



'''simple docstring'''
from typing import Dict, Iterable, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_DEFAULT_MEAN,
    IMAGENET_DEFAULT_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


lowercase      			=	logging.get_logger(__name__)






class    __lowerCamelCase							( UpperCamelCase_   ):







	'''simple docstring'''
	snake_case__		:   List[str]       =				["""pixel_values"""]






	def __init__(      self      ,     a__ = True      ,     a__ = None      ,     a__ = PILImageResampling.BICUBIC      ,     a__ = True      ,     a__ = None      ,     a__ = True      ,     a__ = 1 / 255      ,     a__ = True      ,     a__ = IMAGENET_DEFAULT_MEAN      ,     a__ = IMAGENET_DEFAULT_STD      ,     **a__      ,     ):
						super().__init__(**a__					)
						__SCREAMING_SNAKE_CASE				:    Dict						    =							size if size is not None else {"shortest_edge": 224}
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							get_size_dict(a__      ,     default_to_square=a__					)
						__SCREAMING_SNAKE_CASE				:    str						    =							crop_size if crop_size is not None else {"height": 224, "width": 224}
						__SCREAMING_SNAKE_CASE				:    str						    =							get_size_dict(a__      ,     param_name="crop_size"					)

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							do_resize
						__SCREAMING_SNAKE_CASE				:    Any						    =							size
						__SCREAMING_SNAKE_CASE				:    List[Any]						    =							resample
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							do_center_crop
						__SCREAMING_SNAKE_CASE				:    str						    =							crop_size
						__SCREAMING_SNAKE_CASE				:    Dict						    =							do_rescale
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							rescale_factor
						__SCREAMING_SNAKE_CASE				:    Any						    =							do_normalize
						__SCREAMING_SNAKE_CASE				:    str						    =							image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
						__SCREAMING_SNAKE_CASE				:    Any						    =							image_std if image_std is not None else IMAGENET_DEFAULT_STD






	def  a_					(      self      ,     a__      ,     a__      ,     a__ = PILImageResampling.BICUBIC      ,     a__ = None      ,     **a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    Tuple						    =							get_size_dict(a__      ,     default_to_square=a__					)
						# size_dict is a dict with either keys "height" and "width" or "shortest_edge"
						if "shortest_edge" in size:
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							int((256 / 224) * size["shortest_edge"]					)
											__SCREAMING_SNAKE_CASE				:    Dict						    =							get_resize_output_image_size(a__      ,     size=a__      ,     default_to_square=a__					)
											__SCREAMING_SNAKE_CASE				:    Any						    =							{"height": output_size[0], "width": output_size[1]}
						if "height" not in size_dict or "width" not in size_dict:
											raise ValueError(
											    f'Size dict must have keys \'height\' and \'width\' or \'shortest_edge\'. Got {size_dict.keys()}'					)
						return resize(
						    a__      ,     size=(size_dict["height"], size_dict["width"])      ,     resample=a__      ,     data_format=a__      ,     **a__					)






	def  a_					(      self      ,     a__      ,     a__      ,     a__ = None      ,     **a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    Any						    =							get_size_dict(a__					)
						if "height" not in size or "width" not in size:
											raise ValueError(f'Size dict must have keys \'height\' and \'width\'. Got {size.keys()}'					)
						return center_crop(a__      ,     size=(size["height"], size["width"])      ,     data_format=a__      ,     **a__					)






	def  a_					(      self      ,     a__      ,     a__      ,     a__ = None      ,     **a__      ,     ):
						return rescale(a__      ,     scale=a__      ,     data_format=a__      ,     **a__					)






	def  a_					(      self      ,     a__      ,     a__      ,     a__      ,     a__ = None      ,     **a__      ,     ):
						return normalize(a__      ,     mean=a__      ,     std=a__      ,     data_format=a__      ,     **a__					)






	def  a_					(      self      ,     a__      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = None      ,     a__ = ChannelDimension.FIRST      ,     **a__      ,     ):
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							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				:    Optional[Any]						    =							do_center_crop if do_center_crop is not None else self.do_center_crop
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							do_rescale if do_rescale is not None else self.do_rescale
						__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							rescale_factor if rescale_factor is not None else self.rescale_factor
						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							do_normalize if do_normalize is not None else self.do_normalize
						__SCREAMING_SNAKE_CASE				:    Any						    =							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				:    Optional[Any]						    =							size if size is not None else self.size
						__SCREAMING_SNAKE_CASE				:    List[str]						    =							get_size_dict(a__      ,     default_to_square=a__					)
						__SCREAMING_SNAKE_CASE				:    Optional[Any]						    =							crop_size if crop_size is not None else self.crop_size
						__SCREAMING_SNAKE_CASE				:    Any						    =							get_size_dict(a__      ,     param_name="crop_size"					)

						__SCREAMING_SNAKE_CASE				:    Tuple						    =							make_list_of_images(a__					)

						if not valid_images(a__					):
											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:
											raise ValueError("Size must be specified if do_resize is True."					)

						if do_center_crop and crop_size is None:
											raise ValueError("Crop size must be specified if do_center_crop 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."					)

						# All transformations expect numpy arrays.
						__SCREAMING_SNAKE_CASE				:    int						    =							[to_numpy_array(a__					) for image in images]

						if do_resize:
											__SCREAMING_SNAKE_CASE				:    Optional[int]						    =							[self.resize(a__      ,     a__      ,     a__					) for image in images]

						if do_center_crop:
											__SCREAMING_SNAKE_CASE				:    List[str]						    =							[self.center_crop(a__      ,     a__					) for image in images]

						if do_rescale:
											__SCREAMING_SNAKE_CASE				:    List[Any]						    =							[self.rescale(a__      ,     a__					) for image in images]

						if do_normalize:
											__SCREAMING_SNAKE_CASE				:    Tuple						    =							[self.normalize(a__      ,     a__      ,     a__					) for image in images]

						__SCREAMING_SNAKE_CASE				:    Union[str, Any]						    =							[to_channel_dimension_format(a__      ,     a__					) for image in images]

						__SCREAMING_SNAKE_CASE				:    Any						    =							{"pixel_values": images}
						return BatchFeature(data=a__      ,     tensor_type=a__					)




'''simple docstring'''

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


snake_case									=    logging.get_logger(__name__)

snake_case									=    {
    """google/mobilenet_v2_1.4_224""": """https://huggingface.co/google/mobilenet_v2_1.4_224/resolve/main/config.json""",
    """google/mobilenet_v2_1.0_224""": """https://huggingface.co/google/mobilenet_v2_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v2_0.75_160""": """https://huggingface.co/google/mobilenet_v2_0.75_160/resolve/main/config.json""",
    """google/mobilenet_v2_0.35_96""": """https://huggingface.co/google/mobilenet_v2_0.35_96/resolve/main/config.json""",
    # See all MobileNetV2 models at https://huggingface.co/models?filter=mobilenet_v2
}




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				List[str]	=			"""mobilenet_v2"""


 def __init__(    self  : Optional[int]			,							a__  : Dict=3			,							a__  : str=224			,							a__  : int=1.0			,							a__  : Dict=8			,							a__  : Dict=8			,							a__  : Dict=6			,							a__  : Optional[Any]=32			,							a__  : List[str]=True			,							a__  : int=True			,							a__  : List[str]="relu6"			,							a__  : List[str]=True			,							a__  : List[Any]=0.8			,							a__  : Tuple=0.02			,							a__  : Tuple=0.001			,							a__  : Optional[Any]=255			,							**a__  : str			,							):
  '''simple docstring'''

  super().__init__(**a__    )

  if depth_multiplier <= 0:
   raise ValueError("depth_multiplier must be greater than zero."    )

  lowerCAmelCase__     :   Any     		=	num_channels
  lowerCAmelCase__     :   Union[str, Any]     		=	image_size
  lowerCAmelCase__     :   Tuple     		=	depth_multiplier
  lowerCAmelCase__     :   Optional[int]     		=	depth_divisible_by
  lowerCAmelCase__     :   Union[str, Any]     		=	min_depth
  lowerCAmelCase__     :   List[str]     		=	expand_ratio
  lowerCAmelCase__     :   Union[str, Any]     		=	output_stride
  lowerCAmelCase__     :   Optional[int]     		=	first_layer_is_expansion
  lowerCAmelCase__     :   Tuple     		=	finegrained_output
  lowerCAmelCase__     :   Optional[int]     		=	hidden_act
  lowerCAmelCase__     :   Optional[Any]     		=	tf_padding
  lowerCAmelCase__     :   Union[str, Any]     		=	classifier_dropout_prob
  lowerCAmelCase__     :   Optional[int]     		=	initializer_range
  lowerCAmelCase__     :   str     		=	layer_norm_eps
  lowerCAmelCase__     :   int     		=	semantic_loss_ignore_index




class    lowerCAmelCase       (     UpperCamelCase_  ):
 A_				:				Optional[int]	=			version.parse("""1.11"""  )


 @property
 def    _A	(    self  : List[Any]    ):
  '''simple docstring'''

  return OrderedDict([("pixel_values", {0: "batch"})]    )


 @property
 def    _A	(    self  : Optional[int]    ):
  '''simple docstring'''

  if self.task == "image-classification":
   return OrderedDict([("logits", {0: "batch"})]    )
  else:
   return OrderedDict([("last_hidden_state", {0: "batch"}), ("pooler_output", {0: "batch"})]    )


 @property
 def    _A	(    self  : List[str]    ):
  '''simple docstring'''

  return 1e-4





