






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_      (			snake_case_				):



	'''simple docstring'''




	def __init__(       self       ,			a       ,			a       ,			a=10_24       ,			a=10_24       ,			a=3.6					)       ->    str:
								snake_case_				=							tokenizer
								snake_case_				=							tokenizer.bos_token_id
								snake_case_				=							dataset
								snake_case_				=							seq_length
								snake_case_				=							seq_length * chars_per_token * num_of_sequences






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






def 						__UpperCAmelCase			(     a_):
							snake_case_				=							{'streaming': True}
							snake_case_				=							load_dataset(args.dataset_name							,     split='train'							,     **a_)
							snake_case_				=							ConstantLengthDataset(a_							,     a_							,     seq_length=args.seq_length)
							snake_case_				=							DataLoader(a_							,     batch_size=args.batch_size)
							return eval_dataloader





def 						__UpperCAmelCase			(     a_):
							model.eval()
							snake_case_				=							[]
							for step, batch in enumerate(a_):
														with torch.no_grad():
																					snake_case_				=							model(a_							,     labels=a_)
														snake_case_				=							outputs.loss.repeat(args.batch_size)
														losses.append(accelerator.gather(a_))

														if args.max_eval_steps > 0 and step >= args.max_eval_steps:
																					break
							snake_case_				=							torch.mean(torch.cat(a_))
							try:
														snake_case_				=							torch.exp(a_)
							except OverflowError:
														snake_case_				=							float('inf')
							return loss.item(), perplexity.item()


# Setup Accelerator
lowercase  							=					Accelerator()

# Parse configuration
lowercase  							=					HfArgumentParser(EvaluationArguments)
lowercase  							=					parser.parse_args()
set_seed(args.seed)

# Logging
lowercase  							=					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
lowercase  							=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
lowercase  							=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
lowercase  							=					create_dataloader(args)

# Prepare everything with our `accelerator`.
lowercase							,       lowercase  							=					accelerator.prepare(model, eval_dataloader)

# Evaluate and save the last checkpoint
logger.info("Evaluating and saving model after training")
lowercase							,       lowercase  							=					evaluate(args)




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













from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType




class  UpperCamelCase_      (			snake_case_				):



	'''simple docstring'''




	lowerCAmelCase													= ['''image_processor''', '''tokenizer''']
	lowerCAmelCase													= '''BlipImageProcessor'''
	lowerCAmelCase													= ('''BertTokenizer''', '''BertTokenizerFast''')




	def __init__(       self       ,			a       ,			a					)       ->    Tuple:
								snake_case_				=							False
								super().__init__(a       ,			a					)
								snake_case_				=							self.image_processor




	def __call__(       self       ,			a = None       ,			a = None       ,			a = True       ,			a = False       ,			a = None       ,			a = None       ,			a = 0       ,			a = None       ,			a = None       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = False       ,			a = True       ,			a = None       ,			**a       ,			)       ->    BatchEncoding:
								if images is None and text is None:
															raise ValueError('You have to specify either images or text.'					)

								# Get only text
								if images is None:
															snake_case_				=							self.tokenizer
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
															return text_encoding

								# add pixel_values
								snake_case_				=							self.image_processor(a       ,			return_tensors=a					)

								if text is not None:
															snake_case_				=							self.tokenizer(
															    text=a       ,			add_special_tokens=a       ,			padding=a       ,			truncation=a       ,			max_length=a       ,			stride=a       ,			pad_to_multiple_of=a       ,			return_attention_mask=a       ,			return_overflowing_tokens=a       ,			return_special_tokens_mask=a       ,			return_offsets_mapping=a       ,			return_token_type_ids=a       ,			return_length=a       ,			verbose=a       ,			return_tensors=a       ,			**a       ,			)
								else:
															snake_case_				=							None

								if text_encoding is not None:
															encoding_image_processor.update(a					)

								return encoding_image_processor




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    int:
								return self.tokenizer.batch_decode(*a       ,			**a					)




	def 							_UpperCamelCase			(       self       ,			*a       ,			**a					)       ->    Any:
								return self.tokenizer.decode(*a       ,			**a					)






	@property
	def 							_UpperCamelCase			(       self					)       ->    List[str]:
								snake_case_				=							self.tokenizer.model_input_names
								snake_case_				=							self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)





