





"""simple docstring"""


import sys
from collections.abc import Mapping

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter



class      lowercase_				(     TensorFormatter[Mapping, np.ndarray, Mapping]							):

       def __init__(			self   ,					__SCREAMING_SNAKE_CASE=None   ,					**__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        super().__init__(features=snake_case_     )
        snake_case__    							=				np_array_kwargs

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        if isinstance(snake_case_   ,					snake_case_     ):
         if column and all(
             isinstance(snake_case_   ,					np.ndarray     ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column     ):
          return np.stack(snake_case_     )
         else:
          # don't use np.array(column, dtype=object)
          # since it fails in certain cases
          # see https://stackoverflow.com/q/51005699
          snake_case__    							=				np.empty(len(snake_case_     )   ,					dtype=snake_case_     )
          snake_case__    							=				column
          return out
        return column

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        if isinstance(snake_case_   ,					(str, bytes, type(snake_case_     ))     ):
         return value
        elif isinstance(snake_case_   ,					(np.character, np.ndarray)     ) and np.issubdtype(value.dtype   ,					np.character     ):
         return value
        elif isinstance(snake_case_   ,					np.number     ):
         return value

        snake_case__    							=				{}

        if isinstance(snake_case_   ,					np.ndarray     ) and np.issubdtype(value.dtype   ,					np.integer     ):
         snake_case__    							=				{'''dtype''': np.intaa}
        elif isinstance(snake_case_   ,					np.ndarray     ) and np.issubdtype(value.dtype   ,					np.floating     ):
         snake_case__    							=				{'''dtype''': np.floataa}
        elif config.PIL_AVAILABLE and "PIL" in sys.modules:
         import PIL.Image

         if isinstance(snake_case_   ,					PIL.Image.Image     ):
          return np.asarray(snake_case_   ,					**self.np_array_kwargs     )

        return np.asarray(snake_case_   ,					**{**default_dtype, **self.np_array_kwargs}     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        if config.TORCH_AVAILABLE and "torch" in sys.modules:
         import torch

         if isinstance(snake_case_   ,					torch.Tensor     ):
          return self._tensorize(data_struct.detach().cpu().numpy()[()]     )
        if hasattr(snake_case_   ,					"__array__"     ) and not isinstance(snake_case_   ,					(np.ndarray, np.character, np.number)     ):
         snake_case__    							=				data_struct.__array__()
        # support for nested types like struct of list of struct
        if isinstance(snake_case_   ,					np.ndarray     ):
         if data_struct.dtype == object:
          return self._consolidate([self.recursive_tensorize(snake_case_     ) for substruct in data_struct]     )
        if isinstance(snake_case_   ,					(list, tuple)     ):
         return self._consolidate([self.recursive_tensorize(snake_case_     ) for substruct in data_struct]     )
        return self._tensorize(snake_case_     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        return map_nested(self._recursive_tensorize   ,					snake_case_   ,					map_list=snake_case_     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				self.numpy_arrow_extractor().extract_row(snake_case_     )
        snake_case__    							=				self.python_features_decoder.decode_row(snake_case_     )
        return self.recursive_tensorize(snake_case_     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				self.numpy_arrow_extractor().extract_column(snake_case_     )
        snake_case__    							=				self.python_features_decoder.decode_column(snake_case_   ,					pa_table.column_names[0]     )
        snake_case__    							=				self.recursive_tensorize(snake_case_     )
        snake_case__    							=				self._consolidate(snake_case_     )
        return column

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        snake_case__    							=				self.numpy_arrow_extractor().extract_batch(snake_case_     )
        snake_case__    							=				self.python_features_decoder.decode_batch(snake_case_     )
        snake_case__    							=				self.recursive_tensorize(snake_case_     )
        for column_name in batch:
         snake_case__    							=				self._consolidate(batch[column_name]     )
        return batch








"""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      lowercase_				:
       snake_case__				:							CommonSchedulerState

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

       # running values
       snake_case__				:							Optional[jnp.ndarray]											=     None

       @classmethod
       def 		_SCREAMING_SNAKE_CASE(			cls   ,					__SCREAMING_SNAKE_CASE   ,					__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   ,					sigmas=__SCREAMING_SNAKE_CASE     )



@dataclass
class      lowercase_				(     __lowerCAmelCase							):
       snake_case__				:							LMSDiscreteSchedulerState



class      lowercase_				(     __lowerCAmelCase				,__lowerCAmelCase							):
       snake_case__				:							Any											=     [e.name for e in FlaxKarrasDiffusionSchedulers]

       snake_case__				:							jnp.dtype

       @property
       def 		_SCREAMING_SNAKE_CASE(			self     ):






        '''simple docstring'''

        return True

       @register_to_config
       def __init__(			self   ,					__SCREAMING_SNAKE_CASE = 1000   ,					__SCREAMING_SNAKE_CASE = 0.0001   ,					__SCREAMING_SNAKE_CASE = 0.02   ,					__SCREAMING_SNAKE_CASE = "linear"   ,					__SCREAMING_SNAKE_CASE = None   ,					__SCREAMING_SNAKE_CASE = "epsilon"   ,					__SCREAMING_SNAKE_CASE = jnp.floataa   ,					):






        '''simple docstring'''

        snake_case__    							=				dtype

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE = None     ):






        '''simple docstring'''

        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=__SCREAMING_SNAKE_CASE   ,					init_noise_sigma=__SCREAMING_SNAKE_CASE   ,					timesteps=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        ((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 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ):






        '''simple docstring'''

        def lms_derivative(__SCREAMING_SNAKE_CASE     ):
         snake_case__    							=				1.0
         for k in range(__SCREAMING_SNAKE_CASE     ):
          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(__SCREAMING_SNAKE_CASE   ,					state.sigmas[t]   ,					state.sigmas[t + 1]   ,					epsrel=1e-4     )[0]

        return integrated_coeff

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






        '''simple docstring'''

        snake_case__    							=				jnp.linspace(self.config.num_train_timesteps - 1   ,					0   ,					__SCREAMING_SNAKE_CASE   ,					dtype=self.dtype     )

        snake_case__    							=				jnp.floor(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )
        snake_case__    							=				jnp.ceil(__SCREAMING_SNAKE_CASE     ).astype(jnp.intaa     )

        snake_case__    							=				jnp.mod(__SCREAMING_SNAKE_CASE   ,					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=__SCREAMING_SNAKE_CASE   ,					sigmas=__SCREAMING_SNAKE_CASE   ,					num_inference_steps=__SCREAMING_SNAKE_CASE   ,					derivatives=__SCREAMING_SNAKE_CASE   ,					)

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE = 4   ,					__SCREAMING_SNAKE_CASE = True   ,					):






        '''simple docstring'''

        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   ,					__SCREAMING_SNAKE_CASE     )     )
        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   ,					__SCREAMING_SNAKE_CASE     )
        snake_case__    							=				[self.get_lms_coefficient(__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE     ) for curr_order in range(__SCREAMING_SNAKE_CASE     )]

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

        if not return_dict:
         return (prev_sample, state)

        return FlaxLMSSchedulerOutput(prev_sample=__SCREAMING_SNAKE_CASE   ,					state=__SCREAMING_SNAKE_CASE     )

       def 		_SCREAMING_SNAKE_CASE(			self   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					__SCREAMING_SNAKE_CASE   ,					):






        '''simple docstring'''

        snake_case__    							=				state.sigmas[timesteps].flatten()
        snake_case__    							=				broadcast_to_shape_from_left(__SCREAMING_SNAKE_CASE   ,					noise.shape     )

        snake_case__    							=				original_samples + noise * sigma

        return noisy_samples

       def __len__(			self     ):






        '''simple docstring'''

        return self.config.num_train_timesteps

