

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 	__magic_name__				:
     UpperCamelCase_  :CommonSchedulerState

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

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






     @classmethod
     def   UpperCAmelCase_							(							cls		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Dict:
         return cls(common=_lowercase		,	init_noise_sigma=_lowercase		,	timesteps=_lowercase		,	sigmas=_lowercase  )






@dataclass
class 	__magic_name__				(       snake_case							):
     UpperCamelCase_  :LMSDiscreteSchedulerState






class 	__magic_name__				(       snake_case ,			snake_case							):
     UpperCamelCase_  :List[Any]								=			[e.name for e in FlaxKarrasDiffusionSchedulers]

     UpperCamelCase_  :jnp.dtype






     @property
     def   UpperCAmelCase_							(							self  )->    str:
         return True






     @register_to_config
     def __init__(							self		,	_lowercase = 1_000		,	_lowercase = 0.0_001		,	_lowercase = 0.02		,	_lowercase = "linear"		,	_lowercase = None		,	_lowercase = "epsilon"		,	_lowercase = jnp.floataa		,	)->    Tuple:
         UpperCamelCase_      =	dtype






     def   UpperCAmelCase_							(							self		,	_lowercase = None  )->    LMSDiscreteSchedulerState:
         if common is None:
             UpperCamelCase_      =	CommonSchedulerState.create(self  )

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

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

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase  )->    jnp.ndarray:
         ((UpperCamelCase_)     ,					)      =	jnp.where(state.timesteps == timestep		,	size=1  )
         UpperCamelCase_      =	step_index[0]

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase  )->    Optional[Any]:

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

         UpperCamelCase_      =	integrate.quad(_lowercase		,	state.sigmas[t]		,	state.sigmas[t + 1]		,	epsrel=1e-4  )[0]

         return integrated_coeff






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase = ()  )->    LMSDiscreteSchedulerState:
         UpperCamelCase_      =	jnp.linspace(self.config.num_train_timesteps - 1		,	0		,	_lowercase		,	dtype=self.dtype  )

         UpperCamelCase_      =	jnp.floor(_lowercase  ).astype(jnp.intaa  )
         UpperCamelCase_      =	jnp.ceil(_lowercase  ).astype(jnp.intaa  )

         UpperCamelCase_      =	jnp.mod(_lowercase		,	1.0  )

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

         UpperCamelCase_      =	timesteps.astype(jnp.intaa  )

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

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






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase = 4		,	_lowercase = 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"  )

         UpperCamelCase_      =	state.sigmas[timestep]

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

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

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

         if not return_dict:
             return (prev_sample, state)

         return FlaxLMSSchedulerOutput(prev_sample=_lowercase		,	state=_lowercase  )






     def   UpperCAmelCase_							(							self		,	_lowercase		,	_lowercase		,	_lowercase		,	_lowercase		,	)->    jnp.ndarray:
         UpperCamelCase_      =	state.sigmas[timesteps].flatten()
         UpperCamelCase_      =	broadcast_to_shape_from_left(_lowercase		,	noise.shape  )

         UpperCamelCase_      =	original_samples + noise * sigma

         return noisy_samples






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



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE		:Optional[Any]				  =    {"""configuration_t5""": ["""T5_PRETRAINED_CONFIG_ARCHIVE_MAP""", """T5Config""", """T5OnnxConfig"""]}

try:
 if not is_sentencepiece_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:Dict				  =    ["""T5Tokenizer"""]

try:
 if not is_tokenizers_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:str				  =    ["""T5TokenizerFast"""]

try:
 if not is_torch_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[Any]				  =    [
     """T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """T5EncoderModel""",
     """T5ForConditionalGeneration""",
     """T5Model""",
     """T5PreTrainedModel""",
     """load_tf_weights_in_t5""",
     """T5ForQuestionAnswering""",
 ]

try:
 if not is_tf_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:int				  =    [
     """TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST""",
     """TFT5EncoderModel""",
     """TFT5ForConditionalGeneration""",
     """TFT5Model""",
     """TFT5PreTrainedModel""",
 ]

try:
 if not is_flax_available():
  raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
 pass
else:
 SCREAMING_SNAKE_CASE		:List[str]				  =    [
     """FlaxT5EncoderModel""",
     """FlaxT5ForConditionalGeneration""",
     """FlaxT5Model""",
     """FlaxT5PreTrainedModel""",
 ]


if TYPE_CHECKING:
 from .configuration_ta import T5_PRETRAINED_CONFIG_ARCHIVE_MAP, TaConfig, TaOnnxConfig

 try:
  if not is_sentencepiece_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta import TaTokenizer

 try:
  if not is_tokenizers_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .tokenization_ta_fast import TaTokenizerFast

 try:
  if not is_torch_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_ta import (
      T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TaEncoderModel,
      TaForConditionalGeneration,
      TaForQuestionAnswering,
      TaModel,
      TaPreTrainedModel,
      load_tf_weights_in_ta,
  )

 try:
  if not is_tf_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_tf_ta import (
      TF_T5_PRETRAINED_MODEL_ARCHIVE_LIST,
      TFTaEncoderModel,
      TFTaForConditionalGeneration,
      TFTaModel,
      TFTaPreTrainedModel,
  )

 try:
  if not is_flax_available():
   raise OptionalDependencyNotAvailable()
 except OptionalDependencyNotAvailable:
  pass
 else:
  from .modeling_flax_ta import (
      FlaxTaEncoderModel,
      FlaxTaForConditionalGeneration,
      FlaxTaModel,
      FlaxTaPreTrainedModel,
  )


else:
 import sys

 SCREAMING_SNAKE_CASE		:List[Any]				  =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
