


"""simple docstring"""






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

import torch

from ...models import AutoencoderKL, TransformeraDModel
from ...schedulers import KarrasDiffusionSchedulers
from ...utils import randn_tensor
from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput





class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : Optional[Any] ,       UpperCamelCase    : TransformeraDModel ,       UpperCamelCase    : AutoencoderKL ,       UpperCamelCase    : KarrasDiffusionSchedulers ,       UpperCamelCase    : Optional[Dict[int, str]] = None ,       ):
												'''simple docstring'''






												super().__init__()
												self.register_modules(transformer=UpperCamelCase ,       vae=UpperCamelCase ,       scheduler=UpperCamelCase  )

												# create a imagenet -> id dictionary for easier use
												__UpperCAmelCase   :						Optional[Any]      							=					{}
												if idalabel is not None:
																			for key, value in idalabel.items():
																										for label in value.split(""","""  ):
																																	__UpperCAmelCase   :						List[Any]      							=					int(UpperCamelCase  )
																			__UpperCAmelCase   :						int      							=					dict(sorted(self.labels.items()  )  )






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : Union[str, List[str]]  ):
												'''simple docstring'''






												if not isinstance(UpperCamelCase ,       UpperCamelCase  ):
																			__UpperCAmelCase   :						List[str]      							=					list(UpperCamelCase  )

												for l in label:
																			if l not in self.labels:
																										raise ValueError(
																										    f'''{l} does not exist. Please make sure to select one of the following labels: \n {self.labels}.'''  )

												return [self.labels[l] for l in label]






					@torch.no_grad()
					def __call__(       self    : Any ,       UpperCamelCase    : List[int] ,       UpperCamelCase    : float = 4.0 ,       UpperCamelCase    : Optional[Union[torch.Generator, List[torch.Generator]]] = None ,       UpperCamelCase    : int = 50 ,       UpperCamelCase    : Optional[str] = "pil" ,       UpperCamelCase    : bool = True ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					len(UpperCamelCase  )
												__UpperCAmelCase   :						str      							=					self.transformer.config.sample_size
												__UpperCAmelCase   :						Optional[Any]      							=					self.transformer.config.in_channels

												__UpperCAmelCase   :						Dict      							=					randn_tensor(
												    shape=(batch_size, latent_channels, latent_size, latent_size) ,       generator=UpperCamelCase ,       device=self.device ,       dtype=self.transformer.dtype ,       )
												__UpperCAmelCase   :						List[str]      							=					torch.cat([latents] * 2  ) if guidance_scale > 1 else latents

												__UpperCAmelCase   :						Optional[int]      							=					torch.tensor(UpperCamelCase ,       device=self.device  ).reshape(-1  )
												__UpperCAmelCase   :						Dict      							=					torch.tensor([1_000] * batch_size ,       device=self.device  )
												__UpperCAmelCase   :						Union[str, Any]      							=					torch.cat([class_labels, class_null] ,       0  ) if guidance_scale > 1 else class_labels

												# set step values
												self.scheduler.set_timesteps(UpperCamelCase  )

												for t in self.progress_bar(self.scheduler.timesteps  ):
																			if guidance_scale > 1:
																										__UpperCAmelCase   :						List[Any]      							=					latent_model_input[: len(UpperCamelCase  ) // 2]
																										__UpperCAmelCase   :						Optional[int]      							=					torch.cat([half, half] ,       dim=0  )
																			__UpperCAmelCase   :						Tuple      							=					self.scheduler.scale_model_input(UpperCamelCase ,       UpperCamelCase  )

																			__UpperCAmelCase   :						Optional[int]      							=					t
																			if not torch.is_tensor(UpperCamelCase  ):
																										# TODO: this requires sync between CPU and GPU. So try to pass timesteps as tensors if you can
																										# This would be a good case for the `match` statement (Python 3.10+)
																										__UpperCAmelCase   :						Any      							=					latent_model_input.device.type == """mps"""
																										if isinstance(UpperCamelCase ,       UpperCamelCase  ):
																																	__UpperCAmelCase   :						List[Any]      							=					torch.floataa if is_mps else torch.floataa
																										else:
																																	__UpperCAmelCase   :						Union[str, Any]      							=					torch.intaa if is_mps else torch.intaa
																										__UpperCAmelCase   :						Tuple      							=					torch.tensor([timesteps] ,       dtype=UpperCamelCase ,       device=latent_model_input.device  )
																			elif len(timesteps.shape  ) == 0:
																										__UpperCAmelCase   :						Tuple      							=					timesteps[None].to(latent_model_input.device  )
																			# broadcast to batch dimension in a way that's compatible with ONNX/Core ML
																			__UpperCAmelCase   :						List[Any]      							=					timesteps.expand(latent_model_input.shape[0]  )
																			# predict noise model_output
																			__UpperCAmelCase   :						List[str]      							=					self.transformer(
																			    UpperCamelCase ,       timestep=UpperCamelCase ,       class_labels=UpperCamelCase  ).sample

																			# perform guidance
																			if guidance_scale > 1:
																										__UpperCAmelCase							,__UpperCAmelCase   :						int      							=					noise_pred[:, :latent_channels], noise_pred[:, latent_channels:]
																										__UpperCAmelCase							,__UpperCAmelCase   :						List[str]      							=					torch.split(UpperCamelCase ,       len(UpperCamelCase  ) // 2 ,       dim=0  )

																										__UpperCAmelCase   :						str      							=					uncond_eps + guidance_scale * (cond_eps - uncond_eps)
																										__UpperCAmelCase   :						Tuple      							=					torch.cat([half_eps, half_eps] ,       dim=0  )

																										__UpperCAmelCase   :						Optional[int]      							=					torch.cat([eps, rest] ,       dim=1  )

																			# learned sigma
																			if self.transformer.config.out_channels // 2 == latent_channels:
																										__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					torch.split(UpperCamelCase ,       UpperCamelCase ,       dim=1  )
																			else:
																										__UpperCAmelCase   :						List[Any]      							=					noise_pred

																			# compute previous image: x_t -> x_t-1
																			__UpperCAmelCase   :						List[Any]      							=					self.scheduler.step(UpperCamelCase ,       UpperCamelCase ,       UpperCamelCase  ).prev_sample

												if guidance_scale > 1:
																			__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					latent_model_input.chunk(2 ,       dim=0  )
												else:
																			__UpperCAmelCase   :						int      							=					latent_model_input

												__UpperCAmelCase   :						int      							=					1 / self.vae.config.scaling_factor * latents
												__UpperCAmelCase   :						int      							=					self.vae.decode(UpperCamelCase  ).sample

												__UpperCAmelCase   :						str      							=					(samples / 2 + 0.5).clamp(0 ,       1  )

												# we always cast to float32 as this does not cause significant overhead and is compatible with bfloat16
												__UpperCAmelCase   :						Union[str, Any]      							=					samples.cpu().permute(0 ,       2 ,       3 ,       1  ).float().numpy()

												if output_type == "pil":
																			__UpperCAmelCase   :						Optional[Any]      							=					self.numpy_to_pil(UpperCamelCase  )

												if not return_dict:
																			return (samples,)

												return ImagePipelineOutput(images=UpperCamelCase  )




"""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__   :







												"""simple docstring"""


												@staticmethod
												def lowerCamelCase__							(       *UpperCamelCase    : List[str] ,       **UpperCamelCase    : Any  ):
																			'''simple docstring'''






																			pass





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







					"""simple docstring"""


					__a	       =							MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING






					def lowerCamelCase__							(       self    : List[str] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[int]      							=					pipeline(
												    """zero-shot-object-detection""" ,       model="""hf-internal-testing/tiny-random-owlvit-object-detection"""  )

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






					def lowerCamelCase__							(       self    : int ,       UpperCamelCase    : Optional[int] ,       UpperCamelCase    : List[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					object_detector(examples[0] ,       threshold=0.0  )

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






					@require_tf
					@unittest.skip("""Zero Shot Object Detection not implemented in TF"""  )
					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												pass






					@require_torch
					def lowerCamelCase__							(       self    : Union[str, Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					pipeline(
												    """zero-shot-object-detection""" ,       model="""hf-internal-testing/tiny-random-owlvit-object-detection"""  )

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

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

												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       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 lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					pipeline("""zero-shot-object-detection"""  )

												__UpperCAmelCase   :						int      							=					object_detector(
												    """http://images.cocodataset.org/val2017/000000039769.jpg""" ,       candidate_labels=["""cat""", """remote""", """couch"""] ,       )
												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       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   :						List[str]      							=					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(UpperCamelCase ,       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 lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												pass






					@require_torch
					@slow
					def lowerCamelCase__							(       self    : str  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					0.2
												__UpperCAmelCase   :						List[Any]      							=					pipeline("""zero-shot-object-detection"""  )

												__UpperCAmelCase   :						Any      							=					object_detector(
												    """http://images.cocodataset.org/val2017/000000039769.jpg""" ,       candidate_labels=["""cat""", """remote""", """couch"""] ,       threshold=UpperCamelCase ,       )
												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       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 lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[Any]      							=					2
												__UpperCAmelCase   :						Union[str, Any]      							=					pipeline("""zero-shot-object-detection"""  )

												__UpperCAmelCase   :						List[str]      							=					object_detector(
												    """http://images.cocodataset.org/val2017/000000039769.jpg""" ,       candidate_labels=["""cat""", """remote""", """couch"""] ,       top_k=UpperCamelCase ,       )
												self.assertEqual(
												    nested_simplify(UpperCamelCase ,       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}},
												    ] ,       )
