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  A__       (	__snake_case	,       __snake_case      ):
      _UpperCAmelCase     :Tuple						   =       1





      @register_to_config
      def __init__(			self	,							A_ = 1000	,							A_ = None       ):
       '''simple docstring'''




       self.set_timesteps(A_       )

       # 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     : List[str]	     =       4

       # running values
       UpperCamelCase     : Any	     =       []





      def 	__UpperCamelCase(			self	,							A_	,							A_ = None       ):
       '''simple docstring'''




       UpperCamelCase     : str	     =       num_inference_steps
       UpperCamelCase     : List[str]	     =       torch.linspace(1	,							0	,							num_inference_steps + 1       )[:-1]
       UpperCamelCase     : Tuple	     =       torch.cat([steps, torch.tensor([0.0]       )]       )

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

       UpperCamelCase     : Optional[int]	     =       (1.0 - self.betas**2) ** 0.5

       UpperCamelCase     : List[str]	     =       (torch.atana(self.betas	,							self.alphas       ) / math.pi * 2)[:-1]
       UpperCamelCase     : int	     =       timesteps.to(A_       )

       UpperCamelCase     : Any	     =       []





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_ = True	,							):
       '''simple docstring'''




       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     : Any	     =       (self.timesteps == timestep).nonzero().item()
       UpperCamelCase     : int	     =       timestep_index + 1

       UpperCamelCase     : str	     =       sample * self.betas[timestep_index] + model_output * self.alphas[timestep_index]
       self.ets.append(A_       )

       if len(self.ets       ) == 1:
        UpperCamelCase     : Optional[int]	     =       self.ets[-1]
       elif len(self.ets       ) == 2:
        UpperCamelCase     : int	     =       (3 * self.ets[-1] - self.ets[-2]) / 2
       elif len(self.ets       ) == 3:
        UpperCamelCase     : Dict	     =       (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     : Tuple	     =       self._get_prev_sample(A_	,							A_	,							A_	,							A_       )

       if not return_dict:
        return (prev_sample,)

       return SchedulerOutput(prev_sample=A_       )





      def 	__UpperCamelCase(			self	,							A_	,							*A_	,							**A_       ):
       '''simple docstring'''




       return sample





      def 	__UpperCamelCase(			self	,							A_	,							A_	,							A_	,							A_       ):
       '''simple docstring'''




       UpperCamelCase     : Dict	     =       self.alphas[timestep_index]
       UpperCamelCase     : List[str]	     =       self.betas[timestep_index]

       UpperCamelCase     : List[Any]	     =       self.alphas[prev_timestep_index]
       UpperCamelCase     : List[Any]	     =       self.betas[prev_timestep_index]

       UpperCamelCase     : str	     =       (sample - sigma * ets) / max(A_	,							1e-8       )
       UpperCamelCase     : Tuple	     =       next_alpha * pred + ets * next_sigma

       return prev_sample





      def __len__(			self       ):
       '''simple docstring'''




       return self.config.num_train_timesteps







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 tensorflow as tf

    from transformers import AutoTokenizer, TFAutoModelForSeqaSeqLM



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





      @slow
      def 	__UpperCamelCase(			self       ):
       '''simple docstring'''




       UpperCamelCase     : List[str]	     =       TFAutoModelForSeqaSeqLM.from_pretrained("google/mt5-small"       )
       UpperCamelCase     : Tuple	     =       AutoTokenizer.from_pretrained("google/mt5-small"       )

       UpperCamelCase     : Dict	     =       tokenizer("Hello there"	,							return_tensors="tf"       ).input_ids
       UpperCamelCase     : int	     =       tokenizer("Hi I am"	,							return_tensors="tf"       ).input_ids

       UpperCamelCase     : Union[str, Any]	     =       model(A_	,							labels=A_       ).loss
       UpperCamelCase     : List[str]	     =       -tf.math.reduce_mean(A_       ).numpy()

       UpperCamelCase     : Union[str, Any]	     =       -21.22_81_68
       self.assertTrue(abs(mtf_score - EXPECTED_SCORE       ) < 2e-4       )
