"""simple docstring"""


import json
import logging
import os
import random
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from huggingface_hub import hf_hub_download
from PIL import Image
from torch import nn
from torchvision import transforms
from torchvision.transforms import functional

import transformers
from transformers import (
    AutoConfig,
    AutoImageProcessor,
    AutoModelForSemanticSegmentation,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    default_data_collator,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
a							=logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.31.0')

require_version('datasets>=2.0.0', 'To fix: pip install -r examples/pytorch/semantic-segmentation/requirements.txt')





def 			lowerCamelCase_ (					__lowerCAmelCase   ,    __lowerCAmelCase   ,    __lowerCAmelCase=0				)							->					Tuple:




						'''simple docstring'''



						lowerCamelCase__					=(size, size) if isinstance(lowercase_   ,    lowercase_				) else size
						lowerCamelCase__					,      lowerCamelCase__					=img.size
						lowerCamelCase__					=size[1] - original_height if original_height < size[1] else 0
						lowerCamelCase__					=size[0] - original_width if original_width < size[0] else 0
						lowerCamelCase__					=functional.pad(lowercase_   ,    (0, 0, pad_width, pad_height)   ,    fill=lowercase_				)
						return img






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=transforms






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										for t in self.transforms:
																lowerCamelCase__					,      lowerCamelCase__					=t(lowerCamelCase__	, lowerCamelCase__      )
										return image, target






class    __UpperCAmelCase						:

				def __init__(      self      ):
										pass






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										return image, target






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=size






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=functional.resize(lowerCamelCase__	, self.size      )
										lowerCamelCase__					=functional.resize(lowerCamelCase__	, self.size	, interpolation=transforms.InterpolationMode.NEAREST      )
										return image, target






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase=None      ):
										lowerCamelCase__					=min_size
										if max_size is None:
																lowerCamelCase__					=min_size
										lowerCamelCase__					=max_size






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=random.randint(self.min_size	, self.max_size      )
										lowerCamelCase__					=functional.resize(lowerCamelCase__	, lowerCamelCase__      )
										lowerCamelCase__					=functional.resize(lowerCamelCase__	, lowerCamelCase__	, interpolation=transforms.InterpolationMode.NEAREST      )
										return image, target






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=size if isinstance(lowerCamelCase__	, lowerCamelCase__      ) else (size, size)






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=pad_if_smaller(lowerCamelCase__	, self.size      )
										lowerCamelCase__					=pad_if_smaller(lowerCamelCase__	, self.size	, fill=255      )
										lowerCamelCase__					=transforms.RandomCrop.get_params(lowerCamelCase__	, self.size      )
										lowerCamelCase__					=functional.crop(lowerCamelCase__	, *lowerCamelCase__      )
										lowerCamelCase__					=functional.crop(lowerCamelCase__	, *lowerCamelCase__      )
										return image, target






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=flip_prob






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										if random.random() < self.flip_prob:
																lowerCamelCase__					=functional.hflip(lowerCamelCase__      )
																lowerCamelCase__					=functional.hflip(lowerCamelCase__      )
										return image, target






class    __UpperCAmelCase						:

				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=functional.pil_to_tensor(lowerCamelCase__      )
										lowerCamelCase__					=torch.as_tensor(np.array(lowerCamelCase__      )	, dtype=torch.intaa      )
										return image, target






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase      ):
										lowerCamelCase__					=dtype






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=functional.convert_image_dtype(lowerCamelCase__	, self.dtype      )
										return image, target






class    __UpperCAmelCase						:

				def __init__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=mean
										lowerCamelCase__					=std






				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										lowerCamelCase__					=functional.normalize(lowerCamelCase__	, mean=self.mean	, std=self.std      )
										return image, target






class    __UpperCAmelCase						:

				def __call__(      self	, _lowerCamelCase	, _lowerCamelCase      ):
										if not isinstance(lowerCamelCase__	, np.ndarray      ):
																lowerCamelCase__					=np.array(lowerCamelCase__      ).astype(np.uinta      )
										# avoid using underflow conversion
										lowerCamelCase__					=255
										lowerCamelCase__					=target - 1
										lowerCamelCase__					=255

										lowerCamelCase__					=Image.fromarray(lowerCamelCase__      )
										return image, target






@dataclass
class    __UpperCAmelCase						:
				A__      :   Optional[str]  							=       field(
				    default='''segments/sidewalk-semantic'''      ,      metadata={
				        '''help''': '''Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub).'''
				    }      ,      )
				A__      :   Optional[str]  							=       field(
				    default=A__      ,      metadata={'''help''': '''The configuration name of the dataset to use (via the datasets library).'''}	)
				A__      :   Optional[float]  							=       field(
				    default=0.15      ,      metadata={'''help''': '''Percent to split off of train for validation.'''}	)
				A__      :   Optional[int]  							=       field(
				    default=A__      ,      metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of training examples to this '''
				            '''value if set.'''
				        )
				    }      ,      )
				A__      :   Optional[int]  							=       field(
				    default=A__      ,      metadata={
				        '''help''': (
				            '''For debugging purposes or quicker training, truncate the number of evaluation examples to this '''
				            '''value if set.'''
				        )
				    }      ,      )
				A__      :   Optional[bool]  							=       field(
				    default=A__      ,      metadata={'''help''': '''Whether or not to reduce all labels by 1 and replace background by 255.'''}      ,      )

				def 		_a	(      self      ):
										if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
																raise ValueError(
																    "You must specify either a dataset name from the hub or a train and/or validation directory."      )






@dataclass
class    __UpperCAmelCase						:
				A__      :   str  							=       field(
				    default='''nvidia/mit-b0'''      ,      metadata={'''help''': '''Path to pretrained model or model identifier from huggingface.co/models'''}      ,      )
				A__      :   Optional[str]  							=       field(
				    default=A__      ,      metadata={'''help''': '''Pretrained config name or path if not the same as model_name'''}	)
				A__      :   Optional[str]  							=       field(
				    default=A__      ,      metadata={'''help''': '''Where do you want to store the pretrained models downloaded from s3'''}	)
				A__      :   str  							=       field(
				    default='''main'''      ,      metadata={'''help''': '''The specific model version to use (can be a branch name, tag name or commit id).'''}      ,      )
				A__      :   str  							=       field(default=A__      ,      metadata={'''help''': '''Name or path of preprocessor config.'''}	)
				A__      :   bool  							=       field(
				    default=A__      ,      metadata={
				        '''help''': (
				            '''Will use the token generated when running `huggingface-cli login` (necessary to use this script '''
				            '''with private models).'''
				        )
				    }      ,      )





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




						'''simple docstring'''



						lowerCamelCase__					=HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)				)
						if len(sys.argv				) == 2 and sys.argv[1].endswith(".json"				):
												# If we pass only one argument to the script and it's the path to a json file,
												# let's parse it to get our arguments.
												lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]				)				)
						else:
												lowerCamelCase__					,      lowerCamelCase__					,      lowerCamelCase__					=parser.parse_args_into_dataclasses()

						# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
						# information sent is the one passed as arguments along with your Python/PyTorch versions.
						send_example_telemetry("run_semantic_segmentation"   ,    lowercase_   ,    lowercase_				)

						# Setup logging
						logging.basicConfig(
						    format="%(asctime)s - %(levelname)s - %(name)s - %(message)s"   ,    datefmt="%m/%d/%Y %H:%M:%S"   ,    handlers=[logging.StreamHandler(sys.stdout				)]   ,    )

						if training_args.should_log:
												# The default of training_args.log_level is passive, so we set log level at info here to have that default.
												transformers.utils.logging.set_verbosity_info()

						lowerCamelCase__					=training_args.get_process_log_level()
						logger.setLevel(lowercase_				)
						transformers.utils.logging.set_verbosity(lowercase_				)
						transformers.utils.logging.enable_default_handler()
						transformers.utils.logging.enable_explicit_format()

						# Log on each process the small summary:
						logger.warning(
						    F'''Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'''
						    + F'''distributed training: {bool(training_args.local_rank != -1				)}, 16-bits training: {training_args.fpaa}'''				)
						logger.info(F'''Training/evaluation parameters {training_args}'''				)

						# Detecting last checkpoint.
						lowerCamelCase__					=None
						if os.path.isdir(training_args.output_dir				) and training_args.do_train and not training_args.overwrite_output_dir:
												lowerCamelCase__					=get_last_checkpoint(training_args.output_dir				)
												if last_checkpoint is None and len(os.listdir(training_args.output_dir				)				) > 0:
																		raise ValueError(
																		    F'''Output directory ({training_args.output_dir}) already exists and is not empty. '''
																		    "Use --overwrite_output_dir to overcome."				)
												elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
																		logger.info(
																		    F'''Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change '''
																		    "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."				)

    # Load dataset
    # In distributed training, the load_dataset function guarantees that only one local process can concurrently
    # download the dataset.
    # TODO support datasets from local folders
						lowerCamelCase__					=load_dataset(data_args.dataset_name   ,    cache_dir=model_args.cache_dir				)

						# Rename column names to standardized names (only "image" and "label" need to be present)
						if "pixel_values" in dataset["train"].column_names:
												lowerCamelCase__					=dataset.rename_columns({"pixel_values": "image"}				)
						if "annotation" in dataset["train"].column_names:
												lowerCamelCase__					=dataset.rename_columns({"annotation": "label"}				)

						# If we don't have a validation split, split off a percentage of train as validation.
						lowerCamelCase__					=None if "validation" in dataset.keys() else data_args.train_val_split
						if isinstance(data_args.train_val_split   ,    lowercase_				) and data_args.train_val_split > 0.0:
												lowerCamelCase__					=dataset["train"].train_test_split(data_args.train_val_split				)
												lowerCamelCase__					=split["train"]
												lowerCamelCase__					=split["test"]

						# Prepare label mappings.
						# We'll include these in the model's config to get human readable labels in the Inference API.
						if data_args.dataset_name == "scene_parse_150":
												lowerCamelCase__					="huggingface/label-files"
												lowerCamelCase__					="ade20k-id2label.json"
						else:
												lowerCamelCase__					=data_args.dataset_name
												lowerCamelCase__					="id2label.json"
						lowerCamelCase__					=json.load(open(hf_hub_download(lowercase_   ,    lowercase_   ,    repo_type="dataset"				)   ,    "r"				)				)
						lowerCamelCase__					={int(lowercase_				): v for k, v in idalabel.items()}
						lowerCamelCase__					={v: str(lowercase_				) for k, v in idalabel.items()}

						# Load the mean IoU metric from the datasets package
						lowerCamelCase__					=evaluate.load("mean_iou"				)

						# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
						# predictions and label_ids field) and has to return a dictionary string to float.
						@torch.no_grad()
						def compute_metrics(__lowerCAmelCase				):
												lowerCamelCase__					,      lowerCamelCase__					=eval_pred
												lowerCamelCase__					=torch.from_numpy(lowercase_				)
												# scale the logits to the size of the label
												lowerCamelCase__					=nn.functional.interpolate(
												    lowercase_   ,    size=labels.shape[-2:]   ,    mode="bilinear"   ,    align_corners=lowercase_   ,    ).argmax(dim=1				)

												lowerCamelCase__					=logits_tensor.detach().cpu().numpy()
												lowerCamelCase__					=metric.compute(
												    predictions=lowercase_   ,    references=lowercase_   ,    num_labels=len(lowercase_				)   ,    ignore_index=0   ,    reduce_labels=image_processor.do_reduce_labels   ,    )
												# add per category metrics as individual key-value pairs
												lowerCamelCase__					=metrics.pop("per_category_accuracy"				).tolist()
												lowerCamelCase__					=metrics.pop("per_category_iou"				).tolist()

												metrics.update({F'''accuracy_{idalabel[i]}''': v for i, v in enumerate(lowercase_				)}				)
												metrics.update({F'''iou_{idalabel[i]}''': v for i, v in enumerate(lowercase_				)}				)

												return metrics

						lowerCamelCase__					=AutoConfig.from_pretrained(
						    model_args.config_name or model_args.model_name_or_path   ,    labelaid=lowercase_   ,    idalabel=lowercase_   ,    cache_dir=model_args.cache_dir   ,    revision=model_args.model_revision   ,    use_auth_token=True if model_args.use_auth_token else None   ,    )
						lowerCamelCase__					=AutoModelForSemanticSegmentation.from_pretrained(
						    model_args.model_name_or_path   ,    from_tf=bool(".ckpt" in model_args.model_name_or_path				)   ,    config=lowercase_   ,    cache_dir=model_args.cache_dir   ,    revision=model_args.model_revision   ,    use_auth_token=True if model_args.use_auth_token else None   ,    )
						lowerCamelCase__					=AutoImageProcessor.from_pretrained(
						    model_args.image_processor_name or model_args.model_name_or_path   ,    cache_dir=model_args.cache_dir   ,    revision=model_args.model_revision   ,    use_auth_token=True if model_args.use_auth_token else None   ,    )

						# Define torchvision transforms to be applied to each image + target.
						# Not that straightforward in torchvision: https://github.com/pytorch/vision/issues/9
						# Currently based on official torchvision references: https://github.com/pytorch/vision/blob/main/references/segmentation/transforms.py
						if "shortest_edge" in image_processor.size:
												# We instead set the target size as (shortest_edge, shortest_edge) to here to ensure all images are batchable.
												lowerCamelCase__					=(image_processor.size["shortest_edge"], image_processor.size["shortest_edge"])
						else:
												lowerCamelCase__					=(image_processor.size["height"], image_processor.size["width"])
						lowerCamelCase__					=Compose(
						    [
						        ReduceLabels() if data_args.reduce_labels else Identity(),
						        RandomCrop(size=lowercase_				),
						        RandomHorizontalFlip(flip_prob=0.5				),
						        PILToTensor(),
						        ConvertImageDtype(torch.float				),
						        Normalize(mean=image_processor.image_mean   ,    std=image_processor.image_std				),
						    ]				)
						# Define torchvision transform to be applied to each image.
						# jitter = ColorJitter(brightness=0.25, contrast=0.25, saturation=0.25, hue=0.1)
						lowerCamelCase__					=Compose(
						    [
						        ReduceLabels() if data_args.reduce_labels else Identity(),
						        Resize(size=lowercase_				),
						        PILToTensor(),
						        ConvertImageDtype(torch.float				),
						        Normalize(mean=image_processor.image_mean   ,    std=image_processor.image_std				),
						    ]				)

						def preprocess_train(__lowerCAmelCase				):
												lowerCamelCase__					=[]
												lowerCamelCase__					=[]
												for image, target in zip(example_batch["image"]   ,    example_batch["label"]				):
																		lowerCamelCase__					,      lowerCamelCase__					=train_transforms(image.convert("RGB"				)   ,    lowercase_				)
																		pixel_values.append(lowercase_				)
																		labels.append(lowercase_				)

												lowerCamelCase__					={}
												lowerCamelCase__					=torch.stack(lowercase_				)
												lowerCamelCase__					=torch.stack(lowercase_				)

												return encoding

						def preprocess_val(__lowerCAmelCase				):
												lowerCamelCase__					=[]
												lowerCamelCase__					=[]
												for image, target in zip(example_batch["image"]   ,    example_batch["label"]				):
																		lowerCamelCase__					,      lowerCamelCase__					=val_transforms(image.convert("RGB"				)   ,    lowercase_				)
																		pixel_values.append(lowercase_				)
																		labels.append(lowercase_				)

												lowerCamelCase__					={}
												lowerCamelCase__					=torch.stack(lowercase_				)
												lowerCamelCase__					=torch.stack(lowercase_				)

												return encoding

						if training_args.do_train:
												if "train" not in dataset:
																		raise ValueError("--do_train requires a train dataset"				)
												if data_args.max_train_samples is not None:
																		lowerCamelCase__					=(
																		    dataset["train"].shuffle(seed=training_args.seed				).select(range(data_args.max_train_samples				)				)
																		)
												# Set the training transforms
												dataset["train"].set_transform(lowercase_				)

						if training_args.do_eval:
												if "validation" not in dataset:
																		raise ValueError("--do_eval requires a validation dataset"				)
												if data_args.max_eval_samples is not None:
																		lowerCamelCase__					=(
																		    dataset["validation"].shuffle(seed=training_args.seed				).select(range(data_args.max_eval_samples				)				)
																		)
												# Set the validation transforms
												dataset["validation"].set_transform(lowercase_				)

						# Initalize our trainer
						lowerCamelCase__					=Trainer(
						    model=lowercase_   ,    args=lowercase_   ,    train_dataset=dataset["train"] if training_args.do_train else None   ,    eval_dataset=dataset["validation"] if training_args.do_eval else None   ,    compute_metrics=lowercase_   ,    tokenizer=lowercase_   ,    data_collator=lowercase_   ,    )

						# Training
						if training_args.do_train:
												lowerCamelCase__					=None
												if training_args.resume_from_checkpoint is not None:
																		lowerCamelCase__					=training_args.resume_from_checkpoint
												elif last_checkpoint is not None:
																		lowerCamelCase__					=last_checkpoint
												lowerCamelCase__					=trainer.train(resume_from_checkpoint=lowercase_				)
												trainer.save_model()
												trainer.log_metrics("train"   ,    train_result.metrics				)
												trainer.save_metrics("train"   ,    train_result.metrics				)
												trainer.save_state()

						# Evaluation
						if training_args.do_eval:
												lowerCamelCase__					=trainer.evaluate()
												trainer.log_metrics("eval"   ,    lowercase_				)
												trainer.save_metrics("eval"   ,    lowercase_				)

						# Write model card and (optionally) push to hub
						lowerCamelCase__					={
						    "finetuned_from": model_args.model_name_or_path,
						    "dataset": data_args.dataset_name,
						    "tags": ["image-segmentation", "vision"],
						}
						if training_args.push_to_hub:
												trainer.push_to_hub(**lowercase_				)
						else:
												trainer.create_model_card(**lowercase_				)


if __name__ == "__main__":
	main()







"""simple docstring"""
import numpy as np
from matplotlib import pyplot as plt



def 				lowerCAmelCase_(      lowercase_   :							List[str]							)		->				List[Any]:
				return 1 / (1 + np.exp(-1 * x							))


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self ,				lowerCamelCase__ ,				lowerCamelCase__=None ,				lowerCamelCase__=None ,				lowerCamelCase__=False      ):
									_lowerCamelCase					     = units
									_lowerCamelCase					     = None
									_lowerCamelCase					     = None
									_lowerCamelCase					     = activation
									if learning_rate is None:
													_lowerCamelCase					     = 0.3
									_lowerCamelCase					     = learning_rate
									_lowerCamelCase					     = is_input_layer

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				(self.units, back_units)      )      )
									_lowerCamelCase					     = np.asmatrix(np.random.normal(0 ,				0.5 ,				self.units      )      ).T
									if self.activation is None:
													_lowerCamelCase					     = sigmoid

					def 	snake_case__					(			self      ):
									# activation function may be sigmoid or linear
									if self.activation == sigmoid:
													_lowerCamelCase					     = np.dot(self.output ,				(1 - self.output).T      )
													_lowerCamelCase					     = np.diag(np.diag(lowerCamelCase__      )      )
									else:
													_lowerCamelCase					     = 1
									return gradient_activation

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = xdata
									if self.is_input_layer:
													# input layer
													_lowerCamelCase					     = xdata
													_lowerCamelCase					     = xdata
													return xdata
									else:
													_lowerCamelCase					     = np.dot(self.weight ,				self.xdata      ) - self.bias
													_lowerCamelCase					     = self.activation(self.wx_plus_b      )
													return self.output





					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									_lowerCamelCase					     = self.cal_gradient()  # i * i 维
									_lowerCamelCase					     = np.asmatrix(np.dot(gradient.T ,				lowerCamelCase__      )      )

									_lowerCamelCase					     = np.asmatrix(self.xdata      )
									_lowerCamelCase					     = -1
									_lowerCamelCase					     = self.weight

									_lowerCamelCase					     = np.dot(gradient.T ,				self._gradient_weight.T      )
									_lowerCamelCase					     = gradient * self._gradient_bias
									_lowerCamelCase					     = np.dot(lowerCamelCase__ ,				self._gradient_x      ).T
									# upgrade: the Negative gradient direction
									_lowerCamelCase					     = self.weight - self.learn_rate * self.gradient_weight
									_lowerCamelCase					     = self.bias - self.learn_rate * self.gradient_bias.T
									# updates the weights and bias according to learning rate (0.3 if undefined)
									return self.gradient


class 							lowerCamelCase_:





					'''simple docstring'''



					def __init__(			self      ):
									_lowerCamelCase					     = []
									_lowerCamelCase					     = []
									_lowerCamelCase					     = plt.figure()
									_lowerCamelCase					     = self.fig_loss.add_subplot(1 ,				1 ,				1      )

					def 	snake_case__					(			self ,				lowerCamelCase__      ):
									self.layers.append(lowerCamelCase__      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													if i < 1:
																	_lowerCamelCase					     = True
													else:
																	layer.initializer(self.layers[i - 1].units      )

					def 	snake_case__					(			self      ):
									for i, layer in enumerate(self.layers[:]      ):
													print(F"""------- layer {i} -------"""      )
													print('''weight.shape ''' ,				np.shape(layer.weight      )      )
													print('''bias.shape ''' ,				np.shape(layer.bias      )      )

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = train_round
									_lowerCamelCase					     = accuracy

									self.ax_loss.hlines(self.accuracy ,				0 ,				self.train_round * 1.1      )

									_lowerCamelCase					     = np.shape(lowerCamelCase__      )
									for _ in range(lowerCamelCase__      ):
													_lowerCamelCase					     = 0
													for row in range(x_shape[0]      ):
																	_lowerCamelCase					     = np.asmatrix(xdata[row, :]      ).T
																	_lowerCamelCase					     = np.asmatrix(ydata[row, :]      ).T

																	# forward propagation
																	for layer in self.layers:
																					_lowerCamelCase					     = layer.forward_propagation(_xdata      )

																	_lowerCamelCase     ,   _lowerCamelCase					     = self.cal_loss(_ydata ,				_xdata      )
																	_lowerCamelCase					     = all_loss + loss

																	# back propagation: the input_layer does not upgrade
																	for layer in self.layers[:0:-1]:
																					_lowerCamelCase					     = layer.back_propagation(lowerCamelCase__      )

													_lowerCamelCase					     = all_loss / x_shape[0]
													self.train_mse.append(lowerCamelCase__      )

													self.plot_loss()

													if mse < self.accuracy:
																	print('''----达到精度----'''      )
																	return mse
									return None

					def 	snake_case__					(			self ,				lowerCamelCase__ ,				lowerCamelCase__      ):
									_lowerCamelCase					     = np.sum(np.power((ydata - ydata_) ,				2      )      )
									_lowerCamelCase					     = 2 * (ydata_ - ydata)
									# vector (shape is the same as _ydata.shape)
									return self.loss, self.loss_gradient





					def 	snake_case__					(			self      ):
									if self.ax_loss.lines:
													self.ax_loss.lines.remove(self.ax_loss.lines[0]      )
									self.ax_loss.plot(self.train_mse ,				'''r-'''      )
									plt.ion()
									plt.xlabel('''step'''      )
									plt.ylabel('''loss'''      )
									plt.show()
									plt.pause(0.1      )



def 				lowerCAmelCase_(      )		->				Optional[Any]:
				_lowerCamelCase					     = np.random.randn(10  ,      10							)
				_lowerCamelCase					     = np.asarray(
				    [
				        [0.8, 0.4],
				        [0.4, 0.3],
				        [0.3_4, 0.4_5],
				        [0.6_7, 0.3_2],
				        [0.8_8, 0.6_7],
				        [0.7_8, 0.7_7],
				        [0.5_5, 0.6_6],
				        [0.5_5, 0.4_3],
				        [0.5_4, 0.1],
				        [0.1, 0.5],
				    ]							)
				_lowerCamelCase					     = BPNN()
				for i in (10, 20, 30, 2):
								model.add_layer(DenseLayer(lowercase_							)							)
				model.build()
				model.summary()
				model.train(xdata=lowercase_  ,      ydata=lowercase_  ,      train_round=1_00  ,      accuracy=0.0_1							)


if __name__ == "__main__":
			example()
