



import math
from collections import defaultdict
from typing import List, Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput





def 		a__			(		_UpperCamelCase			: Tuple ,_UpperCamelCase			: Dict=0.999 ,_UpperCamelCase			: List[Any]="cosine" ,):







   if alpha_transform_type == "cosine":

      def alpha_bar_fn(_UpperCamelCase			: Dict							):
         return math.cos((t + 0.008) / 1.008 * math.pi / 2							) ** 2

   elif alpha_transform_type == "exp":

      def alpha_bar_fn(_UpperCamelCase			: Union[str, Any]							):
         return math.exp(t * -12.0							)

   else:
      raise ValueError(F"""Unsupported alpha_tranform_type: {alpha_transform_type}"""							)

   __lowerCamelCase													=    []
   for i in range(__UpperCAmelCase							):
      __lowerCamelCase													=    i / num_diffusion_timesteps
      __lowerCamelCase													=    (i + 1) / num_diffusion_timesteps
      betas.append(min(1 - alpha_bar_fn(__UpperCAmelCase							) / alpha_bar_fn(__UpperCAmelCase							) ,__UpperCAmelCase							)							)
   return torch.tensor(__UpperCAmelCase ,dtype=torch.floataa							)





class  __lowerCAmelCase     (	_snake_case  ,	_snake_case   ):
 lowerCAmelCase__       					=  [e.name for e in KarrasDiffusionSchedulers]
 lowerCAmelCase__       					=  2




 @register_to_config
 def __init__(						self	,				__UpperCAmelCase = 1000	,				__UpperCAmelCase = 0.00_085	,				__UpperCAmelCase = 0.012	,				__UpperCAmelCase = "linear"	,				__UpperCAmelCase = None	,				__UpperCAmelCase = "epsilon"	,				__UpperCAmelCase = False	,				__UpperCAmelCase = False	,				__UpperCAmelCase = 1.0	,				__UpperCAmelCase = "linspace"	,				__UpperCAmelCase = 0	,				):



    '''simple docstring'''

    if trained_betas is not None:
       __lowerCamelCase													=    torch.tensor(__UpperCAmelCase	,				dtype=torch.floataa				)
    elif beta_schedule == "linear":
       __lowerCamelCase													=    torch.linspace(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				dtype=torch.floataa				)
    elif beta_schedule == "scaled_linear":
       # this schedule is very specific to the latent diffusion model.
       __lowerCamelCase													=    (
           torch.linspace(beta_start**0.5	,				beta_end**0.5	,				__UpperCAmelCase	,				dtype=torch.floataa				) ** 2
       )
    elif beta_schedule == "squaredcos_cap_v2":
       # Glide cosine schedule
       __lowerCamelCase													=    betas_for_alpha_bar(__UpperCAmelCase	,				alpha_transform_type='''cosine'''				)
    elif beta_schedule == "exp":
       __lowerCamelCase													=    betas_for_alpha_bar(__UpperCAmelCase	,				alpha_transform_type='''exp'''				)
    else:
       raise NotImplementedError(F"""{beta_schedule} does is not implemented for {self.__class__}"""				)

    __lowerCamelCase													=    1.0 - self.betas
    __lowerCamelCase													=    torch.cumprod(self.alphas	,				dim=0				)

    #  set all values
    self.set_timesteps(__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase				)
    __lowerCamelCase													=    use_karras_sigmas




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



    '''simple docstring'''

    if schedule_timesteps is None:
       __lowerCamelCase													=    self.timesteps

    __lowerCamelCase													=    (schedule_timesteps == timestep).nonzero()

    # The sigma index that is taken for the **very** first `step`
    # is always the second index (or the last index if there is only 1)
    # This way we can ensure we don't accidentally skip a sigma in
    # case we start in the middle of the denoising schedule (e.g. for image-to-image)
    if len(self._index_counter				) == 0:
       __lowerCamelCase													=    1 if len(__UpperCAmelCase				) > 1 else 0
    else:
       __lowerCamelCase													=    timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase				) else timestep
       __lowerCamelCase													=    self._index_counter[timestep_int]

    return indices[pos].item()




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''

    if self.config.timestep_spacing in ["linspace", "trailing"]:
       return self.sigmas.max()

    return (self.sigmas.max() ** 2 + 1) ** 0.5




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.index_for_timestep(__UpperCAmelCase				)

    __lowerCamelCase													=    self.sigmas[step_index]
    __lowerCamelCase													=    sample / ((sigma**2 + 1) ** 0.5)
    return sample




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



    '''simple docstring'''
    __lowerCamelCase													=    num_inference_steps

    __lowerCamelCase													=    num_train_timesteps or self.config.num_train_timesteps

    # "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
    if self.config.timestep_spacing == "linspace":
       __lowerCamelCase													=    np.linspace(0	,				num_train_timesteps - 1	,				__UpperCAmelCase	,				dtype=__UpperCAmelCase				)[::-1].copy()
    elif self.config.timestep_spacing == "leading":
       __lowerCamelCase													=    num_train_timesteps // self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       __lowerCamelCase													=    (np.arange(0	,				__UpperCAmelCase				) * step_ratio).round()[::-1].copy().astype(__UpperCAmelCase				)
       timesteps += self.config.steps_offset
    elif self.config.timestep_spacing == "trailing":
       __lowerCamelCase													=    num_train_timesteps / self.num_inference_steps
       # creates integer timesteps by multiplying by ratio
       # casting to int to avoid issues when num_inference_step is power of 3
       __lowerCamelCase													=    (np.arange(__UpperCAmelCase	,				0	,				-step_ratio				)).round().copy().astype(__UpperCAmelCase				)
       timesteps -= 1
    else:
       raise ValueError(
           F"""{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."""				)

    __lowerCamelCase													=    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5				)
    __lowerCamelCase													=    np.log(__UpperCAmelCase				)
    __lowerCamelCase													=    np.interp(__UpperCAmelCase	,				np.arange(0	,				len(__UpperCAmelCase				)				)	,				__UpperCAmelCase				)

    if self.config.use_karras_sigmas:
       __lowerCamelCase													=    self._convert_to_karras(in_sigmas=__UpperCAmelCase	,				num_inference_steps=self.num_inference_steps				)
       __lowerCamelCase													=    np.array([self._sigma_to_t(__UpperCAmelCase	,				__UpperCAmelCase				) for sigma in sigmas]				)

    __lowerCamelCase													=    np.concatenate([sigmas, [0.0]]				).astype(np.floataa				)
    __lowerCamelCase													=    torch.from_numpy(__UpperCAmelCase				).to(device=__UpperCAmelCase				)
    __lowerCamelCase													=    torch.cat([sigmas[:1], sigmas[1:-1].repeat_interleave(2				), sigmas[-1:]]				)

    __lowerCamelCase													=    torch.from_numpy(__UpperCAmelCase				)
    __lowerCamelCase													=    torch.cat([timesteps[:1], timesteps[1:].repeat_interleave(2				)]				)

    if str(__UpperCAmelCase				).startswith('''mps'''				):
       # mps does not support float64
       __lowerCamelCase													=    timesteps.to(__UpperCAmelCase	,				dtype=torch.floataa				)
    else:
       __lowerCamelCase													=    timesteps.to(device=__UpperCAmelCase				)

    # empty dt and derivative
    __lowerCamelCase													=    None
    __lowerCamelCase													=    None

    # for exp beta schedules, such as the one for `pipeline_shap_e.py`
    # we need an index counter
    __lowerCamelCase													=    defaultdict(__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    np.log(__UpperCAmelCase				)

    # get distribution
    __lowerCamelCase													=    log_sigma - log_sigmas[:, np.newaxis]

    # get sigmas range
    __lowerCamelCase													=    np.cumsum((dists >= 0)	,				axis=0				).argmax(axis=0				).clip(max=log_sigmas.shape[0] - 2				)
    __lowerCamelCase													=    low_idx + 1

    __lowerCamelCase													=    log_sigmas[low_idx]
    __lowerCamelCase													=    log_sigmas[high_idx]

    # interpolate sigmas
    __lowerCamelCase													=    (low - log_sigma) / (low - high)
    __lowerCamelCase													=    np.clip(__UpperCAmelCase	,				0	,				1				)

    # transform interpolation to time range
    __lowerCamelCase													=    (1 - w) * low_idx + w * high_idx
    __lowerCamelCase													=    t.reshape(sigma.shape				)
    return t




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase				):



    '''simple docstring'''
    __lowerCamelCase													=    in_sigmas[-1].item()
    __lowerCamelCase													=    in_sigmas[0].item()

    __lowerCamelCase													=    7.0  # 7.0 is the value used in the paper
    __lowerCamelCase													=    np.linspace(0	,				1	,				__UpperCAmelCase				)
    __lowerCamelCase													=    sigma_min ** (1 / rho)
    __lowerCamelCase													=    sigma_max ** (1 / rho)
    __lowerCamelCase													=    (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
    return sigmas




 @property
 def        lowerCamelCase      (						self				):



    '''simple docstring'''
    return self.dt is None




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase = True	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.index_for_timestep(__UpperCAmelCase				)

    # advance index counter by 1
    __lowerCamelCase													=    timestep.cpu().item() if torch.is_tensor(__UpperCAmelCase				) else timestep
    self._index_counter[timestep_int] += 1

    if self.state_in_first_order:
       __lowerCamelCase													=    self.sigmas[step_index]
       __lowerCamelCase													=    self.sigmas[step_index + 1]
    else:
       # 2nd order / Heun's method
       __lowerCamelCase													=    self.sigmas[step_index - 1]
       __lowerCamelCase													=    self.sigmas[step_index]

    # currently only gamma=0 is supported. This usually works best anyways.
    # We can support gamma in the future but then need to scale the timestep before
    # passing it to the model which requires a change in API
    __lowerCamelCase													=    0
    __lowerCamelCase													=    sigma * (gamma + 1)  # Note: sigma_hat == sigma for now

    # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
    if self.config.prediction_type == "epsilon":
       __lowerCamelCase													=    sigma_hat if self.state_in_first_order else sigma_next
       __lowerCamelCase													=    sample - sigma_input * model_output
    elif self.config.prediction_type == "v_prediction":
       __lowerCamelCase													=    sigma_hat if self.state_in_first_order else sigma_next
       __lowerCamelCase													=    model_output * (-sigma_input / (sigma_input**2 + 1) ** 0.5) + (
           sample / (sigma_input**2 + 1)
       )
    elif self.config.prediction_type == "sample":
       __lowerCamelCase													=    model_output
    else:
       raise ValueError(
           F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"""				)

    if self.config.clip_sample:
       __lowerCamelCase													=    pred_original_sample.clamp(
           -self.config.clip_sample_range	,				self.config.clip_sample_range				)

    if self.state_in_first_order:
       # 2. Convert to an ODE derivative for 1st order
       __lowerCamelCase													=    (sample - pred_original_sample) / sigma_hat
       # 3. delta timestep
       __lowerCamelCase													=    sigma_next - sigma_hat

       # store for 2nd order step
       __lowerCamelCase													=    derivative
       __lowerCamelCase													=    dt
       __lowerCamelCase													=    sample
    else:
       # 2. 2nd order / Heun's method
       __lowerCamelCase													=    (sample - pred_original_sample) / sigma_next
       __lowerCamelCase													=    (self.prev_derivative + derivative) / 2

       # 3. take prev timestep & sample
       __lowerCamelCase													=    self.dt
       __lowerCamelCase													=    self.sample

       # free dt and derivative
       # Note, this puts the scheduler in "first order mode"
       __lowerCamelCase													=    None
       __lowerCamelCase													=    None
       __lowerCamelCase													=    None

    __lowerCamelCase													=    sample + derivative * dt

    if not return_dict:
       return (prev_sample,)

    return SchedulerOutput(prev_sample=__UpperCAmelCase				)




 def        lowerCamelCase      (						self	,				__UpperCAmelCase	,				__UpperCAmelCase	,				__UpperCAmelCase	,				):



    '''simple docstring'''
    __lowerCamelCase													=    self.sigmas.to(device=original_samples.device	,				dtype=original_samples.dtype				)
    if original_samples.device.type == "mps" and torch.is_floating_point(__UpperCAmelCase				):
       # mps does not support float64
       __lowerCamelCase													=    self.timesteps.to(original_samples.device	,				dtype=torch.floataa				)
       __lowerCamelCase													=    timesteps.to(original_samples.device	,				dtype=torch.floataa				)
    else:
       __lowerCamelCase													=    self.timesteps.to(original_samples.device				)
       __lowerCamelCase													=    timesteps.to(original_samples.device				)

    __lowerCamelCase													=    [self.index_for_timestep(__UpperCAmelCase	,				__UpperCAmelCase				) for t in timesteps]

    __lowerCamelCase													=    sigmas[step_indices].flatten()
    while len(sigma.shape				) < len(original_samples.shape				):
       __lowerCamelCase													=    sigma.unsqueeze(-1				)

    __lowerCamelCase													=    original_samples + noise * sigma
    return noisy_samples




 def __len__(						self				):



    '''simple docstring'''
    return self.config.num_train_timesteps











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}")






