





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__     (     UpperCamelCase__			:       str       ,    UpperCamelCase__			:       str						)			-> Optional[int]:



					'''simple docstring'''




					if "xprophetnet" in prophetnet_checkpoint_path:
										_snake_case									=				XLMProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__						)
										_snake_case							,     _snake_case									=				XLMProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__       ,    output_loading_info=UpperCamelCase__						)
					else:
										_snake_case									=				ProphetNetForConditionalGenerationOld.from_pretrained(UpperCamelCase__						)
										_snake_case							,     _snake_case									=				ProphetNetForConditionalGeneration.from_pretrained(
										    UpperCamelCase__       ,    output_loading_info=UpperCamelCase__						)

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

					_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"]:
										_snake_case									=				key.split('.'						)

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

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

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

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

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

																				if old_attribute == "":
																									_snake_case									=				old_model
																				else:
																									if not hasattr(UpperCamelCase__       ,    UpperCamelCase__						):
																														raise ValueError(F'''{old_model} does not have {old_attribute}'''						)
																									_snake_case									=				getattr(UpperCamelCase__       ,    UpperCamelCase__						)

										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(UpperCamelCase__						)


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)










from collections import deque

from .hash_table import HashTable
class 			UpperCamelCase_		(  _lowerCamelCase					):




					def __init__(		self				,						*lowerCAmelCase_				,						**lowerCAmelCase_  )		->							Any:
										super().__init__(*lowerCAmelCase_				,						**lowerCAmelCase_  )




					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_  )		->							Optional[int]:
										_snake_case									=				deque([]  ) if self.values[key] is None else self.values[key]
										self.values[key].appendleft(lowerCAmelCase_  )
										_snake_case									=				self.values[key]




					def 	lowerCAmelCase			(		self  )		->							Optional[int]:
										return (
										    sum(self.charge_factor - len(lowerCAmelCase_  ) for slot in self.values  )
										    / self.size_table
										    * self.charge_factor
										)






					def 	lowerCAmelCase			(		self				,						lowerCAmelCase_				,						lowerCAmelCase_=None  )		->							str:
										if not (
										    len(self.values[key]  ) == self.charge_factor and self.values.count(lowerCAmelCase_  ) == 0
										):
															return key
										return super()._collision_resolution(lowerCAmelCase_				,						lowerCAmelCase_  )



