



import torch

from diffusers import CMStochasticIterativeScheduler

from .test_schedulers import SchedulerCommonTest



class  __lowerCAmelCase     (	lowerCAmelCase__   ):
 lowerCAmelCase__       					=  (CMStochasticIterativeScheduler,)
 lowerCAmelCase__       					=  1_0




 def        lowerCamelCase      (						self	,				**__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    {
        '''num_train_timesteps''': 201,
        '''sigma_min''': 0.002,
        '''sigma_max''': 80.0,
    }

    config.update(**__UpperCAmelCase				)
    return config




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    10

    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    self.scheduler_classes[0](**__UpperCAmelCase				)

    scheduler.set_timesteps(__UpperCAmelCase				)

    __lowerCamelCase													=    scheduler.timesteps[0]
    __lowerCamelCase													=    scheduler.timesteps[1]

    __lowerCamelCase													=    self.dummy_sample
    __lowerCamelCase													=    0.1 * sample

    __lowerCamelCase													=    scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				).prev_sample
    __lowerCamelCase													=    scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				).prev_sample

    self.assertEqual(output_a.shape	,				sample.shape				)
    self.assertEqual(output_a.shape	,				output_a.shape				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for timesteps in [10, 50, 100, 1000]:
       self.check_over_configs(num_train_timesteps=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    for clip_denoised in [True, False]:
       self.check_over_configs(clip_denoised=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    1
    scheduler.set_timesteps(__UpperCAmelCase				)
    __lowerCamelCase													=    scheduler.timesteps

    __lowerCamelCase													=    torch.manual_seed(0				)

    __lowerCamelCase													=    self.dummy_model()
    __lowerCamelCase													=    self.dummy_sample_deter * scheduler.init_noise_sigma

    for i, t in enumerate(__UpperCAmelCase				):
       # 1. scale model input
       __lowerCamelCase													=    scheduler.scale_model_input(__UpperCAmelCase	,				__UpperCAmelCase				)

       # 2. predict noise residual
       __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase				)

       # 3. predict previous sample x_t-1
       __lowerCamelCase													=    scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				generator=__UpperCAmelCase				).prev_sample

       __lowerCamelCase													=    pred_prev_sample

    __lowerCamelCase													=    torch.sum(torch.abs(__UpperCAmelCase				)				)
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_sum.item() - 192.7_614				) < 1E-2
    assert abs(result_mean.item() - 0.2_510				) < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [106, 0]
    scheduler.set_timesteps(timesteps=__UpperCAmelCase				)
    __lowerCamelCase													=    scheduler.timesteps

    __lowerCamelCase													=    torch.manual_seed(0				)

    __lowerCamelCase													=    self.dummy_model()
    __lowerCamelCase													=    self.dummy_sample_deter * scheduler.init_noise_sigma

    for t in timesteps:
       # 1. scale model input
       __lowerCamelCase													=    scheduler.scale_model_input(__UpperCAmelCase	,				__UpperCAmelCase				)

       # 2. predict noise residual
       __lowerCamelCase													=    model(__UpperCAmelCase	,				__UpperCAmelCase				)

       # 3. predict previous sample x_t-1
       __lowerCamelCase													=    scheduler.step(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				generator=__UpperCAmelCase				).prev_sample

       __lowerCamelCase													=    pred_prev_sample

    __lowerCamelCase													=    torch.sum(torch.abs(__UpperCAmelCase				)				)
    __lowerCamelCase													=    torch.mean(torch.abs(__UpperCAmelCase				)				)

    assert abs(result_sum.item() - 347.6_357				) < 1E-2
    assert abs(result_mean.item() - 0.4_527				) < 1E-3




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [39, 30, 12, 15, 0]

    with self.assertRaises(__UpperCAmelCase	,				msg='''`timesteps` must be in descending order.'''				):
       scheduler.set_timesteps(timesteps=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [39, 30, 12, 1, 0]
    __lowerCamelCase													=    len(__UpperCAmelCase				)

    with self.assertRaises(__UpperCAmelCase	,				msg='''Can only pass one of `num_inference_steps` or `timesteps`.'''				):
       scheduler.set_timesteps(num_inference_steps=__UpperCAmelCase	,				timesteps=__UpperCAmelCase				)




 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    __lowerCamelCase													=    self.scheduler_classes[0]
    __lowerCamelCase													=    self.get_scheduler_config()
    __lowerCamelCase													=    scheduler_class(**__UpperCAmelCase				)

    __lowerCamelCase													=    [scheduler.config.num_train_timesteps]

    with self.assertRaises(
        __UpperCAmelCase	,				msg='''`timesteps` must start before `self.config.train_timesteps`: {scheduler.config.num_train_timesteps}}'''	,				):
       scheduler.set_timesteps(timesteps=__UpperCAmelCase				)











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}")






