



import argparse
import os
import pathlib

import fairseq
import torch
from fairseq.modules import TransformerSentenceEncoderLayer
from packaging import version

from transformers import (
    DataaVecTextConfig,
    DataaVecTextForMaskedLM,
    DataaVecTextForSequenceClassification,
    DataaVecTextModel,
)
from transformers.models.bert.modeling_bert import (
    BertIntermediate,
    BertLayer,
    BertOutput,
    BertSelfAttention,
    BertSelfOutput,
)

# IMPORTANT: In order for this script to run, please make sure to download the dictionary: `dict.txt` from wget https://dl.fbaipublicfiles.com/fairseq/models/roberta.large.tar.gz
# File copied from https://github.com/pytorch/fairseq/blob/main/examples/data2vec/models/data2vec_text.py
from transformers.utils import logging


if version.parse(fairseq.__version__) < version.parse('''0.9.0'''):
		raise Exception('''requires fairseq >= 0.9.0''')


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

__UpperCAmelCase    	=  '''Hello world! cécé herlolip'''





def     __lowerCamelCase							(   __magic_name__						:	str  ,			__magic_name__						:	str  ,			__magic_name__						:	bool    ):
							a__				,      a__:							Optional[Any]									=os.path.split(__magic_name__    )
							a__:							List[Any]									=DataaVecTextModel.from_pretrained(
							    __magic_name__  ,			checkpoint_file=__magic_name__    )
							dataavec.eval()  # disable dropout
							a__:							Optional[int]									=dataavec.models[0]
							a__:							int									=dataavec_model.encoder.sentence_encoder
							a__:							str									=DataaVecTextConfig(
							    vocab_size=dataavec_sent_encoder.embed_tokens.num_embeddings  ,			hidden_size=dataavec_model.args.encoder_embed_dim  ,			num_hidden_layers=dataavec_model.args.encoder_layers  ,			num_attention_heads=dataavec_model.args.encoder_attention_heads  ,			intermediate_size=dataavec_model.args.encoder_ffn_embed_dim  ,			max_position_embeddings=514  ,			type_vocab_size=1  ,			layer_norm_eps=1e-5  ,			)
							if classification_head:
														a__:							int									=dataavec.model.classification_heads["mnli"].out_proj.weight.shape[0]
							print("Our BERT config:"  ,			__magic_name__    )

							a__:							Optional[Any]									=DataaVecTextForSequenceClassification(__magic_name__    ) if classification_head else DataaVecTextForMaskedLM(__magic_name__    )
							model.eval()

							# Now let's copy all the weights.
							# Embeddings
							a__:							List[Any]									=dataavec_sent_encoder.embed_tokens.weight
							a__:							Optional[Any]									=dataavec_sent_encoder.embed_positions.weight
							a__:							List[str]									=torch.zeros_like(
							    model.dataavec_text.embeddings.token_type_embeddings.weight    )  # just zero them out b/c data2vec doesn't use them.
							a__:							Dict									=dataavec_sent_encoder.layernorm_embedding.weight
							a__:							Optional[int]									=dataavec_sent_encoder.layernorm_embedding.bias

							for i in range(config.num_hidden_layers    ):
														# Encoder: start of layer
														a__:							BertLayer									=model.dataavec_text.encoder.layer[i]
														a__:							TransformerSentenceEncoderLayer									=dataavec_sent_encoder.layers[i]

														# self attention
														a__:							BertSelfAttention									=layer.attention.self
														assert dataavec_layer.self_attn.k_proj.weight.data.shape == torch.Size(
														    (config.hidden_size, config.hidden_size)    ), (
														    "Shape for data2vec_layer.self_attn.k_proj.weight.data should be"
														    F" {torch.Size((config.hidden_size, config.hidden_size)    )}"
														)
														assert dataavec_layer.self_attn.q_proj.weight.data.shape == torch.Size(
														    (config.hidden_size, config.hidden_size)    ), (
														    "Shape for data2vec_layer.self_attn.q_proj.weight.data should be"
														    F" {torch.Size((config.hidden_size, config.hidden_size)    )}"
														)
														assert dataavec_layer.self_attn.v_proj.weight.data.shape == torch.Size(
														    (config.hidden_size, config.hidden_size)    ), (
														    "Shape for data2vec_layer.self_attn.v_proj.weight.data should be"
														    F" {torch.Size((config.hidden_size, config.hidden_size)    )}"
														)

														a__:							Optional[int]									=dataavec_layer.self_attn.q_proj.weight
														a__:							Union[str, Any]									=dataavec_layer.self_attn.q_proj.bias
														a__:							Optional[Any]									=dataavec_layer.self_attn.k_proj.weight
														a__:							Any									=dataavec_layer.self_attn.k_proj.bias
														a__:							Optional[int]									=dataavec_layer.self_attn.v_proj.weight
														a__:							Union[str, Any]									=dataavec_layer.self_attn.v_proj.bias

														# self-attention output
														a__:							BertSelfOutput									=layer.attention.output
														assert (
														    self_output.dense.weight.shape == dataavec_layer.self_attn.out_proj.weight.shape
														), F"Shape for self_output.dense.weight should be {dataavec_layer.self_attn.out_proj.weight.shape}"
														a__:							Dict									=dataavec_layer.self_attn.out_proj.weight
														a__:							Dict									=dataavec_layer.self_attn.out_proj.bias
														a__:							int									=dataavec_layer.self_attn_layer_norm.weight
														a__:							Tuple									=dataavec_layer.self_attn_layer_norm.bias

														# intermediate
														a__:							BertIntermediate									=layer.intermediate
														assert (
														    intermediate.dense.weight.shape == dataavec_layer.fca.weight.shape
														), F"Shape for intermediate.dense.weight should be {dataavec_layer.fca.weight.shape}"
														a__:							str									=dataavec_layer.fca.weight
														a__:							Tuple									=dataavec_layer.fca.bias

														# output
														a__:							BertOutput									=layer.output
														assert (
														    bert_output.dense.weight.shape == dataavec_layer.fca.weight.shape
														), F"Shape for bert_output.dense.weight should be {dataavec_layer.fca.weight.shape}"
														a__:							str									=dataavec_layer.fca.weight
														a__:							Optional[Any]									=dataavec_layer.fca.bias
														a__:							Optional[int]									=dataavec_layer.final_layer_norm.weight
														a__:							Dict									=dataavec_layer.final_layer_norm.bias
														# end of layer

							if classification_head:
														a__:							Optional[int]									=dataavec.model.classification_heads["mnli"].dense.weight
														a__:							Dict									=dataavec.model.classification_heads["mnli"].dense.bias
														a__:							List[Any]									=dataavec.model.classification_heads["mnli"].out_proj.weight
														a__:							Tuple									=dataavec.model.classification_heads["mnli"].out_proj.bias
							else:
														# LM Head
														a__:							Any									=dataavec_model.encoder.lm_head.dense.weight
														a__:							List[Any]									=dataavec_model.encoder.lm_head.dense.bias
														a__:							List[str]									=dataavec_model.encoder.lm_head.layer_norm.weight
														a__:							Union[str, Any]									=dataavec_model.encoder.lm_head.layer_norm.bias
														a__:							Optional[Any]									=dataavec_model.encoder.lm_head.weight
														a__:							int									=dataavec_model.encoder.lm_head.bias

							# Let's check that we get the same results.
							a__:							torch.Tensor									=dataavec.encode(__magic_name__    ).unsqueeze(0    )  # batch of size 1

							a__:							Tuple									=model(__magic_name__    )[0]
							if classification_head:
														a__:							str									=dataavec.model.classification_heads["mnli"](dataavec.extract_features(__magic_name__    )    )
							else:
														a__:							str									=dataavec_model(__magic_name__    )[0]
							print(our_output.shape  ,			their_output.shape    )
							a__:							Tuple									=torch.max(torch.abs(our_output - their_output    )    ).item()
							print(F"max_absolute_diff = {max_absolute_diff}"    )  # ~ 1e-7
							a__:							Optional[Any]									=torch.allclose(__magic_name__  ,			__magic_name__  ,			atol=1e-3    )
							print("Do both models output the same tensors?"  ,			"🔥" if success else "💩"    )
							if not success:
														raise Exception("Something went wRoNg"    )

							pathlib.Path(__magic_name__    ).mkdir(parents=__magic_name__  ,			exist_ok=__magic_name__    )
							print(F"Saving model to {pytorch_dump_folder_path}"    )
							model.save_pretrained(__magic_name__    )


if __name__ == "__main__":
		__UpperCAmelCase    	=  argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    '''--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.'''
		)
		parser.add_argument(
		    '''--classification_head''', action='''store_true''', help='''Whether to convert a final classification head.'''
		)
		__UpperCAmelCase    	=  parser.parse_args()
		convert_dataavec_checkpoint_to_pytorch(
		    args.checkpoint_path, args.pytorch_dump_folder_path, args.classification_head
		)










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 						lowerCamelCase__			:


						def __init__(			self    :	Optional[Any]      ):
													a__:							Tuple									=False


						def 	_lowerCamelCase      (			self    :	Dict   ,							_a    :	Any   ,							_a    :	Union[str, Any]   ,							_a    :	List[str]   ,							_a    :	Optional[int]      ):
													if not self.initialized:
																				a__:							Any									=RagRetriever(
																				    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)
																				a__:							str									=True


						def 	_lowerCamelCase      (			self    :	List[str]      ):
													self.retriever.index.init_index()


						def 	_lowerCamelCase      (			self    :	Optional[int]      ):
													# delete the old self.retriever object before assigning the new index
													del self.retriever
													a__:							Dict									=False







						def 	_lowerCamelCase      (			self    :	str   ,							_a    :	List[Any]   ,							_a    :	Any      ):
													a__				,      a__:							List[str]									=self.retriever._main_retrieve(_a   ,							_a      )
													a__:							List[str]									=self.retriever.index.get_doc_dicts(_a      )
													return doc_ids, retrieved_doc_embeds, doc_dicts


class 						lowerCamelCase__			(		_a ):


						def __init__(			self    :	Optional[int]   ,							_a    :	List[Any]   ,							_a    :	Any   ,							_a    :	Optional[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	str=None      ):
													if index is not None and index.is_initialized() and len(_a      ) > 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__(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							index=_a   ,							init_retrieval=_a   ,							)

													a__:							Optional[Any]									=retrieval_workers
													a__:							str									=question_encoder_tokenizer
													a__:							Optional[int]									=generator_tokenizer
													if len(self.retrieval_workers      ) > 0:
																				ray.get(
																				    [
																				        worker.create_rag_retriever.remote(_a   ,							_a   ,							_a   ,							_a      )
																				        for worker in self.retrieval_workers
																				    ]      )


						def 	_lowerCamelCase      (			self    :	List[Any]      ):
													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 	_lowerCamelCase      (			self    :	List[Any]   ,							_a    :	Union[str, Any]   ,							_a    :	Union[str, Any]      ):
													if len(self.retrieval_workers      ) > 0:
																				# Select a random retrieval actor.
																				a__:							Tuple									=self.retrieval_workers[random.randint(0   ,							len(self.retrieval_workers      ) - 1      )]
																				a__				,      a__				,      a__:							Union[str, Any]									=ray.get(
																				    random_worker.retrieve.remote(_a   ,							_a      )      )
													else:
																				a__				,      a__:							List[Any]									=self._main_retrieve(_a   ,							_a      )
																				a__:							List[str]									=self.index.get_doc_dicts(_a      )
													return retrieved_doc_embeds, doc_ids, doc_dicts


						@classmethod
						def 	_lowerCamelCase      (			cls    :	str   ,							_a    :	Union[str, Any]   ,							_a    :	Any=None   ,							**_a    :	Dict      ):
													return super(_a   ,							cls      ).get_tokenizers(_a   ,							_a   ,							**_a      )


						@classmethod
						def 	_lowerCamelCase      (			cls    :	Dict   ,							_a    :	Optional[int]   ,							_a    :	Tuple   ,							_a    :	int=None   ,							**_a    :	List[str]      ):
													a__:							Union[str, Any]									=kwargs.pop("config"   ,							_a      ) or RagConfig.from_pretrained(_a   ,							**_a      )
													a__:							List[Any]									=RagTokenizer.from_pretrained(_a   ,							config=_a      )
													a__:							str									=rag_tokenizer.question_encoder
													a__:							Any									=rag_tokenizer.generator

													if indexed_dataset is not None:
																				a__:							Dict									="custom"
																				a__:							Dict									=CustomHFIndex(config.retrieval_vector_size   ,							_a      )
													else:
																				a__:							List[str]									=cls._build_index(_a      )

													return cls(
													    _a   ,							question_encoder_tokenizer=_a   ,							generator_tokenizer=_a   ,							retrieval_workers=_a   ,							index=_a   ,							)







						def 	_lowerCamelCase      (			self    :	Union[str, Any]      ):
													logger.info("re-loading the new dataset with embeddings"      )
													# access from the training loop

													ray.get([worker.clear_object.remote() for worker in self.retrieval_workers]      )

													# build the index object again
													a__:							str									=self._build_index(self.config      )

													ray.get(
													    [
													        worker.create_rag_retriever.remote(
													            self.config   ,							self.question_encoder_tokenizer   ,							self.generator_tokenizer   ,							_a      )
													        for worker in self.retrieval_workers
													    ]      )





