



'''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 							lowercase       :


 """simple docstring"""


 UpperCAmelCase							=  42

 # setable values
 UpperCAmelCase							=  42
 UpperCAmelCase							=  42
 UpperCAmelCase							=  None







 @classmethod
 def _snake_case (							cls  ,a_  ,a_  ,a_							)		->   Dict:
        return cls(common=a_  ,init_noise_sigma=a_  ,timesteps=a_							)







@dataclass
class 							lowercase       (  _lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  42







class 							lowercase       (  _lowerCamelCase					,					_lowerCamelCase				):


 """simple docstring"""


 UpperCAmelCase							=  [e.name for e in FlaxKarrasDiffusionSchedulers]

 UpperCAmelCase							=  42







 @property
 def _snake_case (							self							)		->   Any:
        return True







 @register_to_config
 def __init__(							self  ,a_ = 1_000  ,a_ = 0.0001  ,a_ = 0.02  ,a_ = "linear"  ,a_ = None  ,a_ = "fixed_small"  ,a_ = True  ,a_ = "epsilon"  ,a_ = jnp.floataa  ,)		->   Dict:
        _UpperCAmelCase  :						Any			     = dtype







 def _snake_case (							self  ,a_ = None							)		->   DDPMSchedulerState:
        if common is None:
               _UpperCAmelCase  :						int			     = CommonSchedulerState.create(self							)

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

        _UpperCAmelCase  :						int			     = 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  ,a_  ,a_  ,a_ = None							)		->   jnp.ndarray:
        return sample







 def _snake_case (							self  ,a_  ,a_  ,a_ = ()							)		->   DDPMSchedulerState:
        _UpperCAmelCase  :						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
        _UpperCAmelCase  :						Tuple			     = (jnp.arange(0  ,a_							) * step_ratio).round()[::-1]

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







 def _snake_case (							self  ,a_  ,a_  ,a_=None  ,a_=None							)		->   List[Any]:
        _UpperCAmelCase  :						Optional[Any]			     = state.common.alphas_cumprod[t]
        _UpperCAmelCase  :						List[str]			     = 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
        _UpperCAmelCase  :						Any			     = (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
               _UpperCAmelCase  :						str			     = self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
               _UpperCAmelCase  :						Optional[Any]			     = jnp.clip(a_  ,a_min=1E-2_0							)
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
               _UpperCAmelCase  :						Optional[Any]			     = jnp.log(jnp.clip(a_  ,a_min=1E-2_0							)							)
        elif variance_type == "fixed_large":
               _UpperCAmelCase  :						Tuple			     = state.common.betas[t]
        elif variance_type == "fixed_large_log":
               # Glide max_log
               _UpperCAmelCase  :						Optional[Any]			     = jnp.log(state.common.betas[t]							)
        elif variance_type == "learned":
               return predicted_variance
        elif variance_type == "learned_range":
               _UpperCAmelCase  :						List[Any]			     = variance
               _UpperCAmelCase  :						Any			     = state.common.betas[t]
               _UpperCAmelCase  :						Optional[int]			     = (predicted_variance + 1) / 2
               _UpperCAmelCase  :						List[str]			     = frac * max_log + (1 - frac) * min_log

        return variance







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,a_ = None  ,a_ = True  ,)		->   Union[FlaxDDPMSchedulerOutput, Tuple]:
        _UpperCAmelCase  :						List[Any]			     = timestep

        if key is None:
               _UpperCAmelCase  :						Tuple			     = jax.random.PRNGKey(0							)

        if model_output.shape[1] == sample.shape[1] * 2 and self.config.variance_type in ["learned", "learned_range"]:
               _UpperCAmelCase     ,_UpperCAmelCase  :						Dict			     = jnp.split(a_  ,sample.shape[1]  ,axis=1							)
        else:
               _UpperCAmelCase  :						Optional[int]			     = None

        # 1. compute alphas, betas
        _UpperCAmelCase  :						Optional[int]			     = state.common.alphas_cumprod[t]
        _UpperCAmelCase  :						str			     = jnp.where(t > 0  ,state.common.alphas_cumprod[t - 1]  ,jnp.array(1.0  ,dtype=self.dtype							)							)
        _UpperCAmelCase  :						List[Any]			     = 1 - alpha_prod_t
        _UpperCAmelCase  :						Union[str, Any]			     = 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":
               _UpperCAmelCase  :						Any			     = (sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
        elif self.config.prediction_type == "sample":
               _UpperCAmelCase  :						Tuple			     = model_output
        elif self.config.prediction_type == "v_prediction":
               _UpperCAmelCase  :						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:
               _UpperCAmelCase  :						Dict			     = 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
        _UpperCAmelCase  :						List[str]			     = (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
        _UpperCAmelCase  :						Dict			     = 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
        _UpperCAmelCase  :						str			     = pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

        # 6. Add noise
        def random_variance():
               _UpperCAmelCase  :						Dict			     = jax.random.split(a_  ,num=1							)
               _UpperCAmelCase  :						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

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

        _UpperCAmelCase  :						Union[str, Any]			     = pred_prev_sample + variance

        if not return_dict:
               return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=a_  ,state=a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,)		->   jnp.ndarray:
        return add_noise_common(state.common  ,a_  ,a_  ,a_							)







 def _snake_case (							self  ,a_  ,a_  ,a_  ,a_  ,)		->   jnp.ndarray:
        return get_velocity_common(state.common  ,a_  ,a_  ,a_							)







 def __len__(							self							)		->   Optional[Any]:
        return self.config.num_train_timesteps








'''simple docstring'''






from __future__ import annotations

import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_sentencepiece, require_tf, require_tokenizers, slow


if is_tf_available():
   import numpy as np
   import tensorflow as tf

   from transformers import TFCamembertModel



@require_tf
@require_sentencepiece
@require_tokenizers
class 							lowercase       (  unittest.TestCase				):


 """simple docstring"""


 @slow
 def _snake_case (							self							)		->   Any:
        _UpperCAmelCase  :						Dict			     = TFCamembertModel.from_pretrained("""jplu/tf-camembert-base"""							)

        _UpperCAmelCase  :						Optional[int]			     = tf.convert_to_tensor(
            [[5, 121, 11, 660, 16, 730, 25_543, 110, 83, 6]]  ,dtype=tf.intaa  ,)  # J'aime le camembert !"

        _UpperCAmelCase  :						Dict			     = model(a_							)["""last_hidden_state"""]
        _UpperCAmelCase  :						Dict			     = tf.TensorShape((1, 10, 768)							)
        self.assertEqual(output.shape  ,a_							)
        # compare the actual values for a slice.
        _UpperCAmelCase  :						Tuple			     = tf.convert_to_tensor(
            [[[-0.0254, 0.0235, 0.1027], [0.0606, -0.1811, -0.0418], [-0.1561, -0.1127, 0.2687]]]  ,dtype=tf.floataa  ,)
        # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
        # camembert.eval()
        # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

        self.assertTrue(np.allclose(output[:, :3, :3].numpy()  ,expected_slice.numpy()  ,atol=1E-4							)							)



