
'''simple docstring'''





from dataclasses import dataclass
from typing import Optional, Tuple, Union

import flax
import jax.numpy as jnp
from scipy import integrate

from ..configuration_utils import ConfigMixin, register_to_config
from .scheduling_utils_flax import (
    CommonSchedulerState,
    FlaxKarrasDiffusionSchedulers,
    FlaxSchedulerMixin,
    FlaxSchedulerOutput,
    broadcast_to_shape_from_left,
)



@flax.struct.dataclass
class 			lowerCAmelCase__ :



  '''simple docstring'''






  _UpperCamelCase							=			42

  # setable values
  _UpperCamelCase							=			42
  _UpperCamelCase							=			42
  _UpperCamelCase							=			42
  _UpperCamelCase							=			None

  # running values
  _UpperCamelCase							=			None

  @classmethod
  def 		_lowerCamelCase						(cls	:			List[str]						,			lowerCAmelCase_	:			CommonSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray     ) ->							Optional[Any]:
        return cls(common=lowerCAmelCase_						,			init_noise_sigma=lowerCAmelCase_						,			timesteps=lowerCAmelCase_						,			sigmas=lowerCAmelCase_     )



@dataclass
class 			lowerCAmelCase__ (							UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			42



class 			lowerCAmelCase__ (							UpperCamelCase       ,				UpperCamelCase							):



  '''simple docstring'''






  _UpperCamelCase							=			[e.name for e in FlaxKarrasDiffusionSchedulers]

  _UpperCamelCase							=			42

  @property
  def 		_lowerCamelCase						(self	:			Optional[int]     ) ->							Tuple:
        return True

  @register_to_config
  def __init__(self	:			Tuple						,			lowerCAmelCase_	:			int = 10_00						,			lowerCAmelCase_	:			float = 0.0_001						,			lowerCAmelCase_	:			float = 0.02						,			lowerCAmelCase_	:			str = "linear"						,			lowerCAmelCase_	:			Optional[jnp.ndarray] = None						,			lowerCAmelCase_	:			str = "epsilon"						,			lowerCAmelCase_	:			jnp.dtype = jnp.floataa						,			) ->							Dict:
        snake_case_    							=				dtype

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			Optional[CommonSchedulerState] = None     ) ->							LMSDiscreteSchedulerState:
        if common is None:
              snake_case_    							=				CommonSchedulerState.create(self     )

        snake_case_    							=				jnp.arange(0						,			self.config.num_train_timesteps     ).round()[::-1]
        snake_case_    							=				((1 - common.alphas_cumprod) / common.alphas_cumprod) ** 0.5

        # standard deviation of the initial noise distribution
        snake_case_    							=				sigmas.max()

        return LMSDiscreteSchedulerState.create(
            common=lowerCAmelCase_						,			init_noise_sigma=lowerCAmelCase_						,			timesteps=lowerCAmelCase_						,			sigmas=lowerCAmelCase_						,			)

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			int     ) ->							jnp.ndarray:
        ((snake_case_)							,						)    							=				jnp.where(state.timesteps == timestep						,			size=1     )
        snake_case_    							=				step_index[0]

        snake_case_    							=				state.sigmas[step_index]
        snake_case_    							=				sample / ((sigma**2 + 1) ** 0.5)
        return sample

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			Optional[Any]						,			lowerCAmelCase_	:			str						,			lowerCAmelCase_	:			Any     ) ->							Optional[Any]:

        def lms_derivative(lowerCAmelCase_	:			Tuple     ):
              snake_case_    							=				1.0
              for k in range(lowerCAmelCase_     ):
                    if current_order == k:
                          continue
                    prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
              return prod

        snake_case_    							=				integrate.quad(lowerCAmelCase_						,			state.sigmas[t]						,			state.sigmas[t + 1]						,			epsrel=1E-4     )[0]

        return integrated_coeff

  def 		_lowerCamelCase						(self	:			str						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			Tuple = ()     ) ->							LMSDiscreteSchedulerState:
        snake_case_    							=				jnp.linspace(self.config.num_train_timesteps - 1						,			0						,			lowerCAmelCase_						,			dtype=self.dtype     )

        snake_case_    							=				jnp.floor(lowerCAmelCase_     ).astype(jnp.intaa     )
        snake_case_    							=				jnp.ceil(lowerCAmelCase_     ).astype(jnp.intaa     )

        snake_case_    							=				jnp.mod(lowerCAmelCase_						,			1.0     )

        snake_case_    							=				((1 - state.common.alphas_cumprod) / state.common.alphas_cumprod) ** 0.5
        snake_case_    							=				(1 - frac) * sigmas[low_idx] + frac * sigmas[high_idx]
        snake_case_    							=				jnp.concatenate([sigmas, jnp.array([0.0]						,			dtype=self.dtype     )]     )

        snake_case_    							=				timesteps.astype(jnp.intaa     )

        # initial running values
        snake_case_    							=				jnp.zeros((0,) + shape						,			dtype=self.dtype     )

        return state.replace(
            timesteps=lowerCAmelCase_						,			sigmas=lowerCAmelCase_						,			num_inference_steps=lowerCAmelCase_						,			derivatives=lowerCAmelCase_						,			)

  def 		_lowerCamelCase						(self	:			Optional[int]						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			int						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			int = 4						,			lowerCAmelCase_	:			bool = True						,			) ->							Union[FlaxLMSSchedulerOutput, Tuple]:
        if state.num_inference_steps is None:
              raise ValueError(
                  "Number of inference steps is 'None', you need to run 'set_timesteps' after creating the scheduler"     )

        snake_case_    							=				state.sigmas[timestep]

        # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
        if self.config.prediction_type == "epsilon":
              snake_case_    							=				sample - sigma * model_output
        elif self.config.prediction_type == "v_prediction":
              # * c_out + input * c_skip
              snake_case_    							=				model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
        else:
              raise ValueError(
                  f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`'''     )

        # 2. Convert to an ODE derivative
        snake_case_    							=				(sample - pred_original_sample) / sigma
        snake_case_    							=				state.replace(derivatives=jnp.append(state.derivatives						,			lowerCAmelCase_     )     )
        if len(state.derivatives     ) > order:
              snake_case_    							=				state.replace(derivatives=jnp.delete(state.derivatives						,			0     )     )

        # 3. Compute linear multistep coefficients
        snake_case_    							=				min(timestep + 1						,			lowerCAmelCase_     )
        snake_case_    							=				[self.get_lms_coefficient(lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_						,			lowerCAmelCase_     ) for curr_order in range(lowerCAmelCase_     )]

        # 4. Compute previous sample based on the derivatives path
        snake_case_    							=				sample + sum(
            coeff * derivative for coeff, derivative in zip(lowerCAmelCase_						,			reversed(state.derivatives     )     )     )

        if not return_dict:
              return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=lowerCAmelCase_						,			state=lowerCAmelCase_     )

  def 		_lowerCamelCase						(self	:			Any						,			lowerCAmelCase_	:			LMSDiscreteSchedulerState						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray						,			lowerCAmelCase_	:			jnp.ndarray						,			) ->							jnp.ndarray:
        snake_case_    							=				state.sigmas[timesteps].flatten()
        snake_case_    							=				broadcast_to_shape_from_left(lowerCAmelCase_						,			noise.shape     )

        snake_case_    							=				original_samples + noise * sigma

        return noisy_samples


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







import unittest
from pathlib import Path

from transformers import is_vision_available, load_tool
from transformers.testing_utils import get_tests_dir

from .test_tools_common import ToolTesterMixin


if is_vision_available():
     from PIL import Image




class  __lowerCAmelCase							(				unittest.TestCase   ,			lowerCAmelCase):






       def        SCREAMING_SNAKE_CASE     ( self:       Dict					):
         lowercase     :Any   						=    load_tool("image-segmentation"					)
         self.tool.setup()
         lowercase     :int   						=    load_tool("image-segmentation"							,       remote=_lowerCAmelCase					)






       def        SCREAMING_SNAKE_CASE     ( self:       str					):
         lowercase     :Any   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Union[str, Any]					):
         lowercase     :Dict   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[Any]   						=    self.remote_tool(_lowerCAmelCase							,       "cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       List[Any]					):
         lowercase     :Optional[Any]   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :Dict   						=    self.tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)






       def        SCREAMING_SNAKE_CASE     ( self:       Tuple					):
         lowercase     :int   						=    Image.open(Path(get_tests_dir("fixtures/tests_samples/COCO"					)					) / "000000039769.png"					)
         lowercase     :List[str]   						=    self.remote_tool(image=_lowerCAmelCase							,       label="cat"					)
         self.assertTrue(isinstance(_lowerCAmelCase							,       Image.Image					)					)



