"""simple docstring"""





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   UpperCAmelCase_			:
      # setable values
      lowerCamelCase__	:					Optional[int]										=						None
      lowerCamelCase__	:					Optional[jnp.ndarray]										=						None
      lowerCamelCase__	:					Optional[jnp.ndarray]										=						None  # sigma(t_i)


      @classmethod
      def 	_UpperCAmelCase (     cls							) ->      int:
             return cls()





@dataclass
class   UpperCAmelCase_			(					_a):
      lowerCamelCase__	:					jnp.ndarray
      lowerCamelCase__	:					jnp.ndarray
      lowerCamelCase__	:					KarrasVeSchedulerState





class   UpperCAmelCase_			(					_a				,							_a):


      @property
      def 	_UpperCAmelCase (     self							) ->      Dict:
             return True


      @register_to_config
      def __init__(     self	,    a = 0.02	,    a = 1_0_0	,    a = 1.007	,    a = 8_0	,    a = 0.05	,    a = 5_0	,    ) ->      Optional[int]:
             pass


      def 	_UpperCAmelCase (     self							) ->      int:
             return KarrasVeSchedulerState.create()


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a = ()							) ->      KarrasVeSchedulerState:
             lowercase__    :	Optional[Any]     					=			jnp.arange(0	,    a							)[::-1].copy()
             lowercase__    :	str     					=			[
                 (
                     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=a	,    schedule=jnp.array(a	,    dtype=jnp.floataa							)	,    timesteps=a	,    )


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    ) ->      Tuple[jnp.ndarray, float]:
             if self.config.s_min <= sigma <= self.config.s_max:
                    lowercase__    :	Optional[int]     					=			min(self.config.s_churn / state.num_inference_steps	,    2**0.5 - 1							)
             else:
                    lowercase__    :	int     					=			0

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

             return sample_hat, sigma_hat


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a = True	,    ) ->      Union[FlaxKarrasVeOutput, Tuple]:
             lowercase__    :	Union[str, Any]     					=			sample_hat + sigma_hat * model_output
             lowercase__    :	int     					=			(sample_hat - pred_original_sample) / sigma_hat
             lowercase__    :	Tuple     					=			sample_hat + (sigma_prev - sigma_hat) * derivative

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

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


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a	,    a	,    a	,    a	,    a = True	,    ) ->      Union[FlaxKarrasVeOutput, Tuple]:
             lowercase__    :	Dict     					=			sample_prev + sigma_prev * model_output
             lowercase__    :	List[str]     					=			(sample_prev - pred_original_sample) / sigma_prev
             lowercase__    :	Tuple     					=			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=a	,    derivative=a	,    state=a							)


      def 	_UpperCAmelCase (     self	,    a	,    a	,    a	,    a							) ->      Optional[Any]:
             raise NotImplementedError()


"""simple docstring"""





from typing import TYPE_CHECKING

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


_UpperCamelCase     :		str											=					{
    "configuration_gpt_neo": ["GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP", "GPTNeoConfig", "GPTNeoOnnxConfig"],
}

try:
       if not is_torch_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Tuple											=					[
           "GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST",
           "GPTNeoForCausalLM",
           "GPTNeoForQuestionAnswering",
           "GPTNeoForSequenceClassification",
           "GPTNeoForTokenClassification",
           "GPTNeoModel",
           "GPTNeoPreTrainedModel",
           "load_tf_weights_in_gpt_neo",
       ]

try:
       if not is_flax_available():
              raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
       pass
else:
       _UpperCamelCase     :		Dict											=					[
           "FlaxGPTNeoForCausalLM",
           "FlaxGPTNeoModel",
           "FlaxGPTNeoPreTrainedModel",
       ]


if TYPE_CHECKING:
       from .configuration_gpt_neo import GPT_NEO_PRETRAINED_CONFIG_ARCHIVE_MAP, GPTNeoConfig, GPTNeoOnnxConfig

       try:
              if not is_torch_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_gpt_neo import (
                  GPT_NEO_PRETRAINED_MODEL_ARCHIVE_LIST,
                  GPTNeoForCausalLM,
                  GPTNeoForQuestionAnswering,
                  GPTNeoForSequenceClassification,
                  GPTNeoForTokenClassification,
                  GPTNeoModel,
                  GPTNeoPreTrainedModel,
                  load_tf_weights_in_gpt_neo,
              )

       try:
              if not is_flax_available():
                     raise OptionalDependencyNotAvailable()
       except OptionalDependencyNotAvailable:
              pass
       else:
              from .modeling_flax_gpt_neo import FlaxGPTNeoForCausalLM, FlaxGPTNeoModel, FlaxGPTNeoPreTrainedModel


else:
       import sys

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

