
"""simple docstring"""







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,
)


lowerCamelCase__        =		getLogger(__name__)
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = 8	,  _UpperCamelCase = 1024	,  _UpperCamelCase="val"	,  _UpperCamelCase=None	,  _UpperCamelCase=False	,  _UpperCamelCase="summarization"	,  _UpperCamelCase=None	,  _UpperCamelCase=1	,  _UpperCamelCase = None	,  _UpperCamelCase=""	,  **_UpperCamelCase	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				str(_UpperCamelCase		)
	assert local_rank is not None
	torch.distributed.init_process_group(backend='nccl'	,  rank=_UpperCamelCase		)

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

	__lowerCAmelCase      :						List[str]						      =				AutoTokenizer.from_pretrained(_UpperCamelCase		)
	logger.info(F"Inferred tokenizer type: {tokenizer.__class__}"		)  # if this is wrong, check config.model_type.

	if max_source_length is None:
		__lowerCAmelCase      :						Any						      =				tokenizer.model_max_length
	if prefix is None:
		__lowerCAmelCase      :						str						      =				prefix or getattr(model.config	,  'prefix'	,  ''		) or ''
	__lowerCAmelCase      :						List[str]						      =				SeqaSeqDataset(
	    _UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase	,  max_target_length=1024	,  type_path=_UpperCamelCase	,  n_obs=_UpperCamelCase	,  prefix=_UpperCamelCase	,  **_UpperCamelCase	,  )
	# 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.
	__lowerCAmelCase      :						Any						      =				ds.make_sortish_sampler(_UpperCamelCase	,  distributed=_UpperCamelCase	,  add_extra_examples=_UpperCamelCase	,  shuffle=_UpperCamelCase		)
	__lowerCAmelCase      :						str						      =				DataLoader(_UpperCamelCase	,  sampler=_UpperCamelCase	,  batch_size=_UpperCamelCase	,  collate_fn=ds.collate_fn		)
	__lowerCAmelCase      :						str						      =				[]
	for batch in tqdm(_UpperCamelCase		):
		__lowerCAmelCase      :						int						      =				model.generate(
		    input_ids=batch['input_ids'].to(model.device		)	,  attention_mask=batch['attention_mask'].to(model.device		)	,  num_return_sequences=_UpperCamelCase	,  num_beams=_UpperCamelCase	,  **_UpperCamelCase	,  )
		__lowerCAmelCase      :						str						      =				tokenizer.batch_decode(_UpperCamelCase	,  skip_special_tokens=_UpperCamelCase	,  clean_up_tokenization_spaces=_UpperCamelCase		)
		__lowerCAmelCase      :						Optional[Any]						      =				batch['ids']
		if num_return_sequences > 1:
			__lowerCAmelCase      :						List[str]						      =				chunks(_UpperCamelCase	,  _UpperCamelCase		)  # batch size chunks, each of size num_return_seq
		for i, pred in enumerate(_UpperCamelCase		):
			results.append({'pred': pred, 'id': ids[i].item()}		)
	save_json(_UpperCamelCase	,  _UpperCamelCase		)
	return results, sampler.num_replicas
def    __lowerCAmelCase    ():
	__lowerCAmelCase      :						List[str]						      =				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=_UpperCamelCase	,  help='like cnn_dm/test.source'		)
	parser.add_argument(
	    '--model_name'	,  type=_UpperCamelCase	,  help='like facebook/bart-large-cnn,t5-base, etc.'	,  default='sshleifer/distilbart-xsum-12-3'	,  )
	parser.add_argument('--save_dir'	,  type=_UpperCamelCase	,  help='where to save'	,  default='tmp_gen'		)
	parser.add_argument('--max_source_length'	,  type=_UpperCamelCase	,  default=_UpperCamelCase		)
	parser.add_argument(
	    '--type_path'	,  type=_UpperCamelCase	,  default='test'	,  help='which subset to evaluate typically train/val/test'		)
	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(
	    '--local_rank'	,  type=_UpperCamelCase	,  default=-1	,  required=_UpperCamelCase	,  help='should be passed by distributed.launch'		)

	parser.add_argument(
	    '--n_obs'	,  type=_UpperCamelCase	,  default=_UpperCamelCase	,  required=_UpperCamelCase	,  help='How many observations. Defaults to all.'		)
	parser.add_argument(
	    '--num_return_sequences'	,  type=_UpperCamelCase	,  default=1	,  required=_UpperCamelCase	,  help='How many sequences to return'		)
	parser.add_argument(
	    '--sync_timeout'	,  type=_UpperCamelCase	,  default=600	,  required=_UpperCamelCase	,  help='How long should master process wait for other processes to finish.'	,  )
	parser.add_argument('--src_lang'	,  type=_UpperCamelCase	,  default=_UpperCamelCase	,  required=_UpperCamelCase		)
	parser.add_argument('--tgt_lang'	,  type=_UpperCamelCase	,  default=_UpperCamelCase	,  required=_UpperCamelCase		)
	parser.add_argument(
	    '--prefix'	,  type=_UpperCamelCase	,  required=_UpperCamelCase	,  default=_UpperCamelCase	,  help='will be added to the begininng of src examples'		)
	parser.add_argument('--fp16'	,  action='store_true'		)
	parser.add_argument('--debug'	,  action='store_true'		)
	__lowerCAmelCase      :						List[Any]						      =				time.time()
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Dict						      =				parser.parse_known_args()
	__lowerCAmelCase      :						List[str]						      =				parse_numeric_n_bool_cl_kwargs(_UpperCamelCase		)
	if generate_kwargs and args.local_rank <= 0:
		print(F"parsed the following generate kwargs: {generate_kwargs}"		)
	__lowerCAmelCase      :						Any						      =				Path(args.save_dir + '_tmp'		)
	Path(_UpperCamelCase		).mkdir(exist_ok=_UpperCamelCase		)  # this handles locking.
	__lowerCAmelCase      :						Optional[int]						      =				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.
	__lowerCAmelCase      :						Any						      =				{}
	if args.src_lang is not None:
		__lowerCAmelCase      :						int						      =				args.src_lang
	if args.tgt_lang is not None:
		__lowerCAmelCase      :						str						      =				args.tgt_lang

	Path(args.save_dir		).mkdir(exist_ok=_UpperCamelCase		)
	__lowerCAmelCase      ,       __lowerCAmelCase      :						Any						      =				eval_data_dir(
	    args.data_dir	,  _UpperCamelCase	,  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=_UpperCamelCase	,  **_UpperCamelCase	,  )

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

		# Calculate metrics, save metrics,  and save _generations.txt
		__lowerCAmelCase      :						Tuple						      =				'translation' in args.task
		__lowerCAmelCase      :						Optional[Any]						      =				calculate_bleu if calc_bleu else calculate_rouge
		__lowerCAmelCase      :						List[Any]						      =				'bleu' if calc_bleu else 'rouge'
		__lowerCAmelCase      :						Dict						      =				score_fn(_UpperCamelCase	,  _UpperCamelCase		)
		__lowerCAmelCase      :						List[str]						      =				len(_UpperCamelCase		)
		__lowerCAmelCase      :						int						      =				time.time() - start_time
		__lowerCAmelCase      :						List[str]						      =				round(runtime / metrics['n_obs']	,  4		)
		__lowerCAmelCase      :						List[str]						      =				num_replicas
		# TODO(@stas00): add whatever metadata to metrics
		__lowerCAmelCase      :						Dict						      =				save_dir.joinpath(F"{args.type_path}_{metric_name}.json"		)
		save_json(_UpperCamelCase	,  _UpperCamelCase	,  indent=_UpperCamelCase		)
		print(_UpperCamelCase		)
		write_txt_file(_UpperCamelCase	,  save_dir.joinpath(F"{args.type_path}_generations.txt"		)		)
		if args.debug:
			write_txt_file(_UpperCamelCase	,  save_dir.joinpath(F"{args.type_path}.target"		)		)
		else:
			shutil.rmtree(_UpperCamelCase		)
def    __lowerCAmelCase    (_UpperCamelCase		):
	__lowerCAmelCase      :						Dict						      =				[]
	for partial_result in partial_results:
		records.extend(_UpperCamelCase		)
	__lowerCAmelCase      :						Dict						      =				sorted(_UpperCamelCase	,  key=lambda _UpperCamelCase		: x["id"]		)
	__lowerCAmelCase      :						Tuple						      =				[x['pred'] for x in records]
	return preds
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase		):
	# WAIT FOR lots of .json files
	__lowerCAmelCase      :						Dict						      =				time.time()
	logger.info('waiting for all nodes to finish'		)
	__lowerCAmelCase      :						List[Any]						      =				None
	while (time.time() - start_wait) < timeout:
		__lowerCAmelCase      :						Any						      =				list(save_dir.glob('rank_*.json'		)		)
		if len(_UpperCamelCase		) < num_replicas:
			continue
		try:
			# make sure all json files are fully saved
			__lowerCAmelCase      :						Union[str, Any]						      =				lmap(_UpperCamelCase	,  _UpperCamelCase		)
			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()

"""simple docstring"""







# flake8: noqa
# Lint as: python3

from typing import Dict, List, Optional, Type

from .. import config
from ..utils import logging
from .formatting import (
    ArrowFormatter,
    CustomFormatter,
    Formatter,
    PandasFormatter,
    PythonFormatter,
    TensorFormatter,
    format_table,
    query_table,
)
from .np_formatter import NumpyFormatter


lowerCamelCase__        =		logging.get_logger(__name__)

lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
lowerCamelCase__        =		{}
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None	,  ):
	__lowerCAmelCase      :						Union[str, Any]						      =				aliases if aliases is not None else []
	if format_type in _FORMAT_TYPES:
		logger.warning(
		    F"Overwriting format type '{format_type}' ({_FORMAT_TYPES[format_type].__name__} -> {formatter_cls.__name__})"		)
	__lowerCAmelCase      :						str						      =				formatter_cls
	for alias in set(aliases + [format_type]		):
		if alias in _FORMAT_TYPES_ALIASES:
			logger.warning(
			    F"Overwriting format type alias '{alias}' ({_FORMAT_TYPES_ALIASES[alias]} -> {format_type})"		)
		__lowerCAmelCase      :						Any						      =				format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  _UpperCamelCase	,  _UpperCamelCase = None		):
	__lowerCAmelCase      :						int						      =				aliases if aliases is not None else []
	for alias in set(aliases + [format_type]		):
		__lowerCAmelCase      :						str						      =				unavailable_error


# Here we define all the available formatting functions that can be used by `Dataset.set_format`
_register_formatter(PythonFormatter, None, aliases=["""python"""])
_register_formatter(ArrowFormatter, """arrow""", aliases=["""pa""", """pyarrow"""])
_register_formatter(NumpyFormatter, """numpy""", aliases=["""np"""])
_register_formatter(PandasFormatter, """pandas""", aliases=["""pd"""])
_register_formatter(CustomFormatter, """custom""")

if config.TORCH_AVAILABLE:
							from .torch_formatter import TorchFormatter

							_register_formatter(TorchFormatter, """torch""", aliases=["""pt""", """pytorch"""])
else:
							lowerCamelCase__        =		ValueError("""PyTorch needs to be installed to be able to return PyTorch tensors.""")
							_register_unavailable_formatter(_torch_error, """torch""", aliases=["""pt""", """pytorch"""])

if config.TF_AVAILABLE:
							from .tf_formatter import TFFormatter

							_register_formatter(TFFormatter, """tensorflow""", aliases=["""tf"""])
else:
							lowerCamelCase__        =		ValueError("""Tensorflow needs to be installed to be able to return Tensorflow tensors.""")
							_register_unavailable_formatter(_tf_error, """tensorflow""", aliases=["""tf"""])

if config.JAX_AVAILABLE:
							from .jax_formatter import JaxFormatter

							_register_formatter(JaxFormatter, """jax""", aliases=[])
else:
							lowerCamelCase__        =		ValueError("""JAX needs to be installed to be able to return JAX arrays.""")
							_register_unavailable_formatter(_jax_error, """jax""", aliases=[])
def    __lowerCAmelCase    (_UpperCamelCase		):
	if format_type in _FORMAT_TYPES_ALIASES:
		return _FORMAT_TYPES_ALIASES[format_type]
	else:
		return format_type
def    __lowerCAmelCase    (_UpperCamelCase	,  **_UpperCamelCase		):
	__lowerCAmelCase      :						Any						      =				get_format_type_from_alias(_UpperCamelCase		)
	if format_type in _FORMAT_TYPES:
		return _FORMAT_TYPES[format_type](**_UpperCamelCase		)
	if format_type in _FORMAT_TYPES_ALIASES_UNAVAILABLE:
		raise _FORMAT_TYPES_ALIASES_UNAVAILABLE[format_type]
	else:
		raise ValueError(
		    F"Return type should be None or selected in {list(type for type in _FORMAT_TYPES.keys() if type != None		)}, but got '{format_type}'"		)