

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_       	=	logging.get_logger(__name__)


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





@add_end_docstrings(__A					)
class 	snake_case_   (		__A					):







			'''simple docstring'''

			def __init__(     self :				str							,   *_UpperCamelCase :				Tuple							,   **_UpperCamelCase :				Optional[Any]							)      ->int:
									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 				snake_case__(     self :				Optional[Any]							,   **_UpperCamelCase :				Any							)      ->Optional[int]:
									snake_case_   =	{}
									if "threshold" in kwargs:
															snake_case_   =	kwargs['''threshold''']
									return {}, {}, postprocess_kwargs







			def __call__(     self :				str							,   *_UpperCamelCase :				List[Any]							,   **_UpperCamelCase :				Tuple							)      ->Union[Predictions, List[Prediction]]:
									return super().__call__(*_UpperCamelCase							,   **_UpperCamelCase							)







			def 				snake_case__(     self :				Any							,   _UpperCamelCase :				str							)      ->Union[str, Any]:
									snake_case_   =	load_image(_UpperCamelCase							)
									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 				snake_case__(     self :				int							,   _UpperCamelCase :				List[str]							)      ->List[Any]:
									snake_case_   =	model_inputs.pop('''target_size'''							)
									snake_case_   =	self.model(**_UpperCamelCase							)
									snake_case_   =	outputs.__class__({'''target_size''': target_size, **outputs}							)
									if self.tokenizer is not None:
															snake_case_   =	model_inputs['''bbox''']
									return model_outputs







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				str							,   _UpperCamelCase :				List[str]=0.9							)      ->List[Any]:
									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_,    snake_case_   =	target_size[0].tolist()

															def unnormalize(_UpperCamelCase :				int							):
																					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),
																					        ]							)							)

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

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

									return annotation







			def 				snake_case__(     self :				List[Any]							,   _UpperCamelCase :				"torch.Tensor"							)      ->Dict[str, int]:
									if self.framework != "pt":
															raise ValueError('''The ObjectDetectionPipeline is only available in PyTorch.'''							)
									snake_case_,    snake_case_,    snake_case_,    snake_case_   =	box.int().tolist()
									snake_case_   =	{
									    '''xmin''': xmin,
									    '''ymin''': ymin,
									    '''xmax''': xmax,
									    '''ymax''': ymax,
									}
									return bbox




import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
				import torch

				from transformers.activations import gelu_new, gelu_python, get_activation


@require_torch
class       a							(			unittest.TestCase				):



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      Union[str, Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      int     		=	get_activation('''gelu'''  )
						self.assertTrue(torch.allclose(gelu_python(__lowercase  )   ,torch_builtin(__lowercase  )  )  )
						self.assertFalse(torch.allclose(gelu_python(__lowercase  )   ,gelu_new(__lowercase  )  )  )



		def 	__lowerCamelCase			(     self     :Union[str, Any]  ):
						snake_case__	:      List[Any]     		=	torch.tensor([-1_0_0, -1, -0.1, 0, 0.1, 1.0, 1_0_0]  )
						snake_case__	:      Union[str, Any]     		=	get_activation('''gelu'''  )
						snake_case__	:      int     		=	get_activation('''gelu_10'''  )

						snake_case__	:      Optional[int]     		=	torch_builtin(__lowercase  )
						snake_case__	:      str     		=	geluaa(__lowercase  )

						snake_case__	:      Tuple     		=	torch.where(y_gelu_aa < 10.0   ,1   ,0  )

						self.assertTrue(torch.max(__lowercase  ).item() == 10.0  )
						self.assertTrue(torch.allclose(y_gelu * clipped_mask   ,y_gelu_aa * clipped_mask  )  )



		def 	__lowerCamelCase			(     self     :Any  ):
						get_activation('''gelu'''  )
						get_activation('''gelu_10'''  )
						get_activation('''gelu_fast'''  )
						get_activation('''gelu_new'''  )
						get_activation('''gelu_python'''  )
						get_activation('''gelu_pytorch_tanh'''  )
						get_activation('''linear'''  )
						get_activation('''mish'''  )
						get_activation('''quick_gelu'''  )
						get_activation('''relu'''  )
						get_activation('''sigmoid'''  )
						get_activation('''silu'''  )
						get_activation('''swish'''  )
						get_activation('''tanh'''  )
						with self.assertRaises(__lowercase  ):
										get_activation('''bogus'''  )
						with self.assertRaises(__lowercase  ):
										get_activation(__lowercase  )






		def 	__lowerCamelCase			(     self     :Optional[int]  ):
						snake_case__	:      str     		=	get_activation('''gelu'''  )
						snake_case__	:      List[Any]     		=	1
						snake_case__	:      Optional[Any]     		=	get_activation('''gelu'''  )
						self.assertEqual(acta.a   ,1  )
						with self.assertRaises(__lowercase  ):
										snake_case__	:      str     		=	acta.a

