


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import Callable, Dict, List, Tuple

import timm
import torch
import torch.nn as nn
from classy_vision.models.regnet import RegNet, RegNetParams, RegNetYaagf, RegNetYaagf, RegNetYaaagf
from huggingface_hub import cached_download, hf_hub_url
from torch import Tensor
from vissl.models.model_helpers import get_trunk_forward_outputs

from transformers import AutoImageProcessor, RegNetConfig, RegNetForImageClassification, RegNetModel
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase			=				logging.get_logger()


@dataclass
class 			_a				:
							_lowercase      :     nn.Module
							_lowercase      :     List[nn.Module]     =					field(default_factory=UpperCamelCase__     )
							_lowercase      :     list     =					field(default_factory=UpperCamelCase__     )

							def  lowerCamelCase_					(				self:      str       ,		UpperCamelCase_:      List[Any]       ,		UpperCamelCase_:      Tensor       ,		UpperCamelCase_:      Tensor				)     ->  List[str]:





									"""simple docstring"""







									lowercase__				= len(list(m.modules()				)				) == 1 or isinstance(UpperCamelCase_       ,		nn.Convad				) or isinstance(UpperCamelCase_       ,		nn.BatchNormad				)
									if has_not_submodules:
											self.traced.append(UpperCamelCase_				)

							def __call__(				self:      Optional[Any]       ,		UpperCamelCase_:      Tensor				)     ->  Tuple:





									"""simple docstring"""







									for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook				)				)
									self.module(UpperCamelCase_				)
									[x.remove() for x in self.handles]
									return self


							@property
							def  lowerCamelCase_					(				self:      List[Any]				)     ->  Union[str, Any]:





									"""simple docstring"""







									return list(filter(lambda UpperCamelCase_				: len(list(x.state_dict().keys()				)				) > 0       ,		self.traced				)				)


@dataclass
class 			_a				:
							_lowercase      :     nn.Module
							_lowercase      :     nn.Module
							_lowercase      :     int     =					1
							_lowercase      :     List     =					field(default_factory=UpperCamelCase__     )
							_lowercase      :     List     =					field(default_factory=UpperCamelCase__     )
							_lowercase      :     bool     =					True

							def __call__(				self:      int       ,		UpperCamelCase_:      Tensor				)     ->  Union[str, Any]:





									"""simple docstring"""







									lowercase__				= Tracker(self.dest				)(UpperCamelCase_				).parametrized
									lowercase__				= Tracker(self.src				)(UpperCamelCase_				).parametrized

									lowercase__				= list(filter(lambda UpperCamelCase_				: type(UpperCamelCase_				) not in self.src_skip       ,		UpperCamelCase_				)				)
									lowercase__				= list(filter(lambda UpperCamelCase_				: type(UpperCamelCase_				) not in self.dest_skip       ,		UpperCamelCase_				)				)

									if len(UpperCamelCase_				) != len(UpperCamelCase_				) and self.raise_if_mismatch:
											raise Exception(
											    f'Numbers of operations are different. Source module has {len(UpperCamelCase_				)} operations while'
											    f' destination module has {len(UpperCamelCase_				)}.'				)

									for dest_m, src_m in zip(UpperCamelCase_       ,		UpperCamelCase_				):
											dest_m.load_state_dict(src_m.state_dict()				)
											if self.verbose == 1:
													print(f'Transfered from={src_m} to={dest_m}'				)


class 			_a				(     nn.Module     ):

							def __init__(				self:      List[str]       ,		UpperCamelCase_:      nn.Module				)     ->  str:





									"""simple docstring"""







									super().__init__()

									lowercase__				= []
									# - get the stem
									feature_blocks.append(('''conv1''', model.stem)				)
									# - get all the feature blocks
									for k, v in model.trunk_output.named_children():
											assert k.startswith('''block'''				), f'Unexpected layer name {k}'
											lowercase__				= len(UpperCamelCase_				) + 1
											feature_blocks.append((f'res{block_index}', v)				)

									lowercase__				= nn.ModuleDict(UpperCamelCase_				)


							def  lowerCamelCase_					(				self:      List[str]       ,		UpperCamelCase_:      Tensor				)     ->  Dict:





									"""simple docstring"""







									return get_trunk_forward_outputs(
									    UpperCamelCase_       ,		out_feat_keys=UpperCamelCase_       ,		feature_blocks=self._feature_blocks       ,		)


class 			_a				(     UpperCamelCase__     ):

							def  lowerCamelCase_					(				self:      Optional[int]       ,		UpperCamelCase_:      str				)     ->  str:





									"""simple docstring"""







									lowercase__				= x.split('''-'''				)
									return x_split[0] + x_split[1] + "_" + "".join(x_split[2:]				)


							def __getitem__(				self:      Optional[Any]       ,		UpperCamelCase_:      str				)     ->  Callable[[], Tuple[nn.Module, Dict]]:





									"""simple docstring"""







									if x not in self:
											lowercase__				= self.convert_name_to_timm(UpperCamelCase_				)
											lowercase__				= partial(lambda: (timm.create_model(UpperCamelCase_       ,		pretrained=UpperCamelCase_				).eval(), None)				)

									else:
											lowercase__				= super().__getitem__(UpperCamelCase_				)

									return val


class 			_a				(     UpperCamelCase__     ):

							def __getitem__(				self:      List[Any]       ,		UpperCamelCase_:      str				)     ->  Callable[[], nn.Module]:





									"""simple docstring"""







									if "seer" in x and "in1k" not in x:
											lowercase__				= RegNetModel
									else:
											lowercase__				= RegNetForImageClassification
									return val
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	):


		"""simple docstring"""




		for from_key, to_key in keys:
				lowercase__				= from_state_dict[from_key].clone()
				print(f'Copied key={from_key} to={to_key}'	)
		return to_state_dict
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = True		,							):


		"""simple docstring"""




		print(f'Converting {name}...'	)
		with torch.no_grad():
				lowercase__	,						lowercase__				= from_model_func()
				lowercase__				= our_model_func(SCREAMING_SNAKE_CASE	).eval()
				lowercase__				= ModuleTransfer(src=SCREAMING_SNAKE_CASE		,							dest=SCREAMING_SNAKE_CASE		,							raise_if_mismatch=SCREAMING_SNAKE_CASE	)
				lowercase__				= torch.randn((1, 3, 2_24, 2_24)	)
				module_transfer(SCREAMING_SNAKE_CASE	)

		if from_state_dict is not None:
				lowercase__				= []
				# for seer - in1k finetuned we have to manually copy the head
				if "seer" in name and "in1k" in name:
						lowercase__				= [('''0.clf.0.weight''', '''classifier.1.weight'''), ('''0.clf.0.bias''', '''classifier.1.bias''')]
				lowercase__				= manually_copy_vissl_head(SCREAMING_SNAKE_CASE		,							our_model.state_dict()		,							SCREAMING_SNAKE_CASE	)
				our_model.load_state_dict(SCREAMING_SNAKE_CASE	)

		lowercase__				= our_model(SCREAMING_SNAKE_CASE		,							output_hidden_states=SCREAMING_SNAKE_CASE	)
		lowercase__				= (
		    our_outputs.logits if isinstance(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	) else our_outputs.last_hidden_state
		)

		lowercase__				= from_model(SCREAMING_SNAKE_CASE	)
		lowercase__				= from_output[-1] if type(SCREAMING_SNAKE_CASE	) is list else from_output

		# now since I don't want to use any config files, vissl seer model doesn't actually have an head, so let's just check the last hidden state
		if "seer" in name and "in1k" in name:
				lowercase__				= our_outputs.hidden_states[-1]

		assert torch.allclose(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	), "The model logits don't match the original one."

		if push_to_hub:
				our_model.push_to_hub(
				    repo_path_or_name=save_directory / name		,							commit_message='''Add model'''		,							use_temp_dir=SCREAMING_SNAKE_CASE		,							)

				lowercase__				= 2_24 if '''seer''' not in name else 3_84
				# we can use the convnext one
				lowercase__				= AutoImageProcessor.from_pretrained('''facebook/convnext-base-224-22k-1k'''		,							size=SCREAMING_SNAKE_CASE	)
				image_processor.push_to_hub(
				    repo_path_or_name=save_directory / name		,							commit_message='''Add image processor'''		,							use_temp_dir=SCREAMING_SNAKE_CASE		,							)

				print(f'Pushed {name}'	)
def      _a					( SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE = None		,							SCREAMING_SNAKE_CASE = True	):


		"""simple docstring"""




		lowercase__				= '''imagenet-1k-id2label.json'''
		lowercase__				= 10_00
		lowercase__				= (1, num_labels)

		lowercase__				= '''huggingface/label-files'''
		lowercase__				= num_labels
		lowercase__				= json.load(open(cached_download(hf_hub_url(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							repo_type='''dataset'''	)	)		,							'''r'''	)	)
		lowercase__				= {int(SCREAMING_SNAKE_CASE	): v for k, v in idalabel.items()}

		lowercase__				= idalabel
		lowercase__				= {v: k for k, v in idalabel.items()}

		lowercase__				= partial(SCREAMING_SNAKE_CASE		,							num_labels=SCREAMING_SNAKE_CASE		,							idalabel=SCREAMING_SNAKE_CASE		,							labelaid=SCREAMING_SNAKE_CASE	)

		lowercase__				= {
		    '''regnet-x-002''': ImageNetPreTrainedConfig(
		        depths=[1, 1, 4, 7]		,							hidden_sizes=[24, 56, 1_52, 3_68]		,							groups_width=8		,							layer_type='''x'''	),
		    '''regnet-x-004''': ImageNetPreTrainedConfig(
		        depths=[1, 2, 7, 12]		,							hidden_sizes=[32, 64, 1_60, 3_84]		,							groups_width=16		,							layer_type='''x'''	),
		    '''regnet-x-006''': ImageNetPreTrainedConfig(
		        depths=[1, 3, 5, 7]		,							hidden_sizes=[48, 96, 2_40, 5_28]		,							groups_width=24		,							layer_type='''x'''	),
		    '''regnet-x-008''': ImageNetPreTrainedConfig(
		        depths=[1, 3, 7, 5]		,							hidden_sizes=[64, 1_28, 2_88, 6_72]		,							groups_width=16		,							layer_type='''x'''	),
		    '''regnet-x-016''': ImageNetPreTrainedConfig(
		        depths=[2, 4, 10, 2]		,							hidden_sizes=[72, 1_68, 4_08, 9_12]		,							groups_width=24		,							layer_type='''x'''	),
		    '''regnet-x-032''': ImageNetPreTrainedConfig(
		        depths=[2, 6, 15, 2]		,							hidden_sizes=[96, 1_92, 4_32, 10_08]		,							groups_width=48		,							layer_type='''x'''	),
		    '''regnet-x-040''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 14, 2]		,							hidden_sizes=[80, 2_40, 5_60, 13_60]		,							groups_width=40		,							layer_type='''x'''	),
		    '''regnet-x-064''': ImageNetPreTrainedConfig(
		        depths=[2, 4, 10, 1]		,							hidden_sizes=[1_68, 3_92, 7_84, 16_24]		,							groups_width=56		,							layer_type='''x'''	),
		    '''regnet-x-080''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 15, 1]		,							hidden_sizes=[80, 2_40, 7_20, 19_20]		,							groups_width=1_20		,							layer_type='''x'''	),
		    '''regnet-x-120''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 11, 1]		,							hidden_sizes=[2_24, 4_48, 8_96, 22_40]		,							groups_width=1_12		,							layer_type='''x'''	),
		    '''regnet-x-160''': ImageNetPreTrainedConfig(
		        depths=[2, 6, 13, 1]		,							hidden_sizes=[2_56, 5_12, 8_96, 20_48]		,							groups_width=1_28		,							layer_type='''x'''	),
		    '''regnet-x-320''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 13, 1]		,							hidden_sizes=[3_36, 6_72, 13_44, 25_20]		,							groups_width=1_68		,							layer_type='''x'''	),
		    # y variant
		    '''regnet-y-002''': ImageNetPreTrainedConfig(depths=[1, 1, 4, 7]		,							hidden_sizes=[24, 56, 1_52, 3_68]		,							groups_width=8	),
		    '''regnet-y-004''': ImageNetPreTrainedConfig(
		        depths=[1, 3, 6, 6]		,							hidden_sizes=[48, 1_04, 2_08, 4_40]		,							groups_width=8	),
		    '''regnet-y-006''': ImageNetPreTrainedConfig(
		        depths=[1, 3, 7, 4]		,							hidden_sizes=[48, 1_12, 2_56, 6_08]		,							groups_width=16	),
		    '''regnet-y-008''': ImageNetPreTrainedConfig(
		        depths=[1, 3, 8, 2]		,							hidden_sizes=[64, 1_28, 3_20, 7_68]		,							groups_width=16	),
		    '''regnet-y-016''': ImageNetPreTrainedConfig(
		        depths=[2, 6, 17, 2]		,							hidden_sizes=[48, 1_20, 3_36, 8_88]		,							groups_width=24	),
		    '''regnet-y-032''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 13, 1]		,							hidden_sizes=[72, 2_16, 5_76, 15_12]		,							groups_width=24	),
		    '''regnet-y-040''': ImageNetPreTrainedConfig(
		        depths=[2, 6, 12, 2]		,							hidden_sizes=[1_28, 1_92, 5_12, 10_88]		,							groups_width=64	),
		    '''regnet-y-064''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 14, 2]		,							hidden_sizes=[1_44, 2_88, 5_76, 12_96]		,							groups_width=72	),
		    '''regnet-y-080''': ImageNetPreTrainedConfig(
		        depths=[2, 4, 10, 1]		,							hidden_sizes=[1_68, 4_48, 8_96, 20_16]		,							groups_width=56	),
		    '''regnet-y-120''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 11, 1]		,							hidden_sizes=[2_24, 4_48, 8_96, 22_40]		,							groups_width=1_12	),
		    '''regnet-y-160''': ImageNetPreTrainedConfig(
		        depths=[2, 4, 11, 1]		,							hidden_sizes=[2_24, 4_48, 12_32, 30_24]		,							groups_width=1_12	),
		    '''regnet-y-320''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 12, 1]		,							hidden_sizes=[2_32, 6_96, 13_92, 37_12]		,							groups_width=2_32	),
		    # models created by SEER -> https://arxiv.org/abs/2202.08360
		    '''regnet-y-320-seer''': RegNetConfig(depths=[2, 5, 12, 1]		,							hidden_sizes=[2_32, 6_96, 13_92, 37_12]		,							groups_width=2_32	),
		    '''regnet-y-640-seer''': RegNetConfig(depths=[2, 5, 12, 1]		,							hidden_sizes=[3_28, 9_84, 19_68, 49_20]		,							groups_width=3_28	),
		    '''regnet-y-1280-seer''': RegNetConfig(
		        depths=[2, 7, 17, 1]		,							hidden_sizes=[5_28, 10_56, 29_04, 73_92]		,							groups_width=2_64	),
		    '''regnet-y-2560-seer''': RegNetConfig(
		        depths=[3, 7, 16, 1]		,							hidden_sizes=[6_40, 16_96, 25_44, 50_88]		,							groups_width=6_40	),
		    '''regnet-y-10b-seer''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]		,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]		,							groups_width=10_10	),
		    # finetuned on imagenet
		    '''regnet-y-320-seer-in1k''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 12, 1]		,							hidden_sizes=[2_32, 6_96, 13_92, 37_12]		,							groups_width=2_32	),
		    '''regnet-y-640-seer-in1k''': ImageNetPreTrainedConfig(
		        depths=[2, 5, 12, 1]		,							hidden_sizes=[3_28, 9_84, 19_68, 49_20]		,							groups_width=3_28	),
		    '''regnet-y-1280-seer-in1k''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]		,							hidden_sizes=[5_28, 10_56, 29_04, 73_92]		,							groups_width=2_64	),
		    '''regnet-y-2560-seer-in1k''': ImageNetPreTrainedConfig(
		        depths=[3, 7, 16, 1]		,							hidden_sizes=[6_40, 16_96, 25_44, 50_88]		,							groups_width=6_40	),
		    '''regnet-y-10b-seer-in1k''': ImageNetPreTrainedConfig(
		        depths=[2, 7, 17, 1]		,							hidden_sizes=[20_20, 40_40, 1_11_10, 2_82_80]		,							groups_width=10_10	),
		}

		lowercase__				= NameToOurModelFuncMap()
		lowercase__				= NameToFromModelFuncMap()
		# add seer weights logic

		def load_using_classy_vision(SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE	) -> Tuple[nn.Module, Dict]:
				lowercase__				= torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE		,							model_dir=str(SCREAMING_SNAKE_CASE	)		,							map_location='''cpu'''	)
				lowercase__				= model_func()
				# check if we have a head, if yes add it
				lowercase__				= files['''classy_state_dict''']['''base_model''']['''model''']
				lowercase__				= model_state_dict['''trunk''']
				model.load_state_dict(SCREAMING_SNAKE_CASE	)
				return model.eval(), model_state_dict["heads"]

		# pretrained
		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet32d/seer_regnet32gf_model_iteration244000.torch'''		,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,							)

		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet64/seer_regnet64gf_model_final_checkpoint_phase0.torch'''		,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,							)

		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/swav_ig1b_regnet128Gf_cnstant_bs32_node16_sinkhorn10_proto16k_syncBN64_warmup8k/model_final_checkpoint_phase0.torch'''		,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)		,							)

		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_regnet10B/model_iteration124500_conso.torch'''		,							lambda: FakeRegNetVisslWrapper(
		        RegNet(RegNetParams(depth=27		,							group_width=10_10		,							w_a=17_44		,							w_a=620.83		,							w_m=2.52	)	)	)		,							)

		# IN1K finetuned
		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet32_finetuned_in1k_model_final_checkpoint_phase78.torch'''		,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,							)

		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet64_finetuned_in1k_model_final_checkpoint_phase78.torch'''		,							lambda: FakeRegNetVisslWrapper(RegNetYaagf()	)		,							)

		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_regnet128_finetuned_in1k_model_final_checkpoint_phase78.torch'''		,							lambda: FakeRegNetVisslWrapper(RegNetYaaagf()	)		,							)

		lowercase__				= partial(
		    SCREAMING_SNAKE_CASE		,							'''https://dl.fbaipublicfiles.com/vissl/model_zoo/seer_finetuned/seer_10b_finetuned_in1k_model_phase28_conso.torch'''		,							lambda: FakeRegNetVisslWrapper(
		        RegNet(RegNetParams(depth=27		,							group_width=10_10		,							w_a=17_44		,							w_a=620.83		,							w_m=2.52	)	)	)		,							)

		if model_name:
				convert_weight_and_push(
				    SCREAMING_SNAKE_CASE		,							names_to_from_model_map[model_name]		,							names_to_ours_model_map[model_name]		,							names_to_config[model_name]		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							)
		else:
				for model_name, config in names_to_config.items():
						convert_weight_and_push(
						    SCREAMING_SNAKE_CASE		,							names_to_from_model_map[model_name]		,							names_to_ours_model_map[model_name]		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							SCREAMING_SNAKE_CASE		,							)
		return config, expected_shape


if __name__ == "__main__":
			lowerCAmelCase			=				argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '--model_name',
			    default=None,
			    type=str,
			    help=(
			        'The name of the model you wish to convert, it must be one of the supported regnet* architecture,'
			        ' currently: regnetx-*, regnety-*. If `None`, all of them will the converted.'
			    ),
			)
			parser.add_argument(
			    '--pytorch_dump_folder_path',
			    default=None,
			    type=Path,
			    required=True,
			    help='Path to the output PyTorch model directory.',
			)
			parser.add_argument(
			    '--push_to_hub',
			    default=True,
			    type=bool,
			    required=False,
			    help='If True, push model and image processor to the hub.',
			)

			lowerCAmelCase			=				parser.parse_args()

			lowerCAmelCase			=				args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)










'''simple docstring'''

import numpy as np

# Importing the Keras libraries and packages
import tensorflow as tf
from tensorflow.keras import layers, models

if __name__ == "__main__":
      # Initialising the CNN
      # (Sequential- Building the model layer by layer)
      UpperCamelCase      					=models.Sequential()

      # Step 1 - Convolution
      # Here 64,64 is the length & breadth of dataset images and 3 is for the RGB channel
      # (3,3) is the kernel size (filter matrix)
      classifier.add(
          layers.ConvaD(32, (3, 3), input_shape=(64, 64, 3), activation="relu")
      )

      # Step 2 - Pooling
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Adding a second convolutional layer
      classifier.add(layers.ConvaD(32, (3, 3), activation="relu"))
      classifier.add(layers.MaxPoolingaD(pool_size=(2, 2)))

      # Step 3 - Flattening
      classifier.add(layers.Flatten())

      # Step 4 - Full connection
      classifier.add(layers.Dense(units=128, activation="relu"))
      classifier.add(layers.Dense(units=1, activation="sigmoid"))

      # Compiling the CNN
      classifier.compile(
          optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"]
      )

      # Part 2 - Fitting the CNN to the images

      # Load Trained model weights

      # from keras.models import load_model
      # regressor=load_model('cnn.h5')

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(
          rescale=1.0 / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
      )

      UpperCamelCase      					=tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 255)

      UpperCamelCase      					=train_datagen.flow_from_directory(
          "dataset/training_set", target_size=(64, 64), batch_size=32, class_mode="binary"
      )

      UpperCamelCase      					=test_datagen.flow_from_directory(
          "dataset/test_set", target_size=(64, 64), batch_size=32, class_mode="binary"
      )

      classifier.fit_generator(
          training_set, steps_per_epoch=5, epochs=30, validation_data=test_set
      )

      classifier.save("cnn.h5")

      # Part 3 - Making new predictions

      UpperCamelCase      					=tf.keras.preprocessing.image.load_img(
          "dataset/single_prediction/image.png", target_size=(64, 64)
      )
      UpperCamelCase      					=tf.keras.preprocessing.image.img_to_array(test_image)
      UpperCamelCase      					=np.expand_dims(test_image, axis=0)
      UpperCamelCase      					=classifier.predict(test_image)
      # training_set.class_indices
      if result[0][0] == 0:
            UpperCamelCase      					="Normal"
      if result[0][0] == 1:
            UpperCamelCase      					="Abnormality detected"






