
"""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, is_vision_available, logging


if is_vision_available():
				import PIL


__UpperCAmelCase       =  logging.get_logger(__name__)




class 			_SCREAMING_SNAKE_CASE					(			A__							):
			UpperCAmelCase_  :Dict				 =						["pixel_values"]






			def __init__(					self       ,   __A = True       ,   __A = None       ,   __A = None       ,   __A = PILImageResampling.BILINEAR       ,   __A = True       ,   __A = 1 / 255       ,   __A = True       ,   __A = None       ,   __A = None       ,   **__A       ,   )			->     None:
						super().__init__(**__A       )
						lowerCAmelCase_    :Any			     =  size if size is not None else {"""shortest_edge""": 384}
						lowerCAmelCase_    :List[Any]			     =  get_size_dict(__A       ,   default_to_square=__A       )

						lowerCAmelCase_    :List[str]			     =  do_resize
						lowerCAmelCase_    :int			     =  size
						# Default value set here for backwards compatibility where the value in config is None
						lowerCAmelCase_    :Dict			     =  crop_pct if crop_pct is not None else 224 / 256
						lowerCAmelCase_    :Tuple			     =  resample
						lowerCAmelCase_    :List[Any]			     =  do_rescale
						lowerCAmelCase_    :Optional[Any]			     =  rescale_factor
						lowerCAmelCase_    :int			     =  do_normalize
						lowerCAmelCase_    :Optional[int]			     =  image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
						lowerCAmelCase_    :Dict			     =  image_std if image_std is not None else IMAGENET_STANDARD_STD






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A = PILImageResampling.BICUBIC       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						lowerCAmelCase_    :List[Any]			     =  get_size_dict(__A       ,   default_to_square=__A       )
						if "shortest_edge" not in size:
									raise ValueError(f"""Size dictionary must contain 'shortest_edge' key. Got {size.keys()}"""       )
						lowerCAmelCase_    :Dict			     =  size["""shortest_edge"""]

						if shortest_edge < 384:
									# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
									lowerCAmelCase_    :List[Any]			     =  int(shortest_edge / crop_pct       )
									lowerCAmelCase_    :int			     =  get_resize_output_image_size(__A       ,   size=__A       ,   default_to_square=__A       )
									lowerCAmelCase_    :Tuple			     =  resize(image=__A       ,   size=__A       ,   resample=__A       ,   data_format=__A       ,   **__A       )
									# then crop to (shortest_edge, shortest_edge)
									return center_crop(image=__A       ,   size=(shortest_edge, shortest_edge)       ,   data_format=__A       ,   **__A       )
						else:
									# warping (no cropping) when evaluated at 384 or larger
									return resize(
									    __A       ,   size=(shortest_edge, shortest_edge)       ,   resample=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A = None       ,   **__A       ,   )			->     Optional[Any]:
						return rescale(__A       ,   scale=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A       ,   __A       ,   __A = None       ,   **__A       ,   )			->     np.ndarray:
						return normalize(__A       ,   mean=__A       ,   std=__A       ,   data_format=__A       ,   **__A       )






			def  __lowerCAmelCase (					self       ,   __A       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = None       ,   __A = ChannelDimension.FIRST       ,   **__A       ,   )			->     PIL.Image.Image:
						lowerCAmelCase_    :Any			     =  do_resize if do_resize is not None else self.do_resize
						lowerCAmelCase_    :Dict			     =  crop_pct if crop_pct is not None else self.crop_pct
						lowerCAmelCase_    :Optional[int]			     =  resample if resample is not None else self.resample
						lowerCAmelCase_    :Optional[Any]			     =  do_rescale if do_rescale is not None else self.do_rescale
						lowerCAmelCase_    :int			     =  rescale_factor if rescale_factor is not None else self.rescale_factor
						lowerCAmelCase_    :int			     =  do_normalize if do_normalize is not None else self.do_normalize
						lowerCAmelCase_    :Tuple			     =  image_mean if image_mean is not None else self.image_mean
						lowerCAmelCase_    :str			     =  image_std if image_std is not None else self.image_std

						lowerCAmelCase_    :str			     =  size if size is not None else self.size
						lowerCAmelCase_    :Any			     =  get_size_dict(__A       ,   default_to_square=__A       )

						lowerCAmelCase_    :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 or resample is None:
									raise ValueError("""Size and resample must be specified if do_resize is True."""       )

						if do_resize and size["shortest_edge"] < 384 and crop_pct is None:
									raise ValueError("""crop_pct must be specified if size < 384."""       )

						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.
						lowerCAmelCase_    :str			     =  [to_numpy_array(__A       ) for image in images]

						if do_resize:
									lowerCAmelCase_    :List[Any]			     =  [self.resize(image=__A       ,   size=__A       ,   crop_pct=__A       ,   resample=__A       ) for image in images]

						if do_rescale:
									lowerCAmelCase_    :Any			     =  [self.rescale(image=__A       ,   scale=__A       ) for image in images]

						if do_normalize:
									lowerCAmelCase_    :str			     =  [self.normalize(image=__A       ,   mean=__A       ,   std=__A       ) for image in images]

						lowerCAmelCase_    :Any			     =  [to_channel_dimension_format(__A       ,   __A       ) for image in images]

						lowerCAmelCase_    :Optional[Any]			     =  {"""pixel_values""": images}
						return BatchFeature(data=__A       ,   tensor_type=__A       )













'''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      snake_case     (    __lowerCamelCase       ):




 """simple docstring"""



 def __init__(       self       :							Any							,	__A       :							Dict							,	__A       :							str							,	__A       :							List[Any]=1_0_2_4							,	__A       :							Tuple=1_0_2_4							,	__A       :							str=3.6	):
        __UpperCamelCase       =  tokenizer
        __UpperCamelCase       =  tokenizer.bos_token_id
        __UpperCamelCase       =  dataset
        __UpperCamelCase       =  seq_length
        __UpperCamelCase       =  seq_length * chars_per_token * num_of_sequences






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






def 				lowercase__   (   __lowercase						:  Optional[Any]     ) ->      Union[str, Any]:



       """simple docstring"""






       __UpperCamelCase       =  {'streaming': True}
       __UpperCamelCase       =  load_dataset(args.dataset_name				,  split='train'				,  **__lowercase     )
       __UpperCamelCase       =  ConstantLengthDataset(__lowercase				,  __lowercase				,  seq_length=args.seq_length     )
       __UpperCamelCase       =  DataLoader(__lowercase				,  batch_size=args.batch_size     )
       return eval_dataloader
def 				lowercase__   (   __lowercase						:  Tuple     ) ->      Optional[Any]:



       """simple docstring"""






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

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


# Setup Accelerator
a__							:							int =Accelerator()

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

# Logging
a__							:							List[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
a__							:							Union[str, Any] =AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a__							:							List[Any] =AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a__							:							Union[str, Any] =create_dataloader(args)

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

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


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