
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(    __lowerCamelCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=10_24	,      SCREAMING_SNAKE_CASE_=10_24	,      SCREAMING_SNAKE_CASE_=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						):
       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(SCREAMING_SNAKE_CASE_						)["""content"""]						)
                      buffer_len += len(buffer[-1]						)
                 except StopIteration:
                      UpperCamelCase__				=    False
                      break
            UpperCamelCase__				=    tokenizer(SCREAMING_SNAKE_CASE_	,      truncation=SCREAMING_SNAKE_CASE_						)["""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(SCREAMING_SNAKE_CASE_						)	,      self.seq_length						):
                 UpperCamelCase__				=    all_token_ids[i : i + self.seq_length]
                 if len(SCREAMING_SNAKE_CASE_						) == self.seq_length:
                      yield torch.tensor(SCREAMING_SNAKE_CASE_						)






def 						__magic_name__			(   __a      :	str						):



     '''simple docstring'''




     UpperCamelCase__				=    {"""streaming""": True}
     UpperCamelCase__				=    load_dataset(args.dataset_name	,		split="""train"""	,		**__a						)
     UpperCamelCase__				=    ConstantLengthDataset(__a	,		__a	,		seq_length=args.seq_length						)
     UpperCamelCase__				=    DataLoader(__a	,		batch_size=args.batch_size						)
     return eval_dataloader






def 						__magic_name__			(   __a      :	Tuple						):



     '''simple docstring'''




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

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


# Setup Accelerator
lowerCamelCase_						     =					Accelerator()

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

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

# Load dataset and dataloader
lowerCamelCase_						     =					create_dataloader(args)

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

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




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







import unittest

import numpy as np
import requests

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

    from transformers.pytorch_utils import is_torch_greater_or_equal_than_1_11
else:
    lowerCamelCase_						     =					False

if is_vision_available():
    from PIL import Image

    from transformers import PixaStructImageProcessor




class      __A(    unittest.TestCase			):

  """simple docstring"""







  def __init__(self	,      SCREAMING_SNAKE_CASE_	,      SCREAMING_SNAKE_CASE_=7	,      SCREAMING_SNAKE_CASE_=3	,      SCREAMING_SNAKE_CASE_=18	,      SCREAMING_SNAKE_CASE_=30	,      SCREAMING_SNAKE_CASE_=4_00	,      SCREAMING_SNAKE_CASE_=None	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=True	,      SCREAMING_SNAKE_CASE_=None	,      ):
       UpperCamelCase__				=    size if size is not None else {"""height""": 20, """width""": 20}
       UpperCamelCase__				=    parent
       UpperCamelCase__				=    batch_size
       UpperCamelCase__				=    num_channels
       UpperCamelCase__				=    image_size
       UpperCamelCase__				=    min_resolution
       UpperCamelCase__				=    max_resolution
       UpperCamelCase__				=    size
       UpperCamelCase__				=    do_normalize
       UpperCamelCase__				=    do_convert_rgb
       UpperCamelCase__				=    [5_12, 10_24, 20_48, 40_96]
       UpperCamelCase__				=    patch_size if patch_size is not None else {"""height""": 16, """width""": 16}







  def 	UpperCAmelCase_ (self						):
       return {"do_normalize": self.do_normalize, "do_convert_rgb": self.do_convert_rgb}







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    """https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/australia.jpg"""
       UpperCamelCase__				=    Image.open(requests.get(SCREAMING_SNAKE_CASE_	,      stream=SCREAMING_SNAKE_CASE_						).raw						).convert("""RGB"""						)
       return raw_image






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,				reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,				)
@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self						)







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processor_tester.prepare_dummy_image()

       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       UpperCamelCase__				=    20_48

       UpperCamelCase__				=    image_processor(SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						)
       self.assertTrue(torch.allclose(inputs.flattened_patches.mean()	,      torch.tensor(0.0606						)	,      atol=1E-3	,      rtol=1E-3						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       UpperCamelCase__				=    True

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            with self.assertRaises(SCREAMING_SNAKE_CASE_						):
                 UpperCamelCase__				=    image_processor(
                     image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches

            UpperCamelCase__				=    """Hello"""

            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_	,      header_text=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random numpy tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      numpify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      np.ndarray						)

       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PyTorch tensors
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_	,      torchify=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      torch.Tensor						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * self.image_processor_tester.num_channels
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )






@unittest.skipIf(
    not is_torch_greater_or_equal_than_1_11    ,				reason="""`Pix2StructImageProcessor` requires `torch>=1.11.0`."""    ,				)
@require_torch
@require_vision
class      __A(    __lowerCamelCase    ,				unittest.TestCase			):

  """simple docstring"""
  SCREAMING_SNAKE_CASE__         =      PixaStructImageProcessor if is_vision_available() else None







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    PixaStructImageProcessingTester(self	,      num_channels=4						)
       UpperCamelCase__				=    3







  @property
  def 	UpperCAmelCase_ (self						):
       return self.image_processor_tester.prepare_image_processor_dict()







  def 	UpperCAmelCase_ (self						):
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_normalize"""						)						)
       self.assertTrue(hasattr(SCREAMING_SNAKE_CASE_	,      """do_convert_rgb"""						)						)







  def 	UpperCAmelCase_ (self						):
       # Initialize image_processor
       UpperCamelCase__				=    self.image_processing_class(**self.image_processor_dict						)
       # create random PIL images
       UpperCamelCase__				=    prepare_image_inputs(self.image_processor_tester	,      equal_resolution=SCREAMING_SNAKE_CASE_						)
       for image in image_inputs:
            self.assertIsInstance(SCREAMING_SNAKE_CASE_	,      Image.Image						)

       # Test not batched input
       UpperCamelCase__				=    (
           (self.image_processor_tester.patch_size["""height"""] * self.image_processor_tester.patch_size["""width"""])
           * (self.image_processor_tester.num_channels - 1)
       ) + 2

       for max_patch in self.image_processor_tester.max_patches:
            # Test not batched input
            UpperCamelCase__				=    image_processor(
                image_inputs[0]	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (1, max_patch, expected_hidden_dim)	,      )

            # Test batched
            UpperCamelCase__				=    image_processor(
                SCREAMING_SNAKE_CASE_	,      return_tensors="""pt"""	,      max_patches=SCREAMING_SNAKE_CASE_						).flattened_patches
            self.assertEqual(
                encoded_images.shape	,      (self.image_processor_tester.batch_size, max_patch, expected_hidden_dim)	,      )





