






"""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


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


# General docstring
SCREAMING_SNAKE_CASE						:						Dict					   =       '''MobileNetV1Config'''

# Base docstring
SCREAMING_SNAKE_CASE						:						Dict					   =       '''google/mobilenet_v1_1.0_224'''
SCREAMING_SNAKE_CASE						:						Tuple					   =       [1, 1_024, 7, 7]

# Image classification docstring
SCREAMING_SNAKE_CASE						:						Optional[int]					   =       '''google/mobilenet_v1_1.0_224'''
SCREAMING_SNAKE_CASE						:						Union[str, Any]					   =       '''tabby, tabby cat'''


SCREAMING_SNAKE_CASE						:						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  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__=None				):
				A__							 =       {}

				if isinstance(lowerCAmelCase__       ,lowerCAmelCase__				):
								A__							 =       model.mobilenet_va
				else:
								A__							 =       model

				A__							 =       'MobilenetV1/Conv2d_0/'
				A__							 =       backbone.conv_stem.convolution.weight
				A__							 =       backbone.conv_stem.normalization.bias
				A__							 =       backbone.conv_stem.normalization.weight
				A__							 =       backbone.conv_stem.normalization.running_mean
				A__							 =       backbone.conv_stem.normalization.running_var

				for i in range(13				):
								A__							 =       i + 1
								A__							 =       i * 2

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

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

				if isinstance(lowerCAmelCase__       ,lowerCAmelCase__				):
								A__							 =       'MobilenetV1/Logits/Conv2d_1c_1x1/'
								A__							 =       model.classifier.weight
								A__							 =       model.classifier.bias

				return tf_to_pt_map



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				):
				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
				A__							 =       tf.train.list_variables(lowerCAmelCase__				)
				A__							 =       {}
				for name, shape in init_vars:
								logger.info(f'''Loading TF weight {name} with shape {shape}'''				)
								A__							 =       tf.train.load_variable(lowerCAmelCase__       ,lowerCAmelCase__				)
								A__							 =       array

				# Build TF to PyTorch weights loading map
				A__							 =       _build_tf_to_pytorch_map(lowerCAmelCase__       ,lowerCAmelCase__       ,lowerCAmelCase__				)

				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

								A__							 =       tf_weights[name]

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

								tf_weights.pop(lowerCAmelCase__       ,lowerCAmelCase__				)
								tf_weights.pop(name + '/RMSProp'       ,lowerCAmelCase__				)
								tf_weights.pop(name + '/RMSProp_1'       ,lowerCAmelCase__				)
								tf_weights.pop(name + '/ExponentialMovingAverage'       ,lowerCAmelCase__				)

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



def  __lowerCamelCase					(					lowerCAmelCase__       ,lowerCAmelCase__				):
				A__ ,			A__							 =       features.shape[-2:]
				A__ ,			A__							 =       conv_layer.stride
				A__ ,			A__							 =       conv_layer.kernel_size

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

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

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

				A__							 =       (pad_left, pad_right, pad_top, pad_bottom)
				return nn.functional.pad(lowerCAmelCase__       ,lowerCAmelCase__       ,'constant'       ,0.0				)





class       snake_case_       (    nn.Module       ):



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a  ,					__a  ,					__a  ,					__a = 1  ,					__a = 1  ,					__a = False  ,					__a = True  ,					__a = True  ,					):


					"""simple docstring"""

					super().__init__()
					A__							 =       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.'''						)

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

					A__							 =       nn.Convad(
					    in_channels=__a  ,					out_channels=__a  ,					kernel_size=__a  ,					stride=__a  ,					padding=__a  ,					groups=__a  ,					bias=__a  ,					padding_mode='zeros'  ,					)

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

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






	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					if self.config.tf_padding:
									A__							 =       apply_tf_padding(__a  ,					self.convolution						)
					A__							 =       self.convolution(__a						)
					if self.normalization is not None:
									A__							 =       self.normalization(__a						)
					if self.activation is not None:
									A__							 =       self.activation(__a						)
					return features





class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	SCREAMING_SNAKE_CASE_:							Tuple   			=					MobileNetVaConfig
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					load_tf_weights_in_mobilenet_va
	SCREAMING_SNAKE_CASE_:							Union[str, Any]   			=					"""mobilenet_v1"""
	SCREAMING_SNAKE_CASE_:							List[Any]   			=					"""pixel_values"""
	SCREAMING_SNAKE_CASE_:							Dict   			=					False


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					if isinstance(__a  ,					(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(__a  ,					nn.BatchNormad						):
									module.bias.data.zero_()
									module.weight.data.fill_(1.0						)





SCREAMING_SNAKE_CASE						:						Tuple					   =       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.
'''

SCREAMING_SNAKE_CASE						:						Optional[int]					   =       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."""    ,							_lowerCamelCase    ,							)
class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a  ,					__a = True						):


					"""simple docstring"""

					super().__init__(__a						)
					A__							 =       config

					A__							 =       32
					A__							 =       max(int(depth * config.depth_multiplier						)  ,					config.min_depth						)

					A__							 =       MobileNetVaConvLayer(
					    __a  ,					in_channels=config.num_channels  ,					out_channels=__a  ,					kernel_size=3  ,					stride=2  ,					)

					A__							 =       [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

					A__							 =       nn.ModuleList()
					for i in range(13						):
									A__							 =       out_channels

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

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

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

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

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


	def       _UpperCAmelCase						(      self  ,					__a						):


					"""simple docstring"""

					raise NotImplementedError






	@add_start_docstrings_to_model_forward(__a						)
	@add_code_sample_docstrings(
	    checkpoint=_CHECKPOINT_FOR_DOC  ,					output_type=__a  ,					config_class=_CONFIG_FOR_DOC  ,					modality='vision'  ,					expected_output=_EXPECTED_OUTPUT_SHAPE  ,					)
	def       _UpperCAmelCase						(      self  ,					__a = None  ,					__a = None  ,					__a = None  ,					):


					"""simple docstring"""

					A__							 =       (
					    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
					)
					A__							 =       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'						)

					A__							 =       self.conv_stem(__a						)

					A__							 =       () if output_hidden_states else None

					for i, layer_module in enumerate(self.layer						):
									A__							 =       layer_module(__a						)

									if output_hidden_states:
													A__							 =       all_hidden_states + (hidden_states,)

					A__							 =       hidden_states

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





@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.
    """    ,							_lowerCamelCase    ,							)
class       snake_case_       (    _lowerCamelCase       ):



	"""simple docstring"""

	def __init__(      self  ,					__a						):


					"""simple docstring"""

					super().__init__(__a						)

					A__							 =       config.num_labels
					A__							 =       MobileNetVaModel(__a						)

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

					# Classifier head
					A__							 =       nn.Dropout(config.classifier_dropout_prob  ,					inplace=__a						)
					A__							 =       nn.Linear(__a  ,					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(__a						)
	@add_code_sample_docstrings(
	    checkpoint=_IMAGE_CLASS_CHECKPOINT  ,					output_type=__a  ,					config_class=_CONFIG_FOR_DOC  ,					expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT  ,					)
	def       _UpperCAmelCase						(      self  ,					__a = None  ,					__a = None  ,					__a = None  ,					__a = None  ,					):


					"""simple docstring"""

					A__							 =       return_dict if return_dict is not None else self.config.use_return_dict

					A__							 =       self.mobilenet_va(__a  ,					output_hidden_states=__a  ,					return_dict=__a						)

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

					A__							 =       self.classifier(self.dropout(__a						)						)

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

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

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

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










"""simple docstring"""






import numpy as np



def  __lowerCamelCase					(					lowerCAmelCase__				):
				return 1 / (1 + np.exp(-vector				))



def  __lowerCamelCase					(					lowerCAmelCase__				):
				return vector * sigmoid(lowerCAmelCase__				)


if __name__ == "__main__":
					import doctest

					doctest.testmod()


