


'''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()







'''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							)



