





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					:






    '''simple docstring'''
    _lowerCAmelCase  =						42

    # setable values
    _lowerCAmelCase  =						42
    _lowerCAmelCase  =						42
    _lowerCAmelCase  =						42
    _lowerCAmelCase  =						None

    # running values
    _lowerCAmelCase  =						None





    @classmethod
    def 						A		( cls    :		Union[str, Any]      ,    snake_case    :		CommonSchedulerState      ,    snake_case    :		jnp.ndarray      ,    snake_case    :		jnp.ndarray      ,    snake_case    :		jnp.ndarray				):

      """simple docstring"""





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


@dataclass
class 	__snake_case					(      _a ):






    '''simple docstring'''
    _lowerCAmelCase  =						42


class 	__snake_case					(      _a							,					_a ):






    '''simple docstring'''
    _lowerCAmelCase  =						[e.name for e in FlaxKarrasDiffusionSchedulers]

    _lowerCAmelCase  =						42





    @property
    def 						A		( self    :		List[Any]				):

      """simple docstring"""





      return True





    @register_to_config
    def __init__( self    :		Dict      ,    snake_case    :		int = 1_000      ,    snake_case    :		float = 0.0001      ,    snake_case    :		float = 0.02      ,    snake_case    :		str = "linear"      ,    snake_case    :		Optional[jnp.ndarray] = None      ,    snake_case    :		str = "epsilon"      ,    snake_case    :		jnp.dtype = jnp.floataa      ,    ):

      """simple docstring"""





      __lowerCAmelCase								=					dtype





    def 						A		( self    :		Dict      ,    snake_case    :		Optional[CommonSchedulerState] = None				):

      """simple docstring"""





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

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

      # standard deviation of the initial noise distribution
      __lowerCAmelCase								=					sigmas.max()

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





    def 						A		( self    :		Union[str, Any]      ,    snake_case    :		LMSDiscreteSchedulerState      ,    snake_case    :		jnp.ndarray      ,    snake_case    :		int				):

      """simple docstring"""





      ((__lowerCAmelCase)						,    )								=					jnp.where(state.timesteps == timestep      ,    size=1				)
      __lowerCAmelCase								=					step_index[0]

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





    def 						A		( self    :		Tuple      ,    snake_case    :		LMSDiscreteSchedulerState      ,    snake_case    :		int      ,    snake_case    :		Tuple      ,    snake_case    :		List[Any]				):

      """simple docstring"""





      def lms_derivative(snake_case    :		List[str]				):
        __lowerCAmelCase								=					1.0
        for k in range(snake_case				):
          if current_order == k:
            continue
          prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
        return prod

      __lowerCAmelCase								=					integrate.quad(snake_case      ,    state.sigmas[t]      ,    state.sigmas[t + 1]      ,    epsrel=1e-4				)[0]

      return integrated_coeff





    def 						A		( self    :		List[str]      ,    snake_case    :		LMSDiscreteSchedulerState      ,    snake_case    :		int      ,    snake_case    :		Tuple = ()				):

      """simple docstring"""





      __lowerCAmelCase								=					jnp.linspace(self.config.num_train_timesteps - 1      ,    0      ,    snake_case      ,    dtype=self.dtype				)

      __lowerCAmelCase								=					jnp.floor(snake_case				).astype(jnp.intaa				)
      __lowerCAmelCase								=					jnp.ceil(snake_case				).astype(jnp.intaa				)

      __lowerCAmelCase								=					jnp.mod(snake_case      ,    1.0				)

      __lowerCAmelCase								=					((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
      __lowerCAmelCase								=					(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
      __lowerCAmelCase								=					jnp.concatenate([sigmas, jnp.array([0.0]      ,    dtype=self.dtype				)]				)

      __lowerCAmelCase								=					timesteps.astype(jnp.intaa				)

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

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





    def 						A		( self    :		int      ,    snake_case    :		LMSDiscreteSchedulerState      ,    snake_case    :		jnp.ndarray      ,    snake_case    :		int      ,    snake_case    :		jnp.ndarray      ,    snake_case    :		int = 4      ,    snake_case    :		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'''				)

      __lowerCAmelCase								=					state.sigmas[timestep]

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
        __lowerCAmelCase								=					sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
        # * c_out + input * c_skip
        __lowerCAmelCase								=					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
      __lowerCAmelCase								=					(sample - pred_original_sample) / sigma
      __lowerCAmelCase								=					state.replace(derivatives=jnp.append(state.derivatives      ,    snake_case				)				)
      if len(state.derivatives				) > order:
        __lowerCAmelCase								=					state.replace(derivatives=jnp.delete(state.derivatives      ,    0				)				)

      # 3. Compute linear multistep coefficients
      __lowerCAmelCase								=					min(timestep + 1      ,    snake_case				)
      __lowerCAmelCase								=					[self.get_lms_coefficient(snake_case      ,    snake_case      ,    snake_case      ,    snake_case				) for curr_order in range(snake_case				)]

      # 4. Compute previous sample based on the derivatives path
      __lowerCAmelCase								=					sample + sum(
          coeff * derivative for coeff, derivative in zip(snake_case      ,    reversed(state.derivatives				)				)				)

      if not return_dict:
        return (prev_sample, state)

      return FlaxLMSSchedulerOutput(prev_sample=snake_case      ,    state=snake_case				)





    def 						A		( self    :		Tuple      ,    snake_case    :		LMSDiscreteSchedulerState      ,    snake_case    :		jnp.ndarray      ,    snake_case    :		jnp.ndarray      ,    snake_case    :		jnp.ndarray      ,    ):

      """simple docstring"""





      __lowerCAmelCase								=					state.sigmas[timesteps].flatten()
      __lowerCAmelCase								=					broadcast_to_shape_from_left(snake_case      ,    noise.shape				)

      __lowerCAmelCase								=					original_samples + noise * sigma

      return noisy_samples





    def __len__( self    :		Optional[Any]				):

      """simple docstring"""





      return self.config.num_train_timesteps







import os
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 GPTaLMHeadModel

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






    '''simple docstring'''





    @slow
    @unittest.skip('''Model is not available.'''				)
    def 						A		( self    :		str				):

      """simple docstring"""





      __lowerCAmelCase								=					'''nvidia/megatron-gpt2-345m/'''
      if "MYDIR" in os.environ:
        __lowerCAmelCase								=					os.path.join(os.environ['''MYDIR''']      ,    snake_case				)
      __lowerCAmelCase								=					GPTaLMHeadModel.from_pretrained(snake_case				)
      model.to(snake_case				)
      model.half()

      __lowerCAmelCase								=					torch.tensor(
          [[101, 7_110, 1_005, 1_056, 2_023, 11_333, 17_413, 1_029, 102]]      ,    device=snake_case      ,    dtype=torch.long      ,    )

      with torch.no_grad():
        __lowerCAmelCase								=					model(snake_case				).logits

      __lowerCAmelCase								=					torch.Size((1, 9, 50_257)				)
      self.assertEqual(output.shape      ,    snake_case				)

      __lowerCAmelCase								=					torch.tensor(
          [
              4.9414,
              -0.2920,
              -1.2148,
              -4.0273,
              -0.5161,
              -5.2109,
              -1.2412,
              -1.8301,
              -1.7734,
              -4.7148,
              -0.2317,
              -1.0811,
              -2.1777,
              0.4141,
              -3.7969,
              -4.0586,
              -2.5332,
              -3.3809,
              4.3867,
          ]      ,    device=snake_case      ,    dtype=torch.half      ,    )

      for i in range(19				):
        __lowerCAmelCase						,    __lowerCAmelCase								=					8 * i // 17, 2_792 * i  # along the diagonal
        __lowerCAmelCase						,    __lowerCAmelCase								=					output[0, r, c], expected_diag[i]
        __lowerCAmelCase								=					f'''row={r} col={c} computed={computed} expected={expected}'''
        self.assertAlmostEqual(snake_case      ,    snake_case      ,    delta=1e-4      ,    msg=snake_case				)
