'''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


_lowerCAmelCase	:Dict						=     logging.get_logger(__name__)
logging.set_verbosity_info()


def      __lowerCAmelCase    (							a_							,   a_		)   ->						Optional[int]:





     '''simple docstring'''





     if "xprophetnet" in prophetnet_checkpoint_path:
          SCREAMING_SNAKE_CASE       :      Dict     						=							XLMProphetNetForConditionalGenerationOld.from_pretrained(a_		)
          SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Tuple     						=							XLMProphetNetForConditionalGeneration.from_pretrained(
              a_							,   output_loading_info=a_		)
     else:
          SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							ProphetNetForConditionalGenerationOld.from_pretrained(a_		)
          SCREAMING_SNAKE_CASE						, SCREAMING_SNAKE_CASE       :      Tuple     						=							ProphetNetForConditionalGeneration.from_pretrained(
              a_							,   output_loading_info=a_		)

     SCREAMING_SNAKE_CASE       :      List[Any]     						=							['key_proj', 'value_proj', 'query_proj']

     SCREAMING_SNAKE_CASE       :      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"]:
          SCREAMING_SNAKE_CASE       :      int     						=							key.split('.'		)

          if attributes[0] == "lm_head":
               SCREAMING_SNAKE_CASE       :      int     						=							prophet
               SCREAMING_SNAKE_CASE       :      Any     						=							prophet_old
          else:
               SCREAMING_SNAKE_CASE       :      Any     						=							prophet.prophetnet
               SCREAMING_SNAKE_CASE       :      Optional[Any]     						=							prophet_old.model

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

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

                    elif attribute == "key_proj":
                         SCREAMING_SNAKE_CASE       :      List[str]     						=							nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]		)
                         SCREAMING_SNAKE_CASE       :      Tuple     						=							nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]		)
                    elif attribute == "value_proj":
                         SCREAMING_SNAKE_CASE       :      List[str]     						=							nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]		)
                         SCREAMING_SNAKE_CASE       :      Any     						=							nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]		)
                    SCREAMING_SNAKE_CASE       :      Tuple     						=							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."
                    SCREAMING_SNAKE_CASE       :      Tuple     						=							nn.Parameter(old_model.embed_positions.weight[:512, :]		)
                    SCREAMING_SNAKE_CASE       :      int     						=							True
                    break

               if attribute.isdigit():
                    SCREAMING_SNAKE_CASE       :      Union[str, Any]     						=							model[int(a_		)]
                    SCREAMING_SNAKE_CASE       :      int     						=							old_model[int(a_		)]
               else:
                    SCREAMING_SNAKE_CASE       :      str     						=							getattr(a_							,   a_		)

                    if old_attribute == "":
                         SCREAMING_SNAKE_CASE       :      int     						=							old_model
                    else:
                         if not hasattr(a_							,   a_		):
                              raise ValueError(f"""{old_model} does not have {old_attribute}"""		)
                         SCREAMING_SNAKE_CASE       :      List[Any]     						=							getattr(a_							,   a_		)

          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(a_		)


if __name__ == "__main__":
 _lowerCAmelCase	:List[Any]						=     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."""
 )
 _lowerCAmelCase	:List[str]						=     parser.parse_args()
 convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)











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


lowercase    				=   logging.get_logger(__name__)

lowercase    				=   {
    "transfo-xl-wt103": "https://huggingface.co/transfo-xl-wt103/resolve/main/config.json",
}




class 					UpperCamelCase_ (     snake_case_							):



	'''simple docstring'''




	lowerCAmelCase						=					'''transfo-xl'''
	lowerCAmelCase						=					['''mems''']
	lowerCAmelCase						=					{
	    '''n_token''': '''vocab_size''',
	    '''hidden_size''': '''d_model''',
	    '''num_attention_heads''': '''n_head''',
	    '''num_hidden_layers''': '''n_layer''',
	}




	def __init__(			self			,     a=26_77_35			,     a=[2_00_00, 4_00_00, 20_00_00]			,     a=10_24			,     a=10_24			,     a=16			,     a=64			,     a=40_96			,     a=4			,     a=False			,     a=18			,     a=16_00			,     a=10_00			,     a=True			,     a=True			,     a=0			,     a=-1			,     a=True			,     a=0.1			,     a=0.0			,     a=True			,     a="normal"			,     a=0.01			,     a=0.01			,     a=0.02			,     a=1E-5			,     a=0			,     **a			,     )			->       Union[str, Any]:
								snake_case_           =				vocab_size
								snake_case_           =				[]
								self.cutoffs.extend(a   )
								if proj_share_all_but_first:
															snake_case_           =				[False] + [True] * len(self.cutoffs   )
								else:
															snake_case_           =				[False] + [False] * len(self.cutoffs   )
								snake_case_           =				d_model
								snake_case_           =				d_embed
								snake_case_           =				d_head
								snake_case_           =				d_inner
								snake_case_           =				div_val
								snake_case_           =				pre_lnorm
								snake_case_           =				n_layer
								snake_case_           =				n_head
								snake_case_           =				mem_len
								snake_case_           =				same_length
								snake_case_           =				attn_type
								snake_case_           =				clamp_len
								snake_case_           =				sample_softmax
								snake_case_           =				adaptive
								snake_case_           =				dropout
								snake_case_           =				dropatt
								snake_case_           =				untie_r
								snake_case_           =				init
								snake_case_           =				init_range
								snake_case_           =				proj_init_std
								snake_case_           =				init_std
								snake_case_           =				layer_norm_epsilon
								super().__init__(eos_token_id=a			,     **a   )




	@property
	def 				_UpperCamelCase     (			self   )			->       int:
								# Message copied from Transformer-XL documentation
								logger.info(F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )
								return -1






	@max_position_embeddings.setter
	def 				_UpperCamelCase     (			self			,     a   )			->       List[str]:
								# Message copied from Transformer-XL documentation
								raise NotImplementedError(
								    F'''The model {self.model_type} is one of the few models that has no sequence length limit.'''   )





