





import json
import os
import shutil
import sys
import tempfile
import unittest
from unittest import TestCase
from unittest.mock import patch

import faiss
import numpy as np
from datasets import Dataset

from transformers import BartConfig, BartTokenizer, DPRConfig, DPRQuestionEncoderTokenizer, RagConfig
from transformers.file_utils import is_datasets_available, is_faiss_available, is_psutil_available, is_torch_available
from transformers.integrations import is_ray_available
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_ray


sys.path.append(os.path.join(os.getcwd()))  # noqa: E402 # noqa: E402 # isort:skip

if is_torch_available():
					from distributed_pytorch_retriever import RagPyTorchDistributedRetriever  # noqa: E402 # isort:skip
else:
					UpperCAmelCase_   				=  None

if is_ray_available():
					import ray  # noqa: E402 # isort:skip
					from distributed_ray_retriever import RagRayDistributedRetriever, RayRetriever  # noqa: E402 # isort:skip
else:
					UpperCAmelCase_   				=  None
					UpperCAmelCase_   				=  None
					UpperCAmelCase_   				=  None





def     lowerCamelCase__  (  UpperCamelCase__      :							Tuple    )      ->			List[str]:



					'''simple docstring'''




					if not (is_datasets_available() and is_faiss_available() and is_psutil_available()):
										_snake_case						=					unittest.skip('test requires Datasets, Faiss, psutil'    )(UpperCamelCase__    )
					return test_case




@require_distributed_retrieval
class       UpperCamelCase_		(	_lowerCamelCase       ):




					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					tempfile.mkdtemp()
										_snake_case						=					8

										# DPR tok
										_snake_case						=					[
										    '[UNK]',
										    '[CLS]',
										    '[SEP]',
										    '[PAD]',
										    '[MASK]',
										    'want',
										    '##want',
										    '##ed',
										    'wa',
										    'un',
										    'runn',
										    '##ing',
										    ',',
										    'low',
										    'lowest',
										]
										_snake_case						=					os.path.join(self.tmpdirname					,    'dpr_tokenizer'	)
										os.makedirs(lowerCAmelCase_					,    exist_ok=lowerCAmelCase_	)
										_snake_case						=					os.path.join(lowerCAmelCase_					,    DPR_VOCAB_FILES_NAMES['vocab_file']	)
										with open(self.vocab_file					,    'w'					,    encoding='utf-8'	) as vocab_writer:
															vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)

										# BART tok
										_snake_case						=					[
										    'l',
										    'o',
										    'w',
										    'e',
										    'r',
										    's',
										    't',
										    'i',
										    'd',
										    'n',
										    '\u0120',
										    '\u0120l',
										    '\u0120n',
										    '\u0120lo',
										    '\u0120low',
										    'er',
										    '\u0120lowest',
										    '\u0120newer',
										    '\u0120wider',
										    '<unk>',
										]
										_snake_case						=					dict(zip(lowerCAmelCase_					,    range(len(lowerCAmelCase_	)	)	)	)
										_snake_case						=					['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
										_snake_case						=					{'unk_token': '<unk>'}

										_snake_case						=					os.path.join(self.tmpdirname					,    'bart_tokenizer'	)
										os.makedirs(lowerCAmelCase_					,    exist_ok=lowerCAmelCase_	)
										_snake_case						=					os.path.join(lowerCAmelCase_					,    BART_VOCAB_FILES_NAMES['vocab_file']	)
										_snake_case						=					os.path.join(lowerCAmelCase_					,    BART_VOCAB_FILES_NAMES['merges_file']	)
										with open(self.vocab_file					,    'w'					,    encoding='utf-8'	) as fp:
															fp.write(json.dumps(lowerCAmelCase_	) + '\n'	)
										with open(self.merges_file					,    'w'					,    encoding='utf-8'	) as fp:
															fp.write('\n'.join(lowerCAmelCase_	)	)




					def 						lowerCAmelCase      (		self	)		->				DPRQuestionEncoderTokenizer:
										return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname					,    'dpr_tokenizer'	)	)




					def 						lowerCAmelCase      (		self	)		->				BartTokenizer:
										return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname					,    'bart_tokenizer'	)	)




					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										shutil.rmtree(self.tmpdirname	)




					def 						lowerCAmelCase      (		self	)		->				Dict:
										_snake_case						=					Dataset.from_dict(
										    {
										        'id': ['0', '1'],
										        'text': ['foo', 'bar'],
										        'title': ['Foo', 'Bar'],
										        'embeddings': [np.ones(self.retrieval_vector_size	), 2 * np.ones(self.retrieval_vector_size	)],
										    }	)
										dataset.add_faiss_index('embeddings'					,    string_factory='Flat'					,    metric_type=faiss.METRIC_INNER_PRODUCT	)
										return dataset




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_=1_2345	)		->				RagPyTorchDistributedRetriever:
										_snake_case						=					self.get_dummy_dataset()
										_snake_case						=					RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    )
										with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
															_snake_case						=					dataset
															_snake_case						=					RagPyTorchDistributedRetriever(
															    lowerCAmelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
															if init_retrieval:
																				retriever.init_retrieval(lowerCAmelCase_	)
										return retriever




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_	)		->				RagRayDistributedRetriever:
										# Have to run in local mode because sys.path modifications at top of
										# file are not propogated to remote workers.
										# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
										ray.init(local_mode=lowerCAmelCase_	)
										_snake_case						=					RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    )
										_snake_case						=					ray.remote(lowerCAmelCase_	)
										_snake_case						=					[remote_cls.remote() for _ in range(1	)]
										with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
															_snake_case						=					self.get_dummy_dataset()
															_snake_case						=					RagRayDistributedRetriever(
															    lowerCAmelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCAmelCase_					,    )
															if init_retrieval:
																				retriever.init_retrieval()
										return retriever




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_=1_2345	)		->				Optional[Any]:
										_snake_case						=					self.get_dummy_dataset()
										_snake_case						=					RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    index_name='custom'					,    )
										if from_disk:
															_snake_case						=					os.path.join(self.tmpdirname					,    'dataset'	)
															_snake_case						=					os.path.join(self.tmpdirname					,    'index.faiss'	)
															dataset.get_index('embeddings'	).save(os.path.join(self.tmpdirname					,    'index.faiss'	)	)
															dataset.drop_index('embeddings'	)
															dataset.save_to_disk(os.path.join(self.tmpdirname					,    'dataset'	)	)
															del dataset
															_snake_case						=					RagPyTorchDistributedRetriever(
															    lowerCAmelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    )
										else:
															_snake_case						=					RagPyTorchDistributedRetriever(
															    lowerCAmelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    index=CustomHFIndex(config.retrieval_vector_size					,    lowerCAmelCase_	)					,    )
										if init_retrieval:
															retriever.init_retrieval(lowerCAmelCase_	)
										return retriever




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				List[Any]:
										# Have to run in local mode because sys.path modifications at top of
										# file are not propogated to remote workers.
										# https://stackoverflow.com/questions/54338013/parallel-import-a-python-file-from-sibling-folder
										ray.init(local_mode=lowerCAmelCase_	)
										_snake_case						=					self.get_dummy_dataset()
										_snake_case						=					RagConfig(
										    retrieval_vector_size=self.retrieval_vector_size					,    question_encoder=DPRConfig().to_dict()					,    generator=BartConfig().to_dict()					,    index_name='custom'					,    )
										_snake_case						=					ray.remote(lowerCAmelCase_	)
										_snake_case						=					[remote_cls.remote() for _ in range(1	)]
										if from_disk:
															_snake_case						=					os.path.join(self.tmpdirname					,    'dataset'	)
															_snake_case						=					os.path.join(self.tmpdirname					,    'index.faiss'	)
															dataset.get_index('embeddings'	).save(os.path.join(self.tmpdirname					,    'index.faiss'	)	)
															dataset.drop_index('embeddings'	)
															dataset.save_to_disk(os.path.join(self.tmpdirname					,    'dataset'	)	)
															del dataset
															_snake_case						=					RagRayDistributedRetriever(
															    lowerCAmelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCAmelCase_					,    index=CustomHFIndex.load_from_disk(
															        vector_size=config.retrieval_vector_size					,    dataset_path=config.passages_path					,    index_path=config.index_path					,    )					,    )
										else:
															_snake_case						=					RagRayDistributedRetriever(
															    lowerCAmelCase_					,    question_encoder_tokenizer=self.get_dpr_tokenizer()					,    generator_tokenizer=self.get_bart_tokenizer()					,    retrieval_workers=lowerCAmelCase_					,    index=CustomHFIndex(config.retrieval_vector_size					,    lowerCAmelCase_	)					,    )
										if init_retrieval:
															retriever.init_retrieval()
										return retriever




					def 						lowerCAmelCase      (		self					,    lowerCAmelCase_					,    lowerCAmelCase_					,    lowerCAmelCase_	)		->				None:
										_snake_case				,  _snake_case				,  _snake_case						=					retriever.retrieve(lowerCAmelCase_					,    n_docs=lowerCAmelCase_	)
										self.assertEqual(retrieved_doc_embeds.shape					,    (2, n_docs, self.retrieval_vector_size)	)
										self.assertEqual(len(lowerCAmelCase_	)					,    2	)
										self.assertEqual(sorted(doc_dicts[0]	)					,    ['embeddings', 'id', 'text', 'title']	)
										self.assertEqual(len(doc_dicts[0]['id']	)					,    lowerCAmelCase_	)
										self.assertEqual(doc_dicts[0]['id'][0]					,    '1'	)  # max inner product is reached with second doc
										self.assertEqual(doc_dicts[1]['id'][0]					,    '0'	)  # max inner product is reached with first doc
										self.assertListEqual(doc_ids.tolist()					,    [[1], [0]]	)




					def 						lowerCAmelCase      (		self	)		->				Optional[int]:
										_snake_case						=					1
										_snake_case						=					np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,    dtype=np.floataa	)

										self.distributed_retriever_check(
										    self.get_dummy_pytorch_distributed_retriever(init_retrieval=lowerCAmelCase_	)					,    lowerCAmelCase_					,    lowerCAmelCase_	)




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					1
										_snake_case						=					np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,    dtype=np.floataa	)

										self.distributed_retriever_check(
										    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCAmelCase_					,    from_disk=lowerCAmelCase_	)					,    lowerCAmelCase_					,    lowerCAmelCase_					,    )




					def 						lowerCAmelCase      (		self	)		->				Optional[Any]:
										_snake_case						=					1
										_snake_case						=					np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,    dtype=np.floataa	)

										self.distributed_retriever_check(
										    self.get_dummy_custom_hf_index_pytorch_retriever(init_retrieval=lowerCAmelCase_					,    from_disk=lowerCAmelCase_	)					,    lowerCAmelCase_					,    lowerCAmelCase_					,    )




					@require_ray
					def 						lowerCAmelCase      (		self	)		->				Tuple:
										_snake_case						=					1
										_snake_case						=					np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,    dtype=np.floataa	)

										self.distributed_retriever_check(
										    self.get_dummy_ray_distributed_retriever(init_retrieval=lowerCAmelCase_	)					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										ray.shutdown()




					@require_ray
					def 						lowerCAmelCase      (		self	)		->				Any:
										_snake_case						=					1
										_snake_case						=					np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,    dtype=np.floataa	)
										with self.assertRaises(lowerCAmelCase_	):
															self.distributed_retriever_check(
															    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCAmelCase_					,    from_disk=lowerCAmelCase_	)					,    lowerCAmelCase_					,    lowerCAmelCase_					,    )
										ray.shutdown()






					@require_ray
					def 						lowerCAmelCase      (		self	)		->				Any:
										_snake_case						=					1
										_snake_case						=					np.array(
										    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]					,    dtype=np.floataa	)

										self.distributed_retriever_check(
										    self.get_dummy_custom_hf_index_ray_retriever(init_retrieval=lowerCAmelCase_					,    from_disk=lowerCAmelCase_	)					,    lowerCAmelCase_					,    lowerCAmelCase_	)
										ray.shutdown()










import argparse
import datetime
import json
import time
import warnings
from logging import getLogger
from pathlib import Path
from typing import Dict, List

import torch
from tqdm import tqdm

from transformers import AutoModelForSeqaSeqLM, AutoTokenizer
from utils import calculate_bleu, calculate_rouge, chunks, parse_numeric_n_bool_cl_kwargs, use_task_specific_params


UpperCAmelCase_   				=  getLogger(__name__)


UpperCAmelCase_   				=  """cuda""" if torch.cuda.is_available() else """cpu"""





def     lowerCamelCase__  (  UpperCamelCase__      :							List[str]							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							str							,     UpperCamelCase__      :							int = 8							,     UpperCamelCase__      :							str = DEFAULT_DEVICE							,     UpperCamelCase__      :							str=False							,     UpperCamelCase__      :							str="summarization"							,     UpperCamelCase__      :							Optional[int]=None							,     **UpperCamelCase__      :							int							,     )      ->			Dict:



					'''simple docstring'''




					_snake_case						=					Path(UpperCamelCase__    ).open('w'							,     encoding='utf-8'    )
					_snake_case						=					str(UpperCamelCase__    )
					_snake_case						=					AutoModelForSeqaSeqLM.from_pretrained(UpperCamelCase__    ).to(UpperCamelCase__    )
					if fpaa:
										_snake_case						=					model.half()

					_snake_case						=					AutoTokenizer.from_pretrained(UpperCamelCase__    )
					logger.info(F'''Inferred tokenizer type: {tokenizer.__class__}'''    )  # if this is wrong, check config.model_type.

					_snake_case						=					time.time()
					# update config with task specific params
					use_task_specific_params(UpperCamelCase__							,     UpperCamelCase__    )
					if prefix is None:
										_snake_case						=					prefix or getattr(model.config							,     'prefix'							,     ''    ) or ''
					for examples_chunk in tqdm(list(chunks(UpperCamelCase__							,     UpperCamelCase__    )    )    ):
										_snake_case						=					[prefix + text for text in examples_chunk]
										_snake_case						=					tokenizer(UpperCamelCase__							,     return_tensors='pt'							,     truncation=UpperCamelCase__							,     padding='longest'    ).to(UpperCamelCase__    )
										_snake_case						=					model.generate(
										    input_ids=batch.input_ids							,     attention_mask=batch.attention_mask							,     **UpperCamelCase__							,     )
										_snake_case						=					tokenizer.batch_decode(UpperCamelCase__							,     skip_special_tokens=UpperCamelCase__							,     clean_up_tokenization_spaces=UpperCamelCase__    )
										for hypothesis in dec:
															fout.write(hypothesis + '\n'    )
															fout.flush()
					fout.close()
					_snake_case						=					int(time.time() - start_time    )  # seconds
					_snake_case						=					len(UpperCamelCase__    )
					return {"n_obs": n_obs, "runtime": runtime, "seconds_per_sample": round(runtime / n_obs							,     4    )}





def     lowerCamelCase__  (  )      ->			Dict:



					'''simple docstring'''




					return datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'    )





def     lowerCamelCase__  (  UpperCamelCase__      :							Union[str, Any]=True    )      ->			List[str]:



					'''simple docstring'''




					_snake_case						=					argparse.ArgumentParser()
					parser.add_argument('model_name'							,     type=UpperCamelCase__							,     help='like facebook/bart-large-cnn,t5-base, etc.'    )
					parser.add_argument('input_path'							,     type=UpperCamelCase__							,     help='like cnn_dm/test.source'    )
					parser.add_argument('save_path'							,     type=UpperCamelCase__							,     help='where to save summaries'    )
					parser.add_argument('--reference_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     help='like cnn_dm/test.target'    )
					parser.add_argument('--score_path'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default='metrics.json'							,     help='where to save metrics'    )
					parser.add_argument('--device'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='cuda, cuda:1, cpu etc.'    )
					parser.add_argument(
					    '--prefix'							,     type=UpperCamelCase__							,     required=UpperCamelCase__							,     default=UpperCamelCase__							,     help='will be added to the begininng of src examples'    )
					parser.add_argument('--task'							,     type=UpperCamelCase__							,     default='summarization'							,     help='used for task_specific_params + metrics'    )
					parser.add_argument('--bs'							,     type=UpperCamelCase__							,     default=8							,     required=UpperCamelCase__							,     help='batch size'    )
					parser.add_argument(
					    '--n_obs'							,     type=UpperCamelCase__							,     default=-1							,     required=UpperCamelCase__							,     help='How many observations. Defaults to all.'    )
					parser.add_argument('--fp16'							,     action='store_true'    )
					parser.add_argument('--dump-args'							,     action='store_true'							,     help='print the custom hparams with the results'    )
					parser.add_argument(
					    '--info'							,     nargs='?'							,     type=UpperCamelCase__							,     const=datetime_now()							,     help=(
					        'use in conjunction w/ --dump-args to print with the results whatever other info you\'d like, e.g.'
					        ' lang=en-ru. If no value is passed, the current datetime string will be used.'
					    )							,     )
					# Unspecified args like --num_beams=2 --decoder_start_token_id=4 are passed to model.generate
					_snake_case				,  _snake_case						=					parser.parse_known_args()
					_snake_case						=					parse_numeric_n_bool_cl_kwargs(UpperCamelCase__    )
					if parsed_args and verbose:
										print(F'''parsed the following generate kwargs: {parsed_args}'''    )
					with open(args.input_path    ) as f:
										_snake_case						=					[' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
					if args.n_obs > 0:
										_snake_case						=					examples[: args.n_obs]
					Path(args.save_path    ).parent.mkdir(exist_ok=UpperCamelCase__    )
					if args.reference_path is None and Path(args.score_path    ).exists():
										warnings.warn(F'''score_path {args.score_path} will be overwritten unless you type ctrl-c.'''    )
					_snake_case						=					generate_summaries_or_translations(
					    UpperCamelCase__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **UpperCamelCase__							,     )

					if args.reference_path is None:
										return {}

					# Compute scores
					_snake_case						=					calculate_bleu if 'translation' in args.task else calculate_rouge
					_snake_case						=					[x.rstrip() for x in open(args.save_path    ).readlines()]
					_snake_case						=					[x.rstrip() for x in open(args.reference_path    ).readlines()][: len(UpperCamelCase__    )]
					_snake_case						=					score_fn(UpperCamelCase__							,     UpperCamelCase__    )
					scores.update(UpperCamelCase__    )

					if args.dump_args:
										scores.update(UpperCamelCase__    )
					if args.info:
										_snake_case						=					args.info

					if verbose:
										print(UpperCamelCase__    )

					if args.score_path is not None:
										json.dump(UpperCamelCase__							,     open(args.score_path							,     'w'    )    )

					return scores


if __name__ == "__main__":
					# Usage for MT:
					# python run_eval.py MODEL_NAME $DATA_DIR/test.source $save_dir/test_translations.txt --reference_path $DATA_DIR/test.target --score_path $save_dir/test_bleu.json  --task translation $@
					run_generate(verbose=True)



