




"""simple docstring"""







# 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       lowerCAmelCase__       :


				"""simple docstring"""





				__UpperCAmelCase :     CommonSchedulerState

				# setable values
				__UpperCAmelCase :     jnp.ndarray
				__UpperCAmelCase :     jnp.ndarray
				__UpperCAmelCase :     Optional[int]       =      None



				@classmethod
				def       snake_case    (     cls					:	int    ,   lowercase__					:	CommonSchedulerState    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	jnp.ndarray						):
						return cls(common=lowercase__    ,   init_noise_sigma=lowercase__    ,   timesteps=lowercase__						)





@dataclass
class       lowerCAmelCase__       (			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     DDPMSchedulerState





class       lowerCAmelCase__       (			lowerCAmelCase_  ,			lowerCAmelCase_    ):


				"""simple docstring"""





				__UpperCAmelCase :     Optional[Any]       =      [e.name for e in FlaxKarrasDiffusionSchedulers]

				__UpperCAmelCase :     jnp.dtype



				@property
				def       snake_case    (     self					:	Optional[Any]						):
						return True



				@register_to_config
				def __init__(     self					:	Optional[int]    ,   lowercase__					:	int = 1_0_0_0    ,   lowercase__					:	float = 0.0_0_0_1    ,   lowercase__					:	float = 0.0_2    ,   lowercase__					:	str = "linear"    ,   lowercase__					:	Optional[jnp.ndarray] = None    ,   lowercase__					:	str = "fixed_small"    ,   lowercase__					:	bool = True    ,   lowercase__					:	str = "epsilon"    ,   lowercase__					:	jnp.dtype = jnp.floataa    ,   ):
						__lowercase				:		List[Any]												=				dtype



				def       snake_case    (     self					:	List[Any]    ,   lowercase__					:	Optional[CommonSchedulerState] = None						):
						if common is None:
								__lowercase				:		Optional[Any]												=				CommonSchedulerState.create(self						)

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

						__lowercase				:		Tuple												=				jnp.arange(0    ,   self.config.num_train_timesteps						).round()[::-1]

						return DDPMSchedulerState.create(
						    common=lowercase__    ,   init_noise_sigma=lowercase__    ,   timesteps=lowercase__    ,   )



				def       snake_case    (     self					:	str    ,   lowercase__					:	DDPMSchedulerState    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	Optional[int] = None						):
						return sample



				def       snake_case    (     self					:	Tuple    ,   lowercase__					:	DDPMSchedulerState    ,   lowercase__					:	int    ,   lowercase__					:	Tuple = ()						):
						__lowercase				:		int												=				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				:		List[Any]												=				(jnp.arange(0    ,   lowercase__						) * step_ratio).round()[::-1]

						return state.replace(
						    num_inference_steps=lowercase__    ,   timesteps=lowercase__    ,   )



				def       snake_case    (     self					:	Union[str, Any]    ,   lowercase__					:	DDPMSchedulerState    ,   lowercase__					:	Optional[int]    ,   lowercase__					:	Dict=None    ,   lowercase__					:	str=None						):
						__lowercase				:		Optional[int]												=				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				:		Union[str, Any]												=				(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				:		Union[str, Any]												=				jnp.clip(lowercase__    ,   a_min=1e-20						)
						# for rl-diffuser https://arxiv.org/abs/2205.09991
						elif variance_type == "fixed_small_log":
								__lowercase				:		List[str]												=				jnp.log(jnp.clip(lowercase__    ,   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				:		Tuple												=				variance
								__lowercase				:		Dict												=				state.common.betas[t]
								__lowercase				:		List[Any]												=				(predicted_variance + 1) / 2
								__lowercase				:		Optional[Any]												=				frac * max_log + (1 - frac) * min_log

						return variance



				def       snake_case    (     self					:	Dict    ,   lowercase__					:	DDPMSchedulerState    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	int    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	Optional[jax.random.KeyArray] = None    ,   lowercase__					:	bool = True    ,   ):
						__lowercase				:		int												=				timestep

						if key is None:
								__lowercase				:		Union[str, Any]												=				jax.random.PRNGKey(0						)

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

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

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

						__lowercase				:		Optional[Any]												=				jnp.where(t > 0    ,   random_variance()    ,   jnp.zeros(model_output.shape    ,   dtype=self.dtype						)						)

						__lowercase				:		List[str]												=				pred_prev_sample + variance

						if not return_dict:
								return (pred_prev_sample, state)

						return FlaxDDPMSchedulerOutput(prev_sample=lowercase__    ,   state=lowercase__						)



				def       snake_case    (     self					:	Any    ,   lowercase__					:	DDPMSchedulerState    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	jnp.ndarray    ,   ):
						return add_noise_common(state.common    ,   lowercase__    ,   lowercase__    ,   lowercase__						)



				def       snake_case    (     self					:	Optional[int]    ,   lowercase__					:	DDPMSchedulerState    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	jnp.ndarray    ,   lowercase__					:	jnp.ndarray    ,   ):
						return get_velocity_common(state.common    ,   lowercase__    ,   lowercase__    ,   lowercase__						)



				def __len__(     self					:	Dict						):
						return self.config.num_train_timesteps






"""simple docstring"""







import unittest

from transformers.testing_utils import CaptureStdout
from transformers.tools.python_interpreter import evaluate

def 						snake_case__       (					_lowerCamelCase				)			->Dict:






		"""simple docstring"""


		return x + 2





class       lowerCAmelCase__       (			unittest.TestCase    ):


				"""simple docstring"""





				def       snake_case    (     self					:	List[str]						):
						__lowercase				:		List[Any]												=				"x = 3"
						__lowercase				:		Optional[int]												=				{}
						__lowercase				:		List[Any]												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						assert result == 3
						self.assertDictEqual(lowercase__    ,   {"x": 3}						)

						__lowercase				:		Optional[int]												=				"x = y"
						__lowercase				:		Union[str, Any]												=				{"y": 5}
						__lowercase				:		str												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(lowercase__    ,   {"x": 5, "y": 5}						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Dict												=				"y = add_two(x)"
						__lowercase				:		List[Any]												=				{"x": 3}
						__lowercase				:		Optional[int]												=				evaluate(lowercase__    ,   {"add_two": add_two}    ,   state=lowercase__						)
						assert result == 5
						self.assertDictEqual(lowercase__    ,   {"x": 3, "y": 5}						)

						# Won't work without the tool
						with CaptureStdout() as out:
								__lowercase				:		Any												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						assert result is None
						assert "tried to execute add_two" in out.out



				def       snake_case    (     self					:	Any						):
						__lowercase				:		Optional[Any]												=				"x = 3"
						__lowercase				:		List[Any]												=				{}
						__lowercase				:		Dict												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						assert result == 3
						self.assertDictEqual(lowercase__    ,   {"x": 3}						)



				def       snake_case    (     self					:	str						):
						__lowercase				:		Optional[int]												=				"test_dict = {'x': x, 'y': add_two(x)}"
						__lowercase				:		List[str]												=				{"x": 3}
						__lowercase				:		Tuple												=				evaluate(lowercase__    ,   {"add_two": add_two}    ,   state=lowercase__						)
						self.assertDictEqual(lowercase__    ,   {"x": 3, "y": 5}						)
						self.assertDictEqual(lowercase__    ,   {"x": 3, "test_dict": {"x": 3, "y": 5}}						)



				def       snake_case    (     self					:	int						):
						__lowercase				:		Tuple												=				"x = 3\ny = 5"
						__lowercase				:		Optional[Any]												=				{}
						__lowercase				:		List[Any]												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(lowercase__    ,   {"x": 3, "y": 5}						)



				def       snake_case    (     self					:	Any						):
						__lowercase				:		str												=				"text = f'This is x: {x}.'"
						__lowercase				:		Any												=				{"x": 3}
						__lowercase				:		List[Any]												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						# evaluate returns the value of the last assignment.
						assert result == "This is x: 3."
						self.assertDictEqual(lowercase__    ,   {"x": 3, "text": "This is x: 3."}						)



				def       snake_case    (     self					:	str						):
						__lowercase				:		Optional[Any]												=				"if x <= 3:\n    y = 2\nelse:\n    y = 5"
						__lowercase				:		int												=				{"x": 3}
						__lowercase				:		Optional[int]												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						# evaluate returns the value of the last assignment.
						assert result == 2
						self.assertDictEqual(lowercase__    ,   {"x": 3, "y": 2}						)

						__lowercase				:		Optional[int]												=				{"x": 8}
						__lowercase				:		Union[str, Any]												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						# evaluate returns the value of the last assignment.
						assert result == 5
						self.assertDictEqual(lowercase__    ,   {"x": 8, "y": 5}						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Optional[Any]												=				"test_list = [x, add_two(x)]"
						__lowercase				:		str												=				{"x": 3}
						__lowercase				:		Any												=				evaluate(lowercase__    ,   {"add_two": add_two}    ,   state=lowercase__						)
						self.assertListEqual(lowercase__    ,   [3, 5]						)
						self.assertDictEqual(lowercase__    ,   {"x": 3, "test_list": [3, 5]}						)



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		str												=				"y = x"
						__lowercase				:		str												=				{"x": 3}
						__lowercase				:		int												=				evaluate(lowercase__    ,   {}    ,   state=lowercase__						)
						assert result == 3
						self.assertDictEqual(lowercase__    ,   {"x": 3, "y": 3}						)



				def       snake_case    (     self					:	Optional[int]						):
						__lowercase				:		str												=				"test_list = [x, add_two(x)]\ntest_list[1]"
						__lowercase				:		Optional[Any]												=				{"x": 3}
						__lowercase				:		Any												=				evaluate(lowercase__    ,   {"add_two": add_two}    ,   state=lowercase__						)
						assert result == 5
						self.assertDictEqual(lowercase__    ,   {"x": 3, "test_list": [3, 5]}						)

						__lowercase				:		Tuple												=				"test_dict = {'x': x, 'y': add_two(x)}\ntest_dict['y']"
						__lowercase				:		Union[str, Any]												=				{"x": 3}
						__lowercase				:		Tuple												=				evaluate(lowercase__    ,   {"add_two": add_two}    ,   state=lowercase__						)
						assert result == 5
						self.assertDictEqual(lowercase__    ,   {"x": 3, "test_dict": {"x": 3, "y": 5}}						)



				def       snake_case    (     self					:	Dict						):
						__lowercase				:		Dict												=				"x = 0\nfor i in range(3):\n    x = i"
						__lowercase				:		str												=				{}
						__lowercase				:		int												=				evaluate(lowercase__    ,   {"range": range}    ,   state=lowercase__						)
						assert result == 2
						self.assertDictEqual(lowercase__    ,   {"x": 2, "i": 2}						)
