from importlib import import_module

from .logging import get_logger


lowerCAmelCase__     						=			get_logger(__name__)
class     a_			:






 '''simple docstring'''






 def __init__(      self :  List[Any]      ,   lowercase__ :  Dict      ,   lowercase__ :  Dict=None):







        '''simple docstring'''



        lowerCAmelCase__					     = attrs or []
        if module is not None:
               for key in module.__dict__:
                      if key in attrs or not key.startswith('__'):
                             setattr(self      ,   lowercase__      ,   getattr(lowercase__      ,   lowercase__))
        lowerCAmelCase__					     = module._original_module if isinstance(lowercase__      ,   _PatchedModuleObj) else module






class     a_			:






 '''simple docstring'''






 UpperCAmelCase_     			= []




 def __init__(      self :  str      ,   lowercase__ :  int      ,   lowercase__ :  str      ,   lowercase__ :  Any      ,   lowercase__ :  int=None):







        '''simple docstring'''



        lowerCAmelCase__					     = obj
        lowerCAmelCase__					     = target
        lowerCAmelCase__					     = new
        lowerCAmelCase__					     = target.split('.')[0]
        lowerCAmelCase__					     = {}
        lowerCAmelCase__					     = attrs or []




 def __enter__(      self :  List[Any]):







        '''simple docstring'''



        *lowerCAmelCase__     , lowerCAmelCase__					     = self.target.split('.')

        # Patch modules:
        # it's used to patch attributes of submodules like "os.path.join";
        # in this case we need to patch "os" and "os.path"

        for i in range(len(lowercase__)):
               try:
                      lowerCAmelCase__					     = import_module('.'.join(submodules[: i + 1]))
               except ModuleNotFoundError:
                      continue
               # We iterate over all the globals in self.obj in case we find "os" or "os.path"
               for attr in self.obj.__dir__():
                      lowerCAmelCase__					     = getattr(self.obj      ,   lowercase__)
                      # We don't check for the name of the global, but rather if its value *is* "os" or "os.path".
                      # This allows to patch renamed modules like "from os import path as ospath".
                      if obj_attr is submodule or (
                          (isinstance(lowercase__      ,   _PatchedModuleObj) and obj_attr._original_module is submodule)
                      ):
                             lowerCAmelCase__					     = obj_attr
                             # patch at top level
                             setattr(self.obj      ,   lowercase__      ,   _PatchedModuleObj(lowercase__      ,   attrs=self.attrs))
                             lowerCAmelCase__					     = getattr(self.obj      ,   lowercase__)
                             # construct lower levels patches
                             for key in submodules[i + 1 :]:
                                    setattr(lowercase__      ,   lowercase__      ,   _PatchedModuleObj(getattr(lowercase__      ,   lowercase__      ,   lowercase__)      ,   attrs=self.attrs))
                                    lowerCAmelCase__					     = getattr(lowercase__      ,   lowercase__)
                             # finally set the target attribute
                             setattr(lowercase__      ,   lowercase__      ,   self.new)

        # Patch attribute itself:
        # it's used for builtins like "open",
        # and also to patch "os.path.join" we may also need to patch "join"
        # itself if it was imported as "from os.path import join".

        if submodules:  # if it's an attribute of a submodule like "os.path.join"
               try:
                      lowerCAmelCase__					     = getattr(import_module('.'.join(lowercase__))      ,   lowercase__)
               except (AttributeError, ModuleNotFoundError):
                      return
               # We iterate over all the globals in self.obj in case we find "os.path.join"
               for attr in self.obj.__dir__():
                      # We don't check for the name of the global, but rather if its value *is* "os.path.join".
                      # This allows to patch renamed attributes like "from os.path import join as pjoin".
                      if getattr(self.obj      ,   lowercase__) is attr_value:
                             lowerCAmelCase__					     = getattr(self.obj      ,   lowercase__)
                             setattr(self.obj      ,   lowercase__      ,   self.new)
        elif target_attr in globals()["__builtins__"]:  # if it'a s builtin like "open"
               lowerCAmelCase__					     = globals()['__builtins__'][target_attr]
               setattr(self.obj      ,   lowercase__      ,   self.new)
        else:
               raise RuntimeError(F"""Tried to patch attribute {target_attr} instead of a submodule.""")




 def __exit__(      self :  Tuple      ,   *lowercase__ :  Optional[int]):







        '''simple docstring'''



        for attr in list(self.original):
               setattr(self.obj      ,   lowercase__      ,   self.original.pop(lowercase__))




 def 		__snake_case						(      self :  Dict):







        '''simple docstring'''



        self.__enter__()
        self._active_patches.append(self)




 def 		__snake_case						(      self :  List[Any]):







        '''simple docstring'''



        try:
               self._active_patches.remove(self)
        except ValueError:
               # If the patch hasn't been started this will fail
               return None

        return self.__exit__()

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


lowerCAmelCase__     						=			logging.get_logger(__name__)
class     a_			(						SCREAMING_SNAKE_CASE						):






 '''simple docstring'''






 UpperCAmelCase_     			= ['pixel_values']




 def __init__(      self :  Tuple      ,   lowercase__ :  bool = True      ,   lowercase__ :  Dict[str, int] = None      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  bool = True      ,   lowercase__ :  Union[int, float] = 1 / 255      ,   lowercase__ :  bool = True      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  bool = True      ,   **lowercase__ :  List[Any]      ,   ):







        '''simple docstring'''



        super().__init__(**lowercase__)
        lowerCAmelCase__					     = size if size is not None else {'height': 384, 'width': 384}
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = do_resize
        lowerCAmelCase__					     = size
        lowerCAmelCase__					     = resample
        lowerCAmelCase__					     = do_rescale
        lowerCAmelCase__					     = rescale_factor
        lowerCAmelCase__					     = do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else OPENAI_CLIP_MEAN
        lowerCAmelCase__					     = image_std if image_std is not None else OPENAI_CLIP_STD
        lowerCAmelCase__					     = do_convert_rgb




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Dict[str, int]      ,   lowercase__ :  PILImageResampling = PILImageResampling.BICUBIC      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)
        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()}""")
        lowerCAmelCase__					     = (size['height'], size['width'])
        return resize(lowercase__      ,   size=lowercase__      ,   resample=lowercase__      ,   data_format=lowercase__      ,   **lowercase__)




 def 		__snake_case						(      self :  List[str]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[int, float]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Union[str, Any]      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Optional[Any]      ,   lowercase__ :  np.ndarray      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Union[float, List[float]]      ,   lowercase__ :  Optional[Union[str, ChannelDimension]] = None      ,   **lowercase__ :  Any      ,   ):







        '''simple docstring'''



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




 def 		__snake_case						(      self :  Any      ,   lowercase__ :  ImageInput      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Dict[str, int]] = None      ,   lowercase__ :  PILImageResampling = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[float] = None      ,   lowercase__ :  Optional[bool] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[float, List[float]]] = None      ,   lowercase__ :  Optional[Union[str, TensorType]] = None      ,   lowercase__ :  bool = None      ,   lowercase__ :  ChannelDimension = ChannelDimension.FIRST      ,   **lowercase__ :  Dict      ,   ):







        '''simple docstring'''



        lowerCAmelCase__					     = do_resize if do_resize is not None else self.do_resize
        lowerCAmelCase__					     = resample if resample is not None else self.resample
        lowerCAmelCase__					     = do_rescale if do_rescale is not None else self.do_rescale
        lowerCAmelCase__					     = rescale_factor if rescale_factor is not None else self.rescale_factor
        lowerCAmelCase__					     = do_normalize if do_normalize is not None else self.do_normalize
        lowerCAmelCase__					     = image_mean if image_mean is not None else self.image_mean
        lowerCAmelCase__					     = image_std if image_std is not None else self.image_std
        lowerCAmelCase__					     = do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

        lowerCAmelCase__					     = size if size is not None else self.size
        lowerCAmelCase__					     = get_size_dict(lowercase__      ,   default_to_square=lowercase__)

        lowerCAmelCase__					     = make_list_of_images(lowercase__)

        if not valid_images(lowercase__):
               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:
               lowerCAmelCase__					     = [convert_to_rgb(lowercase__) for image in images]

        # All transformations expect numpy arrays.
        lowerCAmelCase__					     = [to_numpy_array(lowercase__) for image in images]

        if do_resize:
               lowerCAmelCase__					     = [self.resize(image=lowercase__      ,   size=lowercase__      ,   resample=lowercase__) for image in images]

        if do_rescale:
               lowerCAmelCase__					     = [self.rescale(image=lowercase__      ,   scale=lowercase__) for image in images]

        if do_normalize:
               lowerCAmelCase__					     = [self.normalize(image=lowercase__      ,   mean=lowercase__      ,   std=lowercase__) for image in images]

        lowerCAmelCase__					     = [to_channel_dimension_format(lowercase__      ,   lowercase__) for image in images]

        lowerCAmelCase__					     = BatchFeature(data={'pixel_values': images}      ,   tensor_type=lowercase__)

        return encoded_outputs
