






"""simple docstring"""


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

import flax
import jax.numpy as jnp
from scipy import integrate

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






@flax.struct.dataclass
class 						snake_case  :
    __magic_name__         =     42

    # setable values
    __magic_name__         =     42
    __magic_name__         =     42
    __magic_name__         =     42
    __magic_name__         =     None

    # running values
    __magic_name__         =     None





    @classmethod
    def 				lowerCamelCase__      (							cls	:					Any				,		A	:					CommonSchedulerState				,		A	:					jnp.ndarray				,		A	:					jnp.ndarray				,		A	:					jnp.ndarray			):


      '''simple docstring'''




      return cls(common=A				,		init_noise_sigma=A				,		timesteps=A				,		sigmas=A			)






@dataclass
class 						snake_case  (  UpperCAmelCase       ):
    __magic_name__         =     42






class 						snake_case  (  UpperCAmelCase   , UpperCAmelCase       ):
    __magic_name__         =     [e.name for e in FlaxKarrasDiffusionSchedulers]

    __magic_name__         =     42





    @property
    def 				lowerCamelCase__      (							self	:					int			):


      '''simple docstring'''




      return True





    @register_to_config
    def __init__(							self	:					int				,		A	:					int = 1_0_0_0				,		A	:					float = 0.00_01				,		A	:					float = 0.02				,		A	:					str = "linear"				,		A	:					Optional[jnp.ndarray] = None				,		A	:					str = "epsilon"				,		A	:					jnp.dtype = jnp.floataa				,		):


      '''simple docstring'''




      a     :			Any				=   dtype





    def 				lowerCamelCase__      (							self	:					List[str]				,		A	:					Optional[CommonSchedulerState] = None			):


      '''simple docstring'''




      if common is None:
        a     :			List[Any]				=   CommonSchedulerState.create(self			)

      a     :			Tuple				=   jnp.arange(0				,		self.config.num_train_timesteps			).round()[::-1]
      a     :			str				=   ((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

      # standard deviation of the initial noise distribution
      a     :			int				=   sigmas.max()

      return LMSDiscreteSchedulerState.create(
          common=A				,		init_noise_sigma=A				,		timesteps=A				,		sigmas=A				,		)





    def 				lowerCamelCase__      (							self	:					List[str]				,		A	:					LMSDiscreteSchedulerState				,		A	:					jnp.ndarray				,		A	:					int			):


      '''simple docstring'''




      ((a),     )     :			Tuple				=   jnp.where(state.timesteps == timestep				,		size=1			)
      a     :			List[Any]				=   step_index[0]

      a     :			Dict				=   state.sigmas[step_index]
      a     :			Dict				=   sample / ((sigma**2 + 1) ** 0.5)
      return sample





    def 				lowerCamelCase__      (							self	:					str				,		A	:					LMSDiscreteSchedulerState				,		A	:					Dict				,		A	:					Tuple				,		A	:					Optional[Any]			):


      '''simple docstring'''




      def lms_derivative(A	:					Any			):
        a     :			Union[str, Any]				=   1.0
        for k in range(A			):
          if current_order == k:
            continue
          prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

      a     :			int				=   integrate.quad(A				,		state.sigmas[t]				,		state.sigmas[t + 1]				,		epsrel=1E-4			)[0]

      return integrated_coeff





    def 				lowerCamelCase__      (							self	:					int				,		A	:					LMSDiscreteSchedulerState				,		A	:					int				,		A	:					Tuple = ()			):


      '''simple docstring'''




      a     :			List[Any]				=   jnp.linspace(self.config.num_train_timesteps - 1				,		0				,		A				,		dtype=self.dtype			)

      a     :			str				=   jnp.floor(A			).astype(jnp.intaa			)
      a     :			Tuple				=   jnp.ceil(A			).astype(jnp.intaa			)

      a     :			List[Any]				=   jnp.mod(A				,		1.0			)

      a     :			List[str]				=   ((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
      a     :			Any				=   (1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
      a     :			int				=   jnp.concatenate([sigmas, jnp.array([0.0]				,		dtype=self.dtype			)]			)

      a     :			Optional[Any]				=   timesteps.astype(jnp.intaa			)

      # initial running values
      a     :			Any				=   jnp.zeros((0,) + shape				,		dtype=self.dtype			)

      return state.replace(
          timesteps=A				,		sigmas=A				,		num_inference_steps=A				,		derivatives=A				,		)





    def 				lowerCamelCase__      (							self	:					Tuple				,		A	:					LMSDiscreteSchedulerState				,		A	:					jnp.ndarray				,		A	:					int				,		A	:					jnp.ndarray				,		A	:					int = 4				,		A	:					bool = True				,		):


      '''simple docstring'''




      if state.num_inference_steps is None:
        raise ValueError(
            'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'			)

      a     :			List[str]				=   state.sigmas[timestep]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        a     :			Any				=   sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        a     :			Union[str, Any]				=   model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      else:
        raise ValueError(
            F'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''			)

      # 2. Convert to an ODE derivative
      a     :			Any				=   (sample - pred_original_sample) / sigma
      a     :			int				=   state.replace(derivatives=jnp.append(state.derivatives				,		A			)			)
      if len(state.derivatives			) > order:
        a     :			Union[str, Any]				=   state.replace(derivatives=jnp.delete(state.derivatives				,		0			)			)

      # 3. Compute linear multistep coefficients
      a     :			Union[str, Any]				=   min(timestep + 1				,		A			)
      a     :			Tuple				=   [self.get_lms_coefficient(A				,		A				,		A				,		A			) for curr_order in range(A			)]

      # 4. Compute previous sample based on the derivatives path
      a     :			Optional[int]				=   sample + sum(
          coeff * derivative for coeff, derivative in zip(A				,		reversed(state.derivatives			)			)			)

      if not return_dict:
        return (prev_sample, state)

      return FlaxLMSSchedulerOutput(prev_sample=A				,		state=A			)





    def 				lowerCamelCase__      (							self	:					str				,		A	:					LMSDiscreteSchedulerState				,		A	:					jnp.ndarray				,		A	:					jnp.ndarray				,		A	:					jnp.ndarray				,		):


      '''simple docstring'''




      a     :			List[str]				=   state.sigmas[timesteps].flatten()
      a     :			int				=   broadcast_to_shape_from_left(A				,		noise.shape			)

      a     :			List[str]				=   original_samples + noise * sigma

      return noisy_samples





    def __len__(							self	:					Any			):


      '''simple docstring'''




      return self.config.num_train_timesteps












"""simple docstring"""


import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device


if is_torch_available():
   import torch

   from transformers import CamembertModel






@require_torch
@require_sentencepiece
@require_tokenizers
class 						snake_case  (  unittest.TestCase       ):





    @slow
    def 				lowerCamelCase__      (							self	:					str			):


      '''simple docstring'''




      a     :			int				=   CamembertModel.from_pretrained('camembert-base'			)
      model.to(A			)

      a     :			List[Any]				=   torch.tensor(
          [[5, 1_2_1, 1_1, 6_6_0, 1_6, 7_3_0, 2_5_5_4_3, 1_1_0, 8_3, 6]]				,		device=A				,		dtype=torch.long				,		)  # J'aime le camembert !
      with torch.no_grad():
        a     :			int				=   model(A			)['last_hidden_state']
      a     :			Union[str, Any]				=   torch.Size((1, 1_0, 7_6_8)			)
      self.assertEqual(output.shape				,		A			)
      # compare the actual values for a slice.
      a     :			List[str]				=   torch.tensor(
          [[[-0.02_54, 0.02_35, 0.10_27], [0.06_06, -0.18_11, -0.04_18], [-0.15_61, -0.11_27, 0.26_87]]]				,		device=A				,		dtype=torch.float				,		)
      # camembert = torch.hub.load('pytorch/fairseq', 'camembert.v0')
      # camembert.eval()
      # expected_slice = roberta.model.forward(input_ids)[0][:, :3, :3].detach()

      self.assertTrue(torch.allclose(output[:, :3, :3]				,		A				,		atol=1E-4			)			)




