


'''simple docstring'''
# DISCLAIMER: This file is strongly influenced by https://github.com/ermongroup/ddim

from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax
import jax.numpy as jnp

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    add_noise_common,
    get_velocity_common,
)

@flax.struct.dataclass
class 						_lowerCamelCase					:




 '''simple docstring'''
 __lowercase				:		Optional[Any]    							=						4_2

 # setable values
 __lowercase				:		Any    							=						4_2
 __lowercase				:		Optional[Any]    							=						4_2
 __lowercase				:		Optional[int]    							=						None


 @classmethod
 def  snake_case__   (      cls      ,      __lowercase      ,      __lowercase      ,      __lowercase						):

  """simple docstring"""







  return cls(common=__A      ,      init_noise_sigma=__A      ,      timesteps=__A						)


@dataclass
class 						_lowerCamelCase					(    UpperCamelCase_				):




 '''simple docstring'''
 __lowercase				:		List[str]    							=						4_2


class 						_lowerCamelCase					(    UpperCamelCase_    ,		UpperCamelCase_				):




 '''simple docstring'''
 __lowercase				:		Dict    							=						[e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowercase				:		Tuple    							=						4_2


 @property
 def  snake_case__   (      self						):

  """simple docstring"""







  return True


 @register_to_config
 def __init__(      self      ,      __lowercase = 1_000      ,      __lowercase = 0.0_0_0_1      ,      __lowercase = 0.0_2      ,      __lowercase = "linear"      ,      __lowercase = None      ,      __lowercase = "fixed_small"      ,      __lowercase = True      ,      __lowercase = "epsilon"      ,      __lowercase = jnp.floataa      ,      ):

  """simple docstring"""







  __A  :		Any       =			dtype


 def  snake_case__   (      self      ,      __lowercase = None						):

  """simple docstring"""







  if common is None:
   __A  :		str       =			CommonSchedulerState.create(self						)

  # standard deviation of the initial noise distribution
  __A  :		Union[str, Any]       =			jnp.array(1.0      ,      dtype=self.dtype						)

  __A  :		Tuple       =			jnp.arange(0      ,      self.config.num_train_timesteps						).round()[::-1]

  return DDPMSchedulerState.create(
      common=__A      ,      init_noise_sigma=__A      ,      timesteps=__A      ,      )


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = None						):

  """simple docstring"""







  return sample


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = ()						):

  """simple docstring"""







  __A  :		Optional[Any]       =			self.config.num_train_timesteps // num_inference_steps
  # creates integer timesteps by multiplying by ratio
  # rounding to avoid issues when num_inference_step is power of 3
  __A  :		int       =			(jnp.arange(0      ,      __A						) * step_ratio).round()[::-1]

  return state.replace(
      num_inference_steps=__A      ,      timesteps=__A      ,      )


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase=None      ,      __lowercase=None						):

  """simple docstring"""







  __A  :		Union[str, Any]       =			state.common.alphas_cumprod[t]
  __A  :		Tuple       =			jnp.where(t > 0      ,      state.common.alphas_cumprod[t - 1]      ,      jnp.array(1.0      ,      dtype=self.dtype						)						)

  # For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
  # and sample from it to get previous sample
  # x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
  __A  :		List[str]       =			(1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

  if variance_type is None:
   __A  :		Dict       =			self.config.variance_type

  # hacks - were probably added for training stability
  if variance_type == "fixed_small":
   __A  :		Union[str, Any]       =			jnp.clip(__A      ,      a_min=1E-20						)
  # for rl-diffuser https://arxiv.org/abs/2205.09991
  elif variance_type == "fixed_small_log":
   __A  :		Optional[int]       =			jnp.log(jnp.clip(__A      ,      a_min=1E-20						)						)
  elif variance_type == "fixed_large":
   __A  :		Any       =			state.common.betas[t]
  elif variance_type == "fixed_large_log":
   # Glide max_log
   __A  :		int       =			jnp.log(state.common.betas[t]						)
  elif variance_type == "learned":
   return predicted_variance
  elif variance_type == "learned_range":
   __A  :		int       =			variance
   __A  :		Dict       =			state.common.betas[t]
   __A  :		int       =			(predicted_variance + 1) / 2
   __A  :		str       =			frac * max_log + (1 - frac) * min_log

  return variance


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase = None      ,      __lowercase = True      ,      ):

  """simple docstring"""







  __A  :		int       =			timestep

  if key is None:
   __A  :		str       =			jax.random.PRNGKey(0						)

  if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
   __A  :		Tuple       =			jnp.split(__A      ,      sample.shape[1]      ,      axis=1						)
  else:
   __A  :		Any       =			None

  # 1. compute alphas, betas
  __A  :		Optional[int]       =			state.common.alphas_cumprod[t]
  __A  :		List[Any]       =			jnp.where(t > 0      ,      state.common.alphas_cumprod[t - 1]      ,      jnp.array(1.0      ,      dtype=self.dtype						)						)
  __A  :		Optional[int]       =			1 - alpha_prod_t
  __A  :		Tuple       =			1 - alpha_prod_t_prev

  # 2. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
  if self.config.prediction_type == "epsilon":
   __A  :		Optional[Any]       =			(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif self.config.prediction_type == "sample":
   __A  :		str       =			model_output
  elif self.config.prediction_type == "v_prediction":
   __A  :		Optional[int]       =			(alpha_prod_t**0.5) * sample - (beta_prod_t**0.5) * model_output
  else:
   raise ValueError(
       F"""prediction_type given as {self.config.prediction_type} must be one of `epsilon`, `sample` """
       ' for the FlaxDDPMScheduler.'						)

  # 3. Clip "predicted x_0"
  if self.config.clip_sample:
   __A  :		Optional[Any]       =			jnp.clip(__A      ,      -1      ,      1						)

  # 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __A  :		Optional[Any]       =			(alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
  __A  :		List[Any]       =			state.common.alphas[t] ** 0.5 * beta_prod_t_prev / beta_prod_t

  # 5. Compute predicted previous sample µ_t
  # See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
  __A  :		Dict       =			pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  def random_variance():
   __A  :		Optional[int]       =			jax.random.split(__A      ,      num=1						)
   __A  :		Union[str, Any]       =			jax.random.normal(__A      ,      shape=model_output.shape      ,      dtype=self.dtype						)
   return (self._get_variance(__A      ,      __A      ,      predicted_variance=__A						) ** 0.5) * noise

  __A  :		List[Any]       =			jnp.where(t > 0      ,      random_variance()      ,      jnp.zeros(model_output.shape      ,      dtype=self.dtype						)						)

  __A  :		List[str]       =			pred_prev_sample + variance

  if not return_dict:
   return (pred_prev_sample, state)

  return FlaxDDPMSchedulerOutput(prev_sample=__A      ,      state=__A						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  return add_noise_common(state.common      ,      __A      ,      __A      ,      __A						)


 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      __lowercase      ,      ):

  """simple docstring"""







  return get_velocity_common(state.common      ,      __A      ,      __A      ,      __A						)







 def __len__(      self						):

  """simple docstring"""







  return self.config.num_train_timesteps










'''simple docstring'''
import numpy as np
import torch
from torch.nn import CrossEntropyLoss
from transformers import AutoModelForCausalLM, AutoTokenizer

import datasets
from datasets import logging


UpperCAmelCase_ :     Optional[int]								=      '\\n\n'

UpperCAmelCase_ :     Tuple								=      '\nPerplexity (PPL) is one of the most common metrics for evaluating language models.\nIt is defined as the exponentiated average negative log-likelihood of a sequence.\n\nFor more information, see https://huggingface.co/docs/transformers/perplexity\n'

UpperCAmelCase_ :     str								=      '\nArgs:\n    model_id (str): model used for calculating Perplexity\n            NOTE: Perplexity can only be calculated for causal language models.\n                    This includes models such as gpt2, causal variations of bert,\n                    causal versions of t5, and more (the full list can be found\n                    in the AutoModelForCausalLM documentation here:\n                    https://huggingface.co/docs/transformers/master/en/model_doc/auto#transformers.AutoModelForCausalLM )\n\n    input_texts (list of str): input text, each separate text snippet\n        is one list entry.\n    batch_size (int): the batch size to run texts through the model. Defaults to 16.\n    add_start_token (bool): whether to add the start token to the texts,\n        so the perplexity can include the probability of the first word. Defaults to True.\n    device (str): device to run on, defaults to \'cuda\' when available\nReturns:\n    perplexity: dictionary containing the perplexity scores for the texts\n        in the input list, as well as the mean perplexity. If one of the input texts is\n        longer than the max input length of the model, then it is truncated to the\n        max length for the perplexity computation.\nExamples:\n    Example 1:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = ["lorem ipsum", "Happy Birthday!", "Bienvenue"]\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              add_start_token=False,\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        78.22\n        >>> print(round(results["perplexities"][0], 2))\n        11.11\n\n    Example 2:\n        >>> perplexity = datasets.load_metric("perplexity")\n        >>> input_texts = datasets.load_dataset("wikitext",\n        ...                                     "wikitext-2-raw-v1",\n        ...                                     split="test")["text"][:50] # doctest:+ELLIPSIS\n        [...]\n        >>> input_texts = [s for s in input_texts if s!=\'\']\n        >>> results = perplexity.compute(model_id=\'gpt2\',\n        ...                              input_texts=input_texts) # doctest:+ELLIPSIS\n        >>> print(list(results.keys()))\n        [\'perplexities\', \'mean_perplexity\']\n        >>> print(round(results["mean_perplexity"], 2))\n        60.35\n        >>> print(round(results["perplexities"][0], 2))\n        81.12\n'

@datasets.utils.file_utils.add_start_docstrings(_DESCRIPTION    ,		_KWARGS_DESCRIPTION				)
class 						_lowerCamelCase					(    datasets.Metric				):




 '''simple docstring'''


 def  snake_case__   (      self						):

  """simple docstring"""







  return datasets.MetricInfo(
      description=_DESCRIPTION      ,      citation=_CITATION      ,      inputs_description=_KWARGS_DESCRIPTION      ,      features=datasets.Features(
          {
              'input_texts': datasets.Value('string'						),
          }						)      ,      reference_urls=['https://huggingface.co/docs/transformers/perplexity']      ,      )







 def  snake_case__   (      self      ,      __lowercase      ,      __lowercase      ,      __lowercase = 16      ,      __lowercase = True      ,      __lowercase=None						):

  """simple docstring"""







  if device is not None:
   assert device in ["gpu", "cpu", "cuda"], "device should be either gpu or cpu."
   if device == "gpu":
    __A  :		Optional[Any]       =			'cuda'
  else:
   __A  :		Optional[int]       =			'cuda' if torch.cuda.is_available() else 'cpu'

  __A  :		List[Any]       =			AutoModelForCausalLM.from_pretrained(__lowercase						)
  __A  :		Any       =			model.to(__lowercase						)

  __A  :		List[Any]       =			AutoTokenizer.from_pretrained(__lowercase						)

  # if batch_size > 1 (which generally leads to padding being required), and
  # if there is not an already assigned pad_token, assign an existing
  # special token to also be the padding token
  if tokenizer.pad_token is None and batch_size > 1:
   __A  :		Union[str, Any]       =			list(tokenizer.special_tokens_map_extended.values()						)
   # check that the model already has at least one special token defined
   assert (
       len(__lowercase						) > 0
   ), "If batch_size > 1, model must have at least one special token to use for padding. Please use a different model or set batch_size=1."
   # assign one of the special tokens to also be the pad token
   tokenizer.add_special_tokens({'pad_token': existing_special_tokens[0]}						)

  if add_start_token:
   # leave room for <BOS> token to be added:
   assert (
       tokenizer.bos_token is not None
   ), "Input model must already have a BOS token if using add_start_token=True. Please use a different model, or set add_start_token=False"
   __A  :		Tuple       =			model.config.max_length - 1
  else:
   __A  :		str       =			model.config.max_length

  __A  :		int       =			tokenizer(
      __lowercase      ,      add_special_tokens=__lowercase      ,      padding=__lowercase      ,      truncation=__lowercase      ,      max_length=__lowercase      ,      return_tensors='pt'      ,      return_attention_mask=__lowercase      ,      ).to(__lowercase						)

  __A  :		Union[str, Any]       =			encodings['input_ids']
  __A  :		Optional[int]       =			encodings['attention_mask']

  # check that each input is long enough:
  if add_start_token:
   assert torch.all(torch.ge(attn_masks.sum(1						)      ,      1						)						), "Each input text must be at least one token long."
  else:
   assert torch.all(
       torch.ge(attn_masks.sum(1						)      ,      2						)						), "When add_start_token=False, each input text must be at least two tokens long. Run with add_start_token=True if inputting strings of only one token, and remove all empty input strings."

  __A  :		Union[str, Any]       =			[]
  __A  :		str       =			CrossEntropyLoss(reduction='none'						)

  for start_index in logging.tqdm(range(0      ,      len(__lowercase						)      ,      __lowercase						)						):
   __A  :		str       =			min(start_index + batch_size      ,      len(__lowercase						)						)
   __A  :		Tuple       =			encoded_texts[start_index:end_index]
   __A  :		List[str]       =			attn_masks[start_index:end_index]

   if add_start_token:
    __A  :		str       =			torch.tensor([[tokenizer.bos_token_id]] * encoded_batch.size(dim=0						)						).to(__lowercase						)
    __A  :		str       =			torch.cat([bos_tokens_tensor, encoded_batch]      ,      dim=1						)
    __A  :		Union[str, Any]       =			torch.cat(
        [torch.ones(bos_tokens_tensor.size()      ,      dtype=torch.intaa						).to(__lowercase						), attn_mask]      ,      dim=1						)

   __A  :		Tuple       =			encoded_batch

   with torch.no_grad():
    __A  :		List[str]       =			model(__lowercase      ,      attention_mask=__lowercase						).logits

   __A  :		int       =			out_logits[..., :-1, :].contiguous()
   __A  :		Union[str, Any]       =			labels[..., 1:].contiguous()
   __A  :		List[Any]       =			attn_mask[..., 1:].contiguous()

   __A  :		Tuple       =			torch.expa(
       (loss_fct(shift_logits.transpose(1      ,      2						)      ,      __lowercase						) * shift_attention_mask_batch).sum(1						)
       / shift_attention_mask_batch.sum(1						)						)

   ppls += perplexity_batch.tolist()

  return {"perplexities": ppls, "mean_perplexity": np.mean(__lowercase						)}






