





'''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


A   : Dict             =			'scheduler_config.json'







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    1
					_SCREAMING_SNAKE_CASE							   =    2
					_SCREAMING_SNAKE_CASE							   =    3
					_SCREAMING_SNAKE_CASE							   =    4
					_SCREAMING_SNAKE_CASE							   =    5







@dataclass
class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    42







class 			lowerCamelCase					:
					_SCREAMING_SNAKE_CASE							   =    SCHEDULER_CONFIG_NAME
					_SCREAMING_SNAKE_CASE							   =    ["dtype"]
					_SCREAMING_SNAKE_CASE							   =    []
					_SCREAMING_SNAKE_CASE							   =    True


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Dict       ,     __snake_case	:  Dict[str, Any] = None       ,     __snake_case	:  Optional[str] = None       ,     __snake_case	:  List[str]=False       ,     **__snake_case	:  str       ,     ):





											'''simple docstring'''





											_snake_case					,						_snake_case:   List[str]  					=		cls.load_config(
											    pretrained_model_name_or_path=__snake_case       ,     subfolder=__snake_case       ,     return_unused_kwargs=__snake_case       ,     **__snake_case       ,     )
											_snake_case					,						_snake_case:   Optional[int]  					=		cls.from_config(__snake_case       ,     return_unused_kwargs=__snake_case       ,     **__snake_case       )

											if hasattr(__snake_case       ,     'create_state'       ) and getattr(__snake_case       ,     'has_state'       ,     __snake_case       ):
																	_snake_case:   List[str]  					=		scheduler.create_state()

											if return_unused_kwargs:
																	return scheduler, state, unused_kwargs

											return scheduler, state


					def    SCREAMING_SNAKE_CASE_		(					self	:  int       ,     __snake_case	:  Union[str, os.PathLike]       ,     __snake_case	:  bool = False       ,     **__snake_case	:  Tuple       ):





											'''simple docstring'''





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


					@property
					def    SCREAMING_SNAKE_CASE_		(					self	:  List[str]       ):





											'''simple docstring'''





											return self._get_compatibles()




					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  Tuple       ):





											'''simple docstring'''





											_snake_case:   Tuple  					=		list(set([cls.__name__] + cls._compatibles       )       )
											_snake_case:   str  					=		importlib.import_module(__name__.split('.'       )[0]       )
											_snake_case:   str  					=		[
											    getattr(__snake_case       ,     __snake_case       ) for c in compatible_classes_str if hasattr(__snake_case       ,     __snake_case       )
											]
											return compatible_classes





def       lowercase_      (		lowercase__				,  lowercase__						)   ->jnp.ndarray:
						assert len(lowercase__						) >= x.ndim
						return jnp.broadcast_to(x.reshape(x.shape + (1,) * (len(lowercase__						) - x.ndim)						)				,  lowercase__						)





def       lowercase_      (		lowercase__				,  lowercase__=0.9_9_9				,  lowercase__=jnp.floataa						)   ->jnp.ndarray:

						def alpha_bar(lowercase__						):
												return math.cos((time_step + 0.0_0_8) / 1.0_0_8 * math.pi / 2						) ** 2

						_snake_case:   Any  					=		[]
						for i in range(lowercase__						):
												_snake_case:   List[str]  					=		i / num_diffusion_timesteps
												_snake_case:   Optional[Any]  					=		(i + 1) / num_diffusion_timesteps
												betas.append(min(1 - alpha_bar(lowercase__						) / alpha_bar(lowercase__						)				,  lowercase__						)						)
						return jnp.array(lowercase__				,  dtype=lowercase__						)







@flax.struct.dataclass
class 			lowerCamelCase					:
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    42
					_SCREAMING_SNAKE_CASE							   =    42


					@classmethod
					def    SCREAMING_SNAKE_CASE_		(					cls	:  List[str]       ,     __snake_case	:  str       ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		scheduler.config

											if config.trained_betas is not None:
																	_snake_case:   Optional[int]  					=		jnp.asarray(config.trained_betas       ,     dtype=scheduler.dtype       )
											elif config.beta_schedule == "linear":
																	_snake_case:   int  					=		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.
																	_snake_case:   int  					=		(
																	    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
																	_snake_case:   Tuple  					=		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__}'''       )

											_snake_case:   str  					=		1.0 - betas

											_snake_case:   List[Any]  					=		jnp.cumprod(__snake_case       ,     axis=0       )

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





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Tuple:
						_snake_case:   List[Any]  					=		state.alphas_cumprod

						_snake_case:   Any  					=		alphas_cumprod[timesteps] ** 0.5
						_snake_case:   Tuple  					=		sqrt_alpha_prod.flatten()
						_snake_case:   Optional[Any]  					=		broadcast_to_shape_from_left(lowercase__				,  original_samples.shape						)

						_snake_case:   str  					=		(1 - alphas_cumprod[timesteps]) ** 0.5
						_snake_case:   List[Any]  					=		sqrt_one_minus_alpha_prod.flatten()
						_snake_case:   List[str]  					=		broadcast_to_shape_from_left(lowercase__				,  original_samples.shape						)

						return sqrt_alpha_prod, sqrt_one_minus_alpha_prod





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Tuple:
						_snake_case					,						_snake_case:   Dict  					=		get_sqrt_alpha_prod(lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)
						_snake_case:   Optional[Any]  					=		sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
						return noisy_samples





def       lowercase_      (		lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)   ->Optional[int]:
						_snake_case					,						_snake_case:   str  					=		get_sqrt_alpha_prod(lowercase__				,  lowercase__				,  lowercase__				,  lowercase__						)
						_snake_case:   Optional[int]  					=		sqrt_alpha_prod * noise - sqrt_one_minus_alpha_prod * sample
						return velocity












'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


A   : Tuple             =			logging.get_logger(__name__)

A   : Any             =			{
    'microsoft/biogpt': 'https://huggingface.co/microsoft/biogpt/resolve/main/config.json',
    # See all BioGPT models at https://huggingface.co/models?filter=biogpt
}







class 			lowerCamelCase					(						__UpperCAmelCase  ):
					_SCREAMING_SNAKE_CASE							   =    "biogpt"


					def __init__(					self	:  Union[str, Any]       ,     __snake_case	:  Tuple=4_23_84       ,     __snake_case	:  Any=10_24       ,     __snake_case	:  Optional[int]=24       ,     __snake_case	:  int=16       ,     __snake_case	:  str=40_96       ,     __snake_case	:  Dict="gelu"       ,     __snake_case	:  List[str]=0.1       ,     __snake_case	:  List[Any]=0.1       ,     __snake_case	:  List[Any]=10_24       ,     __snake_case	:  Optional[int]=0.02       ,     __snake_case	:  Union[str, Any]=1e-12       ,     __snake_case	:  List[Any]=True       ,     __snake_case	:  Union[str, Any]=True       ,     __snake_case	:  Any=0.0       ,     __snake_case	:  int=0.0       ,     __snake_case	:  Dict=1       ,     __snake_case	:  str=0       ,     __snake_case	:  List[str]=2       ,     **__snake_case	:  Optional[int]       ,     ):





											'''simple docstring'''





											_snake_case:   List[str]  					=		vocab_size
											_snake_case:   Optional[int]  					=		max_position_embeddings
											_snake_case:   Dict  					=		hidden_size
											_snake_case:   Union[str, Any]  					=		num_hidden_layers
											_snake_case:   str  					=		num_attention_heads
											_snake_case:   Any  					=		intermediate_size
											_snake_case:   List[Any]  					=		hidden_act
											_snake_case:   int  					=		hidden_dropout_prob
											_snake_case:   List[str]  					=		attention_probs_dropout_prob
											_snake_case:   Optional[Any]  					=		initializer_range
											_snake_case:   Union[str, Any]  					=		layer_norm_eps
											_snake_case:   Dict  					=		scale_embedding
											_snake_case:   Dict  					=		use_cache
											_snake_case:   List[Any]  					=		layerdrop
											_snake_case:   int  					=		activation_dropout
											super().__init__(pad_token_id=__snake_case       ,     bos_token_id=__snake_case       ,     eos_token_id=__snake_case       ,     **__snake_case       )





