
'''simple docstring'''





import logging
import random

import ray

from transformers import RagConfig, RagRetriever, RagTokenizer
from transformers.models.rag.retrieval_rag import CustomHFIndex


UpperCAmelCase    					=   logging.getLogger(__name__)

class 						a   :




				def __init__(							self	:				Optional[Any]      ):
										snake_case       :     Optional[int] =    False




				def 	__snake_case							(							self	:				str,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				str,    SCREAMING_SNAKE_CASE_	:				List[Any]      ):
										if not self.initialized:
																snake_case       :     Union[str, Any] =    RagRetriever(
																    SCREAMING_SNAKE_CASE_,    question_encoder_tokenizer=SCREAMING_SNAKE_CASE_,    generator_tokenizer=SCREAMING_SNAKE_CASE_,    index=SCREAMING_SNAKE_CASE_,    init_retrieval=SCREAMING_SNAKE_CASE_,    )
																snake_case       :     str =    True




				def 	__snake_case							(							self	:				Any      ):
										self.retriever.index.init_index()




				def 	__snake_case							(							self	:				Dict,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				int      ):
										snake_case,					snake_case       :     int =    self.retriever._main_retrieve(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return doc_ids, retrieved_doc_embeds




class 						a   (				__magic_name__    ):




				def __init__(							self	:				Union[str, Any],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				List[str],    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Tuple=None      ):
										if index is not None and index.is_initialized() and len(SCREAMING_SNAKE_CASE_      ) > 0:
																raise ValueError(
																    '''When using Ray for distributed fine-tuning, '''
																    '''you\'ll need to provide the paths instead, '''
																    '''as the dataset and the index are loaded '''
																    '''separately. More info in examples/rag/use_own_knowledge_dataset.py '''      )
										super().__init__(
										    SCREAMING_SNAKE_CASE_,    question_encoder_tokenizer=SCREAMING_SNAKE_CASE_,    generator_tokenizer=SCREAMING_SNAKE_CASE_,    index=SCREAMING_SNAKE_CASE_,    init_retrieval=SCREAMING_SNAKE_CASE_,    )
										snake_case       :     Dict =    retrieval_workers
										if len(self.retrieval_workers      ) > 0:
																ray.get(
																    [
																        worker.create_rag_retriever.remote(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
																        for worker in self.retrieval_workers
																    ]      )




				def 	__snake_case							(							self	:				Optional[int]      ):
										logger.info('''initializing retrieval'''      )

										if len(self.retrieval_workers      ) > 0:
																ray.get([worker.init_retrieval.remote() for worker in self.retrieval_workers]      )
										else:
																# Non-distributed training. Load index into this same process.
																self.index.init_index()




				def 	__snake_case							(							self	:				Optional[int],    SCREAMING_SNAKE_CASE_	:				Dict,    SCREAMING_SNAKE_CASE_	:				Any      ):
										if len(self.retrieval_workers      ) > 0:
																# Select a random retrieval actor.
																snake_case       :     Optional[int] =    self.retrieval_workers[random.randint(0,    len(self.retrieval_workers      ) - 1      )]
																snake_case,					snake_case       :     Tuple =    ray.get(random_worker.retrieve.remote(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )      )
										else:
																snake_case,					snake_case       :     Optional[int] =    self._main_retrieve(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_      )
										return retrieved_doc_embeds, doc_ids, self.index.get_doc_dicts(SCREAMING_SNAKE_CASE_      )




				@classmethod
				def 	__snake_case							(							cls	:				Optional[Any],    SCREAMING_SNAKE_CASE_	:				List[Any],    SCREAMING_SNAKE_CASE_	:				Any=None,    **SCREAMING_SNAKE_CASE_	:				Dict      ):
										return super(SCREAMING_SNAKE_CASE_,    cls      ).get_tokenizers(SCREAMING_SNAKE_CASE_,    SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )




				@classmethod
				def 	__snake_case							(							cls	:				str,    SCREAMING_SNAKE_CASE_	:				int,    SCREAMING_SNAKE_CASE_	:				Any,    SCREAMING_SNAKE_CASE_	:				Any=None,    **SCREAMING_SNAKE_CASE_	:				Optional[int]      ):
										snake_case       :     Tuple =    kwargs.pop('''config''',    SCREAMING_SNAKE_CASE_      ) or RagConfig.from_pretrained(SCREAMING_SNAKE_CASE_,    **SCREAMING_SNAKE_CASE_      )
										snake_case       :     Tuple =    RagTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_,    config=SCREAMING_SNAKE_CASE_      )
										snake_case       :     Dict =    rag_tokenizer.question_encoder
										snake_case       :     Optional[int] =    rag_tokenizer.generator
										if indexed_dataset is not None:
																snake_case       :     Union[str, Any] =    '''custom'''
																snake_case       :     int =    CustomHFIndex(config.retrieval_vector_size,    SCREAMING_SNAKE_CASE_      )
										else:
																snake_case       :     List[str] =    cls._build_index(SCREAMING_SNAKE_CASE_      )
										return cls(
										    SCREAMING_SNAKE_CASE_,    question_encoder_tokenizer=SCREAMING_SNAKE_CASE_,    generator_tokenizer=SCREAMING_SNAKE_CASE_,    retrieval_workers=SCREAMING_SNAKE_CASE_,    index=SCREAMING_SNAKE_CASE_,    )



'''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


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

def      A (       A_							:					str    ,							A_							:					str       ):
						if "xprophetnet" in prophetnet_checkpoint_path:
												snake_case       :     Any =    XLMProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     Tuple =    XLMProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )
						else:
												snake_case       :     Union[str, Any] =    ProphetNetForConditionalGenerationOld.from_pretrained(A_       )
												snake_case,					snake_case       :     List[Any] =    ProphetNetForConditionalGeneration.from_pretrained(
												    A_    ,							output_loading_info=A_       )

						snake_case       :     Union[str, Any] =    ['''key_proj''', '''value_proj''', '''query_proj''']

						snake_case       :     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"]:
												snake_case       :     Optional[Any] =    key.split('''.'''       )

												if attributes[0] == "lm_head":
																		snake_case       :     Optional[int] =    prophet
																		snake_case       :     Union[str, Any] =    prophet_old
												else:
																		snake_case       :     Optional[int] =    prophet.prophetnet
																		snake_case       :     Any =    prophet_old.model

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

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

																								elif attribute == "key_proj":
																														snake_case       :     str =    nn.Parameter(old_model.in_proj_weight[embed_dim : 2 * embed_dim, :]       )
																														snake_case       :     Any =    nn.Parameter(old_model.in_proj_bias[embed_dim : 2 * embed_dim]       )
																								elif attribute == "value_proj":
																														snake_case       :     Tuple =    nn.Parameter(old_model.in_proj_weight[2 * embed_dim :, :]       )
																														snake_case       :     List[str] =    nn.Parameter(old_model.in_proj_bias[2 * embed_dim :]       )
																								snake_case       :     Optional[Any] =    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       :     List[Any] =    nn.Parameter(old_model.embed_positions.weight[:512, :]       )
																								snake_case       :     Any =    True
																								break

																		if attribute.isdigit():
																								snake_case       :     Optional[Any] =    model[int(A_       )]
																								snake_case       :     List[str] =    old_model[int(A_       )]
																		else:
																								snake_case       :     Optional[Any] =    getattr(A_    ,							A_       )

																								if old_attribute == "":
																														snake_case       :     Union[str, Any] =    old_model
																								else:
																														if not hasattr(A_    ,							A_       ):
																																				raise ValueError(F"""{old_model} does not have {old_attribute}"""       )
																														snake_case       :     Tuple =    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__":
						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)

