


"""simple docstring"""






import copy
import inspect

from transformers.testing_utils import require_torch, torch_device
from transformers.utils.backbone_utils import BackboneType


@require_torch
class 			SCREAMING_SNAKE_CASE_  :




							'''simple docstring'''





							__magic_name__					:     Optional[Any]				  =  ()
							__magic_name__					:     Dict				  =  True




							def        UpperCAmelCase (						self)   ->	List[str]:







								'''simple docstring'''




								snake_case__  :						Union[str, Any]		=      self.config_class

								# test default config
								snake_case__  :						Tuple		=      config_class()
								self.assertIsNotNone(lowerCamelCase__)
								snake_case__  :						str		=      ["stem"] + [f"""stage{idx}""" for idx in range(1	,					len(config.depths) + 1)]
								self.assertEqual(config.stage_names	,					lowerCamelCase__)
								self.assertTrue(set(config.out_features).issubset(set(config.stage_names)))

								# Test out_features and out_indices are correctly set
								# out_features and out_indices both None
								snake_case__  :						Optional[int]		=      config_class(out_features=lowerCamelCase__	,					out_indices=lowerCamelCase__)
								self.assertEqual(config.out_features	,					[config.stage_names[-1]])
								self.assertEqual(config.out_indices	,					[len(config.stage_names) - 1])

								# out_features and out_indices both set
								snake_case__  :						Union[str, Any]		=      config_class(out_features=["stem", "stage1"]	,					out_indices=[0, 1])
								self.assertEqual(config.out_features	,					["stem", "stage1"])
								self.assertEqual(config.out_indices	,					[0, 1])

								# Only out_features set
								snake_case__  :						Optional[int]		=      config_class(out_features=["stage1", "stage3"])
								self.assertEqual(config.out_features	,					["stage1", "stage3"])
								self.assertEqual(config.out_indices	,					[1, 3])

								# Only out_indices set
								snake_case__  :						Union[str, Any]		=      config_class(out_indices=[0, 2])
								self.assertEqual(config.out_features	,					[config.stage_names[0], config.stage_names[2]])
								self.assertEqual(config.out_indices	,					[0, 2])

								# Error raised when out_indices do not correspond to out_features
								with self.assertRaises(lowerCamelCase__):
									snake_case__  :						str		=      config_class(out_features=["stage1", "stage2"]	,					out_indices=[0, 2])




							def        UpperCAmelCase (						self)   ->	Optional[Any]:







								'''simple docstring'''




								snake_case__,					snake_case__  :						int		=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									snake_case__  :						Dict		=      model_class(lowerCamelCase__)
									snake_case__  :						List[str]		=      inspect.signature(model.forward)
									# signature.parameters is an OrderedDict => so arg_names order is deterministic
									snake_case__  :						Optional[int]		=      [*signature.parameters.keys()]
									snake_case__  :						List[Any]		=      ["pixel_values"]
									self.assertListEqual(arg_names[:1]	,					lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__,					snake_case__  :						Union[str, Any]		=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									snake_case__  :						Optional[int]		=      model_class(lowerCamelCase__)
									self.assertEqual(len(model.channels)	,					len(config.out_features))
									snake_case__  :						str		=      model.num_features
									snake_case__  :						int		=      [config.stage_names.index(lowerCamelCase__) for feat in config.out_features]
									snake_case__  :						Tuple		=      [num_features[idx] for idx in out_indices]
									self.assertListEqual(model.channels	,					lowerCamelCase__)

									snake_case__  :						str		=      copy.deepcopy(lowerCamelCase__)
									snake_case__  :						str		=      None
									snake_case__  :						List[str]		=      model_class(lowerCamelCase__)
									self.assertEqual(len(model.channels)	,					1)
									self.assertListEqual(model.channels	,					[num_features[-1]])

									snake_case__  :						int		=      copy.deepcopy(lowerCamelCase__)
									snake_case__  :						Tuple		=      None
									snake_case__  :						Optional[Any]		=      model_class(lowerCamelCase__)
									self.assertEqual(len(model.channels)	,					1)
									self.assertListEqual(model.channels	,					[num_features[-1]])




							def        UpperCAmelCase (						self)   ->	str:







								'''simple docstring'''




								snake_case__,					snake_case__  :						Any		=      self.model_tester.prepare_config_and_inputs_for_common()

								for model_class in self.all_model_classes:
									snake_case__  :						Dict		=      model_class(lowerCamelCase__)
									model.to(lowerCamelCase__)
									model.eval()
									snake_case__  :						Union[str, Any]		=      model(**lowerCamelCase__)

									self.assertEqual(len(result.feature_maps)	,					len(config.out_features))
									self.assertEqual(len(model.channels)	,					len(config.out_features))
									self.assertEqual(len(result.feature_maps)	,					len(config.out_indices))
									self.assertEqual(len(model.channels)	,					len(config.out_indices))

									# Check output of last stage is taken if out_features=None, out_indices=None
									snake_case__  :						Any		=      copy.deepcopy(lowerCamelCase__)
									snake_case__  :						str		=      None
									snake_case__  :						str		=      model_class(lowerCamelCase__)
									model.to(lowerCamelCase__)
									model.eval()
									snake_case__  :						Dict		=      model(**lowerCamelCase__)

									self.assertEqual(len(result.feature_maps)	,					1)
									self.assertEqual(len(model.channels)	,					1)

									snake_case__  :						Optional[Any]		=      copy.deepcopy(lowerCamelCase__)
									snake_case__  :						Optional[Any]		=      None
									snake_case__  :						int		=      model_class(lowerCamelCase__)
									model.to(lowerCamelCase__)
									model.eval()
									snake_case__  :						Any		=      model(**lowerCamelCase__)

									self.assertEqual(len(result.feature_maps)	,					1)
									self.assertEqual(len(model.channels)	,					1)

									# Check backbone can be initialized with fresh weights
									snake_case__  :						List[Any]		=      copy.deepcopy(lowerCamelCase__)
									snake_case__  :						Optional[Any]		=      False
									snake_case__  :						Optional[Any]		=      model_class(lowerCamelCase__)
									model.to(lowerCamelCase__)
									model.eval()
									snake_case__  :						Dict		=      model(**lowerCamelCase__)




							def        UpperCAmelCase (						self)   ->	Union[str, Any]:







								'''simple docstring'''




								snake_case__,					snake_case__  :						int		=      self.model_tester.prepare_config_and_inputs_for_common()

								for backbone_class in self.all_model_classes:
									snake_case__  :						List[Any]		=      backbone_class(lowerCamelCase__)

									self.assertTrue(hasattr(lowerCamelCase__	,					"backbone_type"))
									self.assertTrue(hasattr(lowerCamelCase__	,					"stage_names"))
									self.assertTrue(hasattr(lowerCamelCase__	,					"num_features"))
									self.assertTrue(hasattr(lowerCamelCase__	,					"out_indices"))
									self.assertTrue(hasattr(lowerCamelCase__	,					"out_features"))
									self.assertTrue(hasattr(lowerCamelCase__	,					"out_feature_channels"))
									self.assertTrue(hasattr(lowerCamelCase__	,					"channels"))

									self.assertIsInstance(backbone.backbone_type	,					lowerCamelCase__)
									# Verify num_features has been initialized in the backbone init
									self.assertIsNotNone(backbone.num_features)
									self.assertTrue(len(backbone.channels) == len(backbone.out_indices))
									self.assertTrue(len(backbone.stage_names) == len(backbone.num_features))
									self.assertTrue(len(backbone.channels) <= len(backbone.num_features))
									self.assertTrue(len(backbone.out_feature_channels) == len(backbone.stage_names))







							def        UpperCAmelCase (						self)   ->	Optional[int]:







								'''simple docstring'''




								snake_case__,					snake_case__  :						str		=      self.model_tester.prepare_config_and_inputs_for_common()
								snake_case__  :						Any		=      inputs_dict["pixel_values"].shape[0]

								for backbone_class in self.all_model_classes:
									snake_case__  :						str		=      backbone_class(lowerCamelCase__)
									backbone.to(lowerCamelCase__)
									backbone.eval()

									snake_case__  :						Union[str, Any]		=      backbone(**lowerCamelCase__)

									# Test default outputs and verify feature maps
									self.assertIsInstance(outputs.feature_maps	,					lowerCamelCase__)
									self.assertTrue(len(outputs.feature_maps) == len(backbone.channels))
									for feature_map, n_channels in zip(outputs.feature_maps	,					backbone.channels):
										self.assertTrue(feature_map.shape[:2]	,					(batch_size, n_channels))
									self.assertIsNone(outputs.hidden_states)
									self.assertIsNone(outputs.attentions)

									# Test output_hidden_states=True
									snake_case__  :						Dict		=      backbone(**lowerCamelCase__	,					output_hidden_states=lowerCamelCase__)
									self.assertIsNotNone(outputs.hidden_states)
									self.assertTrue(len(outputs.hidden_states)	,					len(backbone.stage_names))
									for hidden_state, n_channels in zip(outputs.hidden_states	,					backbone.channels):
										self.assertTrue(hidden_state.shape[:2]	,					(batch_size, n_channels))

									# Test output_attentions=True
									if self.has_attentions:
										snake_case__  :						Tuple		=      backbone(**lowerCamelCase__	,					output_attentions=lowerCamelCase__)
										self.assertIsNotNone(outputs.attentions)











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

import numpy as np
import torch

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


__A						:		Union[str, Any]          =						logging.get_logger(__name__)  # pylint: disable=invalid-name

@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerDiscrete
class 		lowercase_       (	lowerCAmelCase__				):
					__UpperCamelCase     	= 42
					__UpperCamelCase     	= None





def      UpperCAmelCase  (						UpperCamelCase__						,			UpperCamelCase__=0.999						,			UpperCamelCase__="cosine"						,			)     ->     Optional[int]:







				'''simple docstring'''


				if alpha_transform_type == "cosine":

								def alpha_bar_fn(UpperCamelCase__   ):
												return math.cos((t + 0.008) / 1.008 * math.pi / 2   ) ** 2

				elif alpha_transform_type == "exp":

								def alpha_bar_fn(UpperCamelCase__   ):
												return math.exp(t * -12.0   )

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

				__lowerCAmelCase					=  []
				for i in range(UpperCamelCase__   ):
								__lowerCAmelCase					=  i / num_diffusion_timesteps
								__lowerCAmelCase					=  (i + 1) / num_diffusion_timesteps
								betas.append(min(1 - alpha_bar_fn(UpperCamelCase__   ) / alpha_bar_fn(UpperCamelCase__   )						,			UpperCamelCase__   )   )
				return torch.tensor(UpperCamelCase__						,			dtype=torch.floataa   )

class 		lowercase_       (	lowerCAmelCase__						,							lowerCAmelCase__				):
					__UpperCamelCase     	= [e.name for e in KarrasDiffusionSchedulers]
					__UpperCamelCase     	= 1




					@register_to_config
					def __init__(   self:		Any,     _lowercase:		int = 1000,     _lowercase:		float = 0.0_001,     _lowercase:		float = 0.02,     _lowercase:		str = "linear",     _lowercase:		Optional[Union[np.ndarray, List[float]]] = None,     _lowercase:		str = "epsilon",     _lowercase:		str = "linear",     _lowercase:		Optional[bool] = False,     _lowercase:		str = "linspace",     _lowercase:		int = 0,     ):


									'''simple docstring'''


									if trained_betas is not None:
													__lowerCAmelCase					=  torch.tensor(_lowercase,     dtype=torch.floataa)
									elif beta_schedule == "linear":
													__lowerCAmelCase					=  torch.linspace(_lowercase,     _lowercase,     _lowercase,     dtype=torch.floataa)
									elif beta_schedule == "scaled_linear":
													# this schedule is very specific to the latent diffusion model.
													__lowerCAmelCase					=  (
													    torch.linspace(beta_start**0.5,     beta_end**0.5,     _lowercase,     dtype=torch.floataa) ** 2
													)
									elif beta_schedule == "squaredcos_cap_v2":
													# Glide cosine schedule
													__lowerCAmelCase					=  betas_for_alpha_bar(_lowercase)
									else:
													raise NotImplementedError(f'''{beta_schedule} does is not implemented for {self.__class__}''')

									__lowerCAmelCase					=  1.0 - self.betas
									__lowerCAmelCase					=  torch.cumprod(self.alphas,     dim=0)

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.concatenate([sigmas[::-1], [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase)

									# setable values
									__lowerCAmelCase					=  None
									__lowerCAmelCase					=  np.linspace(0,     num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[::-1].copy()
									__lowerCAmelCase					=  torch.from_numpy(_lowercase)
									__lowerCAmelCase					=  False
									__lowerCAmelCase					=  use_karras_sigmas




					@property
					def     _lowercase						(   self:		Optional[Any]):


									'''simple docstring'''


									if self.config.timestep_spacing in ["linspace", "trailing"]:
													return self.sigmas.max()

									return (self.sigmas.max() ** 2 + 1) ** 0.5




					def     _lowercase						(   self:		Tuple,     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor]):


									'''simple docstring'''


									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)
									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									__lowerCAmelCase					=  sample / ((sigma**2 + 1) ** 0.5)

									__lowerCAmelCase					=  True
									return sample




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		int,     _lowercase:		Union[str, torch.device] = None):


									'''simple docstring'''


									__lowerCAmelCase					=  num_inference_steps

									# "linspace", "leading", "trailing" corresponds to annotation of Table 2. of https://arxiv.org/abs/2305.08891
									if self.config.timestep_spacing == "linspace":
													__lowerCAmelCase					=  np.linspace(0,     self.config.num_train_timesteps - 1,     _lowercase,     dtype=_lowercase)[
													    ::-1
													].copy()
									elif self.config.timestep_spacing == "leading":
													__lowerCAmelCase					=  self.config.num_train_timesteps // self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__lowerCAmelCase					=  (np.arange(0,     _lowercase) * step_ratio).round()[::-1].copy().astype(_lowercase)
													timesteps += self.config.steps_offset
									elif self.config.timestep_spacing == "trailing":
													__lowerCAmelCase					=  self.config.num_train_timesteps / self.num_inference_steps
													# creates integer timesteps by multiplying by ratio
													# casting to int to avoid issues when num_inference_step is power of 3
													__lowerCAmelCase					=  (np.arange(self.config.num_train_timesteps,     0,     -step_ratio)).round().copy().astype(_lowercase)
													timesteps -= 1
									else:
													raise ValueError(
													    f'''{self.config.timestep_spacing} is not supported. Please make sure to choose one of \'linspace\', \'leading\' or \'trailing\'.''')

									__lowerCAmelCase					=  np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5)
									__lowerCAmelCase					=  np.log(_lowercase)

									if self.config.interpolation_type == "linear":
													__lowerCAmelCase					=  np.interp(_lowercase,     np.arange(0,     len(_lowercase)),     _lowercase)
									elif self.config.interpolation_type == "log_linear":
													__lowerCAmelCase					=  torch.linspace(np.log(sigmas[-1]),     np.log(sigmas[0]),     num_inference_steps + 1).exp()
									else:
													raise ValueError(
													    f'''{self.config.interpolation_type} is not implemented. Please specify interpolation_type to either'''
													    """ 'linear' or 'log_linear'""")

									if self.use_karras_sigmas:
													__lowerCAmelCase					=  self._convert_to_karras(in_sigmas=_lowercase,     num_inference_steps=self.num_inference_steps)
													__lowerCAmelCase					=  np.array([self._sigma_to_t(_lowercase,     _lowercase) for sigma in sigmas])

									__lowerCAmelCase					=  np.concatenate([sigmas, [0.0]]).astype(np.floataa)
									__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)
									if str(_lowercase).startswith("""mps"""):
													# mps does not support float64
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(_lowercase,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  torch.from_numpy(_lowercase).to(device=_lowercase)




					def     _lowercase						(   self:		List[Any],     _lowercase:		Optional[int],     _lowercase:		Tuple):


									'''simple docstring'''


									__lowerCAmelCase					=  np.log(_lowercase)

									# get distribution
									__lowerCAmelCase					=  log_sigma - log_sigmas[:, np.newaxis]

									# get sigmas range
									__lowerCAmelCase					=  np.cumsum((dists >= 0),     axis=0).argmax(axis=0).clip(max=log_sigmas.shape[0] - 2)
									__lowerCAmelCase					=  low_idx + 1

									__lowerCAmelCase					=  log_sigmas[low_idx]
									__lowerCAmelCase					=  log_sigmas[high_idx]

									# interpolate sigmas
									__lowerCAmelCase					=  (low - log_sigma) / (low - high)
									__lowerCAmelCase					=  np.clip(_lowercase,     0,     1)

									# transform interpolation to time range
									__lowerCAmelCase					=  (1 - w) * low_idx + w * high_idx
									__lowerCAmelCase					=  t.reshape(sigma.shape)
									return t




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[str, Any]):


									'''simple docstring'''


									__lowerCAmelCase					=  in_sigmas[-1].item()
									__lowerCAmelCase					=  in_sigmas[0].item()

									__lowerCAmelCase					=  7.0  # 7.0 is the value used in the paper
									__lowerCAmelCase					=  np.linspace(0,     1,     _lowercase)
									__lowerCAmelCase					=  sigma_min ** (1 / rho)
									__lowerCAmelCase					=  sigma_max ** (1 / rho)
									__lowerCAmelCase					=  (max_inv_rho + ramp * (min_inv_rho - max_inv_rho)) ** rho
									return sigmas




					def     _lowercase						(   self:		Optional[int],     _lowercase:		torch.FloatTensor,     _lowercase:		Union[float, torch.FloatTensor],     _lowercase:		torch.FloatTensor,     _lowercase:		float = 0.0,     _lowercase:		float = 0.0,     _lowercase:		float = float("""inf"""),     _lowercase:		float = 1.0,     _lowercase:		Optional[torch.Generator] = None,     _lowercase:		bool = True,     ):


									'''simple docstring'''


									if (
									    isinstance(_lowercase,     _lowercase)
									    or isinstance(_lowercase,     torch.IntTensor)
									    or isinstance(_lowercase,     torch.LongTensor)
									):
													raise ValueError(
													    (
													        """Passing integer indices (e.g. from `enumerate(timesteps)`) as timesteps to"""
													        """ `EulerDiscreteScheduler.step()` is not supported. Make sure to pass"""
													        """ one of the `scheduler.timesteps` as a timestep."""
													    ),     )

									if not self.is_scale_input_called:
													logger.warning(
													    """The `scale_model_input` function should be called before `step` to ensure correct denoising. """
													    """See `StableDiffusionPipeline` for a usage example.""")

									if isinstance(_lowercase,     torch.Tensor):
													__lowerCAmelCase					=  timestep.to(self.timesteps.device)

									__lowerCAmelCase					=  (self.timesteps == timestep).nonzero().item()
									__lowerCAmelCase					=  self.sigmas[step_index]

									__lowerCAmelCase					=  min(s_churn / (len(self.sigmas) - 1),     2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0

									__lowerCAmelCase					=  randn_tensor(
									    model_output.shape,     dtype=model_output.dtype,     device=model_output.device,     generator=_lowercase)

									__lowerCAmelCase					=  noise * s_noise
									__lowerCAmelCase					=  sigma * (gamma + 1)

									if gamma > 0:
													__lowerCAmelCase					=  sample + eps * (sigma_hat**2 - sigma**2) ** 0.5

									# 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
									# NOTE: "original_sample" should not be an expected prediction_type but is left in for
									# backwards compatibility
									if self.config.prediction_type == "original_sample" or self.config.prediction_type == "sample":
													__lowerCAmelCase					=  model_output
									elif self.config.prediction_type == "epsilon":
													__lowerCAmelCase					=  sample - sigma_hat * model_output
									elif self.config.prediction_type == "v_prediction":
													# * c_out + input * c_skip
													__lowerCAmelCase					=  model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
									else:
													raise ValueError(
													    f'''prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`''')

									# 2. Convert to an ODE derivative
									__lowerCAmelCase					=  (sample - pred_original_sample) / sigma_hat

									__lowerCAmelCase					=  self.sigmas[step_index + 1] - sigma_hat

									__lowerCAmelCase					=  sample + derivative * dt

									if not return_dict:
													return (prev_sample,)

									return EulerDiscreteSchedulerOutput(prev_sample=_lowercase,     pred_original_sample=_lowercase)




					def     _lowercase						(   self:		Union[str, Any],     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     _lowercase:		torch.FloatTensor,     ):


									'''simple docstring'''


									__lowerCAmelCase					=  self.sigmas.to(device=original_samples.device,     dtype=original_samples.dtype)
									if original_samples.device.type == "mps" and torch.is_floating_point(_lowercase):
													# mps does not support float64
													__lowerCAmelCase					=  self.timesteps.to(original_samples.device,     dtype=torch.floataa)
													__lowerCAmelCase					=  timesteps.to(original_samples.device,     dtype=torch.floataa)
									else:
													__lowerCAmelCase					=  self.timesteps.to(original_samples.device)
													__lowerCAmelCase					=  timesteps.to(original_samples.device)

									__lowerCAmelCase					=  [(schedule_timesteps == t).nonzero().item() for t in timesteps]

									__lowerCAmelCase					=  sigmas[step_indices].flatten()
									while len(sigma.shape) < len(original_samples.shape):
													__lowerCAmelCase					=  sigma.unsqueeze(-1)

									__lowerCAmelCase					=  original_samples + noise * sigma
									return noisy_samples







					def __len__(   self:		str):


									'''simple docstring'''


									return self.config.num_train_timesteps





