

import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
  import torch

if is_vision_available():
  from PIL import Image

  from transformers import OwlViTImageProcessor







class  UpperCamelCase_      (	unittest.TestCase      ):


  """simple docstring"""



  def __init__(						self     :					int  ,	A_     :					List[str]  ,	A_     :					Dict=7  ,	A_     :					List[str]=3  ,	A_     :					Optional[Any]=18  ,	A_     :					str=30  ,	A_     :					List[Any]=400  ,	A_     :					List[str]=True  ,	A_     :					int=None  ,	A_     :					Dict=True  ,	A_     :					List[str]=None  ,	A_     :					List[str]=True  ,	A_     :					Optional[Any]=[0.48_145_466, 0.4_578_275, 0.40_821_073]  ,	A_     :					Any=[0.26_862_954, 0.26_130_258, 0.27_577_711]  ,	A_     :					Optional[Any]=True  ,	)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    parent
      SCREAMING_SNAKE_CASE__         =    batch_size
      SCREAMING_SNAKE_CASE__         =    num_channels
      SCREAMING_SNAKE_CASE__         =    image_size
      SCREAMING_SNAKE_CASE__         =    min_resolution
      SCREAMING_SNAKE_CASE__         =    max_resolution
      SCREAMING_SNAKE_CASE__         =    do_resize
      SCREAMING_SNAKE_CASE__         =    size if size is not None else {"height": 18, "width": 18}
      SCREAMING_SNAKE_CASE__         =    do_center_crop
      SCREAMING_SNAKE_CASE__         =    crop_size if crop_size is not None else {"height": 18, "width": 18}
      SCREAMING_SNAKE_CASE__         =    do_normalize
      SCREAMING_SNAKE_CASE__         =    image_mean
      SCREAMING_SNAKE_CASE__         =    image_std
      SCREAMING_SNAKE_CASE__         =    do_convert_rgb







  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Union[str, Any]:
      return {
          "do_resize": self.do_resize,
          "size": self.size,
          "do_center_crop": self.do_center_crop,
          "crop_size": self.crop_size,
          "do_normalize": self.do_normalize,
          "image_mean": self.image_mean,
          "image_std": self.image_std,
          "do_convert_rgb": self.do_convert_rgb,
      }







@require_torch
@require_vision
class  UpperCamelCase_      (	_snake_case		,  unittest.TestCase      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   OwlViTImageProcessor if is_vision_available() else None



  def   _UpperCamelCase     (						self     :					int						)       ->			str:
      SCREAMING_SNAKE_CASE__         =    OwlViTImageProcessingTester(self						)



  @property
  def   _UpperCamelCase     (						self     :					Any						)       ->			Optional[Any]:
      return self.image_processor_tester.prepare_image_processor_dict()



  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Any:
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      self.assertTrue(hasattr(A_  ,	"do_resize"						)						)
      self.assertTrue(hasattr(A_  ,	"size"						)						)
      self.assertTrue(hasattr(A_  ,	"do_center_crop"						)						)
      self.assertTrue(hasattr(A_  ,	"center_crop"						)						)
      self.assertTrue(hasattr(A_  ,	"do_normalize"						)						)
      self.assertTrue(hasattr(A_  ,	"image_mean"						)						)
      self.assertTrue(hasattr(A_  ,	"image_std"						)						)
      self.assertTrue(hasattr(A_  ,	"do_convert_rgb"						)						)



  def   _UpperCamelCase     (						self     :					str						)       ->			List[str]:
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class.from_dict(self.image_processor_dict						)
      self.assertEqual(image_processor.size  ,	{"height": 18, "width": 18}						)
      self.assertEqual(image_processor.crop_size  ,	{"height": 18, "width": 18}						)

      SCREAMING_SNAKE_CASE__         =    self.image_processing_class.from_dict(self.image_processor_dict  ,	size=42  ,	crop_size=84						)
      self.assertEqual(image_processor.size  ,	{"height": 42, "width": 42}						)
      self.assertEqual(image_processor.crop_size  ,	{"height": 84, "width": 84}						)



  def   _UpperCamelCase     (						self     :					List[Any]						)       ->			Optional[Any]:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      # create random PIL images
      SCREAMING_SNAKE_CASE__         =    prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=A_						)

      for image in image_inputs:
          self.assertIsInstance(A_  ,	Image.Image						)

      # Test not batched input
      SCREAMING_SNAKE_CASE__         =    image_processing(image_inputs[0]  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      SCREAMING_SNAKE_CASE__         =    image_processing(A_  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)



  def   _UpperCamelCase     (						self     :					str						)       ->			List[str]:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      # create random numpy tensors
      SCREAMING_SNAKE_CASE__         =    prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=A_  ,	numpify=A_						)
      for image in image_inputs:
          self.assertIsInstance(A_  ,	np.ndarray						)

      # Test not batched input
      SCREAMING_SNAKE_CASE__         =    image_processing(image_inputs[0]  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      SCREAMING_SNAKE_CASE__         =    image_processing(A_  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)







  def   _UpperCamelCase     (						self     :					Tuple						)       ->			Optional[int]:
      # Initialize image_processing
      SCREAMING_SNAKE_CASE__         =    self.image_processing_class(**self.image_processor_dict						)
      # create random PyTorch tensors
      SCREAMING_SNAKE_CASE__         =    prepare_image_inputs(self.image_processor_tester  ,	equal_resolution=A_  ,	torchify=A_						)
      for image in image_inputs:
          self.assertIsInstance(A_  ,	torch.Tensor						)

      # Test not batched input
      SCREAMING_SNAKE_CASE__         =    image_processing(image_inputs[0]  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              1,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)

      # Test batched
      SCREAMING_SNAKE_CASE__         =    image_processing(A_  ,	return_tensors="pt"						).pixel_values
      self.assertEqual(
          encoded_images.shape  ,	(
              self.image_processor_tester.batch_size,
              self.image_processor_tester.num_channels,
              self.image_processor_tester.crop_size["height"],
              self.image_processor_tester.crop_size["width"],
          )  ,	)





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__										=      logging.get_logger(__name__)  # pylint: disable=invalid-name







@dataclass
# Copied from diffusers.schedulers.scheduling_ddpm.DDPMSchedulerOutput with DDPM->EulerAncestralDiscrete
class  UpperCamelCase_      (	_snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			torch.FloatTensor
  SCREAMING_SNAKE_CASE__					:			Optional[torch.FloatTensor]					    =   None





def 				__a							(       __A			,							__A=0.999			,							__A="cosine"			,							):
    if alpha_transform_type == "cosine":

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

    elif alpha_transform_type == "exp":

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

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

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







class  UpperCamelCase_      (	_snake_case		,  _snake_case      ):


  """simple docstring"""
  SCREAMING_SNAKE_CASE__					:			Union[str, Any]					    =   [e.name for e in KarrasDiffusionSchedulers]
  SCREAMING_SNAKE_CASE__					:			List[Any]					    =   1



  @register_to_config
  def __init__(						self     :					Tuple  ,	A_     :					int = 1000  ,	A_     :					float = 0.0_001  ,	A_     :					float = 0.02  ,	A_     :					str = "linear"  ,	A_     :					Optional[Union[np.ndarray, List[float]]] = None  ,	A_     :					str = "epsilon"  ,	A_     :					str = "linspace"  ,	A_     :					int = 0  ,	)       ->			Optional[Any]:
      if trained_betas is not None:
          SCREAMING_SNAKE_CASE__         =    torch.tensor(A_  ,	dtype=torch.floataa						)
      elif beta_schedule == "linear":
          SCREAMING_SNAKE_CASE__         =    torch.linspace(A_  ,	A_  ,	A_  ,	dtype=torch.floataa						)
      elif beta_schedule == "scaled_linear":
          # this schedule is very specific to the latent diffusion model.
          SCREAMING_SNAKE_CASE__         =    (
              torch.linspace(beta_start**0.5  ,	beta_end**0.5  ,	A_  ,	dtype=torch.floataa						) ** 2
          )
      elif beta_schedule == "squaredcos_cap_v2":
          # Glide cosine schedule
          SCREAMING_SNAKE_CASE__         =    betas_for_alpha_bar(A_						)
      else:
          raise NotImplementedError(f"{beta_schedule} does is not implemented for {self.__class__}"						)

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

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

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



  @property
  def   _UpperCamelCase     (						self     :					Union[str, Any]						)       ->			Dict:
      # standard deviation of the initial noise distribution
      if self.config.timestep_spacing in ["linspace", "trailing"]:
          return self.sigmas.max()

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



  def   _UpperCamelCase     (						self     :					str  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]						)       ->			torch.FloatTensor:
      if isinstance(A_  ,	torch.Tensor						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)
      SCREAMING_SNAKE_CASE__         =    (self.timesteps == timestep).nonzero().item()
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    sample / ((sigma**2 + 1) ** 0.5)
      SCREAMING_SNAKE_CASE__         =    True
      return sample



  def   _UpperCamelCase     (						self     :					List[Any]  ,	A_     :					int  ,	A_     :					Union[str, torch.device] = None						)       ->			Optional[int]:
      SCREAMING_SNAKE_CASE__         =    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":
          SCREAMING_SNAKE_CASE__         =    np.linspace(0  ,	self.config.num_train_timesteps - 1  ,	A_  ,	dtype=A_						)[
              ::-1
          ].copy()
      elif self.config.timestep_spacing == "leading":
          SCREAMING_SNAKE_CASE__         =    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
          SCREAMING_SNAKE_CASE__         =    (np.arange(0  ,	A_						) * step_ratio).round()[::-1].copy().astype(A_						)
          timesteps += self.config.steps_offset
      elif self.config.timestep_spacing == "trailing":
          SCREAMING_SNAKE_CASE__         =    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
          SCREAMING_SNAKE_CASE__         =    (np.arange(self.config.num_train_timesteps  ,	0  ,	-step_ratio						)).round().copy().astype(A_						)
          timesteps -= 1
      else:
          raise ValueError(
              f"{self.config.timestep_spacing} is not supported. Please make sure to choose one of 'linspace', 'leading' or 'trailing'."						)

      SCREAMING_SNAKE_CASE__         =    np.array(((1 - self.alphas_cumprod) / self.alphas_cumprod) ** 0.5						)
      SCREAMING_SNAKE_CASE__         =    np.interp(A_  ,	np.arange(0  ,	len(A_						)						)  ,	A_						)
      SCREAMING_SNAKE_CASE__         =    np.concatenate([sigmas, [0.0]]						).astype(np.floataa						)
      SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)
      if str(A_						).startswith("mps"						):
          # mps does not support float64
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(A_  ,	dtype=torch.floataa						)
      else:
          SCREAMING_SNAKE_CASE__         =    torch.from_numpy(A_						).to(device=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					Union[float, torch.FloatTensor]  ,	A_     :					torch.FloatTensor  ,	A_     :					Optional[torch.Generator] = None  ,	A_     :					bool = True  ,	)       ->			Union[EulerAncestralDiscreteSchedulerOutput, Tuple]:

      if (
          isinstance(A_  ,	A_						)
          or isinstance(A_  ,	torch.IntTensor						)
          or isinstance(A_  ,	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(A_  ,	torch.Tensor						):
          SCREAMING_SNAKE_CASE__         =    timestep.to(self.timesteps.device						)

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

      # 1. compute predicted original sample (x_0) from sigma-scaled predicted noise
      if self.config.prediction_type == "epsilon":
          SCREAMING_SNAKE_CASE__         =    sample - sigma * model_output
      elif self.config.prediction_type == "v_prediction":
          # * c_out + input * c_skip
          SCREAMING_SNAKE_CASE__         =    model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))
      elif self.config.prediction_type == "sample":
          raise NotImplementedError("prediction_type not implemented yet: sample"						)
      else:
          raise ValueError(
              f"prediction_type given as {self.config.prediction_type} must be one of `epsilon`, or `v_prediction`"						)

      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index]
      SCREAMING_SNAKE_CASE__         =    self.sigmas[step_index + 1]
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 * (sigma_from**2 - sigma_to**2) / sigma_from**2) ** 0.5
      SCREAMING_SNAKE_CASE__         =    (sigma_to**2 - sigma_up**2) ** 0.5

      # 2. Convert to an ODE derivative
      SCREAMING_SNAKE_CASE__         =    (sample - pred_original_sample) / sigma

      SCREAMING_SNAKE_CASE__         =    sigma_down - sigma

      SCREAMING_SNAKE_CASE__         =    sample + derivative * dt

      SCREAMING_SNAKE_CASE__         =    model_output.device
      SCREAMING_SNAKE_CASE__         =    randn_tensor(model_output.shape  ,	dtype=model_output.dtype  ,	device=A_  ,	generator=A_						)

      SCREAMING_SNAKE_CASE__         =    prev_sample + noise * sigma_up

      if not return_dict:
          return (prev_sample,)

      return EulerAncestralDiscreteSchedulerOutput(
          prev_sample=A_  ,	pred_original_sample=A_						)



  def   _UpperCamelCase     (						self     :					Tuple  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	A_     :					torch.FloatTensor  ,	)       ->			torch.FloatTensor:
      # Make sure sigmas and timesteps have the same device and dtype as original_samples
      SCREAMING_SNAKE_CASE__         =    self.sigmas.to(device=original_samples.device  ,	dtype=original_samples.dtype						)
      if original_samples.device.type == "mps" and torch.is_floating_point(A_						):
          # mps does not support float64
          SCREAMING_SNAKE_CASE__         =    self.timesteps.to(original_samples.device  ,	dtype=torch.floataa						)
          SCREAMING_SNAKE_CASE__         =    timesteps.to(original_samples.device  ,	dtype=torch.floataa						)
      else:
          SCREAMING_SNAKE_CASE__         =    self.timesteps.to(original_samples.device						)
          SCREAMING_SNAKE_CASE__         =    timesteps.to(original_samples.device						)

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

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

      SCREAMING_SNAKE_CASE__         =    original_samples + noise * sigma
      return noisy_samples







  def __len__(						self     :					List[Any]						)       ->			Tuple:
      return self.config.num_train_timesteps


