



'''simple docstring'''

import json
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
	import torch

	if is_vision_available():
		from transformers import OneFormerImageProcessor
		from transformers.models.oneformer.image_processing_oneformer import binary_mask_to_rle
		from transformers.models.oneformer.modeling_oneformer import OneFormerForUniversalSegmentationOutput

if is_vision_available():
	from PIL import Image






def       __lowercase  (_SCREAMING_SNAKE_CASE      :int    ,	_SCREAMING_SNAKE_CASE      :Optional[int]="shi-labs/oneformer_demo"       ):
				with open(hf_hub_download(_SCREAMING_SNAKE_CASE    ,	_SCREAMING_SNAKE_CASE    ,	repo_type='''dataset'''       )    ,	'''r'''       ) as f:
								SCREAMING_SNAKE_CASE  :						Dict      					= json.load(_SCREAMING_SNAKE_CASE       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= {}
				SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
				SCREAMING_SNAKE_CASE  :						List[str]      					= []
				for key, info in class_info.items():
								SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= info['''name''']
								class_names.append(info['''name''']       )
								if info["isthing"]:
												thing_ids.append(int(_SCREAMING_SNAKE_CASE       )       )
				SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= thing_ids
				SCREAMING_SNAKE_CASE  :						Tuple      					= class_names
				return metadata


class 	a__     (				unittest.TestCase	):


	def __init__(self : Union[str, Any],					__UpperCAmelCase : int,					__UpperCAmelCase : int=7,					__UpperCAmelCase : List[Any]=3,					__UpperCAmelCase : int=30,					__UpperCAmelCase : Any=400,					__UpperCAmelCase : List[str]=None,					__UpperCAmelCase : Tuple=True,					__UpperCAmelCase : Optional[int]=True,					__UpperCAmelCase : int=[0.5, 0.5, 0.5],					__UpperCAmelCase : Tuple=[0.5, 0.5, 0.5],					__UpperCAmelCase : Optional[int]=10,					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : int=255,					__UpperCAmelCase : List[Any]="shi-labs/oneformer_demo",					__UpperCAmelCase : Any="ade20k_panoptic.json",					__UpperCAmelCase : Union[str, Any]=10,					)     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= parent
					SCREAMING_SNAKE_CASE  :						List[Any]      					= batch_size
					SCREAMING_SNAKE_CASE  :						str      					= num_channels
					SCREAMING_SNAKE_CASE  :						List[Any]      					= min_resolution
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= max_resolution
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= do_resize
					SCREAMING_SNAKE_CASE  :						int      					= {'''shortest_edge''': 32, '''longest_edge''': 1333} if size is None else size
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= do_normalize
					SCREAMING_SNAKE_CASE  :						str      					= image_mean
					SCREAMING_SNAKE_CASE  :						Dict      					= image_std
					SCREAMING_SNAKE_CASE  :						List[str]      					= class_info_file
					SCREAMING_SNAKE_CASE  :						Dict      					= prepare_metadata(__UpperCAmelCase,					__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= num_text
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= repo_path

					# for the post_process_functions
					SCREAMING_SNAKE_CASE  :						Tuple      					= 2
					SCREAMING_SNAKE_CASE  :						int      					= 10
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= 10
					SCREAMING_SNAKE_CASE  :						int      					= 3
					SCREAMING_SNAKE_CASE  :						int      					= 4
					SCREAMING_SNAKE_CASE  :						int      					= num_labels
					SCREAMING_SNAKE_CASE  :						Tuple      					= do_reduce_labels
					SCREAMING_SNAKE_CASE  :						Tuple      					= ignore_index


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

					"""simple docstring"""

					return {
					    "do_resize": self.do_resize,
					    "size": self.size,
					    "do_normalize": self.do_normalize,
					    "image_mean": self.image_mean,
					    "image_std": self.image_std,
					    "num_labels": self.num_labels,
					    "do_reduce_labels": self.do_reduce_labels,
					    "ignore_index": self.ignore_index,
					    "class_info_file": self.class_info_file,
					    "metadata": self.metadata,
					    "num_text": self.num_text,
					}


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : str=False      )     ->			List[str]:

					"""simple docstring"""

					if not batched:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= image_inputs[0]
									if isinstance(__UpperCAmelCase,					Image.Image      ):
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= image.size
									else:
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= image.shape[1], image.shape[2]
									if w < h:
													SCREAMING_SNAKE_CASE  :						List[str]      					= int(self.size['''shortest_edge'''] * h / w      )
													SCREAMING_SNAKE_CASE  :						str      					= self.size['''shortest_edge''']
									elif w > h:
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.size['''shortest_edge''']
													SCREAMING_SNAKE_CASE  :						Tuple      					= int(self.size['''shortest_edge'''] * w / h      )
									else:
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.size['''shortest_edge''']
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.size['''shortest_edge''']

					else:
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
									for image in image_inputs:
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= self.get_expected_values([image]      )
													expected_values.append((expected_height, expected_width)      )
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= max(__UpperCAmelCase,					key=lambda __UpperCAmelCase      : item[0]      )[0]
									SCREAMING_SNAKE_CASE  :						int      					= max(__UpperCAmelCase,					key=lambda __UpperCAmelCase      : item[1]      )[1]

					return expected_height, expected_width






	def 				lowercase__  (self : Tuple      )     ->			List[str]:

					"""simple docstring"""

					return OneFormerForUniversalSegmentationOutput(
					    # +1 for null class
					    class_queries_logits=torch.randn((self.batch_size, self.num_queries, self.num_classes + 1)      ),					masks_queries_logits=torch.randn((self.batch_size, self.num_queries, self.height, self.width)      ),					)



@require_torch
@require_vision
class 	a__     (				_lowercase,			unittest.TestCase	):
	__magic_name__		:    Optional[Any]	    =     OneFormerImageProcessor if (is_vision_available() and is_torch_available()) else None
	# only for test_image_processing_common.test_image_proc_to_json_string
	__magic_name__		:    List[str]	    =     image_processing_class


	def 				lowercase__  (self : List[Any]      )     ->			Dict:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= OneFormerImageProcessorTester(self      )


	@property
	def 				lowercase__  (self : Dict      )     ->			int:

					"""simple docstring"""

					return self.image_processing_tester.prepare_image_processor_dict()


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.image_processing_class(**self.image_processor_dict      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''image_mean'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''image_std'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''do_normalize'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''do_resize'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''size'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''ignore_index'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''class_info_file'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''num_text'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''repo_path'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''metadata'''      )      )
					self.assertTrue(hasattr(__UpperCAmelCase,					'''do_reduce_labels'''      )      )


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

					"""simple docstring"""

					pass


	def 				lowercase__  (self : Optional[Any]      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.image_processing_class(**self.image_processor_dict      )
					# create random PIL images
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= prepare_image_inputs(self.image_processing_tester,					equal_resolution=__UpperCAmelCase      )
					for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase,					Image.Image      )

					# Test not batched input
					SCREAMING_SNAKE_CASE  :						List[Any]      					= image_processor(image_inputs[0],					['''semantic'''],					return_tensors='''pt'''      ).pixel_values

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						List[str]      					= self.image_processing_tester.get_expected_values(__UpperCAmelCase      )

					self.assertEqual(
					    encoded_images.shape,					(1, self.image_processing_tester.num_channels, expected_height, expected_width),					)

					# Test batched
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Dict      					= self.image_processing_tester.get_expected_values(__UpperCAmelCase,					batched=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						str      					= image_processor(
					    __UpperCAmelCase,					['''semantic'''] * len(__UpperCAmelCase      ),					return_tensors='''pt'''      ).pixel_values
					self.assertEqual(
					    encoded_images.shape,					(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),					)


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.image_processing_class(**self.image_processor_dict      )
					# create random numpy tensors
					SCREAMING_SNAKE_CASE  :						str      					= prepare_image_inputs(self.image_processing_tester,					equal_resolution=__UpperCAmelCase,					numpify=__UpperCAmelCase      )
					for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase,					np.ndarray      )

					# Test not batched input
					SCREAMING_SNAKE_CASE  :						str      					= image_processor(image_inputs[0],					['''semantic'''],					return_tensors='''pt'''      ).pixel_values

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Dict      					= self.image_processing_tester.get_expected_values(__UpperCAmelCase      )

					self.assertEqual(
					    encoded_images.shape,					(1, self.image_processing_tester.num_channels, expected_height, expected_width),					)

					# Test batched
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.image_processing_tester.get_expected_values(__UpperCAmelCase,					batched=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						Dict      					= image_processor(
					    __UpperCAmelCase,					['''semantic'''] * len(__UpperCAmelCase      ),					return_tensors='''pt'''      ).pixel_values
					self.assertEqual(
					    encoded_images.shape,					(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),					)


	def 				lowercase__  (self : Any      )     ->			Any:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= self.image_processing_class(**self.image_processor_dict      )
					# create random PyTorch tensors
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= prepare_image_inputs(self.image_processing_tester,					equal_resolution=__UpperCAmelCase,					torchify=__UpperCAmelCase      )
					for image in image_inputs:
									self.assertIsInstance(__UpperCAmelCase,					torch.Tensor      )

					# Test not batched input
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= image_processor(image_inputs[0],					['''semantic'''],					return_tensors='''pt'''      ).pixel_values

					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.image_processing_tester.get_expected_values(__UpperCAmelCase      )

					self.assertEqual(
					    encoded_images.shape,					(1, self.image_processing_tester.num_channels, expected_height, expected_width),					)

					# Test batched
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.image_processing_tester.get_expected_values(__UpperCAmelCase,					batched=__UpperCAmelCase      )

					SCREAMING_SNAKE_CASE  :						int      					= image_processor(
					    __UpperCAmelCase,					['''semantic'''] * len(__UpperCAmelCase      ),					return_tensors='''pt'''      ).pixel_values
					self.assertEqual(
					    encoded_images.shape,					(
					        self.image_processing_tester.batch_size,
					        self.image_processing_tester.num_channels,
					        expected_height,
					        expected_width,
					    ),					)


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : int=False,					__UpperCAmelCase : Optional[Any]=False,					__UpperCAmelCase : Optional[Any]="np"      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= self.image_processing_class(**self.image_processor_dict      )
					# prepare image and target
					SCREAMING_SNAKE_CASE  :						Any      					= self.image_processing_tester.num_labels
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= None
					SCREAMING_SNAKE_CASE  :						List[str]      					= None
					SCREAMING_SNAKE_CASE  :						Any      					= prepare_image_inputs(self.image_processing_tester,					equal_resolution=__UpperCAmelCase      )
					if with_segmentation_maps:
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= num_labels
									if is_instance_map:
													SCREAMING_SNAKE_CASE  :						List[str]      					= list(range(__UpperCAmelCase      )      ) * 2
													SCREAMING_SNAKE_CASE  :						int      					= dict(enumerate(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						List[str]      					= [
									    np.random.randint(0,					high * 2,					(img.size[1], img.size[0])      ).astype(np.uinta      ) for img in image_inputs
									]
									if segmentation_type == "pil":
													SCREAMING_SNAKE_CASE  :						Dict      					= [Image.fromarray(__UpperCAmelCase      ) for annotation in annotations]

					SCREAMING_SNAKE_CASE  :						Tuple      					= image_processor(
					    __UpperCAmelCase,					['''semantic'''] * len(__UpperCAmelCase      ),					__UpperCAmelCase,					return_tensors='''pt''',					instance_id_to_semantic_id=__UpperCAmelCase,					pad_and_return_pixel_mask=__UpperCAmelCase,					)

					return inputs


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

					"""simple docstring"""

					pass


	def 				lowercase__  (self : Any      )     ->			int:

					"""simple docstring"""

					def common(__UpperCAmelCase : Dict=False,					__UpperCAmelCase : Union[str, Any]=None      ):
									SCREAMING_SNAKE_CASE  :						Dict      					= self.comm_get_image_processor_inputs(
									    with_segmentation_maps=__UpperCAmelCase,					is_instance_map=__UpperCAmelCase,					segmentation_type=__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						List[Any]      					= inputs['''mask_labels''']
									SCREAMING_SNAKE_CASE  :						List[Any]      					= inputs['''class_labels''']
									SCREAMING_SNAKE_CASE  :						str      					= inputs['''pixel_values''']
									SCREAMING_SNAKE_CASE  :						List[Any]      					= inputs['''text_inputs''']

									# check the batch_size
									for mask_label, class_label, text_input in zip(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      ):
													self.assertEqual(mask_label.shape[0],					class_label.shape[0]      )
													# this ensure padding has happened
													self.assertEqual(mask_label.shape[1:],					pixel_values.shape[2:]      )
													self.assertEqual(len(__UpperCAmelCase      ),					self.image_processing_tester.num_text      )

					common()
					common(is_instance_map=__UpperCAmelCase      )
					common(is_instance_map=__UpperCAmelCase,					segmentation_type='''pil'''      )
					common(is_instance_map=__UpperCAmelCase,					segmentation_type='''pil'''      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.zeros((20, 50)      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= 1
					SCREAMING_SNAKE_CASE  :						str      					= 1
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= 1

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= binary_mask_to_rle(__UpperCAmelCase      )
					self.assertEqual(len(__UpperCAmelCase      ),					4      )
					self.assertEqual(rle[0],					21      )
					self.assertEqual(rle[1],					45      )


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

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes,					max_seq_length=77,					task_seq_length=77,					class_info_file='''ade20k_panoptic.json''',					num_text=self.image_processing_tester.num_text,					repo_path='''shi-labs/oneformer_demo''',					)
					SCREAMING_SNAKE_CASE  :						List[str]      					= self.image_processing_tester.get_fake_oneformer_outputs()

					SCREAMING_SNAKE_CASE  :						List[Any]      					= fature_extractor.post_process_semantic_segmentation(__UpperCAmelCase      )

					self.assertEqual(len(__UpperCAmelCase      ),					self.image_processing_tester.batch_size      )
					self.assertEqual(
					    segmentation[0].shape,					(
					        self.image_processing_tester.height,
					        self.image_processing_tester.width,
					    ),					)

					SCREAMING_SNAKE_CASE  :						str      					= [(1, 4) for i in range(self.image_processing_tester.batch_size      )]
					SCREAMING_SNAKE_CASE  :						int      					= fature_extractor.post_process_semantic_segmentation(__UpperCAmelCase,					target_sizes=__UpperCAmelCase      )

					self.assertEqual(segmentation[0].shape,					target_sizes[0]      )


	def 				lowercase__  (self : Tuple      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes,					max_seq_length=77,					task_seq_length=77,					class_info_file='''ade20k_panoptic.json''',					num_text=self.image_processing_tester.num_text,					repo_path='''shi-labs/oneformer_demo''',					)
					SCREAMING_SNAKE_CASE  :						int      					= self.image_processing_tester.get_fake_oneformer_outputs()
					SCREAMING_SNAKE_CASE  :						Tuple      					= image_processor.post_process_instance_segmentation(__UpperCAmelCase,					threshold=0      )

					self.assertTrue(len(__UpperCAmelCase      ) == self.image_processing_tester.batch_size      )
					for el in segmentation:
									self.assertTrue('''segmentation''' in el      )
									self.assertTrue('''segments_info''' in el      )
									self.assertEqual(type(el['''segments_info''']      ),					__UpperCAmelCase      )
									self.assertEqual(
									    el['''segmentation'''].shape,					(self.image_processing_tester.height, self.image_processing_tester.width)      )






	def 				lowercase__  (self : Optional[int]      )     ->			str:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						str      					= self.image_processing_class(
					    num_labels=self.image_processing_tester.num_classes,					max_seq_length=77,					task_seq_length=77,					class_info_file='''ade20k_panoptic.json''',					num_text=self.image_processing_tester.num_text,					repo_path='''shi-labs/oneformer_demo''',					)
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.image_processing_tester.get_fake_oneformer_outputs()
					SCREAMING_SNAKE_CASE  :						List[str]      					= image_processor.post_process_panoptic_segmentation(__UpperCAmelCase,					threshold=0      )

					self.assertTrue(len(__UpperCAmelCase      ) == self.image_processing_tester.batch_size      )
					for el in segmentation:
									self.assertTrue('''segmentation''' in el      )
									self.assertTrue('''segments_info''' in el      )
									self.assertEqual(type(el['''segments_info''']      ),					__UpperCAmelCase      )
									self.assertEqual(
									    el['''segmentation'''].shape,					(self.image_processing_tester.height, self.image_processing_tester.width)      )









'''simple docstring'''

import pickle

import numpy as np
from matplotlib import pyplot as plt


class 	a__     :


	def __init__(self : Any,					__UpperCAmelCase : int,					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : int,					__UpperCAmelCase : List[str]=0.2,					__UpperCAmelCase : Dict=0.2      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= bp_numa
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= bp_numa
					SCREAMING_SNAKE_CASE  :						str      					= bp_numa
					SCREAMING_SNAKE_CASE  :						Dict      					= conva_get[:2]
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= conva_get[2]
					SCREAMING_SNAKE_CASE  :						int      					= size_pa
					SCREAMING_SNAKE_CASE  :						int      					= rate_w
					SCREAMING_SNAKE_CASE  :						int      					= rate_t
					SCREAMING_SNAKE_CASE  :						int      					= [
					    np.mat(-1 * np.random.rand(self.conva[0],					self.conva[0]      ) + 0.5      )
					    for i in range(self.conva[1]      )
					]
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.mat(-1 * np.random.rand(self.num_bpa,					self.num_bpa      ) + 0.5      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.mat(-1 * np.random.rand(self.num_bpa,					self.num_bpa      ) + 0.5      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= -2 * np.random.rand(self.conva[1]      ) + 1
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= -2 * np.random.rand(self.num_bpa      ) + 1
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= -2 * np.random.rand(self.num_bpa      ) + 1


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= {
					    '''num_bp1''': self.num_bpa,
					    '''num_bp2''': self.num_bpa,
					    '''num_bp3''': self.num_bpa,
					    '''conv1''': self.conva,
					    '''step_conv1''': self.step_conva,
					    '''size_pooling1''': self.size_poolinga,
					    '''rate_weight''': self.rate_weight,
					    '''rate_thre''': self.rate_thre,
					    '''w_conv1''': self.w_conva,
					    '''wkj''': self.wkj,
					    '''vji''': self.vji,
					    '''thre_conv1''': self.thre_conva,
					    '''thre_bp2''': self.thre_bpa,
					    '''thre_bp3''': self.thre_bpa,
					}
					with open(__UpperCAmelCase,					'''wb'''      ) as f:
									pickle.dump(__UpperCAmelCase,					__UpperCAmelCase      )

					print(F'''Model saved： {save_path}'''      )


	@classmethod
	def 				lowercase__  (cls : str,					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					with open(__UpperCAmelCase,					'''rb'''      ) as f:
									SCREAMING_SNAKE_CASE  :						int      					= pickle.load(__UpperCAmelCase      )  # noqa: S301

					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''conv1'''      )
					conv_get.append(model_dic.get('''step_conv1'''      )      )
					SCREAMING_SNAKE_CASE  :						Dict      					= model_dic.get('''size_pooling1'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= model_dic.get('''num_bp1'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''num_bp2'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''num_bp3'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''rate_weight'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''rate_thre'''      )
					# create model instance
					SCREAMING_SNAKE_CASE  :						List[Any]      					= CNN(__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase,					__UpperCAmelCase      )
					# modify model parameter
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''w_conv1'''      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= model_dic.get('''wkj'''      )
					SCREAMING_SNAKE_CASE  :						Any      					= model_dic.get('''vji'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''thre_conv1'''      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= model_dic.get('''thre_bp2'''      )
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= model_dic.get('''thre_bp3'''      )
					return conv_ins


	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Dict      )     ->			Optional[Any]:

					"""simple docstring"""

					return 1 / (1 + np.exp(-1 * x      ))


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : List[str]      )     ->			str:

					"""simple docstring"""

					return round(__UpperCAmelCase,					3      )


	def 				lowercase__  (self : Union[str, Any],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[Any],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Any,					__UpperCAmelCase : int      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= convs[0]
					SCREAMING_SNAKE_CASE  :						int      					= convs[1]
					SCREAMING_SNAKE_CASE  :						List[str]      					= np.shape(__UpperCAmelCase      )[0]
					# get the data slice of original image data, data_focus
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= []
					for i_focus in range(0,					size_data - size_conv + 1,					__UpperCAmelCase      ):
									for j_focus in range(0,					size_data - size_conv + 1,					__UpperCAmelCase      ):
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= data[
													    i_focus : i_focus + size_conv, j_focus : j_focus + size_conv
													]
													data_focus.append(__UpperCAmelCase      )
        # calculate the feature map of every single kernel, and saved as list of matrix
					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= []
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= int((size_data - size_conv) / conv_step + 1      )
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
									for i_focus in range(len(__UpperCAmelCase      )      ):
													SCREAMING_SNAKE_CASE  :						Any      					= (
													    np.sum(np.multiply(data_focus[i_focus],					w_convs[i_map]      )      )
													    - thre_convs[i_map]
													)
													featuremap.append(self.sig(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Dict      					= np.asmatrix(__UpperCAmelCase      ).reshape(
									    __UpperCAmelCase,					__UpperCAmelCase      )
									data_featuremap.append(__UpperCAmelCase      )

					# expanding the data slice to One dimenssion
					SCREAMING_SNAKE_CASE  :						Optional[int]      					= []
					for each_focus in data_focus:
									focusa_list.extend(self.Expand_Mat(__UpperCAmelCase      )      )
					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asarray(__UpperCAmelCase      )
					return focus_list, data_featuremap


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Tuple,					__UpperCAmelCase : List[str]="average_pool"      )     ->			List[Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Optional[int]      					= len(featuremaps[0]      )
					SCREAMING_SNAKE_CASE  :						str      					= int(size_map / size_pooling      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= []
					for i_map in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						str      					= featuremaps[i_map]
									SCREAMING_SNAKE_CASE  :						Any      					= []
									for i_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j_focus in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= feature_map[
																	    i_focus : i_focus + size_pooling,
																	    j_focus : j_focus + size_pooling,
																	]
																	if pooling_type == "average_pool":
																					# average pooling
																					map_pooled.append(np.average(__UpperCAmelCase      )      )
																	elif pooling_type == "max_pooling":
																					# max pooling
																					map_pooled.append(np.max(__UpperCAmelCase      )      )
									SCREAMING_SNAKE_CASE  :						Tuple      					= np.asmatrix(__UpperCAmelCase      ).reshape(__UpperCAmelCase,					__UpperCAmelCase      )
									featuremap_pooled.append(__UpperCAmelCase      )
					return featuremap_pooled


	def 				lowercase__  (self : Any,					__UpperCAmelCase : List[str]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						int      					= []
					for i in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.shape(data[i]      )
									SCREAMING_SNAKE_CASE  :						str      					= data[i].reshape(1,					shapes[0] * shapes[1]      )
									SCREAMING_SNAKE_CASE  :						int      					= data_listed.getA().tolist()[0]
									data_expanded.extend(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						List[Any]      					= np.asarray(__UpperCAmelCase      )
					return data_expanded


	def 				lowercase__  (self : Any,					__UpperCAmelCase : Optional[Any]      )     ->			List[str]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= np.asarray(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE  :						Tuple      					= data_mat.reshape(1,					shapes[0] * shapes[1]      )
					return data_expanded


	def 				lowercase__  (self : Tuple,					__UpperCAmelCase : List[Any],					__UpperCAmelCase : Dict,					__UpperCAmelCase : Any,					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any]      )     ->			Union[str, Any]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Dict      					= []
					SCREAMING_SNAKE_CASE  :						List[str]      					= 0
					for i_map in range(__UpperCAmelCase      ):
									SCREAMING_SNAKE_CASE  :						Dict      					= np.ones((size_map, size_map)      )
									for i in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
													for j in range(0,					__UpperCAmelCase,					__UpperCAmelCase      ):
																	SCREAMING_SNAKE_CASE  :						str      					= pd_pool[
																	    i_pool
																	]
																	SCREAMING_SNAKE_CASE  :						Dict      					= i_pool + 1
									SCREAMING_SNAKE_CASE  :						List[Any]      					= np.multiply(
									    __UpperCAmelCase,					np.multiply(out_map[i_map],					(1 - out_map[i_map])      )      )
									pd_all.append(__UpperCAmelCase      )
					return pd_all


	def 				lowercase__  (self : Optional[int],					__UpperCAmelCase : Optional[int],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : List[Any],					__UpperCAmelCase : List[str],					__UpperCAmelCase : Union[str, Any],					__UpperCAmelCase : Optional[int]=bool      )     ->			List[Any]:

					"""simple docstring"""

					print('''----------------------Start Training-------------------------'''      )
					print((''' - - Shape: Train_Data  ''', np.shape(__UpperCAmelCase      ))      )
					print((''' - - Shape: Teach_Data  ''', np.shape(__UpperCAmelCase      ))      )
					SCREAMING_SNAKE_CASE  :						Dict      					= 0
					SCREAMING_SNAKE_CASE  :						str      					= []
					SCREAMING_SNAKE_CASE  :						Any      					= 10000
					while rp < n_repeat and mse >= error_accuracy:
									SCREAMING_SNAKE_CASE  :						int      					= 0
									print(F'''-------------Learning Time {rp}--------------'''      )
									for p in range(len(__UpperCAmelCase      )      ):
													# print('------------Learning Image: %d--------------'%p)
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(datas_train[p]      )
													SCREAMING_SNAKE_CASE  :						Tuple      					= np.asarray(datas_teach[p]      )
													SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						int      					= self.convolute(
													    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
													SCREAMING_SNAKE_CASE  :						Dict      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.shape(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						str      					= self._expand(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= data_bp_input

													SCREAMING_SNAKE_CASE  :						Tuple      					= np.dot(__UpperCAmelCase,					self.vji.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.sig(__UpperCAmelCase      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.wkj.T      ) - self.thre_bpa
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )

													# --------------Model Leaning ------------------------
													# calculate error and gradient---------------
													SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.multiply(
													    (data_teach - bp_outa),					np.multiply(__UpperCAmelCase,					(1 - bp_outa)      )      )
													SCREAMING_SNAKE_CASE  :						List[Any]      					= np.multiply(
													    np.dot(__UpperCAmelCase,					self.wkj      ),					np.multiply(__UpperCAmelCase,					(1 - bp_outa)      )      )
													SCREAMING_SNAKE_CASE  :						Dict      					= np.dot(__UpperCAmelCase,					self.vji      )

													SCREAMING_SNAKE_CASE  :						Dict      					= pd_i_all / (self.size_poolinga * self.size_poolinga)
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= pd_conva_pooled.T.getA().tolist()
													SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._calculate_gradient_from_pool(
													    __UpperCAmelCase,					__UpperCAmelCase,					shape_featuremapa[0],					shape_featuremapa[1],					self.size_poolinga,					)
													# weight and threshold learning process---------
													# convolution layer
													for k_conv in range(self.conva[1]      ):
																	SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self._expand_mat(pd_conva_all[k_conv]      )
																	SCREAMING_SNAKE_CASE  :						Any      					= self.rate_weight * np.dot(__UpperCAmelCase,					__UpperCAmelCase      )

																	SCREAMING_SNAKE_CASE  :						List[Any]      					= self.w_conva[k_conv] + delta_w.reshape(
																	    (self.conva[0], self.conva[0])      )

																	SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= (
																	    self.thre_conva[k_conv]
																	    - np.sum(pd_conva_all[k_conv]      ) * self.rate_thre
																	)
													# all connected layer
													SCREAMING_SNAKE_CASE  :						Optional[int]      					= self.wkj + pd_k_all.T * bp_outa * self.rate_weight
													SCREAMING_SNAKE_CASE  :						List[str]      					= self.vji + pd_j_all.T * bp_outa * self.rate_weight
													SCREAMING_SNAKE_CASE  :						List[Any]      					= self.thre_bpa - pd_k_all * self.rate_thre
													SCREAMING_SNAKE_CASE  :						int      					= self.thre_bpa - pd_j_all * self.rate_thre
													# calculate the sum error of all single image
													SCREAMING_SNAKE_CASE  :						List[Any]      					= np.sum(abs(data_teach - bp_outa      )      )
													error_count += errors
													# print('   ----Teach      ',data_teach)
													# print('   ----BP_output  ',bp_out3)
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= rp + 1
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= error_count / patterns
									all_mse.append(__UpperCAmelCase      )

					def draw_error():
									SCREAMING_SNAKE_CASE  :						Tuple      					= [error_accuracy for i in range(int(n_repeat * 1.2      )      )]
									plt.plot(__UpperCAmelCase,					'''+-'''      )
									plt.plot(__UpperCAmelCase,					'''r--'''      )
									plt.xlabel('''Learning Times'''      )
									plt.ylabel('''All_mse'''      )
									plt.grid(__UpperCAmelCase,					alpha=0.5      )
									plt.show()

					print('''------------------Training Complished---------------------'''      )
					print((''' - - Training epoch: ''', rp, F'''     - - Mse: {mse:.6f}''')      )
					if draw_e:
									draw_error()
					return mse


	def 				lowercase__  (self : List[str],					__UpperCAmelCase : List[str]      )     ->			int:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						List[str]      					= []
					print('''-------------------Start Testing-------------------------'''      )
					print((''' - - Shape: Test_Data  ''', np.shape(__UpperCAmelCase      ))      )
					for p in range(len(__UpperCAmelCase      )      ):
									SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(datas_test[p]      )
									SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Any      					= self.convolute(
									    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
									SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )
									SCREAMING_SNAKE_CASE  :						str      					= self._expand(__UpperCAmelCase      )

									SCREAMING_SNAKE_CASE  :						Any      					= data_bp_input
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= bp_outa * self.vji.T - self.thre_bpa
									SCREAMING_SNAKE_CASE  :						Dict      					= self.sig(__UpperCAmelCase      )
									SCREAMING_SNAKE_CASE  :						Any      					= bp_outa * self.wkj.T - self.thre_bpa
									SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.sig(__UpperCAmelCase      )
									produce_out.extend(bp_outa.getA().tolist()      )
					SCREAMING_SNAKE_CASE  :						List[str]      					= [list(map(self.do_round,					__UpperCAmelCase      )      ) for each in produce_out]
					return np.asarray(__UpperCAmelCase      )






	def 				lowercase__  (self : Optional[Any],					__UpperCAmelCase : Union[str, Any]      )     ->			Optional[int]:

					"""simple docstring"""

					SCREAMING_SNAKE_CASE  :						Union[str, Any]      					= np.asmatrix(__UpperCAmelCase      )
					SCREAMING_SNAKE_CASE					,					SCREAMING_SNAKE_CASE  :						Optional[Any]      					= self.convolute(
					    __UpperCAmelCase,					self.conva,					self.w_conva,					self.thre_conva,					conv_step=self.step_conva,					)
					SCREAMING_SNAKE_CASE  :						List[Any]      					= self.pooling(__UpperCAmelCase,					self.size_poolinga      )

					return data_conveda, data_pooleda


if __name__ == "__main__":
	pass




