



'''simple docstring'''



import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


__lowercase     :						Any	=     version.parse(version.parse(torch.__version__).base_version) < version.parse('1.11')






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       tuple						,				_SCREAMING_SNAKE_CASE				:       Path						,				_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       Union[str, Any]						,				_SCREAMING_SNAKE_CASE				:       Optional[int]=False						,				):
					output_path.parent.mkdir(parents=lowerCamelCase__						,				exist_ok=lowerCamelCase__       )
					# PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
					# so we check the torch version for backwards compatibility
					if is_torch_less_than_1_11:
										export(
										    lowerCamelCase__						,				lowerCamelCase__						,				f=output_path.as_posix()						,				input_names=lowerCamelCase__						,				output_names=lowerCamelCase__						,				dynamic_axes=lowerCamelCase__						,				do_constant_folding=lowerCamelCase__						,				use_external_data_format=lowerCamelCase__						,				enable_onnx_checker=lowerCamelCase__						,				opset_version=lowerCamelCase__						,				)
					else:
										export(
										    lowerCamelCase__						,				lowerCamelCase__						,				f=output_path.as_posix()						,				input_names=lowerCamelCase__						,				output_names=lowerCamelCase__						,				dynamic_axes=lowerCamelCase__						,				do_constant_folding=lowerCamelCase__						,				opset_version=lowerCamelCase__						,				)






@torch.no_grad()
def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       bool = False       ):
					__a      :	Tuple    =  torch.floataa if fpaa else torch.floataa
					if fpaa and torch.cuda.is_available():
										__a      :	Tuple    =  '''cuda'''
					elif fpaa and not torch.cuda.is_available():
										raise ValueError('`float16` model export is only supported on GPUs with CUDA'       )
					else:
										__a      :	str    =  '''cpu'''
					__a      :	Dict    =  Path(lowerCamelCase__       )

					# VAE DECODER
					__a      :	Optional[int]    =  AutoencoderKL.from_pretrained(model_path + '/vae'       )
					__a      :	Tuple    =  vae_decoder.config.latent_channels
					# forward only through the decoder part
					__a      :	Any    =  vae_decoder.decode
					onnx_export(
					    lowerCamelCase__						,				model_args=(
					        torch.randn(1						,				lowerCamelCase__						,				25						,				25       ).to(device=lowerCamelCase__						,				dtype=lowerCamelCase__       ),
					        False,
					    )						,				output_path=output_path / 'vae_decoder' / 'model.onnx'						,				ordered_input_names=['latent_sample', 'return_dict']						,				output_names=['sample']						,				dynamic_axes={
					        'latent_sample': {0: 'batch', 1: 'channels', 2: 'height', 3: 'width'},
					    }						,				opset=lowerCamelCase__						,				)
					del vae_decoder


if __name__ == "__main__":
	__lowercase     :						Union[str, Any]	=     argparse.ArgumentParser()

	parser.add_argument(
	    '--model_path',
	    type=str,
	    required=True,
	    help='Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).',
	)

	parser.add_argument('--output_path', type=str, required=True, help='Path to the output model.')
	parser.add_argument(
	    '--opset',
	    default=14,
	    type=int,
	    help='The version of the ONNX operator set to use.',
	)
	parser.add_argument('--fp16', action='store_true', default=False, help='Export the models in `float16` mode')

	__lowercase     :						Optional[int]	=     parser.parse_args()
	print(args.output_path)
	convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
	print('SD: Done: ONNX')







'''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


__lowercase     :						str	=     logging.get_logger(__name__)


# General docstring
__lowercase     :						List[str]	=     'MobileNetV1Config'

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

# Image classification docstring
__lowercase     :						int	=     'google/mobilenet_v1_1.0_224'
__lowercase     :						Any	=     'tabby, tabby cat'


__lowercase     :						Dict	=     [
    '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							(_SCREAMING_SNAKE_CASE				:       List[Any]						,				_SCREAMING_SNAKE_CASE				:       int						,				_SCREAMING_SNAKE_CASE				:       Optional[Any]=None       ):
					__a      :	Dict    =  {}

					if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	Optional[Any]    =  model.mobilenet_va
					else:
										__a      :	List[Any]    =  model

					__a      :	Dict    =  'MobilenetV1/Conv2d_0/'
					__a      :	Dict    =  backbone.conv_stem.convolution.weight
					__a      :	Optional[Any]    =  backbone.conv_stem.normalization.bias
					__a      :	int    =  backbone.conv_stem.normalization.weight
					__a      :	int    =  backbone.conv_stem.normalization.running_mean
					__a      :	Tuple    =  backbone.conv_stem.normalization.running_var

					for i in range(13       ):
										__a      :	int    =  i + 1
										__a      :	Dict    =  i * 2

										__a      :	Dict    =  backbone.layer[pt_index]
										__a      :	Dict    =  F"""MobilenetV1/Conv2d_{tf_index}_depthwise/"""
										__a      :	Union[str, Any]    =  pointer.convolution.weight
										__a      :	Optional[Any]    =  pointer.normalization.bias
										__a      :	Union[str, Any]    =  pointer.normalization.weight
										__a      :	List[Any]    =  pointer.normalization.running_mean
										__a      :	Tuple    =  pointer.normalization.running_var

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

					if isinstance(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       ):
										__a      :	Any    =  'MobilenetV1/Logits/Conv2d_1c_1x1/'
										__a      :	Optional[int]    =  model.classifier.weight
										__a      :	List[Any]    =  model.classifier.bias

					return tf_to_pt_map






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       str						,				_SCREAMING_SNAKE_CASE				:       List[str]						,				_SCREAMING_SNAKE_CASE				:       Dict       ):
					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      :	Union[str, Any]    =  tf.train.list_variables(_SCREAMING_SNAKE_CASE       )
					__a      :	Optional[int]    =  {}
					for name, shape in init_vars:
										logger.info(F"""Loading TF weight {name} with shape {shape}"""       )
										__a      :	List[str]    =  tf.train.load_variable(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )
										__a      :	Optional[Any]    =  array

					# Build TF to PyTorch weights loading map
					__a      :	Optional[int]    =  _build_tf_to_pytorch_map(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE       )

					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      :	Union[str, Any]    =  tf_weights[name]

										if "depthwise_weights" in name:
															logger.info('Transposing depthwise'       )
															__a      :	Optional[Any]    =  np.transpose(_SCREAMING_SNAKE_CASE						,				(2, 3, 0, 1)       )
										elif "weights" in name:
															logger.info('Transposing'       )
															if len(pointer.shape       ) == 2:  # copying into linear layer
																				__a      :	Union[str, Any]    =  array.squeeze().transpose()
															else:
																				__a      :	Dict    =  np.transpose(_SCREAMING_SNAKE_CASE						,				(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      :	List[str]    =  torch.from_numpy(_SCREAMING_SNAKE_CASE       )

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

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






def   lowerCamelCase							(_SCREAMING_SNAKE_CASE				:       torch.Tensor						,				_SCREAMING_SNAKE_CASE				:       nn.Convad       ):
					__a   ,    __a      :	Any    =  features.shape[-2:]
					__a   ,    __a      :	int    =  conv_layer.stride
					__a   ,    __a      :	Any    =  conv_layer.kernel_size

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

					if in_width % stride_width == 0:
										__a      :	Any    =  max(kernel_width - stride_width						,				0       )
					else:
										__a      :	str    =  max(kernel_width - (in_width % stride_width)						,				0       )

					__a      :	int    =  pad_along_width // 2
					__a      :	Dict    =  pad_along_width - pad_left
					__a      :	List[str]    =  pad_along_height // 2
					__a      :	Union[str, Any]    =  pad_along_height - pad_top

					__a      :	str    =  (pad_left, pad_right, pad_top, pad_bottom)
					return nn.functional.pad(_SCREAMING_SNAKE_CASE						,				_SCREAMING_SNAKE_CASE						,				'constant'						,				0.0       )





class 							__UpperCamelCase    (		nn.Module ):




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

						'''simple docstring'''





						super().__init__()
						__a      :	Optional[int]    =  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      :	Dict    =  0 if config.tf_padding else int((kernel_size - 1) / 2							)

						__a      :	Union[str, Any]    =  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      :	List[str]    =  nn.BatchNormad(
											    num_features=__a						,    eps=config.layer_norm_eps						,    momentum=0.9997						,    affine=__a						,    track_running_stats=__a						,    )
						else:
											__a      :	Tuple    =  None

						if use_activation:
											if isinstance(__a						,    __a							):
																__a      :	Tuple    =  ACTaFN[use_activation]
											elif isinstance(config.hidden_act						,    __a							):
																__a      :	Union[str, Any]    =  ACTaFN[config.hidden_act]
											else:
																__a      :	Dict    =  config.hidden_act
						else:
											__a      :	List[Any]    =  None





	def 				__UpperCAmelCase							(       self						,    __a							):

						'''simple docstring'''





						if self.config.tf_padding:
											__a      :	Union[str, Any]    =  apply_tf_padding(__a						,    self.convolution							)
						__a      :	Union[str, Any]    =  self.convolution(__a							)
						if self.normalization is not None:
											__a      :	str    =  self.normalization(__a							)
						if self.activation is not None:
											__a      :	Optional[int]    =  self.activation(__a							)
						return features





class 							__UpperCamelCase    (		lowerCAmelCase_ ):
	A_           =   MobileNetVaConfig
	A_           =   load_tf_weights_in_mobilenet_va
	A_           =   "mobilenet_v1"
	A_           =   "pixel_values"
	A_           =   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							)



__lowercase     :						Any	=     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'

__lowercase     :						Optional[int]	=     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."		,				lowerCAmelCase_		,				)
class 							__UpperCamelCase    (		lowerCAmelCase_ ):




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

						'''simple docstring'''





						super().__init__(__a							)
						__a      :	Optional[int]    =  config

						__a      :	str    =  32
						__a      :	Dict    =  max(int(depth * config.depth_multiplier							)						,    config.min_depth							)

						__a      :	Union[str, Any]    =  MobileNetVaConvLayer(
						    __a						,    in_channels=config.num_channels						,    out_channels=__a						,    kernel_size=3						,    stride=2						,    )

						__a      :	Tuple    =  [1, 2, 1, 2, 1, 2, 1, 1, 1, 1, 1, 2, 1]

						__a      :	Any    =  nn.ModuleList()
						for i in range(13							):
											__a      :	Union[str, Any]    =  out_channels

											if strides[i] == 2 or i == 0:
																depth *= 2
																__a      :	List[Any]    =  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      :	Optional[int]    =  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      :	Dict    =  (
						    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
						)
						__a      :	int    =  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      :	Union[str, Any]    =  self.conv_stem(__a							)

						__a      :	Any    =  () if output_hidden_states else None

						for i, layer_module in enumerate(self.layer							):
											__a      :	List[str]    =  layer_module(__a							)

											if output_hidden_states:
																__a      :	List[Any]    =  all_hidden_states + (hidden_states,)

						__a      :	str    =  hidden_states

						if self.pooler is not None:
											__a      :	Union[str, Any]    =  torch.flatten(self.pooler(__a							)						,    start_dim=1							)
						else:
											__a      :	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=__a						,    pooler_output=__a						,    hidden_states=__a						,    )





@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    "		,				lowerCAmelCase_		,				)
class 							__UpperCamelCase    (		lowerCAmelCase_ ):




	def __init__(       self						,    __a							):

						'''simple docstring'''





						super().__init__(__a							)

						__a      :	Tuple    =  config.num_labels
						__a      :	Tuple    =  MobileNetVaModel(__a							)

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

						# Classifier head
						__a      :	Any    =  nn.Dropout(config.classifier_dropout_prob						,    inplace=__a							)
						__a      :	Any    =  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      :	Union[str, Any]    =  return_dict if return_dict is not None else self.config.use_return_dict

						__a      :	Dict    =  self.mobilenet_va(__a						,    output_hidden_states=__a						,    return_dict=__a							)

						__a      :	List[str]    =  outputs.pooler_output if return_dict else outputs[1]

						__a      :	int    =  self.classifier(self.dropout(__a							)							)

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

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

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

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



