


'''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,
)


UpperCAmelCase_		:   str							   =		getLogger(__name__)







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__ = 8							,      SCREAMING_SNAKE_CASE__ = 1024							,      SCREAMING_SNAKE_CASE__="val"							,      SCREAMING_SNAKE_CASE__=None							,      SCREAMING_SNAKE_CASE__=False							,      SCREAMING_SNAKE_CASE__="summarization"							,      SCREAMING_SNAKE_CASE__=None							,      SCREAMING_SNAKE_CASE__=1							,      SCREAMING_SNAKE_CASE__ = None							,      SCREAMING_SNAKE_CASE__=""							,      **SCREAMING_SNAKE_CASE__							,      ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      str(SCREAMING_SNAKE_CASE__     )
				assert local_rank is not None
				torch.distributed.init_process_group(backend="""nccl"""							,      rank=SCREAMING_SNAKE_CASE__     )

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

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

				if max_source_length is None:
								_SCREAMING_SNAKE_CASE :       str     		=      tokenizer.model_max_length
				if prefix is None:
								_SCREAMING_SNAKE_CASE :       str     		=      prefix or getattr(model.config							,      """prefix"""							,      """"""     ) or """"""
				_SCREAMING_SNAKE_CASE :       str     		=      SeqaSeqDataset(
				    SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      max_target_length=1024							,      type_path=SCREAMING_SNAKE_CASE__							,      n_obs=SCREAMING_SNAKE_CASE__							,      prefix=SCREAMING_SNAKE_CASE__							,      **SCREAMING_SNAKE_CASE__							,      )
				# 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.
				_SCREAMING_SNAKE_CASE :       Any     		=      ds.make_sortish_sampler(SCREAMING_SNAKE_CASE__							,      distributed=SCREAMING_SNAKE_CASE__							,      add_extra_examples=SCREAMING_SNAKE_CASE__							,      shuffle=SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      DataLoader(SCREAMING_SNAKE_CASE__							,      sampler=SCREAMING_SNAKE_CASE__							,      batch_size=SCREAMING_SNAKE_CASE__							,      collate_fn=ds.collate_fn     )
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      []
				for batch in tqdm(SCREAMING_SNAKE_CASE__     ):
								_SCREAMING_SNAKE_CASE :       Tuple     		=      model.generate(
								    input_ids=batch["""input_ids"""].to(model.device     )							,      attention_mask=batch["""attention_mask"""].to(model.device     )							,      num_return_sequences=SCREAMING_SNAKE_CASE__							,      num_beams=SCREAMING_SNAKE_CASE__							,      **SCREAMING_SNAKE_CASE__							,      )
								_SCREAMING_SNAKE_CASE :       List[str]     		=      tokenizer.batch_decode(SCREAMING_SNAKE_CASE__							,      skip_special_tokens=SCREAMING_SNAKE_CASE__							,      clean_up_tokenization_spaces=SCREAMING_SNAKE_CASE__     )
								_SCREAMING_SNAKE_CASE :       Tuple     		=      batch["""ids"""]
								if num_return_sequences > 1:
												_SCREAMING_SNAKE_CASE :       Optional[int]     		=      chunks(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )  # batch size chunks, each of size num_return_seq
								for i, pred in enumerate(SCREAMING_SNAKE_CASE__     ):
												results.append({"""pred""": pred, """id""": ids[i].item()}     )
				save_json(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
				return results, sampler.num_replicas







def 			snake_case_   (  ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      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=SCREAMING_SNAKE_CASE__							,      help="""like cnn_dm/test.source"""     )
				parser.add_argument(
				    """--model_name"""							,      type=SCREAMING_SNAKE_CASE__							,      help="""like facebook/bart-large-cnn,t5-base, etc."""							,      default="""sshleifer/distilbart-xsum-12-3"""							,      )
				parser.add_argument("""--save_dir"""							,      type=SCREAMING_SNAKE_CASE__							,      help="""where to save"""							,      default="""tmp_gen"""     )
				parser.add_argument("""--max_source_length"""							,      type=SCREAMING_SNAKE_CASE__							,      default=SCREAMING_SNAKE_CASE__     )
				parser.add_argument(
				    """--type_path"""							,      type=SCREAMING_SNAKE_CASE__							,      default="""test"""							,      help="""which subset to evaluate typically train/val/test"""     )
				parser.add_argument("""--task"""							,      type=SCREAMING_SNAKE_CASE__							,      default="""summarization"""							,      help="""used for task_specific_params + metrics"""     )
				parser.add_argument("""--bs"""							,      type=SCREAMING_SNAKE_CASE__							,      default=8							,      required=SCREAMING_SNAKE_CASE__							,      help="""batch size"""     )
				parser.add_argument(
				    """--local_rank"""							,      type=SCREAMING_SNAKE_CASE__							,      default=-1							,      required=SCREAMING_SNAKE_CASE__							,      help="""should be passed by distributed.launch"""     )

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

				Path(args.save_dir     ).mkdir(exist_ok=SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       List[str]     		=      eval_data_dir(
				    args.data_dir							,      SCREAMING_SNAKE_CASE__							,      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=SCREAMING_SNAKE_CASE__							,      **SCREAMING_SNAKE_CASE__							,      )

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

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







def 			snake_case_   (  SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       Tuple     		=      []
				for partial_result in partial_results:
								records.extend(SCREAMING_SNAKE_CASE__     )
				_SCREAMING_SNAKE_CASE :       Optional[int]     		=      sorted(SCREAMING_SNAKE_CASE__							,      key=lambda SCREAMING_SNAKE_CASE__     : x["id"]     )
				_SCREAMING_SNAKE_CASE :       Dict     		=      [x["""pred"""] for x in records]
				return preds







def 			snake_case_   (  SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     ):







				"""simple docstring"""





				_SCREAMING_SNAKE_CASE :       str     		=      time.time()
				logger.info("""waiting for all nodes to finish"""     )
				_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      None
				while (time.time() - start_wait) < timeout:
								_SCREAMING_SNAKE_CASE :       int     		=      list(save_dir.glob("""rank_*.json"""     )     )
								if len(SCREAMING_SNAKE_CASE__     ) < num_replicas:
												continue
								try:
												# make sure all json files are fully saved
												_SCREAMING_SNAKE_CASE :       str     		=      lmap(SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     )
												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'''


from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class     lowercase__  (						_snake_case					):





							'''simple docstring'''


							A_							:      UNetaDModel
							A_							:      ScoreSdeVeScheduler






							def __init__(    self    ,					__snake_case    ,					__snake_case  ):
											super().__init__()
											self.register_modules(unet=__snake_case    ,					scheduler=__snake_case  )






							@torch.no_grad()
							def __call__(    self    ,					__snake_case = 1    ,					__snake_case = 2000    ,					__snake_case = None    ,					__snake_case = "pil"    ,					__snake_case = True    ,					**__snake_case    ,					):
											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.unet.config.sample_size
											_SCREAMING_SNAKE_CASE :       Optional[Any]     		=      (batch_size, 3, img_size, img_size)

											_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      self.unet

											_SCREAMING_SNAKE_CASE :       Optional[int]     		=      randn_tensor(__snake_case    ,					generator=__snake_case  ) * self.scheduler.init_noise_sigma
											_SCREAMING_SNAKE_CASE :       Any     		=      sample.to(self.device  )

											self.scheduler.set_timesteps(__snake_case  )
											self.scheduler.set_sigmas(__snake_case  )

											for i, t in enumerate(self.progress_bar(self.scheduler.timesteps  )  ):
															_SCREAMING_SNAKE_CASE :       List[Any]     		=      self.scheduler.sigmas[i] * torch.ones(shape[0]    ,					device=self.device  )

															# correction step
															for _ in range(self.scheduler.config.correct_steps  ):
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.unet(__snake_case    ,					__snake_case  ).sample
																			_SCREAMING_SNAKE_CASE :       Any     		=      self.scheduler.step_correct(__snake_case    ,					__snake_case    ,					generator=__snake_case  ).prev_sample

															# prediction step
															_SCREAMING_SNAKE_CASE :       Union[str, Any]     		=      model(__snake_case    ,					__snake_case  ).sample
															_SCREAMING_SNAKE_CASE :       Tuple     		=      self.scheduler.step_pred(__snake_case    ,					__snake_case    ,					__snake_case    ,					generator=__snake_case  )

															_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE :       int     		=      output.prev_sample, output.prev_sample_mean

											_SCREAMING_SNAKE_CASE :       Tuple     		=      sample_mean.clamp(0    ,					1  )
											_SCREAMING_SNAKE_CASE :       str     		=      sample.cpu().permute(0    ,					2    ,					3    ,					1  ).numpy()
											if output_type == "pil":
															_SCREAMING_SNAKE_CASE :       Optional[int]     		=      self.numpy_to_pil(__snake_case  )

											if not return_dict:
															return (sample,)

											return ImagePipelineOutput(images=__snake_case  )




