



import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import LevitImageProcessor



class  __lowerCAmelCase     (	unittest.TestCase   ):




 def __init__(						self	,				__UpperCAmelCase	,				__UpperCAmelCase=7	,				__UpperCAmelCase=3	,				__UpperCAmelCase=18	,				__UpperCAmelCase=30	,				__UpperCAmelCase=400	,				__UpperCAmelCase=True	,				__UpperCAmelCase=None	,				__UpperCAmelCase=True	,				__UpperCAmelCase=None	,				__UpperCAmelCase=True	,				__UpperCAmelCase=[0.5, 0.5, 0.5]	,				__UpperCAmelCase=[0.5, 0.5, 0.5]	,				):



    '''simple docstring'''
    __lowerCamelCase													=    size if size is not None else {'''shortest_edge''': 18}
    __lowerCamelCase													=    crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
    __lowerCamelCase													=    parent
    __lowerCamelCase													=    batch_size
    __lowerCamelCase													=    num_channels
    __lowerCamelCase													=    image_size
    __lowerCamelCase													=    min_resolution
    __lowerCamelCase													=    max_resolution
    __lowerCamelCase													=    do_resize
    __lowerCamelCase													=    size
    __lowerCamelCase													=    do_center_crop
    __lowerCamelCase													=    crop_size
    __lowerCamelCase													=    do_normalize
    __lowerCamelCase													=    image_mean
    __lowerCamelCase													=    image_std




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return {
        "image_mean": self.image_mean,
        "image_std": self.image_std,
        "do_normalize": self.do_normalize,
        "do_resize": self.do_resize,
        "do_center_crop": self.do_center_crop,
        "size": self.size,
        "crop_size": self.crop_size,
    }



@require_torch
@require_vision
class  __lowerCAmelCase     (	lowerCAmelCase__  ,	unittest.TestCase   ):
 lowerCAmelCase__       					=  LevitImageProcessor if is_vision_available() else None




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    LevitImageProcessingTester(self				)




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self.image_processor_tester.prepare_image_processor_dict()




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.image_processing_class(**self.image_processor_dict				)
    self.assertTrue(hasattr(__UpperCAmelCase	,				'''image_mean'''				)				)
    self.assertTrue(hasattr(__UpperCAmelCase	,				'''image_std'''				)				)
    self.assertTrue(hasattr(__UpperCAmelCase	,				'''do_normalize'''				)				)
    self.assertTrue(hasattr(__UpperCAmelCase	,				'''do_resize'''				)				)
    self.assertTrue(hasattr(__UpperCAmelCase	,				'''do_center_crop'''				)				)
    self.assertTrue(hasattr(__UpperCAmelCase	,				'''size'''				)				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.image_processing_class.from_dict(self.image_processor_dict				)
    self.assertEqual(image_processor.size	,				{'''shortest_edge''': 18}				)
    self.assertEqual(image_processor.crop_size	,				{'''height''': 18, '''width''': 18}				)

    __lowerCamelCase													=    self.image_processing_class.from_dict(self.image_processor_dict	,				size=42	,				crop_size=84				)
    self.assertEqual(image_processor.size	,				{'''shortest_edge''': 42}				)
    self.assertEqual(image_processor.crop_size	,				{'''height''': 84, '''width''': 84}				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    pass




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.image_processing_class(**self.image_processor_dict				)
    # create random PIL images
    __lowerCamelCase													=    prepare_image_inputs(self.image_processor_tester	,				equal_resolution=__UpperCAmelCase				)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase	,				Image.Image				)

    # Test not batched input
    __lowerCamelCase													=    image_processing(image_inputs[0]	,				return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape	,				(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )	,				)

    # Test batched
    __lowerCamelCase													=    image_processing(__UpperCAmelCase	,				return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape	,				(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )	,				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.image_processing_class(**self.image_processor_dict				)
    # create random numpy tensors
    __lowerCamelCase													=    prepare_image_inputs(self.image_processor_tester	,				equal_resolution=__UpperCAmelCase	,				numpify=__UpperCAmelCase				)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase	,				np.ndarray				)

    # Test not batched input
    __lowerCamelCase													=    image_processing(image_inputs[0]	,				return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape	,				(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )	,				)

    # Test batched
    __lowerCamelCase													=    image_processing(__UpperCAmelCase	,				return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape	,				(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )	,				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.image_processing_class(**self.image_processor_dict				)
    # create random PyTorch tensors
    __lowerCamelCase													=    prepare_image_inputs(self.image_processor_tester	,				equal_resolution=__UpperCAmelCase	,				torchify=__UpperCAmelCase				)
    for image in image_inputs:
       self.assertIsInstance(__UpperCAmelCase	,				torch.Tensor				)

    # Test not batched input
    __lowerCamelCase													=    image_processing(image_inputs[0]	,				return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape	,				(
            1,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )	,				)

    # Test batched
    __lowerCamelCase													=    image_processing(__UpperCAmelCase	,				return_tensors='''pt'''				).pixel_values
    self.assertEqual(
        encoded_images.shape	,				(
            self.image_processor_tester.batch_size,
            self.image_processor_tester.num_channels,
            self.image_processor_tester.crop_size['''height'''],
            self.image_processor_tester.crop_size['''width'''],
        )	,				)











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}")






