






'''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_				: Optional[Any]    	=							getLogger(__name__)







def 	__magic_name__( _A						,      _A						,      _A						,      _A = 8						,      _A = 1024						,      _A="val"						,      _A=None						,      _A=False						,      _A="summarization"						,      _A=None						,      _A=1						,      _A = None						,      _A=""						,      **_A						,      ):
						'''simple docstring'''





						UpperCamelCase__         = str(_A					)
						assert local_rank is not None
						torch.distributed.init_process_group(backend="""nccl"""						,      rank=_A					)

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

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

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







def 	__magic_name__( ):
						'''simple docstring'''





						UpperCamelCase__         = 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=_A						,      help="""like cnn_dm/test.source"""					)
						parser.add_argument(
						    """--model_name"""						,      type=_A						,      help="""like facebook/bart-large-cnn,t5-base, etc."""						,      default="""sshleifer/distilbart-xsum-12-3"""						,      )
						parser.add_argument("""--save_dir"""						,      type=_A						,      help="""where to save"""						,      default="""tmp_gen"""					)
						parser.add_argument("""--max_source_length"""						,      type=_A						,      default=_A					)
						parser.add_argument(
						    """--type_path"""						,      type=_A						,      default="""test"""						,      help="""which subset to evaluate typically train/val/test"""					)
						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(
						    """--local_rank"""						,      type=_A						,      default=-1						,      required=_A						,      help="""should be passed by distributed.launch"""					)

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

						Path(args.save_dir					).mkdir(exist_ok=_A					)
						UpperCamelCase__    ,   UpperCamelCase__         = eval_data_dir(
						    args.data_dir						,      _A						,      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=_A						,      **_A						,      )

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

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







def 	__magic_name__( _A					):
						'''simple docstring'''





						UpperCamelCase__         = []
						for partial_result in partial_results:
												records.extend(_A					)
						UpperCamelCase__         = sorted(_A						,      key=lambda _A					: x["id"]					)
						UpperCamelCase__         = [x["""pred"""] for x in records]
						return preds







def 	__magic_name__( _A						,      _A						,      _A					):
						'''simple docstring'''





						UpperCamelCase__         = time.time()
						logger.info("""waiting for all nodes to finish"""					)
						UpperCamelCase__         = None
						while (time.time() - start_wait) < timeout:
												UpperCamelCase__         = list(save_dir.glob("""rank_*.json"""					)					)
												if len(_A					) < num_replicas:
																		continue
												try:
																		# make sure all json files are fully saved
																		UpperCamelCase__         = lmap(_A						,      _A					)
																		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 PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowerCamelCase_				: str    	=							transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 	__magic_name__( _A					):
						'''simple docstring'''





						if isinstance(_A						,      torch.Tensor					):
												return image
						elif isinstance(_A						,      PIL.Image.Image					):
												UpperCamelCase__         = [image]

						UpperCamelCase__         = [trans(img.convert("""RGB"""					)					) for img in image]
						UpperCamelCase__         = torch.stack(_A					)
						return image







class    _SCREAMING_SNAKE_CASE							(		SCREAMING_SNAKE_CASE	):


			'''simple docstring'''





			def __init__(   self							:						Tuple ,      lowercase							:						Dict ,      lowercase							:						Optional[Any]			)							->    Any:

									'''simple docstring'''







									super().__init__()

									# make sure scheduler can always be converted to DDIM
									UpperCamelCase__         = DDIMScheduler.from_config(scheduler.config			)

									self.register_modules(unet=lowercase ,      scheduler=lowercase			)






			def 				A      (   self							:						Tuple ,      lowercase							:						Tuple			)							->    Any:

									'''simple docstring'''







									if strength < 0 or strength > 1:
															raise ValueError(f"The value of strength should in [0.0, 1.0] but is {strength}"			)






			def 				A      (   self							:						str ,      lowercase							:						List[Any] ,      lowercase							:						Dict ,      lowercase							:						List[str]			)							->    str:

									'''simple docstring'''







									UpperCamelCase__         = min(int(num_inference_steps * strength			) ,      lowercase			)

									UpperCamelCase__         = max(num_inference_steps - init_timestep ,      0			)
									UpperCamelCase__         = self.scheduler.timesteps[t_start:]

									return timesteps, num_inference_steps - t_start






			def 				A      (   self							:						Union[str, Any] ,      lowercase							:						Optional[Any] ,      lowercase							:						Any ,      lowercase							:						Union[str, Any] ,      lowercase							:						Dict ,      lowercase							:						Tuple ,      lowercase							:						int=None			)							->    int:

									'''simple docstring'''







									if not isinstance(lowercase ,      (torch.Tensor, PIL.Image.Image, list)			):
															raise ValueError(
															    f"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase			)}"			)

									UpperCamelCase__         = image.to(device=lowercase ,      dtype=lowercase			)

									if isinstance(lowercase ,      lowercase			) and len(lowercase			) != batch_size:
															raise ValueError(
															    f"You have passed a list of generators of length {len(lowercase			)}, but requested an effective batch"
															    f" size of {batch_size}. Make sure the batch size matches the length of the generators."			)

									UpperCamelCase__         = init_latents.shape
									UpperCamelCase__         = randn_tensor(lowercase ,      generator=lowercase ,      device=lowercase ,      dtype=lowercase			)

									# get latents
									print("""add noise to latents at timestep""" ,      lowercase			)
									UpperCamelCase__         = self.scheduler.add_noise(lowercase ,      lowercase ,      lowercase			)
									UpperCamelCase__         = init_latents

									return latents







			@torch.no_grad()
			def __call__(   self							:						Any ,      lowercase							:						Union[torch.FloatTensor, PIL.Image.Image] = None ,      lowercase							:						float = 0.8 ,      lowercase							:						int = 1 ,      lowercase							:						Optional[Union[torch.Generator, List[torch.Generator]]] = None ,      lowercase							:						float = 0.0 ,      lowercase							:						int = 5_0 ,      lowercase							:						Optional[bool] = None ,      lowercase							:						Optional[str] = "pil" ,      lowercase							:						bool = True ,      )							->    Union[ImagePipelineOutput, Tuple]:

									'''simple docstring'''







									self.check_inputs(lowercase			)

									# 2. Preprocess image
									UpperCamelCase__         = preprocess(lowercase			)

									# 3. set timesteps
									self.scheduler.set_timesteps(lowercase ,      device=self.device			)
									UpperCamelCase__    ,   UpperCamelCase__         = self.get_timesteps(lowercase ,      lowercase ,      self.device			)
									UpperCamelCase__         = timesteps[:1].repeat(lowercase			)

									# 4. Prepare latent variables
									UpperCamelCase__         = self.prepare_latents(lowercase ,      lowercase ,      lowercase ,      self.unet.dtype ,      self.device ,      lowercase			)
									UpperCamelCase__         = latents

									# 5. Denoising loop
									for t in self.progress_bar(lowercase			):
															# 1. predict noise model_output
															UpperCamelCase__         = self.unet(lowercase ,      lowercase			).sample

															# 2. predict previous mean of image x_t-1 and add variance depending on eta
															# eta corresponds to η in paper and should be between [0, 1]
															# do x_t -> x_t-1
															UpperCamelCase__         = self.scheduler.step(
															    lowercase ,      lowercase ,      lowercase ,      eta=lowercase ,      use_clipped_model_output=lowercase ,      generator=lowercase ,      ).prev_sample

									UpperCamelCase__         = (image / 2 + 0.5).clamp(0 ,      1			)
									UpperCamelCase__         = image.cpu().permute(0 ,      2 ,      3 ,      1			).numpy()
									if output_type == "pil":
															UpperCamelCase__         = self.numpy_to_pil(lowercase			)

									if not return_dict:
															return (image, latent_timestep.item())

									return ImagePipelineOutput(images=lowercase			)
