


'''simple docstring'''




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 (
    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,
    is_batched,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging


_a   :   Tuple												=   logging.get_logger(__name__)




class 			_lowercase (	__lowercase							):
   _SCREAMING_SNAKE_CASE  :  Optional[Any]          =	["pixel_values"]
   def __init__(						self       :      Union[str, Any]       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      Optional[Dict[str, int]] = None       ,   SCREAMING_SNAKE_CASE_       :      PILImageResampling = PILImageResampling.BICUBIC       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      Union[int, float] = 1 / 255       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, int] = None       ,   SCREAMING_SNAKE_CASE_       :      bool = True       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[float, List[float]]] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[float, List[float]]] = None       ,   **SCREAMING_SNAKE_CASE_       :      Dict       ,   ) ->							None:
        super().__init__(**SCREAMING_SNAKE_CASE_      )
        __snake_case           =						size if size is not None else {'height': 224, 'width': 224}
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_      )
        __snake_case           =						crop_size if crop_size is not None else {'height': 224, 'width': 224}
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_       ,   default_to_square=SCREAMING_SNAKE_CASE_       ,   param_name='crop_size'      )

        __snake_case           =						do_resize
        __snake_case           =						do_rescale
        __snake_case           =						do_normalize
        __snake_case           =						do_center_crop
        __snake_case           =						crop_size
        __snake_case           =						size
        __snake_case           =						resample
        __snake_case           =						rescale_factor
        __snake_case           =						image_mean if image_mean is not None else IMAGENET_DEFAULT_MEAN
        __snake_case           =						image_std if image_std is not None else IMAGENET_DEFAULT_STD
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      np.ndarray       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, int]       ,   SCREAMING_SNAKE_CASE_       :      PILImageResampling = PILImageResampling.BILINEAR       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, ChannelDimension]] = None       ,   **SCREAMING_SNAKE_CASE_       :      Any       ,   ) ->							np.ndarray:
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_      )

        if "shortest_edge" in size:
             __snake_case           =						get_resize_output_image_size(SCREAMING_SNAKE_CASE_       ,   size=size['shortest_edge']       ,   default_to_square=SCREAMING_SNAKE_CASE_      )
             # size = get_resize_output_image_size(image, size["shortest_edge"], size["longest_edge"])
        elif "height" in size and "width" in size:
             __snake_case           =						(size['height'], size['width'])
        else:
             raise ValueError(f'Size must contain \'height\' and \'width\' keys or \'shortest_edge\' key. Got {size.keys()}'      )
        return resize(SCREAMING_SNAKE_CASE_       ,   size=SCREAMING_SNAKE_CASE_       ,   resample=SCREAMING_SNAKE_CASE_       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      List[str]       ,   SCREAMING_SNAKE_CASE_       :      np.ndarray       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, int]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, ChannelDimension]] = None       ,   **SCREAMING_SNAKE_CASE_       :      Optional[int]       ,   ) ->							np.ndarray:
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_      )
        if "height" not in size or "width" not in size:
             raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}'      )
        return center_crop(SCREAMING_SNAKE_CASE_       ,   size=(size['height'], size['width'])       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      np.ndarray       ,   SCREAMING_SNAKE_CASE_       :      float       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, ChannelDimension]] = None       ,   **SCREAMING_SNAKE_CASE_       :      Union[str, Any]      ) ->							np.ndarray:
        return rescale(SCREAMING_SNAKE_CASE_       ,   scale=SCREAMING_SNAKE_CASE_       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )
   def a  (						self       :      Optional[int]       ,   SCREAMING_SNAKE_CASE_       :      np.ndarray       ,   SCREAMING_SNAKE_CASE_       :      Union[float, List[float]]       ,   SCREAMING_SNAKE_CASE_       :      Union[float, List[float]]       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, ChannelDimension]] = None       ,   **SCREAMING_SNAKE_CASE_       :      List[Any]       ,   ) ->							np.ndarray:
        return normalize(SCREAMING_SNAKE_CASE_       ,   mean=SCREAMING_SNAKE_CASE_       ,   std=SCREAMING_SNAKE_CASE_       ,   data_format=SCREAMING_SNAKE_CASE_       ,   **SCREAMING_SNAKE_CASE_      )





   def a  (						self       :      Optional[Any]       ,   SCREAMING_SNAKE_CASE_       :      ImageInput       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = None       ,   SCREAMING_SNAKE_CASE_       :      Dict[str, int] = None       ,   SCREAMING_SNAKE_CASE_       :      PILImageResampling = None       ,   SCREAMING_SNAKE_CASE_       :      bool = None       ,   SCREAMING_SNAKE_CASE_       :      int = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[float] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[bool] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[float, List[float]]] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[float, List[float]]] = None       ,   SCREAMING_SNAKE_CASE_       :      Optional[Union[str, TensorType]] = None       ,   SCREAMING_SNAKE_CASE_       :      Union[str, ChannelDimension] = ChannelDimension.FIRST       ,   **SCREAMING_SNAKE_CASE_       :      str       ,   ) ->							BatchFeature:
        __snake_case           =						do_resize if do_resize is not None else self.do_resize
        __snake_case           =						do_rescale if do_rescale is not None else self.do_rescale
        __snake_case           =						do_normalize if do_normalize is not None else self.do_normalize
        __snake_case           =						do_center_crop if do_center_crop is not None else self.do_center_crop
        __snake_case           =						crop_size if crop_size is not None else self.crop_size
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_       ,   param_name='crop_size'       ,   default_to_square=SCREAMING_SNAKE_CASE_      )
        __snake_case           =						resample if resample is not None else self.resample
        __snake_case           =						rescale_factor if rescale_factor is not None else self.rescale_factor
        __snake_case           =						image_mean if image_mean is not None else self.image_mean
        __snake_case           =						image_std if image_std is not None else self.image_std

        __snake_case           =						size if size is not None else self.size
        __snake_case           =						get_size_dict(SCREAMING_SNAKE_CASE_      )

        if not is_batched(SCREAMING_SNAKE_CASE_      ):
             __snake_case           =						[images]

        if not valid_images(SCREAMING_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_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.'      )

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

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

        if do_center_crop:
             __snake_case           =						[self.center_crop(image=SCREAMING_SNAKE_CASE_       ,   size=SCREAMING_SNAKE_CASE_      ) for image in images]

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

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

        __snake_case           =						[to_channel_dimension_format(SCREAMING_SNAKE_CASE_       ,   SCREAMING_SNAKE_CASE_      ) for image in images]

        __snake_case           =						{'pixel_values': images}
        return BatchFeature(data=SCREAMING_SNAKE_CASE_       ,   tensor_type=SCREAMING_SNAKE_CASE_      )








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


__a	 =      logging.get_logger(__name__)

__a	 =      {
    """google/mobilenet_v1_1.0_224""": """https://huggingface.co/google/mobilenet_v1_1.0_224/resolve/main/config.json""",
    """google/mobilenet_v1_0.75_192""": """https://huggingface.co/google/mobilenet_v1_0.75_192/resolve/main/config.json""",
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
}






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							'''mobilenet_v1'''





      def __init__(		self    :       Any       ,							__lowerCamelCase    :       Union[str, Any]=3       ,							__lowerCamelCase    :       Dict=2_2_4       ,							__lowerCamelCase    :       Union[str, Any]=1.0       ,							__lowerCamelCase    :       Optional[int]=8       ,							__lowerCamelCase    :       Optional[int]="relu6"       ,							__lowerCamelCase    :       Optional[int]=True       ,							__lowerCamelCase    :       List[str]=0.999       ,							__lowerCamelCase    :       Optional[Any]=0.02       ,							__lowerCamelCase    :       str=0.001       ,							**__lowerCamelCase    :       Optional[Any]       ,							)     -> Optional[int]:






         """simple docstring"""







         super().__init__(**__lowerCamelCase							)

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

         UpperCAmelCase      					=  num_channels
         UpperCAmelCase      					=  image_size
         UpperCAmelCase      					=  depth_multiplier
         UpperCAmelCase      					=  min_depth
         UpperCAmelCase      					=  hidden_act
         UpperCAmelCase      					=  tf_padding
         UpperCAmelCase      					=  classifier_dropout_prob
         UpperCAmelCase      					=  initializer_range
         UpperCAmelCase      					=  layer_norm_eps






class 	__lowercase					(	__snake_case	):
      UpperCamelCase										=							version.parse('''1.11'''	)





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






         """simple docstring"""







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





      @property
      def 							_lowercase					(		self    :       Union[str, Any]							)     -> 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 							_lowercase					(		self    :       List[Any]							)     -> float:






         """simple docstring"""







         return 1e-4



