

import argparse
import shutil
import time
from json import JSONDecodeError
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import (
    SeqaSeqDataset,
    calculate_bleu,
    calculate_rouge,
    chunks,
    lmap,
    load_json,
    parse_numeric_n_bool_cl_kwargs,
    save_json,
    use_task_specific_params,
    write_txt_file,
)


A_       : Any         =			getLogger(__name__)

def 						UpperCamelCase					(lowercase_: Union[str, Any]    ,      lowercase_: str    ,      lowercase_: str    ,      lowercase_: int = 8    ,      lowercase_: int = 1024    ,      lowercase_: int="val"    ,      lowercase_: Union[str, Any]=None    ,      lowercase_: Optional[Any]=False    ,      lowercase_: str="summarization"    ,      lowercase_: Union[str, Any]=None    ,      lowercase_: Tuple=1    ,      lowercase_: Dict = None    ,      lowercase_: int=""    ,      **lowercase_: List[str]    ,      )     ->      Dict:
	A__							:    Dict        =		str(lowercase_					)
	assert local_rank is not None
	torch.distributed.init_process_group(backend="""nccl"""    ,      rank=lowercase_					)

	A__							:    List[Any]        =		Path(lowercase_					)
	A__							:    List[str]        =		save_dir.joinpath(f"""rank_{local_rank}_output.json"""					)
	torch.cuda.set_device(lowercase_					)
	A__							:    Tuple        =		AutoModelForSeqaSeqLM.from_pretrained(lowercase_					).cuda()
	if fpaa:
		A__							:    List[Any]        =		model.half()
	# determine if we need to increase num_beams
	use_task_specific_params(lowercase_    ,      lowercase_					)  # update config with task specific params
	A__							:    Dict        =		generate_kwargs.pop("""num_beams"""    ,      model.config.num_beams					)  # AttributeError risk?
	if num_return_sequences > num_beams:
		A__							:    List[Any]        =		num_return_sequences

	A__							:    List[Any]        =		AutoTokenizer.from_pretrained(lowercase_					)
	logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""					)  # if this is wrong, check config.model_type.

	if max_source_length is None:
		A__							:    str        =		tokenizer.model_max_length
	if prefix is None:
		A__							:    Any        =		prefix or getattr(model.config    ,      """prefix"""    ,      """"""					) or """"""
	A__							:    Optional[Any]        =		SeqaSeqDataset(
	    lowercase_    ,      lowercase_    ,      lowercase_    ,      max_target_length=1024    ,      type_path=lowercase_    ,      n_obs=lowercase_    ,      prefix=lowercase_    ,      **lowercase_    ,      )
	# I set shuffle=True for a more accurate progress bar.
	# If all the longest samples are first, the prog bar estimate is too high at the beginning.
	A__							:    Dict        =		ds.make_sortish_sampler(lowercase_    ,      distributed=lowercase_    ,      add_extra_examples=lowercase_    ,      shuffle=lowercase_					)
	A__							:    List[str]        =		DataLoader(lowercase_    ,      sampler=lowercase_    ,      batch_size=lowercase_    ,      collate_fn=ds.collate_fn					)
	A__							:    Optional[int]        =		[]
	for batch in tqdm(lowercase_					):
		A__							:    int        =		model.generate(
		    input_ids=batch["""input_ids"""].to(model.device					)    ,      attention_mask=batch["""attention_mask"""].to(model.device					)    ,      num_return_sequences=lowercase_    ,      num_beams=lowercase_    ,      **lowercase_    ,      )
		A__							:    Any        =		tokenizer.batch_decode(lowercase_    ,      skip_special_tokens=lowercase_    ,      clean_up_tokenization_spaces=lowercase_					)
		A__							:    Tuple        =		batch["""ids"""]
		if num_return_sequences > 1:
			A__							:    str        =		chunks(lowercase_    ,      lowercase_					)  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(lowercase_					):
			results.append({"""pred""": pred, """id""": ids[i].item()}					)
	save_json(lowercase_    ,      lowercase_					)
	return results, sampler.num_replicas

def 						UpperCamelCase					()     ->      str:
	A__							:    Optional[Any]        =		argparse.ArgumentParser(
	    epilog="""Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate"""					)
	parser.add_argument("""--data_dir"""    ,      type=lowercase_    ,      help="""like cnn_dm/test.source"""					)
	parser.add_argument(
	    """--model_name"""    ,      type=lowercase_    ,      help="""like facebook/bart-large-cnn,t5-base, etc."""    ,      default="""sshleifer/distilbart-xsum-12-3"""    ,      )
	parser.add_argument("""--save_dir"""    ,      type=lowercase_    ,      help="""where to save"""    ,      default="""tmp_gen"""					)
	parser.add_argument("""--max_source_length"""    ,      type=lowercase_    ,      default=lowercase_					)
	parser.add_argument(
	    """--type_path"""    ,      type=lowercase_    ,      default="""test"""    ,      help="""which subset to evaluate typically train/val/test"""					)
	parser.add_argument("""--task"""    ,      type=lowercase_    ,      default="""summarization"""    ,      help="""used for task_specific_params + metrics"""					)
	parser.add_argument("""--bs"""    ,      type=lowercase_    ,      default=8    ,      required=lowercase_    ,      help="""batch size"""					)
	parser.add_argument(
	    """--local_rank"""    ,      type=lowercase_    ,      default=-1    ,      required=lowercase_    ,      help="""should be passed by distributed.launch"""					)

	parser.add_argument(
	    """--n_obs"""    ,      type=lowercase_    ,      default=lowercase_    ,      required=lowercase_    ,      help="""How many observations. Defaults to all."""					)
	parser.add_argument(
	    """--num_return_sequences"""    ,      type=lowercase_    ,      default=1    ,      required=lowercase_    ,      help="""How many sequences to return"""					)
	parser.add_argument(
	    """--sync_timeout"""    ,      type=lowercase_    ,      default=600    ,      required=lowercase_    ,      help="""How long should master process wait for other processes to finish."""    ,      )
	parser.add_argument("""--src_lang"""    ,      type=lowercase_    ,      default=lowercase_    ,      required=lowercase_					)
	parser.add_argument("""--tgt_lang"""    ,      type=lowercase_    ,      default=lowercase_    ,      required=lowercase_					)
	parser.add_argument(
	    """--prefix"""    ,      type=lowercase_    ,      required=lowercase_    ,      default=lowercase_    ,      help="""will be added to the begininng of src examples"""					)
	parser.add_argument("""--fp16"""    ,      action="""store_true"""					)
	parser.add_argument("""--debug"""    ,      action="""store_true"""					)
	A__							:    Optional[Any]        =		time.time()
	A__   ,   A__							:    Optional[Any]        =		parser.parse_known_args()
	A__							:    Tuple        =		parse_numeric_n_bool_cl_kwargs(lowercase_					)
	if generate_kwargs and args.local_rank <= 0:
		print(f"""parsed the following generate kwargs: {generate_kwargs}"""					)
	A__							:    Union[str, Any]        =		Path(args.save_dir + """_tmp"""					)
	Path(lowercase_					).mkdir(exist_ok=lowercase_					)  # this handles locking.
	A__							:    Tuple        =		list(json_save_dir.glob("""rank_*.json"""					)					)
	if intermediate_files:
		raise ValueError(f"""Found files at {json_save_dir} please move or remove them."""					)
		# In theory, a node could finish and save before another node hits this. If this happens, we can address later.
	A__							:    Union[str, Any]        =		{}
	if args.src_lang is not None:
		A__							:    Optional[Any]        =		args.src_lang
	if args.tgt_lang is not None:
		A__							:    Any        =		args.tgt_lang

	Path(args.save_dir					).mkdir(exist_ok=lowercase_					)
	A__   ,   A__							:    List[str]        =		eval_data_dir(
	    args.data_dir    ,      lowercase_    ,      args.model_name    ,      type_path=args.type_path    ,      bs=args.bs    ,      fpaa=args.fpaa    ,      task=args.task    ,      local_rank=args.local_rank    ,      n_obs=args.n_obs    ,      max_source_length=args.max_source_length    ,      num_return_sequences=args.num_return_sequences    ,      prefix=args.prefix    ,      dataset_kwargs=lowercase_    ,      **lowercase_    ,      )

	if args.local_rank <= 0:
		A__							:    Dict        =		Path(args.save_dir					)
		save_dir.mkdir(exist_ok=lowercase_					)
		A__							:    Dict        =		gather_results_from_each_node(lowercase_    ,      lowercase_    ,      args.sync_timeout					)
		A__							:    Tuple        =		combine_partial_results(lowercase_					)
		if args.num_return_sequences > 1:
			A__							:    Tuple        =		save_dir.joinpath("""pseudolabel_results.json"""					)
			print(f"""Saving aggregated results at {save_path}, intermediate in {json_save_dir}/"""					)
			save_json(lowercase_    ,      lowercase_					)
			return
		A__							:    Dict        =		Path(args.data_dir					).joinpath(args.type_path + """.target"""					)
		with open(lowercase_					) as f:
			A__							:    Tuple        =		[x.rstrip() for x in f.readlines()][: len(lowercase_					)]

		# Calculate metrics, save metrics,  and save _generations.txt
		A__							:    List[Any]        =		"""translation""" in args.task
		A__							:    Optional[int]        =		calculate_bleu if calc_bleu else calculate_rouge
		A__							:    Dict        =		"""bleu""" if calc_bleu else """rouge"""
		A__							:    Dict        =		score_fn(lowercase_    ,      lowercase_					)
		A__							:    Tuple        =		len(lowercase_					)
		A__							:    List[Any]        =		time.time() - start_time
		A__							:    Optional[Any]        =		round(runtime / metrics["""n_obs"""]    ,      4					)
		A__							:    Any        =		num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		A__							:    Union[str, Any]        =		save_dir.joinpath(f"""{args.type_path}_{metric_name}.json"""					)
		save_json(lowercase_    ,      lowercase_    ,      indent=lowercase_					)
		print(lowercase_					)
		write_txt_file(lowercase_    ,      save_dir.joinpath(f"""{args.type_path}_generations.txt"""					)					)
		if args.debug:
			write_txt_file(lowercase_    ,      save_dir.joinpath(f"""{args.type_path}.target"""					)					)
		else:
			shutil.rmtree(lowercase_					)

def 						UpperCamelCase					(lowercase_: Tuple					)     ->      List:
	A__							:    List[str]        =		[]
	for partial_result in partial_results:
		records.extend(lowercase_					)
	A__							:    str        =		sorted(lowercase_    ,      key=lambda lowercase_					: x["id"]					)
	A__							:    Optional[int]        =		[x["""pred"""] for x in records]
	return preds

def 						UpperCamelCase					(lowercase_: Tuple    ,      lowercase_: str    ,      lowercase_: str					)     ->      List[Dict[str, List]]:
	# WAIT FOR lots of .json files
	A__							:    Optional[int]        =		time.time()
	logger.info("""waiting for all nodes to finish"""					)
	A__							:    Tuple        =		None
	while (time.time() - start_wait) < timeout:
		A__							:    Any        =		list(save_dir.glob("""rank_*.json"""					)					)
		if len(lowercase_					) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			A__							:    Dict        =		lmap(lowercase_    ,      lowercase_					)
			return json_data
		except JSONDecodeError:
			continue
	else:
		raise TimeoutError("""Rank 0 gave up on waiting for other processes"""					)
	# Unreachable


if __name__ == "__main__":
	# Usage for MT:
	run_generate()






import os
import tempfile
from functools import partial
from unittest import TestCase
from unittest.mock import patch

import numpy as np
import pytest

from datasets.arrow_dataset import Dataset
from datasets.search import ElasticSearchIndex, FaissIndex, MissingIndex

from .utils import require_elasticsearch, require_faiss


A_       : Any         =			pytest.mark.integration



@require_faiss
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						A__							:    Tuple        =		Dataset.from_dict({"""filename""": ["""my_name-train""" + """_""" + str(A__			) for x in np.arange(30			).tolist()]}			)
						return dset






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						A__							:    Union[str, Any]        =		dset.map(
						    lambda A__    ,       A__			: {"vecs": i * np.ones(5    ,       dtype=np.floataa			)}    ,       with_indices=A__    ,       keep_in_memory=A__			)
						A__							:    int        =		dset.add_faiss_index("""vecs"""    ,       batch_size=100    ,       metric_type=faiss.METRIC_INNER_PRODUCT			)
						A__   ,   A__							:    Optional[Any]        =		dset.get_nearest_examples("""vecs"""    ,       np.ones(5    ,       dtype=np.floataa			)			)
						self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)
						dset.drop_index("""vecs"""			)






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""    ,       batch_size=100    ,       metric_type=faiss.METRIC_INNER_PRODUCT    ,       )
						A__   ,   A__							:    str        =		dset.get_nearest_examples("""vecs"""    ,       np.ones(5    ,       dtype=np.floataa			)			)
						self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)






					def   __A			( self			):
						import faiss

						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""    ,       metric_type=faiss.METRIC_INNER_PRODUCT    ,       )

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__			) as tmp_file:
							dset.save_faiss_index("""vecs"""    ,       tmp_file.name			)
							dset.load_faiss_index("""vecs2"""    ,       tmp_file.name			)
						os.unlink(tmp_file.name			)

						A__   ,   A__							:    Tuple        =		dset.get_nearest_examples("""vecs2"""    ,       np.ones(5    ,       dtype=np.floataa			)			)
						self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)






					def   __A			( self			):
						A__							:    Dataset        =		self._create_dummy_dataset()
						dset.add_faiss_index_from_external_arrays(
						    external_arrays=np.ones((30, 5)			) * np.arange(30			).reshape(-1    ,       1			)    ,       index_name="""vecs"""			)
						dset.drop_index("""vecs"""			)
						self.assertRaises(A__    ,       partial(dset.get_nearest_examples    ,       """vecs2"""    ,       np.ones(5    ,       dtype=np.floataa			)			)			)






					def   __A			( self			):
						from elasticsearch import Elasticsearch

						A__							:    Dataset        =		self._create_dummy_dataset()
						with patch("""elasticsearch.Elasticsearch.search"""			) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""			) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""			) as mocked_bulk:
							A__							:    List[Any]        =		{"""acknowledged""": True}
							mocked_bulk.return_value([(True, None)] * 30			)
							A__							:    List[Any]        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 29}]}}
							A__							:    Any        =		Elasticsearch()

							dset.add_elasticsearch_index("""filename"""    ,       es_client=A__			)
							A__   ,   A__							:    Any        =		dset.get_nearest_examples("""filename"""    ,       """my_name-train_29"""			)
							self.assertEqual(examples["""filename"""][0]    ,       """my_name-train_29"""			)



@require_faiss
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						import faiss

						A__							:    Optional[int]        =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT			)

						# add vectors
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsNotNone(index.faiss_index			)
						self.assertEqual(index.faiss_index.ntotal    ,       5			)
						index.add_vectors(np.zeros((5, 5)    ,       dtype=np.floataa			)			)
						self.assertEqual(index.faiss_index.ntotal    ,       10			)

						# single query
						A__							:    Any        =		np.zeros(5    ,       dtype=np.floataa			)
						A__							:    str        =		1
						A__   ,   A__							:    Optional[Any]        =		index.search(A__			)
						self.assertRaises(A__    ,       index.search    ,       query.reshape(-1    ,       1			)			)
						self.assertGreater(scores[0]    ,       0			)
						self.assertEqual(indices[0]    ,       1			)

						# batched queries
						A__							:    Optional[int]        =		np.eye(5    ,       dtype=np.floataa			)[::-1]
						A__   ,   A__							:    str        =		index.search_batch(A__			)
						self.assertRaises(A__    ,       index.search_batch    ,       queries[0]			)
						A__							:    str        =		[scores[0] for scores in total_scores]
						A__							:    Tuple        =		[indices[0] for indices in total_indices]
						self.assertGreater(np.min(A__			)    ,       0			)
						self.assertListEqual([4, 3, 2, 1, 0]    ,       A__			)






					def   __A			( self			):
						import faiss

						A__							:    Dict        =		FaissIndex(string_factory="""Flat"""			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexFlat			)
						A__							:    Dict        =		FaissIndex(string_factory="""LSH"""			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexLSH			)
						with self.assertRaises(A__			):
							A__							:    List[Any]        =		FaissIndex(string_factory="""Flat"""    ,       custom_index=faiss.IndexFlat(5			)			)






					def   __A			( self			):
						import faiss

						A__							:    List[Any]        =		faiss.IndexFlat(5			)
						A__							:    Union[str, Any]        =		FaissIndex(custom_index=A__			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)
						self.assertIsInstance(index.faiss_index    ,       faiss.IndexFlat			)






					def   __A			( self			):
						import faiss

						A__							:    Any        =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT			)
						index.add_vectors(np.eye(5    ,       dtype=np.floataa			)			)

						# Setting delete=False and unlinking manually is not pretty... but it is required on Windows to
						# ensure somewhat stable behaviour. If we don't, we get PermissionErrors. This is an age-old issue.
						# see https://bugs.python.org/issue14243 and
						# https://stackoverflow.com/questions/23212435/permission-denied-to-write-to-my-temporary-file/23212515
						with tempfile.NamedTemporaryFile(delete=A__			) as tmp_file:
							index.save(tmp_file.name			)
							A__							:    int        =		FaissIndex.load(tmp_file.name			)
						os.unlink(tmp_file.name			)

						A__							:    Optional[Any]        =		np.zeros(5    ,       dtype=np.floataa			)
						A__							:    Optional[int]        =		1
						A__   ,   A__							:    List[Any]        =		index.search(A__			)
						self.assertGreater(scores[0]    ,       0			)
						self.assertEqual(indices[0]    ,       1			)



@require_faiss
def 						UpperCamelCase					(lowercase_: Dict					)     ->      Optional[Any]:
	import faiss

	A__							:    Dict        =		FaissIndex(metric_type=faiss.METRIC_INNER_PRODUCT					)
	index.add_vectors(np.eye(5    ,      dtype=np.floataa					)					)

	A__							:    Optional[Any]        =		"""index.faiss"""
	A__							:    Any        =		f"""mock://{index_name}"""
	index.save(lowercase_    ,      storage_options=mockfs.storage_options					)
	A__							:    str        =		FaissIndex.load(lowercase_    ,      storage_options=mockfs.storage_options					)

	A__							:    int        =		np.zeros(5    ,      dtype=np.floataa					)
	A__							:    Union[str, Any]        =		1
	A__   ,   A__							:    Any        =		index.search(lowercase_					)
	assert scores[0] > 0
	assert indices[0] == 1



@require_elasticsearch
class     _a						(__magic_name__				):


					'''simple docstring'''






					def   __A			( self			):
						from elasticsearch import Elasticsearch

						with patch("""elasticsearch.Elasticsearch.search"""			) as mocked_search, patch(
						    """elasticsearch.client.IndicesClient.create"""			) as mocked_index_create, patch("""elasticsearch.helpers.streaming_bulk"""			) as mocked_bulk:
							A__							:    List[str]        =		Elasticsearch()
							A__							:    List[Any]        =		{"""acknowledged""": True}
							A__							:    int        =		ElasticSearchIndex(es_client=A__			)
							mocked_bulk.return_value([(True, None)] * 3			)
							index.add_documents(["""foo""", """bar""", """foobar"""]			)

							# single query
							A__							:    Dict        =		"""foo"""
							A__							:    Tuple        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
							A__   ,   A__							:    Tuple        =		index.search(A__			)
							self.assertEqual(scores[0]    ,       1			)
							self.assertEqual(indices[0]    ,       0			)

							# single query with timeout
							A__							:    List[str]        =		"""foo"""
							A__							:    str        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 0}]}}
							A__   ,   A__							:    Dict        =		index.search(A__    ,       request_timeout=30			)
							self.assertEqual(scores[0]    ,       1			)
							self.assertEqual(indices[0]    ,       0			)

							# batched queries
							A__							:    Union[str, Any]        =		["""foo""", """bar""", """foobar"""]
							A__							:    Tuple        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
							A__   ,   A__							:    Dict        =		index.search_batch(A__			)
							A__							:    Tuple        =		[scores[0] for scores in total_scores]
							A__							:    Optional[int]        =		[indices[0] for indices in total_indices]
							self.assertGreater(np.min(A__			)    ,       0			)
							self.assertListEqual([1, 1, 1]    ,       A__			)

							# batched queries with timeout
							A__							:    int        =		["""foo""", """bar""", """foobar"""]
							A__							:    List[Any]        =		{"""hits""": {"""hits""": [{"""_score""": 1, """_id""": 1}]}}
							A__   ,   A__							:    Tuple        =		index.search_batch(A__    ,       request_timeout=30			)
							A__							:    List[Any]        =		[scores[0] for scores in total_scores]
							A__							:    Optional[int]        =		[indices[0] for indices in total_indices]
							self.assertGreater(np.min(A__			)    ,       0			)
							self.assertListEqual([1, 1, 1]    ,       A__			)



