
'''simple docstring'''






from typing import Any, Dict, List, Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging, requires_backends
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_vision_available():
					from PIL import Image

					from ..image_utils import load_image

if is_torch_available():
					import torch

					from transformers.modeling_outputs import BaseModelOutput

					from ..models.auto.modeling_auto import MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING

A			      =      logging.get_logger(__name__)




@add_end_docstrings(a__)
class     __snake_case		(   a__):
					def __init__(     self,    **A      ):

									"""simple docstring"""


									super().__init__(**A      )

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

									requires_backends(self,    'vision'      )
									self.check_model_type(A      )
					def __call__(     self,    A,    A = None,    **A,    ):

									"""simple docstring"""


									if "text_queries" in kwargs:
													lowerCamelCase					:			Dict									= kwargs.pop('text_queries'      )

									if isinstance(A,    (str, Image.Image)      ):
													lowerCamelCase					:			Tuple									= {'image': image, 'candidate_labels': candidate_labels}
									else:
													lowerCamelCase					:			str									= image
									lowerCamelCase					:			Union[str, Any]									= super().__call__(A,    **A      )
									return results
					def     UpperCAmelCase_							(     self,    **A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[str]									= {}
									if "threshold" in kwargs:
													lowerCamelCase					:			int									= kwargs['threshold']
									if "top_k" in kwargs:
													lowerCamelCase					:			List[Any]									= kwargs['top_k']
									return {}, {}, postprocess_params
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= load_image(inputs['image']      )
									lowerCamelCase					:			Optional[int]									= inputs['candidate_labels']
									if isinstance(A,    A      ):
													lowerCamelCase					:			int									= candidate_labels.split(','      )

									lowerCamelCase					:			int									= torch.tensor([[image.height, image.width]],    dtype=torch.intaa      )
									for i, candidate_label in enumerate(A      ):
													lowerCamelCase					:			Tuple									= self.tokenizer(A,    return_tensors=self.framework      )
													lowerCamelCase					:			Union[str, Any]									= self.image_processor(A,    return_tensors=self.framework      )
													yield {
													    "is_last": i == len(A      ) - 1,
													    "target_size": target_size,
													    "candidate_label": candidate_label,
													    **text_inputs,
													    **image_features,
													}
					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= model_inputs.pop('target_size'      )
									lowerCamelCase					:			List[str]									= model_inputs.pop('candidate_label'      )
									lowerCamelCase					:			int									= model_inputs.pop('is_last'      )

									lowerCamelCase					:			Any									= self.model(**A      )

									lowerCamelCase					:			int									= {'target_size': target_size, 'candidate_label': candidate_label, 'is_last': is_last, **outputs}
									return model_outputs
					def     UpperCAmelCase_							(     self,    A,    A=0.1,    A=None      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= []
									for model_output in model_outputs:
													lowerCamelCase					:			int									= model_output['candidate_label']
													lowerCamelCase					:			int									= BaseModelOutput(A      )
													lowerCamelCase					:			Tuple									= self.image_processor.post_process_object_detection(
													    outputs=A,    threshold=A,    target_sizes=model_output['target_size']      )[0]

													for index in outputs["scores"].nonzero():
																	lowerCamelCase					:			Union[str, Any]									= outputs['scores'][index].item()
																	lowerCamelCase					:			int									= self._get_bounding_box(outputs['boxes'][index][0]      )

																	lowerCamelCase					:			Any									= {'score': score, 'label': label, 'box': box}
																	results.append(A      )

									lowerCamelCase					:			Any									= sorted(A,    key=lambda A      : x["score"],    reverse=A      )
									if top_k:
													lowerCamelCase					:			Optional[Any]									= results[:top_k]

									return results




					def     UpperCAmelCase_							(     self,    A      ):

									"""simple docstring"""


									if self.framework != "pt":
													raise ValueError('The ZeroShotObjectDetectionPipeline is only available in PyTorch.'      )
									lowerCamelCase			, lowerCamelCase			, lowerCamelCase			, lowerCamelCase					:			int									= box.int().tolist()
									lowerCamelCase					:			List[str]									= {
									    'xmin': xmin,
									    'ymin': ymin,
									    'xmax': xmax,
									    'ymax': ymax,
									}
									return bbox


'''simple docstring'''






import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort




class     __snake_case		(   a__    ,   unittest.TestCase):
					_lowerCAmelCase			     =	'''hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline'''
					def     UpperCAmelCase_							(     self,    A=0      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= floats_tensor((1, 3, 128, 128),    rng=random.Random(A      )      )
									lowerCamelCase					:			Tuple									= np.random.RandomState(A      )
									lowerCamelCase					:			str									= {
									    'prompt': 'A painting of a squirrel eating a burger',
									    'image': image,
									    'generator': generator,
									    'num_inference_steps': 3,
									    'strength': 0.75,
									    'guidance_scale': 7.5,
									    'output_type': 'numpy',
									}
									return inputs
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Union[str, Any]									= self.get_dummy_inputs()
									lowerCamelCase					:			List[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Union[str, Any]									= image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Union[str, Any]									= np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087]      )
									assert np.abs(image_slice - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			str									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Any									= PNDMScheduler.from_config(pipe.scheduler.config,    skip_prk_steps=A      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Dict									= self.get_dummy_inputs()
									lowerCamelCase					:			Dict									= pipe(**A      ).images
									lowerCamelCase					:			Optional[Any]									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Union[str, Any]									= np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Optional[int]									= LMSDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									# warmup pass to apply optimizations
									lowerCamelCase					:			Union[str, Any]									= pipe(**self.get_dummy_inputs()      )

									lowerCamelCase					:			Any									= self.get_dummy_inputs()
									lowerCamelCase					:			Optional[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Dict									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Dict									= np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Any									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			int									= EulerDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			str									= self.get_dummy_inputs()
									lowerCamelCase					:			List[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Tuple									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			List[str]									= np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]      )

									assert np.abs(image_slice.flatten() - expected_slice      ).max() < 1e-1
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			int									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Union[str, Any]									= EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Union[str, Any]									= self.get_dummy_inputs()
									lowerCamelCase					:			List[Any]									= pipe(**A      ).images
									lowerCamelCase					:			Optional[Any]									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			Optional[int]									= np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304]      )

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint,    provider='CPUExecutionProvider'      )
									lowerCamelCase					:			Any									= DPMSolverMultistepScheduler.from_config(pipe.scheduler.config      )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			str									= self.get_dummy_inputs()
									lowerCamelCase					:			Tuple									= pipe(**A      ).images
									lowerCamelCase					:			Union[str, Any]									= image[0, -3:, -3:, -1]

									assert image.shape == (1, 128, 128, 3)
									lowerCamelCase					:			List[Any]									= np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552]      )

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





@nightly
@require_onnxruntime
@require_torch_gpu
class     __snake_case		(   unittest.TestCase):
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									return (
									    "CUDAExecutionProvider",
									    {
									        "gpu_mem_limit": "15000000000",  # 15GB
									        "arena_extend_strategy": "kSameAsRequested",
									    },
									)
					@property
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Optional[int]									= ort.SessionOptions()
									lowerCamelCase					:			Union[str, Any]									= False
									return options
					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			Tuple									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/img2img/sketch-mountains-input.jpg'      )
									lowerCamelCase					:			int									= init_image.resize((768, 512)      )
									# using the PNDM scheduler by default
									lowerCamelCase					:			Union[str, Any]									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    'CompVis/stable-diffusion-v1-4',    revision='onnx',    safety_checker=A,    feature_extractor=A,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Optional[Any]									= 'A fantasy landscape, trending on artstation'

									lowerCamelCase					:			Dict									= np.random.RandomState(0      )
									lowerCamelCase					:			Any									= pipe(
									    prompt=A,    image=A,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=10,    generator=A,    output_type='np',    )
									lowerCamelCase					:			Union[str, Any]									= output.images
									lowerCamelCase					:			Union[str, Any]									= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowerCamelCase					:			List[Any]									= np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019]      )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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




					def     UpperCAmelCase_							(     self      ):

									"""simple docstring"""


									lowerCamelCase					:			List[Any]									= load_image(
									    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main'
									    '/img2img/sketch-mountains-input.jpg'      )
									lowerCamelCase					:			Any									= init_image.resize((768, 512)      )
									lowerCamelCase					:			List[Any]									= LMSDiscreteScheduler.from_pretrained(
									    'runwayml/stable-diffusion-v1-5',    subfolder='scheduler',    revision='onnx'      )
									lowerCamelCase					:			Dict									= OnnxStableDiffusionImgaImgPipeline.from_pretrained(
									    'runwayml/stable-diffusion-v1-5',    revision='onnx',    scheduler=A,    safety_checker=A,    feature_extractor=A,    provider=self.gpu_provider,    sess_options=self.gpu_options,    )
									pipe.set_progress_bar_config(disable=A      )

									lowerCamelCase					:			Union[str, Any]									= 'A fantasy landscape, trending on artstation'

									lowerCamelCase					:			List[Any]									= np.random.RandomState(0      )
									lowerCamelCase					:			Tuple									= pipe(
									    prompt=A,    image=A,    strength=0.75,    guidance_scale=7.5,    num_inference_steps=20,    generator=A,    output_type='np',    )
									lowerCamelCase					:			List[Any]									= output.images
									lowerCamelCase					:			Union[str, Any]									= images[0, 255:258, 383:386, -1]

									assert images.shape == (1, 512, 768, 3)
									lowerCamelCase					:			int									= np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431]      )
									# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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