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


UpperCamelCase__											=					get_logger(__name__)


UpperCamelCase__											=					Path(__file__).parent / 'model_card_template.md'
UpperCamelCase__											=					uuida().hex
UpperCamelCase__											=					os.getenv('HF_HUB_OFFLINE', '').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__											=					os.getenv('DISABLE_TELEMETRY', '').upper() in ENV_VARS_TRUE_VALUES
UpperCamelCase__											=					HUGGINGFACE_CO_RESOLVE_ENDPOINT + '/api/telemetry/'




def    lowerCAmelCase_ (     __A = None							)     ->				str:







      '''simple docstring'''


      UpperCAmelCase__								=  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    lowerCAmelCase_ (     __A,  __A = None,  __A = None							)     ->				str:







      '''simple docstring'''


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




def    lowerCAmelCase_ (     __A,  __A							)     ->				Tuple:







      '''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

      UpperCAmelCase__								=  args.hub_token if hasattr(__A,  "hub_token"							) else None
      UpperCAmelCase__								=  get_full_repo_name(__A,  token=__A							)

      UpperCAmelCase__								=  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,  )

      UpperCAmelCase__								=  os.path.join(args.output_dir,  "README.md"							)
      model_card.save(__A							)




def    lowerCAmelCase_ (     __A,  __A = None							)     ->				Any:







      '''simple docstring'''


      if resolved_file is None or commit_hash is not None:
            return commit_hash
      UpperCAmelCase__								=  str(Path(__A							).as_posix()							)
      UpperCAmelCase__								=  re.search(r"snapshots/([^/]+)/",  __A							)
      if search is None:
            return None
      UpperCAmelCase__								=  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.
UpperCamelCase__											=					os.path.expanduser(
    os.getenv('HF_HOME', os.path.join(os.getenv('XDG_CACHE_HOME', '~/.cache'), 'huggingface'))
)
UpperCamelCase__											=					os.path.join(hf_cache_home, 'diffusers')




def    lowerCAmelCase_ (     __A = None,  __A = None							)     ->				None:







      '''simple docstring'''


      if new_cache_dir is None:
            UpperCAmelCase__								=  DIFFUSERS_CACHE
      if old_cache_dir is None:
            UpperCAmelCase__								=  old_diffusers_cache

      UpperCAmelCase__								=  Path(__A							).expanduser()
      UpperCAmelCase__								=  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():
                  UpperCAmelCase__								=  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).


UpperCamelCase__											=					os.path.join(DIFFUSERS_CACHE, 'version_diffusers_cache.txt')
if not os.path.isfile(cache_version_file):
      UpperCamelCase__											=					0
else:
      with open(cache_version_file) as f:
            try:
                  UpperCamelCase__											=					int(f.read())
            except ValueError:
                  UpperCamelCase__											=					0

if cache_version < 1:
      UpperCamelCase__											=					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:
                  UpperCamelCase__											=					'\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    lowerCAmelCase_ (     __A,  __A = None							)     ->				str:







      '''simple docstring'''


      if variant is not None:
            UpperCAmelCase__								=  weights_name.split("."							)
            UpperCAmelCase__								=  splits[:-1] + [variant] + splits[-1:]
            UpperCAmelCase__								=  ".".join(__A							)

      return weights_name







def    lowerCAmelCase_ (     __A,  *,
    __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A,  __A=None,  )     ->				Optional[Any]:







      '''simple docstring'''


      UpperCAmelCase__								=  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
                  UpperCAmelCase__								=  os.path.join(__A,  __A							)
                  return model_file
            elif subfolder is not None and os.path.isfile(
                os.path.join(__A,  __A,  __A							)							):
                  UpperCAmelCase__								=  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:
                        UpperCAmelCase__								=  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
                  UpperCAmelCase__								=  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}"""							)










from pathlib import Path

import cva
import numpy as np
from matplotlib import pyplot as plt



def _a				(	lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   np.ndarray					,				lowerCamelCase:   int					,				lowerCamelCase:   int )	->						np.ndarray:







      '''simple docstring'''






      __A          =				cva.getAffineTransform(lowerCamelCase					,				lowerCamelCase )
      return cva.warpAffine(lowerCamelCase					,				lowerCamelCase					,				(rows, cols) )


if __name__ == "__main__":
       # read original image
       snake_case__					:							List[Any]													=     cva.imread(
           str(Path(__file__).resolve().parent.parent / 'image_data' / 'lena.jpg')
       )
       # turn image in gray scale value
       snake_case__					:							List[str]													=     cva.cvtColor(image, cva.COLOR_BGR2GRAY)
       # get image shape
       snake_case__						,    snake_case__					:							str													=     gray_img.shape

       # set different points to rotate image
       snake_case__					:							Any													=     np.array([[50, 50], [200, 50], [50, 200]], np.floataa)
       snake_case__					:							str													=     np.array([[10, 100], [200, 50], [100, 250]], np.floataa)
       snake_case__					:							int													=     np.array([[50, 50], [150, 50], [120, 200]], np.floataa)
       snake_case__					:							List[str]													=     np.array([[10, 100], [80, 50], [180, 250]], np.floataa)

       # add all rotated images in a list
       snake_case__					:							Optional[Any]													=     [
           gray_img,
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
           get_rotation(gray_img, ptsa, ptsa, img_rows, img_cols),
       ]

       # plot different image rotations
       snake_case__					:							Optional[Any]													=     plt.figure(1)
       snake_case__					:							Dict													=     ['Original', 'Rotation 1', 'Rotation 2', 'Rotation 3']
       for i, image in enumerate(images):
              plt.subplot(2, 2, i + 1), plt.imshow(image, 'gray')
              plt.title(titles[i])
              plt.axis('off')
              plt.subplots_adjust(left=0.0, bottom=0.05, right=1.0, top=0.95)
       plt.show()





