






"""simple docstring"""
import math
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 SchedulerMixin, SchedulerOutput







class     UpperCamelCase_ (__A       ,		__A  ):
	__magic_name__			   =       1





	@register_to_config
	def __init__(   self      :				Dict			,			lowerCAmelCase_      :				int = 1_000			,			lowerCAmelCase_      :				Optional[Union[np.ndarray, List[float]]] = None		)		->    Optional[int]:
				# set `betas`, `alphas`, `timesteps`
				self.set_timesteps(lowerCAmelCase_		)

				# standard deviation of the initial noise distribution
				UpperCAmelCase_  :				Union[str, Any]      =      1.0

				# For now we only support F-PNDM, i.e. the runge-kutta method
				# For more information on the algorithm please take a look at the paper: https://arxiv.org/pdf/2202.09778.pdf
				# mainly at formula (9), (12), (13) and the Algorithm 2.
				UpperCAmelCase_  :				Tuple      =      4

				# running values
				UpperCAmelCase_  :				List[Any]      =      []





	def   _SCREAMING_SNAKE_CASE   (   self      :				Tuple			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				Union[str, torch.device] = None		)		->    Optional[int]:
				UpperCAmelCase_  :				int      =      num_inference_steps
				UpperCAmelCase_  :				Dict      =      torch.linspace(1			,			0			,			num_inference_steps + 1		)[:-1]
				UpperCAmelCase_  :				Union[str, Any]      =      torch.cat([steps, torch.tensor([0.0]		)]		)

				if self.config.trained_betas is not None:
							UpperCAmelCase_  :				Dict      =      torch.tensor(self.config.trained_betas			,			dtype=torch.floataa		)
				else:
							UpperCAmelCase_  :				Optional[Any]      =      torch.sin(steps * math.pi / 2		) ** 2

				UpperCAmelCase_  :				Optional[Any]      =      (1.0 - self.betas**2) ** 0.5

				UpperCAmelCase_  :				Union[str, Any]      =      (torch.atana(self.betas			,			self.alphas		) / math.pi * 2)[:-1]
				UpperCAmelCase_  :				str      =      timesteps.to(lowerCAmelCase_		)

				UpperCAmelCase_  :				Dict      =      []





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				int			,			lowerCAmelCase_      :				torch.FloatTensor			,			lowerCAmelCase_      :				bool = True			,			)		->    Union[SchedulerOutput, Tuple]:
				if self.num_inference_steps is None:
							raise ValueError(
							    "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"		)

				UpperCAmelCase_  :				str      =      (self.timesteps == timestep).nonzero().item()
				UpperCAmelCase_  :				List[str]      =      timestep_index + 1

				UpperCAmelCase_  :				Optional[Any]      =      sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
				self.ets.append(lowerCAmelCase_		)

				if len(self.ets		) == 1:
							UpperCAmelCase_  :				Tuple      =      self.ets[-1]
				elif len(self.ets		) == 2:
							UpperCAmelCase_  :				Optional[int]      =      (3 * self.ets[-1] - self.ets[-2]) / 2
				elif len(self.ets		) == 3:
							UpperCAmelCase_  :				str      =      (23 * self.ets[-1] - 16 * self.ets[-2] + 5 * self.ets[-3]) / 12
				else:
							UpperCAmelCase_  :				str      =      (1 / 24) * (55 * self.ets[-1] - 59 * self.ets[-2] + 37 * self.ets[-3] - 9 * self.ets[-4])

				UpperCAmelCase_  :				List[str]      =      self._get_prev_sample(lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_			,			lowerCAmelCase_		)

				if not return_dict:
							return (prev_sample,)

				return SchedulerOutput(prev_sample=lowerCAmelCase_		)





	def   _SCREAMING_SNAKE_CASE   (   self      :				List[str]			,			lowerCAmelCase_      :				torch.FloatTensor			,			*lowerCAmelCase_      :				int			,			**lowerCAmelCase_      :				Union[str, Any]		)		->    torch.FloatTensor:
				return sample





	def   _SCREAMING_SNAKE_CASE   (   self      :				int			,			lowerCAmelCase_      :				List[str]			,			lowerCAmelCase_      :				Optional[int]			,			lowerCAmelCase_      :				Tuple			,			lowerCAmelCase_      :				Optional[Any]		)		->    Tuple:
				UpperCAmelCase_  :				List[Any]      =      self.alphas[timestep_index]
				UpperCAmelCase_  :				Tuple      =      self.betas[timestep_index]

				UpperCAmelCase_  :				Any      =      self.alphas[prev_timestep_index]
				UpperCAmelCase_  :				Any      =      self.betas[prev_timestep_index]

				UpperCAmelCase_  :				Optional[int]      =      (sample - sigma * ets) / max(lowerCAmelCase_			,			1e-8		)
				UpperCAmelCase_  :				Optional[int]      =      next_alpha * pred + ets * next_sigma

				return prev_sample





	def __len__(   self      :				Any		)		->    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     UpperCamelCase_ (unittest.TestCase  ):





	@slow
	def   _SCREAMING_SNAKE_CASE   (   self      :				Optional[Any]		)		->    Optional[Any]:
				UpperCAmelCase_  :				List[Any]      =      TFCamembertModel.from_pretrained("jplu/tf-camembert-base"		)

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

				UpperCAmelCase_  :				Any      =      model(lowerCAmelCase_		)["last_hidden_state"]
				UpperCAmelCase_  :				str      =      tf.TensorShape((1, 10, 768)		)
				self.assertEqual(output.shape			,			lowerCAmelCase_		)
				# compare the actual values for a slice.
				UpperCAmelCase_  :				Tuple      =      tf.convert_to_tensor(
				    [[[-0.0_2_5_4, 0.0_2_3_5, 0.1_0_2_7], [0.0_6_0_6, -0.1_8_1_1, -0.0_4_1_8], [-0.1_5_6_1, -0.1_1_2_7, 0.2_6_8_7]]]			,			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		)		)



