





"""simple docstring"""





from ...configuration_utils import PretrainedConfig
from ...utils import logging


_UpperCamelCase					      =   logging.get_logger(__name__)

_UpperCamelCase					      =   {
    'EleutherAI/gpt-neox-20b': 'https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json',
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}
class 		SCREAMING_SNAKE_CASE_				(     snake_case__ ):
				"""simple docstring"""
				__snake_case :							str									=		"""gpt_neox"""




				def __init__(  self	:int    ,     __lowercase	:Optional[Any]=5_0432    ,     __lowercase	:Dict=6144    ,     __lowercase	:Union[str, Any]=44    ,     __lowercase	:str=64    ,     __lowercase	:str=2_4576    ,     __lowercase	:Union[str, Any]="gelu"    ,     __lowercase	:Dict=0.25    ,     __lowercase	:str=1_0000    ,     __lowercase	:List[str]=0.0    ,     __lowercase	:int=0.0    ,     __lowercase	:Any=0.1    ,     __lowercase	:Optional[Any]=2048    ,     __lowercase	:List[str]=0.02    ,     __lowercase	:Optional[int]=1e-5    ,     __lowercase	:str=True    ,     __lowercase	:List[Any]=0    ,     __lowercase	:Tuple=2    ,     __lowercase	:Any=False    ,     __lowercase	:Any=True    ,     __lowercase	:Union[str, Any]=None    ,     **__lowercase	:Union[str, Any]    ,     ):
											super().__init__(bos_token_id=__lowercase    ,     eos_token_id=__lowercase    ,     **__lowercase      )
											__lowerCamelCase   :   str       =vocab_size
											__lowerCamelCase   :   Optional[Any]       =max_position_embeddings
											__lowerCamelCase   :   Optional[int]       =hidden_size
											__lowerCamelCase   :   List[Any]       =num_hidden_layers
											__lowerCamelCase   :   Optional[int]       =num_attention_heads
											__lowerCamelCase   :   Optional[int]       =intermediate_size
											__lowerCamelCase   :   Optional[Any]       =hidden_act
											__lowerCamelCase   :   Union[str, Any]       =rotary_pct
											__lowerCamelCase   :   List[str]       =rotary_emb_base
											__lowerCamelCase   :   Optional[int]       =attention_dropout
											__lowerCamelCase   :   Tuple       =hidden_dropout
											__lowerCamelCase   :   str       =classifier_dropout
											__lowerCamelCase   :   List[str]       =initializer_range
											__lowerCamelCase   :   List[str]       =layer_norm_eps
											__lowerCamelCase   :   Optional[Any]       =use_cache
											__lowerCamelCase   :   str       =tie_word_embeddings
											__lowerCamelCase   :   Any       =use_parallel_residual
											__lowerCamelCase   :   Union[str, Any]       =rope_scaling
											self._rope_scaling_validation()

											if self.hidden_size % self.num_attention_heads != 0:
																		raise ValueError(
																		    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''      )




				def        __lowercase	(  self	:Optional[Any]      ):
											if self.rope_scaling is None:
																		return

											if not isinstance(self.rope_scaling    ,     __lowercase      ) or len(self.rope_scaling      ) != 2:
																		raise ValueError(
																		    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
																		    f'got {self.rope_scaling}'      )
											__lowerCamelCase   :   List[str]       =self.rope_scaling.get('''type'''    ,     __lowercase      )
											__lowerCamelCase   :   Optional[int]       =self.rope_scaling.get('''factor'''    ,     __lowercase      )
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																		raise ValueError(
																		    f'`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'      )
											if rope_scaling_factor is None or not isinstance(__lowercase    ,     __lowercase      ) or rope_scaling_factor <= 1.0:
																		raise ValueError(f'`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'      )











"""simple docstring"""





import argparse

from torch import nn

# transformers_old should correspond to branch `save_old_prophetnet_model_structure` here
# original prophetnet_checkpoints are saved under `patrickvonplaten/..._old` respectively
from transformers_old.modeling_prophetnet import (
    ProphetNetForConditionalGeneration as ProphetNetForConditionalGenerationOld,
)
from transformers_old.modeling_xlm_prophetnet import (
    XLMProphetNetForConditionalGeneration as XLMProphetNetForConditionalGenerationOld,
)

from transformers import ProphetNetForConditionalGeneration, XLMProphetNetForConditionalGeneration, logging


_UpperCamelCase					      =   logging.get_logger(__name__)
logging.set_verbosity_info()



def 					lowerCAmelCase_      (   SCREAMING_SNAKE_CASE							:				str     ,    SCREAMING_SNAKE_CASE							:				str ):


							'''simple docstring'''







							if "xprophetnet" in prophetnet_checkpoint_path:
														__lowerCamelCase   :   Optional[Any]       =XLMProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   List[Any]       =XLMProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )
							else:
														__lowerCamelCase   :   int       =ProphetNetForConditionalGenerationOld.from_pretrained(SCREAMING_SNAKE_CASE )
														__lowerCamelCase     ,    __lowerCamelCase   :   int       =ProphetNetForConditionalGeneration.from_pretrained(
														    SCREAMING_SNAKE_CASE     ,    output_loading_info=SCREAMING_SNAKE_CASE )

							__lowerCamelCase   :   Optional[Any]       =['''key_proj''', '''value_proj''', '''query_proj''']

							__lowerCamelCase   :   Tuple       ={
							    '''self_attn''': '''ngram_self_attn''',
							    '''cross_attn''': '''encoder_attn''',
							    '''cross_attn_layer_norm''': '''encoder_attn_layer_norm''',
							    '''feed_forward_layer_norm''': '''final_layer_norm''',
							    '''feed_forward''': '''''',
							    '''intermediate''': '''fc1''',
							    '''output''': '''fc2''',
							    '''key_proj''': '''k_proj''',
							    '''query_proj''': '''q_proj''',
							    '''value_proj''': '''v_proj''',
							    '''word_embeddings''': '''embed_tokens''',
							    '''embeddings_layer_norm''': '''emb_layer_norm''',
							    '''relative_pos_embeddings''': '''relative_linear''',
							    '''ngram_embeddings''': '''ngram_input_embed''',
							    '''position_embeddings''': '''embed_positions''',
							}

							for key in loading_info["missing_keys"]:
														__lowerCamelCase   :   int       =key.split('''.''' )

														if attributes[0] == "lm_head":
																					__lowerCamelCase   :   int       =prophet
																					__lowerCamelCase   :   Optional[int]       =prophet_old
														else:
																					__lowerCamelCase   :   Any       =prophet.prophetnet
																					__lowerCamelCase   :   Union[str, Any]       =prophet_old.model

														__lowerCamelCase   :   Optional[Any]       =False
														for attribute in attributes:
																					if attribute in mapping:
																												__lowerCamelCase   :   Optional[Any]       =mapping[attribute]
																												if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ) and len(SCREAMING_SNAKE_CASE ) > 0:
																																			__lowerCamelCase   :   Any       =attribute
																					elif hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																												__lowerCamelCase   :   Any       =attribute

																					if attribute == "weight":
																												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
																												__lowerCamelCase   :   str       =old_model.weight
																												logger.info(F'{attribute} is initialized.' )
																												__lowerCamelCase   :   Any       =True
																												break
																					elif attribute == "bias":
																												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
																												__lowerCamelCase   :   Union[str, Any]       =old_model.bias
																												logger.info(F'{attribute} is initialized' )
																												__lowerCamelCase   :   str       =True
																												break
																					elif attribute in special_keys and hasattr(SCREAMING_SNAKE_CASE     ,    '''in_proj_weight''' ):
																												__lowerCamelCase   :   int       =old_model.in_proj_weight.shape[0] // 3
																												__lowerCamelCase   :   Union[str, Any]       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )
																												param.weight.shape == old_model.in_proj_weight[:embed_dim, :].shape, "Shapes have to match"
																												param.bias.shape == old_model.in_proj_bias[:embed_dim].shape, "Shapes have to match"
																												if attribute == "query_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[:embed_dim, :] )
																																			__lowerCamelCase   :   str       =nn.Parameter(old_model.in_proj_bias[:embed_dim] )

																												elif attribute == "key_proj":
																																			__lowerCamelCase   :   List[str]       =nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :] )
																																			__lowerCamelCase   :   Tuple       =nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim] )
																												elif attribute == "value_proj":
																																			__lowerCamelCase   :   Optional[Any]       =nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :] )
																																			__lowerCamelCase   :   int       =nn.Parameter(old_model.in_proj_bias[2 * embed_dim :] )
																												__lowerCamelCase   :   Dict       =True
																												break
																					elif attribute == "position_embeddings":
																												assert (
																												    model.position_embeddings.weight.shape[-1] == old_model.embed_positions.weight.shape[-1]
																												), "Hidden size has to match"
																												assert model.position_embeddings.weight.shape[0] == 512, "We want 512 position_embeddings."
																												__lowerCamelCase   :   str       =nn.Parameter(old_model.embed_positions.weight[:512, :] )
																												__lowerCamelCase   :   Dict       =True
																												break

																					if attribute.isdigit():
																												__lowerCamelCase   :   List[str]       =model[int(SCREAMING_SNAKE_CASE )]
																												__lowerCamelCase   :   Optional[Any]       =old_model[int(SCREAMING_SNAKE_CASE )]
																					else:
																												__lowerCamelCase   :   int       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

																												if old_attribute == "":
																																			__lowerCamelCase   :   Dict       =old_model
																												else:
																																			if not hasattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE ):
																																										raise ValueError(F'{old_model} does not have {old_attribute}' )
																																			__lowerCamelCase   :   Tuple       =getattr(SCREAMING_SNAKE_CASE     ,    SCREAMING_SNAKE_CASE )

														if not is_key_init:
																					raise ValueError(F'{key} was not correctly initialized!' )

							print(F'Saving model to {pytorch_dump_folder_path}' )
							prophet.save_pretrained(SCREAMING_SNAKE_CASE )


if __name__ == "__main__":
							_UpperCamelCase					      =   argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    '--prophetnet_checkpoint_path', default=None, type=str, required=True, help='Path the official PyTorch dump.'
							)
							parser.add_argument(
							    '--pytorch_dump_folder_path', default=None, type=str, required=True, help='Path to the output PyTorch model.'
							)
							_UpperCamelCase					      =   parser.parse_args()
							convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)




