

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 normalize, rescale, resize, to_channel_dimension_format
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


__UpperCAmelCase     					=  logging.get_logger(__name__)





class A__     (			_lowercase			):



 """simple docstring"""







 _lowercase  :       Tuple  			=    ['''pixel_values''']

 def __init__(		self			:						Union[str, Any]  ,    A_			:						bool = True  ,    A_			:						Optional[Dict[str, int]] = None  ,    A_			:						PILImageResampling = PILImageResampling.BILINEAR  ,    A_			:						bool = True  ,    A_			:						Union[int, float] = 1 / 2_5_5  ,    A_			:						bool = True  ,    A_			:						Optional[Union[float, List[float]]] = None  ,    A_			:						Optional[Union[float, List[float]]] = None  ,    **A_			:						int  ,    ):






      '''simple docstring'''




      super().__init__(**snake_case__							)
      _lowerCAmelCase :       Any	   =     size if size is not None else {"""height""": 2_2_4, """width""": 2_2_4}
      _lowerCAmelCase :       int	   =     get_size_dict(snake_case__							)
      _lowerCAmelCase :       Union[str, Any]	   =     do_resize
      _lowerCAmelCase :       Optional[Any]	   =     do_rescale
      _lowerCAmelCase :       str	   =     do_normalize
      _lowerCAmelCase :       str	   =     size
      _lowerCAmelCase :       Tuple	   =     resample
      _lowerCAmelCase :       Tuple	   =     rescale_factor
      _lowerCAmelCase :       Any	   =     image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
      _lowerCAmelCase :       List[str]	   =     image_std if image_std is not None else IMAGENET_STANDARD_STD

 def  __magic_name__							(		self			:						Union[str, Any]  ,    A_			:						np.ndarray  ,    A_			:						Dict[str, int]  ,    A_			:						PILImageResampling = PILImageResampling.BILINEAR  ,    A_			:						Optional[Union[str, ChannelDimension]] = None  ,    **A_			:						Union[str, Any]  ,    ):






      '''simple docstring'''




      _lowerCAmelCase :       Any	   =     get_size_dict(snake_case__							)
      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()}'''							)
      return resize(
          snake_case__  ,    size=(size["height"], size["width"])  ,    resample=snake_case__  ,    data_format=snake_case__  ,    **snake_case__							)

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






      '''simple docstring'''




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

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






      '''simple docstring'''




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


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






      '''simple docstring'''




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

      _lowerCAmelCase :       str	   =     size if size is not None else self.size
      _lowerCAmelCase :       Optional[int]	   =     get_size_dict(snake_case__							)

      _lowerCAmelCase :       Optional[int]	   =     make_list_of_images(snake_case__							)

      if not valid_images(snake_case__							):
           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_rescale and rescale_factor is None:
           raise ValueError("Rescale factor must be specified if do_rescale is True."							)

      # All transformations expect numpy arrays.
      _lowerCAmelCase :       List[str]	   =     [to_numpy_array(snake_case__							) for image in images]

      if do_resize:
           _lowerCAmelCase :       Tuple	   =     [self.resize(image=snake_case__  ,    size=snake_case__  ,    resample=snake_case__							) for image in images]

      if do_rescale:
           _lowerCAmelCase :       Tuple	   =     [self.rescale(image=snake_case__  ,    scale=snake_case__							) for image in images]

      if do_normalize:
           _lowerCAmelCase :       Optional[Any]	   =     [self.normalize(image=snake_case__  ,    mean=snake_case__  ,    std=snake_case__							) for image in images]

      _lowerCAmelCase :       Tuple	   =     [to_channel_dimension_format(snake_case__  ,    snake_case__							) for image in images]

      _lowerCAmelCase :       Optional[Any]	   =     {"""pixel_values""": images}
      return BatchFeature(data=snake_case__  ,    tensor_type=snake_case__							)








"""simple docstring"""
from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


lowercase		:    Dict			=       {
    'configuration_mobilenet_v1': [
        'MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP',
        'MobileNetV1Config',
        'MobileNetV1OnnxConfig',
    ],
}

try:
			if not is_vision_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Optional[int]			=       ['MobileNetV1FeatureExtractor']
			lowercase		:    Union[str, Any]			=       ['MobileNetV1ImageProcessor']

try:
			if not is_torch_available():
						raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
			pass
else:
			lowercase		:    Tuple			=       [
			    'MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST',
			    'MobileNetV1ForImageClassification',
			    'MobileNetV1Model',
			    'MobileNetV1PreTrainedModel',
			    'load_tf_weights_in_mobilenet_v1',
			]


if TYPE_CHECKING:
			from .configuration_mobilenet_va import (
			    MOBILENET_V1_PRETRAINED_CONFIG_ARCHIVE_MAP,
			    MobileNetVaConfig,
			    MobileNetVaOnnxConfig,
			)

			try:
						if not is_vision_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .feature_extraction_mobilenet_va import MobileNetVaFeatureExtractor
						from .image_processing_mobilenet_va import MobileNetVaImageProcessor

			try:
						if not is_torch_available():
									raise OptionalDependencyNotAvailable()
			except OptionalDependencyNotAvailable:
						pass
			else:
						from .modeling_mobilenet_va import (
						    MOBILENET_V1_PRETRAINED_MODEL_ARCHIVE_LIST,
						    MobileNetVaForImageClassification,
						    MobileNetVaModel,
						    MobileNetVaPreTrainedModel,
						    load_tf_weights_in_mobilenet_va,
						)


else:
			import sys

			lowercase		:    Union[str, Any]			=       _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)
