





'''simple docstring'''


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


lowerCAmelCase_   :   Union[str, Any]       =					logging.get_logger(__name__)

lowerCAmelCase_   :   int       =					{
    "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       UpperCamelCase__  (		__lowerCAmelCase     ):
	lowerCAmelCase__					:			Dict								=						"gptj"
	lowerCAmelCase__					:			List[Any]								=						{
	    "max_position_embeddings": "n_positions",
	    "hidden_size": "n_embd",
	    "num_attention_heads": "n_head",
	    "num_hidden_layers": "n_layer",
	}







	def __init__(				self			:   Tuple , lowerCamelCase			:   Union[str, Any]=5_0_4_0_0 , lowerCamelCase			:   Dict=2_0_4_8 , lowerCamelCase			:   Optional[int]=4_0_9_6 , lowerCamelCase			:   Union[str, Any]=2_8 , lowerCamelCase			:   Tuple=1_6 , lowerCamelCase			:   Dict=6_4 , lowerCamelCase			:   str=None , lowerCamelCase			:   Tuple="gelu_new" , lowerCamelCase			:   Optional[Any]=0.0 , lowerCamelCase			:   Tuple=0.0 , lowerCamelCase			:   Tuple=0.0 , lowerCamelCase			:   List[str]=1e-5 , lowerCamelCase			:   str=0.02 , lowerCamelCase			:   int=True , lowerCamelCase			:   List[str]=5_0_2_5_6 , lowerCamelCase			:   int=5_0_2_5_6 , lowerCamelCase			:   Optional[int]=False , **lowerCamelCase			:   Union[str, Any] , ):




				'''simple docstring'''
				a__     = vocab_size
				a__     = n_positions
				a__     = n_embd
				a__     = n_layer
				a__     = n_head
				a__     = n_inner
				a__     = rotary_dim
				a__     = activation_function
				a__     = resid_pdrop
				a__     = embd_pdrop
				a__     = attn_pdrop
				a__     = layer_norm_epsilon
				a__     = initializer_range
				a__     = use_cache

				a__     = bos_token_id
				a__     = eos_token_id

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






class       UpperCamelCase__  (		__lowerCAmelCase     ):







	def __init__(				self			:   List[str] , lowerCamelCase			:   PretrainedConfig , lowerCamelCase			:   str = "default" , lowerCamelCase			:   List[PatchingSpec] = None , lowerCamelCase			:   bool = False , ):




				'''simple docstring'''
				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?
							a__     = 0







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				a__     = OrderedDict({"input_ids": {0: "batch", 1: "sequence"}}		)
				if self.use_past:
							self.fill_with_past_key_values_(lowerCamelCase , direction="inputs"		)
							a__     = {0: "batch", 1: "past_sequence + sequence"}
				else:
							a__     = {0: "batch", 1: "sequence"}

				return common_inputs







	@property
	def 						__a   (				self			:   str		):




				'''simple docstring'''
				return self._config.n_layer







	@property
	def 						__a   (				self			:   Tuple		):




				'''simple docstring'''
				return self._config.n_head







	def 						__a   (				self			:   str , lowerCamelCase			:   PreTrainedTokenizer , lowerCamelCase			:   int = -1 , lowerCamelCase			:   int = -1 , lowerCamelCase			:   bool = False , lowerCamelCase			:   Optional[TensorType] = None , ):




				'''simple docstring'''
				a__     = 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()
				a__     = 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

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

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

				return ordered_inputs







	@property
	def 						__a   (				self			:   Optional[int]		):




				'''simple docstring'''
				return 1_3










'''simple docstring'''


import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration







def    _lowerCamelCase  (__lowerCamelCase  :     Any     )   ->    Optional[int]:
			a__     = [
			    "encoder.version",
			    "decoder.version",
			    "model.encoder.version",
			    "model.decoder.version",
			    "decoder.output_projection.weight",
			    "_float_tensor",
			    "encoder.embed_positions._float_tensor",
			    "decoder.embed_positions._float_tensor",
			]
			for k in ignore_keys:
						state_dict.pop(__lowerCamelCase					,  __lowerCamelCase     )







def    _lowerCamelCase  (__lowerCamelCase  :     int     )   ->    List[Any]:
			a__   ,				a__     = emb.weight.shape
			a__     = nn.Linear(__lowerCamelCase					,  __lowerCamelCase					,  bias=__lowerCamelCase     )
			a__     = emb.weight.data
			return lin_layer







def    _lowerCamelCase  (__lowerCamelCase  :     List[Any]     )   ->    Any:
			a__     = torch.load(__lowerCamelCase					,  map_location="cpu"     )
			a__     = mam_aaa["args"] or mam_aaa["cfg"]["model"]
			a__     = mam_aaa["model"]
			remove_ignore_keys_(__lowerCamelCase     )
			a__     = state_dict["encoder.embed_tokens.weight"].shape[0]

			a__     = MaMaaaConfig(
			    vocab_size=__lowerCamelCase					,  max_position_embeddings=1024					,  encoder_layers=args.encoder_layers					,  decoder_layers=args.decoder_layers					,  encoder_attention_heads=args.encoder_attention_heads					,  decoder_attention_heads=args.decoder_attention_heads					,  encoder_ffn_dim=args.encoder_ffn_embed_dim					,  decoder_ffn_dim=args.decoder_ffn_embed_dim					,  d_model=args.encoder_embed_dim					,  encoder_layerdrop=args.encoder_layerdrop					,  decoder_layerdrop=args.decoder_layerdrop					,  dropout=args.dropout					,  attention_dropout=args.attention_dropout					,  activation_dropout=args.activation_dropout					,  activation_function="relu"					,  )

			a__     = state_dict["decoder.embed_tokens.weight"]
			a__     = MaMaaaForConditionalGeneration(__lowerCamelCase     )
			model.model.load_state_dict(__lowerCamelCase					,  strict=__lowerCamelCase     )
			a__     = make_linear_from_emb(model.model.shared     )

			return model


if __name__ == "__main__":
		lowerCAmelCase_   :   Tuple       =					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_   :   Tuple       =					parser.parse_args()
		lowerCAmelCase_   :   int       =					convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
		model.save_pretrained(args.pytorch_dump_folder_path)



