


# 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   __a			:
 __lowercase :							CommonSchedulerState

 # setable values
 __lowercase :							jnp.ndarray
 __lowercase :							jnp.ndarray
 __lowercase :							Optional[int]        = None
 @classmethod
 def 		SCREAMING_SNAKE_CASE__   (   cls				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )					->				Optional[int]:




        '''simple docstring'''
        return cls(common=lowerCAmelCase__				, init_noise_sigma=lowerCAmelCase__				, timesteps=lowerCAmelCase__ )





@dataclass
class   __a			(    __UpperCamelCase      ):
 __lowercase :							DDPMSchedulerState





class   __a			(    __UpperCamelCase       ,       __UpperCamelCase      ):
 __lowercase :							int        = [e.name for e in FlaxKarrasDiffusionSchedulers]

 __lowercase :							jnp.dtype
 @property
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        return True
 @register_to_config
 def __init__(   self				, lowerCAmelCase__ = 1_000				, lowerCAmelCase__ = 0.0_0_0_1				, lowerCAmelCase__ = 0.0_2				, lowerCAmelCase__ = "linear"				, lowerCAmelCase__ = None				, lowerCAmelCase__ = "fixed_small"				, lowerCAmelCase__ = True				, lowerCAmelCase__ = "epsilon"				, lowerCAmelCase__ = jnp.floataa				, )					->				List[str]:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   dtype
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__ = None )					->				DDPMSchedulerState:




        '''simple docstring'''
        if common is None:
               lowercase__:   Optional[Any]  			=   CommonSchedulerState.create(self )

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

        lowercase__:   List[Any]  			=   jnp.arange(0				, self.config.num_train_timesteps ).round()[::-1]

        return DDPMSchedulerState.create(
            common=lowerCAmelCase__				, init_noise_sigma=lowerCAmelCase__				, timesteps=lowerCAmelCase__				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = None )					->				jnp.ndarray:




        '''simple docstring'''
        return sample
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = () )					->				DDPMSchedulerState:




        '''simple docstring'''
        lowercase__:   Union[str, 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
        lowercase__:   Optional[Any]  			=   (jnp.arange(0				, lowerCAmelCase__ ) * step_ratio).round()[::-1]

        return state.replace(
            num_inference_steps=lowerCAmelCase__				, timesteps=lowerCAmelCase__				, )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__=None				, lowerCAmelCase__=None )					->				str:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   state.common.alphas_cumprod[t]
        lowercase__:   int  			=   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
        lowercase__:   Dict  			=   (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

        if variance_type is None:
               lowercase__:   Optional[Any]  			=   self.config.variance_type

        # hacks - were probably added for training stability
        if variance_type == "fixed_small":
               lowercase__:   Any  			=   jnp.clip(lowerCAmelCase__				, a_min=1E-20 )
        # for rl-diffuser https://arxiv.org/abs/2205.09991
        elif variance_type == "fixed_small_log":
               lowercase__:   str  			=   jnp.log(jnp.clip(lowerCAmelCase__				, a_min=1E-20 ) )
        elif variance_type == "fixed_large":
               lowercase__:   int  			=   state.common.betas[t]
        elif variance_type == "fixed_large_log":
               # Glide max_log
               lowercase__:   Dict  			=   jnp.log(state.common.betas[t] )
        elif variance_type == "learned":
               return predicted_variance
        elif variance_type == "learned_range":
               lowercase__:   List[Any]  			=   variance
               lowercase__:   Optional[Any]  			=   state.common.betas[t]
               lowercase__:   List[Any]  			=   (predicted_variance + 1) / 2
               lowercase__:   Any  			=   frac * max_log + (1 - frac) * min_log

        return variance
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ = None				, lowerCAmelCase__ = True				, )					->				Union[FlaxDDPMSchedulerOutput, Tuple]:




        '''simple docstring'''
        lowercase__:   Optional[Any]  			=   timestep

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

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

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

        # 6. Add noise
        def random_variance():
               lowercase__:   List[str]  			=   jax.random.split(lowerCAmelCase__				, num=1 )
               lowercase__:   List[Any]  			=   jax.random.normal(lowerCAmelCase__				, shape=model_output.shape				, dtype=self.dtype )
               return (self._get_variance(lowerCAmelCase__				, lowerCAmelCase__				, predicted_variance=lowerCAmelCase__ ) ** 0.5) * noise

        lowercase__:   str  			=   jnp.where(t > 0				, random_variance()				, jnp.zeros(model_output.shape				, dtype=self.dtype ) )

        lowercase__:   Optional[Any]  			=   pred_prev_sample + variance

        if not return_dict:
               return (pred_prev_sample, state)

        return FlaxDDPMSchedulerOutput(prev_sample=lowerCAmelCase__				, state=lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				jnp.ndarray:




        '''simple docstring'''
        return add_noise_common(state.common				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )
 def 		SCREAMING_SNAKE_CASE__   (   self				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__				, )					->				jnp.ndarray:




        '''simple docstring'''
        return get_velocity_common(state.common				, lowerCAmelCase__				, lowerCAmelCase__				, lowerCAmelCase__ )
 def __len__(   self )					->				str:




        '''simple docstring'''
        return self.config.num_train_timesteps






import os
import unittest

from transformers.models.cpmant.tokenization_cpmant import VOCAB_FILES_NAMES, CpmAntTokenizer
from transformers.testing_utils import require_jieba, tooslow

from ...test_tokenization_common import TokenizerTesterMixin





@require_jieba
class   __a			(    __UpperCamelCase       ,       unittest.TestCase      ):
 __lowercase :							str        = CpmAntTokenizer
 __lowercase :							List[Any]        = False
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				str:




        '''simple docstring'''
        super().setUp()

        lowercase__:   Any  			=   [
            '<d>',
            '</d>',
            '<s>',
            '</s>',
            '</_>',
            '<unk>',
            '<pad>',
            '</n>',
            '我',
            '是',
            'C',
            'P',
            'M',
            'A',
            'n',
            't',
        ]
        lowercase__:   List[Any]  			=   os.path.join(self.tmpdirname				, VOCAB_FILES_NAMES['vocab_file'] )
        with open(self.vocab_file				, 'w'				, encoding='utf-8' ) as vocab_writer:
               vocab_writer.write(''.join([x + '\n' for x in vocab_tokens] ) )
 @tooslow
 def 		SCREAMING_SNAKE_CASE__   (   self )					->				Tuple:




        '''simple docstring'''
        lowercase__:   Optional[int]  			=   CpmAntTokenizer.from_pretrained('openbmb/cpm-ant-10b' )
        lowercase__:   Optional[Any]  			=   '今天天气真好！'
        lowercase__:   str  			=   ['今天', '天气', '真', '好', '！']
        lowercase__:   Optional[Any]  			=   tokenizer.tokenize(lowerCAmelCase__ )
        self.assertListEqual(lowerCAmelCase__				, lowerCAmelCase__ )
        lowercase__:   List[str]  			=   '今天天气真好！'
        lowercase__:   List[str]  			=   [tokenizer.bos_token] + tokens

        lowercase__:   Tuple  			=   [6, 9_802, 14_962, 2_082, 831, 244]
        self.assertListEqual(tokenizer.convert_tokens_to_ids(lowerCAmelCase__ )				, lowerCAmelCase__ )

        lowercase__:   Any  			=   tokenizer.decode(lowerCAmelCase__ )
        self.assertEqual(lowerCAmelCase__				, lowerCAmelCase__ )


