






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

import flax
import jax.numpy as jnp
from jax import random

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput
from .scheduling_utils_flax import FlaxSchedulerMixin


@flax.struct.dataclass
class  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     UpperCamelCase_							:		Optional[int]			=	None
     UpperCamelCase_							:		Optional[jnp.ndarray]			=	None
     UpperCamelCase_							:		Optional[jnp.ndarray]			=	None  # sigma(t_i)


     @classmethod
     def 				_A       (				cls       :	Optional[Any]      ):
       return cls()





@dataclass
class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		jnp.ndarray
     UpperCamelCase_							:		jnp.ndarray
     UpperCamelCase_							:		KarrasVeSchedulerState





class  SCREAMING_SNAKE_CASE			(			lowerCAmelCase      ,		lowerCAmelCase					):
     '''simple docstring'''


     @property
     def 				_A       (				self       :	List[Any]      ):
       return True


     @register_to_config
     def __init__(				self       :	int					, UpperCAmelCase_       :	float = 0.02					, UpperCAmelCase_       :	float = 100					, UpperCAmelCase_       :	float = 1.007					, UpperCAmelCase_       :	float = 80					, UpperCAmelCase_       :	float = 0.05					, UpperCAmelCase_       :	float = 50					, ):
       pass


     def 				_A       (				self       :	List[Any]      ):
       return KarrasVeSchedulerState.create()


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	int					, UpperCAmelCase_       :	Tuple = ()      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    jnp.arange(0					, UpperCAmelCase_      )[::-1].copy()
       SCREAMING_SNAKE_CASE       :      int							=    [
           (
               self.config.sigma_max**2
               * (self.config.sigma_min**2 / self.config.sigma_max**2) ** (i / (num_inference_steps - 1))
           )
           for i in timesteps
       ]

       return state.replace(
           num_inference_steps=UpperCAmelCase_					, schedule=jnp.array(UpperCAmelCase_					, dtype=jnp.floataa      )					, timesteps=UpperCAmelCase_					, )


     def 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	random.KeyArray					, ):
       if self.config.s_min <= sigma <= self.config.s_max:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    min(self.config.s_churn / state.num_inference_steps					, 2**0.5 - 1      )
       else:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    0

       # sample eps ~ N(0, S_noise^2 * I)
       SCREAMING_SNAKE_CASE       :      Tuple							=    random.split(UpperCAmelCase_					, num=1      )
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.config.s_noise * random.normal(key=UpperCAmelCase_					, shape=sample.shape      )
       SCREAMING_SNAKE_CASE       :      Tuple							=    sigma + gamma * sigma
       SCREAMING_SNAKE_CASE       :      Any							=    sample + ((sigma_hat**2 - sigma**2) ** 0.5 * eps)

       return sample_hat, sigma_hat


     def 				_A       (				self       :	str					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	bool = True					, ):
       SCREAMING_SNAKE_CASE       :      int							=    sample_hat + sigma_hat * model_output
       SCREAMING_SNAKE_CASE       :      List[Any]							=    (sample_hat - pred_original_sample) / sigma_hat
       SCREAMING_SNAKE_CASE       :      List[Any]							=    sample_hat + (sigma_prev - sigma_hat) * derivative

       if not return_dict:
         return (sample_prev, derivative, state)

       return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase_					, derivative=UpperCAmelCase_					, state=UpperCAmelCase_      )


     def 				_A       (				self       :	Union[str, Any]					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	float					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	jnp.ndarray					, UpperCAmelCase_       :	bool = True					, ):
       SCREAMING_SNAKE_CASE       :      str							=    sample_prev + sigma_prev * model_output
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    (sample_prev - pred_original_sample) / sigma_prev
       SCREAMING_SNAKE_CASE       :      List[Any]							=    sample_hat + (sigma_prev - sigma_hat) * (0.5 * derivative + 0.5 * derivative_corr)

       if not return_dict:
         return (sample_prev, derivative, state)

       return FlaxKarrasVeOutput(prev_sample=UpperCAmelCase_					, derivative=UpperCAmelCase_					, state=UpperCAmelCase_      )






     def 				_A       (				self       :	Optional[Any]					, UpperCAmelCase_       :	KarrasVeSchedulerState					, UpperCAmelCase_       :	Any					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	Optional[Any]      ):
       raise NotImplementedError()














from typing import TYPE_CHECKING

from ....utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case    				=		{
    """configuration_trajectory_transformer""": [
        """TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """TrajectoryTransformerConfig""",
    ],
}

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   snake_case    				=		[
       """TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """TrajectoryTransformerModel""",
       """TrajectoryTransformerPreTrainedModel""",
       """load_tf_weights_in_trajectory_transformer""",
   ]


if TYPE_CHECKING:
   from .configuration_trajectory_transformer import (
       TRAJECTORY_TRANSFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
       TrajectoryTransformerConfig,
   )

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_trajectory_transformer import (
          TRAJECTORY_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          TrajectoryTransformerModel,
          TrajectoryTransformerPreTrainedModel,
          load_tf_weights_in_trajectory_transformer,
      )


else:
   import sys

   snake_case    				=		_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)






