
"""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


SCREAMING_SNAKE_CASE_ 			=     logging.get_logger(__name__)
logging.set_verbosity_info()

def       lowercase			(_lowerCAmelCase      ,  _lowerCAmelCase		):

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

						__lowerCAmelCase   					=   ["""key_proj""", """value_proj""", """query_proj"""]

						__lowerCAmelCase   					=   {
						    """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"]:
												__lowerCAmelCase   					=   key.split("""."""		)

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

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

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

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

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

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






from __future__ import annotations







def SCREAMING_SNAKE_CASE				(       lowercase_ ,				lowercase_ ,				lowercase_						)   ->       tuple[float, list[float]]:



       """simple docstring"""

       A__       =    list(range(len(lowercase_						)						)						)
       A__       =    [v / w for v, w in zip(lowercase_ ,				lowercase_						)]
       index.sort(key=lambda lowercase_						: ratio[i] ,				reverse=lowercase_						)

       A__       =    0
       A__       =    [0] * len(lowercase_						)
       for i in index:
              if weight[i] <= capacity:
                     A__       =    1
                     max_value += value[i]
                     capacity -= weight[i]
              else:
                     A__       =    capacity / weight[i]
                     max_value += value[i] * capacity / weight[i]
                     break

       return max_value, fractions


if __name__ == "__main__":
  import doctest

  doctest.testmod()


