



"""simple docstring"""







import argparse
import os
import re

import tensorflow as tf
import torch

from transformers import BertConfig, BertModel
from transformers.utils import logging


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

def       _lowerCamelCase				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Dict,	UpperCAmelCase_ :       str     )	-> List[str]:






	"""simple docstring"""





	A__											=					os.path.abspath(UpperCAmelCase_     )
	logger.info(F"""Converting TensorFlow checkpoint from {tf_path}"""     )
	# Load weights from TF model
	A__											=					tf.train.list_variables(UpperCAmelCase_     )
	A__											=					[]
	A__											=					[]
	A__											=					[]
	for full_name, shape in init_vars:
		# logger.info(f"Loading TF weight {name} with shape {shape}")
		A__											=					full_name.split("/"     )
		if full_name == "_CHECKPOINTABLE_OBJECT_GRAPH" or name[0] in ["global_step", "save_counter"]:
			logger.info(F"""Skipping non-model layer {full_name}"""     )
			continue
		if "optimizer" in full_name:
			logger.info(F"""Skipping optimization layer {full_name}"""     )
			continue
		if name[0] == "model":
			# ignore initial 'model'
			A__											=					name[1:]
		# figure out how many levels deep the name is
		A__											=					0
		for _name in name:
			if _name.startswith("layer_with_weights"     ):
				depth += 1
			else:
				break
		layer_depth.append(UpperCAmelCase_     )
		# read data
		A__											=					tf.train.load_variable(UpperCAmelCase_,	UpperCAmelCase_     )
		names.append("/".join(UpperCAmelCase_     )     )
		arrays.append(UpperCAmelCase_     )
	logger.info(F"""Read a total of {len(UpperCAmelCase_     ):,} layers"""     )

	# Sanity check
	if len(set(UpperCAmelCase_     )     ) != 1:
		raise ValueError(F"""Found layer names with different depths (layer depth {list(set(UpperCAmelCase_     )     )})"""     )
	A__											=					list(set(UpperCAmelCase_     )     )[0]
	if layer_depth != 1:
		raise ValueError(
		    "The model contains more than just the embedding/encoder layers. This script does not handle MLM/NSP"
		    " heads."     )

	# convert layers
	logger.info("Converting weights..."     )
	for full_name, array in zip(UpperCAmelCase_,	UpperCAmelCase_     ):
		A__											=					full_name.split("/"     )
		A__											=					model
		A__											=					[]
		for i, m_name in enumerate(UpperCAmelCase_     ):
			if m_name == ".ATTRIBUTES":
				# variable names end with .ATTRIBUTES/VARIABLE_VALUE
				break
			if m_name.startswith("layer_with_weights"     ):
				A__											=					int(m_name.split("-"     )[-1]     )
				if layer_num <= 2:
					# embedding layers
					# layer_num 0: word_embeddings
					# layer_num 1: position_embeddings
					# layer_num 2: token_type_embeddings
					continue
				elif layer_num == 3:
					# embedding LayerNorm
					trace.extend(["embeddings", "LayerNorm"]     )
					A__											=					getattr(UpperCAmelCase_,	"embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"LayerNorm"     )
				elif layer_num > 3 and layer_num < config.num_hidden_layers + 4:
					# encoder layers
					trace.extend(["encoder", "layer", str(layer_num - 4     )]     )
					A__											=					getattr(UpperCAmelCase_,	"encoder"     )
					A__											=					getattr(UpperCAmelCase_,	"layer"     )
					A__											=					pointer[layer_num - 4]
				elif layer_num == config.num_hidden_layers + 4:
					# pooler layer
					trace.extend(["pooler", "dense"]     )
					A__											=					getattr(UpperCAmelCase_,	"pooler"     )
					A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "embeddings":
				trace.append("embeddings"     )
				A__											=					getattr(UpperCAmelCase_,	"embeddings"     )
				if layer_num == 0:
					trace.append("word_embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"word_embeddings"     )
				elif layer_num == 1:
					trace.append("position_embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"position_embeddings"     )
				elif layer_num == 2:
					trace.append("token_type_embeddings"     )
					A__											=					getattr(UpperCAmelCase_,	"token_type_embeddings"     )
				else:
					raise ValueError(F"""Unknown embedding layer with name {full_name}"""     )
				trace.append("weight"     )
				A__											=					getattr(UpperCAmelCase_,	"weight"     )
			elif m_name == "_attention_layer":
				# self-attention layer
				trace.extend(["attention", "self"]     )
				A__											=					getattr(UpperCAmelCase_,	"attention"     )
				A__											=					getattr(UpperCAmelCase_,	"self"     )
			elif m_name == "_attention_layer_norm":
				# output attention norm
				trace.extend(["attention", "output", "LayerNorm"]     )
				A__											=					getattr(UpperCAmelCase_,	"attention"     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"LayerNorm"     )
			elif m_name == "_attention_output_dense":
				# output attention dense
				trace.extend(["attention", "output", "dense"]     )
				A__											=					getattr(UpperCAmelCase_,	"attention"     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "_output_dense":
				# output dense
				trace.extend(["output", "dense"]     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "_output_layer_norm":
				# output dense
				trace.extend(["output", "LayerNorm"]     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
				A__											=					getattr(UpperCAmelCase_,	"LayerNorm"     )
			elif m_name == "_key_dense":
				# attention key
				trace.append("key"     )
				A__											=					getattr(UpperCAmelCase_,	"key"     )
			elif m_name == "_query_dense":
				# attention query
				trace.append("query"     )
				A__											=					getattr(UpperCAmelCase_,	"query"     )
			elif m_name == "_value_dense":
				# attention value
				trace.append("value"     )
				A__											=					getattr(UpperCAmelCase_,	"value"     )
			elif m_name == "_intermediate_dense":
				# attention intermediate dense
				trace.extend(["intermediate", "dense"]     )
				A__											=					getattr(UpperCAmelCase_,	"intermediate"     )
				A__											=					getattr(UpperCAmelCase_,	"dense"     )
			elif m_name == "_output_layer_norm":
				# output layer norm
				trace.append("output"     )
				A__											=					getattr(UpperCAmelCase_,	"output"     )
			# weights & biases
			elif m_name in ["bias", "beta"]:
				trace.append("bias"     )
				A__											=					getattr(UpperCAmelCase_,	"bias"     )
			elif m_name in ["kernel", "gamma"]:
				trace.append("weight"     )
				A__											=					getattr(UpperCAmelCase_,	"weight"     )
			else:
				logger.warning(F"""Ignored {m_name}"""     )
        # for certain layers reshape is necessary
		A__											=					".".join(UpperCAmelCase_     )
		if re.match(r"(\S+)\.attention\.self\.(key|value|query)\.(bias|weight)",	UpperCAmelCase_     ) or re.match(
		    r"(\S+)\.attention\.output\.dense\.weight",	UpperCAmelCase_     ):
			A__											=					array.reshape(pointer.data.shape     )
		if "kernel" in full_name:
			A__											=					array.transpose()
		if pointer.shape == array.shape:
			A__											=					torch.from_numpy(UpperCAmelCase_     )
		else:
			raise ValueError(
			    F"""Shape mismatch in layer {full_name}: Model expects shape {pointer.shape} but layer contains shape:"""
			    F""" {array.shape}"""     )
		logger.info(F"""Successfully set variable {full_name} to PyTorch layer {trace}"""     )
	return model

def       _lowerCamelCase				(							UpperCAmelCase_ :       Any,	UpperCAmelCase_ :       Union[str, Any],	UpperCAmelCase_ :       Any     )	-> List[str]:






	"""simple docstring"""





	logger.info(F"""Loading model based on config from {config_path}..."""     )
	A__											=					BertConfig.from_json_file(UpperCAmelCase_     )
	A__											=					BertModel(UpperCAmelCase_     )

	# Load weights from checkpoint
	logger.info(F"""Loading weights from checkpoint {tf_checkpoint_path}..."""     )
	load_tfa_weights_in_bert(UpperCAmelCase_,	UpperCAmelCase_,	UpperCAmelCase_     )

	# Save pytorch-model
	logger.info(F"""Saving PyTorch model to {pytorch_dump_path}..."""     )
	torch.save(model.state_dict(),	UpperCAmelCase_     )


if __name__ == "__main__":
			UpperCamelCase     =							argparse.ArgumentParser()
			parser.add_argument(
			    """--tf_checkpoint_path""", type=str, required=True, help="""Path to the TensorFlow 2.x checkpoint path."""
			)
			parser.add_argument(
			    """--bert_config_file""",
			    type=str,
			    required=True,
			    help="""The config json file corresponding to the BERT model. This specifies the model architecture.""",
			)
			parser.add_argument(
			    """--pytorch_dump_path""",
			    type=str,
			    required=True,
			    help="""Path to the output PyTorch model (must include filename).""",
			)
			UpperCamelCase     =							parser.parse_args()
			convert_tfa_checkpoint_to_pytorch(args.tf_checkpoint_path, args.bert_config_file, args.pytorch_dump_path)







'''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


UpperCamelCase_    						=       logging.getLogger(__name__)
torch.set_grad_enabled(False)
UpperCamelCase_    						=       """cuda""" if torch.cuda.is_available() else """cpu"""






def 				_lowerCAmelCase			(   __magic_name__						:     str   , __magic_name__						:     Tuple=100   , __magic_name__						:     Optional[int]=" "						)				->  List[str]:
						lowercase :							List[Any]    		=text.split(__magic_name__						)
						return [character.join(text[i : i + n]						).strip() for i in range(0   , len(__magic_name__						)   , __magic_name__						)]






def 				_lowerCAmelCase			(   __magic_name__						:     dict						)				->  dict:
						lowercase		,	lowercase :							int    		=[], []
						for title, text in zip(documents['''title''']   , documents['''text''']						):
												if text is not None:
																		for passage in split_text(__magic_name__						):
																								titles.append(title if title is not None else ''''''						)
																								texts.append(__magic_name__						)
						return {"title": titles, "text": texts}






def 				_lowerCAmelCase			(   __magic_name__						:     dict   , __magic_name__						:     DPRContextEncoder   , __magic_name__						:     DPRContextEncoderTokenizerFast						)				->  dict:
						lowercase :							Dict    		=ctx_tokenizer(
						    documents['''title''']   , documents['''text''']   , truncation=__magic_name__   , padding='''longest'''   , return_tensors='''pt'''						)['''input_ids''']
						lowercase :							Optional[int]    		=ctx_encoder(input_ids.to(device=__magic_name__						)   , return_dict=__magic_name__						).pooler_output
						return {"embeddings": embeddings.detach().cpu().numpy()}






def 				_lowerCAmelCase			(   __magic_name__						:     "RagExampleArguments"   , __magic_name__						:     "ProcessingArguments"   , __magic_name__						:     "IndexHnswArguments"   , )				->  str:
						######################################
						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
						lowercase :							Tuple    		=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
						lowercase :							Optional[int]    		=dataset.map(__magic_name__   , batched=__magic_name__   , num_proc=processing_args.num_proc						)

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

						# And finally save your dataset
						lowercase :							Optional[Any]    		=os.path.join(rag_example_args.output_dir   , '''my_knowledge_dataset'''						)
						dataset.save_to_disk(__magic_name__						)
						# 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
						lowercase :							Union[str, Any]    		=faiss.IndexHNSWFlat(index_hnsw_args.d   , index_hnsw_args.m   , faiss.METRIC_INNER_PRODUCT						)
						dataset.add_faiss_index('''embeddings'''   , custom_index=__magic_name__						)

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





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





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





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    field(
				    default=7_68						,							metadata={'help': 'The dimension of the embeddings to pass to the HNSW Faiss index.'}						,							)
				lowerCamelCase_		       =    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)

							UpperCamelCase_    						=       HfArgumentParser((RagExampleArguments, ProcessingArguments, IndexHnswArguments))
							UpperCamelCase_						,   UpperCamelCase_						,   UpperCamelCase_    						=       parser.parse_args_into_dataclasses()
							with TemporaryDirectory() as tmp_dir:
														UpperCamelCase_    						=       rag_example_args.output_dir or tmp_dir
														main(rag_example_args, processing_args, index_hnsw_args)





