



'''simple docstring'''

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


__lowerCAmelCase		     =  logging.getLogger(__name__)
torch.set_grad_enabled(False)
__lowerCAmelCase		     =  '''cuda''' if torch.cuda.is_available() else '''cpu'''



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_=100		,     lowerCAmelCase_=" "    )     -> List[str]:
		_a	: List[Any]								=				text.split(lowerCAmelCase_    )
		return [character.join(text[i : i + n]    ).strip() for i in range(0		,     len(lowerCAmelCase_    )		,     lowerCAmelCase_    )]



def 	__lowerCamelCase	( lowerCAmelCase_    )     -> dict:
		_a			,   _a	: Any								=				[], []
		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_    )     -> dict:
		_a	: int								=				ctx_tokenizer(
		    documents['title']		,     documents['text']		,     truncation=lowerCAmelCase_		,     padding='longest'		,     return_tensors='pt'    )['input_ids']
		_a	: Any								=				ctx_encoder(input_ids.to(device=lowerCAmelCase_    )		,     return_dict=lowerCAmelCase_    ).pooler_output
		return {"embeddings": embeddings.detach().cpu().numpy()}



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     )     -> int:
		######################################
		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	: Optional[Any]								=				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	: Dict								=				dataset.map(lowerCAmelCase_		,     batched=lowerCAmelCase_		,     num_proc=processing_args.num_proc    )

		# And compute the embeddings
		_a	: Dict								=				DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    ).to(device=lowerCAmelCase_    )
		_a	: Tuple								=				DPRContextEncoderTokenizerFast.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name    )
		_a	: List[Any]								=				Features(
		    {'text': Value('string'    ), 'title': Value('string'    ), 'embeddings': Sequence(Value('float32'    )    )}    )  # optional, save as float32 instead of float64 to save space
		_a	: Dict								=				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	: List[Any]								=				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	: Any								=				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	: List[Any]								=				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

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       str  							=   field(
							    default=str(Path(_UpperCamelCase  ).parent / 'test_run' / 'dummy-kb' / 'my_knowledge_dataset.csv'  )     ,		metadata={'help': 'Path to a tab-separated csv file with columns \'title\' and \'text\''}     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=_UpperCamelCase     ,		metadata={'help': 'Question that is passed as input to RAG. Default is \'What does Moses\' rod turn into ?\'.'}     ,		)
							lowerCAmelCase			:       str  							=   field(
							    default='facebook/rag-sequence-nq'     ,		metadata={'help': 'The RAG model to use. Either \'facebook/rag-sequence-nq\' or \'facebook/rag-token-nq\''}     ,		)
							lowerCAmelCase			:       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\''
							        )
							    }     ,		)
							lowerCAmelCase			:       Optional[str]  							=   field(
							    default=str(Path(_UpperCamelCase  ).parent / 'test_run' / 'dummy-kb'  )     ,		metadata={'help': 'Path to a directory where the dataset passages and the index will be saved'}     ,		)

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       Optional[int]  							=   field(
							    default=_UpperCamelCase     ,		metadata={
							        'help': 'The number of processes to use to split the documents into passages. Default is single process.'
							    }     ,		)
							lowerCAmelCase			:       int  							=   field(
							    default=1_6     ,		metadata={
							        'help': 'The batch size to use when computing the passages embeddings using the DPR context encoder.'
							    }     ,		)

@dataclass
class       __magic_name__		:
							lowerCAmelCase			:       int  							=   field(
							    default=7_6_8     ,		metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}     ,		)
							lowerCAmelCase			:       int  							=   field(
							    default=1_2_8     ,		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		     =  HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							__lowerCAmelCase     ,   __lowerCAmelCase     ,   __lowerCAmelCase		     =  parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														__lowerCAmelCase		     =  rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)









'''simple docstring'''

import argparse
import collections

import numpy as np
import torch
from flax import traverse_util
from tax import checkpoints

from transformers import MTaConfig, UMTaEncoderModel, UMTaForConditionalGeneration
from transformers.utils import logging


logging.set_verbosity_info()



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Dict:
		return params[f"""{prefix}/{prefix}/relpos_bias/rel_embedding"""][:, i, :]



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_="attention"    )     -> int:
		_a	: int								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/key/kernel"""][:, i, :, :]    )
		_a	: Dict								=				k_tmp.reshape(k_tmp.shape[0]		,     k_tmp.shape[1] * k_tmp.shape[2]    )
		_a	: Any								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/out/kernel"""][:, i, :, :]    )
		_a	: int								=				o_tmp.reshape(o_tmp.shape[0] * o_tmp.shape[1]		,     o_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/query/kernel"""][:, i, :, :]    )
		_a	: Dict								=				q_tmp.reshape(q_tmp.shape[0]		,     q_tmp.shape[1] * q_tmp.shape[2]    )
		_a	: List[str]								=				np.ascontiguousarray(params[f"""{prefix}/{prefix}/{layer_name}/value/kernel"""][:, i, :, :]    )
		_a	: int								=				v_tmp.reshape(v_tmp.shape[0]		,     v_tmp.shape[1] * v_tmp.shape[2]    )
		return k, o, q, v



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_=False    )     -> Optional[Any]:
		if split_mlp_wi:
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_0/kernel"""][:, i, :]
				_a	: Dict								=				params[f"""{prefix}/{prefix}/mlp/wi_1/kernel"""][:, i, :]
				_a	: Optional[int]								=				(wi_a, wi_a)
		else:
				_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wi/kernel"""][:, i, :]

		_a	: Tuple								=				params[f"""{prefix}/{prefix}/mlp/wo/kernel"""][:, i, :]
		return wi, wo



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> Any:
		return params[f"""{prefix}/{prefix}/{layer_name}/scale"""][:, i]



def 	__lowerCamelCase	( lowerCAmelCase_		,     *, lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False    )     -> Any:
		_a	: Dict								=				traverse_util.flatten_dict(variables['target']    )
		_a	: Tuple								=				{'/'.join(lowerCAmelCase_    ): v for k, v in old.items()}

		# v1.1 models have a gated GeLU with wi_0 and wi_1 instead of wi
		_a	: int								=				'encoder/encoder/mlp/wi_0/kernel' in old
		print('Split MLP:'		,     lowerCAmelCase_    )

		_a	: str								=				collections.OrderedDict()

		# Shared embeddings.
		_a	: List[str]								=				old['token_embedder/embedding']

		# Encoder.
		for i in range(lowerCAmelCase_    ):
				# Block i, layer 0 (Self Attention).
				_a	: Any								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_attention_layer_norm'    )
				_a			,   _a			,   _a			,   _a	: List[str]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'attention'    )
				_a	: str								=				layer_norm
				_a	: Union[str, Any]								=				k.T
				_a	: Dict								=				o.T
				_a	: int								=				q.T
				_a	: List[str]								=				v.T

				# Block i, layer 1 (MLP).
				_a	: Optional[int]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     'pre_mlp_layer_norm'    )
				_a			,   _a	: Any								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'		,     lowerCAmelCase_    )
				_a	: Optional[Any]								=				layer_norm
				if split_mlp_wi:
						_a	: Tuple								=				wi[0].T
						_a	: List[str]								=				wi[1].T
				else:
						_a	: Union[str, Any]								=				wi.T
				_a	: Any								=				wo.T
				if scalable_attention:
						# convert the rel_embedding of each layer
						_a	: Any								=				tax_relpos_bias_lookup(
						    lowerCAmelCase_		,     lowerCAmelCase_		,     'encoder'    ).T

		_a	: Optional[Any]								=				old['encoder/encoder_norm/scale']

		if not scalable_attention:
				_a	: Union[str, Any]								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'encoder'    ).T
				_a	: Any								=				tax_relpos_bias_lookup(
				    lowerCAmelCase_		,     0		,     'decoder'    ).T

		if not is_encoder_only:
				# Decoder.
				for i in range(lowerCAmelCase_    ):
						# Block i, layer 0 (Self Attention).
						_a	: Dict								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_self_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Union[str, Any]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'self_attention'    )
						_a	: str								=				layer_norm
						_a	: List[Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: int								=				q.T
						_a	: List[Any]								=				v.T

						# Block i, layer 1 (Cross Attention).
						_a	: List[Any]								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_cross_attention_layer_norm'    )
						_a			,   _a			,   _a			,   _a	: Optional[int]								=				tax_attention_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'encoder_decoder_attention'    )
						_a	: str								=				layer_norm
						_a	: Union[str, Any]								=				k.T
						_a	: Union[str, Any]								=				o.T
						_a	: Any								=				q.T
						_a	: str								=				v.T

						# Block i, layer 2 (MLP).
						_a	: str								=				tax_layer_norm_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     'pre_mlp_layer_norm'    )
						_a			,   _a	: int								=				tax_mlp_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'		,     lowerCAmelCase_    )
						_a	: List[str]								=				layer_norm
						if split_mlp_wi:
								_a	: List[str]								=				wi[0].T
								_a	: Union[str, Any]								=				wi[1].T
						else:
								_a	: Optional[Any]								=				wi.T
						_a	: List[str]								=				wo.T

						if scalable_attention:
								# convert the rel_embedding of each layer
								_a	: int								=				tax_relpos_bias_lookup(lowerCAmelCase_		,     lowerCAmelCase_		,     'decoder'    ).T

				_a	: List[Any]								=				old['decoder/decoder_norm/scale']

				# LM Head (only in v1.1 checkpoints, in v1.0 embeddings are used instead)
				if "decoder/logits_dense/kernel" in old:
						_a	: List[Any]								=				old['decoder/logits_dense/kernel'].T

		return new



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_    )     -> str:
		_a	: List[str]								=				collections.OrderedDict([(k, torch.from_numpy(v.copy()    )) for (k, v) in converted_params.items()]    )

		# Add what is missing.
		if "encoder.embed_tokens.weight" not in state_dict:
				_a	: Tuple								=				state_dict['shared.weight']

		if not is_encoder_only:
				if "decoder.embed_tokens.weight" not in state_dict:
						_a	: Union[str, Any]								=				state_dict['shared.weight']

				if "lm_head.weight" not in state_dict:  # For old 1.0 models.
						print('Using shared word embeddings as lm_head.'    )
						_a	: Optional[Any]								=				state_dict['shared.weight']

		return state_dict



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )     -> int:
		_a	: Any								=				checkpoints.load_tax_checkpoint(lowerCAmelCase_    )
		_a	: str								=				convert_tax_to_pytorch(
		    lowerCAmelCase_		,     num_layers=config.num_layers		,     is_encoder_only=lowerCAmelCase_		,     scalable_attention=lowerCAmelCase_    )
		_a	: Dict								=				make_state_dict(lowerCAmelCase_		,     lowerCAmelCase_    )
		model.load_state_dict(lowerCAmelCase_		,     strict=lowerCAmelCase_    )



def 	__lowerCamelCase	( lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_ = False		,     lowerCAmelCase_ = False		,     )     -> str:
		_a	: int								=				MTaConfig.from_json_file(lowerCAmelCase_    )
		print(f"""Building PyTorch model from configuration: {config}"""    )
		# Non-v1.1 checkpoints could also use T5Model, but this works for all.
		# The v1.0 checkpoints will simply have an LM head that is the word embeddings.
		if is_encoder_only:
				_a	: Any								=				UMTaEncoderModel(lowerCAmelCase_    )
		else:
				_a	: Optional[int]								=				UMTaForConditionalGeneration(lowerCAmelCase_    )

		# Load weights from tf checkpoint
		load_tax_weights_in_ta(lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_		,     lowerCAmelCase_    )

		# Save pytorch-model
		print(f"""Save PyTorch model to {pytorch_dump_path}"""    )
		model.save_pretrained(lowerCAmelCase_    )

		# Verify that we can load the checkpoint.
		model.from_pretrained(lowerCAmelCase_    )
		print('Done'    )


if __name__ == "__main__":
							__lowerCAmelCase		     =  argparse.ArgumentParser(description='''Converts a native T5X checkpoint into a PyTorch checkpoint.''')
							# Required parameters
							parser.add_argument(
							    '''--t5x_checkpoint_path''', default=None, type=str, required=True, help='''Path to the T5X checkpoint.'''
							)
							parser.add_argument(
							    '''--config_file''',
							    default=None,
							    type=str,
							    required=True,
							    help='''The config json file corresponding to the pre-trained T5 model.\nThis specifies the model architecture.''',
							)
							parser.add_argument(
							    '''--pytorch_dump_path''', default=None, type=str, required=True, help='''Path to the output PyTorch model.'''
							)
							parser.add_argument(
							    '''--is_encoder_only''', action='''store_true''', help='''Check if the model is encoder-decoder model''', default=False
							)
							parser.add_argument(
							    '''--scalable_attention''',
							    action='''store_true''',
							    help='''Whether the model uses scaled attention (umt5 model)''',
							    default=False,
							)
							__lowerCAmelCase		     =  parser.parse_args()
							convert_tax_checkpoint_to_pytorch(
							    args.tax_checkpoint_path,
							    args.config_file,
							    args.pytorch_dump_path,
							    args.is_encoder_only,
							    args.scalable_attention,
							)




