
import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''
	_lowerCAmelCase							       =				(UniPCMultistepScheduler,)
	_lowerCAmelCase							       =				(("num_inference_steps", 25),)





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					A__  :	Optional[int]         =							{
					    '''num_train_timesteps''': 1000,
					    '''beta_start''': 0.0_0_0_1,
					    '''beta_end''': 0.0_2,
					    '''beta_schedule''': '''linear''',
					    '''solver_order''': 2,
					    '''solver_type''': '''bh2''',
					}

					config.update(**UpperCamelCase__		)
					return config





	def   __snake_case				(			self				,       UpperCamelCase__=0				,       **UpperCamelCase__		):
					A__  :	List[Any]         =							dict(self.forward_default_kwargs		)
					A__  :	List[Any]         =							kwargs.pop('''num_inference_steps'''				,       UpperCamelCase__		)
					A__  :	Optional[int]         =							self.dummy_sample
					A__  :	Tuple         =							0.1 * sample
					A__  :	Dict         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									A__  :	int         =							self.get_scheduler_config(**UpperCamelCase__		)
									A__  :	Optional[int]         =							scheduler_class(**UpperCamelCase__		)
									scheduler.set_timesteps(UpperCamelCase__		)
									# copy over dummy past residuals
									A__  :	Dict         =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(UpperCamelCase__		)
													A__  :	Optional[int]         =							scheduler_class.from_pretrained(UpperCamelCase__		)
													new_scheduler.set_timesteps(UpperCamelCase__		)
													# copy over dummy past residuals
													A__  :	Dict         =							dummy_past_residuals[: new_scheduler.config.solver_order]

									A__       ,							A__  :	Union[str, Any]         =							sample, sample
									for t in range(UpperCamelCase__				,       time_step + scheduler.config.solver_order + 1		):
													A__  :	int         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample
													A__  :	Dict         =							new_scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample

													assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"





	def   __snake_case				(			self				,       UpperCamelCase__=0				,       **UpperCamelCase__		):
					A__  :	int         =							dict(self.forward_default_kwargs		)
					A__  :	List[Any]         =							kwargs.pop('''num_inference_steps'''				,       UpperCamelCase__		)
					A__  :	int         =							self.dummy_sample
					A__  :	Optional[int]         =							0.1 * sample
					A__  :	str         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]

					for scheduler_class in self.scheduler_classes:
									A__  :	Optional[int]         =							self.get_scheduler_config()
									A__  :	Union[str, Any]         =							scheduler_class(**UpperCamelCase__		)
									scheduler.set_timesteps(UpperCamelCase__		)

									# copy over dummy past residuals (must be after setting timesteps)
									A__  :	List[str]         =							dummy_past_residuals[: scheduler.config.solver_order]

									with tempfile.TemporaryDirectory() as tmpdirname:
													scheduler.save_config(UpperCamelCase__		)
													A__  :	Dict         =							scheduler_class.from_pretrained(UpperCamelCase__		)
													# copy over dummy past residuals
													new_scheduler.set_timesteps(UpperCamelCase__		)

													# copy over dummy past residual (must be after setting timesteps)
													A__  :	int         =							dummy_past_residuals[: new_scheduler.config.solver_order]

									A__  :	Optional[Any]         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample
									A__  :	Any         =							new_scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample

									assert torch.sum(torch.abs(output - new_output		)		) < 1e-5, "Scheduler outputs are not identical"





	def   __snake_case				(			self				,       UpperCamelCase__=None				,       **UpperCamelCase__		):
					if scheduler is None:
									A__  :	List[str]         =							self.scheduler_classes[0]
									A__  :	str         =							self.get_scheduler_config(**UpperCamelCase__		)
									A__  :	Dict         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Dict         =							self.scheduler_classes[0]
					A__  :	Tuple         =							self.get_scheduler_config(**UpperCamelCase__		)
					A__  :	List[str]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	Tuple         =							10
					A__  :	Tuple         =							self.dummy_model()
					A__  :	int         =							self.dummy_sample_deter
					scheduler.set_timesteps(UpperCamelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
									A__  :	Optional[Any]         =							model(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	Tuple         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		).prev_sample

					return sample





	def   __snake_case				(			self		):
					A__  :	int         =							dict(self.forward_default_kwargs		)

					A__  :	Dict         =							kwargs.pop('''num_inference_steps'''				,       UpperCamelCase__		)

					for scheduler_class in self.scheduler_classes:
									A__  :	Dict         =							self.get_scheduler_config()
									A__  :	List[Any]         =							scheduler_class(**UpperCamelCase__		)

									A__  :	List[str]         =							self.dummy_sample
									A__  :	Dict         =							0.1 * sample

									if num_inference_steps is not None and hasattr(UpperCamelCase__				,       '''set_timesteps'''		):
													scheduler.set_timesteps(UpperCamelCase__		)
									elif num_inference_steps is not None and not hasattr(UpperCamelCase__				,       '''set_timesteps'''		):
													A__  :	Any         =							num_inference_steps

									# copy over dummy past residuals (must be done after set_timesteps)
									A__  :	int         =							[residual + 0.2, residual + 0.1_5, residual + 0.1_0]
									A__  :	Tuple         =							dummy_past_residuals[: scheduler.config.solver_order]

									A__  :	Union[str, Any]         =							scheduler.timesteps[5]
									A__  :	Optional[int]         =							scheduler.timesteps[6]

									A__  :	Dict         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample
									A__  :	Tuple         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__				,       **UpperCamelCase__		).prev_sample

									self.assertEqual(output_a.shape				,       sample.shape		)
									self.assertEqual(output_a.shape				,       output_a.shape		)





	def   __snake_case				(			self		):
					# make sure that iterating over schedulers with same config names gives same results
					# for defaults
					A__  :	Optional[Any]         =							UniPCMultistepScheduler(**self.get_scheduler_config()		)
					A__  :	int         =							self.full_loop(scheduler=UpperCamelCase__		)
					A__  :	List[Any]         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.2_4_6_4		) < 1e-3

					A__  :	Union[str, Any]         =							DPMSolverSinglestepScheduler.from_config(scheduler.config		)
					A__  :	Any         =							DEISMultistepScheduler.from_config(scheduler.config		)
					A__  :	Union[str, Any]         =							DPMSolverMultistepScheduler.from_config(scheduler.config		)
					A__  :	str         =							UniPCMultistepScheduler.from_config(scheduler.config		)

					A__  :	Union[str, Any]         =							self.full_loop(scheduler=UpperCamelCase__		)
					A__  :	Union[str, Any]         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.2_4_6_4		) < 1e-3





	def   __snake_case				(			self		):
					for timesteps in [25, 50, 100, 999, 1000]:
									self.check_over_configs(num_train_timesteps=UpperCamelCase__		)





	def   __snake_case				(			self		):
					self.check_over_configs(thresholding=UpperCamelCase__		)
					for order in [1, 2, 3]:
									for solver_type in ["bh1", "bh2"]:
													for threshold in [0.5, 1.0, 2.0]:
																	for prediction_type in ["epsilon", "sample"]:
																					self.check_over_configs(
																					    thresholding=UpperCamelCase__				,       prediction_type=UpperCamelCase__				,       sample_max_value=UpperCamelCase__				,       solver_order=UpperCamelCase__				,       solver_type=UpperCamelCase__				,       )





	def   __snake_case				(			self		):
					for prediction_type in ["epsilon", "v_prediction"]:
									self.check_over_configs(prediction_type=UpperCamelCase__		)





	def   __snake_case				(			self		):
					for solver_type in ["bh1", "bh2"]:
									for order in [1, 2, 3]:
													for prediction_type in ["epsilon", "sample"]:
																	self.check_over_configs(
																	    solver_order=UpperCamelCase__				,       solver_type=UpperCamelCase__				,       prediction_type=UpperCamelCase__				,       )
																	A__  :	List[str]         =							self.full_loop(
																	    solver_order=UpperCamelCase__				,       solver_type=UpperCamelCase__				,       prediction_type=UpperCamelCase__				,       )
																	assert not torch.isnan(UpperCamelCase__		).any(), "Samples have nan numbers"





	def   __snake_case				(			self		):
					self.check_over_configs(lower_order_final=UpperCamelCase__		)
					self.check_over_configs(lower_order_final=UpperCamelCase__		)





	def   __snake_case				(			self		):
					for num_inference_steps in [1, 2, 3, 5, 10, 50, 100, 999, 1000]:
									self.check_over_forward(num_inference_steps=UpperCamelCase__				,       time_step=0		)





	def   __snake_case				(			self		):
					A__  :	Optional[int]         =							self.full_loop()
					A__  :	Optional[int]         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.2_4_6_4		) < 1e-3





	def   __snake_case				(			self		):
					A__  :	str         =							self.full_loop(prediction_type='''v_prediction'''		)
					A__  :	str         =							torch.mean(torch.abs(UpperCamelCase__		)		)

					assert abs(result_mean.item() - 0.1_0_1_4		) < 1e-3





	def   __snake_case				(			self		):
					A__  :	Tuple         =							self.scheduler_classes[0]
					A__  :	Any         =							self.get_scheduler_config(thresholding=UpperCamelCase__				,       dynamic_thresholding_ratio=0		)
					A__  :	Optional[Any]         =							scheduler_class(**UpperCamelCase__		)

					A__  :	int         =							10
					A__  :	str         =							self.dummy_model()
					A__  :	str         =							self.dummy_sample_deter.half()
					scheduler.set_timesteps(UpperCamelCase__		)

					for i, t in enumerate(scheduler.timesteps		):
									A__  :	List[Any]         =							model(UpperCamelCase__				,       UpperCamelCase__		)
									A__  :	int         =							scheduler.step(UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__		).prev_sample

					assert sample.dtype == torch.floataa





	def   __snake_case				(			self				,       **UpperCamelCase__		):
					for scheduler_class in self.scheduler_classes:
									A__  :	List[Any]         =							self.get_scheduler_config(**UpperCamelCase__		)
									A__  :	Optional[Any]         =							scheduler_class(**UpperCamelCase__		)

									scheduler.set_timesteps(scheduler.config.num_train_timesteps		)
									assert len(scheduler.timesteps.unique()		) == scheduler.num_inference_steps

import logging

import torch
from accelerate import Accelerator
from arguments import EvaluationArguments
from datasets import load_dataset
from torch.utils.data import IterableDataset
from torch.utils.data.dataloader import DataLoader

from transformers import AutoModelForCausalLM, AutoTokenizer, HfArgumentParser, set_seed







class  UpperCamelCase__		(    SCREAMING_SNAKE_CASE_   ):







	'''simple docstring'''





	def __init__(			self				,       UpperCamelCase__				,       UpperCamelCase__				,       UpperCamelCase__=1024				,       UpperCamelCase__=1024				,       UpperCamelCase__=3.6		):
					A__  :	str         =							tokenizer
					A__  :	int         =							tokenizer.bos_token_id
					A__  :	List[Any]         =							dataset
					A__  :	Tuple         =							seq_length
					A__  :	Any         =							seq_length * chars_per_token * num_of_sequences





	def __iter__(			self		):
					A__  :	Dict         =							iter(self.dataset		)
					A__  :	Tuple         =							True
					while more_examples:
									A__       ,							A__  :	Optional[Any]         =							[], 0
									while True:
													if buffer_len >= self.input_characters:
																	break
													try:
																	buffer.append(next(UpperCamelCase__		)['''content''']		)
																	buffer_len += len(buffer[-1]		)
													except StopIteration:
																	A__  :	Dict         =							False
																	break
									A__  :	str         =							tokenizer(UpperCamelCase__				,       truncation=UpperCamelCase__		)['''input_ids''']
									A__  :	Optional[int]         =							[]
									for tokenized_input in tokenized_inputs:
													all_token_ids.extend(tokenized_input + [self.concat_token_id]		)
									for i in range(0				,       len(UpperCamelCase__		)				,       self.seq_length		):
													A__  :	Optional[int]         =							all_token_ids[i : i + self.seq_length]
													if len(UpperCamelCase__		) == self.seq_length:
																	yield torch.tensor(UpperCamelCase__		)




def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[Any]       ) ->       Any:

				"""simple docstring"""







				A__  :	Any         =							{'''streaming''': True}
				A__  :	List[str]         =							load_dataset(args.dataset_name							,						split='''train'''							,						**__UpperCamelCase       )
				A__  :	List[str]         =							ConstantLengthDataset(__UpperCamelCase							,						__UpperCamelCase							,						seq_length=args.seq_length       )
				A__  :	int         =							DataLoader(__UpperCamelCase							,						batch_size=args.batch_size       )
				return eval_dataloader

def 				SCREAMING_SNAKE_CASE     (      __UpperCamelCase :						List[str]       ) ->       Dict:

				"""simple docstring"""







				model.eval()
				A__  :	Dict         =							[]
				for step, batch in enumerate(__UpperCamelCase       ):
								with torch.no_grad():
												A__  :	Any         =							model(__UpperCamelCase							,						labels=__UpperCamelCase       )
								A__  :	Tuple         =							outputs.loss.repeat(args.batch_size       )
								losses.append(accelerator.gather(__UpperCamelCase       )       )

								if args.max_eval_steps > 0 and step >= args.max_eval_steps:
												break
				A__  :	Tuple         =							torch.mean(torch.cat(__UpperCamelCase       )       )
				try:
								A__  :	Optional[Any]         =							torch.exp(__UpperCamelCase       )
				except OverflowError:
								A__  :	Union[str, Any]         =							float('''inf'''       )
				return loss.item(), perplexity.item()


# Setup Accelerator
_SCREAMING_SNAKE_CASE    :      List[Any]          =			Accelerator()

# Parse configuration
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			HfArgumentParser(EvaluationArguments)
_SCREAMING_SNAKE_CASE    :      Union[str, Any]          =			parser.parse_args()
set_seed(args.seed)

# Logging
_SCREAMING_SNAKE_CASE    :      Dict          =			logging.getLogger(__name__)
logging.basicConfig(
    format='%(asctime)s - %(levelname)s - %(name)s - %(message)s', datefmt='%m/%d/%Y %H:%M:%S', level=logging.INFO
)

# Load model and tokenizer
_SCREAMING_SNAKE_CASE    :      Optional[int]          =			AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_SCREAMING_SNAKE_CASE    :      List[str]          =			AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_SCREAMING_SNAKE_CASE    :      Optional[Any]          =			create_dataloader(args)

# Prepare everything with our `accelerator`.
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[Any]          =			accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info('Evaluating and saving model after training')
_SCREAMING_SNAKE_CASE   ,     _SCREAMING_SNAKE_CASE    :      Optional[int]          =			evaluate(args)



logger.info(f"""loss/eval: {eval_loss}, perplexity: {perplexity}""")