
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

_snake_case					:		Optional[Any]      =    logging.get_logger(__name__)


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

@add_end_docstrings(lowercase__				)
class     a		(lowercase__				):




							"""simple docstring"""


							def __init__(							self	:  List[str]   ,     *lowerCamelCase	:  Optional[int]   ,     **lowerCamelCase	:  Optional[int]    )     ->   Any:
														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	:  Union[str, Any]   ,     **lowerCamelCase	:  str    )     ->   Dict:
														__snake_case		:      List[Any]										=       {}
														if "threshold" in kwargs:
																					__snake_case		:      Optional[int]										=       kwargs["""threshold"""]
														return {}, {}, postprocess_kwargs

							def __call__(							self	:  Tuple   ,     *lowerCamelCase	:  List[Any]   ,     **lowerCamelCase	:  int    )     ->   List[Any]:
														return super().__call__(*_UpperCamelCase   ,     **_UpperCamelCase    )

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Tuple    )     ->   Tuple:
														__snake_case		:      List[Any]										=       load_image(_UpperCamelCase    )
														__snake_case		:      Optional[int]										=       torch.IntTensor([[image.height, image.width]]    )
														__snake_case		:      int										=       self.image_processor(images=[image]   ,     return_tensors="pt"    )
														if self.tokenizer is not None:
																					__snake_case		:      List[Any]										=       self.tokenizer(text=inputs["words"]   ,     boxes=inputs["boxes"]   ,     return_tensors="pt"    )
														__snake_case		:      int										=       target_size
														return inputs

							def    __snake_case (							self	:  Any   ,     lowerCamelCase	:  Dict    )     ->   Optional[int]:
														__snake_case		:      List[Any]										=       model_inputs.pop("target_size"    )
														__snake_case		:      Dict										=       self.model(**_UpperCamelCase    )
														__snake_case		:      Dict										=       outputs.__class__({"target_size": target_size, **outputs}    )
														if self.tokenizer is not None:
																					__snake_case		:      Optional[int]										=       model_inputs["""bbox"""]
														return model_outputs

							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  Tuple   ,     lowerCamelCase	:  Optional[Any]=0.9    )     ->   Any:
														__snake_case		:      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.
																					__snake_case		:      Any										=       target_size[0].tolist()

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

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

																					__snake_case		:      Any										=       scores.tolist()
																					__snake_case		:      str										=       [self.model.config.idalabel[label.item()] for label in labels]
																					__snake_case		:      int										=       [self._get_bounding_box(_UpperCamelCase    ) for box in boxes]

																					# {"scores": [...], ...} --> [{"score":x, ...}, ...]
																					__snake_case		:      Any										=       ["""score""", """label""", """box"""]
																					__snake_case		:      str										=       [
																					    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]   ,     lowerCamelCase	:  "torch.Tensor"    )     ->   Tuple:
														if self.framework != "pt":
																					raise ValueError("The ObjectDetectionPipeline is only available in PyTorch."    )
														__snake_case		:      Dict										=       box.int().tolist()
														__snake_case		:      Union[str, Any]										=       {
														    """xmin""": xmin,
														    """ymin""": ymin,
														    """xmax""": xmax,
														    """ymax""": ymax,
														}
														return bbox





import numpy

class     a		:




							"""simple docstring"""


							def __init__(							self	:  str   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  numpy.ndarray    )     ->   None:
														__snake_case		:      Any										=       input_array

														# Random initial weights are assigned where first argument is the
														# number of nodes in previous layer and second argument is the
														# number of nodes in the next layer.

														# Random initial weights are assigned.
														# self.input_array.shape[1] is used to represent number of nodes in input layer.
														# First hidden layer consists of 4 nodes.
														__snake_case		:      int										=       numpy.random.rand(
														    self.input_array.shape[1]   ,     4    )

														# Random initial values for the first hidden layer.
														# First hidden layer has 4 nodes.
														# Second hidden layer has 3 nodes.
														__snake_case		:      Optional[int]										=       numpy.random.rand(
														    4   ,     3    )

														# Random initial values for the second hidden layer.
														# Second hidden layer has 3 nodes.
														# Output layer has 1 node.
														__snake_case		:      int										=       numpy.random.rand(3   ,     1    )

														# Real output values provided.
														__snake_case		:      Optional[Any]										=       output_array

														# Predicted output values by the neural network.
														# Predicted_output array initially consists of zeroes.
														__snake_case		:      Optional[int]										=       numpy.zeros(output_array.shape    )

							def    __snake_case (							self	:  List[Any]    )     ->   numpy.ndarray:
														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.input_array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														# layer_between_first_hidden_layer_and_second_hidden_layer is the layer
														# connecting the first hidden set of nodes with the second hidden set of nodes.
														__snake_case		:      str										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														# layer_between_second_hidden_layer_and_output is the layer connecting
														# second hidden layer with the output node.
														__snake_case		:      str										=       sigmoid(
														    numpy.dot(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer   ,     self.second_hidden_layer_and_output_layer_weights   ,     )    )

														return self.layer_between_second_hidden_layer_and_output

							def    __snake_case (							self	:  Union[str, Any]    )     ->   None:
														__snake_case		:      Optional[int]										=       numpy.dot(
														    self.layer_between_first_hidden_layer_and_second_hidden_layer.T   ,     2
														    * (self.output_array - self.predicted_output)
														    * sigmoid_derivative(self.predicted_output    )   ,     )
														__snake_case		:      Dict										=       numpy.dot(
														    self.layer_between_input_and_first_hidden_layer.T   ,     numpy.dot(
														        2
														        * (self.output_array - self.predicted_output)
														        * sigmoid_derivative(self.predicted_output    )   ,     self.second_hidden_layer_and_output_layer_weights.T   ,     )
														    * sigmoid_derivative(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer    )   ,     )
														__snake_case		:      Optional[Any]										=       numpy.dot(
														    self.input_array.T   ,     numpy.dot(
														        numpy.dot(
														            2
														            * (self.output_array - self.predicted_output)
														            * sigmoid_derivative(self.predicted_output    )   ,     self.second_hidden_layer_and_output_layer_weights.T   ,     )
														        * sigmoid_derivative(
														            self.layer_between_first_hidden_layer_and_second_hidden_layer    )   ,     self.first_hidden_layer_and_second_hidden_layer_weights.T   ,     )
														    * sigmoid_derivative(self.layer_between_input_and_first_hidden_layer    )   ,     )

														self.input_layer_and_first_hidden_layer_weights += (
														    updated_input_layer_and_first_hidden_layer_weights
														)
														self.first_hidden_layer_and_second_hidden_layer_weights += (
														    updated_first_hidden_layer_and_second_hidden_layer_weights
														)
														self.second_hidden_layer_and_output_layer_weights += (
														    updated_second_hidden_layer_and_output_layer_weights
														)

							def    __snake_case (							self	:  List[str]   ,     lowerCamelCase	:  numpy.ndarray   ,     lowerCamelCase	:  int   ,     lowerCamelCase	:  bool    )     ->   None:
														for iteration in range(1   ,     iterations + 1    ):
																					__snake_case		:      Any										=       self.feedforward()
																					self.back_propagation()
																					if give_loss:
																												__snake_case		:      str										=       numpy.mean(numpy.square(output - self.feedforward()    )    )
																												print(F'Iteration {iteration} Loss: {loss}'    )






							def    __snake_case (							self	:  Optional[Any]   ,     lowerCamelCase	:  numpy.ndarray    )     ->   int:
														__snake_case		:      Any										=       input_arr

														__snake_case		:      List[str]										=       sigmoid(
														    numpy.dot(self.array   ,     self.input_layer_and_first_hidden_layer_weights    )    )

														__snake_case		:      List[Any]										=       sigmoid(
														    numpy.dot(
														        self.layer_between_input_and_first_hidden_layer   ,     self.first_hidden_layer_and_second_hidden_layer_weights   ,     )    )

														__snake_case		:      Any										=       sigmoid(
														    numpy.dot(
														        self.layer_between_first_hidden_layer_and_second_hidden_layer   ,     self.second_hidden_layer_and_output_layer_weights   ,     )    )

														return int(self.layer_between_second_hidden_layer_and_output > 0.6    )







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return 1 / (1 + numpy.exp(-value     ))







def     lowerCAmelCase_					( __lowerCamelCase     ):
							return (value) * (1 - (value))







def     lowerCAmelCase_					( ):
							__snake_case		:      Dict										=       numpy.array(
							    (
							        [0, 0, 0],
							        [0, 0, 1],
							        [0, 1, 0],
							        [0, 1, 1],
							        [1, 0, 0],
							        [1, 0, 1],
							        [1, 1, 0],
							        [1, 1, 1],
							    )							,     dtype=numpy.floataa							,     )

							# True output values for the given input values.
							__snake_case		:      Union[str, Any]										=       numpy.array(([0], [1], [1], [0], [1], [0], [0], [1])							,     dtype=numpy.floataa     )

							# Calling neural network class.
							__snake_case		:      int										=       TwoHiddenLayerNeuralNetwork(
							    input_array=__lowerCamelCase							,     output_array=__lowerCamelCase     )

							# Calling training function.
							# Set give_loss to True if you want to see loss in every iteration.
							neural_network.train(output=__lowerCamelCase							,     iterations=1_0							,     give_loss=__lowerCamelCase     )

							return neural_network.predict(numpy.array(([1, 1, 1])							,     dtype=numpy.floataa     )     )


if __name__ == "__main__":
			example()

