



import math
from typing import Dict, Iterable, List, Optional, Tuple, Union

import numpy as np

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


if is_torch_available():
  import torch

if is_vision_available():
  import PIL


a_			=					logging.get_logger(__name__)





def 		a__			(		_UpperCamelCase			: np.ndarray ,_UpperCamelCase			: Union[int, Iterable[int]] ,_UpperCamelCase			: bool ,_UpperCamelCase			: int							):
   def constraint_to_multiple_of(_UpperCamelCase			: List[Any] ,_UpperCamelCase			: Optional[Any] ,_UpperCamelCase			: str=0 ,_UpperCamelCase			: List[Any]=None							):
      __lowerCamelCase													=    round(val / multiple							) * multiple

      if max_val is not None and x > max_val:
         __lowerCamelCase													=    math.floor(val / multiple							) * multiple

      if x < min_val:
         __lowerCamelCase													=    math.ceil(val / multiple							) * multiple

      return x

   __lowerCamelCase													=    (output_size, output_size) if isinstance(_UpperCamelCase ,_UpperCamelCase							) else output_size

   __lowerCamelCase							,__lowerCamelCase													=    get_image_size(_UpperCamelCase							)
   __lowerCamelCase							,__lowerCamelCase													=    output_size

   # determine new height and width
   __lowerCamelCase													=    output_height / input_height
   __lowerCamelCase													=    output_width / input_width

   if keep_aspect_ratio:
      # scale as little as possible
      if abs(1 - scale_width							) < abs(1 - scale_height							):
         # fit width
         __lowerCamelCase													=    scale_width
      else:
         # fit height
         __lowerCamelCase													=    scale_height

   __lowerCamelCase													=    constraint_to_multiple_of(scale_height * input_height ,multiple=_UpperCamelCase							)
   __lowerCamelCase													=    constraint_to_multiple_of(scale_width * input_width ,multiple=_UpperCamelCase							)

   return (new_height, new_width)





class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  ["""pixel_values"""]




 def __init__(						self	,				__UpperCAmelCase = True	,				__UpperCAmelCase = None	,				__UpperCAmelCase = PILImageResampling.BILINEAR	,				__UpperCAmelCase = False	,				__UpperCAmelCase = 1	,				__UpperCAmelCase = True	,				__UpperCAmelCase = 1 / 255	,				__UpperCAmelCase = True	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    super().__init__(**__UpperCAmelCase				)
    __lowerCamelCase													=    size if size is not None else {'''height''': 384, '''width''': 384}
    __lowerCamelCase													=    get_size_dict(__UpperCAmelCase				)
    __lowerCamelCase													=    do_resize
    __lowerCamelCase													=    size
    __lowerCamelCase													=    keep_aspect_ratio
    __lowerCamelCase													=    ensure_multiple_of
    __lowerCamelCase													=    resample
    __lowerCamelCase													=    do_rescale
    __lowerCamelCase													=    rescale_factor
    __lowerCamelCase													=    do_normalize
    __lowerCamelCase													=    image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
    __lowerCamelCase													=    image_std if image_std is not None else IMAGENET_STANDARD_STD




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = False	,				__UpperCAmelCase = 1	,				__UpperCAmelCase = PILImageResampling.BICUBIC	,				__UpperCAmelCase = None	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    get_size_dict(__UpperCAmelCase				)
    if "height" not in size or "width" not in size:
       raise ValueError(F"""The size dictionary must contain the keys 'height' and 'width'. Got {size.keys()}"""				)
    __lowerCamelCase													=    get_resize_output_image_size(
        __UpperCAmelCase	,				output_size=(size['''height'''], size['''width'''])	,				keep_aspect_ratio=__UpperCAmelCase	,				multiple=__UpperCAmelCase	,				)
    return resize(__UpperCAmelCase	,				size=__UpperCAmelCase	,				resample=__UpperCAmelCase	,				data_format=__UpperCAmelCase	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    return rescale(__UpperCAmelCase	,				scale=__UpperCAmelCase	,				data_format=__UpperCAmelCase	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    return normalize(__UpperCAmelCase	,				mean=__UpperCAmelCase	,				std=__UpperCAmelCase	,				data_format=__UpperCAmelCase	,				**__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = None	,				__UpperCAmelCase = ChannelDimension.FIRST	,				**__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    do_resize if do_resize is not None else self.do_resize
    __lowerCamelCase													=    size if size is not None else self.size
    __lowerCamelCase													=    get_size_dict(__UpperCAmelCase				)
    __lowerCamelCase													=    keep_aspect_ratio if keep_aspect_ratio is not None else self.keep_aspect_ratio
    __lowerCamelCase													=    ensure_multiple_of if ensure_multiple_of is not None else self.ensure_multiple_of
    __lowerCamelCase													=    resample if resample is not None else self.resample
    __lowerCamelCase													=    do_rescale if do_rescale is not None else self.do_rescale
    __lowerCamelCase													=    rescale_factor if rescale_factor is not None else self.rescale_factor
    __lowerCamelCase													=    do_normalize if do_normalize is not None else self.do_normalize
    __lowerCamelCase													=    image_mean if image_mean is not None else self.image_mean
    __lowerCamelCase													=    image_std if image_std is not None else self.image_std

    __lowerCamelCase													=    make_list_of_images(__UpperCAmelCase				)

    if not valid_images(__UpperCAmelCase				):
       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_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													=    [to_numpy_array(__UpperCAmelCase				) for image in images]

    if do_resize:
       __lowerCamelCase													=    [self.resize(image=__UpperCAmelCase	,				size=__UpperCAmelCase	,				resample=__UpperCAmelCase				) for image in images]

    if do_rescale:
       __lowerCamelCase													=    [self.rescale(image=__UpperCAmelCase	,				scale=__UpperCAmelCase				) for image in images]

    if do_normalize:
       __lowerCamelCase													=    [self.normalize(image=__UpperCAmelCase	,				mean=__UpperCAmelCase	,				std=__UpperCAmelCase				) for image in images]

    __lowerCamelCase													=    [to_channel_dimension_format(__UpperCAmelCase	,				__UpperCAmelCase				) for image in images]

    __lowerCamelCase													=    {'''pixel_values''': images}
    return BatchFeature(data=__UpperCAmelCase	,				tensor_type=__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase = None				):



    '''simple docstring'''
    __lowerCamelCase													=    outputs.logits

    # Resize logits and compute semantic segmentation maps
    if target_sizes is not None:
       if len(__UpperCAmelCase				) != len(__UpperCAmelCase				):
          raise ValueError(
              '''Make sure that you pass in as many target sizes as the batch dimension of the logits'''				)

       if is_torch_tensor(__UpperCAmelCase				):
          __lowerCamelCase													=    target_sizes.numpy()

       __lowerCamelCase													=    []

       for idx in range(len(__UpperCAmelCase				)				):
          __lowerCamelCase													=    torch.nn.functional.interpolate(
              logits[idx].unsqueeze(dim=0				)	,				size=target_sizes[idx]	,				mode='''bilinear'''	,				align_corners=__UpperCAmelCase				)
          __lowerCamelCase													=    resized_logits[0].argmax(dim=0				)
          semantic_segmentation.append(__UpperCAmelCase				)
    else:
       __lowerCamelCase													=    logits.argmax(dim=1				)
       __lowerCamelCase													=    [semantic_segmentation[i] for i in range(semantic_segmentation.shape[0]				)]

    return semantic_segmentation











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__   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=1024	,				__UpperCAmelCase=3.6				):



    '''simple docstring'''
    __lowerCamelCase													=    tokenizer
    __lowerCamelCase													=    tokenizer.bos_token_id
    __lowerCamelCase													=    dataset
    __lowerCamelCase													=    seq_length
    __lowerCamelCase													=    seq_length * chars_per_token * num_of_sequences




 def __iter__(						self				):



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





def 		a__			(		_UpperCamelCase			: List[Any]							):
   __lowerCamelCase													=    {'''streaming''': True}
   __lowerCamelCase													=    load_dataset(args.dataset_name ,split='''train''' ,**_UpperCamelCase							)
   __lowerCamelCase													=    ConstantLengthDataset(_UpperCamelCase ,_UpperCamelCase ,seq_length=args.seq_length							)
   __lowerCamelCase													=    DataLoader(_UpperCamelCase ,batch_size=args.batch_size							)
   return eval_dataloader





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

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


# Setup Accelerator
a_			=					Accelerator()

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

# Logging
a_			=					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_			=					AutoModelForCausalLM.from_pretrained(args.model_ckpt)
a_			=					AutoTokenizer.from_pretrained(args.model_ckpt)

# Load dataset and dataloader
a_			=					create_dataloader(args)

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

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





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






