"""simple docstring"""
from typing import Optional, Tuple, Union

import torch
from einops import rearrange, reduce

from diffusers import DDIMScheduler, DDPMScheduler, DiffusionPipeline, ImagePipelineOutput, UNetaDConditionModel
from diffusers.schedulers.scheduling_ddim import DDIMSchedulerOutput
from diffusers.schedulers.scheduling_ddpm import DDPMSchedulerOutput


lowercase__					       =	8







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=BITS   )	->Optional[Any]:
  a__:     Any										=	x.device

  a__:     List[str]										=	(x * 255).int().clamp(0		,     255   )

  a__:     Union[str, Any]										=	2 ** torch.arange(bits - 1		,     -1		,     -1		,     device=_SCREAMING_SNAKE_CASE   )
  a__:     List[str]										=	rearrange(_SCREAMING_SNAKE_CASE		,     'd -> d 1 1'   )
  a__:     int										=	rearrange(_SCREAMING_SNAKE_CASE		,     'b c h w -> b c 1 h w'   )

  a__:     Dict										=	((x & mask) != 0).float()
  a__:     Optional[Any]										=	rearrange(_SCREAMING_SNAKE_CASE		,     'b c d h w -> b (c d) h w'   )
  a__:     Optional[Any]										=	bits * 2 - 1
  return bits







def 						__a   (	_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE=BITS   )	->Dict:
  a__:     Optional[int]										=	x.device

  a__:     List[Any]										=	(x > 0).int()
  a__:     Any										=	2 ** torch.arange(bits - 1		,     -1		,     -1		,     device=_SCREAMING_SNAKE_CASE		,     dtype=torch.intaa   )

  a__:     Optional[int]										=	rearrange(_SCREAMING_SNAKE_CASE		,     'd -> d 1 1'   )
  a__:     str										=	rearrange(_SCREAMING_SNAKE_CASE		,     'b (c d) h w -> b c d h w'		,     d=8   )
  a__:     Optional[int]										=	reduce(x * mask		,     'b c d h w -> b c h w'		,     'sum'   )
  return (dec / 255).clamp(0.0		,     1.0   )







def 						__a   (	self		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE = 0.0		,     _SCREAMING_SNAKE_CASE = True		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE = True		,     )	->Union[DDIMSchedulerOutput, Tuple]:
  if self.num_inference_steps is None:
    raise ValueError(
        'Number of inference steps is \'None\', you need to run \'set_timesteps\' after creating the scheduler'   )

  # See formulas (12) and (16) of DDIM paper https://arxiv.org/pdf/2010.02502.pdf
  # Ideally, read DDIM paper in-detail understanding

  # Notation (<variable name> -> <name in paper>
  # - pred_noise_t -> e_theta(x_t, t)
  # - pred_original_sample -> f_theta(x_t, t) or x_0
  # - std_dev_t -> sigma_t
  # - eta -> η
  # - pred_sample_direction -> "direction pointing to x_t"
  # - pred_prev_sample -> "x_t-1"

  # 1. get previous step value (=t-1)
  a__:     Tuple										=	timestep - self.config.num_train_timesteps // self.num_inference_steps

  # 2. compute alphas, betas
  a__:     List[Any]										=	self.alphas_cumprod[timestep]
  a__:     int										=	self.alphas_cumprod[prev_timestep] if prev_timestep >= 0 else self.final_alpha_cumprod

  a__:     Optional[Any]										=	1 - alpha_prod_t

  # 3. compute predicted original sample from predicted noise also called
  # "predicted x_0" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  a__:     List[str]										=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5

  # 4. Clip "predicted x_0"
  a__:     Any										=	self.bit_scale
  if self.config.clip_sample:
    a__:     List[Any]										=	torch.clamp(_SCREAMING_SNAKE_CASE		,     -scale		,     _SCREAMING_SNAKE_CASE   )

  # 5. compute variance: "sigma_t(η)" -> see formula (16)
  # σ_t = sqrt((1 − α_t−1)/(1 − α_t)) * sqrt(1 − α_t/α_t−1)
  a__:     int										=	self._get_variance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   )
  a__:     Optional[int]										=	eta * variance ** 0.5

  if use_clipped_model_output:
    # the model_output is always re-derived from the clipped x_0 in Glide
    a__:     Dict										=	(sample - alpha_prod_t ** 0.5 * pred_original_sample) / beta_prod_t ** 0.5

  # 6. compute "direction pointing to x_t" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  a__:     str										=	(1 - alpha_prod_t_prev - std_dev_t**2) ** 0.5 * model_output

  # 7. compute x_t without "random noise" of formula (12) from https://arxiv.org/pdf/2010.02502.pdf
  a__:     Union[str, Any]										=	alpha_prod_t_prev ** 0.5 * pred_original_sample + pred_sample_direction

  if eta > 0:
    # randn_like does not support generator https://github.com/pytorch/pytorch/issues/27072
    a__:     Tuple										=	model_output.device if torch.is_tensor(_SCREAMING_SNAKE_CASE   ) else 'cpu'
    a__:     List[str]										=	torch.randn(model_output.shape		,     dtype=model_output.dtype		,     generator=_SCREAMING_SNAKE_CASE   ).to(_SCREAMING_SNAKE_CASE   )
    a__:     List[Any]										=	self._get_variance(_SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE   ) ** 0.5 * eta * noise

    a__:     str										=	prev_sample + variance

  if not return_dict:
    return (prev_sample,)

  return DDIMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE		,     pred_original_sample=_SCREAMING_SNAKE_CASE   )







def 						__a   (	self		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE		,     _SCREAMING_SNAKE_CASE="epsilon"		,     _SCREAMING_SNAKE_CASE=None		,     _SCREAMING_SNAKE_CASE = True		,     )	->Union[DDPMSchedulerOutput, Tuple]:
  a__:     Optional[Any]										=	timestep

  if model_output.shape[1] == sample.shape[1] * 2 and self.variance_type in ["learned", "learned_range"]:
    a__							,    a__:     Optional[int]										=	torch.split(_SCREAMING_SNAKE_CASE		,     sample.shape[1]		,     dim=1   )
  else:
    a__:     str										=	None

  # 1. compute alphas, betas
  a__:     List[Any]										=	self.alphas_cumprod[t]
  a__:     List[Any]										=	self.alphas_cumprod[t - 1] if t > 0 else self.one
  a__:     Optional[Any]										=	1 - alpha_prod_t
  a__:     int										=	1 - alpha_prod_t_prev

  # 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 prediction_type == "epsilon":
    a__:     Any										=	(sample - beta_prod_t ** 0.5 * model_output) / alpha_prod_t ** 0.5
  elif prediction_type == "sample":
    a__:     Optional[int]										=	model_output
  else:
    raise ValueError(F'Unsupported prediction_type {prediction_type}.'   )

  # 3. Clip "predicted x_0"
  a__:     List[Any]										=	self.bit_scale
  if self.config.clip_sample:
    a__:     str										=	torch.clamp(_SCREAMING_SNAKE_CASE		,     -scale		,     _SCREAMING_SNAKE_CASE   )

  # 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
  a__:     Dict										=	(alpha_prod_t_prev ** 0.5 * self.betas[t]) / beta_prod_t
  a__:     Optional[int]										=	self.alphas[t] ** 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
  a__:     str										=	pred_original_sample_coeff * pred_original_sample + current_sample_coeff * sample

  # 6. Add noise
  a__:     str										=	0
  if t > 0:
    a__:     int										=	torch.randn(
        model_output.size()		,     dtype=model_output.dtype		,     layout=model_output.layout		,     generator=_SCREAMING_SNAKE_CASE   ).to(model_output.device   )
    a__:     Any										=	(self._get_variance(_SCREAMING_SNAKE_CASE		,     predicted_variance=_SCREAMING_SNAKE_CASE   ) ** 0.5) * noise

  a__:     Tuple										=	pred_prev_sample + variance

  if not return_dict:
    return (pred_prev_sample,)

  return DDPMSchedulerOutput(prev_sample=_SCREAMING_SNAKE_CASE		,     pred_original_sample=_SCREAMING_SNAKE_CASE   )




class    __snake_case       (     __lowerCAmelCase				):



      def __init__(						self    ,			lowercase    ,			lowercase    ,			lowercase = 1.0    ,			)	->			str:





        '''simple docstring'''





        super().__init__()
        a__:     List[Any]										=	bit_scale
        a__:     str										=	(
            ddim_bit_scheduler_step if isinstance(lowercase    ,			lowercase) else ddpm_bit_scheduler_step
        )

        self.register_modules(unet=lowercase    ,			scheduler=lowercase)



      @torch.no_grad()
      def __call__(						self    ,			lowercase = 2_56    ,			lowercase = 2_56    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = 1    ,			lowercase = "pil"    ,			lowercase = True    ,			**lowercase    ,			)	->			Union[Tuple, ImagePipelineOutput]:





        '''simple docstring'''





        a__:     Optional[Any]										=	torch.randn(
            (batch_size, self.unet.config.in_channels, height, width)    ,			generator=lowercase    ,			)
        a__:     Union[str, Any]										=	decimal_to_bits(lowercase) * self.bit_scale
        a__:     Union[str, Any]										=	latents.to(self.device)

        self.scheduler.set_timesteps(lowercase)

        for t in self.progress_bar(self.scheduler.timesteps):
          # predict the noise residual
          a__:     int										=	self.unet(lowercase    ,			lowercase).sample

          # compute the previous noisy sample x_t -> x_t-1
          a__:     List[str]										=	self.scheduler.step(lowercase    ,			lowercase    ,			lowercase).prev_sample

        a__:     str										=	bits_to_decimal(lowercase)

        if output_type == "pil":
          a__:     int										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image,)

        return ImagePipelineOutput(images=lowercase)



"""simple docstring"""
import shutil
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_tf_cross_test,
    require_tf,
    require_torch,
    require_torchvision,
    require_vision,
)
from transformers.utils import is_tf_available, is_torch_available, is_vision_available


if is_vision_available():
 from PIL import Image

 from transformers import AutoProcessor, SamImageProcessor, SamProcessor

if is_torch_available():
 import torch

if is_tf_available():
 import tensorflow as tf




@require_vision
@require_torchvision
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     Optional[int]										=	SamImageProcessor()
        a__:     Tuple										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			List[Any]:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



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





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Any										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Optional[Any]										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Optional[int]										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     List[Any]										=	SamProcessor.from_pretrained(self.tmpdirname    ,			do_normalize=lowercase    ,			padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Union[str, Any]										=	self.get_image_processor()

        a__:     List[Any]										=	SamProcessor(image_processor=lowercase)

        a__:     Optional[int]										=	self.prepare_image_inputs()

        a__:     Optional[Any]										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Tuple										=	processor(images=lowercase    ,			return_tensors='np')

        input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('reshaped_input_sizes')  # pop original_sizes as it is popped in the processor

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()    ,			input_processor[key].sum()    ,			delta=1e-2)



      @require_torch
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     int										=	self.get_image_processor()

        a__:     List[str]										=	SamProcessor(image_processor=lowercase)
        a__:     Optional[Any]										=	[torch.ones((1, 3, 5, 5))]

        a__:     Union[str, Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     int										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase)
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Optional[int]										=	processor.post_process_masks(
            lowercase    ,			torch.tensor(lowercase)    ,			torch.tensor(lowercase))
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        # should also work with np
        a__:     Dict										=	[np.ones((1, 3, 5, 5))]
        a__:     Tuple										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))

        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Tuple										=	[[1, 0], [0, 1]]
        with self.assertRaises(lowercase):
          a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			np.array(lowercase)    ,			np.array(lowercase))







@require_vision
@require_tf
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			List[Any]:





        '''simple docstring'''





        a__:     Optional[Any]										=	tempfile.mkdtemp()
        a__:     List[Any]										=	SamImageProcessor()
        a__:     Optional[int]										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			int:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Optional[Any]										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Optional[int]										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     List[str]										=	SamProcessor(image_processor=self.get_image_processor())
        processor.save_pretrained(self.tmpdirname)

        a__:     Dict										=	self.get_image_processor(do_normalize=lowercase    ,			padding_value=1.0)

        a__:     Union[str, Any]										=	SamProcessor.from_pretrained(self.tmpdirname    ,			do_normalize=lowercase    ,			padding_value=1.0)

        self.assertEqual(processor.image_processor.to_json_string()    ,			image_processor_add_kwargs.to_json_string())
        self.assertIsInstance(processor.image_processor    ,			lowercase)



      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[Any]										=	self.get_image_processor()

        a__:     str										=	SamProcessor(image_processor=lowercase)

        a__:     int										=	self.prepare_image_inputs()

        a__:     int										=	image_processor(lowercase    ,			return_tensors='np')
        a__:     Dict										=	processor(images=lowercase    ,			return_tensors='np')

        input_feat_extract.pop('original_sizes')  # pop original_sizes as it is popped in the processor
        input_feat_extract.pop('reshaped_input_sizes')  # pop reshaped_input_sizes as it is popped in the processor

        for key in input_feat_extract.keys():
          self.assertAlmostEqual(input_feat_extract[key].sum()    ,			input_processor[key].sum()    ,			delta=1e-2)



      @require_tf
      def   lowerCamelCase_   (						self)	->			Any:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Any										=	SamProcessor(image_processor=lowercase)
        a__:     str										=	[tf.ones((1, 3, 5, 5))]

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     List[Any]										=	[[6_83, 10_24]]
        a__:     List[Any]										=	processor.post_process_masks(lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     Tuple										=	processor.post_process_masks(
            lowercase    ,			tf.convert_to_tensor(lowercase)    ,			tf.convert_to_tensor(lowercase)    ,			return_tensors='tf'    ,			)
        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        # should also work with np
        a__:     Optional[Any]										=	[np.ones((1, 3, 5, 5))]
        a__:     int										=	processor.post_process_masks(
            lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')

        self.assertEqual(masks[0].shape    ,			(1, 3, 17_64, 26_46))

        a__:     List[str]										=	[[1, 0], [0, 1]]
        with self.assertRaises(tf.errors.InvalidArgumentError):
          a__:     Any										=	processor.post_process_masks(
              lowercase    ,			np.array(lowercase)    ,			np.array(lowercase)    ,			return_tensors='tf')







@require_vision
@require_torchvision
class    __snake_case       (     unittest.TestCase				):



      def   lowerCamelCase_   (						self)	->			Dict:





        '''simple docstring'''





        a__:     str										=	tempfile.mkdtemp()
        a__:     int										=	SamImageProcessor()
        a__:     Union[str, Any]										=	SamProcessor(lowercase)
        processor.save_pretrained(self.tmpdirname)



      def   lowerCamelCase_   (						self    ,			**lowercase)	->			Optional[int]:





        '''simple docstring'''





        return AutoProcessor.from_pretrained(self.tmpdirname    ,			**lowercase).image_processor



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        shutil.rmtree(self.tmpdirname)



      def   lowerCamelCase_   (						self)	->			Tuple:





        '''simple docstring'''





        a__:     Any										=	[np.random.randint(2_55    ,			size=(3, 30, 4_00)    ,			dtype=np.uinta)]

        a__:     Any										=	[Image.fromarray(np.moveaxis(lowercase    ,			0    ,			-1)) for x in image_inputs]

        return image_inputs



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			int:





        '''simple docstring'''





        a__:     Optional[int]										=	self.get_image_processor()

        a__:     int										=	SamProcessor(image_processor=lowercase)
        a__:     int										=	np.random.randint(0    ,			2    ,			size=(1, 3, 5, 5)).astype(np.floataa)
        a__:     Dict										=	[tf.convert_to_tensor(lowercase)]
        a__:     Union[str, Any]										=	[torch.tensor(lowercase)]

        a__:     List[Any]										=	[[17_64, 26_46]]

        a__:     Optional[Any]										=	[[6_83, 10_24]]
        a__:     Tuple										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='tf')
        a__:     str										=	processor.post_process_masks(
            lowercase    ,			lowercase    ,			lowercase    ,			return_tensors='pt')

        self.assertTrue(np.all(tf_masks[0].numpy() == pt_masks[0].numpy()))



      @is_pt_tf_cross_test
      def   lowerCamelCase_   (						self)	->			str:





        '''simple docstring'''





        a__:     Tuple										=	self.get_image_processor()

        a__:     Dict										=	SamProcessor(image_processor=lowercase)

        a__:     Any										=	self.prepare_image_inputs()

        a__:     List[Any]										=	image_processor(lowercase    ,			return_tensors='pt')['pixel_values'].numpy()
        a__:     Tuple										=	processor(images=lowercase    ,			return_tensors='pt')['pixel_values'].numpy()

        a__:     Any										=	image_processor(lowercase    ,			return_tensors='tf')['pixel_values'].numpy()
        a__:     Any										=	processor(images=lowercase    ,			return_tensors='tf')['pixel_values'].numpy()

        self.assertTrue(np.allclose(lowercase    ,			lowercase))
        self.assertTrue(np.allclose(lowercase    ,			lowercase))
        self.assertTrue(np.allclose(lowercase    ,			lowercase))


