs["""input_ids"""]}					)

							# Need to add the past_keys
							if self.use_past:
													if not is_torch_available():
																			raise ValueError("""Cannot generate dummy past_keys inputs without PyTorch installed."""					)
													else:
																			import torch

																			UpperCAmelCase_=       common_inputs["""input_ids"""].shape
																			# Not using the same length for past_key_values
																			UpperCAmelCase_=       seqlen + 2
																			UpperCAmelCase_=       (
																			    batch,
																			    self.num_attention_heads,
																			    past_key_values_length,
																			    self._config.hidden_size // self.num_attention_heads,
																			)
																			UpperCAmelCase_=       [
																			    (torch.zeros(_lowerCAmelCase					), torch.zeros(_lowerCAmelCase					)) for _ in range(self.num_layers					)
																			]

							UpperCAmelCase_=       common_inputs["""attention_mask"""]
							if self.use_past:
													UpperCAmelCase_=       ordered_inputs["""attention_mask"""].dtype
													UpperCAmelCase_=       torch.cat(
													    [ordered_inputs["""attention_mask"""], torch.ones(_lowerCAmelCase						,  _lowerCAmelCase						,  dtype=_lowerCAmelCase					)]						,  dim=1					)

							return ordered_inputs




	@property
	def   _SCREAMING_SNAKE_CASE		(     self     :						List[str]					)      ->			int:
							return 13









import argparse
from argparse import Namespace

import torch
from torch import nn

from transformers import XGLMConfig, XGLMForCausalLM



def 	__a					(   lowerCAmelCase_       : Optional[int]    )				->			List[Any]:






						'''simple docstring'''



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



def 	__a					(   lowerCAmelCase_       : Any    )				->			str:






						'''simple docstring'''



						UpperCAmelCase_,       UpperCAmelCase_=       emb.weight.shape
						UpperCAmelCase_=       nn.Linear(lowerCAmelCase_      ,lowerCAmelCase_      ,bias=lowerCAmelCase_    )
						UpperCAmelCase_=       emb.weight.data
						return lin_layer



def 	__a					(   lowerCAmelCase_       : List[str]    )				->			List[str]:






						'''simple docstring'''



						UpperCAmelCase_=       torch.load(lowerCAmelCase_      ,map_location="""cpu"""    )
						UpperCAmelCase_=       Namespace(**checkpoint["""cfg"""]["""model"""]    )
						UpperCAmelCase_=       checkpoint["""model"""]
						remove_ignore_keys_(lowerCAmelCase_    )
						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=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_=       XGLMForCausalLM(lowerCAmelCase_    )
						UpperCAmelCase_=       model.load_state_dict(lowerCAmelCase_      ,strict=lowerCAmelCase_    )
						print(lowerCAmelCase_    )
						UpperCAmelCase_=       make_linear_from_emb(model.model.embed_tokens    )

						return model


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




