


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(	lowercase__   , lowercase__	)						->	List[str]:

		'''simple docstring'''







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

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

		__lowercase=			{
		    '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"]:
				__lowercase=			key.split('.'	)

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

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

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

								elif attribute == "key_proj":
										__lowercase=			nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]	)
										__lowercase=			nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]	)
								elif attribute == "value_proj":
										__lowercase=			nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]	)
										__lowercase=			nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]	)
								__lowercase=			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."
								__lowercase=			nn.Parameter(old_model.embed_positions.weight[:5_1_2, :]	)
								__lowercase=			True
								break

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

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

				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(lowercase__	)


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)










from math import isqrt







def        _lowerCamelCase(	lowercase__	)						->	bool:

		'''simple docstring'''







		return all(number % divisor != 0 for divisor in range(2   , isqrt(lowercase__	) + 1	)	)







def        _lowerCamelCase(	lowercase__ = 1_0**6	)						->	int:

		'''simple docstring'''







		__lowercase=			0
		__lowercase=			1
		__lowercase=			7
		while prime_candidate < max_prime:
				primes_count += is_prime(lowercase__	)

				cube_index += 1
				prime_candidate += 6 * cube_index

		return primes_count


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






