from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

a  :      Optional[Any]            =    logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase__			)
class 					_lowercase    (     UpperCAmelCase__			):






    '''simple docstring'''




    def __init__(    self       ,						**lowerCamelCase__ ):
       super().__init__(**lowerCamelCase__ )
       requires_backends(self       ,						"vision" )
       requires_backends(self       ,						"torch" )

       if self.framework != "pt":
          raise ValueError(F'''The {self.__class__} is only available in PyTorch.''' )

       self.check_model_type(lowerCamelCase__ )




    def      _a    (    self       ,						**lowerCamelCase__ ):
       lowerCAmelCase_:							Tuple       =	{}
       lowerCAmelCase_:							Optional[Any]       =	{}
       lowerCAmelCase_:							List[Any]       =	{}
       # preprocess args
       if "points_per_batch" in kwargs:
          lowerCAmelCase_:							Union[str, Any]       =	kwargs["points_per_batch"]
       if "points_per_crop" in kwargs:
          lowerCAmelCase_:							Optional[Any]       =	kwargs["points_per_crop"]
       if "crops_n_layers" in kwargs:
          lowerCAmelCase_:							Any       =	kwargs["crops_n_layers"]
       if "crop_overlap_ratio" in kwargs:
          lowerCAmelCase_:							Any       =	kwargs["crop_overlap_ratio"]
       if "crop_n_points_downscale_factor" in kwargs:
          lowerCAmelCase_:							Optional[Any]       =	kwargs["crop_n_points_downscale_factor"]
       # postprocess args
       if "pred_iou_thresh" in kwargs:
          lowerCAmelCase_:							Union[str, Any]       =	kwargs["pred_iou_thresh"]
       if "stability_score_offset" in kwargs:
          lowerCAmelCase_:							List[Any]       =	kwargs["stability_score_offset"]
       if "mask_threshold" in kwargs:
          lowerCAmelCase_:							Union[str, Any]       =	kwargs["mask_threshold"]
       if "stability_score_thresh" in kwargs:
          lowerCAmelCase_:							Dict       =	kwargs["stability_score_thresh"]
       if "crops_nms_thresh" in kwargs:
          lowerCAmelCase_:							Optional[Any]       =	kwargs["crops_nms_thresh"]
       if "output_rle_mask" in kwargs:
          lowerCAmelCase_:							Dict       =	kwargs["output_rle_mask"]
       if "output_bboxes_mask" in kwargs:
          lowerCAmelCase_:							str       =	kwargs["output_bboxes_mask"]
       return preprocess_kwargs, forward_params, postprocess_kwargs




    def __call__(    self       ,						lowerCamelCase__       ,						*lowerCamelCase__       ,						lowerCamelCase__=None       ,						lowerCamelCase__=None       ,						**lowerCamelCase__ ):
       return super().__call__(lowerCamelCase__       ,						*lowerCamelCase__       ,						num_workers=lowerCamelCase__       ,						batch_size=lowerCamelCase__       ,						**lowerCamelCase__ )




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__=64       ,						lowerCamelCase__ = 0       ,						lowerCamelCase__ = 512 / 1_500       ,						lowerCamelCase__ = 32       ,						lowerCamelCase__ = 1       ,						):
       lowerCAmelCase_:							List[Any]       =	load_image(lowerCamelCase__ )
       lowerCAmelCase_:							Optional[int]       =	self.image_processor.size["longest_edge"]
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	self.image_processor.generate_crop_boxes(
           lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )
       lowerCAmelCase_:							Any       =	self.image_processor(images=lowerCamelCase__       ,						return_tensors="pt" )

       with self.device_placement():
          if self.framework == "pt":
             lowerCAmelCase_:							Union[str, Any]       =	self.get_inference_context()
             with inference_context():
                lowerCAmelCase_:							List[Any]       =	self._ensure_tensor_on_device(lowerCamelCase__       ,						device=self.device )
                lowerCAmelCase_:							Any       =	self.model.get_image_embeddings(model_inputs.pop("pixel_values" ) )
                lowerCAmelCase_:							str       =	image_embeddings

       lowerCAmelCase_:							Any       =	grid_points.shape[1]
       lowerCAmelCase_:							Dict       =	points_per_batch if points_per_batch is not None else n_points

       if points_per_batch <= 0:
          raise ValueError(
              "Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. "
              "To return all points at once, set points_per_batch to None" )

       for i in range(0       ,						lowerCamelCase__       ,						lowerCamelCase__ ):
          lowerCAmelCase_:							List[Any]       =	grid_points[:, i : i + points_per_batch, :, :]
          lowerCAmelCase_:							Optional[Any]       =	input_labels[:, i : i + points_per_batch]
          lowerCAmelCase_:							List[str]       =	i == n_points - points_per_batch
          yield {
              "input_points": batched_points,
              "input_labels": labels,
              "input_boxes": crop_boxes,
              "is_last": is_last,
              **model_inputs,
          }




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__=0.8_8       ,						lowerCamelCase__=0.9_5       ,						lowerCamelCase__=0       ,						lowerCamelCase__=1       ,						):
       lowerCAmelCase_:							Optional[Any]       =	model_inputs.pop("input_boxes" )
       lowerCAmelCase_:							str       =	model_inputs.pop("is_last" )
       lowerCAmelCase_:							Any       =	model_inputs.pop("original_sizes" ).tolist()
       lowerCAmelCase_:							Optional[int]       =	model_inputs.pop("reshaped_input_sizes" ).tolist()

       lowerCAmelCase_:							List[str]       =	self.model(**lowerCamelCase__ )

       # post processing happens here in order to avoid CPU GPU copies of ALL the masks
       lowerCAmelCase_:							Optional[int]       =	model_outputs["pred_masks"]
       lowerCAmelCase_:							int       =	self.image_processor.post_process_masks(
           lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						binarize=lowerCamelCase__ )
       lowerCAmelCase_:							Optional[Any]       =	model_outputs["iou_scores"]
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							Optional[int]       =	self.image_processor.filter_masks(
           masks[0]       ,						iou_scores[0]       ,						original_sizes[0]       ,						input_boxes[0]       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						)
       return {
           "masks": masks,
           "is_last": is_last,
           "boxes": boxes,
           "iou_scores": iou_scores,
       }




    def      _a    (    self       ,						lowerCamelCase__       ,						lowerCamelCase__=False       ,						lowerCamelCase__=False       ,						lowerCamelCase__=0.7       ,						):
       lowerCAmelCase_:							Dict       =	[]
       lowerCAmelCase_:							Dict       =	[]
       lowerCAmelCase_:							Tuple       =	[]
       for model_output in model_outputs:
          all_scores.append(model_output.pop("iou_scores" ) )
          all_masks.extend(model_output.pop("masks" ) )
          all_boxes.append(model_output.pop("boxes" ) )

       lowerCAmelCase_:							Optional[Any]       =	torch.cat(lowerCamelCase__ )
       lowerCAmelCase_:							str       =	torch.cat(lowerCamelCase__ )
       lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_  ,	lowerCAmelCase_:							str       =	self.image_processor.post_process_for_mask_generation(
           lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__       ,						lowerCamelCase__ )

       lowerCAmelCase_:							List[Any]       =	defaultdict(lowerCamelCase__ )
       for output in model_outputs:
          for k, v in output.items():
             extra[k].append(lowerCamelCase__ )

       lowerCAmelCase_:							str       =	{}
       if output_rle_mask:
          lowerCAmelCase_:							Tuple       =	rle_mask

       if output_bboxes_mask:
          lowerCAmelCase_:							Tuple       =	bounding_boxes

       return {"masks": output_masks, "scores": iou_scores, **optional, **extra}
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 					_lowercase    (     unittest.TestCase			):






    '''simple docstring'''
    pass





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






    '''simple docstring'''




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




    def      _a    (    self ):
       lowerCAmelCase_:							List[str]       =	VersatileDiffusionTextToImagePipeline.from_pretrained("shi-labs/versatile-diffusion" )
       # remove text_unet
       pipe.remove_unused_weights()
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							Tuple       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							List[str]       =	torch.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	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__ )
          lowerCAmelCase_:							int       =	VersatileDiffusionTextToImagePipeline.from_pretrained(lowerCamelCase__ )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							List[str]       =	generator.manual_seed(0 )
       lowerCAmelCase_:							Dict       =	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      _a    (    self ):
       lowerCAmelCase_:							Optional[int]       =	VersatileDiffusionTextToImagePipeline.from_pretrained(
           "shi-labs/versatile-diffusion"       ,						torch_dtype=torch.floataa )
       pipe.to(lowerCamelCase__ )
       pipe.set_progress_bar_config(disable=lowerCamelCase__ )

       lowerCAmelCase_:							str       =	"A painting of a squirrel eating a burger "
       lowerCAmelCase_:							int       =	torch.manual_seed(0 )
       lowerCAmelCase_:							List[str]       =	pipe(
           prompt=lowerCamelCase__       ,						generator=lowerCamelCase__       ,						guidance_scale=7.5       ,						num_inference_steps=50       ,						output_type="numpy" ).images

       lowerCAmelCase_:							List[Any]       =	image[0, 253:256, 253:256, -1]

       assert image.shape == (1, 512, 512, 3)
       lowerCAmelCase_:							Optional[int]       =	np.array([0.3_3_6_7, 0.3_1_6_9, 0.2_6_5_6, 0.3_8_7_0, 0.4_7_9_0, 0.3_7_9_6, 0.4_0_0_9, 0.4_8_7_8, 0.4_7_7_8] )

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