



"""simple docstring"""




from typing import Optional, Union

import torch
from torch import nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithPoolingAndNoAttention, ImageClassifierOutputWithNoAttention
from ...modeling_utils import PreTrainedModel
from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging
from .configuration_mobilenet_va import MobileNetVaConfig


_lowerCAmelCase    : List[str]							=							logging.get_logger(__name__)


# General docstring
_lowerCAmelCase    : Optional[Any]							=							'MobileNetV1Config'

# Base docstring
_lowerCAmelCase    : List[Any]							=							'google/mobilenet_v1_1.0_224'
_lowerCAmelCase    : Dict							=							[1, 10_24, 7, 7]

# Image classification docstring
_lowerCAmelCase    : Dict							=							'google/mobilenet_v1_1.0_224'
_lowerCAmelCase    : Tuple							=							'tabby, tabby cat'


_lowerCAmelCase    : List[Any]							=							[
    'google/mobilenet_v1_1.0_224',
    'google/mobilenet_v1_0.75_192',
    # See all MobileNetV1 models at https://huggingface.co/models?filter=mobilenet_v1
]

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						Optional[Any]						,  SCREAMING_SNAKE_CASE__     :						Union[str, Any]						,  SCREAMING_SNAKE_CASE__     :						str=None    )  ->       str:





     '''simple docstring'''

     _UpperCAmelCase      :     int        =   {}

     if isinstance(a__						,  a__    ):
          _UpperCAmelCase      :     List[str]        =   model.mobilenet_va
     else:
          _UpperCAmelCase      :     List[Any]        =   model

     _UpperCAmelCase      :     Tuple        =   "MobilenetV1/Conv2d_0/"
     _UpperCAmelCase      :     int        =   backbone.conv_stem.convolution.weight
     _UpperCAmelCase      :     Optional[Any]        =   backbone.conv_stem.normalization.bias
     _UpperCAmelCase      :     Dict        =   backbone.conv_stem.normalization.weight
     _UpperCAmelCase      :     Optional[int]        =   backbone.conv_stem.normalization.running_mean
     _UpperCAmelCase      :     List[str]        =   backbone.conv_stem.normalization.running_var

     for i in range(13    ):
          _UpperCAmelCase      :     List[Any]        =   i + 1
          _UpperCAmelCase      :     int        =   i * 2

          _UpperCAmelCase      :     List[str]        =   backbone.layer[pt_index]
          _UpperCAmelCase      :     str        =   f'MobilenetV1/Conv2d_{tf_index}_depthwise/'
          _UpperCAmelCase      :     Optional[Any]        =   pointer.convolution.weight
          _UpperCAmelCase      :     Optional[int]        =   pointer.normalization.bias
          _UpperCAmelCase      :     Any        =   pointer.normalization.weight
          _UpperCAmelCase      :     List[str]        =   pointer.normalization.running_mean
          _UpperCAmelCase      :     List[Any]        =   pointer.normalization.running_var

          _UpperCAmelCase      :     Optional[int]        =   backbone.layer[pt_index + 1]
          _UpperCAmelCase      :     Dict        =   f'MobilenetV1/Conv2d_{tf_index}_pointwise/'
          _UpperCAmelCase      :     Tuple        =   pointer.convolution.weight
          _UpperCAmelCase      :     Tuple        =   pointer.normalization.bias
          _UpperCAmelCase      :     Optional[Any]        =   pointer.normalization.weight
          _UpperCAmelCase      :     Tuple        =   pointer.normalization.running_mean
          _UpperCAmelCase      :     List[str]        =   pointer.normalization.running_var

     if isinstance(a__						,  a__    ):
          _UpperCAmelCase      :     Union[str, Any]        =   "MobilenetV1/Logits/Conv2d_1c_1x1/"
          _UpperCAmelCase      :     Tuple        =   model.classifier.weight
          _UpperCAmelCase      :     List[Any]        =   model.classifier.bias

     return tf_to_pt_map

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						List[str]						,  SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Tuple    )  ->       List[str]:





     '''simple docstring'''

     try:
          import numpy as np
          import tensorflow as tf
     except ImportError:
          logger.error(
              "Loading a TensorFlow models in PyTorch, requires TensorFlow to be installed. Please see "
              "https://www.tensorflow.org/install/ for installation instructions."    )
          raise

     # Load weights from TF model
     _UpperCAmelCase      :     List[Any]        =   tf.train.list_variables(a__    )
     _UpperCAmelCase      :     Optional[Any]        =   {}
     for name, shape in init_vars:
          logger.info(f'Loading TF weight {name} with shape {shape}'    )
          _UpperCAmelCase      :     List[Any]        =   tf.train.load_variable(a__						,  a__    )
          _UpperCAmelCase      :     str        =   array

     # Build TF to PyTorch weights loading map
     _UpperCAmelCase      :     Any        =   _build_tf_to_pytorch_map(a__						,  a__						,  a__    )

     for name, pointer in tf_to_pt_map.items():
          logger.info(f'Importing {name}'    )
          if name not in tf_weights:
               logger.info(f'{name} not in tf pre-trained weights, skipping'    )
               continue

          _UpperCAmelCase      :     Dict        =   tf_weights[name]

          if "depthwise_weights" in name:
               logger.info("Transposing depthwise"    )
               _UpperCAmelCase      :     Optional[int]        =   np.transpose(a__						,  (2, 3, 0, 1)    )
          elif "weights" in name:
               logger.info("Transposing"    )
               if len(pointer.shape    ) == 2:  # copying into linear layer
                    _UpperCAmelCase      :     Any        =   array.squeeze().transpose()
               else:
                    _UpperCAmelCase      :     str        =   np.transpose(a__						,  (3, 2, 0, 1)    )

          if pointer.shape != array.shape:
               raise ValueError(f'Pointer shape {pointer.shape} and array shape {array.shape} mismatched'    )

          logger.info(f'Initialize PyTorch weight {name} {array.shape}'    )
          _UpperCAmelCase      :     List[str]        =   torch.from_numpy(a__    )

          tf_weights.pop(a__						,  a__    )
          tf_weights.pop(name + "/RMSProp"						,  a__    )
          tf_weights.pop(name + "/RMSProp_1"						,  a__    )
          tf_weights.pop(name + "/ExponentialMovingAverage"						,  a__    )

     logger.info(f'Weights not copied to PyTorch model: {", ".join(tf_weights.keys()    )}'    )
     return model

def 						__snake_case						(				SCREAMING_SNAKE_CASE__     :						int						,  SCREAMING_SNAKE_CASE__     :						Dict    )  ->       torch.Tensor:





     '''simple docstring'''

     _UpperCAmelCase    ,	_UpperCAmelCase      :     Optional[Any]        =   features.shape[-2:]
     _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   conv_layer.stride
     _UpperCAmelCase    ,	_UpperCAmelCase      :     Tuple        =   conv_layer.kernel_size

     if in_height % stride_height == 0:
          _UpperCAmelCase      :     str        =   max(kernel_height - stride_height						,  0    )
     else:
          _UpperCAmelCase      :     Union[str, Any]        =   max(kernel_height - (in_height % stride_height)						,  0    )

     if in_width % stride_width == 0:
          _UpperCAmelCase      :     str        =   max(kernel_width - stride_width						,  0    )
     else:
          _UpperCAmelCase      :     Union[str, Any]        =   max(kernel_width - (in_width % stride_width)						,  0    )

     _UpperCAmelCase      :     int        =   pad_along_width // 2
     _UpperCAmelCase      :     List[Any]        =   pad_along_width - pad_left
     _UpperCAmelCase      :     List[str]        =   pad_along_height // 2
     _UpperCAmelCase      :     Optional[Any]        =   pad_along_height - pad_top

     _UpperCAmelCase      :     Optional[int]        =   (pad_left, pad_right, pad_top, pad_bottom)
     return nn.functional.pad(a__						,  a__						,  "constant"						,  0.0    )






class      UpperCAmelCase_  (    nn.Module						):



      def __init__(					self					:     Union[str, Any]					,     A					:     int					,     A					:     Dict					,     A					:     str					,     A					:     Dict					,     A					:     List[Any] = 1					,     A					:     Any = 1					,     A					:     int = False					,     A					:     List[str] = True					,     A					:     List[str] = True					,     ):
           super().__init__()
           _UpperCAmelCase      :     Dict        =   config

           if in_channels % groups != 0:
                raise ValueError(f'Input channels ({in_channels}) are not divisible by {groups} groups.'						)
           if out_channels % groups != 0:
                raise ValueError(f'Output channels ({out_channels}) are not divisible by {groups} groups.'						)

           _UpperCAmelCase      :     Union[str, Any]        =   0 if config.tf_padding else int((kernel_size - 1) / 2						)

           _UpperCAmelCase      :     Optional[Any]        =   nn.Convad(
               in_channels=_snake_case					,     out_channels=_snake_case					,     kernel_size=_snake_case					,     stride=_snake_case					,     padding=_snake_case					,     groups=_snake_case					,     bias=_snake_case					,     padding_mode="zeros"					,     )

           if use_normalization:
                _UpperCAmelCase      :     Any        =   nn.BatchNormad(
                    num_features=_snake_case					,     eps=config.layer_norm_eps					,     momentum=0.9_997					,     affine=_snake_case					,     track_running_stats=_snake_case					,     )
           else:
                _UpperCAmelCase      :     Any        =   None

           if use_activation:
                if isinstance(_snake_case					,     _snake_case						):
                     _UpperCAmelCase      :     Any        =   ACTaFN[use_activation]
                elif isinstance(config.hidden_act					,     _snake_case						):
                     _UpperCAmelCase      :     List[str]        =   ACTaFN[config.hidden_act]
                else:
                     _UpperCAmelCase      :     Optional[Any]        =   config.hidden_act
           else:
                _UpperCAmelCase      :     List[str]        =   None



      def 						snake_case_ (					self					:     str					,     A					:     List[str]						):




           if self.config.tf_padding:
                _UpperCAmelCase      :     Dict        =   apply_tf_padding(_snake_case					,     self.convolution						)
           _UpperCAmelCase      :     Optional[Any]        =   self.convolution(_snake_case						)
           if self.normalization is not None:
                _UpperCAmelCase      :     Optional[int]        =   self.normalization(_snake_case						)
           if self.activation is not None:
                _UpperCAmelCase      :     List[Any]        =   self.activation(_snake_case						)
           return features







class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       List[Any]								=			MobileNetVaConfig
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			load_tf_weights_in_mobilenet_va
      __SCREAMING_SNAKE_CASE						:       List[str]								=			'mobilenet_v1'
      __SCREAMING_SNAKE_CASE						:       Dict								=			'pixel_values'
      __SCREAMING_SNAKE_CASE						:       Optional[int]								=			False



      def 						snake_case_ (					self					:     Union[str, Any]					,     A					:     Any						):




           if isinstance(_snake_case					,     (nn.Linear, nn.Convad)						):
                module.weight.data.normal_(mean=0.0					,     std=self.config.initializer_range						)
                if module.bias is not None:
                     module.bias.data.zero_()
           elif isinstance(_snake_case					,     nn.BatchNormad						):
                module.bias.data.zero_()
                module.weight.data.fill_(1.0						)







_lowerCAmelCase    : Dict							=							R'\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it\n    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n\n    Parameters:\n        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

_lowerCAmelCase    : Tuple							=							R'\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`MobileNetV1ImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'






@add_start_docstrings(
    'The bare MobileNetV1 model outputting raw hidden-states without any specific head on top.'	,      _UpperCamelCase	,      )
class      UpperCAmelCase_  (    _UpperCamelCase						):



      def __init__(					self					:     str					,     A					:     Dict					,     A					:     Optional[int] = True						):
           super().__init__(_snake_case						)
           _UpperCAmelCase      :     Dict        =   config

           _UpperCAmelCase      :     List[Any]        =   3_2
           _UpperCAmelCase      :     Any        =   max(int(depth * config.depth_multiplier						)					,     config.min_depth						)

           _UpperCAmelCase      :     Dict        =   MobileNetVaConvLayer(
               _snake_case					,     in_channels=config.num_channels					,     out_channels=_snake_case					,     kernel_size=3					,     stride=2					,     )

           _UpperCAmelCase      :     Union[str, Any]        =   [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

           _UpperCAmelCase      :     Dict        =   nn.ModuleList()
           for i in range(1_3						):
                _UpperCAmelCase      :     Optional[int]        =   out_channels

                if strides[i] == 2 or i == 0:
                     depth *= 2
                     _UpperCAmelCase      :     Optional[Any]        =   max(int(depth * config.depth_multiplier						)					,     config.min_depth						)

                self.layer.append(
                    MobileNetVaConvLayer(
                        _snake_case					,     in_channels=_snake_case					,     out_channels=_snake_case					,     kernel_size=3					,     stride=strides[i]					,     groups=_snake_case					,     )						)

                self.layer.append(
                    MobileNetVaConvLayer(
                        _snake_case					,     in_channels=_snake_case					,     out_channels=_snake_case					,     kernel_size=1					,     )						)

           _UpperCAmelCase      :     Union[str, Any]        =   nn.AdaptiveAvgPoolad((1, 1)						) if add_pooling_layer else None

           # Initialize weights and apply final processing
           self.post_init()



      def 						snake_case_ (					self					:     List[str]					,     A					:     Optional[Any]						):
           raise NotImplementedError



      @add_start_docstrings_to_model_forward(_snake_case						)
      @add_code_sample_docstrings(
          checkpoint=_CHECKPOINT_FOR_DOC					,     output_type=_snake_case					,     config_class=_CONFIG_FOR_DOC					,     modality="vision"					,     expected_output=_EXPECTED_OUTPUT_SHAPE					,     )
      def 						snake_case_ (					self					:     str					,     A					:     Union[str, Any] = None					,     A					:     List[Any] = None					,     A					:     Optional[Any] = None					,     ):
           _UpperCAmelCase      :     List[str]        =   (
               output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
           )
           _UpperCAmelCase      :     List[str]        =   return_dict if return_dict is not None else self.config.use_return_dict

           if pixel_values is None:
                raise ValueError("You have to specify pixel_values"						)

           _UpperCAmelCase      :     List[str]        =   self.conv_stem(_snake_case						)

           _UpperCAmelCase      :     str        =   () if output_hidden_states else None

           for i, layer_module in enumerate(self.layer						):
                _UpperCAmelCase      :     Optional[Any]        =   layer_module(_snake_case						)

                if output_hidden_states:
                     _UpperCAmelCase      :     List[str]        =   all_hidden_states + (hidden_states,)

           _UpperCAmelCase      :     Union[str, Any]        =   hidden_states

           if self.pooler is not None:
                _UpperCAmelCase      :     List[Any]        =   torch.flatten(self.pooler(_snake_case						)					,     start_dim=1						)
           else:
                _UpperCAmelCase      :     Optional[int]        =   None

           if not return_dict:
                return tuple(v for v in [last_hidden_state, pooled_output, all_hidden_states] if v is not None						)

           return BaseModelOutputWithPoolingAndNoAttention(
               last_hidden_state=_snake_case					,     pooler_output=_snake_case					,     hidden_states=_snake_case					,     )







@add_start_docstrings(
    '\n    MobileNetV1 model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '	,      _UpperCamelCase	,      )
class      UpperCAmelCase_  (    _UpperCamelCase						):



      def __init__(					self					:     List[str]					,     A					:     Any						):
           super().__init__(_snake_case						)

           _UpperCAmelCase      :     Union[str, Any]        =   config.num_labels
           _UpperCAmelCase      :     str        =   MobileNetVaModel(_snake_case						)

           _UpperCAmelCase      :     int        =   self.mobilenet_va.layer[-1].convolution.out_channels

           # Classifier head
           _UpperCAmelCase      :     Optional[int]        =   nn.Dropout(config.classifier_dropout_prob					,     inplace=_snake_case						)
           _UpperCAmelCase      :     Optional[int]        =   nn.Linear(_snake_case					,     config.num_labels						) if config.num_labels > 0 else nn.Identity()

           # Initialize weights and apply final processing
           self.post_init()



      @add_start_docstrings_to_model_forward(_snake_case						)
      @add_code_sample_docstrings(
          checkpoint=_IMAGE_CLASS_CHECKPOINT					,     output_type=_snake_case					,     config_class=_CONFIG_FOR_DOC					,     expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT					,     )
      def 						snake_case_ (					self					:     List[Any]					,     A					:     List[str] = None					,     A					:     Union[str, Any] = None					,     A					:     Optional[Any] = None					,     A					:     int = None					,     ):
           _UpperCAmelCase      :     Tuple        =   return_dict if return_dict is not None else self.config.use_return_dict

           _UpperCAmelCase      :     int        =   self.mobilenet_va(_snake_case					,     output_hidden_states=_snake_case					,     return_dict=_snake_case						)

           _UpperCAmelCase      :     Optional[Any]        =   outputs.pooler_output if return_dict else outputs[1]

           _UpperCAmelCase      :     Tuple        =   self.classifier(self.dropout(_snake_case						)						)

           _UpperCAmelCase      :     Union[str, Any]        =   None
           if labels is not None:
                if self.config.problem_type is None:
                     if self.num_labels == 1:
                          _UpperCAmelCase      :     Tuple        =   "regression"
                     elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                          _UpperCAmelCase      :     Union[str, Any]        =   "single_label_classification"
                     else:
                          _UpperCAmelCase      :     List[Any]        =   "multi_label_classification"

                if self.config.problem_type == "regression":
                     _UpperCAmelCase      :     int        =   MSELoss()
                     if self.num_labels == 1:
                          _UpperCAmelCase      :     Dict        =   loss_fct(logits.squeeze()					,     labels.squeeze()						)
                     else:
                          _UpperCAmelCase      :     Optional[int]        =   loss_fct(_snake_case					,     _snake_case						)
                elif self.config.problem_type == "single_label_classification":
                     _UpperCAmelCase      :     Optional[Any]        =   CrossEntropyLoss()
                     _UpperCAmelCase      :     Optional[int]        =   loss_fct(logits.view(-1					,     self.num_labels						)					,     labels.view(-1						)						)
                elif self.config.problem_type == "multi_label_classification":
                     _UpperCAmelCase      :     Tuple        =   BCEWithLogitsLoss()
                     _UpperCAmelCase      :     List[str]        =   loss_fct(_snake_case					,     _snake_case						)

           if not return_dict:
                _UpperCAmelCase      :     Any        =   (logits,) + outputs[2:]
                return ((loss,) + output) if loss is not None else output

           return ImageClassifierOutputWithNoAttention(
               loss=_snake_case					,     logits=_snake_case					,     hidden_states=outputs.hidden_states					,     )










"""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


_lowerCAmelCase    : Tuple							=							logging.get_logger(__name__)






class      UpperCAmelCase_  (    _UpperCamelCase						):
      __SCREAMING_SNAKE_CASE						:       str								=			['pixel_values']



      def __init__(					self					:     List[Any]					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     bool = True					,     A					:     Dict[str, int] = None					,     A					:     bool = True					,     A					:     Union[int, float] = 1 / 2_5_5					,     A					:     bool = True					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_MEAN					,     A					:     Optional[Union[float, Iterable[float]]] = IMAGENET_DEFAULT_STD					,     **A					:     List[Any]					,     ):
           super().__init__(**A						)
           _UpperCAmelCase      :     int        =   size if size is not None else {"shortest_edge": 2_2_4}
           _UpperCAmelCase      :     Tuple        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     int        =   crop_size if crop_size is not None else {"height": 2_2_4, "width": 2_2_4}
           _UpperCAmelCase      :     Union[str, Any]        =   get_size_dict(A					,     param_name="crop_size"						)

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



      def 						snake_case_ (					self					:     Optional[int]					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     PILImageResampling = PILImageResampling.BICUBIC					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Union[str, Any]					,     ):
           _UpperCAmelCase      :     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:
                _UpperCAmelCase      :     Any        =   int((2_5_6 / 2_2_4) * size["shortest_edge"]						)
                _UpperCAmelCase      :     Union[str, Any]        =   get_resize_output_image_size(A					,     size=A					,     default_to_square=A						)
                _UpperCAmelCase      :     str        =   {"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 						snake_case_ (					self					:     int					,     A					:     np.ndarray					,     A					:     Dict[str, int]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Optional[Any]					,     ):
           _UpperCAmelCase      :     Dict        =   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 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[int, float]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     Dict					,     ):
           return rescale(A					,     scale=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     List[str]					,     A					:     np.ndarray					,     A					:     Union[float, List[float]]					,     A					:     Union[float, List[float]]					,     A					:     Optional[Union[str, ChannelDimension]] = None					,     **A					:     str					,     ):
           return normalize(A					,     mean=A					,     std=A					,     data_format=A					,     **A						)



      def 						snake_case_ (					self					:     Tuple					,     A					:     ImageInput					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     PILImageResampling = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Dict[str, int]] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[float] = None					,     A					:     Optional[bool] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[Union[float, Iterable[float]]] = None					,     A					:     Optional[TensorType] = None					,     A					:     ChannelDimension = ChannelDimension.FIRST					,     **A					:     Dict					,     ):
           _UpperCAmelCase      :     Dict        =   do_resize if do_resize is not None else self.do_resize
           _UpperCAmelCase      :     List[str]        =   resample if resample is not None else self.resample
           _UpperCAmelCase      :     int        =   do_center_crop if do_center_crop is not None else self.do_center_crop
           _UpperCAmelCase      :     str        =   do_rescale if do_rescale is not None else self.do_rescale
           _UpperCAmelCase      :     Tuple        =   rescale_factor if rescale_factor is not None else self.rescale_factor
           _UpperCAmelCase      :     Optional[int]        =   do_normalize if do_normalize is not None else self.do_normalize
           _UpperCAmelCase      :     List[str]        =   image_mean if image_mean is not None else self.image_mean
           _UpperCAmelCase      :     Tuple        =   image_std if image_std is not None else self.image_std

           _UpperCAmelCase      :     Any        =   size if size is not None else self.size
           _UpperCAmelCase      :     Optional[int]        =   get_size_dict(A					,     default_to_square=A						)
           _UpperCAmelCase      :     List[str]        =   crop_size if crop_size is not None else self.crop_size
           _UpperCAmelCase      :     List[str]        =   get_size_dict(A					,     param_name="crop_size"						)

           _UpperCAmelCase      :     List[Any]        =   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.
           _UpperCAmelCase      :     Any        =   [to_numpy_array(A						) for image in images]

           if do_resize:
                _UpperCAmelCase      :     str        =   [self.resize(A					,     A					,     A						) for image in images]

           if do_center_crop:
                _UpperCAmelCase      :     Dict        =   [self.center_crop(A					,     A						) for image in images]

           if do_rescale:
                _UpperCAmelCase      :     Dict        =   [self.rescale(A					,     A						) for image in images]

           if do_normalize:
                _UpperCAmelCase      :     Optional[int]        =   [self.normalize(A					,     A					,     A						) for image in images]

           _UpperCAmelCase      :     Union[str, Any]        =   [to_channel_dimension_format(A					,     A						) for image in images]

           _UpperCAmelCase      :     List[Any]        =   {"pixel_values": images}
           return BatchFeature(data=A					,     tensor_type=A						)
