






"""simple docstring"""



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        lowercase__      (							snake_case__       ):
			_UpperCAmelCase		:torch.FloatTensor
			_UpperCAmelCase		:Optional[torch.FloatTensor]             =			None


def 			_snake_case    (					lowerCamelCase__     :   Tuple    ,						lowerCamelCase__     :   Tuple=0.999    ,						lowerCamelCase__     :   Tuple="cosine"    ,						)    ->     Any:
							if alpha_transform_type == "cosine":

														def alpha_bar_fn(lowerCamelCase__     :   List[str]  ):
																					return math.cos((t + 0.008) / 1.008 * math.pi / 2  ) ** 2

							elif alpha_transform_type == "exp":

														def alpha_bar_fn(lowerCamelCase__     :   List[str]  ):
																					return math.exp(t * -12.0  )

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

							lowerCamelCase_	:							Dict									=[]
							for i in range(lowerCamelCase__  ):
														lowerCamelCase_	:							str									=i / num_diffusion_timesteps
														lowerCamelCase_	:							Tuple									=(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        lowercase__      (							snake_case__,       snake_case__       ):


			@register_to_config
			def __init__(				self					:  List[str]       ,	snake_case__					:  int = 1000       ,	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"       ,	):
										if beta_schedule != "squaredcos_cap_v2":
																	raise ValueError("UnCLIPScheduler only supports `beta_schedule`: 'squaredcos_cap_v2'" )

										lowerCamelCase_	:							str									=betas_for_alpha_bar(snake_case__ )

										lowerCamelCase_	:							str									=1.0 - self.betas
										lowerCamelCase_	:							str									=torch.cumprod(self.alphas       ,	dim=0 )
										lowerCamelCase_	:							Union[str, Any]									=torch.tensor(1.0 )

										# standard deviation of the initial noise distribution
										lowerCamelCase_	:							Optional[Any]									=1.0

										# setable values
										lowerCamelCase_	:							int									=None
										lowerCamelCase_	:							str									=torch.from_numpy(np.arange(0       ,	snake_case__ )[::-1].copy() )

										lowerCamelCase_	:							List[str]									=variance_type


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  Optional[int] = None ):
										return sample


			def      UpperCAmelCase__						(				self					:  Tuple       ,	snake_case__					:  int       ,	snake_case__					:  Union[str, torch.device] = None ):
										lowerCamelCase_	:							List[str]									=num_inference_steps
										lowerCamelCase_	:							Union[str, Any]									=(self.config.num_train_timesteps - 1) / (self.num_inference_steps - 1)
										lowerCamelCase_	:							List[Any]									=(np.arange(0       ,	snake_case__ ) * step_ratio).round()[::-1].copy().astype(np.intaa )
										lowerCamelCase_	:							Dict									=torch.from_numpy(snake_case__ ).to(snake_case__ )


			def      UpperCAmelCase__						(				self					:  Dict       ,	snake_case__					:  Optional[Any]       ,	snake_case__					:  List[str]=None       ,	snake_case__					:  Union[str, Any]=None       ,	snake_case__					:  str=None ):
										if prev_timestep is None:
																	lowerCamelCase_	:							Dict									=t - 1

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

										if prev_timestep == t - 1:
																	lowerCamelCase_	:							str									=self.betas[t]
										else:
																	lowerCamelCase_	:							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
										lowerCamelCase_	:							int									=beta_prod_t_prev / beta_prod_t * beta

										if variance_type is None:
																	lowerCamelCase_	:							str									=self.config.variance_type

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

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

										return variance


			def      UpperCAmelCase__						(				self					:  int       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  int       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  Optional[int] = None       ,	snake_case__					:  List[Any]=None       ,	snake_case__					:  bool = True       ,	):
										lowerCamelCase_	:							Dict									=timestep

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

										# 1. compute alphas, betas
										if prev_timestep is None:
																	lowerCamelCase_	:							List[str]									=t - 1

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

										if prev_timestep == t - 1:
																	lowerCamelCase_	:							str									=self.betas[t]
																	lowerCamelCase_	:							Any									=self.alphas[t]
										else:
																	lowerCamelCase_	:							List[Any]									=1 - alpha_prod_t / alpha_prod_t_prev
																	lowerCamelCase_	:							Optional[int]									=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":
																	lowerCamelCase_	:							List[str]									=(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
										elif self.config.prediction_type == "sample":
																	lowerCamelCase_	:							Any									=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:
																	lowerCamelCase_	:							Tuple									=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
										lowerCamelCase_	:							str									=(alpha_prod_t_prev ** 0.5 * beta) / beta_prod_t
										lowerCamelCase_	:							int									=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
										lowerCamelCase_	:							Optional[int]									=pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

										# 6. Add noise
										lowerCamelCase_	:							Optional[Any]									=0
										if t > 0:
																	lowerCamelCase_	:							Union[str, Any]									=randn_tensor(
																	    model_output.shape       ,	dtype=model_output.dtype       ,	generator=snake_case__       ,	device=model_output.device )

																	lowerCamelCase_	:							Any									=self._get_variance(
																	    snake_case__       ,	predicted_variance=snake_case__       ,	prev_timestep=snake_case__       ,	)

																	if self.variance_type == "fixed_small_log":
																								lowerCamelCase_	:							Tuple									=variance
																	elif self.variance_type == "learned_range":
																								lowerCamelCase_	:							Tuple									=(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." )

																	lowerCamelCase_	:							Dict									=variance * variance_noise

										lowerCamelCase_	:							List[Any]									=pred_prev_sample + variance

										if not return_dict:
																	return (pred_prev_sample,)

										return UnCLIPSchedulerOutput(prev_sample=snake_case__       ,	pred_original_sample=snake_case__ )


			def      UpperCAmelCase__						(				self					:  Union[str, Any]       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  torch.FloatTensor       ,	snake_case__					:  torch.IntTensor       ,	):
										# Make sure alphas_cumprod and timestep have same device and dtype as original_samples
										lowerCamelCase_	:							Optional[Any]									=self.alphas_cumprod.to(device=original_samples.device       ,	dtype=original_samples.dtype )
										lowerCamelCase_	:							List[str]									=timesteps.to(original_samples.device )

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

										lowerCamelCase_	:							Any									=(1 - alphas_cumprod[timesteps]) ** 0.5
										lowerCamelCase_	:							Dict									=sqrt_one_minus_alpha_prod.flatten()
										while len(sqrt_one_minus_alpha_prod.shape ) < len(original_samples.shape ):
																	lowerCamelCase_	:							Optional[int]									=sqrt_one_minus_alpha_prod.unsqueeze(-1 )

										lowerCamelCase_	:							List[Any]									=sqrt_alpha_prod * original_samples + sqrt_one_minus_alpha_prod * noise
										return noisy_samples











"""simple docstring"""



import copy
import json
import os
import tempfile

from transformers import is_torch_available

from .test_configuration_utils import config_common_kwargs







class        lowercase__      (							snake_case__       ):


			def __init__(				self					:  Tuple       ,	snake_case__					:  Optional[int]       ,	snake_case__					:  int=None       ,	snake_case__					:  Union[str, Any]=True       ,	snake_case__					:  Optional[int]=None       ,	**snake_case__					:  Optional[int] ):
										lowerCamelCase_	:							Dict									=parent
										lowerCamelCase_	:							List[str]									=config_class
										lowerCamelCase_	:							Union[str, Any]									=has_text_modality
										lowerCamelCase_	:							Optional[int]									=kwargs
										lowerCamelCase_	:							List[str]									=common_properties


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							List[str]									=self.config_class(**self.inputs_dict )
										lowerCamelCase_	:							Any									=(
										    ["hidden_size", "num_attention_heads", "num_hidden_layers"]
										    if self.common_properties is None
										    else self.common_properties
										)

										# Add common fields for text models
										if self.has_text_modality:
																	common_properties.extend(["vocab_size"] )

										# Test that config has the common properties as getters
										for prop in common_properties:
																	self.parent.assertTrue(hasattr(snake_case__       ,	snake_case__ )       ,	msg=F"""`{prop}` does not exist""" )

										# Test that config has the common properties as setter
										for idx, name in enumerate(snake_case__ ):
																	try:
																								setattr(snake_case__       ,	snake_case__       ,	snake_case__ )
																								self.parent.assertEqual(
																								    getattr(snake_case__       ,	snake_case__ )       ,	snake_case__       ,	msg=F"""`{name} value {idx} expected, but was {getattr(snake_case__       ,	snake_case__ )}""" )
																	except NotImplementedError:
																								# Some models might not be able to implement setters for common_properties
																								# In that case, a NotImplementedError is raised
																								pass

        # Test if config class can be called with Config(prop_name=..)
										for idx, name in enumerate(snake_case__ ):
																	try:
																								lowerCamelCase_	:							Dict									=self.config_class(**{name: idx} )
																								self.parent.assertEqual(
																								    getattr(snake_case__       ,	snake_case__ )       ,	snake_case__       ,	msg=F"""`{name} value {idx} expected, but was {getattr(snake_case__       ,	snake_case__ )}""" )
																	except NotImplementedError:
																								# Some models might not be able to implement setters for common_properties
																								# In that case, a NotImplementedError is raised
																								pass


			def      UpperCAmelCase__						(				self					:  Any ):
										lowerCamelCase_	:							Tuple									=self.config_class(**self.inputs_dict )
										lowerCamelCase_	:							Any									=json.loads(config.to_json_string() )
										for key, value in self.inputs_dict.items():
																	self.parent.assertEqual(obj[key]       ,	snake_case__ )


			def      UpperCAmelCase__						(				self					:  int ):
										lowerCamelCase_	:							Tuple									=self.config_class(**self.inputs_dict )

										with tempfile.TemporaryDirectory() as tmpdirname:
																	lowerCamelCase_	:							List[Any]									=os.path.join(snake_case__       ,	"config.json" )
																	config_first.to_json_file(snake_case__ )
																	lowerCamelCase_	:							Optional[int]									=self.config_class.from_json_file(snake_case__ )

										self.parent.assertEqual(config_second.to_dict()       ,	config_first.to_dict() )


			def      UpperCAmelCase__						(				self					:  Dict ):
										lowerCamelCase_	:							Dict									=self.config_class(**self.inputs_dict )

										with tempfile.TemporaryDirectory() as tmpdirname:
																	config_first.save_pretrained(snake_case__ )
																	lowerCamelCase_	:							Optional[int]									=self.config_class.from_pretrained(snake_case__ )

										self.parent.assertEqual(config_second.to_dict()       ,	config_first.to_dict() )


			def      UpperCAmelCase__						(				self					:  str ):
										lowerCamelCase_	:							Dict									=self.config_class(**self.inputs_dict )

										lowerCamelCase_	:							Dict									="test"
										with tempfile.TemporaryDirectory() as tmpdirname:
																	lowerCamelCase_	:							str									=os.path.join(snake_case__       ,	snake_case__ )
																	config_first.save_pretrained(snake_case__ )
																	lowerCamelCase_	:							Optional[Any]									=self.config_class.from_pretrained(snake_case__       ,	subfolder=snake_case__ )

										self.parent.assertEqual(config_second.to_dict()       ,	config_first.to_dict() )


			def      UpperCAmelCase__						(				self					:  Optional[Any] ):
										lowerCamelCase_	:							Optional[Any]									=self.config_class(**self.inputs_dict       ,	num_labels=5 )
										self.parent.assertEqual(len(config.idalabel )       ,	5 )
										self.parent.assertEqual(len(config.labelaid )       ,	5 )

										lowerCamelCase_	:							List[Any]									=3
										self.parent.assertEqual(len(config.idalabel )       ,	3 )
										self.parent.assertEqual(len(config.labelaid )       ,	3 )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										if self.config_class.is_composition:
																	return
										lowerCamelCase_	:							Tuple									=self.config_class()
										self.parent.assertIsNotNone(snake_case__ )


			def      UpperCAmelCase__						(				self					:  List[Any] ):
										lowerCamelCase_	:							List[str]									=copy.deepcopy(snake_case__ )
										lowerCamelCase_	:							Optional[int]									=self.config_class(**snake_case__ )
										lowerCamelCase_	:							Union[str, Any]									=[]
										for key, value in config_common_kwargs.items():
																	if key == "torch_dtype":
																								if not is_torch_available():
																															continue
																								else:
																															import torch

																															if config.torch_dtype != torch.floataa:
																																						wrong_values.append(("torch_dtype", config.torch_dtype, torch.floataa) )
																	elif getattr(snake_case__       ,	snake_case__ ) != value:
																								wrong_values.append((key, getattr(snake_case__       ,	snake_case__ ), value) )

										if len(snake_case__ ) > 0:
																	lowerCamelCase_	:							Any									="\n".join([F"""- {v[0]}: got {v[1]} instead of {v[2]}""" for v in wrong_values] )
																	raise ValueError(F"""The following keys were not properly set in the config:\n{errors}""" )


			def      UpperCAmelCase__						(				self					:  int ):
										self.create_and_test_config_common_properties()
										self.create_and_test_config_to_json_string()
										self.create_and_test_config_to_json_file()
										self.create_and_test_config_from_and_save_pretrained()
										self.create_and_test_config_from_and_save_pretrained_subfolder()
										self.create_and_test_config_with_num_labels()
										self.check_config_can_be_init_without_params()
										self.check_config_arguments_init()



