


"""simple docstring"""


from typing import Dict, List, Optional, Union

import numpy as np

from transformers.utils import is_vision_available
from transformers.utils.generic import TensorType

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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    is_valid_image,
    to_numpy_array,
    valid_images,
)
from ...utils import logging


if is_vision_available():
	import PIL

A     :   Union[str, Any]			    =			logging.get_logger(__name__)


def _lowerCamelCase	(		_UpperCamelCase      ):

			'''simple docstring'''




			if isinstance(_UpperCamelCase    , (list, tuple)      ) and isinstance(videos[0]    , (list, tuple)      ) and is_valid_image(videos[0][0]      ):
						return videos

			elif isinstance(_UpperCamelCase    , (list, tuple)      ) and is_valid_image(videos[0]      ):
						return [videos]

			elif is_valid_image(_UpperCamelCase      ):
						return [[videos]]

			raise ValueError(f"Could not make batched video from {videos}"      )


class   _UpperCamelCase  (   lowerCAmelCase__		):
							'''simple docstring'''







							__UpperCAmelCase						:    List[str]				      =["""pixel_values"""]

							def __init__(     self    ,   __a = True    ,   __a = None    ,   __a = PILImageResampling.BILINEAR    ,   __a = True    ,   __a = None    ,   __a = True    ,   __a = 1 / 2_55    ,   __a = True    ,   __a = True    ,   __a = None    ,   __a = None    ,   **__a    ,   ):
										super().__init__(**__a	)
										__lowerCAmelCase				    =			size if size is not None else {"shortest_edge": 2_56}
										__lowerCAmelCase				    =			get_size_dict(__a    ,   default_to_square=__a	)
										__lowerCAmelCase				    =			crop_size if crop_size is not None else {"height": 2_24, "width": 2_24}
										__lowerCAmelCase				    =			get_size_dict(__a    ,   param_name="crop_size"	)

										__lowerCAmelCase				    =			do_resize
										__lowerCAmelCase				    =			size
										__lowerCAmelCase				    =			do_center_crop
										__lowerCAmelCase				    =			crop_size
										__lowerCAmelCase				    =			resample
										__lowerCAmelCase				    =			do_rescale
										__lowerCAmelCase				    =			rescale_factor
										__lowerCAmelCase				    =			offset
										__lowerCAmelCase				    =			do_normalize
										__lowerCAmelCase				    =			image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
										__lowerCAmelCase				    =			image_std if image_std is not None else IMAGENET_STANDARD_STD

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a = PILImageResampling.BILINEAR    ,   __a = None    ,   **__a    ,   ):
										__lowerCAmelCase				    =			get_size_dict(__a    ,   default_to_square=__a	)
										if "shortest_edge" in size:
													__lowerCAmelCase				    =			get_resize_output_image_size(__a    ,   size["shortest_edge"]    ,   default_to_square=__a	)
										elif "height" in size and "width" in size:
													__lowerCAmelCase				    =			(size["height"], size["width"])
										else:
													raise ValueError(f"Size must have 'height' and 'width' or 'shortest_edge' as keys. Got {size.keys()}"	)
										return resize(__a    ,   size=__a    ,   resample=__a    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a = None    ,   **__a    ,   ):
										__lowerCAmelCase				    =			get_size_dict(__a	)
										if "height" not in size or "width" not in size:
													raise ValueError(f"Size must have 'height' and 'width' as keys. Got {size.keys()}"	)
										return center_crop(__a    ,   size=(size["height"], size["width"])    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a = True    ,   __a = None    ,   **__a    ,   ):
										__lowerCAmelCase				    =			image.astype(np.floataa	)
										if offset:
													__lowerCAmelCase				    =			image - (scale / 2)
										return rescale(__a    ,   scale=__a    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     self    ,   __a    ,   __a    ,   __a    ,   __a = None    ,   **__a    ,   ):
										return normalize(__a    ,   mean=__a    ,   std=__a    ,   data_format=__a    ,   **__a	)

							def 			snake_case				(     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    ,   ):
										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_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."	)

										if offset and not do_rescale:
													raise ValueError("For offset, do_rescale must also be set to True."	)

										# All transformations expect numpy arrays.
										__lowerCAmelCase				    =			to_numpy_array(__a	)

										if do_resize:
													__lowerCAmelCase				    =			self.resize(image=__a    ,   size=__a    ,   resample=__a	)

										if do_center_crop:
													__lowerCAmelCase				    =			self.center_crop(__a    ,   size=__a	)

										if do_rescale:
													__lowerCAmelCase				    =			self.rescale(image=__a    ,   scale=__a    ,   offset=__a	)

										if do_normalize:
													__lowerCAmelCase				    =			self.normalize(image=__a    ,   mean=__a    ,   std=__a	)

										__lowerCAmelCase				    =			to_channel_dimension_format(__a    ,   __a	)
										return image



							def 			snake_case				(     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 = None    ,   __a = ChannelDimension.FIRST    ,   **__a    ,   ):
										__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_center_crop if do_center_crop is not None else self.do_center_crop
										__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				    =			offset if offset is not None else self.offset
										__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				    =			size if size is not None else self.size
										__lowerCAmelCase				    =			get_size_dict(__a    ,   default_to_square=__a	)
										__lowerCAmelCase				    =			crop_size if crop_size is not None else self.crop_size
										__lowerCAmelCase				    =			get_size_dict(__a    ,   param_name="crop_size"	)

										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."	)

										__lowerCAmelCase				    =			make_batched(__a	)

										__lowerCAmelCase				    =			[
										    [
										        self._preprocess_image(
										            image=__a    ,   do_resize=__a    ,   size=__a    ,   resample=__a    ,   do_center_crop=__a    ,   crop_size=__a    ,   do_rescale=__a    ,   rescale_factor=__a    ,   offset=__a    ,   do_normalize=__a    ,   image_mean=__a    ,   image_std=__a    ,   data_format=__a    ,   )
										        for img in video
										    ]
										    for video in videos
										]

										__lowerCAmelCase				    =			{"pixel_values": videos}
										return BatchFeature(data=__a    ,   tensor_type=__a	)











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


UpperCAmelCase_     							= logging.get_logger(__name__)

UpperCAmelCase_     							= {
    '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     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							List[str]   		=		"mobilenet_v2"




   def __init__(    self,		__magic_name__=3,		__magic_name__=224,		__magic_name__=1.0,		__magic_name__=8,		__magic_name__=8,		__magic_name__=6,		__magic_name__=32,		__magic_name__=True,		__magic_name__=True,		__magic_name__="relu6",		__magic_name__=True,		__magic_name__=0.8,		__magic_name__=0.02,		__magic_name__=0.001,		__magic_name__=255,		**__magic_name__,		) ->      List[Any]:






       """simple docstring"""
       super().__init__(**__magic_name__							)

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

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






class     lowercase__       (	__lowerCamelCase				):

   '''simple docstring'''


   a						:							Union[str, Any]   		=		version.parse("1.11"				)




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






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




   @property
   def       UpperCamelCase__  (    self							) ->      Mapping[str, Mapping[int, str]]:






       """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       UpperCamelCase__  (    self							) ->      float:






       """simple docstring"""
       return 1E-4

