




'''simple docstring'''


import copy
import random

from transformers import CLIPTokenizer

class       A   (		_a   ):







							def __init__(  self	:  str							,      *lowerCAmelCase_	:  int							,      **lowerCAmelCase_	:  List[str] )      ->							List[Any]:


														"""simple docstring"""


														super().__init__(*lowerCAmelCase_							,      **lowerCAmelCase_ )
														_a     					=    {}







							def        __lowerCAmelCase      (  self	:  Any							,      lowerCAmelCase_	:  Tuple							,      *lowerCAmelCase_	:  Any							,      **lowerCAmelCase_	:  Tuple )      ->							str:


														"""simple docstring"""


														_a     					=    super().add_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
														if num_added_tokens == 0:
																					raise ValueError(
																					    F'The tokenizer already contains the token {placeholder_token}. Please pass a different'
																					    ''' `placeholder_token` that is not already in the tokenizer.''' )







							def        __lowerCAmelCase      (  self	:  Optional[Any]							,      lowerCAmelCase_	:  Dict							,      *lowerCAmelCase_	:  Tuple							,      lowerCAmelCase_	:  Tuple=1							,      **lowerCAmelCase_	:  int )      ->							Any:


														"""simple docstring"""


														_a     					=    []
														if num_vec_per_token == 1:
																					self.try_adding_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
																					output.append(lowerCAmelCase_ )
														else:
																					_a     					=    []
																					for i in range(lowerCAmelCase_ ):
																												_a     					=    placeholder_token + F'_{i}'
																												self.try_adding_tokens(lowerCAmelCase_							,      *lowerCAmelCase_							,      **lowerCAmelCase_ )
																												output.append(lowerCAmelCase_ )
        # handle cases where there is a new placeholder token that contains the current placeholder token but is larger
														for token in self.token_map:
																					if token in placeholder_token:
																												raise ValueError(
																												    F'The tokenizer already has placeholder token {token} that can get confused with'
																												    F' {placeholder_token}keep placeholder tokens independent' )
														_a     					=    output







							def        __lowerCAmelCase      (  self	:  Dict							,      lowerCAmelCase_	:  List[Any]							,      lowerCAmelCase_	:  Tuple=False							,      lowerCAmelCase_	:  Optional[int]=1.0 )      ->							Tuple:


														"""simple docstring"""


														if isinstance(lowerCAmelCase_							,      lowerCAmelCase_ ):
																					_a     					=    []
																					for i in range(len(lowerCAmelCase_ ) ):
																												output.append(self.replace_placeholder_tokens_in_text(text[i]							,      vector_shuffle=lowerCAmelCase_ ) )
																					return output
														for placeholder_token in self.token_map:
																					if placeholder_token in text:
																												_a     					=    self.token_map[placeholder_token]
																												_a     					=    tokens[: 1 + int(len(lowerCAmelCase_ ) * prop_tokens_to_load )]
																												if vector_shuffle:
																																			_a     					=    copy.copy(lowerCAmelCase_ )
																																			random.shuffle(lowerCAmelCase_ )
																												_a     					=    text.replace(lowerCAmelCase_							,      ''' '''.join(lowerCAmelCase_ ) )
														return text







							def __call__(  self	:  List[str]							,      lowerCAmelCase_	:  str							,      *lowerCAmelCase_	:  int							,      lowerCAmelCase_	:  Optional[int]=False							,      lowerCAmelCase_	:  Union[str, Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Union[str, Any]:


														"""simple docstring"""


														return super().__call__(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )







							def        __lowerCAmelCase      (  self	:  List[str]							,      lowerCAmelCase_	:  int							,      *lowerCAmelCase_	:  str							,      lowerCAmelCase_	:  Any=False							,      lowerCAmelCase_	:  List[Any]=1.0							,      **lowerCAmelCase_	:  Union[str, Any] )      ->							Dict:


														"""simple docstring"""


														return super().encode(
														    self.replace_placeholder_tokens_in_text(
														        lowerCAmelCase_							,      vector_shuffle=lowerCAmelCase_							,      prop_tokens_to_load=lowerCAmelCase_ )							,      *lowerCAmelCase_							,      **lowerCAmelCase_							,      )







'''simple docstring'''


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       A   (		_a   ):







							def __init__(  self	:  Optional[int]							,      lowerCAmelCase_	:  Optional[Any]							,      lowerCAmelCase_	:  Union[str, Any]							,      lowerCAmelCase_	:  List[str]=10_24							,      lowerCAmelCase_	:  Optional[Any]=10_24							,      lowerCAmelCase_	:  Tuple=3.6 )      ->							List[Any]:


														"""simple docstring"""


														_a     					=    tokenizer
														_a     					=    tokenizer.bos_token_id
														_a     					=    dataset
														_a     					=    seq_length
														_a     					=    seq_length * chars_per_token * num_of_sequences







							def __iter__(  self	:  Any )      ->							int:


														"""simple docstring"""


														_a     					=    iter(self.dataset )
														_a     					=    True
														while more_examples:
																					_a					,							_a     					=    [], 0
																					while True:
																												if buffer_len >= self.input_characters:
																																			break
																												try:
																																			buffer.append(next(lowerCAmelCase_ )['''content'''] )
																																			buffer_len += len(buffer[-1] )
																												except StopIteration:
																																			_a     					=    False
																																			break
																					_a     					=    tokenizer(lowerCAmelCase_							,      truncation=lowerCAmelCase_ )['''input_ids''']
																					_a     					=    []
																					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 ):
																												_a     					=    all_token_ids[i : i + self.seq_length]
																												if len(lowerCAmelCase_ ) == self.seq_length:
																																			yield torch.tensor(lowerCAmelCase_ )



def 		snake_case_   (UpperCamelCase	:  int				):





							'''simple docstring'''


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





							'''simple docstring'''


							model.eval()
							_a     					=    []
							for step, batch in enumerate(UpperCamelCase				):
														with torch.no_grad():
																					_a     					=    model(UpperCamelCase			,						labels=UpperCamelCase				)
														_a     					=    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     					=    torch.mean(torch.cat(UpperCamelCase				)				)
							try:
														_a     					=    torch.exp(UpperCamelCase				)
							except OverflowError:
														_a     					=    float('''inf'''				)
							return loss.item(), perplexity.item()


# Setup Accelerator
_snake_case				:				List[str]       							=		Accelerator()

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

# Logging
_snake_case				:				Any       							=		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
_snake_case				:				Dict       							=		AutoModelForCausalLM.from_pretrained(args.model_ckpt)
_snake_case				:				Optional[Any]       							=		AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
_snake_case				:				List[str]       							=		create_dataloader(args)

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

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

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

