


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__			:      Optional[Any]								=logging.get_logger(__name__)
logging.set_verbosity_info()






def 						a__			(				A__,      A__				):
			if "xprophetnet" in prophetnet_checkpoint_path:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					XLMProphetNetForConditionalGenerationOld.from_pretrained(A__				)
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     int					=					XLMProphetNetForConditionalGeneration.from_pretrained(
						    A__,      output_loading_info=A__				)
			else:
						SCREAMING_SNAKE_CASE_		:     List[str]					=					ProphetNetForConditionalGenerationOld.from_pretrained(A__				)
						SCREAMING_SNAKE_CASE_		,      SCREAMING_SNAKE_CASE_		:     Any					=					ProphetNetForConditionalGeneration.from_pretrained(
						    A__,      output_loading_info=A__				)

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

			SCREAMING_SNAKE_CASE_		:     List[str]					=					{
			    '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_		:     Any					=					key.split('.'				)

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

						SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					False
						for attribute in attributes:
									if attribute in mapping:
												SCREAMING_SNAKE_CASE_		:     Any					=					mapping[attribute]
												if not hasattr(A__,      A__				) and len(A__				) > 0:
															SCREAMING_SNAKE_CASE_		:     Tuple					=					attribute
									elif hasattr(A__,      A__				):
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					attribute

									if attribute == "weight":
												assert old_model.weight.shape == model.weight.shape, "Shapes have to match!"
												SCREAMING_SNAKE_CASE_		:     Tuple					=					old_model.weight
												logger.info(F'''{attribute} is initialized.'''				)
												SCREAMING_SNAKE_CASE_		:     Tuple					=					True
												break
									elif attribute == "bias":
												assert old_model.bias.shape == model.bias.shape, "Shapes have to match!"
												SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					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_		:     Union[str, Any]					=					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_		:     List[Any]					=					nn.Parameter(old_model.in_proj_weight[:embed_dim, :]				)
															SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					nn.Parameter(old_model.in_proj_bias[:embed_dim]				)

												elif attribute == "key_proj":
															SCREAMING_SNAKE_CASE_		:     List[Any]					=					nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]				)
															SCREAMING_SNAKE_CASE_		:     Any					=					nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]				)
												elif attribute == "value_proj":
															SCREAMING_SNAKE_CASE_		:     Optional[int]					=					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_		:     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] == 5_1_2, "We want 512 position_embeddings."
												SCREAMING_SNAKE_CASE_		:     Optional[Any]					=					nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]				)
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					True
												break

									if attribute.isdigit():
												SCREAMING_SNAKE_CASE_		:     Union[str, Any]					=					model[int(A__				)]
												SCREAMING_SNAKE_CASE_		:     List[Any]					=					old_model[int(A__				)]
									else:
												SCREAMING_SNAKE_CASE_		:     Tuple					=					getattr(A__,      A__				)

												if old_attribute == "":
															SCREAMING_SNAKE_CASE_		:     List[str]					=					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__			:      Dict								=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[Any]								=parser.parse_args()
				convert_prophetnet_checkpoint_to_pytorch(args.prophetnet_checkpoint_path, args.pytorch_dump_folder_path)









'''simple docstring'''





from __future__ import annotations

from functools import lru_cache
from math import ceil

UpperCAmelCase__  			=				1_0_0

UpperCAmelCase__  			=				set(range(3, NUM_PRIMES, 2))
primes.add(2)
UpperCAmelCase__  			=				42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
					if prime not in primes:
										continue
					primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))






@lru_cache(maxsize=100 )
def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int ):






			"""simple docstring"""




			if number_to_partition < 0:
						return set()
			elif number_to_partition == 0:
						return {1}

			__A=	set()
			__A=	42
			__A=	42

			for prime in primes:
						if prime > number_to_partition:
									continue
						for sub in partition(number_to_partition - prime ):
									ret.add(sub * prime )

			return ret






def 						UpperCAmelCase__(				_SCREAMING_SNAKE_CASE					: int = 5000 ):






			"""simple docstring"""




			for number_to_partition in range(1,_SCREAMING_SNAKE_CASE ):
						if len(partition(_SCREAMING_SNAKE_CASE ) ) > number_unique_partitions:
									return number_to_partition
			return None


if __name__ == "__main__":
					print(F"""{solution() = }""")



