
"""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__							=				logging.get_logger(__name__)
logging.set_verbosity_info()



def        _lowerCamelCase ( __a,							__a    ):
		if "xprophetnet" in prophetnet_checkpoint_path:
				SCREAMING_SNAKE_CASE_						 = XLMProphetNetForConditionalGenerationOld.from_pretrained(__a    )
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = XLMProphetNetForConditionalGeneration.from_pretrained(
				    __a,							output_loading_info=__a    )
		else:
				SCREAMING_SNAKE_CASE_						 = ProphetNetForConditionalGenerationOld.from_pretrained(__a    )
				SCREAMING_SNAKE_CASE_ ,SCREAMING_SNAKE_CASE_						 = ProphetNetForConditionalGeneration.from_pretrained(
				    __a,							output_loading_info=__a    )

		SCREAMING_SNAKE_CASE_						 = ['''key_proj''', '''value_proj''', '''query_proj''']

		SCREAMING_SNAKE_CASE_						 = {
		    '''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_						 = key.split('''.'''    )

				if attributes[0] == "lm_head":
						SCREAMING_SNAKE_CASE_						 = prophet
						SCREAMING_SNAKE_CASE_						 = prophet_old
				else:
						SCREAMING_SNAKE_CASE_						 = prophet.prophetnet
						SCREAMING_SNAKE_CASE_						 = prophet_old.model

				SCREAMING_SNAKE_CASE_						 = False
				for attribute in attributes:
						if attribute in mapping:
								SCREAMING_SNAKE_CASE_						 = mapping[attribute]
								if not hasattr(__a,							__a    ) and len(__a    ) > 0:
										SCREAMING_SNAKE_CASE_						 = attribute
						elif hasattr(__a,							__a    ):
								SCREAMING_SNAKE_CASE_						 = attribute

						if attribute == "weight":
								assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
								SCREAMING_SNAKE_CASE_						 = old_model.weight
								logger.info(F'{attribute} is initialized.'    )
								SCREAMING_SNAKE_CASE_						 = True
								break
						elif attribute == "bias":
								assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
								SCREAMING_SNAKE_CASE_						 = old_model.bias
								logger.info(F'{attribute} is initialized'    )
								SCREAMING_SNAKE_CASE_						 = True
								break
						elif attribute in special_keys and hasattr(__a,							'''in_proj_weight'''    ):
								SCREAMING_SNAKE_CASE_						 = old_model.in_proj_weight.shape[0] // 3
								SCREAMING_SNAKE_CASE_						 = 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_						 = nn.Parameter(old_model.in_proj_weight[:embed_dim, :]    )
										SCREAMING_SNAKE_CASE_						 = nn.Parameter(old_model.in_proj_bias[:embed_dim]    )

								elif attribute == "key_proj":
										SCREAMING_SNAKE_CASE_						 = nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]    )
										SCREAMING_SNAKE_CASE_						 = nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]    )
								elif attribute == "value_proj":
										SCREAMING_SNAKE_CASE_						 = nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]    )
										SCREAMING_SNAKE_CASE_						 = nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]    )
								SCREAMING_SNAKE_CASE_						 = 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_						 = nn.Parameter(old_model.embed_positions.weight[:512, :]    )
								SCREAMING_SNAKE_CASE_						 = True
								break

						if attribute.isdigit():
								SCREAMING_SNAKE_CASE_						 = model[int(__a    )]
								SCREAMING_SNAKE_CASE_						 = old_model[int(__a    )]
						else:
								SCREAMING_SNAKE_CASE_						 = getattr(__a,							__a    )

								if old_attribute == "":
										SCREAMING_SNAKE_CASE_						 = old_model
								else:
										if not hasattr(__a,							__a    ):
												raise ValueError(F'{old_model} does not have {old_attribute}'    )
										SCREAMING_SNAKE_CASE_						 = 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__							=				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__							=				parser.parse_args()
						convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)

"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput





@dataclass
class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None
							UpperCAmelCase__						     =      None







class  snake_case     (							__lowercase   ):

							def __init__(self       ,       SCREAMING_SNAKE_CASE_=1       ,       SCREAMING_SNAKE_CASE_=0       ,       SCREAMING_SNAKE_CASE_=2       ,       SCREAMING_SNAKE_CASE_=5_12       ,       SCREAMING_SNAKE_CASE_="cls"       ,       SCREAMING_SNAKE_CASE_=False       ,       SCREAMING_SNAKE_CASE_=True       ,       **SCREAMING_SNAKE_CASE_       ,       ):

									"""simple docstring"""







									super().__init__(pad_token_id=SCREAMING_SNAKE_CASE_       ,       bos_token_id=SCREAMING_SNAKE_CASE_       ,       eos_token_id=SCREAMING_SNAKE_CASE_       ,       **SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = project_dim
									SCREAMING_SNAKE_CASE_						 = pooler_fn
									SCREAMING_SNAKE_CASE_						 = learn_encoder
									SCREAMING_SNAKE_CASE_						 = use_attention_mask







class  snake_case     (							__lowercase   ):
							UpperCAmelCase__						     =      [R'''pooler''', R'''logit_scale''']
							UpperCAmelCase__						     =      [R'''position_ids''', R'''predictions.decoder.bias''']
							UpperCAmelCase__						     =      '''roberta'''
							UpperCAmelCase__						     =      RobertaSeriesConfig

							def __init__(self       ,       SCREAMING_SNAKE_CASE_    ):

									"""simple docstring"""







									super().__init__(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = XLMRobertaModel(SCREAMING_SNAKE_CASE_    )
									SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
									SCREAMING_SNAKE_CASE_						 = getattr(SCREAMING_SNAKE_CASE_       ,       '''has_pre_transformation'''       ,       SCREAMING_SNAKE_CASE_    )
									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = nn.Linear(config.hidden_size       ,       config.project_dim    )
											SCREAMING_SNAKE_CASE_						 = nn.LayerNorm(config.hidden_size       ,       eps=config.layer_norm_eps    )
									self.post_init()







							def _lowercase					(self       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       SCREAMING_SNAKE_CASE_ = None       ,       ):

									"""simple docstring"""







									SCREAMING_SNAKE_CASE_						 = return_dict if return_dict is not None else self.config.use_return_dict

									SCREAMING_SNAKE_CASE_						 = self.base_model(
									    input_ids=SCREAMING_SNAKE_CASE_       ,       attention_mask=SCREAMING_SNAKE_CASE_       ,       token_type_ids=SCREAMING_SNAKE_CASE_       ,       position_ids=SCREAMING_SNAKE_CASE_       ,       head_mask=SCREAMING_SNAKE_CASE_       ,       inputs_embeds=SCREAMING_SNAKE_CASE_       ,       encoder_hidden_states=SCREAMING_SNAKE_CASE_       ,       encoder_attention_mask=SCREAMING_SNAKE_CASE_       ,       output_attentions=SCREAMING_SNAKE_CASE_       ,       output_hidden_states=True if self.has_pre_transformation else output_hidden_states       ,       return_dict=SCREAMING_SNAKE_CASE_       ,       )

									if self.has_pre_transformation:
											SCREAMING_SNAKE_CASE_						 = outputs['''hidden_states'''][-2]
											SCREAMING_SNAKE_CASE_						 = self.pre_LN(SCREAMING_SNAKE_CASE_    )
											SCREAMING_SNAKE_CASE_						 = self.transformation_pre(SCREAMING_SNAKE_CASE_    )

											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )
									else:
											SCREAMING_SNAKE_CASE_						 = self.transformation(outputs.last_hidden_state    )
											return TransformationModelOutput(
											    projection_state=SCREAMING_SNAKE_CASE_       ,       last_hidden_state=outputs.last_hidden_state       ,       hidden_states=outputs.hidden_states       ,       attentions=outputs.attentions       ,       )