

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   __lowercase				( UpperCAmelCase_	):




			"""simple docstring"""







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




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







def 			A_      (    _UpperCAmelCase    ):
						SCREAMING_SNAKE_CASE_: Any  		=       {"streaming": True}
						SCREAMING_SNAKE_CASE_: Any  		=       load_dataset(args.dataset_name				,	split="train"				,	**_UpperCAmelCase    )
						SCREAMING_SNAKE_CASE_: int  		=       ConstantLengthDataset(_UpperCAmelCase				,	_UpperCAmelCase				,	seq_length=args.seq_length    )
						SCREAMING_SNAKE_CASE_: Tuple  		=       DataLoader(_UpperCAmelCase				,	batch_size=args.batch_size    )
						return eval_dataloader







def 			A_      (    _UpperCAmelCase    ):
						model.eval()
						SCREAMING_SNAKE_CASE_: Optional[Any]  		=       []
						for step, batch in enumerate(_UpperCAmelCase    ):
												with torch.no_grad():
																		SCREAMING_SNAKE_CASE_: Optional[int]  		=       model(_UpperCAmelCase				,	labels=_UpperCAmelCase    )
												SCREAMING_SNAKE_CASE_: int  		=       outputs.loss.repeat(args.batch_size    )
												losses.append(accelerator.gather(_UpperCAmelCase    )    )

												if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																		break
						SCREAMING_SNAKE_CASE_: Optional[int]  		=       torch.mean(torch.cat(_UpperCAmelCase    )    )
						try:
												SCREAMING_SNAKE_CASE_: Dict  		=       torch.exp(_UpperCAmelCase    )
						except OverflowError:
												SCREAMING_SNAKE_CASE_: Any  		=       float("inf"    )
						return loss.item(), perplexity.item()


# Setup Accelerator
lowerCAmelCase							:  Optional[Any]   	=						Accelerator()

# Parse configuration
lowerCAmelCase							:  List[str]   	=						HfArgumentParser(EvaluationArguments)
lowerCAmelCase							:  List[str]   	=						parser.parse_args()
set_seed(args.seed)

# Logging
lowerCAmelCase							:  List[str]   	=						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
lowerCAmelCase							:  Optional[Any]   	=						AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowerCAmelCase							:  Any   	=						AutoTokenizer.from_pretrained(args.model_ckpt)

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

# Prepare everything with our `accelerator`.
lowerCAmelCase	,			lowerCAmelCase							:  List[str]   	=						accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("""Evaluating and saving model after training""")
lowerCAmelCase	,			lowerCAmelCase							:  List[str]   	=						evaluate(args)



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










"""simple docstring"""
import gc
import unittest

import numpy as np
import torch

from diffusers import StableDiffusionKDiffusionPipeline
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu


enable_full_determinism()



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






		'''simple docstring'''

		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

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



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Tuple						=				StableDiffusionKDiffusionPipeline.from_pretrained('''CompVis/stable-diffusion-v1-4'''					)
			__snake_case	:						List[str]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						Tuple						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						str						=				torch.manual_seed(0					)
			__snake_case	:						int						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						List[str]						=				output.images

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

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Dict						=				np.array([0.0447, 0.0492, 0.0468, 0.0408, 0.0383, 0.0408, 0.0354, 0.0380, 0.0339]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Optional[Any]						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_euler'''					)

			__snake_case	:						str						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						List[str]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe([prompt]				,       generator=a_				,       guidance_scale=9.0				,       num_inference_steps=20				,       output_type='''np'''					)

			__snake_case	:						Any						=				output.images

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

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Tuple						=				np.array([0.1237, 0.1320, 0.1438, 0.1359, 0.1390, 0.1132, 0.1277, 0.1175, 0.1112]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 5E-1



		def 				SCREAMING_SNAKE_CASE  (self					):





			'''simple docstring'''

			__snake_case	:						Any						=				StableDiffusionKDiffusionPipeline.from_pretrained('''stabilityai/stable-diffusion-2-1-base'''					)
			__snake_case	:						Union[str, Any]						=				sd_pipe.to(a_					)
			sd_pipe.set_progress_bar_config(disable=a_					)

			sd_pipe.set_scheduler('''sample_dpmpp_2m'''					)

			__snake_case	:						int						=				'''A painting of a squirrel eating a burger'''
			__snake_case	:						Optional[Any]						=				torch.manual_seed(0					)
			__snake_case	:						Dict						=				sd_pipe(
			    [prompt]				,       generator=a_				,       guidance_scale=7.5				,       num_inference_steps=15				,       output_type='''np'''				,       use_karras_sigmas=a_				,       )

			__snake_case	:						Optional[int]						=				output.images

			__snake_case	:						Any						=				image[0, -3:, -3:, -1]

			assert image.shape == (1, 5_12, 5_12, 3)
			__snake_case	:						Any						=				np.array(
			    [0.1138_1689, 0.1211_2921, 0.138_9457, 0.1254_9606, 0.124_4964, 0.1083_1517, 0.1156_2866, 0.1086_7816, 0.1049_9048]					)

			assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2





