






"""simple docstring"""







import unittest

from transformers import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING, is_vision_available, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_vision_available():
  from PIL import Image
else:







  class  lowerCamelCase__ :






      @staticmethod
      def     _UpperCamelCase							(			*A     ,**A ):
            pass







@is_pipeline_test
@require_vision
@require_torch
class  lowerCamelCase__ (	unittest.TestCase			):
    SCREAMING_SNAKE_CASE		=      MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






    def     _UpperCamelCase							(			self     ,A     ,A     ,A ):
          UpperCAmelCase          =	pipeline(
              """zero-shot-object-detection"""     ,model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )

          UpperCAmelCase          =	[
              {
                  """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
                  """candidate_labels""": ["""cat""", """remote""", """couch"""],
              }
          ]
          return object_detector, examples






    def     _UpperCamelCase							(			self     ,A     ,A ):
          UpperCAmelCase          =	object_detector(examples[0]     ,threshold=0.0 )

          UpperCAmelCase          =	len(A )
          self.assertGreater(A     ,0 )
          self.assertEqual(
              A     ,[
                  {
                      """score""": ANY(A ),
                      """label""": ANY(A ),
                      """box""": {"""xmin""": ANY(A ), """ymin""": ANY(A ), """xmax""": ANY(A ), """ymax""": ANY(A )},
                  }
                  for i in range(A )
              ]     ,)






    @require_tf
    @unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
    def     _UpperCamelCase							(			self ):
          pass






    @require_torch
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	pipeline(
              """zero-shot-object-detection"""     ,model="""hf-internal-testing/tiny-random-owlvit-object-detection""" )

          UpperCAmelCase          =	object_detector(
              """./tests/fixtures/tests_samples/COCO/000000039769.png"""     ,candidate_labels=["""cat""", """remote""", """couch"""]     ,threshold=0.64     ,)

          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
                  {"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
                  {"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
                  {"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
                  {"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
                  {"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
                  {"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
                  {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
                  {"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
              ]     ,)

          UpperCAmelCase          =	object_detector(
              [
                  {
                      """image""": """./tests/fixtures/tests_samples/COCO/000000039769.png""",
                      """candidate_labels""": ["""cat""", """remote""", """couch"""],
                  }
              ]     ,threshold=0.64     ,)

          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  [
                      {"""score""": 0.7235, """label""": """cat""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
                      {"""score""": 0.7218, """label""": """remote""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
                      {"""score""": 0.7184, """label""": """couch""", """box""": {"""xmin""": 204, """ymin""": 167, """xmax""": 232, """ymax""": 190}},
                      {"""score""": 0.6748, """label""": """remote""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
                      {"""score""": 0.6656, """label""": """cat""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
                      {"""score""": 0.6614, """label""": """couch""", """box""": {"""xmin""": 571, """ymin""": 83, """xmax""": 598, """ymax""": 103}},
                      {"""score""": 0.6456, """label""": """remote""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
                      {"""score""": 0.642, """label""": """remote""", """box""": {"""xmin""": 67, """ymin""": 274, """xmax""": 93, """ymax""": 297}},
                      {"""score""": 0.6419, """label""": """cat""", """box""": {"""xmin""": 494, """ymin""": 105, """xmax""": 521, """ymax""": 127}},
                  ]
              ]     ,)






    @require_torch
    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	pipeline("""zero-shot-object-detection""" )

          UpperCAmelCase          =	object_detector(
              """http://images.cocodataset.org/val2017/000000039769.jpg"""     ,candidate_labels=["""cat""", """remote""", """couch"""]     ,)
          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
                  {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
                  {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
                  {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
                  {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
              ]     ,)

          UpperCAmelCase          =	object_detector(
              [
                  {
                      """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
                      """candidate_labels""": ["""cat""", """remote""", """couch"""],
                  },
                  {
                      """image""": """http://images.cocodataset.org/val2017/000000039769.jpg""",
                      """candidate_labels""": ["""cat""", """remote""", """couch"""],
                  },
              ]     ,)
          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  [
                      {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
                      {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
                      {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
                      {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
                      {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
                  ],
                  [
                      {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
                      {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
                      {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
                      {"""score""": 0.1474, """label""": """remote""", """box""": {"""xmin""": 335, """ymin""": 74, """xmax""": 371, """ymax""": 187}},
                      {"""score""": 0.1208, """label""": """couch""", """box""": {"""xmin""": 4, """ymin""": 0, """xmax""": 642, """ymax""": 476}},
                  ],
              ]     ,)






    @require_tf
    @unittest.skip("""Zero Shot Object Detection not implemented in TF""" )
    def     _UpperCamelCase							(			self ):
          pass






    @require_torch
    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	0.2
          UpperCAmelCase          =	pipeline("""zero-shot-object-detection""" )

          UpperCAmelCase          =	object_detector(
              """http://images.cocodataset.org/val2017/000000039769.jpg"""     ,candidate_labels=["""cat""", """remote""", """couch"""]     ,threshold=A     ,)
          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
                  {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
                  {"""score""": 0.2537, """label""": """cat""", """box""": {"""xmin""": 1, """ymin""": 55, """xmax""": 315, """ymax""": 472}},
              ]     ,)






    @require_torch
    @slow
    def     _UpperCamelCase							(			self ):
          UpperCAmelCase          =	2
          UpperCAmelCase          =	pipeline("""zero-shot-object-detection""" )

          UpperCAmelCase          =	object_detector(
              """http://images.cocodataset.org/val2017/000000039769.jpg"""     ,candidate_labels=["""cat""", """remote""", """couch"""]     ,top_k=A     ,)
          self.assertEqual(
              nested_simplify(A     ,decimals=4 )     ,[
                  {"""score""": 0.2868, """label""": """cat""", """box""": {"""xmin""": 324, """ymin""": 20, """xmax""": 640, """ymax""": 373}},
                  {"""score""": 0.277, """label""": """remote""", """box""": {"""xmin""": 40, """ymin""": 72, """xmax""": 177, """ymax""": 115}},
              ]     ,)











"""simple docstring"""







from typing import List, Optional, Tuple, Union

import torch

from ...models import UNetaDModel
from ...schedulers import ScoreSdeVeScheduler
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput







class  lowerCamelCase__ (	snake_case			):
    SCREAMING_SNAKE_CASE		=      42
    SCREAMING_SNAKE_CASE		=      42






    def __init__(			self     ,A     ,A ):
          super().__init__()
          self.register_modules(unet=A     ,scheduler=A )






    @torch.no_grad()
    def __call__(			self     ,A = 1     ,A = 2_000     ,A = None     ,A = "pil"     ,A = True     ,**A     ,):
          UpperCAmelCase          =	self.unet.config.sample_size
          UpperCAmelCase          =	(batch_size, 3, img_size, img_size)

          UpperCAmelCase          =	self.unet

          UpperCAmelCase          =	randn_tensor(A     ,generator=A ) * self.scheduler.init_noise_sigma
          UpperCAmelCase          =	sample.to(self.device )

          self.scheduler.set_timesteps(A )
          self.scheduler.set_sigmas(A )

          for i, t in enumerate(self.progress_bar(self.scheduler.timesteps ) ):
                UpperCAmelCase          =	self.scheduler.sigmas[i] * torch.ones(shape[0]     ,device=self.device )

                # correction step
                for _ in range(self.scheduler.config.correct_steps ):
                      UpperCAmelCase          =	self.unet(A     ,A ).sample
                      UpperCAmelCase          =	self.scheduler.step_correct(A     ,A     ,generator=A ).prev_sample

                # prediction step
                UpperCAmelCase          =	model(A     ,A ).sample
                UpperCAmelCase          =	self.scheduler.step_pred(A     ,A     ,A     ,generator=A )

                UpperCAmelCase      , UpperCAmelCase          =	output.prev_sample, output.prev_sample_mean

          UpperCAmelCase          =	sample_mean.clamp(0     ,1 )
          UpperCAmelCase          =	sample.cpu().permute(0     ,2     ,3     ,1 ).numpy()
          if output_type == "pil":
                UpperCAmelCase          =	self.numpy_to_pil(A )

          if not return_dict:
                return (sample,)

          return ImagePipelineOutput(images=A )



