import argparse

# utilities to import the model weights and config file
import os
import pickle as pkl

# PyTorch + new model classes
import torch
from torch import nn

from transformers import AutoTokenizer, MegaConfig, MegaForMaskedLM


# import the EncoderLayer class used to pretrain
# !! NOTE !! this requires the version of fairseq that is built when you install the Mega source
try:
	from fairseq.modules.mega_layer import MegaEncoderLayer
except ImportError:
	raise ImportError("""You need to install the version of fairseq from the Mega repo!""")


class 						a__							(   nn.Module		):



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Optional[Any]:
								super().__init__()
								__a							=   mega_args
								__a							=   nn.Embedding(UpperCAmelCase      ,						self.mega_args.encoder_embed_dim    )
								__a							=   nn.ModuleList([MegaEncoderLayer(self.mega_args    ) for _ in range(UpperCAmelCase    )]    )
								__a							=   depth







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=True      ,						UpperCAmelCase=0    ) ->    int:

								# Mega expects embeddings to be (time, batch, embedding size), but
								# Hugging Face returns tokens as (batch, time)
								if batch_first:
													__a							=   input_ids.T

								# to make things more confusing, Mega expects the attention mask to
								# be (batch, time), but with values of 0 (normal token) and 1 (ignore token)
								# which is the opposite of what HF returns
								if ignore_mask_value == 0:
													__a							=   1 - attention_mask

								# get token embeddings from IDs
								__a							=   self.embedding_layer(UpperCAmelCase    )

								# pass through the Mega layers
								# input is (time, batch, encoder dim) and output is the same
								for encoder in self.encoders:
													__a							=   encoder(UpperCAmelCase      ,						UpperCAmelCase    )

								# return according to the shape specified
								if batch_first:
													# (T, B, H) --> (B, T, H)
													return torch.transpose(UpperCAmelCase      ,						0      ,						1    )
								else:
													return embeds






class 						a__							(   nn.Module		):



			def __init__(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    ) ->    Tuple:
								super().__init__()
								__a							=   MegaLM(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								__a							=   nn.Linear(mega_args.encoder_embed_dim      ,						UpperCAmelCase    )
								__a							=   nn.Dropout(p=0.1    )







			def __SCREAMING_SNAKE_CASE							(						self      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase=True      ,						UpperCAmelCase=0    ) ->    int:
								__a							=   self.mega(UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase      ,						UpperCAmelCase    )
								return self.mlm_head(self.dropout(UpperCAmelCase    )    )






def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					with open(os.path.join(__lowerCamelCase      ,				'model_args.pkl'						)      ,				'rb'						) as f:
										__a							=   pkl.load(__lowerCamelCase						)

					# load the original encoder
					__a							=   OriginalMegaForMaskedLM(**__lowerCamelCase						).eval()

					# load its weights
					print(
					    'Original Mega encoder:'      ,				original_mlm.mega.load_state_dict(
					        torch.load(os.path.join(__lowerCamelCase      ,				'encoder_weights.pt'						)      ,				map_location='cpu'						)						)      ,				)
					print(
					    'Original Mega MLM layer:'      ,				original_mlm.mlm_head.load_state_dict(
					        torch.load(os.path.join(__lowerCamelCase      ,				'mlm_head_weights.pt'						)      ,				map_location='cpu'						)						)      ,				)

					# create a new config from the old one
					__a							=   MegaConfig(
					    num_hidden_layers=mega_original_args['depth']      ,				vocab_size=mega_original_args['vocab_size']      ,				hidden_size=mega_original_args['mega_args'].encoder_embed_dim      ,				shared_representation_size=mega_original_args['mega_args'].encoder_z_dim      ,				intermediate_size=mega_original_args['mega_args'].encoder_hidden_dim      ,				ema_projection_size=mega_original_args['mega_args'].encoder_n_dim      ,				dropout_prob=mega_original_args['mega_args'].dropout      ,				attention_probs_dropout_prob=mega_original_args['mega_args'].attention_dropout      ,				hidden_dropout_prob=mega_original_args['mega_args'].hidden_dropout      ,				activation=mega_original_args['mega_args'].activation_fn      ,				attention_activation=mega_original_args['mega_args'].attention_activation_fn      ,				bidirectional=mega_original_args['mega_args'].bidirectional      ,				use_chunking=mega_original_args['mega_args'].encoder_chunk_size > 0      ,				chunk_size=mega_original_args['mega_args'].encoder_chunk_size      ,				truncation=mega_original_args['mega_args'].truncation_length      ,				normalization_type=mega_original_args['mega_args'].normalization_type      ,				normalize_before_mega=__lowerCamelCase      ,				norm_affine=__lowerCamelCase      ,				use_feature_dropout=mega_original_args['mega_args'].feature_dropout      ,				relative_positional_bias=mega_original_args['mega_args'].rel_pos_bias      ,				max_positions=mega_original_args['mega_args'].max_source_positions      ,				nffn_hidden_size=mega_original_args['mega_args'].encoder_ffn_embed_dim      ,				normalize_before_ffn=mega_original_args['mega_args'].normalize_before      ,				nffn_activation_dropout_prob=0.0      ,				add_token_type_embeddings=__lowerCamelCase      ,				add_lm_hidden_dense_layer=__lowerCamelCase      ,				)

					__a							=   MegaForMaskedLM(__lowerCamelCase						).eval()

					# the originl checkpoint just uses nn.Embedding for the word embeddings
					# we use a wrapper module for embeddings to add support for positional embeddings
					__a							=   original_mlm.mega.embedding_layer.weight

					# modify the state dictionary of the original checkpoint to account for naming issues in the Hugging Face
					# ecosystem -- any names containing "beta" or "gamma" aren't safe to use and are renamed upon _load_pretrained,
					# also renaming previously confusing parameter names
					__a							=   original_mlm.mega.encoders.state_dict()
					__a							=   {}
					for module_name in original_state_dict.keys():
										__a							=   None
										# have to handle gamma, beta, and alpha differently due to their use
										# in multiple modules within the original repository;
										# beta is used in EMA, MovingAverageGatedAttention, and RotaryRelativePositionalBias, and must be renamed due to flax/tf weights
										# the EMA sublayer was renamed from "move" to "ema_gate" for readability, so that is also done here
										if "beta" in module_name:
															# EMA sub-layers were always called "move" in the original repo
															if "move.beta" in module_name:
																				__a							=   module_name.replace('move.beta'      ,				'ema_gate.ema_expansion_matrix'						)
															elif "mega_layer.beta" in module_name:
																				__a							=   module_name.replace('beta'      ,				'qk_bias'						)
															else:
																				__a							=   module_name.replace('beta'      ,				'b_param'						)
        # beta is used in EMA and MovingAverageGatedAttention, and must be renamed due to flax/tf weights
										elif "gamma" in module_name:
															if "move.gamma" in module_name:
																				__a							=   module_name.replace('move.gamma'      ,				'ema_gate.kernel_projection_matrix'						)
															elif "mega_layer.gamma" in module_name:
																				__a							=   module_name.replace('gamma'      ,				'qk_weight'						)
															else:
																				__a							=   module_name.replace('gamma'      ,				'g_param'						)
        # alpha is used in EMA and positional bias; renaming to improve readability
										elif "move.alpha" in module_name:
															__a							=   module_name.replace('move.alpha'      ,				'ema_gate.decay_factor'						)
										# delta is only used in EMA; renaming to improve readability
										elif "move.delta" in module_name:
															__a							=   module_name.replace('move.delta'      ,				'ema_gate.damping_factor'						)
										# omega is only used in EMA; renaming to improve readability
										elif "omega" in module_name:
															__a							=   module_name.replace('move.omega'      ,				'ema_gate.residual_weight'						)

										if new_module_name:
															__a							=   new_module_name

					if len(__lowerCamelCase						) != 0:
										print(f'''Renaming these keys: {updated_keys.keys()}'''						)
					else:
										print('No need to rename state dict entries'						)
					for old, new in updated_keys.items():
										__a							=   original_state_dict.pop(__lowerCamelCase						)

					# now attempt to load the state dictionary with updated names
					# note that we now call it `mega.layers` instead of `mega.encoders` due to hugging face style
					print('HF Mega encoder:'      ,				hf_mlm.mega.layers.load_state_dict(__lowerCamelCase						)						)

					# load the MLM head weights directly
					print(
					    'HF Mega MLM layer:'      ,				hf_mlm.mlm_head.load_state_dict(
					        torch.load(os.path.join(__lowerCamelCase      ,				'mlm_head_weights.pt'						)      ,				map_location='cpu'						)						)      ,				)

					# test on a randomly generated input sequence
					__a							=   torch.randint(0      ,				hf_config.vocab_size      ,				size=(4, 256)						)
					__a							=   torch.ones_like(__lowerCamelCase						)
					# mask a few tokens to make sure masking is applied appropriately :)
					__a							=   0

					# run forward passes
					__a							=   original_mlm(__lowerCamelCase      ,				__lowerCamelCase      ,				batch_first=__lowerCamelCase      ,				ignore_mask_value=0						)
					__a							=   hf_mlm(__lowerCamelCase      ,				__lowerCamelCase						)[0]

					# print shapes and diff
					print(f'''original output {original_output.shape}'''						)
					print(f'''hf output {hf_output.shape}'''						)
					print(f'''max diff: {(original_output - hf_output).max()}'''						)  # 0.0
					__a							=   torch.allclose(__lowerCamelCase      ,				__lowerCamelCase      ,				atol=1e-3						)

					if success:
										print('Yay!'						)
										hf_mlm.save_pretrained(__lowerCamelCase						)
					else:
										raise RuntimeError(f'''Something\'s broken :(\nOriginal:\n{original_output}\n\nHF\n{hf_output}\n{hf_mlm}'''						)

					if includes_tokenizer:
										print('Transferring tokenizer'						)
										__a							=   AutoTokenizer.from_pretrained(__lowerCamelCase						)
										tokenizer.save_pretrained(__lowerCamelCase						)


if __name__ == "__main__":
	lowerCamelCase_				:		Any       						=       argparse.ArgumentParser()

	parser.add_argument(
	    """--pretrained_checkpoint_path""",
	    default=None,
	    type=str,
	    required=True,
	    help="""Point to the directory containing your model weights using the official Mega repo""",
	)

	parser.add_argument(
	    """--output_path""", default=None, type=str, required=True, help="""Location to save the Hugging Face version"""
	)

	parser.add_argument(
	    """--includes_tokenizer""",
	    action="""store_true""",
	    help="""Use this flag if there is a Hugging Face tokenizer in the original checkpoint repo""",
	)

	lowerCamelCase_				:		int       						=       parser.parse_args()

	convert_checkpoint_to_huggingface(args.pretrained_checkpoint_path, args.output_path, args.includes_tokenizer)





import logging
import os
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from tempfile import TemporaryDirectory
from typing import List, Optional

import faiss
import torch
from datasets import Features, Sequence, Value, load_dataset

from transformers import (
    DPRContextEncoder,
    DPRContextEncoderTokenizerFast,
    HfArgumentParser,
    RagRetriever,
    RagSequenceForGeneration,
    RagTokenizer,
)


lowerCamelCase_				:		Union[str, Any]       						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
lowerCamelCase_				:		List[Any]       						=       """cuda""" if torch.cuda.is_available() else """cpu"""
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase=100      ,				__lowerCamelCase=" "						):
					__a							=   text.split(__lowerCamelCase						)
					return [character.join(text[i : i + n]						).strip() for i in range(0      ,				len(__lowerCamelCase						)      ,				__lowerCamelCase						)]
def 							lowerCAmelCase(	__lowerCamelCase						):
					__a					,      __a							=   [], []
					for title, text in zip(documents['title']      ,				documents['text']						):
										if text is not None:
															for passage in split_text(__lowerCamelCase						):
																				titles.append(title if title is not None else ''						)
																				texts.append(__lowerCamelCase						)
					return {"title": titles, "text": texts}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase						):
					__a							=   ctx_tokenizer(
					    documents['title']      ,				documents['text']      ,				truncation=__lowerCamelCase      ,				padding='longest'      ,				return_tensors='pt'						)['input_ids']
					__a							=   ctx_encoder(input_ids.to(device=__lowerCamelCase						)      ,				return_dict=__lowerCamelCase						).pooler_output
					return {"embeddings": embeddings.detach().cpu().numpy()}
def 							lowerCAmelCase(	__lowerCamelCase      ,				__lowerCamelCase      ,				__lowerCamelCase      ,				):
					######################################
					logger.info('Step 1 - Create the dataset'						)
					######################################

					# The dataset needed for RAG must have three columns:
					# - title (string): title of the document
					# - text (string): text of a passage of the document
					# - embeddings (array of dimension d): DPR representation of the passage

					# Let's say you have documents in tab-separated csv files with columns "title" and "text"
					assert os.path.isfile(rag_example_args.csv_path						), "Please provide a valid path to a csv file"

					# You can load a Dataset object this way
					__a							=   load_dataset(
					    'csv'      ,				data_files=[rag_example_args.csv_path]      ,				split='train'      ,				delimiter='\t'      ,				column_names=['title', 'text']						)

					# More info about loading csv files in the documentation: https://huggingface.co/docs/datasets/loading_datasets.html?highlight=csv#csv-files

					# Then split the documents into passages of 100 words
					__a							=   dataset.map(__lowerCamelCase      ,				batched=__lowerCamelCase      ,				num_proc=processing_args.num_proc						)

					# And compute the embeddings
					__a							=   DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						).to(device=__lowerCamelCase						)
					__a							=   DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name						)
					__a							=   Features(
					    {'text': Value('string'						), 'title': Value('string'						), 'embeddings': Sequence(Value('float32'						)						)}						)  # optional, save as float32 instead of float64 to save space
					__a							=   dataset.map(
					    partial(__lowerCamelCase      ,				ctx_encoder=__lowerCamelCase      ,				ctx_tokenizer=__lowerCamelCase						)      ,				batched=__lowerCamelCase      ,				batch_size=processing_args.batch_size      ,				features=__lowerCamelCase      ,				)

					# And finally save your dataset
					__a							=   os.path.join(rag_example_args.output_dir      ,				'my_knowledge_dataset'						)
					dataset.save_to_disk(__lowerCamelCase						)
					# from datasets import load_from_disk
					# dataset = load_from_disk(passages_path)  # to reload the dataset

					######################################
					logger.info('Step 2 - Index the dataset'						)
					######################################

					# Let's use the Faiss implementation of HNSW for fast approximate nearest neighbor search
					__a							=   faiss.IndexHNSWFlat(index_hnsw_args.d      ,				index_hnsw_args.m      ,				faiss.METRIC_INNER_PRODUCT						)
					dataset.add_faiss_index('embeddings'      ,				custom_index=__lowerCamelCase						)

					# And save the index
					__a							=   os.path.join(rag_example_args.output_dir      ,				'my_knowledge_dataset_hnsw_index.faiss'						)
					dataset.get_index('embeddings'						).save(__lowerCamelCase						)
					# dataset.load_faiss_index("embeddings", index_path)  # to reload the index

					######################################
					logger.info('Step 3 - Load RAG'						)
					######################################

					# Easy way to load the model
					__a							=   RagRetriever.from_pretrained(
					    rag_example_args.rag_model_name      ,				index_name='custom'      ,				indexed_dataset=__lowerCamelCase						)
					__a							=   RagSequenceForGeneration.from_pretrained(rag_example_args.rag_model_name      ,				retriever=__lowerCamelCase						)
					__a							=   RagTokenizer.from_pretrained(rag_example_args.rag_model_name						)

					# For distributed fine-tuning you'll need to provide the paths instead, as the dataset and the index are loaded separately.
					# retriever = RagRetriever.from_pretrained(rag_model_name, index_name="custom", passages_path=passages_path, index_path=index_path)

					######################################
					logger.info('Step 4 - Have fun'						)
					######################################

					__a							=   rag_example_args.question or 'What does Moses\' rod turn into ?'
					__a							=   tokenizer.question_encoder(__lowerCamelCase      ,				return_tensors='pt'						)['input_ids']
					__a							=   model.generate(__lowerCamelCase						)
					__a							=   tokenizer.batch_decode(__lowerCamelCase      ,				skip_special_tokens=__lowerCamelCase						)[0]
					logger.info('Q: ' + question						)
					logger.info('A: ' + generated_string						)


@dataclass
class 						a__							:
			A__				:   str								=			field(
			    default=str(Path(__snake_case		).parent / 'test_data' / 'my_knowledge_dataset.csv'		)      ,						metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}      ,						)
			A__				:   str								=			field(
			    default='facebook/rag-sequence-nq'      ,						metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}      ,						)
			A__				:   str								=			field(
			    default='facebook/dpr-ctx_encoder-multiset-base'      ,						metadata={
			        'help': (
			            'The DPR context encoder model to use. Either \'facebook/dpr-ctx_encoder-single-nq-base\' or'
			            ' \'facebook/dpr-ctx_encoder-multiset-base\''
			        )
			    }      ,						)
			A__				:   Optional[str]								=			field(
			    default=__snake_case      ,						metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}      ,						)






@dataclass
class 						a__							:
			A__				:   Optional[int]								=			field(
			    default=__snake_case      ,						metadata={
			        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
			    }      ,						)
			A__				:   int								=			field(
			    default=16      ,						metadata={
			        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
			    }      ,						)






@dataclass
class 						a__							:
			A__				:   int								=			field(
			    default=7_68      ,						metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}      ,						)
			A__				:   int								=			field(
			    default=1_28      ,						metadata={
			        'help': (
			            'The number of bi-directional links created for every new element during the HNSW index construction.'
			        )
			    }      ,						)


if __name__ == "__main__":
	logging.basicConfig(level=logging.WARNING)
	logger.setLevel(logging.INFO)

	lowerCamelCase_				:		Tuple       						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
	lowerCamelCase_						,  lowerCamelCase_						,  lowerCamelCase_				:		Union[str, Any]       						=       parser.parse_args_into_dataclasses()
	with TemporaryDirectory() as tmp_dir:
		lowerCamelCase_				:		Union[str, Any]       						=       rag_example_args.output_dir or tmp_dir
		main(rag_example_args, processing_args, index_hnsw_args)




