





'''simple docstring'''





import importlib
import inspect
import json
import os
import re
import shutil
import sys
from pathlib import Path
from typing import Dict, Optional, Union
from urllib import request

from huggingface_hub import HfFolder, cached_download, hf_hub_download, model_info
from packaging import version

from .. import __version__
from . import DIFFUSERS_DYNAMIC_MODULE_NAME, HF_MODULES_CACHE, logging


_UpperCAmelCase        =				(
    'https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py'
)


_UpperCAmelCase        =				logging.get_logger(__name__)  # pylint: disable=invalid-name




def  lowerCAmelCase_ (							)     ->		List[Any]:
       UpperCamelCase_          =  "https://pypi.org/pypi/diffusers/json"
       UpperCamelCase_          =  json.loads(request.urlopen(UpperCamelCase_  ).read()  )["releases"].keys()
       return sorted(UpperCamelCase_					,		key=lambda UpperCamelCase_  : version.Version(UpperCamelCase_  )  )




def  lowerCAmelCase_ (							)     ->		Any:
       # This function has already been executed if HF_MODULES_CACHE already is in the Python path.
       if HF_MODULES_CACHE in sys.path:
              return

       sys.path.append(UpperCamelCase_  )
       os.makedirs(UpperCamelCase_					,		exist_ok=UpperCamelCase_  )
       UpperCamelCase_          =  Path(UpperCamelCase_  ) / "__init__.py"
       if not init_path.exists():
              init_path.touch()




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		int:
       init_hf_modules()
       UpperCamelCase_          =  Path(UpperCamelCase_  ) / name
       # If the parent module does not exist yet, recursively create it.
       if not dynamic_module_path.parent.exists():
              create_dynamic_module(dynamic_module_path.parent  )
       os.makedirs(UpperCamelCase_					,		exist_ok=UpperCamelCase_  )
       UpperCamelCase_          =  dynamic_module_path / "__init__.py"
       if not init_path.exists():
              init_path.touch()




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Any:
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.read()

       # Imports of the form `import .xxx`
       UpperCamelCase_          =  re.findall("^\s*import\s+\.(\S+)\s*$"					,		UpperCamelCase_					,		flags=re.MULTILINE  )
       # Imports of the form `from .xxx import yyy`
       relative_imports += re.findall("^\s*from\s+\.(\S+)\s+import"					,		UpperCamelCase_					,		flags=re.MULTILINE  )
       # Unique-ify
       return list(set(UpperCamelCase_  )  )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Dict:
       UpperCamelCase_          =  False
       UpperCamelCase_          =  [module_file]
       UpperCamelCase_          =  []

       # Let's recurse through all relative imports
       while not no_change:
              UpperCamelCase_          =  []
              for f in files_to_check:
                     new_imports.extend(get_relative_imports(UpperCamelCase_  )  )

              UpperCamelCase_          =  Path(UpperCamelCase_  ).parent
              UpperCamelCase_          =  [str(module_path / m  ) for m in new_imports]
              UpperCamelCase_          =  [f for f in new_import_files if f not in all_relative_imports]
              UpperCamelCase_          =  [F'''{f}.py''' for f in new_import_files]

              UpperCamelCase_          =  len(UpperCamelCase_  ) == 0
              all_relative_imports.extend(UpperCamelCase_  )

       return all_relative_imports




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		Dict:
       with open(UpperCamelCase_					,		"r"					,		encoding="utf-8"  ) as f:
              UpperCamelCase_          =  f.read()

       # Imports of the form `import xxx`
       UpperCamelCase_          =  re.findall("^\s*import\s+(\S+)\s*$"					,		UpperCamelCase_					,		flags=re.MULTILINE  )
       # Imports of the form `from xxx import yyy`
       imports += re.findall("^\s*from\s+(\S+)\s+import"					,		UpperCamelCase_					,		flags=re.MULTILINE  )
       # Only keep the top-level module
       UpperCamelCase_          =  [imp.split("."  )[0] for imp in imports if not imp.startswith("."  )]

       # Unique-ify and test we got them all
       UpperCamelCase_          =  list(set(UpperCamelCase_  )  )
       UpperCamelCase_          =  []
       for imp in imports:
              try:
                     importlib.import_module(UpperCamelCase_  )
              except ImportError:
                     missing_packages.append(UpperCamelCase_  )

       if len(UpperCamelCase_  ) > 0:
              raise ImportError(
                  "This modeling file requires the following packages that were not found in your environment: "
                  F'''{", ".join(UpperCamelCase_  )}. Run `pip install {" ".join(UpperCamelCase_  )}`'''  )

       return get_relative_imports(UpperCamelCase_  )




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_  )     ->		Tuple:
       UpperCamelCase_          =  module_path.replace(os.path.sep					,		"."  )
       UpperCamelCase_          =  importlib.import_module(UpperCamelCase_  )

       if class_name is None:
              return find_pipeline_class(UpperCamelCase_  )
       return getattr(UpperCamelCase_					,		UpperCamelCase_  )




def  lowerCAmelCase_ (							UpperCamelCase_  )     ->		List[str]:
       from ..pipelines import DiffusionPipeline

       UpperCamelCase_          =  dict(inspect.getmembers(UpperCamelCase_					,		inspect.isclass  )  )

       UpperCamelCase_          =  None
       for cls_name, cls in cls_members.items():
              if (
                  cls_name != DiffusionPipeline.__name__
                  and issubclass(cls					,		UpperCamelCase_  )
                  and cls.__module__.split("."  )[0] != "diffusers"
              ):
                     if pipeline_class is not None:
                            raise ValueError(
                                F'''Multiple classes that inherit from {DiffusionPipeline.__name__} have been found:'''
                                F''' {pipeline_class.__name__}, and {cls_name}. Please make sure to define only one in'''
                                F''' {loaded_module}.'''  )
                     UpperCamelCase_          =  cls

       return pipeline_class




def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = False					,		UpperCamelCase_ = False					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None					,		UpperCamelCase_ = False					,		)     ->		List[str]:
       UpperCamelCase_          =  str(UpperCamelCase_  )

       UpperCamelCase_          =  os.path.join(UpperCamelCase_					,		UpperCamelCase_  )

       if os.path.isfile(UpperCamelCase_  ):
              UpperCamelCase_          =  module_file_or_url
              UpperCamelCase_          =  "local"
       elif pretrained_model_name_or_path.count("/"  ) == 0:
              UpperCamelCase_          =  get_diffusers_versions()
              # cut ".dev0"
              UpperCamelCase_          =  "v" + ".".join(__version__.split("."  )[:3]  )

              # retrieve github version that matches
              if revision is None:
                     UpperCamelCase_          =  latest_version if latest_version[1:] in available_versions else "main"
                     logger.info(F'''Defaulting to latest_version: {revision}.'''  )
              elif revision in available_versions:
                     UpperCamelCase_          =  F'''v{revision}'''
              elif revision == "main":
                     UpperCamelCase_          =  revision
              else:
                     raise ValueError(
                         F'''`custom_revision`: {revision} does not exist. Please make sure to choose one of'''
                         F''' {", ".join(available_versions + ["main"]  )}.'''  )

              # community pipeline on GitHub
              UpperCamelCase_          =  COMMUNITY_PIPELINES_URL.format(revision=UpperCamelCase_					,		pipeline=UpperCamelCase_  )
              try:
                     UpperCamelCase_          =  cached_download(
                         UpperCamelCase_					,		cache_dir=UpperCamelCase_					,		force_download=UpperCamelCase_					,		proxies=UpperCamelCase_					,		resume_download=UpperCamelCase_					,		local_files_only=UpperCamelCase_					,		use_auth_token=UpperCamelCase_					,		)
                     UpperCamelCase_          =  "git"
                     UpperCamelCase_          =  pretrained_model_name_or_path + ".py"
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''  )
                     raise
       else:
              try:
                     # Load from URL or cache if already cached
                     UpperCamelCase_          =  hf_hub_download(
                         UpperCamelCase_					,		UpperCamelCase_					,		cache_dir=UpperCamelCase_					,		force_download=UpperCamelCase_					,		proxies=UpperCamelCase_					,		resume_download=UpperCamelCase_					,		local_files_only=UpperCamelCase_					,		use_auth_token=UpperCamelCase_					,		)
                     UpperCamelCase_          =  os.path.join("local"					,		"--".join(pretrained_model_name_or_path.split("/"  )  )  )
              except EnvironmentError:
                     logger.error(F'''Could not locate the {module_file} inside {pretrained_model_name_or_path}.'''  )
                     raise

    # Check we have all the requirements in our environment
       UpperCamelCase_          =  check_imports(UpperCamelCase_  )

       # Now we move the module inside our cached dynamic modules.
       UpperCamelCase_          =  DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
       create_dynamic_module(UpperCamelCase_  )
       UpperCamelCase_          =  Path(UpperCamelCase_  ) / full_submodule
       if submodule == "local" or submodule == "git":
              # We always copy local files (we could hash the file to see if there was a change, and give them the name of
              # that hash, to only copy when there is a modification but it seems overkill for now).
              # The only reason we do the copy is to avoid putting too many folders in sys.path.
              shutil.copy(UpperCamelCase_					,		submodule_path / module_file  )
              for module_needed in modules_needed:
                     UpperCamelCase_          =  F'''{module_needed}.py'''
                     shutil.copy(os.path.join(UpperCamelCase_					,		UpperCamelCase_  )					,		submodule_path / module_needed  )
       else:
              # Get the commit hash
              # TODO: we will get this info in the etag soon, so retrieve it from there and not here.
              if isinstance(UpperCamelCase_					,		UpperCamelCase_  ):
                     UpperCamelCase_          =  use_auth_token
              elif use_auth_token is True:
                     UpperCamelCase_          =  HfFolder.get_token()
              else:
                     UpperCamelCase_          =  None

              UpperCamelCase_          =  model_info(UpperCamelCase_					,		revision=UpperCamelCase_					,		token=UpperCamelCase_  ).sha

              # The module file will end up being placed in a subfolder with the git hash of the repo. This way we get the
              # benefit of versioning.
              UpperCamelCase_          =  submodule_path / commit_hash
              UpperCamelCase_          =  full_submodule + os.path.sep + commit_hash
              create_dynamic_module(UpperCamelCase_  )

              if not (submodule_path / module_file).exists():
                     shutil.copy(UpperCamelCase_					,		submodule_path / module_file  )
              # Make sure we also have every file with relative
              for module_needed in modules_needed:
                     if not (submodule_path / module_needed).exists():
                            get_cached_module_file(
                                UpperCamelCase_					,		F'''{module_needed}.py'''					,		cache_dir=UpperCamelCase_					,		force_download=UpperCamelCase_					,		resume_download=UpperCamelCase_					,		proxies=UpperCamelCase_					,		use_auth_token=UpperCamelCase_					,		revision=UpperCamelCase_					,		local_files_only=UpperCamelCase_					,		)
       return os.path.join(UpperCamelCase_					,		UpperCamelCase_  )







def  lowerCAmelCase_ (							UpperCamelCase_					,		UpperCamelCase_					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None					,		UpperCamelCase_ = False					,		UpperCamelCase_ = False					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None					,		UpperCamelCase_ = None					,		UpperCamelCase_ = False					,		**UpperCamelCase_					,		)     ->		Any:
       UpperCamelCase_          =  get_cached_module_file(
           UpperCamelCase_					,		UpperCamelCase_					,		cache_dir=UpperCamelCase_					,		force_download=UpperCamelCase_					,		resume_download=UpperCamelCase_					,		proxies=UpperCamelCase_					,		use_auth_token=UpperCamelCase_					,		revision=UpperCamelCase_					,		local_files_only=UpperCamelCase_					,		)
       return get_class_in_module(UpperCamelCase_					,		final_module.replace(".py"					,		""  )  )








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(3_2, (3, 3), input_shape=(6_4, 6_4, 3), activation='relu')
     )

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

     # Adding a second convolutional layer
     classifier.add(layers.ConvaD(3_2, (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=1_2_8, 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 / 2_5_5, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
     )

     _UpperCAmelCase        =				tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_5_5)

     _UpperCAmelCase        =				train_datagen.flow_from_directory(
         'dataset/training_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     _UpperCAmelCase        =				test_datagen.flow_from_directory(
         'dataset/test_set', target_size=(6_4, 6_4), batch_size=3_2, class_mode='binary'
     )

     classifier.fit_generator(
         training_set, steps_per_epoch=5, epochs=3_0, 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=(6_4, 6_4)
     )
     _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'

