code: 
  
  
  
  
  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
  SCREAMING_SNAKE_CASE__    :    List[str]								=					{
      "translation": ["bleu"],
      "summarization": ROUGE_KEYS,
  }
  
  def lowercase						(     SCREAMING_SNAKE_CASE   ) ->       Any:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	search.split()
  			SCREAMING_SNAKE_CASE_			=	dict((g.split('='   ) for g in groups)   )
  			SCREAMING_SNAKE_CASE_			=	list(entries.keys()   )
  			SCREAMING_SNAKE_CASE_			=	[[F'--{k} {v}' for v in vs.split(':'   )] for k, vs in entries.items()]
  			SCREAMING_SNAKE_CASE_			=	[list(SCREAMING_SNAKE_CASE   ) for x in itertools.product(*SCREAMING_SNAKE_CASE   )]
  			return matrix, entry_names
  
  def lowercase						(     ) ->       Union[str, Any]:
  
  
  			'''simple docstring'''
  
  
  
  
  
  
  
  			SCREAMING_SNAKE_CASE_			=	sys.argv[0]
  
  			SCREAMING_SNAKE_CASE_			=	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=SCREAMING_SNAKE_CASE					,						required=SCREAMING_SNAKE_CASE					,						help='param space to search, e.g. "num_beams=5:10 length_penalty=0.8:1.0:1.2"'					,						)
  			parser.add_argument(
  			    '--bs'					,						type=SCREAMING_SNAKE_CASE					,						default=8					,						required=SCREAMING_SNAKE_CASE					,						help='initial batch size (may get reduced if it\'s too big)'   )
  			parser.add_argument('--task'					,						type=SCREAMING_SNAKE_CASE					,						help='used for task_specific_params + metrics'   )
  			parser.add_argument(
  			    '--info'					,						nargs='?'					,						type=SCREAMING_SNAKE_CASE					,						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.'
  			    )					,						)
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	parser.parse_known_args()
  			# we share some of the args
  			args_main.extend(['--task', args.task]   )
  			SCREAMING_SNAKE_CASE_			=	[prog] + args_main
  
  			# to support variations like translation_en_to_de"
  			SCREAMING_SNAKE_CASE_			=	'translation' if 'translation' in args.task else 'summarization'
  
  			SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	parse_search_arg(args.search   )
  			SCREAMING_SNAKE_CASE_			=	task_score_names[task]  # score cols first
  			SCREAMING_SNAKE_CASE_			=	{col: len(str(SCREAMING_SNAKE_CASE   )   ) for col in col_names}
  			SCREAMING_SNAKE_CASE_			=	[]
  			for r in matrix:
  						SCREAMING_SNAKE_CASE_			=	dict((x.replace('--'					,						''   ).split() for x in r)   )
  						SCREAMING_SNAKE_CASE_			=	' '.join(SCREAMING_SNAKE_CASE   ).split()
  						args_exp.extend(['--bs', str(args.bs   )]   )  # in case we need to reduce its size due to CUDA OOM
  						SCREAMING_SNAKE_CASE_			=	args_normal + args_exp
  
  						# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
  						SCREAMING_SNAKE_CASE_			=	run_generate(verbose=SCREAMING_SNAKE_CASE   )
  						# make sure scores are first in the table
  						SCREAMING_SNAKE_CASE_			=	OrderedDict()
  						for score in task_score_names[task]:
  									SCREAMING_SNAKE_CASE_			=	scores[score]
  						result.update(SCREAMING_SNAKE_CASE   )
  						results.append(SCREAMING_SNAKE_CASE   )
  
  						# find widest entries
  						for k, v in result.items():
  									SCREAMING_SNAKE_CASE_			=	len(str(SCREAMING_SNAKE_CASE   )   )
  									if l > col_widths[k]:
  												SCREAMING_SNAKE_CASE_			=	l
  
  			SCREAMING_SNAKE_CASE_			=	sorted(SCREAMING_SNAKE_CASE					,						key=operator.itemgetter(*task_score_names[task]   )					,						reverse=SCREAMING_SNAKE_CASE   )
  			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]   )   )
  
  			SCREAMING_SNAKE_CASE_			=	results_sorted[0]
  			for score in task_score_names[task]:
  						del best[score]
  			SCREAMING_SNAKE_CASE_			=	[F'--{k} {v}' for k, v in best.items()]
  			SCREAMING_SNAKE_CASE_			=	['--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()
  
style_context: 
  
  
  
  
  import warnings
  from typing import List, Optional, Union
  
  import numpy as np
  import PIL
  import torch
  from PIL import Image
  
  from .configuration_utils import ConfigMixin, register_to_config
  from .utils import CONFIG_NAME, PIL_INTERPOLATION, deprecate
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   CONFIG_NAME
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = False			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[None, ...]
  				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
  				if images.shape[-1] == 1:
  							# special case for grayscale (single channel) images
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
  				else:
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			) for image in images]
  
  				return pil_images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	[images]
  				SCREAMING_SNAKE_CASE_			=	[np.array(SCREAMING_SNAKE_CASE			).astype(np.floataa			) / 2_5_5.0 for image in images]
  				SCREAMING_SNAKE_CASE_			=	np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				torch.FloatTensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[..., None]
  
  				SCREAMING_SNAKE_CASE_			=	torch.from_numpy(images.transpose(0			,    3			,    1			,    2			)			)
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				np.ndarray:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	images.cpu().permute(0			,    2			,    3			,    1			).float().numpy()
  				return images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				str:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return 2.0 * images - 1.0
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return (images / 2 + 0.5).clamp(0			,    1			)
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	image.convert('RGB'			)
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				PIL.Image.Image:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if height is None:
  							SCREAMING_SNAKE_CASE_			=	image.height
  				if width is None:
  							SCREAMING_SNAKE_CASE_			=	image.width
  
  				SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	(
  				    x - x % self.config.vae_scale_factor for x in (width, height)
  				)  # resize to integer multiple of vae_scale_factor
  				SCREAMING_SNAKE_CASE_			=	image.resize((width, height)			,    resample=PIL_INTERPOLATION[self.config.resample]			)
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = None			,    SCREAMING_SNAKE_CASE = None			,    )     ->				torch.Tensor:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				SCREAMING_SNAKE_CASE_			=	(PIL.Image.Image, np.ndarray, torch.Tensor)
  				if isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			):
  							SCREAMING_SNAKE_CASE_			=	[image]
  				elif not (isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) and all(isinstance(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image			)):
  							raise ValueError(
  							    f'Input is in incorrect format: {[type(SCREAMING_SNAKE_CASE			) for i in image]}. Currently, we only support {", ".join(SCREAMING_SNAKE_CASE			)}'			)
  
  				if isinstance(image[0]			,    PIL.Image.Image			):
  							if self.config.do_convert_rgb:
  										SCREAMING_SNAKE_CASE_			=	[self.convert_to_rgb(SCREAMING_SNAKE_CASE			) for i in image]
  							if self.config.do_resize:
  										SCREAMING_SNAKE_CASE_			=	[self.resize(SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE			) for i in image]
  							SCREAMING_SNAKE_CASE_			=	self.pil_to_numpy(SCREAMING_SNAKE_CASE			)  # to np
  							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)  # to pt
  
  				elif isinstance(image[0]			,    np.ndarray			):
  							SCREAMING_SNAKE_CASE_			=	np.concatenate(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else np.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  							SCREAMING_SNAKE_CASE_			=	self.numpy_to_pt(SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape
  							if self.config.do_resize and (
  							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
  							):
  										raise ValueError(
  										    f'Currently we only support resizing for PIL image - please resize your numpy array to be divisible by {self.config.vae_scale_factor}'
  										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)
  
  				elif isinstance(image[0]			,    torch.Tensor			):
  							SCREAMING_SNAKE_CASE_			=	torch.cat(SCREAMING_SNAKE_CASE			,    axis=0			) if image[0].ndim == 4 else torch.stack(SCREAMING_SNAKE_CASE			,    axis=0			)
  							SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_	,		SCREAMING_SNAKE_CASE_			=	image.shape
  
  							# don't need any preprocess if the image is latents
  							if channel == 4:
  										return image
  
  							if self.config.do_resize and (
  							    height % self.config.vae_scale_factor != 0 or width % self.config.vae_scale_factor != 0
  							):
  										raise ValueError(
  										    f'Currently we only support resizing for PIL image - please resize your pytorch tensor to be divisible by {self.config.vae_scale_factor}'
  										    f'currently the sizes are {height} and {width}. You can also pass a PIL image instead to use resize option in VAEImageProcessor'			)
  
          # expected range [0,1], normalize to [-1,1]
  				SCREAMING_SNAKE_CASE_			=	self.config.do_normalize
  				if image.min() < 0:
  							warnings.warn(
  							    'Passing `image` as torch tensor with value range in [-1,1] is deprecated. The expected value range for image tensor is [0,1] '
  							    f'when passing as pytorch tensor or numpy Array. You passed `image` with value range [{image.min()},{image.max()}]'			,    SCREAMING_SNAKE_CASE			,    )
  							SCREAMING_SNAKE_CASE_			=	False
  
  				if do_normalize:
  							SCREAMING_SNAKE_CASE_			=	self.normalize(SCREAMING_SNAKE_CASE			)
  
  				return image
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Any:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
  							raise ValueError(
  							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
  				if output_type not in ["latent", "pt", "np", "pil"]:
  							SCREAMING_SNAKE_CASE_			=	(
  							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
  							    '`pil`, `np`, `pt`, `latent`'
  							)
  							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	'np'
  
  				if output_type == "latent":
  							return image
  
  				if do_denormalize is None:
  							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]
  
  				SCREAMING_SNAKE_CASE_			=	torch.stack(
  				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
  				if output_type == "pt":
  							return image
  
  				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)
  
  				if output_type == "np":
  							return image
  
  				if output_type == "pil":
  							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			)
  
  
  
  
  class 			a_					(    SCREAMING_SNAKE_CASE__    ):
  	A						       =   CONFIG_NAME
  
  
  
  
  
  	@register_to_config
  	def __init__(       self			,    SCREAMING_SNAKE_CASE = True			,    SCREAMING_SNAKE_CASE = 8			,    SCREAMING_SNAKE_CASE = "lanczos"			,    SCREAMING_SNAKE_CASE = True			,    )     ->				List[str]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				super().__init__()
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Tuple:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[None, ...]
  				SCREAMING_SNAKE_CASE_			=	(images * 255).round().astype('uint8'			)
  				if images.shape[-1] == 1:
  							# special case for grayscale (single channel) images
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image.squeeze()			,    mode='L'			) for image in images]
  				else:
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(image[:, :, :3]			) for image in images]
  
  				return pil_images
  
  
  
  
  
  	@staticmethod
  	def 	A_(       SCREAMING_SNAKE_CASE			)     ->				Optional[Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				return image[:, :, 1] * 2**8 + image[:, :, 2]
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			)     ->				Optional[int]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if images.ndim == 3:
  							SCREAMING_SNAKE_CASE_			=	images[None, ...]
  				SCREAMING_SNAKE_CASE_			=	images[:, :, :, 3:]
  				if images.shape[-1] == 6:
  							SCREAMING_SNAKE_CASE_			=	(images_depth * 255).round().astype('uint8'			)
  							SCREAMING_SNAKE_CASE_			=	[
  							    Image.fromarray(self.rgblike_to_depthmap(SCREAMING_SNAKE_CASE			)			,    mode='I;16'			) for image_depth in images_depth
  							]
  				elif images.shape[-1] == 4:
  							SCREAMING_SNAKE_CASE_			=	(images_depth * 6_5_5_3_5.0).astype(np.uintaa			)
  							SCREAMING_SNAKE_CASE_			=	[Image.fromarray(SCREAMING_SNAKE_CASE			,    mode='I;16'			) for image_depth in images_depth]
  				else:
  							raise Exception('Not supported'			)
  
  				return pil_images
  
  
  
  
  
  	def 	A_(       self			,    SCREAMING_SNAKE_CASE			,    SCREAMING_SNAKE_CASE = "pil"			,    SCREAMING_SNAKE_CASE = None			,    )     ->				Union[str, Any]:
  
  
  
  
  				"""simple docstring"""
  
  
  
  				if not isinstance(SCREAMING_SNAKE_CASE			,    torch.Tensor			):
  							raise ValueError(
  							    f'Input for postprocessing is in incorrect format: {type(SCREAMING_SNAKE_CASE			)}. We only support pytorch tensor'			)
  				if output_type not in ["latent", "pt", "np", "pil"]:
  							SCREAMING_SNAKE_CASE_			=	(
  							    f'the output_type {output_type} is outdated and has been set to `np`. Please make sure to set it to one of these instead: '
  							    '`pil`, `np`, `pt`, `latent`'
  							)
  							deprecate('Unsupported output_type'			,    '1.0.0'			,    SCREAMING_SNAKE_CASE			,    standard_warn=SCREAMING_SNAKE_CASE			)
  							SCREAMING_SNAKE_CASE_			=	'np'
  
  				if do_denormalize is None:
  							SCREAMING_SNAKE_CASE_			=	[self.config.do_normalize] * image.shape[0]
  
  				SCREAMING_SNAKE_CASE_			=	torch.stack(
  				    [self.denormalize(image[i]			) if do_denormalize[i] else image[i] for i in range(image.shape[0]			)]			)
  
  				SCREAMING_SNAKE_CASE_			=	self.pt_to_numpy(SCREAMING_SNAKE_CASE			)
  
  				if output_type == "np":
  							if image.shape[-1] == 6:
  										SCREAMING_SNAKE_CASE_			=	np.stack([self.rgblike_to_depthmap(im[:, :, 3:]			) for im in image]			,    axis=0			)
  							else:
  										SCREAMING_SNAKE_CASE_			=	image[:, :, :, 3:]
  							return image[:, :, :, :3], image_depth
  
  				if output_type == "pil":
  							return self.numpy_to_pil(SCREAMING_SNAKE_CASE			), self.numpy_to_depth(SCREAMING_SNAKE_CASE			)
  				else:
  							raise Exception(f'This type {output_type} is not supported'			)
  