



import argparse
import itertools
import operator
import sys
from collections import OrderedDict

from run_eval import datetime_now, run_generate

from utils import ROUGE_KEYS


# A table of supported tasks and the list of scores in the order of importance to be sorted by.
# To add a new task, simply list the score names that `run_eval.run_generate()` returns
A_		:  Optional[Any]										=  {
    '''translation''': ['''bleu'''],
    '''summarization''': ROUGE_KEYS,
}


def 						UpperCAmelCase__   (   UpperCAmelCase__			:Tuple       ):






 '''simple docstring'''

 a							=		search.split()
 a							=		dict((g.split("="       ) for g in groups)       )
 a							=		list(entries.keys()       )
 a							=		[[F"""--{k} {v}""" for v in vs.split(":"       )] for k, vs in entries.items()]
 a							=		[list(UpperCAmelCase__       ) for x in itertools.product(*UpperCAmelCase__       )]
 return matrix, entry_names


def 						UpperCAmelCase__   (   ):






 '''simple docstring'''

 a							=		sys.argv[0]

 a							=		argparse.ArgumentParser(
     usage=(
         "\n\nImportant: this script accepts all arguments `run_eval.py` accepts and then a few extra, therefore"
         " refer to `run_eval.py -h` for the complete list."
     )       )
 parser.add_argument(
     "--search"			,       type=UpperCAmelCase__			,       required=UpperCAmelCase__			,       help="param space to search, e.g. \"num_beams=5:10 length_penalty=0.8:1.0:1.2\""			,       )
 parser.add_argument(
     "--bs"			,       type=UpperCAmelCase__			,       default=8			,       required=UpperCAmelCase__			,       help="initial batch size (may get reduced if it's too big)"       )
 parser.add_argument("--task"			,       type=UpperCAmelCase__			,       help="used for task_specific_params + metrics"       )
 parser.add_argument(
     "--info"			,       nargs="?"			,       type=UpperCAmelCase__			,       const=datetime_now()			,       help=(
         "add custom notes to be printed before the results table. If no value is passed, the current datetime"
         " string will be used."
     )			,       )
 a		,	a							=		parser.parse_known_args()
 # we share some of the args
 args_main.extend(["--task", args.task]       )
 a							=		[prog] + args_main

 # to support variations like translation_en_to_de"
 a							=		"translation" if "translation" in args.task else "summarization"

 a		,	a							=		parse_search_arg(args.search       )
 a							=		task_score_names[task]  # score cols first
 a							=		{col: len(str(UpperCAmelCase__       )       ) for col in col_names}
 a							=		[]
 for r in matrix:
  a							=		dict((x.replace("--"			,       ""       ).split() for x in r)       )
  a							=		" ".join(UpperCAmelCase__       ).split()
  args_exp.extend(["--bs", str(args.bs       )]       )  # in case we need to reduce its size due to CUDA OOM
  a							=		args_normal + args_exp

  # XXX: need to trap CUDA OOM and lower args.bs if that happens and retry

  a							=		run_generate(verbose=UpperCAmelCase__       )
  # make sure scores are first in the table
  a							=		OrderedDict()
  for score in task_score_names[task]:
   a							=		scores[score]
  result.update(UpperCAmelCase__       )
  results.append(UpperCAmelCase__       )

  # find widest entries
  for k, v in result.items():
   a							=		len(str(UpperCAmelCase__       )       )
   if l > col_widths[k]:
    a							=		l

 a							=		sorted(UpperCAmelCase__			,       key=operator.itemgetter(*task_score_names[task]       )			,       reverse=UpperCAmelCase__       )
 print(" | ".join([F"""{col:{col_widths[col]}}""" for col in col_names]       )       )
 print(" | ".join([F"""{"-"*col_widths[col]}""" for col in col_names]       )       )
 for row in results_sorted:
  print(" | ".join([F"""{row[col]:{col_widths[col]}}""" for col in col_names]       )       )

 a							=		results_sorted[0]
 for score in task_score_names[task]:
  del best[score]
 a							=		[F"""--{k} {v}""" for k, v in best.items()]
 a							=		["--bs", str(args.bs       )]
 if args.info:
  print(F"""\nInfo: {args.info}"""       )
 print("\nBest score args:"       )
 print(" ".join(args_main + best_args + dyn_args       )       )

 return results_sorted


if __name__ == "__main__":
     # Usage:
     # [normal-run_eval_search.py cmd plus] \
     # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
     #
     # Example:
     # PYTHONPATH="src:examples/seq2seq" python examples/seq2seq/run_eval_search.py $MODEL_NAME \
     # $DATA_DIR/val.source $SAVE_DIR/test_translations.txt --reference_path $DATA_DIR/val.target \
     # --score_path $SAVE_DIR/test_bleu.json --bs $BS --task translation \
     # --search="num_beams=1:5:10 length_penalty=0.8:1:1.2 early_stopping=true:false"
     run_search()






"""simple docstring"""


from typing import List, Optional, Tuple, Union

import torch

from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  A_     (	SCREAMING_SNAKE_CASE_							):



						"""simple docstring"""






						def __init__(   self					:List[str]  ,    lowercase_					:List[str]  ,    lowercase_					:List[Any]   )							->     List[str]:
												super().__init__()
												self.register_modules(unet=lowercase_  ,    scheduler=lowercase_   )






						@torch.no_grad()
						def __call__(   self					:int  ,    lowercase_					:int = 1  ,    lowercase_					:Optional[Union[torch.Generator, List[torch.Generator]]] = None  ,    lowercase_					:int = 10_00  ,    lowercase_					:Optional[str] = "pil"  ,    lowercase_					:bool = True  ,    )							->     Union[ImagePipelineOutput, Tuple]:
												# Sample gaussian noise to begin loop
												if isinstance(self.unet.config.sample_size  ,    lowercase_   ):
																		UpperCAmelCase			 =					(
																		    batch_size,
																		    self.unet.config.in_channels,
																		    self.unet.config.sample_size,
																		    self.unet.config.sample_size,
																		)
												else:
																		UpperCAmelCase			 =					(batch_size, self.unet.config.in_channels, *self.unet.config.sample_size)

												if self.device.type == "mps":
																		# randn does not work reproducibly on mps
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_   )
																		UpperCAmelCase			 =					image.to(self.device   )
												else:
																		UpperCAmelCase			 =					randn_tensor(lowercase_  ,    generator=lowercase_  ,    device=self.device   )

												# set step values
												self.scheduler.set_timesteps(lowercase_   )

												for t in self.progress_bar(self.scheduler.timesteps   ):
																		# 1. predict noise model_output
																		UpperCAmelCase			 =					self.unet(lowercase_  ,    lowercase_   ).sample

																		# 2. compute previous image: x_t -> x_t-1
																		UpperCAmelCase			 =					self.scheduler.step(lowercase_  ,    lowercase_  ,    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,)

												return ImagePipelineOutput(images=lowercase_   )





