

from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import convert_to_rgb, normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
       import PIL


UpperCAmelCase__ :     Tuple				    =    logging.get_logger(__name__)







class        a__       (   UpperCAmelCase       ):







  """simple docstring"""




  UpperCAmelCase__      : Any			       =["""pixel_values"""]


  def __init__(			self    : Tuple				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Dict[str, int] = None				,				UpperCAmelCase__    : PILImageResampling = PILImageResampling.BICUBIC				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Union[int, float] = 1 / 2_5_5				,				UpperCAmelCase__    : bool = True				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : bool = True				,				**UpperCAmelCase__    : int				,				)					->None:
        """simple docstring"""







        super().__init__(**UpperCAmelCase__		)
        SCREAMING_SNAKE_CASE :      str       =   size if size is not None else {"""height""": 3_8_4, """width""": 3_8_4}
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      Optional[Any]       =   do_resize
        SCREAMING_SNAKE_CASE :      Dict       =   size
        SCREAMING_SNAKE_CASE :      Union[str, Any]       =   resample
        SCREAMING_SNAKE_CASE :      List[Any]       =   do_rescale
        SCREAMING_SNAKE_CASE :      List[str]       =   rescale_factor
        SCREAMING_SNAKE_CASE :      Any       =   do_normalize
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        SCREAMING_SNAKE_CASE :      Optional[int]       =   image_std if image_std is not None else OPENAI_CLIP_STD
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   do_convert_rgb


  def  _lowercase      (			self    : int				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Dict[str, int]				,				UpperCAmelCase__    : PILImageResampling = PILImageResampling.BICUBIC				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : str				,				)					->np.ndarray:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__		)
        if "height" not in size or "width" not in size:
              raise ValueError(f"The `size` dictionary must contain the keys `height` and `width`. Got {size.keys()}"		)
        SCREAMING_SNAKE_CASE :      Tuple       =   (size["""height"""], size["""width"""])
        return resize(UpperCAmelCase__				,				size=UpperCAmelCase__				,				resample=UpperCAmelCase__				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[Any]				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Union[int, float]				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : str				,				)					->Union[str, Any]:
        """simple docstring"""







        return rescale(UpperCAmelCase__				,				scale=UpperCAmelCase__				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)


  def  _lowercase      (			self    : Optional[int]				,				UpperCAmelCase__    : np.ndarray				,				UpperCAmelCase__    : Union[float, List[float]]				,				UpperCAmelCase__    : Union[float, List[float]]				,				UpperCAmelCase__    : Optional[Union[str, ChannelDimension]] = None				,				**UpperCAmelCase__    : Dict				,				)					->np.ndarray:
        """simple docstring"""







        return normalize(UpperCAmelCase__				,				mean=UpperCAmelCase__				,				std=UpperCAmelCase__				,				data_format=UpperCAmelCase__				,				**UpperCAmelCase__		)




  def  _lowercase      (			self    : List[str]				,				UpperCAmelCase__    : ImageInput				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[Dict[str, int]] = None				,				UpperCAmelCase__    : PILImageResampling = None				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[float] = None				,				UpperCAmelCase__    : Optional[bool] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[float, List[float]]] = None				,				UpperCAmelCase__    : Optional[Union[str, TensorType]] = None				,				UpperCAmelCase__    : bool = None				,				UpperCAmelCase__    : ChannelDimension = ChannelDimension.FIRST				,				**UpperCAmelCase__    : List[str]				,				)					->PIL.Image.Image:
        """simple docstring"""







        SCREAMING_SNAKE_CASE :      Dict       =   do_resize if do_resize is not None else self.do_resize
        SCREAMING_SNAKE_CASE :      str       =   resample if resample is not None else self.resample
        SCREAMING_SNAKE_CASE :      int       =   do_rescale if do_rescale is not None else self.do_rescale
        SCREAMING_SNAKE_CASE :      Any       =   rescale_factor if rescale_factor is not None else self.rescale_factor
        SCREAMING_SNAKE_CASE :      List[str]       =   do_normalize if do_normalize is not None else self.do_normalize
        SCREAMING_SNAKE_CASE :      int       =   image_mean if image_mean is not None else self.image_mean
        SCREAMING_SNAKE_CASE :      Optional[Any]       =   image_std if image_std is not None else self.image_std
        SCREAMING_SNAKE_CASE :      Any       =   do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        SCREAMING_SNAKE_CASE :      List[Any]       =   size if size is not None else self.size
        SCREAMING_SNAKE_CASE :      Dict       =   get_size_dict(UpperCAmelCase__				,				default_to_square=UpperCAmelCase__		)

        SCREAMING_SNAKE_CASE :      List[str]       =   make_list_of_images(UpperCAmelCase__		)

        if not valid_images(UpperCAmelCase__		):
              raise ValueError(
                  """Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, """
                  """torch.Tensor, tf.Tensor or jax.ndarray."""		)

        if do_resize and size is None or resample is None:
              raise ValueError("""Size and resample must be specified if do_resize is True."""		)

        if do_rescale and rescale_factor is None:
              raise ValueError("""Rescale factor must be specified if do_rescale is True."""		)

        if do_normalize and (image_mean is None or image_std is None):
              raise ValueError("""Image mean and std must be specified if do_normalize is True."""		)

        # PIL RGBA images are converted to RGB
        if do_convert_rgb:
              SCREAMING_SNAKE_CASE :      Optional[Any]       =   [convert_to_rgb(UpperCAmelCase__		) for image in images]

        # All transformations expect numpy arrays.
        SCREAMING_SNAKE_CASE :      Any       =   [to_numpy_array(UpperCAmelCase__		) for image in images]

        if do_resize:
              SCREAMING_SNAKE_CASE :      Union[str, Any]       =   [self.resize(image=UpperCAmelCase__				,				size=UpperCAmelCase__				,				resample=UpperCAmelCase__		) for image in images]

        if do_rescale:
              SCREAMING_SNAKE_CASE :      List[Any]       =   [self.rescale(image=UpperCAmelCase__				,				scale=UpperCAmelCase__		) for image in images]

        if do_normalize:
              SCREAMING_SNAKE_CASE :      Optional[int]       =   [self.normalize(image=UpperCAmelCase__				,				mean=UpperCAmelCase__				,				std=UpperCAmelCase__		) for image in images]

        SCREAMING_SNAKE_CASE :      List[str]       =   [to_channel_dimension_format(UpperCAmelCase__				,				UpperCAmelCase__		) for image in images]

        SCREAMING_SNAKE_CASE :      str       =   BatchFeature(data={"""pixel_values""": images}				,				tensor_type=UpperCAmelCase__		)

        return encoded_outputs







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__ :     List[str]				    =    (
    """https://raw.githubusercontent.com/huggingface/diffusers/{revision}/examples/community/{pipeline}.py"""
)


UpperCAmelCase__ :     Union[str, Any]				    =    logging.get_logger(__name__)  # pylint: disable=invalid-name






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






def 					__lowercase      (     )     ->						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(_A			)
      os.makedirs(_A						,    exist_ok=_A			)
      SCREAMING_SNAKE_CASE :      Dict       =   Path(_A			) / """__init__.py"""
      if not init_path.exists():
            init_path.touch()






def 					__lowercase      (     _A			)     ->						Any:
      init_hf_modules()
      SCREAMING_SNAKE_CASE :      Any       =   Path(_A			) / 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(_A						,    exist_ok=_A			)
      SCREAMING_SNAKE_CASE :      str       =   dynamic_module_path / """__init__.py"""
      if not init_path.exists():
            init_path.touch()






def 					__lowercase      (     _A			)     ->						Optional[int]:
      with open(_A						,    """r"""						,    encoding="""utf-8"""			) as f:
            SCREAMING_SNAKE_CASE :      Any       =   f.read()

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






def 					__lowercase      (     _A			)     ->						Optional[Any]:
      SCREAMING_SNAKE_CASE :      Union[str, Any]       =   False
      SCREAMING_SNAKE_CASE :      List[Any]       =   [module_file]
      SCREAMING_SNAKE_CASE :      Any       =   []

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

            SCREAMING_SNAKE_CASE :      List[str]       =   Path(_A			).parent
            SCREAMING_SNAKE_CASE :      int       =   [str(module_path / m			) for m in new_imports]
            SCREAMING_SNAKE_CASE :      int       =   [f for f in new_import_files if f not in all_relative_imports]
            SCREAMING_SNAKE_CASE :      Optional[Any]       =   [F"{f}.py" for f in new_import_files]

            SCREAMING_SNAKE_CASE :      List[Any]       =   len(_A			) == 0
            all_relative_imports.extend(_A			)

      return all_relative_imports






def 					__lowercase      (     _A			)     ->						Union[str, Any]:
      with open(_A						,    """r"""						,    encoding="""utf-8"""			) as f:
            SCREAMING_SNAKE_CASE :      List[Any]       =   f.read()

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

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

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

      return get_relative_imports(_A			)






def 					__lowercase      (     _A						,    _A			)     ->						List[str]:
      SCREAMING_SNAKE_CASE :      int       =   module_path.replace(os.path.sep						,    """."""			)
      SCREAMING_SNAKE_CASE :      List[str]       =   importlib.import_module(_A			)

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






def 					__lowercase      (     _A			)     ->						Optional[int]:
      from ..pipelines import DiffusionPipeline

      SCREAMING_SNAKE_CASE :      Tuple       =   dict(inspect.getmembers(_A						,    inspect.isclass			)			)

      SCREAMING_SNAKE_CASE :      Dict       =   None
      for cls_name, cls in cls_members.items():
            if (
                cls_name != DiffusionPipeline.__name__
                and issubclass(cls						,    _A			)
                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}."			)
                  SCREAMING_SNAKE_CASE :      List[str]       =   cls

      return pipeline_class






def 					__lowercase      (     _A						,    _A						,    _A = None						,    _A = False						,    _A = False						,    _A = None						,    _A = None						,    _A = None						,    _A = False						,    )     ->						List[str]:
      SCREAMING_SNAKE_CASE :      List[str]       =   str(_A			)

      SCREAMING_SNAKE_CASE :      List[Any]       =   os.path.join(_A						,    _A			)

      if os.path.isfile(_A			):
            SCREAMING_SNAKE_CASE :      Union[str, Any]       =   module_file_or_url
            SCREAMING_SNAKE_CASE :      Dict       =   """local"""
      elif pretrained_model_name_or_path.count("""/"""			) == 0:
            SCREAMING_SNAKE_CASE :      Tuple       =   get_diffusers_versions()
            # cut ".dev0"
            SCREAMING_SNAKE_CASE :      str       =   """v""" + """.""".join(__version__.split("""."""			)[:3]			)

            # retrieve github version that matches
            if revision is None:
                  SCREAMING_SNAKE_CASE :      Any       =   latest_version if latest_version[1:] in available_versions else """main"""
                  logger.info(F"Defaulting to latest_version: {revision}."			)
            elif revision in available_versions:
                  SCREAMING_SNAKE_CASE :      List[Any]       =   F"v{revision}"
            elif revision == "main":
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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
            SCREAMING_SNAKE_CASE :      Tuple       =   COMMUNITY_PIPELINES_URL.format(revision=_A						,    pipeline=_A			)
            try:
                  SCREAMING_SNAKE_CASE :      str       =   cached_download(
                      _A						,    cache_dir=_A						,    force_download=_A						,    proxies=_A						,    resume_download=_A						,    local_files_only=_A						,    use_auth_token=_A						,    )
                  SCREAMING_SNAKE_CASE :      Tuple       =   """git"""
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   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
                  SCREAMING_SNAKE_CASE :      int       =   hf_hub_download(
                      _A						,    _A						,    cache_dir=_A						,    force_download=_A						,    proxies=_A						,    resume_download=_A						,    local_files_only=_A						,    use_auth_token=_A						,    )
                  SCREAMING_SNAKE_CASE :      Union[str, Any]       =   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
      SCREAMING_SNAKE_CASE :      int       =   check_imports(_A			)

      # Now we move the module inside our cached dynamic modules.
      SCREAMING_SNAKE_CASE :      List[Any]       =   DIFFUSERS_DYNAMIC_MODULE_NAME + os.path.sep + submodule
      create_dynamic_module(_A			)
      SCREAMING_SNAKE_CASE :      Tuple       =   Path(_A			) / 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(_A						,    submodule_path / module_file			)
            for module_needed in modules_needed:
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   F"{module_needed}.py"
                  shutil.copy(os.path.join(_A						,    _A			)						,    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(_A						,    _A			):
                  SCREAMING_SNAKE_CASE :      Tuple       =   use_auth_token
            elif use_auth_token is True:
                  SCREAMING_SNAKE_CASE :      str       =   HfFolder.get_token()
            else:
                  SCREAMING_SNAKE_CASE :      Optional[int]       =   None

            SCREAMING_SNAKE_CASE :      List[Any]       =   model_info(_A						,    revision=_A						,    token=_A			).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.
            SCREAMING_SNAKE_CASE :      int       =   submodule_path / commit_hash
            SCREAMING_SNAKE_CASE :      Tuple       =   full_submodule + os.path.sep + commit_hash
            create_dynamic_module(_A			)

            if not (submodule_path / module_file).exists():
                  shutil.copy(_A						,    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(
                            _A						,    F"{module_needed}.py"						,    cache_dir=_A						,    force_download=_A						,    resume_download=_A						,    proxies=_A						,    use_auth_token=_A						,    revision=_A						,    local_files_only=_A						,    )
      return os.path.join(_A						,    _A			)







def 					__lowercase      (     _A						,    _A						,    _A = None						,    _A = None						,    _A = False						,    _A = False						,    _A = None						,    _A = None						,    _A = None						,    _A = False						,    **_A						,    )     ->						Tuple:
      SCREAMING_SNAKE_CASE :      Dict       =   get_cached_module_file(
          _A						,    _A						,    cache_dir=_A						,    force_download=_A						,    resume_download=_A						,    proxies=_A						,    use_auth_token=_A						,    revision=_A						,    local_files_only=_A						,    )
      return get_class_in_module(_A						,    final_module.replace(""".py"""						,    """"""			)			)




