






# DISCLAIMER: This file is strongly influenced by https://github.com/yang-song/score_sde_pytorch

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 .scheduling_utils_flax import FlaxSchedulerMixin, FlaxSchedulerOutput, broadcast_to_shape_from_left
@flax.struct.dataclass
class 	a_:



     """simple docstring"""



     __snake_case		:				Optional[jnp.ndarray]  					=None
     __snake_case		:				Optional[jnp.ndarray]  					=None
     __snake_case		:				Optional[jnp.ndarray]  					=None





     @classmethod
     def     __UpperCamelCase						(		cls					:	Optional[Any])							->	int:

      """simple docstring"""
      return cls()

@dataclass
class 	a_(		lowercase__	):



     """simple docstring"""



     __snake_case		:				ScoreSdeVeSchedulerState
     __snake_case		:				jnp.ndarray
     __snake_case		:				Optional[jnp.ndarray]  					=None

class 	a_(		lowercase__			,							lowercase__	):



     """simple docstring"""



     @property
     def     __UpperCamelCase						(		self					:	Tuple)							->	str:

      """simple docstring"""
      return True





     @register_to_config
     def __init__(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	int = 2_0_0_0  ,  lowerCAmelCase__					:	float = 0.15  ,  lowerCAmelCase__					:	float = 0.01  ,  lowerCAmelCase__					:	float = 13_48.0  ,  lowerCAmelCase__					:	float = 1e-5  ,  lowerCAmelCase__					:	int = 1  ,  )							->	str:

      """simple docstring"""
      pass





     def     __UpperCamelCase						(		self					:	Optional[Any])							->	List[Any]:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		ScoreSdeVeSchedulerState.create()
      return self.set_sigmas(
          lowerCAmelCase__  ,  self.config.num_train_timesteps  ,  self.config.sigma_min  ,  self.config.sigma_max  ,  self.config.sampling_eps  ,  )





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Tuple = ()  ,  lowerCAmelCase__					:	float = None)							->	ScoreSdeVeSchedulerState:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		sampling_eps if sampling_eps is not None else self.config.sampling_eps

      SCREAMING_SNAKE_CASE				    =		jnp.linspace(1  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      return state.replace(timesteps=lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	float = None  ,  lowerCAmelCase__					:	float = None  ,  lowerCAmelCase__					:	float = None  ,  )							->	ScoreSdeVeSchedulerState:

      """simple docstring"""
      SCREAMING_SNAKE_CASE				    =		sigma_min if sigma_min is not None else self.config.sigma_min
      SCREAMING_SNAKE_CASE				    =		sigma_max if sigma_max is not None else self.config.sigma_max
      SCREAMING_SNAKE_CASE				    =		sampling_eps if sampling_eps is not None else self.config.sampling_eps
      if state.timesteps is None:
       SCREAMING_SNAKE_CASE				    =		self.set_timesteps(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)

      SCREAMING_SNAKE_CASE				    =		jnp.exp(jnp.linspace(jnp.log(lowerCAmelCase__)  ,  jnp.log(lowerCAmelCase__)  ,  lowerCAmelCase__))
      SCREAMING_SNAKE_CASE				    =		jnp.array([sigma_min * (sigma_max / sigma_min) ** t for t in state.timesteps])

      return state.replace(discrete_sigmas=lowerCAmelCase__  ,  sigmas=lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Optional[Any]  ,  lowerCAmelCase__					:	List[Any]  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	Dict)							->	Dict:

      """simple docstring"""
      return jnp.where(timesteps == 0  ,  jnp.zeros_like(lowerCAmelCase__)  ,  state.discrete_sigmas[timesteps - 1])





     def     __UpperCamelCase						(		self					:	Any  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	int  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	random.KeyArray  ,  lowerCAmelCase__					:	bool = True  ,  )							->	Union[FlaxSdeVeOutput, Tuple]:

      """simple docstring"""
      if state.timesteps is None:
       raise ValueError(
           '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

      SCREAMING_SNAKE_CASE				    =		timestep * jnp.ones(
          sample.shape[0]  ,  )
      SCREAMING_SNAKE_CASE				    =		(timestep * (len(state.timesteps) - 1)).long()

      SCREAMING_SNAKE_CASE				    =		state.discrete_sigmas[timesteps]
      SCREAMING_SNAKE_CASE				    =		self.get_adjacent_sigma(lowerCAmelCase__  ,  lowerCAmelCase__  ,  lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		jnp.zeros_like(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(sigma**2 - adjacent_sigma**2) ** 0.5

      # equation 6 in the paper: the model_output modeled by the network is grad_x log pt(x)
      # also equation 47 shows the analog from SDE models to ancestral sampling methods
      SCREAMING_SNAKE_CASE				    =		diffusion.flatten()
      SCREAMING_SNAKE_CASE				    =		broadcast_to_shape_from_left(lowerCAmelCase__  ,  sample.shape)
      SCREAMING_SNAKE_CASE				    =		drift - diffusion**2 * model_output

      #  equation 6: sample noise for the diffusion term of
      SCREAMING_SNAKE_CASE				    =		random.split(lowerCAmelCase__  ,  num=1)
      SCREAMING_SNAKE_CASE				    =		random.normal(key=lowerCAmelCase__  ,  shape=sample.shape)
      SCREAMING_SNAKE_CASE				    =		sample - drift  # subtract because `dt` is a small negative timestep
      # TODO is the variable diffusion the correct scaling term for the noise?
      SCREAMING_SNAKE_CASE				    =		prev_sample_mean + diffusion * noise  # add impact of diffusion field g

      if not return_dict:
       return (prev_sample, prev_sample_mean, state)

      return FlaxSdeVeOutput(prev_sample=lowerCAmelCase__  ,  prev_sample_mean=lowerCAmelCase__  ,  state=lowerCAmelCase__)





     def     __UpperCamelCase						(		self					:	Dict  ,  lowerCAmelCase__					:	ScoreSdeVeSchedulerState  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	jnp.ndarray  ,  lowerCAmelCase__					:	random.KeyArray  ,  lowerCAmelCase__					:	bool = True  ,  )							->	Union[FlaxSdeVeOutput, Tuple]:

      """simple docstring"""
      if state.timesteps is None:
       raise ValueError(
           '`state.timesteps` is not set, you need to run \'set_timesteps\' after creating the scheduler')

      # For small batch sizes, the paper "suggest replacing norm(z) with sqrt(d), where d is the dim. of z"
      # sample noise for correction
      SCREAMING_SNAKE_CASE				    =		random.split(lowerCAmelCase__  ,  num=1)
      SCREAMING_SNAKE_CASE				    =		random.normal(key=lowerCAmelCase__  ,  shape=sample.shape)

      # compute step size from the model_output, the noise, and the snr
      SCREAMING_SNAKE_CASE				    =		jnp.linalg.norm(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		jnp.linalg.norm(lowerCAmelCase__)
      SCREAMING_SNAKE_CASE				    =		(self.config.snr * noise_norm / grad_norm) ** 2 * 2
      SCREAMING_SNAKE_CASE				    =		step_size * jnp.ones(sample.shape[0])

      # compute corrected sample: model_output term and noise term
      SCREAMING_SNAKE_CASE				    =		step_size.flatten()
      SCREAMING_SNAKE_CASE				    =		broadcast_to_shape_from_left(lowerCAmelCase__  ,  sample.shape)
      SCREAMING_SNAKE_CASE				    =		sample + step_size * model_output
      SCREAMING_SNAKE_CASE				    =		prev_sample_mean + ((step_size * 2) ** 0.5) * noise

      if not return_dict:
       return (prev_sample, state)

      return FlaxSdeVeOutput(prev_sample=lowerCAmelCase__  ,  state=lowerCAmelCase__)





     def __len__(		self					:	Any)							->	List[Any]:

      """simple docstring"""
      return self.config.num_train_timesteps









'''simple docstring'''




# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import TYPE_CHECKING

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


a  :     Optional[Any]       =       {
    """agents""": ["""Agent""", """AzureOpenAiAgent""", """HfAgent""", """LocalAgent""", """OpenAiAgent"""],
    """base""": ["""PipelineTool""", """RemoteTool""", """Tool""", """launch_gradio_demo""", """load_tool"""],
}

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							a  :     Dict       =       ["""DocumentQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""ImageCaptioningTool"""]
							a  :     Tuple       =       ["""ImageQuestionAnsweringTool"""]
							a  :     Tuple       =       ["""ImageSegmentationTool"""]
							a  :     List[str]       =       ["""SpeechToTextTool"""]
							a  :     Dict       =       ["""TextClassificationTool"""]
							a  :     Union[str, Any]       =       ["""TextQuestionAnsweringTool"""]
							a  :     List[str]       =       ["""TextSummarizationTool"""]
							a  :     Optional[Any]       =       ["""TextToSpeechTool"""]
							a  :     str       =       ["""TranslationTool"""]

if TYPE_CHECKING:
							from .agents import Agent, AzureOpenAiAgent, HfAgent, LocalAgent, OpenAiAgent
							from .base import PipelineTool, RemoteTool, Tool, launch_gradio_demo, load_tool

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .document_question_answering import DocumentQuestionAnsweringTool
														from .image_captioning import ImageCaptioningTool
														from .image_question_answering import ImageQuestionAnsweringTool
														from .image_segmentation import ImageSegmentationTool
														from .speech_to_text import SpeechToTextTool
														from .text_classification import TextClassificationTool
														from .text_question_answering import TextQuestionAnsweringTool
														from .text_summarization import TextSummarizationTool
														from .text_to_speech import TextToSpeechTool
														from .translation import TranslationTool
else:
							import sys

							a  :     Dict       =       _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

