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

import PIL
import torch
from torchvision import transforms

from diffusers.pipeline_utils import DiffusionPipeline, ImagePipelineOutput
from diffusers.schedulers import DDIMScheduler
from diffusers.utils import randn_tensor


lowercase__					       =	transforms.Compose(
    [
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize([0.5], [0.5]),
    ]
)







def 						__a   (	_SCREAMING_SNAKE_CASE   )	->Any:
  if isinstance(_SCREAMING_SNAKE_CASE		,     torch.Tensor   ):
    return image
  elif isinstance(_SCREAMING_SNAKE_CASE		,     PIL.Image.Image   ):
    a__:     Optional[int]										=	[image]

  a__:     str										=	[trans(img.convert('RGB'   )   ) for img in image]
  a__:     Any										=	torch.stack(_SCREAMING_SNAKE_CASE   )
  return image




class    __snake_case       (     __lowerCAmelCase				):



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





        '''simple docstring'''





        super().__init__()

        # make sure scheduler can always be converted to DDIM
        a__:     Dict										=	DDIMScheduler.from_config(scheduler.config)

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



      def   lowerCamelCase_   (						self    ,			lowercase)	->			int:





        '''simple docstring'''





        if strength < 0 or strength > 1:
          raise ValueError(f'The value of strength should in [0.0, 1.0] but is {strength}')



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase)	->			Dict:





        '''simple docstring'''





        a__:     int										=	min(int(num_inference_steps * strength)    ,			lowercase)

        a__:     Any										=	max(num_inference_steps - init_timestep    ,			0)
        a__:     Union[str, Any]										=	self.scheduler.timesteps[t_start:]

        return timesteps, num_inference_steps - t_start



      def   lowerCamelCase_   (						self    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase    ,			lowercase=None)	->			List[Any]:





        '''simple docstring'''





        if not isinstance(lowercase    ,			(torch.Tensor, PIL.Image.Image, list)):
          raise ValueError(
              f'`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(lowercase)}')

        a__:     Tuple										=	image.to(device=lowercase    ,			dtype=lowercase)

        if isinstance(lowercase    ,			lowercase) and len(lowercase) != batch_size:
          raise ValueError(
              f'You have passed a list of generators of length {len(lowercase)}, but requested an effective batch'
              f' size of {batch_size}. Make sure the batch size matches the length of the generators.')

        a__:     List[str]										=	init_latents.shape
        a__:     List[Any]										=	randn_tensor(lowercase    ,			generator=lowercase    ,			device=lowercase    ,			dtype=lowercase)

        # get latents
        print('add noise to latents at timestep'    ,			lowercase)
        a__:     int										=	self.scheduler.add_noise(lowercase    ,			lowercase    ,			lowercase)
        a__:     Dict										=	init_latents

        return latents



      @torch.no_grad()
      def __call__(						self    ,			lowercase = None    ,			lowercase = 0.8    ,			lowercase = 1    ,			lowercase = None    ,			lowercase = 0.0    ,			lowercase = 50    ,			lowercase = None    ,			lowercase = "pil"    ,			lowercase = True    ,			)	->			Union[ImagePipelineOutput, Tuple]:





        '''simple docstring'''





        self.check_inputs(lowercase)

        # 2. Preprocess image
        a__:     Tuple										=	preprocess(lowercase)

        # 3. set timesteps
        self.scheduler.set_timesteps(lowercase    ,			device=self.device)
        a__							,    a__:     Union[str, Any]										=	self.get_timesteps(lowercase    ,			lowercase    ,			self.device)
        a__:     Optional[int]										=	timesteps[:1].repeat(lowercase)

        # 4. Prepare latent variables
        a__:     Union[str, Any]										=	self.prepare_latents(lowercase    ,			lowercase    ,			lowercase    ,			self.unet.dtype    ,			self.device    ,			lowercase)
        a__:     Optional[Any]										=	latents

        # 5. Denoising loop
        for t in self.progress_bar(lowercase):
          # 1. predict noise model_output
          a__:     Dict										=	self.unet(lowercase    ,			lowercase).sample

          # 2. predict previous mean of image x_t-1 and add variance depending on eta
          # eta corresponds to η in paper and should be between [0, 1]
          # do x_t -> x_t-1
          a__:     Optional[Any]										=	self.scheduler.step(
              lowercase    ,			lowercase    ,			lowercase    ,			eta=lowercase    ,			use_clipped_model_output=lowercase    ,			generator=lowercase    ,			).prev_sample

        a__:     Union[str, Any]										=	(image / 2 + 0.5).clamp(0    ,			1)
        a__:     Optional[int]										=	image.cpu().permute(0    ,			2    ,			3    ,			1).numpy()
        if output_type == "pil":
          a__:     Dict										=	self.numpy_to_pil(lowercase)

        if not return_dict:
          return (image, latent_timestep.item())

        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))


