


'''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 _lowerCamelCase	(						_lowerCAmelCase							):



		'''simple docstring'''







		def __init__( self		:						List[Any]  ,					_A		:						str  ,					_A		:						int  ,					_A		:						Tuple=1024  ,					_A		:						Optional[int]=1024  ,					_A		:						Any=3.6      )  ->			str:
			__magic_name__	:   List[Any]					     =					tokenizer
			__magic_name__	:   Dict					     =					tokenizer.bos_token_id
			__magic_name__	:   Optional[Any]					     =					dataset
			__magic_name__	:   Union[str, Any]					     =					seq_length
			__magic_name__	:   Tuple					     =					seq_length * chars_per_token * num_of_sequences







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







def lowerCamelCase      (      lowerCAmelCase    :     Optional[int]		) ->    Tuple:





	"""simple docstring"""







	__magic_name__	:   Dict					     =					{"""streaming""": True}
	__magic_name__	:   Union[str, Any]					     =					load_dataset(args.dataset_name		,		split='train'		,		**lowerCAmelCase__		)
	__magic_name__	:   str					     =					ConstantLengthDataset(lowerCAmelCase__		,		lowerCAmelCase__		,		seq_length=args.seq_length		)
	__magic_name__	:   List[Any]					     =					DataLoader(lowerCAmelCase__		,		batch_size=args.batch_size		)
	return eval_dataloader





def lowerCamelCase      (      lowerCAmelCase    :     str		) ->    Dict:





	"""simple docstring"""







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

		if args.max_eval_steps > 0 and step >= args.max_eval_steps:
			break
	__magic_name__	:   str					     =					torch.mean(torch.cat(lowerCAmelCase__		)		)
	try:
		__magic_name__	:   Dict					     =					torch.exp(lowerCAmelCase__		)
	except OverflowError:
		__magic_name__	:   List[str]					     =					float('inf'		)
	return loss.item(), perplexity.item()


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

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

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

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

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

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





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



'''simple docstring'''

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowerCAmelCase				:Tuple        =      logging.get_logger(__name__)
class _lowerCamelCase	(						lowercase__							):



		'''simple docstring'''







		A_     :      int				   =    ["""pixel_values"""]







		def __init__( self		:						Any  ,					_A		:						bool = True  ,					_A		:						Optional[Dict[str, int]] = None  ,					_A		:						PILImageResampling = PILImageResampling.BILINEAR  ,					_A		:						bool = True  ,					_A		:						Dict[str, int] = None  ,					_A		:						bool = True  ,					_A		:						Union[int, float] = 1 / 255  ,					_A		:						bool = True  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					**_A		:						Optional[Any]  ,					)  ->			None:
			super().__init__(**_A      )
			__magic_name__	:   List[str]					     =					size if size is not None else {'shortest_edge': 256}
			__magic_name__	:   str					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   List[str]					     =					crop_size if crop_size is not None else {'height': 224, 'width': 224}
			__magic_name__	:   Optional[int]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_resize
			__magic_name__	:   List[Any]					     =					size
			__magic_name__	:   List[str]					     =					resample
			__magic_name__	:   Dict					     =					do_center_crop
			__magic_name__	:   List[str]					     =					crop_size
			__magic_name__	:   int					     =					do_rescale
			__magic_name__	:   Tuple					     =					rescale_factor
			__magic_name__	:   List[str]					     =					do_normalize
			__magic_name__	:   Union[str, Any]					     =					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
			__magic_name__	:   Dict					     =					image_std if image_std is not None else IMAGENET_STANDARD_STD







		def        __lowerCAmelCase		( self		:						Optional[Any]  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						PILImageResampling = PILImageResampling.BICUBIC  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						List[str]  ,					)  ->			np.ndarray:
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			if "shortest_edge" not in size:
				raise ValueError(F'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}'      )
			__magic_name__	:   Dict					     =					get_resize_output_image_size(_A  ,					size=size['shortest_edge']  ,					default_to_square=_A      )
			return resize(_A  ,					size=_A  ,					resample=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						Dict  ,					_A		:						np.ndarray  ,					_A		:						Dict[str, int]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Optional[int]  ,					)  ->			np.ndarray:
			__magic_name__	:   int					     =					get_size_dict(_A      )
			return center_crop(_A  ,					size=(size['height'], size['width'])  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						np.ndarray  ,					_A		:						float  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						Tuple      )  ->			np.ndarray:
			return rescale(_A  ,					scale=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						str  ,					_A		:						np.ndarray  ,					_A		:						Union[float, List[float]]  ,					_A		:						Union[float, List[float]]  ,					_A		:						Optional[Union[str, ChannelDimension]] = None  ,					**_A		:						int  ,					)  ->			np.ndarray:
			return normalize(_A  ,					mean=_A  ,					std=_A  ,					data_format=_A  ,					**_A      )







		def        __lowerCAmelCase		( self		:						List[str]  ,					_A		:						ImageInput  ,					_A		:						Optional[bool] = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						PILImageResampling = None  ,					_A		:						bool = None  ,					_A		:						Dict[str, int] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[float] = None  ,					_A		:						Optional[bool] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[float, List[float]]] = None  ,					_A		:						Optional[Union[str, TensorType]] = None  ,					_A		:						Union[str, ChannelDimension] = ChannelDimension.FIRST  ,					**_A		:						List[Any]  ,					)  ->			List[str]:
			__magic_name__	:   int					     =					do_resize if do_resize is not None else self.do_resize
			__magic_name__	:   Tuple					     =					size if size is not None else self.size
			__magic_name__	:   Optional[Any]					     =					get_size_dict(_A  ,					default_to_square=_A      )
			__magic_name__	:   Dict					     =					resample if resample is not None else self.resample
			__magic_name__	:   Optional[Any]					     =					do_center_crop if do_center_crop is not None else self.do_center_crop
			__magic_name__	:   Dict					     =					crop_size if crop_size is not None else self.crop_size
			__magic_name__	:   List[str]					     =					get_size_dict(_A      )
			__magic_name__	:   Union[str, Any]					     =					do_rescale if do_rescale is not None else self.do_rescale
			__magic_name__	:   str					     =					rescale_factor if rescale_factor is not None else self.rescale_factor
			__magic_name__	:   Any					     =					do_normalize if do_normalize is not None else self.do_normalize
			__magic_name__	:   Tuple					     =					image_mean if image_mean is not None else self.image_mean
			__magic_name__	:   Union[str, Any]					     =					image_std if image_std is not None else self.image_std

			__magic_name__	:   int					     =					make_list_of_images(_A      )

			if not valid_images(_A      ):
				raise ValueError(
				    'Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '
				    'torch.Tensor, tf.Tensor or jax.ndarray.'      )

			if do_resize and size is None:
				raise ValueError('Size must be specified if do_resize is True.'      )

			if do_center_crop and crop_size is None:
				raise ValueError('Crop size must be specified if do_center_crop is True.'      )

			if do_rescale and rescale_factor is None:
				raise ValueError('Rescale factor must be specified if do_rescale is True.'      )

			if do_normalize and (image_mean is None or image_std is None):
				raise ValueError('Image mean and std must be specified if do_normalize is True.'      )

			# All transformations expect numpy arrays.
			__magic_name__	:   List[Any]					     =					[to_numpy_array(_A      ) for image in images]

			if do_resize:
				__magic_name__	:   Union[str, Any]					     =					[self.resize(image=_A  ,					size=_A  ,					resample=_A      ) for image in images]

			if do_center_crop:
				__magic_name__	:   Union[str, Any]					     =					[self.center_crop(image=_A  ,					size=_A      ) for image in images]

			if do_rescale:
				__magic_name__	:   List[Any]					     =					[self.rescale(image=_A  ,					scale=_A      ) for image in images]

			if do_normalize:
				__magic_name__	:   Optional[Any]					     =					[self.normalize(image=_A  ,					mean=_A  ,					std=_A      ) for image in images]

			__magic_name__	:   Union[str, Any]					     =					[to_channel_dimension_format(_A  ,					_A      ) for image in images]

			__magic_name__	:   List[str]					     =					{'pixel_values': images}
			return BatchFeature(data=_A  ,					tensor_type=_A      )