


"""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     =  logging.get_logger(__name__)


# General docstring
_lowerCamelCase     =  'MobileNetV1Config'

# Base docstring
_lowerCamelCase     =  'google/mobilenet_v1_1.0_224'
_lowerCamelCase     =  [1, 1024, 7, 7]

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


_lowerCamelCase     =  [
    '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 __lowercase     (      lowerCamelCase_  :      Any						,					lowerCamelCase_  :      List[Any]						,					lowerCamelCase_  :      Union[str, Any]=None  ):
				SCREAMING_SNAKE_CASE__     							=				{}

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

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

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

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

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

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

				return tf_to_pt_map


def __lowercase     (      lowerCamelCase_  :      List[str]						,					lowerCamelCase_  :      Tuple						,					lowerCamelCase_  :      int  ):
				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
				SCREAMING_SNAKE_CASE__     							=				tf.train.list_variables(lowerCamelCase_  )
				SCREAMING_SNAKE_CASE__     							=				{}
				for name, shape in init_vars:
								logger.info(F'''Loading TF weight {name} with shape {shape}'''  )
								SCREAMING_SNAKE_CASE__     							=				tf.train.load_variable(lowerCamelCase_						,					lowerCamelCase_  )
								SCREAMING_SNAKE_CASE__     							=				array

				# Build TF to PyTorch weights loading map
				SCREAMING_SNAKE_CASE__     							=				_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

								SCREAMING_SNAKE_CASE__     							=				tf_weights[name]

								if "depthwise_weights" in name:
												logger.info("Transposing depthwise"  )
												SCREAMING_SNAKE_CASE__     							=				np.transpose(lowerCamelCase_						,					(2, 3, 0, 1)  )
								elif "weights" in name:
												logger.info("Transposing"  )
												if len(pointer.shape  ) == 2:  # copying into linear layer
																SCREAMING_SNAKE_CASE__     							=				array.squeeze().transpose()
												else:
																SCREAMING_SNAKE_CASE__     							=				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}'''  )
								SCREAMING_SNAKE_CASE__     							=				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 __lowercase     (      lowerCamelCase_  :      torch.Tensor						,					lowerCamelCase_  :      nn.Convad  ):
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				features.shape[-2:]
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				conv_layer.stride
				SCREAMING_SNAKE_CASE__							,      SCREAMING_SNAKE_CASE__     							=				conv_layer.kernel_size

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

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

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

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


class 			lowerCamelCase_		(			nn.Module   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = 1       ,		UpperCAmelCase__ = 1       ,		UpperCAmelCase__ = False       ,		UpperCAmelCase__ = True       ,		UpperCAmelCase__ = True       ,		):
								super().__init__()
								SCREAMING_SNAKE_CASE__     							=				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.'''     )

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

								SCREAMING_SNAKE_CASE__     							=				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:
												SCREAMING_SNAKE_CASE__     							=				nn.BatchNormad(
												    num_features=UpperCAmelCase__       ,		eps=config.layer_norm_eps       ,		momentum=0.9_997       ,		affine=UpperCAmelCase__       ,		track_running_stats=UpperCAmelCase__       ,		)
								else:
												SCREAMING_SNAKE_CASE__     							=				None

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






				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								if self.config.tf_padding:
												SCREAMING_SNAKE_CASE__     							=				apply_tf_padding(UpperCAmelCase__       ,		self.convolution     )
								SCREAMING_SNAKE_CASE__     							=				self.convolution(UpperCAmelCase__     )
								if self.normalization is not None:
												SCREAMING_SNAKE_CASE__     							=				self.normalization(UpperCAmelCase__     )
								if self.activation is not None:
												SCREAMING_SNAKE_CASE__     							=				self.activation(UpperCAmelCase__     )
								return features


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      Any      							=					MobileNetVaConfig
				_lowerCAmelCase      :      Tuple      							=					load_tf_weights_in_mobilenet_va
				_lowerCAmelCase      :      int      							=					"mobilenet_v1"
				_lowerCAmelCase      :      Any      							=					"pixel_values"
				_lowerCAmelCase      :      Any      							=					False



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								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     )


_lowerCamelCase     =  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     =  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."       ,  lowercase       ,  )
class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__       ,		UpperCAmelCase__ = True     ):
								super().__init__(UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				config

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

								SCREAMING_SNAKE_CASE__     							=				MobileNetVaConvLayer(
								    UpperCAmelCase__       ,		in_channels=config.num_channels       ,		out_channels=UpperCAmelCase__       ,		kernel_size=3       ,		stride=2       ,		)

								SCREAMING_SNAKE_CASE__     							=				[1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

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

												if strides[i] == 2 or i == 0:
																depth *= 2
																SCREAMING_SNAKE_CASE__     							=				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       ,		)     )

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

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



				def   lowerCAmelCase__		(						self       ,		UpperCAmelCase__     ):
								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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		):
								SCREAMING_SNAKE_CASE__     							=				(
								    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
								)
								SCREAMING_SNAKE_CASE__     							=				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"     )

								SCREAMING_SNAKE_CASE__     							=				self.conv_stem(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				() if output_hidden_states else None

								for i, layer_module in enumerate(self.layer     ):
												SCREAMING_SNAKE_CASE__     							=				layer_module(UpperCAmelCase__     )

												if output_hidden_states:
																SCREAMING_SNAKE_CASE__     							=				all_hidden_states + (hidden_states,)

								SCREAMING_SNAKE_CASE__     							=				hidden_states

								if self.pooler is not None:
												SCREAMING_SNAKE_CASE__     							=				torch.flatten(self.pooler(UpperCAmelCase__     )       ,		start_dim=1     )
								else:
												SCREAMING_SNAKE_CASE__     							=				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(
    "\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    "       ,  lowercase       ,  )
class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				def __init__(						self       ,		UpperCAmelCase__     ):
								super().__init__(UpperCAmelCase__     )

								SCREAMING_SNAKE_CASE__     							=				config.num_labels
								SCREAMING_SNAKE_CASE__     							=				MobileNetVaModel(UpperCAmelCase__     )

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

								# Classifier head
								SCREAMING_SNAKE_CASE__     							=				nn.Dropout(config.classifier_dropout_prob       ,		inplace=UpperCAmelCase__     )
								SCREAMING_SNAKE_CASE__     							=				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   lowerCAmelCase__		(						self       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		UpperCAmelCase__ = None       ,		):
								SCREAMING_SNAKE_CASE__     							=				return_dict if return_dict is not None else self.config.use_return_dict

								SCREAMING_SNAKE_CASE__     							=				self.mobilenet_va(UpperCAmelCase__       ,		output_hidden_states=UpperCAmelCase__       ,		return_dict=UpperCAmelCase__     )

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

								SCREAMING_SNAKE_CASE__     							=				self.classifier(self.dropout(UpperCAmelCase__     )     )

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

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

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

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




"""simple docstring"""






from transformers import BertTokenizerFast

from .custom_tokenization import CustomTokenizer


class 			lowerCamelCase_		(			lowercase   ):





				"""simple docstring"""







				_lowerCAmelCase      :      List[Any]      							=					CustomTokenizer
				pass
