


"""simple docstring"""

import warnings
from typing import List, Optional, Tuple, Union

import numpy as np
import PIL
import torch

from ...models import UNetaDModel
from ...schedulers import RePaintScheduler
from ...utils import PIL_INTERPOLATION, logging, randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput


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


def lowerCAmelCase_   (					UpperCamelCase__						:			Union[List, PIL.Image.Image, torch.Tensor]			):






 """simple docstring"""







 warnings.warn(
     """The preprocess method is deprecated and will be removed in a future version. Please"""
     """ use VaeImageProcessor.preprocess instead"""					, UpperCamelCase__					, )
 if isinstance(UpperCamelCase__					, torch.Tensor			):
  return image
 elif isinstance(UpperCamelCase__					, PIL.Image.Image			):
  __lowercase						=						[image]

 if isinstance(image[0]					, PIL.Image.Image			):
  __lowercase				,		__lowercase						=						image[0].size
  __lowercase				,		__lowercase						=						(x - x % 8 for x in (w, h))  # resize to integer multiple of 8

  __lowercase						=						[np.array(i.resize((w, h)					, resample=PIL_INTERPOLATION["""lanczos"""]			)			)[None, :] for i in image]
  __lowercase						=						np.concatenate(UpperCamelCase__					, axis=0			)
  __lowercase						=						np.array(UpperCamelCase__			).astype(np.floataa			) / 255.0
  __lowercase						=						image.transpose(0					, 3					, 1					, 2			)
  __lowercase						=						2.0 * image - 1.0
  __lowercase						=						torch.from_numpy(UpperCamelCase__			)
 elif isinstance(image[0]					, torch.Tensor			):
  __lowercase						=						torch.cat(UpperCamelCase__					, dim=0			)
 return image


def lowerCAmelCase_   (					UpperCamelCase__						:			Union[List, PIL.Image.Image, torch.Tensor]			):






 """simple docstring"""







 if isinstance(UpperCamelCase__					, torch.Tensor			):
  return mask
 elif isinstance(UpperCamelCase__					, PIL.Image.Image			):
  __lowercase						=						[mask]

 if isinstance(mask[0]					, PIL.Image.Image			):
  __lowercase				,		__lowercase						=						mask[0].size
  __lowercase				,		__lowercase						=						(x - x % 32 for x in (w, h))  # resize to integer multiple of 32
  __lowercase						=						[np.array(m.convert("""L"""			).resize((w, h)					, resample=PIL_INTERPOLATION["""nearest"""]			)			)[None, :] for m in mask]
  __lowercase						=						np.concatenate(UpperCamelCase__					, axis=0			)
  __lowercase						=						mask.astype(np.floataa			) / 255.0
  __lowercase						=						0
  __lowercase						=						1
  __lowercase						=						torch.from_numpy(UpperCamelCase__			)
 elif isinstance(mask[0]					, torch.Tensor			):
  __lowercase						=						torch.cat(UpperCamelCase__					, dim=0			)
 return mask





class  lowerCamelCase__    (		_a     ):
      a			:  UNetaDModel
      a			:  RePaintScheduler
      def __init__(		self						:						List[str]   ,							A_						:						Union[str, Any]   ,							A_						:						Union[str, Any]				):






       '''simple docstring'''


       super().__init__()
       self.register_modules(unet=A_   ,							scheduler=A_				)






      @torch.no_grad()
      def __call__(		self						:						str   ,							A_						:						Union[torch.Tensor, PIL.Image.Image]   ,							A_						:						Union[torch.Tensor, PIL.Image.Image]   ,							A_						:						int = 2_5_0   ,							A_						:						float = 0.0   ,							A_						:						int = 1_0   ,							A_						:						int = 1_0   ,							A_						:						Optional[Union[torch.Generator, List[torch.Generator]]] = None   ,							A_						:						Optional[str] = "pil"   ,							A_						:						bool = True   ,							):






       '''simple docstring'''


       __lowercase						=						image

       __lowercase						=						_preprocess_image(A_				)
       __lowercase						=						original_image.to(device=self.device   ,							dtype=self.unet.dtype				)
       __lowercase						=						_preprocess_mask(A_				)
       __lowercase						=						mask_image.to(device=self.device   ,							dtype=self.unet.dtype				)

       __lowercase						=						original_image.shape[0]

       # sample gaussian noise to begin the loop
       if isinstance(A_   ,							A_				) and len(A_				) != batch_size:
        raise ValueError(
            F'''You have passed a list of generators of length {len(A_				)}, but requested an effective batch'''
            F''' size of {batch_size}. Make sure the batch size matches the length of the generators.'''				)

       __lowercase						=						original_image.shape
       __lowercase						=						randn_tensor(A_   ,							generator=A_   ,							device=self.device   ,							dtype=self.unet.dtype				)

       # set step values
       self.scheduler.set_timesteps(A_   ,							A_   ,							A_   ,							self.device				)
       __lowercase						=						eta

       __lowercase						=						self.scheduler.timesteps[0] + 1
       __lowercase						=						generator[0] if isinstance(A_   ,							A_				) else generator
       for i, t in enumerate(self.progress_bar(self.scheduler.timesteps				)				):
        if t < t_last:
         # predict the noise residual
         __lowercase						=						self.unet(A_   ,							A_				).sample
         # compute previous image: x_t -> x_t-1
         __lowercase						=						self.scheduler.step(A_   ,							A_   ,							A_   ,							A_   ,							A_   ,							A_				).prev_sample

        else:
         # compute the reverse: x_t-1 -> x_t
         __lowercase						=						self.scheduler.undo_step(A_   ,							A_   ,							A_				)
        __lowercase						=						t

       __lowercase						=						(image / 2 + 0.5).clamp(0   ,							1				)
       __lowercase						=						image.cpu().permute(0   ,							2   ,							3   ,							1				).numpy()
       if output_type == "pil":
        __lowercase						=						self.numpy_to_pil(A_				)

       if not return_dict:
        return (image,)

       return ImagePipelineOutput(images=A_				)




"""simple docstring"""

import unittest

import numpy as np

from transformers.testing_utils import require_flax, require_tf, require_torch
from transformers.utils import (
    expand_dims,
    flatten_dict,
    is_flax_available,
    is_tf_available,
    is_torch_available,
    reshape,
    squeeze,
    transpose,
)


if is_flax_available():
       import jax.numpy as jnp

if is_tf_available():
       import tensorflow as tf

if is_torch_available():
       import torch





class  lowerCamelCase__    (		unittest.TestCase     ):
      def SCREAMING_SNAKE_CASE_						(		self						:						Any				):






       '''simple docstring'''


       __lowercase						=						{
           """task_specific_params""": {
               """summarization""": {"""length_penalty""": 1.0, """max_length""": 1_2_8, """min_length""": 1_2, """num_beams""": 4},
               """summarization_cnn""": {"""length_penalty""": 2.0, """max_length""": 1_4_2, """min_length""": 5_6, """num_beams""": 4},
               """summarization_xsum""": {"""length_penalty""": 1.0, """max_length""": 6_2, """min_length""": 1_1, """num_beams""": 6},
           }
       }
       __lowercase						=						{
           """task_specific_params.summarization.length_penalty""": 1.0,
           """task_specific_params.summarization.max_length""": 1_2_8,
           """task_specific_params.summarization.min_length""": 1_2,
           """task_specific_params.summarization.num_beams""": 4,
           """task_specific_params.summarization_cnn.length_penalty""": 2.0,
           """task_specific_params.summarization_cnn.max_length""": 1_4_2,
           """task_specific_params.summarization_cnn.min_length""": 5_6,
           """task_specific_params.summarization_cnn.num_beams""": 4,
           """task_specific_params.summarization_xsum.length_penalty""": 1.0,
           """task_specific_params.summarization_xsum.max_length""": 6_2,
           """task_specific_params.summarization_xsum.min_length""": 1_1,
           """task_specific_params.summarization_xsum.num_beams""": 6,
       }

       self.assertEqual(flatten_dict(A_				)   ,							A_				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Union[str, Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							x.transpose()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							x.transpose((1, 2, 0)				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							transpose(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							transpose(A_   ,							axes=(1, 2, 0)				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						int				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							transpose(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							transpose(A_   ,							axes=(1, 2, 0)				).numpy()				)				)
      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(transpose(A_				)   ,							np.asarray(transpose(A_				)				)				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(transpose(A_   ,							axes=(1, 2, 0)				)   ,							np.asarray(transpose(A_   ,							axes=(1, 2, 0)				)				)				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							np.reshape(A_   ,							(4, 3)				)				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							np.reshape(A_   ,							(1_2, 5)				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							reshape(A_   ,							(4, 3)				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							reshape(A_   ,							(1_2, 5)				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							reshape(A_   ,							(4, 3)				).numpy()				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							reshape(A_   ,							(1_2, 5)				).numpy()				)				)
      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						str				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(4, 3)				)   ,							np.asarray(reshape(A_   ,							(4, 3)				)				)				)				)

       __lowercase						=						np.random.randn(3   ,							4   ,							5				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(reshape(A_   ,							(1_2, 5)				)   ,							np.asarray(reshape(A_   ,							(1_2, 5)				)				)				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Dict				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							np.squeeze(A_				)				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							np.squeeze(A_   ,							axis=2				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							squeeze(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							squeeze(A_   ,							axis=2				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							squeeze(A_				).numpy()				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							squeeze(A_   ,							axis=2				).numpy()				)				)
      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						List[Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(1   ,							3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(squeeze(A_				)   ,							np.asarray(squeeze(A_				)				)				)				)

       __lowercase						=						np.random.randn(1   ,							4   ,							1   ,							5				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(squeeze(A_   ,							axis=2				)   ,							np.asarray(squeeze(A_   ,							axis=2				)				)				)				)
      def SCREAMING_SNAKE_CASE_						(		self						:						Tuple				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							np.expand_dims(A_   ,							axis=1				)				)				)
      @require_torch
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[int]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						torch.tensor(A_				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							expand_dims(A_   ,							axis=1				).numpy()				)				)
      @require_tf
      def SCREAMING_SNAKE_CASE_						(		self						:						Optional[Any]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						tf.constant(A_				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							expand_dims(A_   ,							axis=1				).numpy()				)				)






      @require_flax
      def SCREAMING_SNAKE_CASE_						(		self						:						List[str]				):






       '''simple docstring'''


       __lowercase						=						np.random.randn(3   ,							4				)
       __lowercase						=						jnp.array(A_				)
       self.assertTrue(np.allclose(expand_dims(A_   ,							axis=1				)   ,							np.asarray(expand_dims(A_   ,							axis=1				)				)				)				)
