'''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 							lowercase						(						__snake_case ):


   def __init__(   self				:	str   ,		__lowerCAmelCase				:	int   ,		__lowerCAmelCase				:	Union[str, Any]   ,		__lowerCAmelCase				:	List[Any]=1024   ,		__lowerCAmelCase				:	int=1024   ,		__lowerCAmelCase				:	List[str]=3.6)      ->					Any:
         lowercase_     =      tokenizer
         lowercase_     =      tokenizer.bos_token_id
         lowercase_     =      dataset
         lowercase_     =      seq_length
         lowercase_     =      seq_length * chars_per_token * num_of_sequences


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




def __a (	__lowerCamelCase			:						Union[str, Any]				)	->							Optional[int]:
      '''simple docstring'''


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


def __a (	__lowerCamelCase			:						Dict				)	->							Optional[Any]:
      '''simple docstring'''


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

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


# Setup Accelerator
lowerCAmelCase_ : Tuple						  =      Accelerator()

# Parse configuration
lowerCAmelCase_ : Tuple						  =      HfArgumentParser(EvaluationArguments)
lowerCAmelCase_ : List[str]						  =      parser.parse_args()
set_seed(args.seed)

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

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

# Prepare everything with our `accelerator`.
lowerCAmelCase_ : Any						  =      accelerator.prepare(model, eval_dataloader)

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






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







'''simple docstring'''

import gc
import unittest

from diffusers import FlaxControlNetModel, FlaxStableDiffusionControlNetPipeline
from diffusers.utils import is_flax_available, load_image, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
       import jax
       import jax.numpy as jnp
       from flax.jax_utils import replicate
       from flax.training.common_utils import shard





@slow
@require_flax
class 							lowercase						(						unittest.TestCase ):


   def __UpperCAmelCase       (   self				:	int)      ->					List[str]:
         # clean up the VRAM after each test
         super().tearDown()
         gc.collect()


   def __UpperCAmelCase       (   self				:	Dict)      ->					Any:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-canny"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "bird"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/bird_canny.png")
         lowercase_     =      pipe.prepare_image_inputs([canny_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [0.16_7969, 0.11_6699, 0.08_1543, 0.15_4297, 0.13_2812, 0.10_8887, 0.16_9922, 0.16_9922, 0.20_5078])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2


   def __UpperCAmelCase       (   self				:	Tuple)      ->					List[str]:
         lowercase_ ,							lowercase_     =      FlaxControlNetModel.from_pretrained(
             "lllyasviel/sd-controlnet-openpose"   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_ ,							lowercase_     =      FlaxStableDiffusionControlNetPipeline.from_pretrained(
             "runwayml/stable-diffusion-v1-5"   ,		controlnet=__lowerCAmelCase   ,		from_pt=__lowerCAmelCase   ,		dtype=jnp.bfloataa)
         lowercase_     =      controlnet_params

         lowercase_     =      "Chef in the kitchen"
         lowercase_     =      jax.device_count()
         lowercase_     =      pipe.prepare_text_inputs([prompts] * num_samples)

         lowercase_     =      load_image(
             "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/sd_controlnet/pose.png")
         lowercase_     =      pipe.prepare_image_inputs([pose_image] * num_samples)

         lowercase_     =      jax.random.PRNGKey(0)
         lowercase_     =      jax.random.split(__lowerCAmelCase   ,		jax.device_count())

         lowercase_     =      replicate(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)
         lowercase_     =      shard(__lowerCAmelCase)

         lowercase_     =      pipe(
             prompt_ids=__lowerCAmelCase   ,		image=__lowerCAmelCase   ,		params=__lowerCAmelCase   ,		prng_seed=__lowerCAmelCase   ,		num_inference_steps=50   ,		jit=__lowerCAmelCase   ,		).images
         assert images.shape == (jax.device_count(), 1, 768, 512, 3)

         lowercase_     =      images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:])
         lowercase_     =      images[0, 253:256, 253:256, -1]

         lowercase_     =      jnp.asarray(jax.device_get(image_slice.flatten()))
         lowercase_     =      jnp.array(
             [[0.27_1484, 0.26_1719, 0.27_5391, 0.27_7344, 0.27_9297, 0.29_1016, 0.29_4922, 0.30_2734, 0.30_2734]])
         print(F'output_slice: {output_slice}')
         assert jnp.abs(output_slice - expected_slice).max() < 1e-2






