




import math
from dataclasses import dataclass
from typing import Optional, Tuple, Union

import numpy as np
import torch

from ..configuration_utils import ConfigMixin, register_to_config
from ..utils import BaseOutput, randn_tensor
from .scheduling_utils import SchedulerMixin




@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->UnCLIP
class 		__A   (      UpperCamelCase__      ):
						UpperCamelCase      =       42
						UpperCamelCase      =       None




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=0.9_9_9						,  lowerCamelCase="cosine"						,  ):
				if alpha_transform_type == "cosine":

								def alpha_bar_fn(lowerCamelCase ):
												return math.cos((t + 0.0_0_8) / 1.0_0_8 * math.pi / 2 ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(lowerCamelCase ):
												return math.exp(t * -1_2.0 )

				else:
								raise ValueError(F"Unsupported alpha_tranform_type: {alpha_transform_type}" )

				__magic_name__						:						List[str]										=[]
				for i in range(lowerCamelCase ):
								__magic_name__						:						int										=i / num_diffusion_timesteps
								__magic_name__						:						Optional[int]										=(i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(lowerCamelCase ) / alpha_bar_fn(lowerCamelCase )						,  lowerCamelCase ) )
				return torch.tensor(lowerCamelCase						,  dtype=torch.floataa )




class 		__A   (      UpperCamelCase__       ,    UpperCamelCase__      ):

						@register_to_config
						def __init__(		self :Union[str, Any]     ,			__snake_case :int = 10_00     ,			__snake_case :str = "fixed_small_log"     ,			__snake_case :bool = True     ,			__snake_case :Optional[float] = 1.0     ,			__snake_case :str = "epsilon"     ,			__snake_case :str = "squaredcos_cap_v2"     ,			):
										'''simple docstring'''

										if beta_schedule != "squaredcos_cap_v2":
														raise ValueError("""UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'"""			)

										__magic_name__						:						Optional[int]										=betas_for_alpha_bar(__snake_case			)

										__magic_name__						:						Dict										=1.0 - self.betas
										__magic_name__						:						Tuple										=torch.cumprod(self.alphas     ,			dim=0			)
										__magic_name__						:						List[str]										=torch.tensor(1.0			)

										# standard deviation of the initial noise distribution
										__magic_name__						:						Optional[int]										=1.0

										# setable values
										__magic_name__						:						str										=None
										__magic_name__						:						List[str]										=torch.from_numpy(np.arange(0     ,			__snake_case			)[::-1].copy()			)

										__magic_name__						:						Dict										=variance_type

						def      A__    (		self :List[str]     ,			__snake_case :torch.FloatTensor     ,			__snake_case :Optional[int] = None			):
										'''simple docstring'''

										return sample

						def      A__    (		self :str     ,			__snake_case :int     ,			__snake_case :Union[str, torch.device] = None			):
										'''simple docstring'''

										__magic_name__						:						Tuple										=num_inference_steps
										__magic_name__						:						List[str]										=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
										__magic_name__						:						Dict										=(np.arange(0     ,			__snake_case			) * step_ratio).round()[::-1].copy().astype(np.intaa			)
										__magic_name__						:						Union[str, Any]										=torch.from_numpy(__snake_case			).to(__snake_case			)

						def      A__    (		self :List[Any]     ,			__snake_case :str     ,			__snake_case :Any=None     ,			__snake_case :List[Any]=None     ,			__snake_case :Union[str, Any]=None			):
										'''simple docstring'''

										if prev_timestep is None:
														__magic_name__						:						List[Any]										=t - 1

										__magic_name__						:						Any										=self.alphas_cumprod[t]
										__magic_name__						:						List[str]										=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										__magic_name__						:						int										=1 - alpha_prod_t
										__magic_name__						:						str										=1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
														__magic_name__						:						Union[str, Any]										=self.betas[t]
										else:
														__magic_name__						:						Optional[Any]										=1 - alpha_prod_t / alpha_prod_t_prev

										# For t > 0, compute predicted variance βt (see formula (6) and (7) from https://arxiv.org/pdf/2006.11239.pdf)
										# and sample from it to get previous sample
										# x_{t-1} ~ N(pred_prev_sample, variance) == add variance to pred_sample
										__magic_name__						:						Optional[Any]										=beta_prod_t_prev / beta_prod_t * beta

										if variance_type is None:
														__magic_name__						:						Dict										=self.config.variance_type

										# hacks - were probably added for training stability
										if variance_type == "fixed_small_log":
														__magic_name__						:						Optional[int]										=torch.log(torch.clamp(__snake_case     ,			min=1E-20			)			)
														__magic_name__						:						Dict										=torch.exp(0.5 * variance			)
										elif variance_type == "learned_range":
														# NOTE difference with DDPM scheduler
														__magic_name__						:						str										=variance.log()
														__magic_name__						:						str										=beta.log()

														__magic_name__						:						List[str]										=(predicted_variance + 1) / 2
														__magic_name__						:						str										=frac * max_log + (1 - frac) * min_log

										return variance

						def      A__    (		self :List[str]     ,			__snake_case :torch.FloatTensor     ,			__snake_case :int     ,			__snake_case :torch.FloatTensor     ,			__snake_case :Optional[int] = None     ,			__snake_case :Dict=None     ,			__snake_case :bool = True     ,			):
										'''simple docstring'''

										__magic_name__						:						List[str]										=timestep

										if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type == "learned_range":
														__magic_name__					,			__magic_name__						:						int										=torch.split(__snake_case     ,			sample.shape[1]     ,			dim=1			)
										else:
														__magic_name__						:						str										=None

										# 1. compute alphas, betas
										if prev_timestep is None:
														__magic_name__						:						Optional[Any]										=t - 1

										__magic_name__						:						Union[str, Any]										=self.alphas_cumprod[t]
										__magic_name__						:						Optional[int]										=self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.one
										__magic_name__						:						Dict										=1 - alpha_prod_t
										__magic_name__						:						Optional[int]										=1 - alpha_prod_t_prev

										if prev_timestep == t - 1:
														__magic_name__						:						Optional[Any]										=self.betas[t]
														__magic_name__						:						Any										=self.alphas[t]
										else:
														__magic_name__						:						List[Any]										=1 - alpha_prod_t / alpha_prod_t_prev
														__magic_name__						:						Tuple										=1 - beta

										# 2. compute predicted original sample from predicted noise also called
										# "predicted x_0" of formula (15) from https://arxiv.org/pdf/2006.11239.pdf
										if self.config.prediction_type == "epsilon":
														__magic_name__						:						Optional[Any]										=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
														__magic_name__						:						str										=model_output
										else:
														raise ValueError(
														    f"prediction_type given as {self.config.prediction_type} must be one of `epsilon` or `sample`"
														    """ for the UnCLIPScheduler."""			)

										# 3. Clip "predicted x_0"
										if self.config.clip_sample:
														__magic_name__						:						int										=torch.clamp(
														    __snake_case     ,			-self.config.clip_sample_range     ,			self.config.clip_sample_range			)

										# 4. Compute coefficients for pred_original_sample x_0 and current sample x_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										__magic_name__						:						Optional[Any]										=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
										__magic_name__						:						Dict										=alpha ** 0.5 * beta_prod_t_prev / beta_prod_t

										# 5. Compute predicted previous sample µ_t
										# See formula (7) from https://arxiv.org/pdf/2006.11239.pdf
										__magic_name__						:						Any										=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										__magic_name__						:						Any										=0
										if t > 0:
														__magic_name__						:						str										=randn_tensor(
														    model_output.shape     ,			dtype=model_output.dtype     ,			generator=__snake_case     ,			device=model_output.device			)

														__magic_name__						:						Optional[Any]										=self._get_variance(
														    __snake_case     ,			predicted_variance=__snake_case     ,			prev_timestep=__snake_case     ,			)

														if self.variance_type == "fixed_small_log":
																		__magic_name__						:						List[str]										=variance
														elif self.variance_type == "learned_range":
																		__magic_name__						:						int										=(0.5 * variance).exp()
														else:
																		raise ValueError(
																		    f"variance_type given as {self.variance_type} must be one of `fixed_small_log` or `learned_range`"
																		    """ for the UnCLIPScheduler."""			)

														__magic_name__						:						Union[str, Any]										=variance * variance_noise

										__magic_name__						:						str										=pred_prev_sample + variance

										if not return_dict:
														return (pred_prev_sample,)

										return UnCLIPSchedulerOutput(prev_sample=__snake_case     ,			pred_original_sample=__snake_case			)


						def      A__    (		self :List[str]     ,			__snake_case :torch.FloatTensor     ,			__snake_case :torch.FloatTensor     ,			__snake_case :torch.IntTensor     ,			):
										'''simple docstring'''

										__magic_name__						:						str										=self.alphas_cumprod.to(device=original_samples.device     ,			dtype=original_samples.dtype			)
										__magic_name__						:						Union[str, Any]										=timesteps.to(original_samples.device			)

										__magic_name__						:						List[Any]										=alphas_cumprod[timesteps] ** 0.5
										__magic_name__						:						Dict										=sqrt_alpha_prod.flatten()
										while len(sqrt_alpha_prod.shape			) < len(original_samples.shape			):
														__magic_name__						:						Union[str, Any]										=sqrt_alpha_prod.unsqueeze(-1			)

										__magic_name__						:						List[Any]										=(1 - alphas_cumprod[timesteps]) ** 0.5
										__magic_name__						:						str										=sqrt_one_minus_alpha_prod.flatten()
										while len(sqrt_one_minus_alpha_prod.shape			) < len(original_samples.shape			):
														__magic_name__						:						Union[str, Any]										=sqrt_one_minus_alpha_prod.unsqueeze(-1			)

										__magic_name__						:						List[str]										=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
										return noisy_samples











'''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


_SCREAMING_SNAKE_CASE									=				logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE									=				{
    "facebook/deit-base-distilled-patch16-224": (
        "https://huggingface.co/facebook/deit-base-patch16-224/resolve/main/config.json"
    ),
    # See all DeiT models at https://huggingface.co/models?filter=deit
}

class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				Optional[int]								=       "deit"




 def __init__(						self      ,  _lowerCAmelCase=768      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=12      ,  _lowerCAmelCase=3072      ,  _lowerCAmelCase="gelu"      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.0      ,  _lowerCAmelCase=0.02      ,  _lowerCAmelCase=1E-12      ,  _lowerCAmelCase=224      ,  _lowerCAmelCase=16      ,  _lowerCAmelCase=3      ,  _lowerCAmelCase=True      ,  _lowerCAmelCase=16      ,  **_lowerCAmelCase      ,  )     ->				Dict:
     super().__init__(**_lowerCAmelCase		)

     _lowerCAmelCase						      =   hidden_size
     _lowerCAmelCase						      =   num_hidden_layers
     _lowerCAmelCase						      =   num_attention_heads
     _lowerCAmelCase						      =   intermediate_size
     _lowerCAmelCase						      =   hidden_act
     _lowerCAmelCase						      =   hidden_dropout_prob
     _lowerCAmelCase						      =   attention_probs_dropout_prob
     _lowerCAmelCase						      =   initializer_range
     _lowerCAmelCase						      =   layer_norm_eps
     _lowerCAmelCase						      =   image_size
     _lowerCAmelCase						      =   patch_size
     _lowerCAmelCase						      =   num_channels
     _lowerCAmelCase						      =   qkv_bias
     _lowerCAmelCase						      =   encoder_stride







class  lowerCAmelCase_		(		__magic_name__		):
 __lowerCamelCase    :				List[str]								=       version.parse("1.11"		)




 @property
 def 				_snake_case (						self		)     ->				Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ("pixel_values", {0: "batch", 1: "num_channels", 2: "height", 3: "width"}),
         ]		)





 @property
 def 				_snake_case (						self		)     ->				float:
     return 1E-4


