


"""simple docstring"""







import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
		import torch

		from transformers.generation import DisjunctiveConstraint




@require_torch
class  UpperCamelCase_	( unittest.TestCase):

	"""simple docstring"""





	def   UpperCAmelCase_      ( self			:				Optional[int]   )       ->	List[str]:
						# For consistency across different places the DisjunctiveConstraint is called,
						# dc.token_ids is a list of integers. It is also initialized only by integers.

						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 4], [1, 2, 3, 4]]
						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )
						self.assertTrue(isinstance(dc.token_ids			,			UpperCAmelCase__   )   )

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]   )   )

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint([torch.LongTensor([1, 2, 4]   ), torch.LongTensor([1, 2, 3, 4, 5]   )]   )




	def   UpperCAmelCase_      ( self			:				Any   )       ->	int:
						# We can't have constraints that are complete subsets of another. This leads to a preverse
						# interpretation of "constraint fulfillment": does generating [1,2,3] fulfill the constraint?
						# It would mean that it generated [1,2] which fulfills it, but it's in the middle of potentially
						# fulfilling [1,2,3,4]. If we believe that [1,2,3] does fulfill the constraint, then the algorithm
						# will necessarily never reach [1,2,3,4], giving users a false sense of control (better to just not allow it).
						__SCREAMING_SNAKE_CASE						   =				[[1, 2], [1, 2, 3, 4]]

						with self.assertRaises(UpperCAmelCase__   ):
											DisjunctiveConstraint(UpperCAmelCase__   )  # fails here




	def   UpperCAmelCase_      ( self			:				List[Any]   )       ->	Any:
						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 3], [1, 2, 4]]

						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is False and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is False and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(3   )
						__SCREAMING_SNAKE_CASE						   =				stepped is True and completed is True and reset is False
						self.assertTrue(UpperCAmelCase__   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 3]   )




	def   UpperCAmelCase_      ( self			:				str   )       ->	List[str]:
						__SCREAMING_SNAKE_CASE						   =				[[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

						__SCREAMING_SNAKE_CASE						   =				DisjunctiveConstraint(UpperCAmelCase__   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(4   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.current_seq == [1, 2, 4]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(5   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.current_seq == [1, 2, 4, 5]   )

						dc.reset()

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(1   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.remaining() == 3   )
						self.assertTrue(dc.current_seq == [1]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(2   )
						self.assertTrue(not dc.completed   )
						self.assertTrue(dc.remaining() == 2   )
						self.assertTrue(dc.current_seq == [1, 2]   )

						__SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE   ,  __SCREAMING_SNAKE_CASE						   =				dc.update(5   )
						self.assertTrue(dc.completed   )  # Completed!
						self.assertTrue(dc.remaining() == 0   )
						self.assertTrue(dc.current_seq == [1, 2, 5]   )


'''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__   :
					lowerCAmelCase_        =     42

					# setable values
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     42
					lowerCAmelCase_        =     None






					@classmethod
					def 	_snake_case    (   cls				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											return cls(common=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE		)







@dataclass
class  lowerCAmelCase__   (  lowerCamelCase_     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  lowerCamelCase_    ,						lowerCamelCase_     ):
					lowerCAmelCase_        =     [e.name for e in FlaxKarrasDiffusionSchedulers]

					lowerCAmelCase_        =     42






					@property
					def 	_snake_case    (   self		):


											"""simple docstring"""





											return True






					@register_to_config
					def __init__(   self				,							__SCREAMING_SNAKE_CASE = 10_00				,							__SCREAMING_SNAKE_CASE = 0.0_001				,							__SCREAMING_SNAKE_CASE = 0.02				,							__SCREAMING_SNAKE_CASE = "linear"				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = "fixed_small"				,							__SCREAMING_SNAKE_CASE = True				,							__SCREAMING_SNAKE_CASE = "epsilon"				,							__SCREAMING_SNAKE_CASE = jnp.floataa				,							):


											"""simple docstring"""





											lowercase_	:     Dict													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





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

											# standard deviation of the initial noise distribution
											lowercase_	:     Union[str, 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=__SCREAMING_SNAKE_CASE				,							init_noise_sigma=__SCREAMING_SNAKE_CASE				,							timesteps=__SCREAMING_SNAKE_CASE				,							)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											return sample






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = ()		):


											"""simple docstring"""





											lowercase_	:     Optional[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_	:     int													=       (jnp.arange(0				,							__SCREAMING_SNAKE_CASE		) * step_ratio).round()[::-1]

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






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE=None				,							__SCREAMING_SNAKE_CASE=None		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       state.common.alphas_cumprod[t]
											lowercase_	:     str													=       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_	:     int													=       (1 - alpha_prod_t_prev) / (1 - alpha_prod_t) * state.common.betas[t]

											if variance_type is None:
																	lowercase_	:     str													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     int													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)
											# for rl-diffuser https://arxiv.org/abs/2205.09991
											elif variance_type == "fixed_small_log":
																	lowercase_	:     List[str]													=       jnp.log(jnp.clip(__SCREAMING_SNAKE_CASE				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     List[Any]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     List[Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     Optional[Any]													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Union[str, Any]													=       (predicted_variance + 1) / 2
																	lowercase_	:     Any													=       frac * max_log + (1 - frac) * min_log

											return variance






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE = None				,							__SCREAMING_SNAKE_CASE = True				,							):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       timestep

											if key is None:
																	lowercase_	:     int													=       jax.random.PRNGKey(0		)

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

											# 1. compute alphas, betas
											lowercase_	:     Any													=       state.common.alphas_cumprod[t]
											lowercase_	:     Optional[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_	:     str													=       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_	:     Any													=       model_output
											elif self.config.prediction_type == "v_prediction":
																	lowercase_	:     List[Any]													=       (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_	:     Optional[Any]													=       jnp.clip(__SCREAMING_SNAKE_CASE				,							-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_	:     List[Any]													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     Optional[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_	:     str													=       jax.random.split(__SCREAMING_SNAKE_CASE				,							num=1		)
																	lowercase_	:     List[Any]													=       jax.random.normal(__SCREAMING_SNAKE_CASE				,							shape=model_output.shape				,							dtype=self.dtype		)
																	return (self._get_variance(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							predicted_variance=__SCREAMING_SNAKE_CASE		) ** 0.5) * noise

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

											lowercase_	:     Any													=       pred_prev_sample + variance

											if not return_dict:
																	return (pred_prev_sample, state)

											return FlaxDDPMSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE				,							state=__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return add_noise_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							):


											"""simple docstring"""





											return get_velocity_common(state.common				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def __len__(   self		):


											"""simple docstring"""





											return self.config.num_train_timesteps



