code: 
  
  
  '''simple docstring'''
  
  
  
  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
  __lowercase			:		Dict =			{
      'translation': ['bleu'],
      'summarization': ROUGE_KEYS,
  }
  
  
  
  
  
  
  def 						lowerCamelCase							(_SCREAMING_SNAKE_CASE   :     Tuple     ):
  					__a    : Union[str, Any]      					=						search.split()
  					__a    : Tuple      					=						dict((g.split('='     ) for g in groups)     )
  					__a    : str      					=						list(entries.keys()     )
  					__a    : str      					=						[[F"""--{k} {v}""" for v in vs.split(':'     )] for k, vs in entries.items()]
  					__a    : Optional[Any]      					=						[list(_SCREAMING_SNAKE_CASE     ) for x in itertools.product(*_SCREAMING_SNAKE_CASE     )]
  					return matrix, entry_names
  
  
  
  
  
  
  def 						lowerCamelCase							():
  					__a    : List[str]      					=						sys.argv[0]
  
  					__a    : Tuple      					=						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.'
  					    )		,   )
  					__a					,       __a    : Optional[int]      					=						parser.parse_known_args()
  					# we share some of the args
  					args_main.extend(['--task', args.task]     )
  					__a    : List[Any]      					=						[prog] + args_main
  
  					# to support variations like translation_en_to_de"
  					__a    : Tuple      					=						'translation' if 'translation' in args.task else 'summarization'
  
  					__a					,       __a    : Dict      					=						parse_search_arg(args.search     )
  					__a    : Optional[Any]      					=						task_score_names[task]  # score cols first
  					__a    : Any      					=						{col: len(str(_SCREAMING_SNAKE_CASE     )     ) for col in col_names}
  					__a    : Tuple      					=						[]
  					for r in matrix:
  										__a    : List[Any]      					=						dict((x.replace('--'		,   ''     ).split() for x in r)     )
  										__a    : Any      					=						' '.join(_SCREAMING_SNAKE_CASE     ).split()
  										args_exp.extend(['--bs', str(args.bs     )]     )  # in case we need to reduce its size due to CUDA OOM
  										__a    : List[str]      					=						args_normal + args_exp
  
  										# XXX: need to trap CUDA OOM and lower args.bs if that happens and retry
  
  										__a    : Dict      					=						run_generate(verbose=_SCREAMING_SNAKE_CASE     )
  										# make sure scores are first in the table
  										__a    : List[str]      					=						OrderedDict()
  										for score in task_score_names[task]:
  															__a    : Union[str, Any]      					=						scores[score]
  										result.update(_SCREAMING_SNAKE_CASE     )
  										results.append(_SCREAMING_SNAKE_CASE     )
  
  										# find widest entries
  										for k, v in result.items():
  															__a    : Optional[Any]      					=						len(str(_SCREAMING_SNAKE_CASE     )     )
  															if l > col_widths[k]:
  																				__a    : str      					=						l
  
  					__a    : Optional[Any]      					=						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]     )     )
  
  					__a    : Optional[Any]      					=						results_sorted[0]
  					for score in task_score_names[task]:
  										del best[score]
  					__a    : int      					=						[F"""--{k} {v}""" for k, v in best.items()]
  					__a    : int      					=						['--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: 
  
  
  '''simple docstring'''
  
  
  
  from typing import Callable, List, Optional, Union
  
  import torch
  
  from ...models import UNetaDModel
  from ...schedulers import CMStochasticIterativeScheduler
  from ...utils import (
      is_accelerate_available,
      is_accelerate_version,
      logging,
      randn_tensor,
      replace_example_docstring,
  )
  from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput
  
  
  __lowercase			:		List[str] =			logging.get_logger(__name__)  # pylint: disable=invalid-name
  
  
  __lowercase			:		Optional[int] =			'\n    Examples:\n        ```py\n        >>> import torch\n\n        >>> from diffusers import ConsistencyModelPipeline\n\n        >>> device = "cuda"\n        >>> # Load the cd_imagenet64_l2 checkpoint.\n        >>> model_id_or_path = "openai/diffusers-cd_imagenet64_l2"\n        >>> pipe = ConsistencyModelPipeline.from_pretrained(model_id_or_path, torch_dtype=torch.float16)\n        >>> pipe.to(device)\n\n        >>> # Onestep Sampling\n        >>> image = pipe(num_inference_steps=1).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample.png")\n\n        >>> # Onestep sampling, class-conditional image generation\n        >>> # ImageNet-64 class label 145 corresponds to king penguins\n        >>> image = pipe(num_inference_steps=1, class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_onestep_sample_penguin.png")\n\n        >>> # Multistep sampling, class-conditional image generation\n        >>> # Timesteps can be explicitly specified; the particular timesteps below are from the original Github repo:\n        >>> # https://github.com/openai/consistency_models/blob/main/scripts/launch.sh#L77\n        >>> image = pipe(num_inference_steps=None, timesteps=[22, 0], class_labels=145).images[0]\n        >>> image.save("cd_imagenet64_l2_multistep_sample_penguin.png")\n        ```\n'
  
  
  
  
  
  class 							__UpperCamelCase       (	lowerCAmelCase_      ):
  
  
  
  
  	def __init__(     self		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						super().__init__()
  
  						self.register_modules(
  						    unet=__a		,       scheduler=__a		,       )
  
  						__a    : List[str]      					=						None
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=0							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if is_accelerate_available() and is_accelerate_version('>='		,       '0.14.0'							):
  											from accelerate import cpu_offload
  						else:
  											raise ImportError('`enable_sequential_cpu_offload` requires `accelerate v0.14.0` or higher'							)
  
  						__a    : str      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						for cpu_offloaded_model in [self.unet]:
  											cpu_offload(__a		,       __a							)
  
  						if self.safety_checker is not None:
  											cpu_offload(self.safety_checker		,       execution_device=__a		,       offload_buffers=__a							)
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a=0							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if is_accelerate_available() and is_accelerate_version('>='		,       '0.17.0.dev0'							):
  											from accelerate import cpu_offload_with_hook
  						else:
  											raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'							)
  
  						__a    : Tuple      					=						torch.device(f"""cuda:{gpu_id}"""							)
  
  						if self.device.type != "cpu":
  											self.to('cpu'		,       silence_dtype_warnings=__a							)
  											torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)
  
  						__a    : List[Any]      					=						None
  						for cpu_offloaded_model in [self.unet]:
  											__a					,       __a    : List[str]      					=						cpu_offload_with_hook(__a		,       __a		,       prev_module_hook=__a							)
  
  						if self.safety_checker is not None:
  											__a					,       __a    : str      					=						cpu_offload_with_hook(self.safety_checker		,       __a		,       prev_module_hook=__a							)
  
  						# We'll offload the last model manually.
  						__a    : List[Any]      					=						hook
  
  
  
  
  	@property
  	# Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
  	def    __UpperCAmelCase							(     self							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if not hasattr(self.unet		,       '_hf_hook'							):
  											return self.device
  						for module in self.unet.modules():
  											if (
  											    hasattr(__a		,       '_hf_hook'							)
  											    and hasattr(module._hf_hook		,       'execution_device'							)
  											    and module._hf_hook.execution_device is not None
  											):
  																return torch.device(module._hf_hook.execution_device							)
  						return self.device
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : str      					=						(batch_size, num_channels, height, width)
  						if isinstance(__a		,       __a							) and len(__a							) != batch_size:
  											raise ValueError(
  											    f"""You have passed a list of generators of length {len(__a							)}, but requested an effective batch"""
  											    f""" size of {batch_size}. Make sure the batch size matches the length of the generators."""							)
  
  						if latents is None:
  											__a    : Optional[Any]      					=						randn_tensor(__a		,       generator=__a		,       device=__a		,       dtype=__a							)
  						else:
  											__a    : Dict      					=						latents.to(device=__a		,       dtype=__a							)
  
  						# scale the initial noise by the standard deviation required by the scheduler
  						__a    : Union[str, Any]      					=						latents * self.scheduler.init_noise_sigma
  						return latents
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a = "pil"							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if output_type not in ["pt", "np", "pil"]:
  											raise ValueError(
  											    f"""output_type={output_type} is not supported. Make sure to choose one of ['pt', 'np', or 'pil']"""							)
  
  						# Equivalent to diffusers.VaeImageProcessor.denormalize
  						__a    : List[Any]      					=						(sample / 2 + 0.5).clamp(0		,       1							)
  						if output_type == "pt":
  											return sample
  
  						# Equivalent to diffusers.VaeImageProcessor.pt_to_numpy
  						__a    : str      					=						sample.cpu().permute(0		,       2		,       3		,       1							).numpy()
  						if output_type == "np":
  											return sample
  
  						# Output_type must be 'pil'
  						__a    : Union[str, Any]      					=						self.numpy_to_pil(__a							)
  						return sample
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a=None							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if self.unet.config.num_class_embeds is not None:
  											if isinstance(__a		,       __a							):
  																__a    : Tuple      					=						torch.tensor(__a		,       dtype=torch.int							)
  											elif isinstance(__a		,       __a							):
  																assert batch_size == 1, "Batch size must be 1 if classes is an int"
  																__a    : int      					=						torch.tensor([class_labels]		,       dtype=torch.int							)
  											elif class_labels is None:
  																# Randomly generate batch_size class labels
  																# TODO: should use generator here? int analogue of randn_tensor is not exposed in ...utils
  																__a    : str      					=						torch.randint(0		,       self.unet.config.num_class_embeds		,       size=(batch_size,)							)
  											__a    : str      					=						class_labels.to(__a							)
  						else:
  											__a    : Optional[int]      					=						None
  						return class_labels
  
  
  
  
  	def    __UpperCAmelCase							(     self		,       __a		,       __a		,       __a		,       __a		,       __a		,       __a							):
  
  						'''simple docstring'''
  
  
  
  
  
  						if num_inference_steps is None and timesteps is None:
  											raise ValueError('Exactly one of `num_inference_steps` or `timesteps` must be supplied.'							)
  
  						if num_inference_steps is not None and timesteps is not None:
  											logger.warning(
  											    f"""Both `num_inference_steps`: {num_inference_steps} and `timesteps`: {timesteps} are supplied;"""
  											    ' `timesteps` will be used over `num_inference_steps`.'							)
  
  						if latents is not None:
  											__a    : Optional[Any]      					=						(batch_size, 3, img_size, img_size)
  											if latents.shape != expected_shape:
  																raise ValueError(f"""The shape of latents is {latents.shape} but is expected to be {expected_shape}."""							)
  
  						if (callback_steps is None) or (
  						    callback_steps is not None and (not isinstance(__a		,       __a							) or callback_steps <= 0)
  						):
  											raise ValueError(
  											    f"""`callback_steps` has to be a positive integer but is {callback_steps} of type"""
  											    f""" {type(__a							)}."""							)
  
  
  
  
  
  	@torch.no_grad()
  	@replace_example_docstring(__a							)
  	def __call__(     self		,       __a = 1		,       __a = None		,       __a = 1		,       __a = None		,       __a = None		,       __a = None		,       __a = "pil"		,       __a = True		,       __a = None		,       __a = 1		,       ):
  
  						'''simple docstring'''
  
  
  
  
  
  						__a    : Dict      					=						self.unet.config.sample_size
  						__a    : Union[str, Any]      					=						self._execution_device
  
  						# 1. Check inputs
  						self.check_inputs(__a		,       __a		,       __a		,       __a		,       __a		,       __a							)
  
  						# 2. Prepare image latents
  						# Sample image latents x_0 ~ N(0, sigma_0^2 * I)
  						__a    : Union[str, Any]      					=						self.prepare_latents(
  						    batch_size=__a		,       num_channels=self.unet.config.in_channels		,       height=__a		,       width=__a		,       dtype=self.unet.dtype		,       device=__a		,       generator=__a		,       latents=__a		,       )
  
  						# 3. Handle class_labels for class-conditional models
  						__a    : Optional[Any]      					=						self.prepare_class_labels(__a		,       __a		,       class_labels=__a							)
  
  						# 4. Prepare timesteps
  						if timesteps is not None:
  											self.scheduler.set_timesteps(timesteps=__a		,       device=__a							)
  											__a    : Union[str, Any]      					=						self.scheduler.timesteps
  											__a    : Tuple      					=						len(__a							)
  						else:
  											self.scheduler.set_timesteps(__a							)
  											__a    : Tuple      					=						self.scheduler.timesteps
  
  						# 5. Denoising loop
  						# Multistep sampling: implements Algorithm 1 in the paper
  						with self.progress_bar(total=__a							) as progress_bar:
  											for i, t in enumerate(__a							):
  																__a    : Optional[int]      					=						self.scheduler.scale_model_input(__a		,       __a							)
  																__a    : List[str]      					=						self.unet(__a		,       __a		,       class_labels=__a		,       return_dict=__a							)[0]
  
  																__a    : int      					=						self.scheduler.step(__a		,       __a		,       __a		,       generator=__a							)[0]
  
  																# call the callback, if provided
  																progress_bar.update()
  																if callback is not None and i % callback_steps == 0:
  																					callback(__a		,       __a		,       __a							)
  
          # 6. Post-process image sample
  						__a    : Tuple      					=						self.postprocess_image(__a		,       output_type=__a							)
  
  						# Offload last model to CPU
  						if hasattr(self		,       'final_offload_hook'							) and self.final_offload_hook is not None:
  											self.final_offload_hook.offload()
  
  						if not return_dict:
  											return (image,)
  
  						return ImagePipelineOutput(images=__a							)
  
  
  
  