




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 	A	:
     """simple docstring"""

     _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        __snake_case      (			cls     :      str		,     A__     :      CommonSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray	) ->				List[str]:
           return cls(common=A__		,     init_noise_sigma=A__		,     timesteps=A__		,     sigmas=A__	)






@dataclass
class 	A	(						_lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		LMSDiscreteSchedulerState






class 	A	(						_lowerCAmelCase	,  _lowerCAmelCase						):
     """simple docstring"""

     _snake_case:		List[Any]   =[e.name for e in FlaxKarrasDiffusionSchedulers]

     _snake_case:		jnp.dtype




     @property
     def        __snake_case      (			self     :      List[Any]	) ->				Tuple:
           return True




     @register_to_config
     def __init__(			self     :      Any		,     A__     :      int = 1_0_0_0		,     A__     :      float = 0.0001		,     A__     :      float = 0.02		,     A__     :      str = "linear"		,     A__     :      Optional[jnp.ndarray] = None		,     A__     :      str = "epsilon"		,     A__     :      jnp.dtype = jnp.floataa		,     ) ->				Any:
           lowerCamelCase__					     = dtype




     def        __snake_case      (			self     :      Optional[int]		,     A__     :      Optional[CommonSchedulerState] = None	) ->				LMSDiscreteSchedulerState:
           if common is None:
                 lowerCamelCase__					     = CommonSchedulerState.create(self	)

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

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

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




     def        __snake_case      (			self     :      Any		,     A__     :      LMSDiscreteSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      int	) ->				jnp.ndarray:
           ((lowerCamelCase__)     ,						)					     = jnp.where(state.timesteps == timestep		,     size=1	)
           lowerCamelCase__					     = step_index[0]

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




     def        __snake_case      (			self     :      str		,     A__     :      LMSDiscreteSchedulerState		,     A__     :      Any		,     A__     :      Optional[Any]		,     A__     :      Any	) ->				List[Any]:

           def lms_derivative(A__     :      Union[str, Any]	):
                 lowerCamelCase__					     = 1.0
                 for k in range(A__	):
                       if current_order == k:
                             continue
                       prod *= (tau - state.sigmas[t - k]) / (state.sigmas[t - current_order] - state.sigmas[t - k])
                 return prod

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

           return integrated_coeff




     def        __snake_case      (			self     :      str		,     A__     :      LMSDiscreteSchedulerState		,     A__     :      int		,     A__     :      Tuple = ()	) ->				LMSDiscreteSchedulerState:
           lowerCamelCase__					     = jnp.linspace(self.config.num_train_timesteps - 1		,     0		,     A__		,     dtype=self.dtype	)

           lowerCamelCase__					     = jnp.floor(A__	).astype(jnp.intaa	)
           lowerCamelCase__					     = jnp.ceil(A__	).astype(jnp.intaa	)

           lowerCamelCase__					     = jnp.mod(A__		,     1.0	)

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

           lowerCamelCase__					     = timesteps.astype(jnp.intaa	)

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

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




     def        __snake_case      (			self     :      Union[str, Any]		,     A__     :      LMSDiscreteSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      int		,     A__     :      jnp.ndarray		,     A__     :      int = 4		,     A__     :      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'	)

           lowerCamelCase__					     = state.sigmas[timestep]

           # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
           if self.config.prediction_type == "epsilon":
                 lowerCamelCase__					     = sample - sigma * model_output
           elif self.config.prediction_type == "v_prediction":
                 # * c_out + input * c_skip
                 lowerCamelCase__					     = 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
           lowerCamelCase__					     = (sample - pred_original_sample) / sigma
           lowerCamelCase__					     = state.replace(derivatives=jnp.append(state.derivatives		,     A__	)	)
           if len(state.derivatives	) > order:
                 lowerCamelCase__					     = state.replace(derivatives=jnp.delete(state.derivatives		,     0	)	)

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

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

           if not return_dict:
                 return (prev_sample, state)

           return FlaxLMSSchedulerOutput(prev_sample=A__		,     state=A__	)




     def        __snake_case      (			self     :      int		,     A__     :      LMSDiscreteSchedulerState		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     A__     :      jnp.ndarray		,     ) ->				jnp.ndarray:
           lowerCamelCase__					     = state.sigmas[timesteps].flatten()
           lowerCamelCase__					     = broadcast_to_shape_from_left(A__		,     noise.shape	)

           lowerCamelCase__					     = original_samples + noise * sigma

           return noisy_samples




     def __len__(			self     :      List[str]	) ->				str:
           return self.config.num_train_timesteps







import glob
import os

from get_test_info import get_tester_classes


if __name__ == "__main__":
							lowercase     :							List[Any]	     =			[]

							lowercase     :							Optional[int]	     =			os.path.join('''tests''', '''models''', '''**''', '''test_modeling_*.py''')
							lowercase     :							Optional[Any]	     =			glob.glob(pattern)
							# TODO: deal with TF/Flax too
							lowercase     :							Tuple	     =			[
							    x for x in test_files if not (x.startswith('''test_modeling_tf_''') or x.startswith('''test_modeling_flax_'''))
							]

							for test_file in test_files:
														lowercase     :							Union[str, Any]	     =			get_tester_classes(test_file)
														for tester_class in tester_classes:
																					# A few tester classes don't have `parent` parameter in `__init__`.
																					# TODO: deal this better
																					try:
																												lowercase     :							Union[str, Any]	     =			tester_class(parent=None)
																					except Exception:
																												continue
																					if hasattr(tester, '''get_config'''):
																												lowercase     :							Tuple	     =			tester.get_config()
																												for k, v in config.to_dict().items():
																																			if isinstance(v, int):
																																										lowercase     :							Tuple	     =			None
																																										if k in ["vocab_size"]:
																																																	lowercase     :							str	     =			1_00
																																										elif k in ["max_position_embeddings"]:
																																																	lowercase     :							List[str]	     =			1_28
																																										elif k in ["hidden_size", "d_model"]:
																																																	lowercase     :							Union[str, Any]	     =			40
																																										elif k == ["num_layers", "num_hidden_layers", "num_encoder_layers", "num_decoder_layers"]:
																																																	lowercase     :							List[str]	     =			5
																																										if target is not None and v > target:
																																																	failures.append(
																																																	    F"""{tester_class.__name__} will produce a `config` of type `{config.__class__.__name__}`"""
																																																	    F""" with config[\"{k}\"] = {v} which is too large for testing! Set its value to be smaller"""
																																																	    F""" than {target}."""
																																																	)

							if len(failures) > 0:
														raise Exception(F"""There were {len(failures)} failures:\n""" + '''\n'''.join(failures))
