

import gc
import unittest

import numpy as np
import torch
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()
@skip_mps
class  _A		(  __magic_name__   ,     __magic_name__   ,     unittest.TestCase):
  SCREAMING_SNAKE_CASE    :      Any       =  StableDiffusionPanoramaPipeline
  SCREAMING_SNAKE_CASE    :      Optional[int]       =  TEXT_TO_IMAGE_PARAMS
  SCREAMING_SNAKE_CASE    :      List[str]       =  TEXT_TO_IMAGE_BATCH_PARAMS
  SCREAMING_SNAKE_CASE    :      List[str]       =  TEXT_TO_IMAGE_IMAGE_PARAMS
  SCREAMING_SNAKE_CASE    :      Dict       =  TEXT_TO_IMAGE_IMAGE_PARAMS

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  int           =     UNetaDConditionModel(
            block_out_channels=(32, 64) ,					layers_per_block=1 ,					sample_size=32 ,					in_channels=4 ,					out_channels=4 ,					down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D') ,					up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D') ,					cross_attention_dim=32 ,					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     DDIMScheduler()
        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     AutoencoderKL(
            block_out_channels=[32, 64] ,					in_channels=3 ,					out_channels=3 ,					down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D'] ,					up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D'] ,					latent_channels=4 ,					)
        torch.manual_seed(0					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     CLIPTextConfig(
            bos_token_id=0 ,					eos_token_id=2 ,					hidden_size=32 ,					intermediate_size=37 ,					layer_norm_eps=1e-05 ,					num_attention_heads=4 ,					num_hidden_layers=5 ,					pad_token_id=1 ,					vocab_size=1000 ,					)
        SCREAMING_SNAKE_CASE_				:  Any           =     CLIPTextModel(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     CLIPTokenizer.from_pretrained('hf-internal-testing/tiny-random-clip'					)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     {
            'unet': unet,
            'scheduler': scheduler,
            'vae': vae,
            'text_encoder': text_encoder,
            'tokenizer': tokenizer,
            'safety_checker': None,
            'feature_extractor': None,
        }
        return components

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  int           =     torch.manual_seed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     {
            'prompt': 'a photo of the dolomites',
            'generator': generator,
            # Setting height and width to None to prevent OOMs on CPU.
            'height': None,
            'width': None,
            'num_inference_steps': 1,
            'guidance_scale': 6.0,
            'output_type': 'numpy',
        }
        return inputs

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Dict           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  Dict           =     StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     sd_pipe.to(_SCREAMING_SNAKE_CASE					)
        sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     sd_pipe(**_SCREAMING_SNAKE_CASE					).images
        SCREAMING_SNAKE_CASE_				:  int           =     image[0, -3:, -3:, -1]
        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     np.array([0.6186, 0.5374, 0.4915, 0.4135, 0.4114, 0.4563, 0.5128, 0.4977, 0.4757]					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_inference_batch_consistent(batch_sizes=[1, 2]					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().test_inference_batch_single_identical(batch_size=2 ,					expected_max_diff=3.25e-3					)

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  Dict           =     StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Tuple           =     sd_pipe.to(_SCREAMING_SNAKE_CASE					)
        sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     'french fries'
        SCREAMING_SNAKE_CASE_				:  Any           =     sd_pipe(**_SCREAMING_SNAKE_CASE ,					negative_prompt=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     output.images
        SCREAMING_SNAKE_CASE_				:  Any           =     image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_				:  Dict           =     np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Any           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     sd_pipe.to(_SCREAMING_SNAKE_CASE					)
        sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  str           =     sd_pipe(**_SCREAMING_SNAKE_CASE ,					view_batch_size=2					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     output.images
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.array([0.6187, 0.5375, 0.4915, 0.4136, 0.4114, 0.4563, 0.5128, 0.4976, 0.4757]					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[str]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     EulerAncestralDiscreteScheduler(
            beta_start=0.00085 ,					beta_end=0.012 ,					beta_schedule='scaled_linear'					)
        SCREAMING_SNAKE_CASE_				:  int           =     StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  int           =     sd_pipe.to(_SCREAMING_SNAKE_CASE					)
        sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  str           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     sd_pipe(**_SCREAMING_SNAKE_CASE					).images
        SCREAMING_SNAKE_CASE_				:  List[str]           =     image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_				:  Dict           =     np.array([0.4024, 0.6510, 0.4901, 0.5378, 0.5813, 0.5622, 0.4795, 0.4467, 0.4952]					)

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



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'cpu'  # ensure determinism for the device-dependent torch.Generator
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.get_dummy_components()
        SCREAMING_SNAKE_CASE_				:  int           =     PNDMScheduler(
            beta_start=0.00085 ,					beta_end=0.012 ,					beta_schedule='scaled_linear' ,					skip_prk_steps=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     StableDiffusionPanoramaPipeline(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Any           =     sd_pipe.to(_SCREAMING_SNAKE_CASE					)
        sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  int           =     self.get_dummy_inputs(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     sd_pipe(**_SCREAMING_SNAKE_CASE					).images
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     image[0, -3:, -3:, -1]

        assert image.shape == (1, 64, 64, 3)

        SCREAMING_SNAKE_CASE_				:  Tuple           =     np.array([0.6391, 0.6291, 0.4861, 0.5134, 0.5552, 0.4578, 0.5032, 0.5023, 0.4539]					)

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

@slow
@require_torch_gpu
class  _A		(  unittest.TestCase):

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        super().tearDown()
        gc.collect()
        torch.cuda.empty_cache()

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE=0					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     torch.manual_seed(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     {
            'prompt': 'a photo of the dolomites',
            'generator': generator,
            'num_inference_steps': 3,
            'guidance_scale': 7.5,
            'output_type': 'numpy',
        }
        return inputs

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     'stabilityai/stable-diffusion-2-base'
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE ,					subfolder='scheduler'					)
        SCREAMING_SNAKE_CASE_				:  int           =     StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE ,					safety_checker=_SCREAMING_SNAKE_CASE					)
        pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)
        pipe.enable_attention_slicing()

        SCREAMING_SNAKE_CASE_				:  Tuple           =     self.get_inputs()
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     pipe(**_SCREAMING_SNAKE_CASE					).images
        SCREAMING_SNAKE_CASE_				:  Any           =     image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2048, 3)

        SCREAMING_SNAKE_CASE_				:  Any           =     np.array(
            [
                0.36968392,
                0.27025372,
                0.32446766,
                0.28379387,
                0.36363274,
                0.30733347,
                0.27100027,
                0.27054125,
                0.25536096,
            ]					)

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

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     StableDiffusionPanoramaPipeline.from_pretrained(
            'stabilityai/stable-diffusion-2-base' ,					safety_checker=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
        pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)
        pipe.enable_attention_slicing()

        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_inputs()
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     pipe(**_SCREAMING_SNAKE_CASE					).images
        SCREAMING_SNAKE_CASE_				:  Dict           =     image[0, -3:, -3:, -1].flatten()

        assert image.shape == (1, 512, 2048, 3)

        SCREAMING_SNAKE_CASE_				:  Any           =     np.array(
            [
                [
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                    0.0,
                ]
            ]					)

        assert np.abs(expected_slice - image_slice					).max() < 1e-3

  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     0

        def callback_fn(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) -> None:
              SCREAMING_SNAKE_CASE_				:  str           =     True
              nonlocal number_of_steps
              number_of_steps += 1
              if step == 1:
                    SCREAMING_SNAKE_CASE_				:  Dict           =     latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 256)
                    SCREAMING_SNAKE_CASE_				:  int           =     latents[0, -3:, -3:, -1]

                    SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     np.array(
                        [
                            0.18681869,
                            0.33907816,
                            0.5361276,
                            0.14432865,
                            -0.02856611,
                            -0.73941123,
                            0.23397987,
                            0.47322682,
                            -0.37823164,
                        ]					)
                    assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2
              elif step == 2:
                    SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     latents.detach().cpu().numpy()
                    assert latents.shape == (1, 4, 64, 256)
                    SCREAMING_SNAKE_CASE_				:  Optional[int]           =     latents[0, -3:, -3:, -1]

                    SCREAMING_SNAKE_CASE_				:  List[Any]           =     np.array(
                        [
                            0.18539645,
                            0.33987248,
                            0.5378559,
                            0.14437142,
                            -0.02455261,
                            -0.7338317,
                            0.23990755,
                            0.47356272,
                            -0.3786505,
                        ]					)

                    assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5e-2

        SCREAMING_SNAKE_CASE_				:  List[str]           =     False

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     'stabilityai/stable-diffusion-2-base'
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE ,					subfolder='scheduler'					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE ,					safety_checker=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)
        pipe.enable_attention_slicing()

        SCREAMING_SNAKE_CASE_				:  Any           =     self.get_inputs()
        pipe(**_SCREAMING_SNAKE_CASE ,					callback=_SCREAMING_SNAKE_CASE ,					callback_steps=1					)
        assert callback_fn.has_been_called
        assert number_of_steps == 3



  def  UpperCAmelCase    (				self					):





        """simple docstring"""







        torch.cuda.empty_cache()
        torch.cuda.reset_max_memory_allocated()
        torch.cuda.reset_peak_memory_stats()

        SCREAMING_SNAKE_CASE_				:  Dict           =     'stabilityai/stable-diffusion-2-base'
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     DDIMScheduler.from_pretrained(_SCREAMING_SNAKE_CASE ,					subfolder='scheduler'					)
        SCREAMING_SNAKE_CASE_				:  str           =     StableDiffusionPanoramaPipeline.from_pretrained(_SCREAMING_SNAKE_CASE ,					scheduler=_SCREAMING_SNAKE_CASE ,					safety_checker=_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     pipe.to(_SCREAMING_SNAKE_CASE					)
        pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE					)
        pipe.enable_attention_slicing(1					)
        pipe.enable_sequential_cpu_offload()

        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.get_inputs()
        SCREAMING_SNAKE_CASE_				:  List[str]           =     pipe(**_SCREAMING_SNAKE_CASE					)

        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     torch.cuda.max_memory_allocated()
        # make sure that less than 5.2 GB is allocated
        assert mem_bytes < 5.5 * 10**9









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, Pipeline


if is_vision_available():
    from ..image_utils import load_image


if is_torch_available():
    import torch

    from ..models.auto.modeling_auto import MODEL_FOR_OBJECT_DETECTION_MAPPING, MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING

lowerCAmelCase     :   Optional[int]						     =  logging.get_logger(__name__)


lowerCAmelCase     :   Optional[int]						     =  Dict[str, Any]
lowerCAmelCase     :   Any						     =  List[Prediction]
@add_end_docstrings(__magic_name__)
class  _A		(  __magic_name__):

  def __init__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        super().__init__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

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

        requires_backends(self ,					'vision'					)
        self.check_model_type(
            dict(MODEL_FOR_OBJECT_DETECTION_MAPPING.items() + MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING.items()					)					)

  def  UpperCAmelCase    (				self ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Any           =     {}
        if "threshold" in kwargs:
              SCREAMING_SNAKE_CASE_				:  List[str]           =     kwargs['threshold']
        return {}, {}, postprocess_kwargs

  def __call__(				self ,					*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        return super().__call__(*_SCREAMING_SNAKE_CASE ,					**_SCREAMING_SNAKE_CASE					)

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     load_image(_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     torch.IntTensor([[image.height, image.width]]					)
        SCREAMING_SNAKE_CASE_				:  List[Any]           =     self.image_processor(images=[image] ,					return_tensors='pt'					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  Tuple           =     self.tokenizer(text=inputs['words'] ,					boxes=inputs['boxes'] ,					return_tensors='pt'					)
        SCREAMING_SNAKE_CASE_				:  Optional[int]           =     target_size
        return inputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  Dict           =     model_inputs.pop('target_size'					)
        SCREAMING_SNAKE_CASE_				:  List[str]           =     self.model(**_SCREAMING_SNAKE_CASE					)
        SCREAMING_SNAKE_CASE_				:  Dict           =     outputs.__class__({'target_size': target_size, **outputs}					)
        if self.tokenizer is not None:
              SCREAMING_SNAKE_CASE_				:  str           =     model_inputs['bbox']
        return model_outputs

  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE=0.9					):





        """simple docstring"""







        SCREAMING_SNAKE_CASE_				:  List[Any]           =     model_outputs['target_size']
        if self.tokenizer is not None:
              # This is a LayoutLMForTokenClassification variant.
              # The OCR got the boxes and the model classified the words.
              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Any           =     target_size[0].tolist()

              def unnormalize(_SCREAMING_SNAKE_CASE					):
                    return self._get_bounding_box(
                        torch.Tensor(
                            [
                                (width * bbox[0] / 1000),
                                (height * bbox[1] / 1000),
                                (width * bbox[2] / 1000),
                                (height * bbox[3] / 1000),
                            ]					)					)

              SCREAMING_SNAKE_CASE_       ,						SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     model_outputs['logits'].squeeze(0					).softmax(dim=-1					).max(dim=-1					)
              SCREAMING_SNAKE_CASE_				:  str           =     [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
              SCREAMING_SNAKE_CASE_				:  List[Any]           =     [unnormalize(_SCREAMING_SNAKE_CASE					) for bbox in model_outputs['bbox'].squeeze(0					)]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Tuple           =     [dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					) for vals in zip(scores.tolist() ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					) if vals[0] > threshold]
        else:
              # This is a regular ForObjectDetectionModel
              SCREAMING_SNAKE_CASE_				:  Dict           =     self.image_processor.post_process_object_detection(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)
              SCREAMING_SNAKE_CASE_				:  int           =     raw_annotations[0]
              SCREAMING_SNAKE_CASE_				:  Optional[Any]           =     raw_annotation['scores']
              SCREAMING_SNAKE_CASE_				:  Any           =     raw_annotation['labels']
              SCREAMING_SNAKE_CASE_				:  Tuple           =     raw_annotation['boxes']

              SCREAMING_SNAKE_CASE_				:  Union[str, Any]           =     scores.tolist()
              SCREAMING_SNAKE_CASE_				:  Any           =     [self.model.config.idalabel[label.item()] for label in labels]
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [self._get_bounding_box(_SCREAMING_SNAKE_CASE					) for box in boxes]

              # {"scores": [...], ...} --> [{"score":x, ...}, ...]
              SCREAMING_SNAKE_CASE_				:  str           =     ['score', 'label', 'box']
              SCREAMING_SNAKE_CASE_				:  Optional[int]           =     [
                  dict(zip(_SCREAMING_SNAKE_CASE ,					_SCREAMING_SNAKE_CASE					)					)
                  for vals in zip(raw_annotation['scores'] ,					raw_annotation['labels'] ,					raw_annotation['boxes']					)
              ]

        return annotation



  def  UpperCAmelCase    (				self ,					_SCREAMING_SNAKE_CASE					):





        """simple docstring"""







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






