






import os
import re
import sys
import traceback
import warnings
from pathlib import Path
from typing import Dict, Optional, Union
from uuid import uuida

from huggingface_hub import HfFolder, ModelCard, ModelCardData, hf_hub_download, whoami
from huggingface_hub.file_download import REGEX_COMMIT_HASH
from huggingface_hub.utils import (
    EntryNotFoundError,
    RepositoryNotFoundError,
    RevisionNotFoundError,
    is_jinja_available,
)
from packaging import version
from requests import HTTPError

from .. import __version__
from .constants import (
    DEPRECATED_REVISION_ARGS,
    DIFFUSERS_CACHE,
    HUGGINGFACE_CO_RESOLVE_ENDPOINT,
    SAFETENSORS_WEIGHTS_NAME,
    WEIGHTS_NAME,
)
from .import_utils import (
    ENV_VARS_TRUE_VALUES,
    _flax_version,
    _jax_version,
    _onnxruntime_version,
    _torch_version,
    is_flax_available,
    is_onnx_available,
    is_torch_available,
)
from .logging import get_logger


__lowerCamelCase	:		Optional[Any]										=   get_logger(__name__)


__lowerCamelCase	:		int										=   Path(__file__).parent / """model_card_template.md"""
__lowerCamelCase	:		Tuple										=   uuida().hex
__lowerCamelCase	:		List[str]										=   os.getenv("""HF_HUB_OFFLINE""", """""").upper() in ENV_VARS_TRUE_VALUES
__lowerCamelCase	:		Dict										=   os.getenv("""DISABLE_TELEMETRY""", """""").upper() in ENV_VARS_TRUE_VALUES
__lowerCamelCase	:		Any										=   HUGGINGFACE_CO_RESOLVE_ENDPOINT + """/api/telemetry/"""







def 			A__							(      _a  :					Union[Dict, str, None] = None	):





 '''simple docstring'''







 snake_case__  :    Dict							       =f"diffusers/{__version__}; python/{sys.version.split()[0]}; session_id/{SESSION_ID}"
 if DISABLE_TELEMETRY or HF_HUB_OFFLINE:
  return ua + "; telemetry/off"
 if is_torch_available():
  ua += f"; torch/{_torch_version}"
 if is_flax_available():
  ua += f"; jax/{_jax_version}"
  ua += f"; flax/{_flax_version}"
 if is_onnx_available():
  ua += f"; onnxruntime/{_onnxruntime_version}"
 # CI will set this value to True
 if os.environ.get("""DIFFUSERS_IS_CI"""		,  """"""	).upper() in ENV_VARS_TRUE_VALUES:
  ua += "; is_ci/true"
 if isinstance(__a		,  __a	):
  ua += "; " + "; ".join(f"{k}/{v}" for k, v in user_agent.items()	)
 elif isinstance(__a		,  __a	):
  ua += "; " + user_agent
 return ua







def 			A__							(      _a  :					str		,  _a  :					Optional[str] = None		,  _a  :					Optional[str] = None	):





 '''simple docstring'''







 if token is None:
  snake_case__  :    int							       =HfFolder.get_token()
 if organization is None:
  snake_case__  :    str							       =whoami(__a	)["""name"""]
  return f"{username}/{model_id}"
 else:
  return f"{organization}/{model_id}"







def 			A__							(      _a  :					int		,  _a  :					List[str]	):





 '''simple docstring'''







 if not is_jinja_available():
  raise ValueError(
      """Modelcard rendering is based on Jinja templates."""
      """ Please make sure to have `jinja` installed before using `create_model_card`."""
      """ To install it, please run `pip install Jinja2`."""	)

 if hasattr(__a		,  """local_rank"""	) and args.local_rank not in [-1, 0]:
  return

 snake_case__  :    str							       =args.hub_token if hasattr(__a		,  """hub_token"""	) else None
 snake_case__  :    Optional[Any]							       =get_full_repo_name(__a		,  token=__a	)

 snake_case__  :    List[Any]							       =ModelCard.from_template(
     card_data=ModelCardData(  # Card metadata object that will be converted to YAML block
         language="""en"""		,  license="""apache-2.0"""		,  library_name="""diffusers"""		,  tags=[]		,  datasets=args.dataset_name		,  metrics=[]		,  )		,  template_path=__a		,  model_name=__a		,  repo_name=__a		,  dataset_name=args.dataset_name if hasattr(__a		,  """dataset_name"""	) else None		,  learning_rate=args.learning_rate		,  train_batch_size=args.train_batch_size		,  eval_batch_size=args.eval_batch_size		,  gradient_accumulation_steps=(
         args.gradient_accumulation_steps if hasattr(__a		,  """gradient_accumulation_steps"""	) else None
     )		,  adam_betaa=args.adam_betaa if hasattr(__a		,  """adam_beta1"""	) else None		,  adam_betaa=args.adam_betaa if hasattr(__a		,  """adam_beta2"""	) else None		,  adam_weight_decay=args.adam_weight_decay if hasattr(__a		,  """adam_weight_decay"""	) else None		,  adam_epsilon=args.adam_epsilon if hasattr(__a		,  """adam_epsilon"""	) else None		,  lr_scheduler=args.lr_scheduler if hasattr(__a		,  """lr_scheduler"""	) else None		,  lr_warmup_steps=args.lr_warmup_steps if hasattr(__a		,  """lr_warmup_steps"""	) else None		,  ema_inv_gamma=args.ema_inv_gamma if hasattr(__a		,  """ema_inv_gamma"""	) else None		,  ema_power=args.ema_power if hasattr(__a		,  """ema_power"""	) else None		,  ema_max_decay=args.ema_max_decay if hasattr(__a		,  """ema_max_decay"""	) else None		,  mixed_precision=args.mixed_precision		,  )

 snake_case__  :    int							       =os.path.join(args.output_dir		,  """README.md"""	)
 model_card.save(__a	)







def 			A__							(      _a  :					Optional[str]		,  _a  :					Optional[str] = None	):





 '''simple docstring'''







 if resolved_file is None or commit_hash is not None:
  return commit_hash
 snake_case__  :    List[Any]							       =str(Path(__a	).as_posix()	)
 snake_case__  :    List[Any]							       =re.search(R"""snapshots/([^/]+)/"""		,  __a	)
 if search is None:
  return None
 snake_case__  :    Union[str, Any]							       =search.groups()[0]
 return commit_hash if REGEX_COMMIT_HASH.match(__a	) else None


# Old default cache path, potentially to be migrated.
# This logic was more or less taken from `transformers`, with the following differences:
# - Diffusers doesn't use custom environment variables to specify the cache path.
# - There is no need to migrate the cache format, just move the files to the new location.
__lowerCamelCase	:		Any										=   os.path.expanduser(
    os.getenv("""HF_HOME""", os.path.join(os.getenv("""XDG_CACHE_HOME""", """~/.cache"""), """huggingface"""))
)
__lowerCamelCase	:		Tuple										=   os.path.join(hf_cache_home, """diffusers""")







def 			A__							(      _a  :					Optional[str] = None		,  _a  :					Optional[str] = None	):





 '''simple docstring'''







 if new_cache_dir is None:
  snake_case__  :    List[str]							       =DIFFUSERS_CACHE
 if old_cache_dir is None:
  snake_case__  :    List[str]							       =old_diffusers_cache

 snake_case__  :    Optional[Any]							       =Path(__a	).expanduser()
 snake_case__  :    Optional[Any]							       =Path(__a	).expanduser()
 for old_blob_path in old_cache_dir.glob("""**/blobs/*"""	):
  if old_blob_path.is_file() and not old_blob_path.is_symlink():
   snake_case__  :    Union[str, Any]							       =new_cache_dir / old_blob_path.relative_to(__a	)
   new_blob_path.parent.mkdir(parents=__a		,  exist_ok=__a	)
   os.replace(__a		,  __a	)
   try:
    os.symlink(__a		,  __a	)
   except OSError:
    logger.warning(
        """Could not create symlink between old cache and new cache. If you use an older version of diffusers again, files will be re-downloaded."""	)
 # At this point, old_cache_dir contains symlinks to the new cache (it can still be used).


__lowerCamelCase	:		str										=   os.path.join(DIFFUSERS_CACHE, """version_diffusers_cache.txt""")
if not os.path.isfile(cache_version_file):
       __lowerCamelCase	:		Any										=   0
else:
       with open(cache_version_file) as f:
              try:
                     __lowerCamelCase	:		Tuple										=   int(f.read())
              except ValueError:
                     __lowerCamelCase	:		Union[str, Any]										=   0

if cache_version < 1:
       __lowerCamelCase	:		Union[str, Any]										=   os.path.isdir(old_diffusers_cache) and len(os.listdir(old_diffusers_cache)) > 0
       if old_cache_is_not_empty:
              logger.warning(
                  """The cache for model files in Diffusers v0.14.0 has moved to a new location. Moving your """
                  """existing cached models. This is a one-time operation, you can interrupt it or run it """
                  """later by calling `diffusers.utils.hub_utils.move_cache()`."""
              )
              try:
                     move_cache()
              except Exception as e:
                     __lowerCamelCase	:		Union[str, Any]										=   """\n""".join(traceback.format_tb(e.__traceback__))
                     logger.error(
                         F"There was a problem when trying to move your cache:\n\n{trace}\n{e.__class__.__name__}: {e}\n\nPlease "
                         """file an issue at https://github.com/huggingface/diffusers/issues/new/choose, copy paste this whole """
                         """message and we will do our best to help."""
                     )

if cache_version < 1:
       try:
              os.makedirs(DIFFUSERS_CACHE, exist_ok=True)
              with open(cache_version_file, """w""") as f:
                     f.write("""1""")
       except Exception:
              logger.warning(
                  F"There was a problem when trying to write in your cache folder ({DIFFUSERS_CACHE}). Please, ensure "
                  """the directory exists and can be written to."""
              )







def 			A__							(      _a  :					str		,  _a  :					Optional[str] = None	):





 '''simple docstring'''







 if variant is not None:
  snake_case__  :    str							       =weights_name.split("""."""	)
  snake_case__  :    Union[str, Any]							       =splits[:-1] + [variant] + splits[-1:]
  snake_case__  :    Dict							       =""".""".join(__a	)

 return weights_name







def 			A__							(      _a  :					Optional[int]		,  *,
    _a  :					List[str]		,  _a  :					List[str]		,  _a  :					List[str]		,  _a  :					Tuple		,  _a  :					Any		,  _a  :					int		,  _a  :					Tuple		,  _a  :					List[Any]		,  _a  :					List[Any]		,  _a  :					List[Any]		,  _a  :					List[Any]=None		,  ):





 '''simple docstring'''







 snake_case__  :    Union[str, Any]							       =str(__a	)
 if os.path.isfile(__a	):
  return pretrained_model_name_or_path
 elif os.path.isdir(__a	):
  if os.path.isfile(os.path.join(__a		,  __a	)	):
   # Load from a PyTorch checkpoint
   snake_case__  :    List[str]							       =os.path.join(__a		,  __a	)
   return model_file
  elif subfolder is not None and os.path.isfile(
      os.path.join(__a		,  __a		,  __a	)	):
   snake_case__  :    List[Any]							       =os.path.join(__a		,  __a		,  __a	)
   return model_file
  else:
   raise EnvironmentError(
       f"Error no file named {weights_name} found in directory {pretrained_model_name_or_path}."	)
 else:
  # 1. First check if deprecated way of loading from branches is used
  if (
      revision in DEPRECATED_REVISION_ARGS
      and (weights_name == WEIGHTS_NAME or weights_name == SAFETENSORS_WEIGHTS_NAME)
      and version.parse(version.parse(__a	).base_version	) >= version.parse("""0.20.0"""	)
  ):
   try:
    snake_case__  :    Tuple							       =hf_hub_download(
        __a		,  filename=_add_variant(__a		,  __a	)		,  cache_dir=__a		,  force_download=__a		,  proxies=__a		,  resume_download=__a		,  local_files_only=__a		,  use_auth_token=__a		,  user_agent=__a		,  subfolder=__a		,  revision=revision or commit_hash		,  )
    warnings.warn(
        f"Loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'` is deprecated. Loading instead from `revision='main'` with `variant={revision}`. Loading model variants via `revision='{revision}'` will be removed in diffusers v1. Please use `variant='{revision}'` instead."		,  __a		,  )
    return model_file
   except:  # noqa: E722
    warnings.warn(
        f"You are loading the variant {revision} from {pretrained_model_name_or_path} via `revision='{revision}'`. This behavior is deprecated and will be removed in diffusers v1. One should use `variant='{revision}'` instead. However, it appears that {pretrained_model_name_or_path} currently does not have a {_add_variant(__a		,  __a	)} file in the 'main' branch of {pretrained_model_name_or_path}. \n The Diffusers team and community would be very grateful if you could open an issue: https://github.com/huggingface/diffusers/issues/new with the title '{pretrained_model_name_or_path} is missing {_add_variant(__a		,  __a	)}' so that the correct variant file can be added."		,  __a		,  )
  try:
   # 2. Load model file as usual
   snake_case__  :    Tuple							       =hf_hub_download(
       __a		,  filename=__a		,  cache_dir=__a		,  force_download=__a		,  proxies=__a		,  resume_download=__a		,  local_files_only=__a		,  use_auth_token=__a		,  user_agent=__a		,  subfolder=__a		,  revision=revision or commit_hash		,  )
   return model_file

  except RepositoryNotFoundError:
   raise EnvironmentError(
       f"{pretrained_model_name_or_path} is not a local folder and is not a valid model identifier "
       """listed on 'https://huggingface.co/models'\nIf this is a private repository, make sure to pass a """
       """token having permission to this repo with `use_auth_token` or log in with `huggingface-cli """
       """login`."""	)
  except RevisionNotFoundError:
   raise EnvironmentError(
       f"{revision} is not a valid git identifier (branch name, tag name or commit id) that exists for "
       """this model name. Check the model page at """
       f"'https://huggingface.co/{pretrained_model_name_or_path}' for available revisions."	)
  except EntryNotFoundError:
   raise EnvironmentError(
       f"{pretrained_model_name_or_path} does not appear to have a file named {weights_name}."	)
  except HTTPError as err:
   raise EnvironmentError(
       f"There was a specific connection error when trying to load {pretrained_model_name_or_path}:\n{err}"	)
  except ValueError:
   raise EnvironmentError(
       f"We couldn't connect to '{HUGGINGFACE_CO_RESOLVE_ENDPOINT}' to load this model, couldn't find it"
       f" in the cached files and it looks like {pretrained_model_name_or_path} is not the path to a"
       f" directory containing a file named {weights_name} or"
       """ \nCheckout your internet connection or see how to run the library in"""
       """ offline mode at 'https://huggingface.co/docs/diffusers/installation#offline-mode'."""	)
  except EnvironmentError:
   raise EnvironmentError(
       f"Can't load the model for '{pretrained_model_name_or_path}'. If you were trying to load it from "
       """'https://huggingface.co/models', make sure you don't have a local directory with the same name. """
       f"Otherwise, make sure '{pretrained_model_name_or_path}' is the correct path to a directory "
       f"containing a file named {weights_name}"	)













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)
       __lowerCamelCase	:		str										=   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=1_28, 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')

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(
           rescale=1.0 / 2_55, shear_range=0.2, zoom_range=0.2, horizontal_flip=True
       )

       __lowerCamelCase	:		Dict										=   tf.keras.preprocessing.image.ImageDataGenerator(rescale=1.0 / 2_55)

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

       __lowerCamelCase	:		Optional[Any]										=   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

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





