'''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, 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

a_							=      logging.get_logger(__name__)


a_							=      Dict[str, Any]
a_							=      List[Prediction]


@add_end_docstrings(snake_case					)
class 			UpperCAmelCase__    (		snake_case					):



  """simple docstring"""






  def __init__(				self:			Any				,				*__lowerCAmelCase:			Tuple				,				**__lowerCAmelCase:			Optional[int]   ) -> Union[str, Any]:




    '''simple docstring'''




    super().__init__(*__lowerCAmelCase				,				**__lowerCAmelCase   )

    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:			List[Any]				,				**__lowerCAmelCase:			str   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       {}
    if "threshold" in kwargs:
      __UpperCAmelCase						    =       kwargs["threshold"]
    return {}, {}, postprocess_kwargs






  def __call__(				self:			Optional[Any]				,				*__lowerCAmelCase:			Union[str, Any]				,				**__lowerCAmelCase:			Optional[int]   ) -> Union[Predictions, List[Prediction]]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			int				,				__lowerCAmelCase:			Any   ) -> str:




    '''simple docstring'''




    __UpperCAmelCase						    =       load_image(__lowerCAmelCase   )
    __UpperCAmelCase						    =       torch.IntTensor([[image.height, image.width]]   )
    __UpperCAmelCase						    =       self.image_processor(images=[image]				,				return_tensors="pt"   )
    if self.tokenizer is not None:
      __UpperCAmelCase						    =       self.tokenizer(text=inputs["words"]				,				boxes=inputs["boxes"]				,				return_tensors="pt"   )
    __UpperCAmelCase						    =       target_size
    return inputs






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			List[Any]   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       model_inputs.pop("target_size"   )
    __UpperCAmelCase						    =       self.model(**__lowerCAmelCase   )
    __UpperCAmelCase						    =       outputs.__class__({"target_size": target_size, **outputs}   )
    if self.tokenizer is not None:
      __UpperCAmelCase						    =       model_inputs["bbox"]
    return model_outputs






  def 	_UpperCAmelCase				(				self:			Dict				,				__lowerCAmelCase:			Optional[int]				,				__lowerCAmelCase:			List[Any]=0.9   ) -> Tuple:




    '''simple docstring'''




    __UpperCAmelCase						    =       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.
      __UpperCAmelCase    ,		__UpperCAmelCase						    =       target_size[0].tolist()

      def unnormalize(__lowerCAmelCase:			str   ):
        return self._get_bounding_box(
            torch.Tensor(
                [
                    (width * bbox[0] / 1_000),
                    (height * bbox[1] / 1_000),
                    (width * bbox[2] / 1_000),
                    (height * bbox[3] / 1_000),
                ]   )   )

      __UpperCAmelCase    ,		__UpperCAmelCase						    =       model_outputs["logits"].squeeze(0   ).softmax(dim=-1   ).max(dim=-1   )
      __UpperCAmelCase						    =       [self.model.config.idalabel[prediction] for prediction in classes.tolist()]
      __UpperCAmelCase						    =       [unnormalize(__lowerCAmelCase   ) for bbox in model_outputs["bbox"].squeeze(0   )]
      __UpperCAmelCase						    =       ["score", "label", "box"]
      __UpperCAmelCase						    =       [dict(zip(__lowerCAmelCase				,				__lowerCAmelCase   )   ) for vals in zip(scores.tolist()				,				__lowerCAmelCase				,				__lowerCAmelCase   ) if vals[0] > threshold]
    else:
      # This is a regular ForObjectDetectionModel
      __UpperCAmelCase						    =       self.image_processor.post_process_object_detection(__lowerCAmelCase				,				__lowerCAmelCase				,				__lowerCAmelCase   )
      __UpperCAmelCase						    =       raw_annotations[0]
      __UpperCAmelCase						    =       raw_annotation["scores"]
      __UpperCAmelCase						    =       raw_annotation["labels"]
      __UpperCAmelCase						    =       raw_annotation["boxes"]

      __UpperCAmelCase						    =       scores.tolist()
      __UpperCAmelCase						    =       [self.model.config.idalabel[label.item()] for label in labels]
      __UpperCAmelCase						    =       [self._get_bounding_box(__lowerCAmelCase   ) for box in boxes]

      # {"scores": [...], ...} --> [{"score":x, ...}, ...]
      __UpperCAmelCase						    =       ["score", "label", "box"]
      __UpperCAmelCase						    =       [
          dict(zip(__lowerCAmelCase				,				__lowerCAmelCase   )   )
          for vals in zip(raw_annotation["scores"]				,				raw_annotation["labels"]				,				raw_annotation["boxes"]   )
      ]

    return annotation






  def 	_UpperCAmelCase				(				self:			Optional[Any]				,				__lowerCAmelCase:			"torch.Tensor"   ) -> Dict[str, int]:




    '''simple docstring'''




    if self.framework != "pt":
      raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."   )
    __UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase    ,		__UpperCAmelCase						    =       box.int().tolist()
    __UpperCAmelCase						    =       {
        "xmin": xmin,
        "ymin": ymin,
        "xmax": xmax,
        "ymax": ymax,
    }
    return bbox




import gc
import unittest

import numpy as np
import torch

from diffusers import AutoencoderKL, DDIMScheduler, DiTPipeline, DPMSolverMultistepScheduler, TransformeraDModel
from diffusers.utils import is_xformers_available, load_numpy, slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

from ..pipeline_params import (
    CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS,
    CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS,
)
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()


class 			UpperCAmelCase__    (		snake_case		,	unittest.TestCase					):



  """simple docstring"""






  lowerCAmelCase__		:	int    		= DiTPipeline
  lowerCAmelCase__		:	Any    		= CLASS_CONDITIONED_IMAGE_GENERATION_PARAMS
  lowerCAmelCase__		:	Optional[Any]    		= PipelineTesterMixin.required_optional_params - {
      'latents',
      'num_images_per_prompt',
      'callback',
      'callback_steps',
  }
  lowerCAmelCase__		:	List[Any]    		= CLASS_CONDITIONED_IMAGE_GENERATION_BATCH_PARAMS
  lowerCAmelCase__		:	Optional[int]    		= False






  def 	_UpperCAmelCase				(				self:			str   ) -> List[Any]:




    '''simple docstring'''




    torch.manual_seed(0   )
    __UpperCAmelCase						    =       TransformeraDModel(
        sample_size=16				,				num_layers=2				,				patch_size=4				,				attention_head_dim=8				,				num_attention_heads=2				,				in_channels=4				,				out_channels=8				,				attention_bias=__lowerCAmelCase				,				activation_fn="gelu-approximate"				,				num_embeds_ada_norm=1_000				,				norm_type="ada_norm_zero"				,				norm_elementwise_affine=__lowerCAmelCase				,				)
    __UpperCAmelCase						    =       AutoencoderKL()
    __UpperCAmelCase						    =       DDIMScheduler()
    __UpperCAmelCase						    =       {"transformer": transformer.eval(), "vae": vae.eval(), "scheduler": scheduler}
    return components






  def 	_UpperCAmelCase				(				self:			str				,				__lowerCAmelCase:			str				,				__lowerCAmelCase:			Optional[int]=0   ) -> int:




    '''simple docstring'''




    if str(__lowerCAmelCase   ).startswith("mps"   ):
      __UpperCAmelCase						    =       torch.manual_seed(__lowerCAmelCase   )
    else:
      __UpperCAmelCase						    =       torch.Generator(device=__lowerCAmelCase   ).manual_seed(__lowerCAmelCase   )
    __UpperCAmelCase						    =       {
        "class_labels": [1],
        "generator": generator,
        "num_inference_steps": 2,
        "output_type": "numpy",
    }
    return inputs






  def 	_UpperCAmelCase				(				self:			Tuple   ) -> Dict:




    '''simple docstring'''




    __UpperCAmelCase						    =       "cpu"

    __UpperCAmelCase						    =       self.get_dummy_components()
    __UpperCAmelCase						    =       self.pipeline_class(**__lowerCAmelCase   )
    pipe.to(__lowerCAmelCase   )
    pipe.set_progress_bar_config(disable=__lowerCAmelCase   )

    __UpperCAmelCase						    =       self.get_dummy_inputs(__lowerCAmelCase   )
    __UpperCAmelCase						    =       pipe(**__lowerCAmelCase   ).images
    __UpperCAmelCase						    =       image[0, -3:, -3:, -1]

    self.assertEqual(image.shape				,				(1, 16, 16, 3)   )
    __UpperCAmelCase						    =       np.array([0.2946, 0.6601, 0.4329, 0.3296, 0.4144, 0.5319, 0.7273, 0.5013, 0.4457]   )
    __UpperCAmelCase						    =       np.abs(image_slice.flatten() - expected_slice   ).max()
    self.assertLessEqual(__lowerCAmelCase				,				1E-3   )






  def 	_UpperCAmelCase				(				self:			int   ) -> str:




    '''simple docstring'''




    self._test_inference_batch_single_identical(relax_max_difference=__lowerCAmelCase				,				expected_max_diff=1E-3   )






  @unittest.skipIf(
      torch_device != "cuda" or not is_xformers_available()				,				reason="XFormers attention is only available with CUDA and `xformers` installed"				,				)
  def 	_UpperCAmelCase				(				self:			Optional[int]   ) -> str:




    '''simple docstring'''




    self._test_xformers_attention_forwardGenerator_pass(expected_max_diff=1E-3   )






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



  """simple docstring"""






  def 	_UpperCAmelCase				(				self:			Any   ) -> Optional[Any]:




    '''simple docstring'''




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






  def 	_UpperCAmelCase				(				self:			Dict   ) -> List[Any]:




    '''simple docstring'''




    __UpperCAmelCase						    =       torch.manual_seed(0   )

    __UpperCAmelCase						    =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-256"   )
    pipe.to("cuda"   )

    __UpperCAmelCase						    =       ["vase", "umbrella", "white shark", "white wolf"]
    __UpperCAmelCase						    =       pipe.get_label_ids(__lowerCAmelCase   )

    __UpperCAmelCase						    =       pipe(__lowerCAmelCase				,				generator=__lowerCAmelCase				,				num_inference_steps=40				,				output_type="np"   ).images

    for word, image in zip(__lowerCAmelCase				,				__lowerCAmelCase   ):
      __UpperCAmelCase						    =       load_numpy(
          F'''https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/dit/{word}.npy'''   )
      assert np.abs((expected_image - image).max()   ) < 1E-2






  def 	_UpperCAmelCase				(				self:			Any   ) -> List[str]:




    '''simple docstring'''




    __UpperCAmelCase						    =       DiTPipeline.from_pretrained("facebook/DiT-XL-2-512"   )
    __UpperCAmelCase						    =       DPMSolverMultistepScheduler.from_config(pipe.scheduler.config   )
    pipe.to("cuda"   )

    __UpperCAmelCase						    =       ["vase", "umbrella"]
    __UpperCAmelCase						    =       pipe.get_label_ids(__lowerCAmelCase   )

    __UpperCAmelCase						    =       torch.manual_seed(0   )
    __UpperCAmelCase						    =       pipe(__lowerCAmelCase				,				generator=__lowerCAmelCase				,				num_inference_steps=25				,				output_type="np"   ).images

    for word, image in zip(__lowerCAmelCase				,				__lowerCAmelCase   ):
      __UpperCAmelCase						    =       load_numpy(
          "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
          F'''/dit/{word}_512.npy'''   )

      assert np.abs((expected_image - image).max()   ) < 1E-1



