



"""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				(							UpperCAmelCase_ :       str,	UpperCAmelCase_ :       Tuple=100,	UpperCAmelCase_ :       str=" "     )	-> List[str]:






	"""simple docstring"""





	A__											=					text.split(UpperCAmelCase_     )
	return [character.join(text[i : i + n]     ).strip() for i in range(0,	len(UpperCAmelCase_     ),	UpperCAmelCase_     )]

def       _lowerCamelCase				(							UpperCAmelCase_ :       dict     )	-> dict:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       dict,	UpperCAmelCase_ :       DPRContextEncoder,	UpperCAmelCase_ :       DPRContextEncoderTokenizerFast     )	-> dict:






	"""simple docstring"""





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

def       _lowerCamelCase				(							UpperCAmelCase_ :       "RagExampleArguments",	UpperCAmelCase_ :       "ProcessingArguments",	UpperCAmelCase_ :       "IndexHnswArguments",	)	-> int:






	"""simple docstring"""





	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(UpperCAmelCase_,	batched=UpperCAmelCase_,	num_proc=processing_args.num_proc     )

	# And compute the embeddings
	A__											=					DPRContextEncoder.from_pretrained(rag_example_args.dpr_ctx_encoder_model_name     ).to(device=UpperCAmelCase_     )
	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(UpperCAmelCase_,	ctx_encoder=UpperCAmelCase_,	ctx_tokenizer=UpperCAmelCase_     ),	batched=UpperCAmelCase_,	batch_size=processing_args.batch_size,	features=UpperCAmelCase_,	)

	# And finally save your dataset
	A__											=					os.path.join(rag_example_args.output_dir,	"my_knowledge_dataset"     )
	dataset.save_to_disk(UpperCAmelCase_     )
	# 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=UpperCAmelCase_     )

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






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					str							  =     field(
			    default=str(Path(_lowerCAmelCase      ).parent / "test_run" / "dummy-kb" / "my_knowledge_dataset.csv"      )       ,		metadata={"help": "Path to a tab-separated csv file with columns 'title' and 'text'"}       ,		)
			A__	:					Optional[str]							  =     field(
			    default=_lowerCAmelCase       ,		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=str(Path(_lowerCAmelCase      ).parent / "test_run" / "dummy-kb"      )       ,		metadata={"help": "Path to a directory where the dataset passages and the index will be saved"}       ,		)






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


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






@dataclass
class       UpperCamelCase__			:

			"""simple docstring"""


			A__	:					int							  =     field(
			    default=7_6_8       ,		metadata={"help": "The dimension of the embeddings to pass to the HNSW Faiss index."}       ,		)
			A__	:					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)

			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)












# This script creates a super tiny model that is useful inside tests, when we just want to test that
# the machinery works, without needing to the check the quality of the outcomes.
#
# This version creates a tiny vocab first, and then a tiny model - so the outcome is truly tiny -
# all files ~60KB. As compared to taking a full-size model, reducing to the minimum its layers and
# emb dimensions, but keeping the full vocab + merges files, leading to ~3MB in total for all files.
# The latter is done by `fsmt-make-super-tiny-model.py`.
#
# It will be used then as "stas/tiny-wmt19-en-ru"

from pathlib import Path
import json
import tempfile

from transformers import FSMTTokenizer, FSMTConfig, FSMTForConditionalGeneration
from transformers.models.fsmt.tokenization_fsmt import VOCAB_FILES_NAMES

lowerCamelCase      :       List[str]    =						'''tiny-wmt19-en-ru'''

# Build

# borrowed from a test
lowerCamelCase      :       str    =						[
    '''l''',
    '''o''',
    '''w''',
    '''e''',
    '''r''',
    '''s''',
    '''t''',
    '''i''',
    '''d''',
    '''n''',
    '''w</w>''',
    '''r</w>''',
    '''t</w>''',
    '''lo''',
    '''low''',
    '''er</w>''',
    '''low</w>''',
    '''lowest</w>''',
    '''newer</w>''',
    '''wider</w>''',
    '''<unk>''',
]
lowerCamelCase      :       List[Any]    =						dict(zip(vocab, range(len(vocab))))
lowerCamelCase      :       List[str]    =						['''l o 123''', '''lo w 1456''', '''e r</w> 1789''', '''''']

with tempfile.TemporaryDirectory() as tmpdirname:
 lowerCamelCase      :       int    =						Path(tmpdirname)
 lowerCamelCase      :       Optional[int]    =						build_dir / VOCAB_FILES_NAMES['''src_vocab_file''']
 lowerCamelCase      :       List[str]    =						build_dir / VOCAB_FILES_NAMES['''tgt_vocab_file''']
 lowerCamelCase      :       int    =						build_dir / VOCAB_FILES_NAMES['''merges_file''']
 with open(src_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(tgt_vocab_file, '''w''') as fp:
  fp.write(json.dumps(vocab_tokens))
 with open(merges_file, '''w''') as fp:
  fp.write('''\n'''.join(merges))

 lowerCamelCase      :       Dict    =						FSMTTokenizer(
     langs=['''en''', '''ru'''],
     src_vocab_size=len(vocab),
     tgt_vocab_size=len(vocab),
     src_vocab_file=src_vocab_file,
     tgt_vocab_file=tgt_vocab_file,
     merges_file=merges_file,
 )

lowerCamelCase      :       int    =						FSMTConfig(
    langs=['''ru''', '''en'''],
    src_vocab_size=1000,
    tgt_vocab_size=1000,
    d_model=4,
    encoder_layers=1,
    decoder_layers=1,
    encoder_ffn_dim=4,
    decoder_ffn_dim=4,
    encoder_attention_heads=1,
    decoder_attention_heads=1,
)

lowerCamelCase      :       Any    =						FSMTForConditionalGeneration(config)
print(F'''num of params {tiny_model.num_parameters()}''')

# Test
lowerCamelCase      :       int    =						tokenizer(['''Making tiny model'''], return_tensors='''pt''')
lowerCamelCase      :       Dict    =						tiny_model(**batch)

print('''test output:''', len(outputs.logits[0]))

# Save
tiny_model.half()  # makes it smaller
tiny_model.save_pretrained(mname_tiny)
tokenizer.save_pretrained(mname_tiny)

print(F'''Generated {mname_tiny}''')

# Upload
# transformers-cli upload tiny-wmt19-en-ru






