
'''simple docstring'''







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__   (  _UpperCAmelCase     ):
					lowerCAmelCase_        =     42







class  lowerCAmelCase__   (  _UpperCAmelCase    ,						_UpperCAmelCase     ):
					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_	:     List[str]													=       dtype






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE = None		):


											"""simple docstring"""





											if common is None:
																	lowercase_	:     Optional[int]													=       CommonSchedulerState.create(self		)

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

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

											if variance_type is None:
																	lowercase_	:     Union[str, Any]													=       self.config.variance_type

											# hacks - were probably added for training stability
											if variance_type == "fixed_small":
																	lowercase_	:     str													=       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_	:     Optional[Any]													=       jnp.log(jnp.clip(SCREAMING_SNAKE_CASE_				,							a_min=1E-2_0		)		)
											elif variance_type == "fixed_large":
																	lowercase_	:     Optional[int]													=       state.common.betas[t]
											elif variance_type == "fixed_large_log":
																	# Glide max_log
																	lowercase_	:     Union[str, Any]													=       jnp.log(state.common.betas[t]		)
											elif variance_type == "learned":
																	return predicted_variance
											elif variance_type == "learned_range":
																	lowercase_	:     str													=       variance
																	lowercase_	:     Union[str, Any]													=       state.common.betas[t]
																	lowercase_	:     Dict													=       (predicted_variance + 1) / 2
																	lowercase_	:     Dict													=       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_	:     List[str]													=       timestep

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

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

											# 1. compute alphas, betas
											lowercase_	:     str													=       state.common.alphas_cumprod[t]
											lowercase_	:     Union[str, Any]													=       jnp.where(t > 0				,							state.common.alphas_cumprod[t - 1]				,							jnp.array(1.0				,							dtype=self.dtype		)		)
											lowercase_	:     Union[str, Any]													=       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_	:     Any													=       (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_	:     Optional[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_	:     List[str]													=       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_	:     Tuple													=       (alpha_prod_t_prev ** 0.5 * state.common.betas[t]) / beta_prod_t
											lowercase_	:     List[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_	:     str													=       pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

											# 6. Add noise
											def random_variance():
																	lowercase_	:     Union[str, Any]													=       jax.random.split(SCREAMING_SNAKE_CASE_				,							num=1		)
																	lowercase_	:     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_	:     List[str]													=       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







'''simple docstring'''



import unittest

from transformers import is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_vision, slow, torch_device


if is_torch_available():
      import torch

      from transformers import AutoModelForImageClassification

if is_vision_available():
      from transformers import AutoImageProcessor





@require_torch
@require_vision
class    UpperCAmelCase_  (unittest.TestCase  ):





    """simple docstring"""






    @slow
    def       lowercase_				(							self	)				->     List[str]:
         __lowerCamelCase   :	Any								=		AutoImageProcessor.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         __lowerCamelCase   :	Tuple								=		AutoModelForImageClassification.from_pretrained('microsoft/dit-base-finetuned-rvlcdip'	)
         model.to(SCREAMING_SNAKE_CASE_	)

         from datasets import load_dataset

         __lowerCamelCase   :	str								=		load_dataset('nielsr/rvlcdip-demo'	)

         __lowerCamelCase   :	List[Any]								=		dataset['train'][0]['image'].convert('RGB'	)

         __lowerCamelCase   :	str								=		image_processor(SCREAMING_SNAKE_CASE_			,     return_tensors='pt'	).to(SCREAMING_SNAKE_CASE_	)

         # forward pass
         with torch.no_grad():
              __lowerCamelCase   :	str								=		model(**SCREAMING_SNAKE_CASE_	)
              __lowerCamelCase   :	int								=		outputs.logits

         __lowerCamelCase   :	List[Any]								=		torch.Size((1, 16)	)
         self.assertEqual(logits.shape			,     SCREAMING_SNAKE_CASE_	)

         __lowerCamelCase   :	Optional[Any]								=		torch.tensor(
             [-0.4_1_5_8, -0.4_0_9_2, -0.4_3_4_7]			,     device=SCREAMING_SNAKE_CASE_			,     dtype=torch.float			,     )
         self.assertTrue(torch.allclose(logits[0, :3]			,     SCREAMING_SNAKE_CASE_			,     atol=1E-4	)	)






