



import os
import sys
from pathlib import Path

import pytest

from datasets import Dataset, IterableDataset
from datasets.distributed import split_dataset_by_node

from .utils import execute_subprocess_async, get_torch_dist_unique_port, require_torch






def 			__lowercase     (				)       ->				Tuple:
							__SCREAMING_SNAKE_CASE								=      Dataset.from_dict({'i': range(17     )}     )
							__SCREAMING_SNAKE_CASE								=      len(a__     )
							__SCREAMING_SNAKE_CASE								=      3
							__SCREAMING_SNAKE_CASE								=      [
							    split_dataset_by_node(a__							,     rank=a__							,     world_size=a__     ) for rank in range(a__     )
							]
							assert sum(len(a__     ) for ds in datasets_per_rank     ) == full_size
							assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size






def 			__lowercase     (				)       ->				List[Any]:
							def gen():
														return ({"i": i} for i in range(17     ))

							__SCREAMING_SNAKE_CASE								=      3
							__SCREAMING_SNAKE_CASE								=      IterableDataset.from_generator(a__     )
							__SCREAMING_SNAKE_CASE								=      len(list(a__     )     )
							__SCREAMING_SNAKE_CASE								=      [
							    split_dataset_by_node(a__							,     rank=a__							,     world_size=a__     ) for rank in range(a__     )
							]
							assert sum(len(list(a__     )     ) for ds in datasets_per_rank     ) == full_size
							assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size






@pytest.mark.parametrize('shards_per_node'							,     [1, 2, 3]     )
def 			__lowercase     (				a__     )       ->				Dict:
							def gen(a__     ):
														for shard in shards:
																					yield from ({"i": i, "shard": shard} for i in range(17     ))

							__SCREAMING_SNAKE_CASE								=      3
							__SCREAMING_SNAKE_CASE								=      shards_per_node * world_size
							__SCREAMING_SNAKE_CASE								=      {'shards': [f"""shard_{shard_idx}.txt""" for shard_idx in range(a__     )]}
							__SCREAMING_SNAKE_CASE								=      IterableDataset.from_generator(a__							,     gen_kwargs=a__     )
							__SCREAMING_SNAKE_CASE								=      len(list(a__     )     )
							assert full_ds.n_shards == world_size * shards_per_node
							__SCREAMING_SNAKE_CASE								=      [
							    split_dataset_by_node(a__							,     rank=a__							,     world_size=a__     ) for rank in range(a__     )
							]
							assert [ds.n_shards for ds in datasets_per_rank] == [shards_per_node] * world_size
							assert sum(len(list(a__     )     ) for ds in datasets_per_rank     ) == full_size
							assert len({tuple(x.values()     ) for ds in datasets_per_rank for x in ds}     ) == full_size






def 			__lowercase     (				)       ->				List[Any]:
							def gen():
														return ({"i": i} for i in range(17     ))

							__SCREAMING_SNAKE_CASE								=      2
							__SCREAMING_SNAKE_CASE								=      IterableDataset.from_generator(a__     )
							__SCREAMING_SNAKE_CASE								=      len(list(a__     )     )

							__SCREAMING_SNAKE_CASE								=      split_dataset_by_node(a__							,     rank=0							,     world_size=a__     ).shuffle(seed=42     )
							assert len(list(a__     )     ) == 1 + full_size // world_size
							with pytest.raises(a__     ):
														split_dataset_by_node(a__							,     rank=0							,     world_size=a__     ).shuffle()

							__SCREAMING_SNAKE_CASE								=      split_dataset_by_node(full_ds.shuffle(seed=42     )							,     rank=0							,     world_size=a__     )
							assert len(list(a__     )     ) == 1 + full_size // world_size
							with pytest.raises(a__     ):
														split_dataset_by_node(full_ds.shuffle()							,     rank=0							,     world_size=a__     )






@pytest.mark.parametrize('streaming'							,     [False, True]     )
@require_torch
@pytest.mark.skipif(os.name == 'nt'							,     reason='execute_subprocess_async doesn\'t support windows'     )
@pytest.mark.integration
def 			__lowercase     (				a__     )       ->				Tuple:
							__SCREAMING_SNAKE_CASE								=      2
							__SCREAMING_SNAKE_CASE								=      get_torch_dist_unique_port()
							__SCREAMING_SNAKE_CASE								=      Path(a__     ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
							__SCREAMING_SNAKE_CASE								=      f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
							__SCREAMING_SNAKE_CASE								=      f"""
        --streaming={streaming}
    """.split()
							__SCREAMING_SNAKE_CASE								=      [sys.executable] + distributed_args + args
							execute_subprocess_async(a__							,     env=os.environ.copy()     )






@pytest.mark.parametrize(
    'nproc_per_node, num_workers'							,     [
        (2, 2),  # each node has 2 shards and each worker has 1 shards
        (3, 2),  # each node uses all the shards but skips examples, and each worker has 2 shards
    ]							,     )
@require_torch
@pytest.mark.skipif(os.name == 'nt'							,     reason='execute_subprocess_async doesn\'t support windows'     )
@pytest.mark.integration
def 			__lowercase     (				a__							,     a__     )       ->				List[str]:
							__SCREAMING_SNAKE_CASE								=      True
							__SCREAMING_SNAKE_CASE								=      get_torch_dist_unique_port()
							__SCREAMING_SNAKE_CASE								=      Path(a__     ).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
							__SCREAMING_SNAKE_CASE								=      f"""
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    """.split()
							__SCREAMING_SNAKE_CASE								=      f"""
        --streaming={streaming}
        --num_workers={num_workers}
    """.split()
							__SCREAMING_SNAKE_CASE								=      [sys.executable] + distributed_args + args
							execute_subprocess_async(a__							,     env=os.environ.copy()     )











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


lowerCAmelCase__	:  Union[str, Any] 		=getLogger(__name__)


lowerCAmelCase__	:  Optional[int] 		='''cuda''' if torch.cuda.is_available() else '''cpu'''






def 			__lowercase     (				a__							,     a__							,     a__							,     a__ = 8							,     a__ = DEFAULT_DEVICE							,     a__=False							,     a__="summarization"							,     a__=None							,     **a__							,     )       ->				Dict:
							__SCREAMING_SNAKE_CASE								=      Path(a__     ).open('w'							,     encoding='utf-8'     )
							__SCREAMING_SNAKE_CASE								=      str(a__     )
							__SCREAMING_SNAKE_CASE								=      AutoModelForSeqaSeqLM.from_pretrained(a__     ).to(a__     )
							if fpaa:
														__SCREAMING_SNAKE_CASE								=      model.half()

							__SCREAMING_SNAKE_CASE								=      AutoTokenizer.from_pretrained(a__     )
							logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""     )  # if this is wrong, check config.model_type.

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






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






def 			__lowercase     (				a__=True     )       ->				Optional[Any]:
							__SCREAMING_SNAKE_CASE								=      argparse.ArgumentParser()
							parser.add_argument('model_name'							,     type=a__							,     help='like facebook/bart-large-cnn,t5-base, etc.'     )
							parser.add_argument('input_path'							,     type=a__							,     help='like cnn_dm/test.source'     )
							parser.add_argument('save_path'							,     type=a__							,     help='where to save summaries'     )
							parser.add_argument('--reference_path'							,     type=a__							,     required=a__							,     help='like cnn_dm/test.target'     )
							parser.add_argument('--score_path'							,     type=a__							,     required=a__							,     default='metrics.json'							,     help='where to save metrics'     )
							parser.add_argument('--device'							,     type=a__							,     required=a__							,     default=a__							,     help='cuda, cuda:1, cpu etc.'     )
							parser.add_argument(
							    '--prefix'							,     type=a__							,     required=a__							,     default=a__							,     help='will be added to the begininng of src examples'     )
							parser.add_argument('--task'							,     type=a__							,     default='summarization'							,     help='used for task_specific_params + metrics'     )
							parser.add_argument('--bs'							,     type=a__							,     default=8							,     required=a__							,     help='batch size'     )
							parser.add_argument(
							    '--n_obs'							,     type=a__							,     default=-1							,     required=a__							,     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=a__							,     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
							__SCREAMING_SNAKE_CASE							, __SCREAMING_SNAKE_CASE								=      parser.parse_known_args()
							__SCREAMING_SNAKE_CASE								=      parse_numeric_n_bool_cl_kwargs(a__     )
							if parsed_args and verbose:
														print(f"""parsed the following generate kwargs: {parsed_args}"""     )
							with open(args.input_path     ) as f:
														__SCREAMING_SNAKE_CASE								=      [' ' + x.rstrip() if 't5' in args.model_name else x.rstrip() for x in f.readlines()]
							if args.n_obs > 0:
														__SCREAMING_SNAKE_CASE								=      examples[: args.n_obs]
							Path(args.save_path     ).parent.mkdir(exist_ok=a__     )
							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."""     )
							__SCREAMING_SNAKE_CASE								=      generate_summaries_or_translations(
							    a__							,     args.save_path							,     args.model_name							,     batch_size=args.bs							,     device=args.device							,     fpaa=args.fpaa							,     task=args.task							,     prefix=args.prefix							,     **a__							,     )

							if args.reference_path is None:
														return {}

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

							if args.dump_args:
														scores.update(a__     )
							if args.info:
														__SCREAMING_SNAKE_CASE								=      args.info

							if verbose:
														print(a__     )

							if args.score_path is not None:
														json.dump(a__							,     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)






