import gc
import math
import unittest

import torch

from diffusers import UNetaDModel
from diffusers.utils import floats_tensor, logging, slow, torch_all_close, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


UpperCamelCase__    							= logging.get_logger(__name__)

enable_full_determinism()





class 		A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			Optional[Any]              =      UNetaDModel
   __UpperCAmelCase			:			Union[str, Any]              =      'sample'


   @property
   def       lowercase_				(self					:						int		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  3
         UpperCAmelCase__								=  (3_2, 3_2)

         UpperCAmelCase__								=  floats_tensor((batch_size, num_channels) + sizes		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor([1_0]		).to(__UpperCAmelCase		)

         return {"sample": noise, "timestep": time_step}


   @property
   def       lowercase_				(self					:						Tuple		)						->    Union[str, Any]:
         """simple docstring"""



         return (3, 3_2, 3_2)


   @property
   def       lowercase_				(self					:						int		)						->    Dict:
         """simple docstring"""



         return (3, 3_2, 3_2)





   def       lowercase_				(self					:						Optional[Any]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__								=  {
             "block_out_channels": (3_2, 6_4),
             "down_block_types": ("DownBlock2D", "AttnDownBlock2D"),
             "up_block_types": ("AttnUpBlock2D", "UpBlock2D"),
             "attention_head_dim": 3,
             "out_channels": 3,
             "in_channels": 3,
             "layers_per_block": 2,
             "sample_size": 3_2,
         }
         UpperCAmelCase__								=  self.dummy_input
         return init_dict, inputs_dict





class 		A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			Optional[int]              =      UNetaDModel
   __UpperCAmelCase			:			str              =      'sample'


   @property
   def       lowercase_				(self					:						int		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  (3_2, 3_2)

         UpperCAmelCase__								=  floats_tensor((batch_size, num_channels) + sizes		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor([1_0]		).to(__UpperCAmelCase		)

         return {"sample": noise, "timestep": time_step}


   @property
   def       lowercase_				(self					:						Dict		)						->    int:
         """simple docstring"""



         return (4, 3_2, 3_2)


   @property
   def       lowercase_				(self					:						int		)						->    List[str]:
         """simple docstring"""



         return (4, 3_2, 3_2)


   def       lowercase_				(self					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  {
             "sample_size": 3_2,
             "in_channels": 4,
             "out_channels": 4,
             "layers_per_block": 2,
             "block_out_channels": (3_2, 6_4),
             "attention_head_dim": 3_2,
             "down_block_types": ("DownBlock2D", "DownBlock2D"),
             "up_block_types": ("UpBlock2D", "UpBlock2D"),
         }
         UpperCAmelCase__								=  self.dummy_input
         return init_dict, inputs_dict


   def       lowercase_				(self					:						int		)						->    str:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,  output_loading_info=__UpperCAmelCase		)

         self.assertIsNotNone(__UpperCAmelCase		)
         self.assertEqual(len(loading_info["missing_keys"]		)	,  0		)

         model.to(__UpperCAmelCase		)
         UpperCAmelCase__								=  model(**self.dummy_input		).sample

         assert image is not None, "Make sure output is not None"


   @unittest.skipIf(torch_device != "cuda"	,  "This test is supposed to run on GPU"		)
   def       lowercase_				(self					:						Optional[Any]		)						->    int:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,  output_loading_info=__UpperCAmelCase		)
         model.to(__UpperCAmelCase		)
         UpperCAmelCase__								=  model(**self.dummy_input		).sample

         assert image is not None, "Make sure output is not None"


   @unittest.skipIf(torch_device != "cuda"	,  "This test is supposed to run on GPU"		)
   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"	,  output_loading_info=__UpperCAmelCase		)
         model_accelerate.to(__UpperCAmelCase		)
         model_accelerate.eval()

         UpperCAmelCase__								=  torch.randn(
             1	,  model_accelerate.config.in_channels	,  model_accelerate.config.sample_size	,  model_accelerate.config.sample_size	,  generator=torch.manual_seed(0		)	,  )
         UpperCAmelCase__								=  noise.to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor([1_0] * noise.shape[0]		).to(__UpperCAmelCase		)

         UpperCAmelCase__								=  model_accelerate(__UpperCAmelCase	,  __UpperCAmelCase		)["sample"]

         # two models don't need to stay in the device at the same time
         del model_accelerate
         torch.cuda.empty_cache()
         gc.collect()

         UpperCAmelCase__		,				UpperCAmelCase__								=  UNetaDModel.from_pretrained(
             "fusing/unet-ldm-dummy-update"	,  output_loading_info=__UpperCAmelCase	,  low_cpu_mem_usage=__UpperCAmelCase		)
         model_normal_load.to(__UpperCAmelCase		)
         model_normal_load.eval()
         UpperCAmelCase__								=  model_normal_load(__UpperCAmelCase	,  __UpperCAmelCase		)["sample"]

         assert torch_all_close(__UpperCAmelCase	,  __UpperCAmelCase	,  rtol=1E-3		)





   def       lowercase_				(self					:						int		)						->    Union[str, Any]:
         """simple docstring"""



         UpperCAmelCase__								=  UNetaDModel.from_pretrained("fusing/unet-ldm-dummy-update"		)
         model.eval()
         model.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  torch.randn(
             1	,  model.config.in_channels	,  model.config.sample_size	,  model.config.sample_size	,  generator=torch.manual_seed(0		)	,  )
         UpperCAmelCase__								=  noise.to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor([1_0] * noise.shape[0]		).to(__UpperCAmelCase		)

         with torch.no_grad():
               UpperCAmelCase__								=  model(__UpperCAmelCase	,  __UpperCAmelCase		).sample

         UpperCAmelCase__								=  output[0, -1, -3:, -3:].flatten().cpu()
         # fmt: off
         UpperCAmelCase__								=  torch.tensor([-13.3258, -20.1100, -15.9873, -17.6617, -23.0596, -17.9419, -13.3675, -16.1889, -12.3800]		)
         # fmt: on

         self.assertTrue(torch_all_close(__UpperCAmelCase	,  __UpperCAmelCase	,  rtol=1E-3		)		)





class 		A							(    UpperCAmelCase_		,    UpperCAmelCase_		,    unittest.TestCase					):
   __UpperCAmelCase			:			int              =      UNetaDModel
   __UpperCAmelCase			:			Any              =      'sample'


   @property
   def       lowercase_				(self					:						str	,  __UpperCAmelCase					:						str=(3_2, 3_2)		)						->    int:
         """simple docstring"""



         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  3

         UpperCAmelCase__								=  floats_tensor((batch_size, num_channels) + sizes		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor(batch_size * [1_0]		).to(dtype=torch.intaa	,  device=__UpperCAmelCase		)

         return {"sample": noise, "timestep": time_step}


   @property
   def       lowercase_				(self					:						Union[str, Any]		)						->    List[str]:
         """simple docstring"""



         return (3, 3_2, 3_2)


   @property
   def       lowercase_				(self					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         return (3, 3_2, 3_2)


   def       lowercase_				(self					:						Union[str, Any]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  {
             "block_out_channels": [3_2, 6_4, 6_4, 6_4],
             "in_channels": 3,
             "layers_per_block": 1,
             "out_channels": 3,
             "time_embedding_type": "fourier",
             "norm_eps": 1E-6,
             "mid_block_scale_factor": math.sqrt(2.0		),
             "norm_num_groups": None,
             "down_block_types": [
                 "SkipDownBlock2D",
                 "AttnSkipDownBlock2D",
                 "SkipDownBlock2D",
                 "SkipDownBlock2D",
             ],
             "up_block_types": [
                 "SkipUpBlock2D",
                 "SkipUpBlock2D",
                 "AttnSkipUpBlock2D",
                 "SkipUpBlock2D",
             ],
         }
         UpperCAmelCase__								=  self.dummy_input
         return init_dict, inputs_dict


   @slow
   def       lowercase_				(self					:						List[str]		)						->    str:
         """simple docstring"""



         UpperCAmelCase__		,				UpperCAmelCase__								=  UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"	,  output_loading_info=__UpperCAmelCase		)
         self.assertIsNotNone(__UpperCAmelCase		)
         self.assertEqual(len(loading_info["missing_keys"]		)	,  0		)

         model.to(__UpperCAmelCase		)
         UpperCAmelCase__								=  self.dummy_input
         UpperCAmelCase__								=  floats_tensor((4, 3) + (2_5_6, 2_5_6)		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  noise
         UpperCAmelCase__								=  model(**__UpperCAmelCase		)

         assert image is not None, "Make sure output is not None"


   @slow
   def       lowercase_				(self					:						Tuple		)						->    Tuple:
         """simple docstring"""



         UpperCAmelCase__								=  UNetaDModel.from_pretrained("google/ncsnpp-celebahq-256"		)
         model.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  3
         UpperCAmelCase__								=  (2_5_6, 2_5_6)

         UpperCAmelCase__								=  torch.ones((batch_size, num_channels) + sizes		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor(batch_size * [1E-4]		).to(__UpperCAmelCase		)

         with torch.no_grad():
               UpperCAmelCase__								=  model(__UpperCAmelCase	,  __UpperCAmelCase		).sample

         UpperCAmelCase__								=  output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         UpperCAmelCase__								=  torch.tensor([-4842.8691, -6499.6631, -3800.1953, -7978.2686, -10980.7129, -20028.8535, 8148.2822, 2342.2905, 567.7608]		)
         # fmt: on

         self.assertTrue(torch_all_close(__UpperCAmelCase	,  __UpperCAmelCase	,  rtol=1E-2		)		)


   def       lowercase_				(self					:						Union[str, Any]		)						->    Dict:
         """simple docstring"""



         UpperCAmelCase__								=  UNetaDModel.from_pretrained("fusing/ncsnpp-ffhq-ve-dummy-update"		)
         model.to(__UpperCAmelCase		)

         UpperCAmelCase__								=  4
         UpperCAmelCase__								=  3
         UpperCAmelCase__								=  (3_2, 3_2)

         UpperCAmelCase__								=  torch.ones((batch_size, num_channels) + sizes		).to(__UpperCAmelCase		)
         UpperCAmelCase__								=  torch.tensor(batch_size * [1E-4]		).to(__UpperCAmelCase		)

         with torch.no_grad():
               UpperCAmelCase__								=  model(__UpperCAmelCase	,  __UpperCAmelCase		).sample

         UpperCAmelCase__								=  output[0, -3:, -3:, -1].flatten().cpu()
         # fmt: off
         UpperCAmelCase__								=  torch.tensor([-0.0325, -0.0900, -0.0869, -0.0332, -0.0725, -0.0270, -0.0101, 0.0227, 0.0256]		)
         # fmt: on

         self.assertTrue(torch_all_close(__UpperCAmelCase	,  __UpperCAmelCase	,  rtol=1E-2		)		)





   def       lowercase_				(self					:						str		)						->    Dict:
         """simple docstring"""



         pass




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

UpperCamelCase__    							= logging.get_logger(__name__)


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





@add_end_docstrings(UpperCAmelCase_					)
class 					A							(    UpperCAmelCase_					):


   def __init__(self					:						Any	,  *__UpperCAmelCase					:						List[str]	,  **__UpperCAmelCase					:						Any		)						->    Union[str, Any]:
         """simple docstring"""



         super().__init__(*__UpperCAmelCase	,  **__UpperCAmelCase		)

         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       lowercase_				(self					:						Optional[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Any:
         """simple docstring"""



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


   def __call__(self					:						Optional[int]	,  *__UpperCAmelCase					:						List[Any]	,  **__UpperCAmelCase					:						Dict		)						->    Union[Predictions, List[Prediction]]:
         """simple docstring"""



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


   def       lowercase_				(self					:						Optional[int]	,  __UpperCAmelCase					:						Any		)						->    Optional[Any]:
         """simple docstring"""



         UpperCAmelCase__								=  load_image(__UpperCAmelCase		)
         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       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						str		)						->    Optional[Any]:
         """simple docstring"""



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


   def       lowercase_				(self					:						List[Any]	,  __UpperCAmelCase					:						Any	,  __UpperCAmelCase					:						Dict=0.9		)						->    List[Any]:
         """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(__UpperCAmelCase					:						List[str]		):
                     return self._get_bounding_box(
                         torch.Tensor(
                             [
                                 (width * bbox[0] / 1_0_0_0),
                                 (height * bbox[1] / 1_0_0_0),
                                 (width * bbox[2] / 1_0_0_0),
                                 (height * bbox[3] / 1_0_0_0),
                             ]		)		)

               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(__UpperCAmelCase		) for bbox in model_outputs["bbox"].squeeze(0		)]
               UpperCAmelCase__								=  ["score", "label", "box"]
               UpperCAmelCase__								=  [dict(zip(__UpperCAmelCase	,  __UpperCAmelCase		)		) for vals in zip(scores.tolist()	,  __UpperCAmelCase	,  __UpperCAmelCase		) if vals[0] > threshold]
         else:
               # This is a regular ForObjectDetectionModel
               UpperCAmelCase__								=  self.image_processor.post_process_object_detection(__UpperCAmelCase	,  __UpperCAmelCase	,  __UpperCAmelCase		)
               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(__UpperCAmelCase		) for box in boxes]

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

         return annotation





   def       lowercase_				(self					:						List[str]	,  __UpperCAmelCase					:						"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



