


"""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

__SCREAMING_SNAKE_CASE							:    Union[str, Any]		=    logging.get_logger(__name__)


__SCREAMING_SNAKE_CASE							:    Any		=    Dict[str, Any]
__SCREAMING_SNAKE_CASE							:    List[Any]		=    List[Prediction]





@add_end_docstrings(__SCREAMING_SNAKE_CASE)
class      __A     (__SCREAMING_SNAKE_CASE):





							'''simple docstring'''

							def __init__(				self							:      Optional[Any]		,       *UpperCAmelCase_							:      List[str]		,       **UpperCAmelCase_							:      List[str]       )					->Any:



														"""simple docstring"""



														super().__init__(*_a		,       **_a       )

														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 		lowerCAmelCase       (				self							:      List[Any]		,       **UpperCAmelCase_							:      Union[str, Any]       )					->Union[str, Any]:



														"""simple docstring"""



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



							def __call__(				self							:      Optional[int]		,       *UpperCAmelCase_							:      Tuple		,       **UpperCAmelCase_							:      Tuple       )					->int:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Optional[Any]		,       UpperCAmelCase_							:      Any       )					->str:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Any		,       UpperCAmelCase_							:      List[str]       )					->List[str]:



														"""simple docstring"""



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



							def 		lowerCAmelCase       (				self							:      Optional[int]		,       UpperCAmelCase_							:      Optional[Any]		,       UpperCAmelCase_							:      Dict=0.9       )					->str:



														"""simple docstring"""



														snake_case_           =		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.
																					snake_case_           =		target_size[0].tolist()

																					def unnormalize(UpperCAmelCase_							:      List[Any]       ):
																												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),
																												        ]       )       )

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

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

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

														return annotation



							def 		lowerCAmelCase       (				self							:      int		,       UpperCAmelCase_							:      "torch.Tensor"       )					->Tuple:



														"""simple docstring"""



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







"""simple docstring"""
import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()





class      __A     (snake_case__						,		snake_case__						,		unittest.TestCase):





							'''simple docstring'''

							__lowercase:		str							      =    VQModel
							__lowercase:		Union[str, Any]							      =    """sample"""



							@property
							def 		lowerCAmelCase       (				self							:      List[str]		,       UpperCAmelCase_							:      List[str]=(32, 32)       )					->Tuple:



														"""simple docstring"""



														snake_case_           =		4
														snake_case_           =		3

														snake_case_           =		floats_tensor((batch_size, num_channels) + sizes       ).to(UpperCAmelCase_       )

														return {"sample": image}



							@property
							def 		lowerCAmelCase       (				self							:      Tuple       )					->str:



														"""simple docstring"""



														return (3, 32, 32)



							@property
							def 		lowerCAmelCase       (				self							:      List[Any]       )					->Any:



														"""simple docstring"""



														return (3, 32, 32)



							def 		lowerCAmelCase       (				self							:      Optional[int]       )					->Dict:



														"""simple docstring"""



														snake_case_           =		{
														    """block_out_channels""": [32, 64],
														    """in_channels""": 3,
														    """out_channels""": 3,
														    """down_block_types""": ["""DownEncoderBlock2D""", """DownEncoderBlock2D"""],
														    """up_block_types""": ["""UpDecoderBlock2D""", """UpDecoderBlock2D"""],
														    """latent_channels""": 3,
														}
														snake_case_           =		self.dummy_input
														return init_dict, inputs_dict



							def 		lowerCAmelCase       (				self							:      List[str]       )					->Dict:



														"""simple docstring"""



														pass



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



														"""simple docstring"""



														pass



							def 		lowerCAmelCase       (				self							:      Any       )					->Union[str, Any]:



														"""simple docstring"""



														snake_case_       ,						snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""		,       output_loading_info=UpperCAmelCase_       )
														self.assertIsNotNone(UpperCAmelCase_       )
														self.assertEqual(len(loading_info["""missing_keys"""]       )		,       0       )

														model.to(UpperCAmelCase_       )
														snake_case_           =		model(**self.dummy_input       )

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



							def 		lowerCAmelCase       (				self							:      Tuple       )					->Optional[Any]:



														"""simple docstring"""



														snake_case_           =		VQModel.from_pretrained("""fusing/vqgan-dummy"""       )
														model.to(UpperCAmelCase_       ).eval()

														torch.manual_seed(0       )
														if torch.cuda.is_available():
																					torch.cuda.manual_seed_all(0       )

														snake_case_           =		torch.randn(1		,       model.config.in_channels		,       model.config.sample_size		,       model.config.sample_size       )
														snake_case_           =		image.to(UpperCAmelCase_       )
														with torch.no_grad():
																					snake_case_           =		model(UpperCAmelCase_       ).sample

														snake_case_           =		output[0, -1, -3:, -3:].flatten().cpu()
														# fmt: off
														snake_case_           =		torch.tensor([-0.0_153, -0.4_044, -0.1_880, -0.5_161, -0.2_418, -0.4_072, -0.1_612, -0.0_633, -0.0_143]       )
														# fmt: on
														self.assertTrue(torch.allclose(UpperCAmelCase_		,       UpperCAmelCase_		,       atol=1E-3       )       )



