



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


snake_case__					:     Tuple    		=   logging.getLogger(__name__)
torch.set_grad_enabled(False)
snake_case__					:     int    		=   """cuda""" if torch.cuda.is_available() else """cpu"""



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=1_0_0  ,	_SCREAMING_SNAKE_CASE=" "   ):
	__lowercase		       =			text.split(_SCREAMING_SNAKE_CASE   )
	return [character.join(text[i : i + n]   ).strip() for i in range(0  ,	len(_SCREAMING_SNAKE_CASE   )  ,	_SCREAMING_SNAKE_CASE   )]



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase       ,    __lowercase		       =			[], []
	for title, text in zip(documents["title"]  ,	documents["text"]   ):
		if text is not None:
			for passage in split_text(_SCREAMING_SNAKE_CASE   ):
				titles.append(title if title is not None else ""   )
				texts.append(_SCREAMING_SNAKE_CASE   )
	return {"title": titles, "text": texts}



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			ctx_tokenizer(
	    documents["title"]  ,	documents["text"]  ,	truncation=_SCREAMING_SNAKE_CASE  ,	padding="longest"  ,	return_tensors="pt"   )["input_ids"]
	__lowercase		       =			ctx_encoder(input_ids.to(device=_SCREAMING_SNAKE_CASE   )  ,	return_dict=_SCREAMING_SNAKE_CASE   ).pooler_output
	return {"embeddings": embeddings.detach().cpu().numpy()}



def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	):
	######################################
	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		       =			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		       =			dataset.map(_SCREAMING_SNAKE_CASE  ,	batched=_SCREAMING_SNAKE_CASE  ,	num_proc=processing_args.num_proc   )

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

	# And finally save your dataset
	__lowercase		       =			os.path.join(rag_example_args.output_dir  ,	"my_knowledge_dataset"   )
	dataset.save_to_disk(_SCREAMING_SNAKE_CASE   )
	# 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		       =			faiss.IndexHNSWFlat(index_hnsw_args.d  ,	index_hnsw_args.m  ,	faiss.METRIC_INNER_PRODUCT   )
	dataset.add_faiss_index("embeddings"  ,	custom_index=_SCREAMING_SNAKE_CASE   )

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

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     str      							=      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'"""}  ,						)
						_snake_case			:     Optional[str]      							=      field(
						    default=_lowercase  ,						metadata={"""help""": """Question that is passed as input to RAG. Default is 'What does Moses' rod turn into ?'."""}  ,						)
						_snake_case			:     str      							=      field(
						    default="""facebook/rag-sequence-nq"""  ,						metadata={"""help""": """The RAG model to use. Either 'facebook/rag-sequence-nq' or 'facebook/rag-token-nq'"""}  ,						)
						_snake_case			:     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'"""
						        )
						    }  ,						)
						_snake_case			:     Optional[str]      							=      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   _A   :


						'''simple docstring'''





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

@dataclass
class   _A   :


						'''simple docstring'''





						_snake_case			:     int      							=      field(
						    default=768  ,						metadata={"""help""": """The dimension of the embeddings to pass to the HNSW Faiss index."""}  ,						)
						_snake_case			:     int      							=      field(
						    default=128  ,						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)

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








import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
snake_case__					:     Optional[int]    		=   logging.get_logger(__name__)



def    snake_case_				(    _SCREAMING_SNAKE_CASE   ):
	__lowercase		       =			MobileNetVaConfig(layer_norm_eps=0.0_0_1   )

	if "_quant" in model_name:
		raise ValueError("Quantized models are not supported."   )

	__lowercase		       =			re.match(R"^mobilenet_v1_([^_]*)_([^_]*)$"  ,	_SCREAMING_SNAKE_CASE   )
	if matches:
		__lowercase		       =			float(matches[1]   )
		__lowercase		       =			int(matches[2]   )

	# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
	# the usual 1000. The first class (index 0) is "background".
	__lowercase		       =			1_0_0_1
	__lowercase		       =			"imagenet-1k-id2label.json"
	__lowercase		       =			"huggingface/label-files"
	__lowercase		       =			json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"   )  ,	"r"   )   )
	__lowercase		       =			{int(_SCREAMING_SNAKE_CASE   ) + 1: v for k, v in idalabel.items()}
	__lowercase		       =			"background"
	__lowercase		       =			idalabel
	__lowercase		       =			{v: k for k, v in idalabel.items()}

	return config



def    snake_case_				(    ):
	__lowercase		       =			"http://images.cocodataset.org/val2017/000000039769.jpg"
	__lowercase		       =			Image.open(requests.get(_SCREAMING_SNAKE_CASE  ,	stream=_SCREAMING_SNAKE_CASE   ).raw   )
	return im



@torch.no_grad()
def    snake_case_				(    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=False   ):
	__lowercase		       =			get_mobilenet_va_config(_SCREAMING_SNAKE_CASE   )

	# Load 🤗 model
	__lowercase		       =			MobileNetVaForImageClassification(_SCREAMING_SNAKE_CASE   ).eval()

	# Load weights from TensorFlow checkpoint
	load_tf_weights_in_mobilenet_va(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE   )

	# Check outputs on an image, prepared by MobileNetV1ImageProcessor
	__lowercase		       =			MobileNetVaImageProcessor(
	    crop_size={"width": config.image_size, "height": config.image_size}  ,	size={"shortest_edge": config.image_size + 3_2}  ,	)
	__lowercase		       =			image_processor(images=prepare_img()  ,	return_tensors="pt"   )
	__lowercase		       =			model(**_SCREAMING_SNAKE_CASE   )
	__lowercase		       =			outputs.logits

	assert logits.shape == (1, 1_0_0_1)

	if model_name == "mobilenet_v1_1.0_224":
		__lowercase		       =			torch.tensor([-4.1_7_3_9, -1.1_2_3_3, 3.1_2_0_5]   )
	elif model_name == "mobilenet_v1_0.75_192":
		__lowercase		       =			torch.tensor([-3.9_4_4_0, -2.3_1_4_1, -0.3_3_3_3]   )
	else:
		__lowercase		       =			None

	if expected_logits is not None:
		assert torch.allclose(logits[0, :3]  ,	_SCREAMING_SNAKE_CASE  ,	atol=1E-4   )

	Path(_SCREAMING_SNAKE_CASE   ).mkdir(exist_ok=_SCREAMING_SNAKE_CASE   )
	print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""   )
	model.save_pretrained(_SCREAMING_SNAKE_CASE   )
	print(F"""Saving image processor to {pytorch_dump_folder_path}"""   )
	image_processor.save_pretrained(_SCREAMING_SNAKE_CASE   )

	if push_to_hub:
		print("Pushing to the hub..."   )
		__lowercase		       =			"google/" + model_name
		image_processor.push_to_hub(_SCREAMING_SNAKE_CASE   )
		model.push_to_hub(_SCREAMING_SNAKE_CASE   )


if __name__ == "__main__":
	snake_case__					:     Tuple    		=   argparse.ArgumentParser()
	# Required parameters
	parser.add_argument(
	    """--model_name""",
	    default="""mobilenet_v1_1.0_224""",
	    type=str,
	    help="""Name of the MobileNetV1 model you'd like to convert. Should in the form 'mobilenet_v1_<depth>_<size>'.""",
	)
	parser.add_argument(
	    """--checkpoint_path""", required=True, type=str, help="""Path to the original TensorFlow checkpoint (.ckpt file)."""
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
	)

	snake_case__					:     Dict    		=   parser.parse_args()
	convert_movilevit_checkpoint(
	    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
	)



