


'''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


_lowercase      :      List[Any]            =					"""scheduler_config.json"""






class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				Union[str, Any]  		=	1
				__magic_name__    :				int  		=	2
				__magic_name__    :				List[Any]  		=	3
				__magic_name__    :				int  		=	4
				__magic_name__    :				str  		=	5







@dataclass
class 	UpperCamelCase__(       lowerCAmelCase	):
				__magic_name__    :				jnp.ndarray







class 	UpperCamelCase__:
				__magic_name__    :				Optional[int]  		=	SCHEDULER_CONFIG_NAME
				__magic_name__    :				List[Any]  		=	["dtype"]
				__magic_name__    :				Any  		=	[]
				__magic_name__    :				int  		=	True




				@classmethod
				def 					a__(		cls      :			Any				,   lowerCAmelCase      :			Dict[str, Any] = None				,   lowerCAmelCase      :			Optional[str] = None				,   lowerCAmelCase      :			List[str]=False				,   **lowerCAmelCase      :			List[str]				,   )->					Tuple:

										"""simple docstring"""




										UpperCAmelCase    ,    UpperCAmelCase					    =     cls.load_config(
										    pretrained_model_name_or_path=lowerCAmelCase				,   subfolder=lowerCAmelCase				,   return_unused_kwargs=lowerCAmelCase				,   **lowerCAmelCase				,   )
										UpperCAmelCase    ,    UpperCAmelCase					    =     cls.from_config(lowerCAmelCase				,   return_unused_kwargs=lowerCAmelCase				,   **lowerCAmelCase				)

										if hasattr(lowerCAmelCase				,   '''create_state'''				) and getattr(lowerCAmelCase				,   '''has_state'''				,   lowerCAmelCase				):
																UpperCAmelCase					    =     scheduler.create_state()

										if return_unused_kwargs:
																return scheduler, state, unused_kwargs

										return scheduler, state




				def 					a__(		self      :			List[str]				,   lowerCAmelCase      :			Union[str, os.PathLike]				,   lowerCAmelCase      :			bool = False				,   **lowerCAmelCase      :			List[Any]				)->					Optional[int]:

										"""simple docstring"""




										self.save_config(save_directory=lowerCAmelCase				,   push_to_hub=lowerCAmelCase				,   **lowerCAmelCase				)




				@property
				def 					a__(		self      :			Dict				)->					List[Any]:

										"""simple docstring"""




										return self._get_compatibles()





				@classmethod
				def 					a__(		cls      :			Union[str, Any]				)->					str:

										"""simple docstring"""




										UpperCAmelCase					    =     list(set([cls.__name__] + cls._compatibles				)				)
										UpperCAmelCase					    =     importlib.import_module(__name__.split('''.'''				)[0]				)
										UpperCAmelCase					    =     [
										    getattr(lowerCAmelCase				,   lowerCAmelCase				) for c in compatible_classes_str if hasattr(lowerCAmelCase				,   lowerCAmelCase				)
										]
										return compatible_classes







def 				lowerCamelCase__				(	A       :      jnp.ndarray				,       A       :      Tuple[int]					):




						'''simple docstring'''






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


def 				lowerCamelCase__				(	A       :      int				,       A       :      int=0.999				,       A       :      str=jnp.floataa					):




						'''simple docstring'''






						def alpha_bar(A       :      List[str]					):
												return math.cos((time_step + 0.008) / 1.008 * math.pi / 2					) ** 2

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






@flax.struct.dataclass
class 	UpperCamelCase__:
				__magic_name__    :				jnp.ndarray
				__magic_name__    :				jnp.ndarray
				__magic_name__    :				jnp.ndarray




				@classmethod
				def 					a__(		cls      :			int				,   lowerCAmelCase      :			Dict				)->					List[Any]:

										"""simple docstring"""




										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(lowerCAmelCase				,   axis=0				)

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







def 				lowerCamelCase__				(	A       :      CommonSchedulerState				,       A       :      jnp.ndarray				,       A       :      jnp.ndarray				,       A       :      jnp.ndarray					):




						'''simple docstring'''






						UpperCAmelCase					    =     state.alphas_cumprod

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

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

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod


def 				lowerCamelCase__				(	A       :      CommonSchedulerState				,       A       :      jnp.ndarray				,       A       :      jnp.ndarray				,       A       :      jnp.ndarray					):




						'''simple docstring'''






						UpperCAmelCase    ,    UpperCAmelCase					    =     get_sqrt_alpha_prod(A				,       A				,       A				,       A					)
						UpperCAmelCase					    =     sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples




def 				lowerCamelCase__				(	A       :      CommonSchedulerState				,       A       :      jnp.ndarray				,       A       :      jnp.ndarray				,       A       :      jnp.ndarray					):




						'''simple docstring'''






						UpperCAmelCase    ,    UpperCAmelCase					    =     get_sqrt_alpha_prod(A				,       A				,       A				,       A					)
						UpperCAmelCase					    =     sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity









'''simple docstring'''



from typing import TYPE_CHECKING

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


_lowercase      :      Union[str, Any]            =					{
    """configuration_poolformer""": [
        """POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """PoolFormerConfig""",
        """PoolFormerOnnxConfig""",
    ]
}

try:
	if not is_vision_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      List[Any]            =					["""PoolFormerFeatureExtractor"""]
	_lowercase      :      Any            =					["""PoolFormerImageProcessor"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	_lowercase      :      Tuple            =					[
	    """POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """PoolFormerForImageClassification""",
	    """PoolFormerModel""",
	    """PoolFormerPreTrainedModel""",
	]


if TYPE_CHECKING:
	from .configuration_poolformer import (
	    POOLFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    PoolFormerConfig,
	    PoolFormerOnnxConfig,
	)

	try:
		if not is_vision_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .feature_extraction_poolformer import PoolFormerFeatureExtractor
		from .image_processing_poolformer import PoolFormerImageProcessor

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_poolformer import (
		    POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
		    PoolFormerForImageClassification,
		    PoolFormerModel,
		    PoolFormerPreTrainedModel,
		)


else:
	import sys

	_lowercase      :      Dict            =					_LazyModule(__name__, globals()["""__file__"""], _import_structure)





