
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


lowercase        =  logging.get_logger(__name__)


# General docstring
lowercase        =  '''MobileNetV1Config'''

# Base docstring
lowercase        =  '''google/mobilenet_v1_1.0_224'''
lowercase        =  [1, 1_0_2_4, 7, 7]

# Image classification docstring
lowercase        =  '''google/mobilenet_v1_1.0_224'''
lowercase        =  '''tabby, tabby cat'''


lowercase        =  [
    '''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      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      List[str]			,				UpperCAmelCase__				:      str=None       )  ->    str:
							lowerCamelCase_							  =					{}

							if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					model.mobilenet_va
							else:
														lowerCamelCase_							  =					model

							lowerCamelCase_							  =					"""MobilenetV1/Conv2d_0/"""
							lowerCamelCase_							  =					backbone.conv_stem.convolution.weight
							lowerCamelCase_							  =					backbone.conv_stem.normalization.bias
							lowerCamelCase_							  =					backbone.conv_stem.normalization.weight
							lowerCamelCase_							  =					backbone.conv_stem.normalization.running_mean
							lowerCamelCase_							  =					backbone.conv_stem.normalization.running_var

							for i in range(1_3       ):
														lowerCamelCase_							  =					i + 1
														lowerCamelCase_							  =					i * 2

														lowerCamelCase_							  =					backbone.layer[pt_index]
														lowerCamelCase_							  =					F'''MobilenetV1/Conv2d_{tf_index}_depthwise/'''
														lowerCamelCase_							  =					pointer.convolution.weight
														lowerCamelCase_							  =					pointer.normalization.bias
														lowerCamelCase_							  =					pointer.normalization.weight
														lowerCamelCase_							  =					pointer.normalization.running_mean
														lowerCamelCase_							  =					pointer.normalization.running_var

														lowerCamelCase_							  =					backbone.layer[pt_index + 1]
														lowerCamelCase_							  =					F'''MobilenetV1/Conv2d_{tf_index}_pointwise/'''
														lowerCamelCase_							  =					pointer.convolution.weight
														lowerCamelCase_							  =					pointer.normalization.bias
														lowerCamelCase_							  =					pointer.normalization.weight
														lowerCamelCase_							  =					pointer.normalization.running_mean
														lowerCamelCase_							  =					pointer.normalization.running_var

							if isinstance(UpperCAmelCase__			,				UpperCAmelCase__       ):
														lowerCamelCase_							  =					"""MobilenetV1/Logits/Conv2d_1c_1x1/"""
														lowerCamelCase_							  =					model.classifier.weight
														lowerCamelCase_							  =					model.classifier.bias

							return tf_to_pt_map



def      __lowerCAmelCase						(							UpperCAmelCase__				:      Dict			,				UpperCAmelCase__				:      Union[str, Any]			,				UpperCAmelCase__				:      Any       )  ->    List[str]:
							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
							lowerCamelCase_							  =					tf.train.list_variables(UpperCAmelCase__       )
							lowerCamelCase_							  =					{}
							for name, shape in init_vars:
														logger.info(F'''Loading TF weight {name} with shape {shape}'''       )
														lowerCamelCase_							  =					tf.train.load_variable(UpperCAmelCase__			,				UpperCAmelCase__       )
														lowerCamelCase_							  =					array

							# Build TF to PyTorch weights loading map
							lowerCamelCase_							  =					_build_tf_to_pytorch_map(UpperCAmelCase__			,				UpperCAmelCase__			,				UpperCAmelCase__       )

							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

														lowerCamelCase_							  =					tf_weights[name]

														if "depthwise_weights" in name:
																					logger.info("""Transposing depthwise"""       )
																					lowerCamelCase_							  =					np.transpose(UpperCAmelCase__			,				(2, 3, 0, 1)       )
														elif "weights" in name:
																					logger.info("""Transposing"""       )
																					if len(pointer.shape       ) == 2:  # copying into linear layer
																												lowerCamelCase_							  =					array.squeeze().transpose()
																					else:
																												lowerCamelCase_							  =					np.transpose(UpperCAmelCase__			,				(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}'''       )
														lowerCamelCase_							  =					torch.from_numpy(UpperCAmelCase__       )

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

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



def      __lowerCAmelCase						(							UpperCAmelCase__				:      torch.Tensor			,				UpperCAmelCase__				:      nn.Convad       )  ->    torch.Tensor:
							lowerCamelCase_  ,   lowerCamelCase_							  =					features.shape[-2:]
							lowerCamelCase_  ,   lowerCamelCase_							  =					conv_layer.stride
							lowerCamelCase_  ,   lowerCamelCase_							  =					conv_layer.kernel_size

							if in_height % stride_height == 0:
														lowerCamelCase_							  =					max(kernel_height - stride_height			,				0       )
							else:
														lowerCamelCase_							  =					max(kernel_height - (in_height % stride_height)			,				0       )

							if in_width % stride_width == 0:
														lowerCamelCase_							  =					max(kernel_width - stride_width			,				0       )
							else:
														lowerCamelCase_							  =					max(kernel_width - (in_width % stride_width)			,				0       )

							lowerCamelCase_							  =					pad_along_width // 2
							lowerCamelCase_							  =					pad_along_width - pad_left
							lowerCamelCase_							  =					pad_along_height // 2
							lowerCamelCase_							  =					pad_along_height - pad_top

							lowerCamelCase_							  =					(pad_left, pad_right, pad_top, pad_bottom)
							return nn.functional.pad(UpperCAmelCase__			,				UpperCAmelCase__			,				"""constant"""			,				0.0       )





class       __A(				nn.Module       ):

					def __init__(	self					:     str   ,					__UpperCamelCase					:     MobileNetVaConfig   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     int   ,					__UpperCamelCase					:     Optional[int] = 1   ,					__UpperCamelCase					:     Optional[int] = 1   ,					__UpperCamelCase					:     bool = False   ,					__UpperCamelCase					:     Optional[bool] = True   ,					__UpperCamelCase					:     Optional[bool or str] = True   ,					):
												super().__init__()
												lowerCamelCase_							  =					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.'''   )

												lowerCamelCase_							  =					0 if config.tf_padding else int((kernel_size - 1) / 2   )

												lowerCamelCase_							  =					nn.Convad(
												    in_channels=__UpperCamelCase   ,					out_channels=__UpperCamelCase   ,					kernel_size=__UpperCamelCase   ,					stride=__UpperCamelCase   ,					padding=__UpperCamelCase   ,					groups=__UpperCamelCase   ,					bias=__UpperCamelCase   ,					padding_mode="""zeros"""   ,					)

												if use_normalization:
																			lowerCamelCase_							  =					nn.BatchNormad(
																			    num_features=__UpperCamelCase   ,					eps=config.layer_norm_eps   ,					momentum=0.9997   ,					affine=__UpperCamelCase   ,					track_running_stats=__UpperCamelCase   ,					)
												else:
																			lowerCamelCase_							  =					None

												if use_activation:
																			if isinstance(__UpperCamelCase   ,					__UpperCamelCase   ):
																										lowerCamelCase_							  =					ACTaFN[use_activation]
																			elif isinstance(config.hidden_act   ,					__UpperCamelCase   ):
																										lowerCamelCase_							  =					ACTaFN[config.hidden_act]
																			else:
																										lowerCamelCase_							  =					config.hidden_act
												else:
																			lowerCamelCase_							  =					None



					def    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     torch.Tensor   ):
												if self.config.tf_padding:
																			lowerCamelCase_							  =					apply_tf_padding(__UpperCamelCase   ,					self.convolution   )
												lowerCamelCase_							  =					self.convolution(__UpperCamelCase   )
												if self.normalization is not None:
																			lowerCamelCase_							  =					self.normalization(__UpperCamelCase   )
												if self.activation is not None:
																			lowerCamelCase_							  =					self.activation(__UpperCamelCase   )
												return features





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    MobileNetVaConfig
					SCREAMING_SNAKE_CASE			    =    load_tf_weights_in_mobilenet_va
					SCREAMING_SNAKE_CASE			    =    '''mobilenet_v1'''
					SCREAMING_SNAKE_CASE			    =    '''pixel_values'''
					SCREAMING_SNAKE_CASE			    =    False

					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Union[nn.Linear, nn.Convad]   ):
												if isinstance(__UpperCamelCase   ,					(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(__UpperCamelCase   ,					nn.BatchNormad   ):
																			module.bias.data.zero_()
																			module.weight.data.fill_(1.0   )
lowercase        =  r'''
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it
    as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`MobileNetV1Config`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
'''

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





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

					def __init__(	self					:     str   ,					__UpperCamelCase					:     MobileNetVaConfig   ,					__UpperCamelCase					:     bool = True   ):
												super().__init__(__UpperCamelCase   )
												lowerCamelCase_							  =					config

												lowerCamelCase_							  =					3_2
												lowerCamelCase_							  =					max(int(depth * config.depth_multiplier   )   ,					config.min_depth   )

												lowerCamelCase_							  =					MobileNetVaConvLayer(
												    __UpperCamelCase   ,					in_channels=config.num_channels   ,					out_channels=__UpperCamelCase   ,					kernel_size=3   ,					stride=2   ,					)

												lowerCamelCase_							  =					[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

												lowerCamelCase_							  =					nn.ModuleList()
												for i in range(1_3   ):
																			lowerCamelCase_							  =					out_channels

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

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

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

												lowerCamelCase_							  =					nn.AdaptiveAvgPoolad((1, 1)   ) if add_pooling_layer else None

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

					def    lowercase__      (	self					:     Dict   ,					__UpperCamelCase					:     Tuple   ):
												raise NotImplementedError



					@add_start_docstrings_to_model_forward(__UpperCamelCase   )
					@add_code_sample_docstrings(
					    checkpoint=_CHECKPOINT_FOR_DOC   ,					output_type=__UpperCamelCase   ,					config_class=_CONFIG_FOR_DOC   ,					modality="""vision"""   ,					expected_output=_EXPECTED_OUTPUT_SHAPE   ,					)
					def    lowercase__      (	self					:     Optional[Any]   ,					__UpperCamelCase					:     Optional[torch.Tensor] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					):
												lowerCamelCase_							  =					(
												    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
												)
												lowerCamelCase_							  =					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"""   )

												lowerCamelCase_							  =					self.conv_stem(__UpperCamelCase   )

												lowerCamelCase_							  =					() if output_hidden_states else None

												for i, layer_module in enumerate(self.layer   ):
																			lowerCamelCase_							  =					layer_module(__UpperCamelCase   )

																			if output_hidden_states:
																										lowerCamelCase_							  =					all_hidden_states + (hidden_states,)

												lowerCamelCase_							  =					hidden_states

												if self.pooler is not None:
																			lowerCamelCase_							  =					torch.flatten(self.pooler(__UpperCamelCase   )   ,					start_dim=1   )
												else:
																			lowerCamelCase_							  =					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=__UpperCamelCase   ,					pooler_output=__UpperCamelCase   ,					hidden_states=__UpperCamelCase   ,					)





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

					def __init__(	self					:     Tuple   ,					__UpperCamelCase					:     MobileNetVaConfig   ):
												super().__init__(__UpperCamelCase   )

												lowerCamelCase_							  =					config.num_labels
												lowerCamelCase_							  =					MobileNetVaModel(__UpperCamelCase   )

												lowerCamelCase_							  =					self.mobilenet_va.layer[-1].convolution.out_channels

												# Classifier head
												lowerCamelCase_							  =					nn.Dropout(config.classifier_dropout_prob   ,					inplace=__UpperCamelCase   )
												lowerCamelCase_							  =					nn.Linear(__UpperCamelCase   ,					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(__UpperCamelCase   )
					@add_code_sample_docstrings(
					    checkpoint=_IMAGE_CLASS_CHECKPOINT   ,					output_type=__UpperCamelCase   ,					config_class=_CONFIG_FOR_DOC   ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT   ,					)
					def    lowercase__      (	self					:     Union[str, Any]   ,					__UpperCamelCase					:     Optional[torch.Tensor] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					__UpperCamelCase					:     Optional[torch.Tensor] = None   ,					__UpperCamelCase					:     Optional[bool] = None   ,					):
												lowerCamelCase_							  =					return_dict if return_dict is not None else self.config.use_return_dict

												lowerCamelCase_							  =					self.mobilenet_va(__UpperCamelCase   ,					output_hidden_states=__UpperCamelCase   ,					return_dict=__UpperCamelCase   )

												lowerCamelCase_							  =					outputs.pooler_output if return_dict else outputs[1]

												lowerCamelCase_							  =					self.classifier(self.dropout(__UpperCamelCase   )   )

												lowerCamelCase_							  =					None
												if labels is not None:
																			if self.config.problem_type is None:
																										if self.num_labels == 1:
																																	lowerCamelCase_							  =					"""regression"""
																										elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																																	lowerCamelCase_							  =					"""single_label_classification"""
																										else:
																																	lowerCamelCase_							  =					"""multi_label_classification"""

																			if self.config.problem_type == "regression":
																										lowerCamelCase_							  =					MSELoss()
																										if self.num_labels == 1:
																																	lowerCamelCase_							  =					loss_fct(logits.squeeze()   ,					labels.squeeze()   )
																										else:
																																	lowerCamelCase_							  =					loss_fct(__UpperCamelCase   ,					__UpperCamelCase   )
																			elif self.config.problem_type == "single_label_classification":
																										lowerCamelCase_							  =					CrossEntropyLoss()
																										lowerCamelCase_							  =					loss_fct(logits.view(-1   ,					self.num_labels   )   ,					labels.view(-1   )   )
																			elif self.config.problem_type == "multi_label_classification":
																										lowerCamelCase_							  =					BCEWithLogitsLoss()
																										lowerCamelCase_							  =					loss_fct(__UpperCamelCase   ,					__UpperCamelCase   )

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

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







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_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


if is_vision_available():
		import PIL


lowercase        =  logging.get_logger(__name__)





class       __A(				UpperCAmelCase       ):
					SCREAMING_SNAKE_CASE			    =    ['''pixel_values''']

					def __init__(	self					:     Any   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BILINEAR   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Union[int, float] = 1 / 2_5_5   ,					__UpperCamelCase					:     bool = True   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					**__UpperCamelCase					:     Optional[int]   ,					):
												super().__init__(**__UpperCamelCase   )
												lowerCamelCase_							  =					size if size is not None else {"""shortest_edge""": 3_8_4}
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					do_resize
												lowerCamelCase_							  =					size
												# Default value set here for backwards compatibility where the value in config is None
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else 2_2_4 / 2_5_6
												lowerCamelCase_							  =					resample
												lowerCamelCase_							  =					do_rescale
												lowerCamelCase_							  =					rescale_factor
												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    lowercase__      (	self					:     List[Any]   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Dict[str, int]   ,					__UpperCamelCase					:     float   ,					__UpperCamelCase					:     PILImageResampling = PILImageResampling.BICUBIC   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     Tuple   ,					):
												lowerCamelCase_							  =					get_size_dict(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
												if "shortest_edge" not in size:
																			raise ValueError(F'''Size dictionary must contain \'shortest_edge\' key. Got {size.keys()}'''   )
												lowerCamelCase_							  =					size["""shortest_edge"""]

												if shortest_edge < 3_8_4:
																			# maintain same ratio, resizing shortest edge to shortest_edge/crop_pct
																			lowerCamelCase_							  =					int(shortest_edge / crop_pct   )
																			lowerCamelCase_							  =					get_resize_output_image_size(__UpperCamelCase   ,					size=__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )
																			lowerCamelCase_							  =					resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
																			# then crop to (shortest_edge, shortest_edge)
																			return center_crop(image=__UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )
												else:
																			# warping (no cropping) when evaluated at 384 or larger
																			return resize(
																			    __UpperCamelCase   ,					size=(shortest_edge, shortest_edge)   ,					resample=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

					def    lowercase__      (	self					:     str   ,					__UpperCamelCase					:     np.ndarray   ,					__UpperCamelCase					:     Union[int, float]   ,					__UpperCamelCase					:     Optional[Union[str, ChannelDimension]] = None   ,					**__UpperCamelCase					:     int   ,					):
												return rescale(__UpperCamelCase   ,					scale=__UpperCamelCase   ,					data_format=__UpperCamelCase   ,					**__UpperCamelCase   )

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



					def    lowercase__      (	self					:     List[str]   ,					__UpperCamelCase					:     ImageInput   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Dict[str, int] = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     PILImageResampling = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     float = None   ,					__UpperCamelCase					:     bool = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[float, List[float]]] = None   ,					__UpperCamelCase					:     Optional[Union[str, TensorType]] = None   ,					__UpperCamelCase					:     ChannelDimension = ChannelDimension.FIRST   ,					**__UpperCamelCase					:     List[str]   ,					):
												lowerCamelCase_							  =					do_resize if do_resize is not None else self.do_resize
												lowerCamelCase_							  =					crop_pct if crop_pct is not None else self.crop_pct
												lowerCamelCase_							  =					resample if resample is not None else self.resample
												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_							  =					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(__UpperCamelCase   ,					default_to_square=__UpperCamelCase   )

												lowerCamelCase_							  =					make_list_of_images(__UpperCamelCase   )

												if not valid_images(__UpperCamelCase   ):
																			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 or resample is None:
																			raise ValueError("""Size and resample must be specified if do_resize is True."""   )

												if do_resize and size["shortest_edge"] < 3_8_4 and crop_pct is None:
																			raise ValueError("""crop_pct must be specified if size < 384."""   )

												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.
												lowerCamelCase_							  =					[to_numpy_array(__UpperCamelCase   ) for image in images]

												if do_resize:
																			lowerCamelCase_							  =					[self.resize(image=__UpperCamelCase   ,					size=__UpperCamelCase   ,					crop_pct=__UpperCamelCase   ,					resample=__UpperCamelCase   ) for image in images]

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

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

												lowerCamelCase_							  =					[to_channel_dimension_format(__UpperCamelCase   ,					__UpperCamelCase   ) for image in images]

												lowerCamelCase_							  =					{"""pixel_values""": images}
												return BatchFeature(data=__UpperCamelCase   ,					tensor_type=__UpperCamelCase   )





