





import argparse
import dataclasses
import json
import logging
import os
import shutil
from typing import List, Optional

import datasets
from accelerate import Accelerator
from datasets import load_dataset
from finetuning import finetune
from tqdm.auto import tqdm

import transformers
from transformers import AutoConfig, set_seed
from transformers.trainer_utils import IntervalStrategy


lowerCAmelCase							:       List[Any]					 =	logging.getLogger(__name__)

lowerCAmelCase							:       Union[str, Any]					 =	"""pytorch_model.bin"""





@dataclasses.dataclass
class 				a			:
				SCREAMING_SNAKE_CASE__     :   str		      =     dataclasses.field(
				    metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models.'''}			)
				SCREAMING_SNAKE_CASE__     :   Optional[str]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''Where do you want to store the pretrained models downloaded from huggingface.co.'''}   ,)





@dataclasses.dataclass
class 				a			:
				SCREAMING_SNAKE_CASE__     :   str		      =     dataclasses.field(metadata={'''help''': '''A csv or a json file containing the training data.'''}			)
				SCREAMING_SNAKE_CASE__     :   str		      =     dataclasses.field(metadata={'''help''': '''A csv or a json file containing the data to predict on.'''}			)
				SCREAMING_SNAKE_CASE__     :   Optional[str]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''A csv or a json file containing the validation data.'''}			)
				SCREAMING_SNAKE_CASE__     :   Optional[str]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''The name of the task to train on.'''}   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[List[str]]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''The list of labels for the task.'''}			)





@dataclasses.dataclass
class 				a			:
				SCREAMING_SNAKE_CASE__     :   str		      =     dataclasses.field(
				    metadata={'''help''': '''The output directory where the model predictions and checkpoints will be written.'''}			)
				SCREAMING_SNAKE_CASE__     :   Optional[str]		      =     dataclasses.field(
				    default='''accuracy'''   ,metadata={'''help''': '''The evaluation metric used for the task.'''}			)
				SCREAMING_SNAKE_CASE__     :   Optional[str]		      =     dataclasses.field(
				    default='''no'''   ,metadata={
				        '''help''': '''The evaluation strategy to adopt during training. Possible values are: ["no", "step", "epoch]'''
				    }   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     dataclasses.field(
				    default=10   ,metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[float]		      =     dataclasses.field(
				    default=0.0   ,metadata={
				        '''help''': '''How much the specified evaluation metric must improve to satisfy early stopping conditions.'''
				    }   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[bool]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the confidence score.'''}   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[bool]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''Whether to filter the pseudo-labeled data based on the validation performance.'''}   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[bool]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''Whether to fine-tune on labeled data after pseudo training.'''}   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[float]		      =     dataclasses.field(
				    default=0.0   ,metadata={'''help''': '''Confidence threshold for pseudo-labeled data filtering.'''}   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     dataclasses.field(
				    default=100   ,metadata={'''help''': '''Number of evaluation calls with no improvement after which training will be stopped.'''}   ,)
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     dataclasses.field(
				    default=__lowercase   ,metadata={'''help''': '''Random seed for initialization.'''}   ,)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   List[str]       ,			UpperCamelCase__   :   List[Any]       ,			UpperCamelCase__   :   Dict       ,			UpperCamelCase__   :   List[str]					)      ->    Optional[Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   datasets.concatenate_datasets([infer_input, infer_output]       ,			axis=1					)

						if args.do_filter_by_confidence:
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   dataset.filter(lambda UpperCamelCase__					: example["probability"] > args.confidence_threshold					)

						if args.do_filter_by_val_performance:
												assert eval_result >= 0.0 and eval_result <= 1.0
												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   int(eval_result * len(UpperCamelCase__					)					)
												print(UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				List[Any]           =   dataset.sort('''probability'''       ,			reverse=UpperCamelCase__					)
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   dataset.select(range(UpperCamelCase__					)					)

						__SCREAMING_SNAKE_CASE:				List[Any]           =   dataset.remove_columns(['''label''', '''probability''']					)
						__SCREAMING_SNAKE_CASE:				Any           =   dataset.rename_column('''prediction'''       ,			'''label'''					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   dataset.map(lambda UpperCamelCase__					: {"label": idalabel[example["label"]]}					)
						__SCREAMING_SNAKE_CASE:				Any           =   dataset.shuffle(seed=args.seed					)

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   os.path.join(UpperCamelCase__       ,			F"""train_pseudo.{args.data_file_extension}"""					)
						if args.data_file_extension == "csv":
												dataset.to_csv(UpperCamelCase__       ,			index=UpperCamelCase__					)
						else:
												dataset.to_json(UpperCamelCase__					)






def 		lowerCAmelCase  (	UpperCamelCase__   :   Tuple       ,			UpperCamelCase__   :   Union[str, Any]       ,			UpperCamelCase__   :   Optional[int]       ,			UpperCamelCase__   :   int       ,			**UpperCamelCase__   :   List[Any]					)      ->    Union[str, Any]:

						"""simple docstring"""





						__SCREAMING_SNAKE_CASE:				Optional[int]           =   Accelerator()
						# Make one log on every process with the configuration for debugging.
						logging.basicConfig(
						    format='''%(asctime)s - %(levelname)s - %(name)s - %(message)s'''       ,			datefmt='''%m/%d/%Y %H:%M:%S'''       ,			level=logging.INFO       ,			)
						logger.info(accelerator.state					)

						# Setup logging, we only want one process per machine to log things on the
						# screen. accelerator.is_local_main_process is only True for one process per
						# machine.
						logger.setLevel(logging.INFO if accelerator.is_local_main_process else logging.ERROR					)

						if accelerator.is_local_main_process:
												datasets.utils.logging.set_verbosity_warning()
												transformers.utils.logging.set_verbosity_info()
						else:
												datasets.utils.logging.set_verbosity_error()
												transformers.utils.logging.set_verbosity_error()

						__SCREAMING_SNAKE_CASE:				List[str]           =   STModelArguments(model_name_or_path=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Dict           =   STDataArguments(train_file=UpperCamelCase__       ,			infer_file=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Any           =   STTrainingArguments(output_dir=UpperCamelCase__					)
						__SCREAMING_SNAKE_CASE:				Tuple           =   argparse.Namespace()

						for arg_class in (model_args, data_args, training_args):
												for key, value in vars(UpperCamelCase__					).items():
																		setattr(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)

						for key, value in kwargs.items():
												if hasattr(UpperCamelCase__       ,			UpperCamelCase__					):
																		setattr(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)

    # Sanity checks
						__SCREAMING_SNAKE_CASE:				List[Any]           =   {}
						__SCREAMING_SNAKE_CASE:				Dict           =   None

						# You need to provide the training data and the data to predict on
						assert args.train_file is not None
						assert args.infer_file is not None
						__SCREAMING_SNAKE_CASE:				Any           =   args.train_file
						__SCREAMING_SNAKE_CASE:				List[str]           =   args.infer_file

						if args.evaluation_strategy != IntervalStrategy.NO.value:
												assert args.eval_file is not None
												__SCREAMING_SNAKE_CASE:				int           =   args.eval_file

						for key in data_files:
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   data_files[key].split('''.'''					)[-1]
												assert extension in ["csv", "json"], F"""`{key}_file` should be a csv or a json file."""
												if args.data_file_extension is None:
																		__SCREAMING_SNAKE_CASE:				Tuple           =   extension
												else:
																		assert extension == args.data_file_extension, F"""`{key}_file` should be a {args.data_file_extension} file`."""

						assert (
						    args.eval_metric in datasets.list_metrics()
						), F"""{args.eval_metric} not in the list of supported metrics {datasets.list_metrics()}."""

						# If passed along, set the training seed now.
						if args.seed is not None:
												set_seed(args.seed					)

						logger.info('''Creating the initial data directory for self-training...'''					)
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   F"""{args.output_dir}/self-train_iter-{{}}""".format
						__SCREAMING_SNAKE_CASE:				Optional[Any]           =   data_dir_format(0					)

						if accelerator.is_main_process:
												if args.output_dir is not None:
																		os.makedirs(args.output_dir       ,			exist_ok=UpperCamelCase__					)
																		os.makedirs(UpperCamelCase__       ,			exist_ok=UpperCamelCase__					)
						accelerator.wait_for_everyone()

						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   None
						__SCREAMING_SNAKE_CASE:				Any           =   None
						__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   0
						__SCREAMING_SNAKE_CASE:				List[Any]           =   False
						# Show the progress bar
						__SCREAMING_SNAKE_CASE:				str           =   tqdm(range(args.max_selftrain_iterations					)       ,			disable=not accelerator.is_local_main_process					)

						# Self-train
						for iteration in range(0       ,			int(args.max_selftrain_iterations					)					):
												__SCREAMING_SNAKE_CASE:				List[str]           =   data_dir_format(UpperCamelCase__					)
												assert os.path.exists(UpperCamelCase__					)

												# Stage 1: initial fine-tuning for iteration = 0 or pseudo-training for
												# iteration > 0
												__SCREAMING_SNAKE_CASE:				List[str]           =   os.path.join(UpperCamelCase__       ,			'''stage-1'''					)
												__SCREAMING_SNAKE_CASE:				Any           =   {
												    '''accelerator''': accelerator,
												    '''model_name_or_path''': args.model_name_or_path,
												    '''cache_dir''': args.cache_dir,
												    '''do_train''': True,
												    '''train_file''': data_files['''train'''] if iteration == 0 else data_files['''train_pseudo'''],
												    '''do_eval''': True if args.eval_file is not None else False,
												    '''eval_file''': data_files['''eval'''],
												    '''do_predict''': True,
												    '''infer_file''': data_files['''infer'''],
												    '''task_name''': args.task_name,
												    '''label_list''': args.label_list,
												    '''output_dir''': current_output_dir,
												    '''eval_metric''': args.eval_metric,
												    '''evaluation_strategy''': args.evaluation_strategy,
												    '''early_stopping_patience''': args.early_stopping_patience,
												    '''early_stopping_threshold''': args.early_stopping_threshold,
												    '''seed''': args.seed,
												}
												# Add additional training arguments
												for key, value in kwargs.items():
																		if key not in arguments_dict and not hasattr(UpperCamelCase__       ,			UpperCamelCase__					):
																								arguments_dict.update({key: value}					)

												__SCREAMING_SNAKE_CASE:				List[str]           =   os.path.join(UpperCamelCase__       ,			'''best-checkpoint'''       ,			UpperCamelCase__					)
												if os.path.exists(UpperCamelCase__					):
																		logger.info(
																		    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 1.'''       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			)
												else:
																		logger.info('''***** Running self-training: iteration: %d, stage: 1 *****'''       ,			UpperCamelCase__					)
																		finetune(**UpperCamelCase__					)
																		accelerator.wait_for_everyone()
																		assert os.path.exists(UpperCamelCase__					)
																		logger.info('''Self-training job completed: iteration: %d, stage: 1.'''       ,			UpperCamelCase__					)

												if iteration > 0 and args.finetune_on_labeled_data:
																		# Stage 2 (optional): fine-tuning on the original labeled data
																		__SCREAMING_SNAKE_CASE:				List[Any]           =   os.path.join(UpperCamelCase__       ,			'''best-checkpoint'''					)
																		__SCREAMING_SNAKE_CASE:				List[str]           =   os.path.join(UpperCamelCase__       ,			'''stage-2'''					)
																		# Update arguments_dict
																		__SCREAMING_SNAKE_CASE:				str           =   model_path
																		__SCREAMING_SNAKE_CASE:				int           =   data_files['''train''']
																		__SCREAMING_SNAKE_CASE:				Dict           =   current_output_dir

																		__SCREAMING_SNAKE_CASE:				int           =   os.path.join(UpperCamelCase__       ,			'''best-checkpoint'''       ,			UpperCamelCase__					)
																		if os.path.exists(UpperCamelCase__					):
																								logger.info(
																								    '''Found existing model checkpoint at %s. Skipping self-training: iteration: %d, stage: 2.'''       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			)
																		else:
																								logger.info('''***** Running self-training: iteration: %d, stage: 2 *****'''       ,			UpperCamelCase__					)
																								finetune(**UpperCamelCase__					)
																								accelerator.wait_for_everyone()
																								assert os.path.exists(UpperCamelCase__					)
																								logger.info('''Self-training job completed: iteration: %d, stage: 2.'''       ,			UpperCamelCase__					)

												__SCREAMING_SNAKE_CASE:				int           =   iteration
												__SCREAMING_SNAKE_CASE:				Optional[Any]           =   data_dir_format(iteration + 1					)

												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   AutoConfig.from_pretrained(os.path.join(UpperCamelCase__       ,			'''best-checkpoint'''					)					)
												__SCREAMING_SNAKE_CASE:				List[Any]           =   config.idalabel
												__SCREAMING_SNAKE_CASE:				Any           =   os.path.join(UpperCamelCase__       ,			'''eval_results_best-checkpoint.json'''					)
												__SCREAMING_SNAKE_CASE:				Dict           =   os.path.join(UpperCamelCase__       ,			'''test_results_best-checkpoint.json'''					)
												assert os.path.exists(UpperCamelCase__					)

												with open(UpperCamelCase__       ,			'''r'''					) as f:
																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   float(json.load(UpperCamelCase__					)[args.eval_metric]					)
												__SCREAMING_SNAKE_CASE:				List[Any]           =   os.path.join(UpperCamelCase__       ,			'''infer_output_best-checkpoint.csv'''					)
												assert os.path.exists(UpperCamelCase__					)
												# Loading the dataset from local csv or json files.
												__SCREAMING_SNAKE_CASE:				Any           =   load_dataset(args.data_file_extension       ,			data_files={'''data''': data_files['''infer''']}					)['''data''']
												__SCREAMING_SNAKE_CASE:				int           =   load_dataset('''csv'''       ,			data_files={'''data''': infer_output_file}					)['''data''']

												if accelerator.is_main_process:
																		os.makedirs(UpperCamelCase__       ,			exist_ok=UpperCamelCase__					)
																		shutil.copy(UpperCamelCase__       ,			os.path.join(UpperCamelCase__       ,			F"""eval_results_iter-{iteration}.json"""					)					)
																		if os.path.exists(UpperCamelCase__					):
																								shutil.copy(UpperCamelCase__       ,			os.path.join(UpperCamelCase__       ,			F"""test_results_iter-{iteration}.json"""					)					)
																		create_pseudo_labeled_data(UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__       ,			UpperCamelCase__					)
												accelerator.wait_for_everyone()

												__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   os.path.join(UpperCamelCase__       ,			F"""train_pseudo.{args.data_file_extension}"""					)

												if args.evaluation_strategy != IntervalStrategy.NO.value:
																		__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   eval_result

																		if best_iteration is None:
																								__SCREAMING_SNAKE_CASE:				Any           =   new_iteration
																								__SCREAMING_SNAKE_CASE:				Optional[int]           =   new_eval_result
																		else:
																								if new_eval_result - best_eval_result > args.early_stopping_threshold:
																														__SCREAMING_SNAKE_CASE:				Tuple           =   new_iteration
																														__SCREAMING_SNAKE_CASE:				Any           =   new_eval_result
																														__SCREAMING_SNAKE_CASE:				List[Any]           =   0
																								else:
																														if new_eval_result == best_eval_result:
																																				__SCREAMING_SNAKE_CASE:				List[Any]           =   new_iteration
																																				__SCREAMING_SNAKE_CASE:				Any           =   new_eval_result
																														early_stopping_patience_counter += 1

																								if early_stopping_patience_counter >= args.early_stopping_patience:
																														__SCREAMING_SNAKE_CASE:				Optional[int]           =   True

												progress_bar.update(1					)

												if should_training_stop:
																		break

						if best_iteration is not None:
												# Save the best iteration
												logger.info('''Best iteration: %d'''       ,			UpperCamelCase__					)
												logger.info('''Best evaluation result: %s = %f'''       ,			args.eval_metric       ,			UpperCamelCase__					)
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(UpperCamelCase__       ,			F"""eval_results_iter-{iteration}.json"""					)       ,			os.path.join(UpperCamelCase__       ,			'''eval_results_best-iteration.json'''					)       ,			)
						else:
												# Assume that the last iteration is the best
												logger.info('''Best iteration: %d'''       ,			args.max_selftrain_iterations - 1					)
												logger.info('''Best evaluation result: %s = %f'''       ,			args.eval_metric       ,			UpperCamelCase__					)
												accelerator.wait_for_everyone()
												if accelerator.is_main_process:
																		shutil.copy(
																		    os.path.join(UpperCamelCase__       ,			F"""eval_results_iter-{args.max_selftrain_iterations - 1}.json"""					)       ,			os.path.join(UpperCamelCase__       ,			'''eval_results_best-iteration.json'''					)       ,			)










import gc
import unittest

import numpy as np
import torch
from torch.backends.cuda import sdp_kernel

from diffusers import (
    CMStochasticIterativeScheduler,
    ConsistencyModelPipeline,
    UNetaDModel,
)
from diffusers.utils import randn_tensor, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_a, require_torch_gpu

from ..pipeline_params import UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS, UNCONDITIONAL_IMAGE_GENERATION_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()





class 				a			(      __lowercase   ,unittest.TestCase			):
				SCREAMING_SNAKE_CASE__     :   List[Any]		      =     ConsistencyModelPipeline
				SCREAMING_SNAKE_CASE__     :   Any		      =     UNCONDITIONAL_IMAGE_GENERATION_PARAMS
				SCREAMING_SNAKE_CASE__     :   str		      =     UNCONDITIONAL_IMAGE_GENERATION_BATCH_PARAMS

				# Override required_optional_params to remove num_images_per_prompt
				SCREAMING_SNAKE_CASE__     :   Optional[int]		      =     frozenset(
				    [
				        '''num_inference_steps''',
				        '''generator''',
				        '''latents''',
				        '''output_type''',
				        '''return_dict''',
				        '''callback''',
				        '''callback_steps''',
				    ]			)






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   UNetaDModel.from_pretrained(
										    '''diffusers/consistency-models-test'''			, subfolder='''test_unet'''			, )
										return unet






				@property
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   UNetaDModel.from_pretrained(
										    '''diffusers/consistency-models-test'''			, subfolder='''test_unet_class_cond'''			, )
										return unet






				def 			snake_case_    (  self			, _lowerCAmelCase=False					):

										"""simple docstring"""




										if class_cond:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   self.dummy_cond_unet
										else:
																__SCREAMING_SNAKE_CASE:				Tuple           =   self.dummy_uncond_unet

										# Default to CM multistep sampler
										__SCREAMING_SNAKE_CASE:				Tuple           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )

										__SCREAMING_SNAKE_CASE:				List[str]           =   {
										    '''unet''': unet,
										    '''scheduler''': scheduler,
										}

										return components






				def 			snake_case_    (  self			, _lowerCAmelCase			, _lowerCAmelCase=0					):

										"""simple docstring"""




										if str(_lowerCAmelCase					).startswith('''mps'''					):
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.manual_seed(_lowerCAmelCase					)
										else:
																__SCREAMING_SNAKE_CASE:				List[str]           =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   {
										    '''batch_size''': 1,
										    '''num_inference_steps''': None,
										    '''timesteps''': [22, 0],
										    '''generator''': generator,
										    '''output_type''': '''np''',
										}

										return inputs






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dummy_components()
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				int           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Dict           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				str           =   self.get_dummy_components(class_cond=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Dict           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   0
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array([0.3572, 0.6273, 0.4031, 0.3961, 0.4321, 0.5730, 0.5266, 0.4780, 0.5004]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[int]           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   self.get_dummy_components()
										__SCREAMING_SNAKE_CASE:				Tuple           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   1
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   None
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				Tuple           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				int           =   '''cpu'''  # ensure determinism for the device-dependent torch.Generator
										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   self.get_dummy_components(class_cond=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   ConsistencyModelPipeline(**_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				List[Any]           =   pipe.to(_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Tuple           =   self.get_dummy_inputs(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Any           =   1
										__SCREAMING_SNAKE_CASE:				List[Any]           =   None
										__SCREAMING_SNAKE_CASE:				Tuple           =   0
										__SCREAMING_SNAKE_CASE:				List[str]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 32, 32, 3)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   image[0, -3:, -3:, -1]
										__SCREAMING_SNAKE_CASE:				Any           =   np.array([0.5004, 0.5004, 0.4994, 0.5008, 0.4976, 0.5018, 0.4990, 0.4982, 0.4987]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3





@slow
@require_torch_gpu
class 				a			(      unittest.TestCase			):






				def 			snake_case_    (  self					):

										"""simple docstring"""




										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()






				def 			snake_case_    (  self			, _lowerCAmelCase=0			, _lowerCAmelCase=False			, _lowerCAmelCase="cpu"			, _lowerCAmelCase=torch.floataa			, _lowerCAmelCase=(1, 3, 64, 64)					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				str           =   torch.manual_seed(_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				Dict           =   {
										    '''num_inference_steps''': None,
										    '''timesteps''': [22, 0],
										    '''class_labels''': 0,
										    '''generator''': generator,
										    '''output_type''': '''np''',
										}

										if get_fixed_latents:
																__SCREAMING_SNAKE_CASE:				Dict           =   self.get_fixed_latents(seed=_lowerCAmelCase			, device=_lowerCAmelCase			, dtype=_lowerCAmelCase			, shape=_lowerCAmelCase					)
																__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   latents

										return inputs






				def 			snake_case_    (  self			, _lowerCAmelCase=0			, _lowerCAmelCase="cpu"			, _lowerCAmelCase=torch.floataa			, _lowerCAmelCase=(1, 3, 64, 64)					):

										"""simple docstring"""




										if type(_lowerCAmelCase					) == str:
																__SCREAMING_SNAKE_CASE:				List[Any]           =   torch.device(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Dict           =   torch.Generator(device=_lowerCAmelCase					).manual_seed(_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				int           =   randn_tensor(_lowerCAmelCase			, generator=_lowerCAmelCase			, device=_lowerCAmelCase			, dtype=_lowerCAmelCase					)
										return latents






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   self.get_inputs()
										__SCREAMING_SNAKE_CASE:				List[Any]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

										__SCREAMING_SNAKE_CASE:				Dict           =   image[0, -3:, -3:, -1]

										__SCREAMING_SNAKE_CASE:				Any           =   np.array([0.0888, 0.0881, 0.0666, 0.0479, 0.0292, 0.0195, 0.0201, 0.0163, 0.0254]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2






				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				List[Any]           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				str           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				List[str]           =   self.get_inputs()
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   1
										__SCREAMING_SNAKE_CASE:				List[Any]           =   None
										__SCREAMING_SNAKE_CASE:				Optional[int]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

										__SCREAMING_SNAKE_CASE:				Dict           =   image[0, -3:, -3:, -1]

										__SCREAMING_SNAKE_CASE:				Any           =   np.array([0.0340, 0.0152, 0.0063, 0.0267, 0.0221, 0.0107, 0.0416, 0.0186, 0.0217]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 2e-2






				@require_torch_a
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Tuple           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				List[Any]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase			, torch_dtype=torch.floataa					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   self.get_inputs(get_fixed_latents=_lowerCAmelCase			, device=_lowerCAmelCase					)
										# Ensure usage of flash attention in torch 2.0
										with sdp_kernel(enable_flash=_lowerCAmelCase			, enable_math=_lowerCAmelCase			, enable_mem_efficient=_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Dict           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

										__SCREAMING_SNAKE_CASE:				List[Any]           =   image[0, -3:, -3:, -1]

										__SCREAMING_SNAKE_CASE:				Optional[int]           =   np.array([0.1875, 0.1428, 0.1289, 0.2151, 0.2092, 0.1477, 0.1877, 0.1641, 0.1353]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3






				@require_torch_a
				def 			snake_case_    (  self					):

										"""simple docstring"""




										__SCREAMING_SNAKE_CASE:				Optional[Any]           =   UNetaDModel.from_pretrained('''diffusers/consistency_models'''			, subfolder='''diffusers_cd_imagenet64_l2'''					)
										__SCREAMING_SNAKE_CASE:				List[str]           =   CMStochasticIterativeScheduler(
										    num_train_timesteps=40			, sigma_min=0.002			, sigma_max=80.0			, )
										__SCREAMING_SNAKE_CASE:				List[Any]           =   ConsistencyModelPipeline(unet=_lowerCAmelCase			, scheduler=_lowerCAmelCase					)
										pipe.to(torch_device=_lowerCAmelCase			, torch_dtype=torch.floataa					)
										pipe.set_progress_bar_config(disable=_lowerCAmelCase					)

										__SCREAMING_SNAKE_CASE:				int           =   self.get_inputs(get_fixed_latents=_lowerCAmelCase			, device=_lowerCAmelCase					)
										__SCREAMING_SNAKE_CASE:				Union[str, Any]           =   1
										__SCREAMING_SNAKE_CASE:				List[Any]           =   None
										# Ensure usage of flash attention in torch 2.0
										with sdp_kernel(enable_flash=_lowerCAmelCase			, enable_math=_lowerCAmelCase			, enable_mem_efficient=_lowerCAmelCase					):
																__SCREAMING_SNAKE_CASE:				Optional[int]           =   pipe(**_lowerCAmelCase					).images
										assert image.shape == (1, 64, 64, 3)

										__SCREAMING_SNAKE_CASE:				Tuple           =   image[0, -3:, -3:, -1]

										__SCREAMING_SNAKE_CASE:				Tuple           =   np.array([0.1663, 0.1948, 0.2275, 0.1680, 0.1204, 0.1245, 0.1858, 0.1338, 0.2095]					)

										assert np.abs(image_slice.flatten() - expected_slice					).max() < 1e-3



