

'''simple docstring'''

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      __magic_name__							(			)      ->		int:
	snake_case							=					Dataset.from_dict({'i': range(1_7				)}				)
	snake_case							=					len(A				)
	snake_case							=					3
	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      __magic_name__							(			)      ->		List[str]:
	def gen():
		return ({"i": i} for i in range(1_7				))

	snake_case							=					3
	snake_case							=					IterableDataset.from_generator(A				)
	snake_case							=					len(list(A				)				)
	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      __magic_name__							(			A				)      ->		Any:
	def gen(A				):
		for shard in shards:
			yield from ({"i": i, "shard": shard} for i in range(1_7				))

	snake_case							=					3
	snake_case							=					shards_per_node * world_size
	snake_case							=					{'shards': [F'''shard_{shard_idx}.txt''' for shard_idx in range(A				)]}
	snake_case							=					IterableDataset.from_generator(A       , gen_kwargs=A				)
	snake_case							=					len(list(A				)				)
	assert full_ds.n_shards == world_size * shards_per_node
	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      __magic_name__							(			)      ->		Any:
	def gen():
		return ({"i": i} for i in range(1_7				))

	snake_case							=					2
	snake_case							=					IterableDataset.from_generator(A				)
	snake_case							=					len(list(A				)				)

	snake_case							=					split_dataset_by_node(A       , rank=0       , world_size=A				).shuffle(seed=4_2				)
	assert len(list(A				)				) == 1 + full_size // world_size
	with pytest.raises(A				):
		split_dataset_by_node(A       , rank=0       , world_size=A				).shuffle()

	snake_case							=					split_dataset_by_node(full_ds.shuffle(seed=4_2				)       , 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      __magic_name__							(			A				)      ->		Tuple:
	snake_case							=					2
	snake_case							=					get_torch_dist_unique_port()
	snake_case							=					Path(A				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	snake_case							=					F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
	snake_case							=					F'''
        --streaming={streaming}
    '''.split()
	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      __magic_name__							(			A       , A				)      ->		Union[str, Any]:
	snake_case							=					True
	snake_case							=					get_torch_dist_unique_port()
	snake_case							=					Path(A				).resolve().parent / 'distributed_scripts' / 'launch_torch_distributed.py'
	snake_case							=					F'''
        -m torch.distributed.launch
        --nproc_per_node={nproc_per_node}
        --master_port={master_port}
        {test_script}
    '''.split()
	snake_case							=					F'''
        --streaming={streaming}
        --num_workers={num_workers}
    '''.split()
	snake_case							=					[sys.executable] + distributed_args + args
	execute_subprocess_async(A       , env=os.environ.copy()				)







"""simple docstring"""
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


lowercase		:    Dict			=       getLogger(__name__)


lowercase		:    Dict			=       'cuda' if torch.cuda.is_available() else 'cpu'


def      lowerCamelCase_     (						lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_						,					lowerCamelCase_ = 8						,					lowerCamelCase_ = DEFAULT_DEVICE						,					lowerCamelCase_=False						,					lowerCamelCase_="summarization"						,					lowerCamelCase_=None						,					**lowerCamelCase_						,					)  ->      Dict:


					'''simple docstring'''
					__a:    str		     =       Path(lowerCamelCase_			).open("""w"""						,					encoding="""utf-8"""			)
					__a:    Tuple		     =       str(lowerCamelCase_			)
					__a:    List[Any]		     =       AutoModelForSeqaSeqLM.from_pretrained(lowerCamelCase_			).to(lowerCamelCase_			)
					if fpaa:
										__a:    List[str]		     =       model.half()

					__a:    Optional[int]		     =       AutoTokenizer.from_pretrained(lowerCamelCase_			)
					logger.info(f"""Inferred tokenizer type: {tokenizer.__class__}"""			)  # if this is wrong, check config.model_type.

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


def      lowerCamelCase_     (						)  ->      str:


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


def      lowerCamelCase_     (						lowerCamelCase_=True			)  ->      Any:


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

					if args.reference_path is None:
										return {}

					# Compute scores
					__a:    Optional[Any]		     =       calculate_bleu if """translation""" in args.task else calculate_rouge
					__a:    Union[str, Any]		     =       [x.rstrip() for x in open(args.save_path			).readlines()]
					__a:    List[str]		     =       [x.rstrip() for x in open(args.reference_path			).readlines()][: len(lowerCamelCase_			)]
					__a:    dict		     =       score_fn(lowerCamelCase_						,					lowerCamelCase_			)
					scores.update(lowerCamelCase_			)

					if args.dump_args:
										scores.update(lowerCamelCase_			)
					if args.info:
										__a:    Optional[Any]		     =       args.info

					if verbose:
										print(lowerCamelCase_			)

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