





from copy import deepcopy
from typing import Optional, Union

import numpy as np

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_tf_available, is_torch_available


if is_torch_available():
       import torch

if is_tf_available():
       import tensorflow as tf




class     SCREAMING_SNAKE_CASE__ (						UpperCamelCase__      ):
 __SCREAMING_SNAKE_CASE							       =	['''image_processor''']
 __SCREAMING_SNAKE_CASE							       =	'''SamImageProcessor'''



 def __init__(							self,__lowerCamelCase ):
        super().__init__(__lowerCamelCase )
        A__											=						self.image_processor
        A__											=						-10
        A__											=						self.image_processor.size['''longest_edge''']



 def __call__(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase = None,**__lowerCamelCase,):
        A__											=						self.image_processor(
            __lowerCamelCase,return_tensors=__lowerCamelCase,**__lowerCamelCase,)

        # pop arguments that are not used in the foward but used nevertheless
        A__											=						encoding_image_processor['''original_sizes''']

        if hasattr(__lowerCamelCase,'''numpy''' ):  # Checks if Torch or TF tensor
               A__											=						original_sizes.numpy()

        A__					,	A__					,	A__											=						self._check_and_preprocess_points(
            input_points=__lowerCamelCase,input_labels=__lowerCamelCase,input_boxes=__lowerCamelCase,)

        A__											=						self._normalize_and_convert(
            __lowerCamelCase,__lowerCamelCase,input_points=__lowerCamelCase,input_labels=__lowerCamelCase,input_boxes=__lowerCamelCase,return_tensors=__lowerCamelCase,)

        return encoding_image_processor



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase="pt",):
        if input_points is not None:
               if len(__lowerCamelCase ) != len(__lowerCamelCase ):
                      A__											=						[
                          self._normalize_coordinates(self.target_size,__lowerCamelCase,original_sizes[0] ) for point in input_points
                      ]
               else:
                      A__											=						[
                          self._normalize_coordinates(self.target_size,__lowerCamelCase,__lowerCamelCase )
                          for point, original_size in zip(__lowerCamelCase,__lowerCamelCase )
                      ]
               # check that all arrays have the same shape
               if not all(point.shape == input_points[0].shape for point in input_points ):
                      if input_labels is not None:
                             A__					,	A__											=						self._pad_points_and_labels(__lowerCamelCase,__lowerCamelCase )

               A__											=						np.array(__lowerCamelCase )

        if input_labels is not None:
               A__											=						np.array(__lowerCamelCase )

        if input_boxes is not None:
               if len(__lowerCamelCase ) != len(__lowerCamelCase ):
                      A__											=						[
                          self._normalize_coordinates(self.target_size,__lowerCamelCase,original_sizes[0],is_bounding_box=__lowerCamelCase )
                          for box in input_boxes
                      ]
               else:
                      A__											=						[
                          self._normalize_coordinates(self.target_size,__lowerCamelCase,__lowerCamelCase,is_bounding_box=__lowerCamelCase )
                          for box, original_size in zip(__lowerCamelCase,__lowerCamelCase )
                      ]
               A__											=						np.array(__lowerCamelCase )

        if input_boxes is not None:
               if return_tensors == "pt":
                      A__											=						torch.from_numpy(__lowerCamelCase )
                      # boxes batch size of 1 by default
                      A__											=						input_boxes.unsqueeze(1 ) if len(input_boxes.shape ) != 3 else input_boxes
               elif return_tensors == "tf":
                      A__											=						tf.convert_to_tensor(__lowerCamelCase )
                      # boxes batch size of 1 by default
                      A__											=						tf.expand_dims(__lowerCamelCase,1 ) if len(input_boxes.shape ) != 3 else input_boxes
               encoding_image_processor.update({'''input_boxes''': input_boxes} )
        if input_points is not None:
               if return_tensors == "pt":
                      A__											=						torch.from_numpy(__lowerCamelCase )
                      # point batch size of 1 by default
                      A__											=						input_points.unsqueeze(1 ) if len(input_points.shape ) != 4 else input_points
               elif return_tensors == "tf":
                      A__											=						tf.convert_to_tensor(__lowerCamelCase )
                      # point batch size of 1 by default
                      A__											=						tf.expand_dims(__lowerCamelCase,1 ) if len(input_points.shape ) != 4 else input_points
               encoding_image_processor.update({'''input_points''': input_points} )
        if input_labels is not None:
               if return_tensors == "pt":
                      A__											=						torch.from_numpy(__lowerCamelCase )
                      # point batch size of 1 by default
                      A__											=						input_labels.unsqueeze(1 ) if len(input_labels.shape ) != 3 else input_labels
               elif return_tensors == "tf":
                      A__											=						tf.convert_to_tensor(__lowerCamelCase )
                      # point batch size of 1 by default
                      A__											=						tf.expand_dims(__lowerCamelCase,1 ) if len(input_labels.shape ) != 3 else input_labels
               encoding_image_processor.update({'''input_labels''': input_labels} )

        return encoding_image_processor



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase ):
        A__											=						max([point.shape[0] for point in input_points] )
        A__											=						[]
        for i, point in enumerate(__lowerCamelCase ):
               if point.shape[0] != expected_nb_points:
                      A__											=						np.concatenate(
                          [point, np.zeros((expected_nb_points - point.shape[0], 2) ) + self.point_pad_value],axis=0 )
                      A__											=						np.append(input_labels[i],[self.point_pad_value] )
               processed_input_points.append(__lowerCamelCase )
        A__											=						processed_input_points
        return input_points, input_labels



 def 				UpperCamelCase			(							self,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase,__lowerCamelCase=False ):
        A__					,	A__											=						original_size
        A__					,	A__											=						self.image_processor._get_preprocess_shape(__lowerCamelCase,longest_edge=__lowerCamelCase )
        A__											=						deepcopy(__lowerCamelCase ).astype(__lowerCamelCase )

        if is_bounding_box:
               A__											=						coords.reshape(-1,2,2 )

        A__											=						coords[..., 0] * (new_w / old_w)
        A__											=						coords[..., 1] * (new_h / old_h)

        if is_bounding_box:
               A__											=						coords.reshape(-1,4 )

        return coords



 def 				UpperCamelCase			(							self,__lowerCamelCase=None,__lowerCamelCase=None,__lowerCamelCase=None,):
        if input_points is not None:
               if hasattr(__lowerCamelCase,'''numpy''' ):  # Checks for TF or Torch tensor
                      A__											=						input_points.numpy().tolist()

               if not isinstance(__lowerCamelCase,__lowerCamelCase ) or not isinstance(input_points[0],__lowerCamelCase ):
                      raise ValueError('''Input points must be a list of list of floating points.''' )
               A__											=						[np.array(__lowerCamelCase ) for input_point in input_points]
        else:
               A__											=						None

        if input_labels is not None:
               if hasattr(__lowerCamelCase,'''numpy''' ):
                      A__											=						input_labels.numpy().tolist()

               if not isinstance(__lowerCamelCase,__lowerCamelCase ) or not isinstance(input_labels[0],__lowerCamelCase ):
                      raise ValueError('''Input labels must be a list of list integers.''' )
               A__											=						[np.array(__lowerCamelCase ) for label in input_labels]
        else:
               A__											=						None

        if input_boxes is not None:
               if hasattr(__lowerCamelCase,'''numpy''' ):
                      A__											=						input_boxes.numpy().tolist()

               if (
                   not isinstance(__lowerCamelCase,__lowerCamelCase )
                   or not isinstance(input_boxes[0],__lowerCamelCase )
                   or not isinstance(input_boxes[0][0],__lowerCamelCase )
               ):
                      raise ValueError('''Input boxes must be a list of list of list of floating points.''' )
               A__											=						[np.array(__lowerCamelCase ).astype(np.floataa ) for box in input_boxes]
        else:
               A__											=						None

        return input_points, input_labels, input_boxes



 @property
 def 				UpperCamelCase			(							self ):
        A__											=						self.image_processor.model_input_names
        return list(dict.fromkeys(__lowerCamelCase ) )




 def 				UpperCamelCase			(							self,*__lowerCamelCase,**__lowerCamelCase ):
        return self.image_processor.post_process_masks(*__lowerCamelCase,**__lowerCamelCase )












import gc
import tempfile
import unittest

import numpy as np
import torch

from diffusers import VersatileDiffusionTextToImagePipeline
from diffusers.utils.testing_utils import nightly, require_torch_gpu, torch_device


a__:      List[str]									=		False




class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):
 pass




@nightly
@require_torch_gpu
class     SCREAMING_SNAKE_CASE__ (						unittest.TestCase      ):



 def 				UpperCamelCase			(							self ):
        # clean up the VRAM after each test
        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()



 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained('''shi-labs/versatile-diffusion''' )
        # remove text_unet
        pipe.remove_unused_weights()
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        with tempfile.TemporaryDirectory() as tmpdirname:
               pipe.save_pretrained(__lowerCamelCase )
               A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(__lowerCamelCase )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						generator.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=2,output_type='''numpy''' ).images

        assert np.abs(image - new_image ).sum() < 1E-5, "Models don't have the same forward pass"




 def 				UpperCamelCase			(							self ):
        A__											=						VersatileDiffusionTextToImagePipeline.from_pretrained(
            '''shi-labs/versatile-diffusion''',torch_dtype=torch.floataa )
        pipe.to(__lowerCamelCase )
        pipe.set_progress_bar_config(disable=__lowerCamelCase )

        A__											=						'''A painting of a squirrel eating a burger '''
        A__											=						torch.manual_seed(0 )
        A__											=						pipe(
            prompt=__lowerCamelCase,generator=__lowerCamelCase,guidance_scale=7.5,num_inference_steps=50,output_type='''numpy''' ).images

        A__											=						image[0, 253:256, 253:256, -1]

        assert image.shape == (1, 512, 512, 3)
        A__											=						np.array([0.3367, 0.3169, 0.2656, 0.3870, 0.4790, 0.3796, 0.4009, 0.4878, 0.4778] )

        assert np.abs(image_slice.flatten() - expected_slice ).max() < 1E-2





