'''simple docstring'''





import importlib
import math
import os
from dataclasses import dataclass
from enum import Enum
from typing import Any, Dict, Optional, Tuple, Union

import flax
import jax.numpy as jnp

from ..utils import BaseOutput


SCREAMING_SNAKE_CASE_:					Tuple  ='scheduler_config.json'




class 	__A				(			UpperCamelCase__     ):
    a__	:						Dict	       =  1
    a__	:						Optional[int]	       =  2
    a__	:						List[str]	       =  3
    a__	:						Any	       =  4
    a__	:						Optional[int]	       =  5




@dataclass
class 	__A				(			UpperCamelCase__     ):
    a__	:						jnp.ndarray




class 	__A				:
    a__	:						List[Any]	       =  SCHEDULER_CONFIG_NAME
    a__	:						List[str]	       =  ["""dtype"""]
    a__	:						List[Any]	       =  []
    a__	:						Any	       =  True



    @classmethod
    def  _lowercase						(cls   :   Tuple       ,      __a   :   Dict[str, Any] = None       ,      __a   :   Optional[str] = None       ,      __a   :   Any=False       ,      **__a   :   Dict       ,      ):
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       cls.load_config(
             pretrained_model_name_or_path=__a       ,      subfolder=__a       ,      return_unused_kwargs=__a       ,      **__a       ,      )
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       cls.from_config(__a       ,      return_unused_kwargs=__a       ,      **__a			)

         if hasattr(__a       ,      "create_state"			) and getattr(__a       ,      "has_state"       ,      __a			):
              UpperCAmelCase_	   =       scheduler.create_state()

         if return_unused_kwargs:
              return scheduler, state, unused_kwargs

         return scheduler, state



    def  _lowercase						(self   :   Any       ,      __a   :   Union[str, os.PathLike]       ,      __a   :   bool = False       ,      **__a   :   Dict			):
         self.save_config(save_directory=__a       ,      push_to_hub=__a       ,      **__a			)



    @property
    def  _lowercase						(self   :   List[str]			):
         return self._get_compatibles()






    @classmethod
    def  _lowercase						(cls   :   List[str]			):
         UpperCAmelCase_	   =       list(set([cls.__name__] + cls._compatibles			)			)
         UpperCAmelCase_	   =       importlib.import_module(__name__.split("."			)[0]			)
         UpperCAmelCase_	   =       [
             getattr(__a       ,      __a			) for c in compatible_classes_str if hasattr(__a       ,      __a			)
         ]
         return compatible_classes





def      lowerCAmelCase_    ( snake_case_	:			jnp.ndarray ,   snake_case_	:			Tuple[int]      )   ->				jnp.ndarray:


     '''simple docstring'''




     assert len(snake_case_      ) >= x.ndim
     return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(snake_case_      ) - x.ndim)      ) ,   snake_case_      )





def      lowerCAmelCase_    ( snake_case_	:			int ,   snake_case_	:			Union[str, Any]=0.999 ,   snake_case_	:			Dict=jnp.floataa      )   ->				jnp.ndarray:


     '''simple docstring'''




     def alpha_bar(snake_case_	:			Union[str, Any]      ):
          return math.cos((time_step + 0.008) / 1.008 * math.pi / 2      ) ** 2

     UpperCAmelCase_	   =       []
     for i in range(snake_case_      ):
          UpperCAmelCase_	   =       i / num_diffusion_timesteps
          UpperCAmelCase_	   =       (i + 1) / num_diffusion_timesteps
          betas.append(min(1 - alpha_bar(snake_case_      ) / alpha_bar(snake_case_      ) ,   snake_case_      )      )
     return jnp.array(snake_case_ ,   dtype=snake_case_      )




@flax.struct.dataclass
class 	__A				:
    a__	:						jnp.ndarray
    a__	:						jnp.ndarray
    a__	:						jnp.ndarray



    @classmethod
    def  _lowercase						(cls   :   Tuple       ,      __a   :   Optional[Any]			):
         UpperCAmelCase_	   =       scheduler.config

         if config.trained_betas is not None:
              UpperCAmelCase_	   =       jnp.asarray(config.trained_betas       ,      dtype=scheduler.dtype			)
         elif config.beta_schedule == "linear":
              UpperCAmelCase_	   =       jnp.linspace(config.beta_start       ,      config.beta_end       ,      config.num_train_timesteps       ,      dtype=scheduler.dtype			)
         elif config.beta_schedule == "scaled_linear":
              # this schedule is very specific to the latent diffusion model.
              UpperCAmelCase_	   =       (
                  jnp.linspace(
                      config.beta_start**0.5       ,      config.beta_end**0.5       ,      config.num_train_timesteps       ,      dtype=scheduler.dtype			)
                  ** 2
              )
         elif config.beta_schedule == "squaredcos_cap_v2":
              # Glide cosine schedule
              UpperCAmelCase_	   =       betas_for_alpha_bar(config.num_train_timesteps       ,      dtype=scheduler.dtype			)
         else:
              raise NotImplementedError(
                  f"""beta_schedule {config.beta_schedule} is not implemented for scheduler {scheduler.__class__.__name__}"""			)

         UpperCAmelCase_	   =       1.0 - betas

         UpperCAmelCase_	   =       jnp.cumprod(__a       ,      axis=0			)

         return cls(
             alphas=__a       ,      betas=__a       ,      alphas_cumprod=__a       ,      )





def      lowerCAmelCase_    ( snake_case_	:			CommonSchedulerState ,   snake_case_	:			jnp.ndarray ,   snake_case_	:			jnp.ndarray ,   snake_case_	:			jnp.ndarray      )   ->				List[str]:


     '''simple docstring'''




     UpperCAmelCase_	   =       state.alphas_cumprod

     UpperCAmelCase_	   =       alphas_cumprod[timesteps] ** 0.5
     UpperCAmelCase_	   =       sqrt_alpha_prod.flatten()
     UpperCAmelCase_	   =       broadcast_to_shape_from_left(snake_case_ ,   original_samples.shape      )

     UpperCAmelCase_	   =       (1 - alphas_cumprod[timesteps]) ** 0.5
     UpperCAmelCase_	   =       sqrt_one_minus_alpha_prod.flatten()
     UpperCAmelCase_	   =       broadcast_to_shape_from_left(snake_case_ ,   original_samples.shape      )

     return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def      lowerCAmelCase_    ( snake_case_	:			CommonSchedulerState ,   snake_case_	:			jnp.ndarray ,   snake_case_	:			jnp.ndarray ,   snake_case_	:			jnp.ndarray      )   ->				int:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_sqrt_alpha_prod(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
     return noisy_samples






def      lowerCAmelCase_    ( snake_case_	:			CommonSchedulerState ,   snake_case_	:			jnp.ndarray ,   snake_case_	:			jnp.ndarray ,   snake_case_	:			jnp.ndarray      )   ->				Any:


     '''simple docstring'''




     UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_sqrt_alpha_prod(snake_case_ ,   snake_case_ ,   snake_case_ ,   snake_case_      )
     UpperCAmelCase_	   =       sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
     return velocity





'''simple docstring'''





from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


SCREAMING_SNAKE_CASE_:					List[str]  =logging.get_logger(__name__)

SCREAMING_SNAKE_CASE_:					Tuple  ={
    'microsoft/resnet-50': 'https://huggingface.co/microsoft/resnet-50/blob/main/config.json',
}




class 	__A				(			UpperCamelCase__		,     UpperCamelCase__     ):
    a__	:						Optional[Any]	       =  """resnet"""
    a__	:						Tuple	       =  ["""basic""", """bottleneck"""]



    def __init__(self   :   List[Any]       ,      __a   :   Any=3       ,      __a   :   Dict=64       ,      __a   :   Union[str, Any]=[256, 512, 1024, 2048]       ,      __a   :   str=[3, 4, 6, 3]       ,      __a   :   Optional[Any]="bottleneck"       ,      __a   :   Tuple="relu"       ,      __a   :   int=False       ,      __a   :   Optional[int]=None       ,      __a   :   str=None       ,      **__a   :   Dict       ,      ):
         super().__init__(**__a			)
         if layer_type not in self.layer_types:
              raise ValueError(f"""layer_type={layer_type} is not one of {",".join(self.layer_types			)}"""			)
         UpperCAmelCase_	   =       num_channels
         UpperCAmelCase_	   =       embedding_size
         UpperCAmelCase_	   =       hidden_sizes
         UpperCAmelCase_	   =       depths
         UpperCAmelCase_	   =       layer_type
         UpperCAmelCase_	   =       hidden_act
         UpperCAmelCase_	   =       downsample_in_first_stage
         UpperCAmelCase_	   =       ["stem"] + [f"""stage{idx}""" for idx in range(1       ,      len(__a			) + 1			)]
         UpperCAmelCase_   ,			UpperCAmelCase_	   =       get_aligned_output_features_output_indices(
             out_features=__a       ,      out_indices=__a       ,      stage_names=self.stage_names			)




class 	__A				(			UpperCamelCase__     ):
    a__	:						int	       =  version.parse("""1.11"""     )



    @property
    def  _lowercase						(self   :   Optional[int]			):
         return OrderedDict(
             [
                 ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
             ]			)






    @property
    def  _lowercase						(self   :   str			):
         return 1E-3




