'''simple docstring'''




import argparse
from pathlib import Path

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

from diffusers import AutoencoderKL


_lowercase		:				List[Any]	       =		version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")




def 							lowerCamelCase			(   UpperCAmelCase__ :      List[str]   ,   UpperCAmelCase__ :      tuple   ,   UpperCAmelCase__ :      Path   ,   UpperCAmelCase__ :      Tuple   ,   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      Optional[int]   ,   UpperCAmelCase__ :      Optional[Any]   ,   UpperCAmelCase__ :      List[str]=False   ,   )    ->   List[str]:
							output_path.parent.mkdir(parents=UpperCAmelCase__   ,   exist_ok=UpperCAmelCase__				)
							# 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(
														    UpperCAmelCase__   ,   UpperCAmelCase__   ,   f=output_path.as_posix()   ,   input_names=UpperCAmelCase__   ,   output_names=UpperCAmelCase__   ,   dynamic_axes=UpperCAmelCase__   ,   do_constant_folding=UpperCAmelCase__   ,   use_external_data_format=UpperCAmelCase__   ,   enable_onnx_checker=UpperCAmelCase__   ,   opset_version=UpperCAmelCase__   ,   )
							else:
														export(
														    UpperCAmelCase__   ,   UpperCAmelCase__   ,   f=output_path.as_posix()   ,   input_names=UpperCAmelCase__   ,   output_names=UpperCAmelCase__   ,   dynamic_axes=UpperCAmelCase__   ,   do_constant_folding=UpperCAmelCase__   ,   opset_version=UpperCAmelCase__   ,   )




@torch.no_grad()
def 							lowerCamelCase			(   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      str   ,   UpperCAmelCase__ :      int   ,   UpperCAmelCase__ :      bool = False				)    ->   List[Any]:
							lowercase_      :    Dict       		= torch.floataa if fpaa else torch.floataa
							if fpaa and torch.cuda.is_available():
														lowercase_      :    Tuple       		= """cuda"""
							elif fpaa and not torch.cuda.is_available():
														raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""				)
							else:
														lowercase_      :    Union[str, Any]       		= """cpu"""
							lowercase_      :    List[str]       		= Path(UpperCAmelCase__				)

							# VAE DECODER
							lowercase_      :    Dict       		= AutoencoderKL.from_pretrained(model_path + """/vae"""				)
							lowercase_      :    Any       		= vae_decoder.config.latent_channels
							# forward only through the decoder part
							lowercase_      :    Optional[int]       		= vae_decoder.decode
							onnx_export(
							    UpperCAmelCase__   ,   model_args=(
							        torch.randn(1   ,   UpperCAmelCase__   ,   25   ,   25				).to(device=UpperCAmelCase__   ,   dtype=UpperCAmelCase__				),
							        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=UpperCAmelCase__   ,   )
							del vae_decoder


if __name__ == "__main__":
						_lowercase		:				Optional[int]	       =		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		:				Dict	       =		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

import torch
import torch.utils.checkpoint
from torch import Tensor, nn
from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss

from ...activations import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_outputs import (
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import logging
from .configuration_regnet import RegNetConfig


_lowercase		:				Tuple	       =		logging.get_logger(__name__)

# General docstring
_lowercase		:				List[str]	       =		"RegNetConfig"

# Base docstring
_lowercase		:				Dict	       =		"facebook/regnet-y-040"
_lowercase		:				Union[str, Any]	       =		[1, 1088, 7, 7]

# Image classification docstring
_lowercase		:				Optional[Any]	       =		"facebook/regnet-y-040"
_lowercase		:				Union[str, Any]	       =		"tabby, tabby cat"

_lowercase		:				str	       =		[
    "facebook/regnet-y-040",
    # See all regnet models at https://huggingface.co/models?filter=regnet
]





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Union[str, Any]       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 3       ,    lowercase_       :						int = 1       ,    lowercase_       :						int = 1       ,    lowercase_       :						Optional[str] = "relu"       ,    ):
										super().__init__()
										lowercase_      :    List[Any]       		= nn.Convad(
										    lowercase_       ,    lowercase_       ,    kernel_size=lowercase_       ,    stride=lowercase_       ,    padding=kernel_size // 2       ,    groups=lowercase_       ,    bias=lowercase_       ,    )
										lowercase_      :    str       		= nn.BatchNormad(lowercase_					)
										lowercase_      :    Optional[int]       		= ACTaFN[activation] if activation is not None else nn.Identity()







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Dict       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						List[Any]       ,    lowercase_       :						RegNetConfig					):
										super().__init__()
										lowercase_      :    str       		= RegNetConvLayer(
										    config.num_channels       ,    config.embedding_size       ,    kernel_size=3       ,    stride=2       ,    activation=config.hidden_act					)
										lowercase_      :    Any       		= config.num_channels







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Dict       ,    lowercase_       :						Optional[Any]					):
										lowercase_      :    List[str]       		= pixel_values.shape[1]
										if num_channels != self.num_channels:
																	raise ValueError(
																	    """Make sure that the channel dimension of the pixel values match with the one set in the configuration."""					)
										lowercase_      :    Any       		= self.embedder(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 2					):
										super().__init__()
										lowercase_      :    Optional[Any]       		= nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    stride=lowercase_       ,    bias=lowercase_					)
										lowercase_      :    Union[str, Any]       		= nn.BatchNormad(lowercase_					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Tuple       ,    lowercase_       :						Tensor					):
										lowercase_      :    Tuple       		= self.convolution(lowercase_					)
										lowercase_      :    str       		= self.normalization(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						int       ,    lowercase_       :						int					):
										super().__init__()

										lowercase_      :    int       		= nn.AdaptiveAvgPoolad((1, 1)					)
										lowercase_      :    int       		= nn.Sequential(
										    nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1					)       ,    nn.ReLU()       ,    nn.Convad(lowercase_       ,    lowercase_       ,    kernel_size=1					)       ,    nn.Sigmoid()       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Optional[int]       ,    lowercase_       :						Any					):
										# b c h w -> b c 1 1
										lowercase_      :    List[str]       		= self.pooler(lowercase_					)
										lowercase_      :    Optional[int]       		= self.attention(lowercase_					)
										lowercase_      :    Any       		= hidden_state * attention
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[int]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    List[Any]       		= in_channels != out_channels or stride != 1
										lowercase_      :    Optional[int]       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    Dict       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    List[Any]       		= nn.Sequential(
										    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=config.hidden_act					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    stride=lowercase_       ,    groups=lowercase_       ,    activation=config.hidden_act					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=lowercase_					)       ,    )
										lowercase_      :    int       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Any       ,    lowercase_       :						Any					):
										lowercase_      :    Any       		= hidden_state
										lowercase_      :    Union[str, Any]       		= self.layer(lowercase_					)
										lowercase_      :    Union[str, Any]       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    str       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Optional[Any]       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 1					):
										super().__init__()
										lowercase_      :    str       		= in_channels != out_channels or stride != 1
										lowercase_      :    int       		= max(1       ,    out_channels // config.groups_width					)
										lowercase_      :    int       		= (
										    RegNetShortCut(lowercase_       ,    lowercase_       ,    stride=lowercase_					) if should_apply_shortcut else nn.Identity()
										)
										lowercase_      :    Union[str, Any]       		= nn.Sequential(
										    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=config.hidden_act					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    stride=lowercase_       ,    groups=lowercase_       ,    activation=config.hidden_act					)       ,    RegNetSELayer(lowercase_       ,    reduced_channels=int(round(in_channels / 4					)					)					)       ,    RegNetConvLayer(lowercase_       ,    lowercase_       ,    kernel_size=1       ,    activation=lowercase_					)       ,    )
										lowercase_      :    Optional[int]       		= ACTaFN[config.hidden_act]







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Union[str, Any]					):
										lowercase_      :    Optional[int]       		= hidden_state
										lowercase_      :    str       		= self.layer(lowercase_					)
										lowercase_      :    int       		= self.shortcut(lowercase_					)
										hidden_state += residual
										lowercase_      :    Optional[Any]       		= self.activation(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						str       ,    lowercase_       :						RegNetConfig       ,    lowercase_       :						int       ,    lowercase_       :						int       ,    lowercase_       :						int = 2       ,    lowercase_       :						int = 2       ,    ):
										super().__init__()

										lowercase_      :    str       		= RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

										lowercase_      :    str       		= nn.Sequential(
										    # downsampling is done in the first layer with stride of 2
										    layer(
										        lowercase_       ,    lowercase_       ,    lowercase_       ,    stride=lowercase_       ,    )       ,    *[layer(lowercase_       ,    lowercase_       ,    lowercase_					) for _ in range(depth - 1					)]       ,    )







			def 			SCREAMING_SNAKE_CASE_  (			self       :						Union[str, Any]       ,    lowercase_       :						List[str]					):
										lowercase_      :    Tuple       		= self.layers(lowercase_					)
										return hidden_state





class 					__magic_name__		(					nn.Module):







			def __init__(			self       :						Dict       ,    lowercase_       :						RegNetConfig					):
										super().__init__()
										lowercase_      :    Optional[Any]       		= nn.ModuleList([]					)
										# based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
										self.stages.append(
										    RegNetStage(
										        lowercase_       ,    config.embedding_size       ,    config.hidden_sizes[0]       ,    stride=2 if config.downsample_in_first_stage else 1       ,    depth=config.depths[0]       ,    )					)
										lowercase_      :    Optional[Any]       		= zip(config.hidden_sizes       ,    config.hidden_sizes[1:]					)
										for (in_channels, out_channels), depth in zip(lowercase_       ,    config.depths[1:]					):
																	self.stages.append(RegNetStage(lowercase_       ,    lowercase_       ,    lowercase_       ,    depth=lowercase_					)					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						bool = False       ,    lowercase_       :						bool = True					):
										lowercase_      :    Tuple       		= () if output_hidden_states else None

										for stage_module in self.stages:
																	if output_hidden_states:
																								lowercase_      :    Union[str, Any]       		= hidden_states + (hidden_state,)

																	lowercase_      :    Dict       		= stage_module(lowercase_					)

										if output_hidden_states:
																	lowercase_      :    Optional[Any]       		= hidden_states + (hidden_state,)

										if not return_dict:
																	return tuple(v for v in [hidden_state, hidden_states] if v is not None					)

										return BaseModelOutputWithNoAttention(last_hidden_state=lowercase_       ,    hidden_states=lowercase_					)





class 					__magic_name__		(					_UpperCAmelCase):
			UpperCamelCase__       = RegNetConfig
			UpperCamelCase__       = '''regnet'''
			UpperCamelCase__       = '''pixel_values'''
			UpperCamelCase__       = True







			def 			SCREAMING_SNAKE_CASE_  (			self       :						int       ,    lowercase_       :						Optional[Any]					):
										if isinstance(lowercase_       ,    nn.Convad					):
																	nn.init.kaiming_normal_(module.weight       ,    mode="""fan_out"""       ,    nonlinearity="""relu"""					)
										elif isinstance(lowercase_       ,    (nn.BatchNormad, nn.GroupNorm)					):
																	nn.init.constant_(module.weight       ,    1					)
																	nn.init.constant_(module.bias       ,    0					)







			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[Any]       ,    lowercase_       :						Optional[int]       ,    lowercase_       :						Any=False					):
										if isinstance(lowercase_       ,    lowercase_					):
																	lowercase_      :    List[str]       		= value




_lowercase		:				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 ([`RegNetConfig`]): 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		:				Any	       =		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            [`ConvNextImageProcessor.__call__`] for details.\n\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 [`~file_utils.ModelOutput`] instead of a plain tuple.\n"





@add_start_docstrings(
    '''The bare RegNet model outputting raw features without any specific head on top.''',  _UpperCAmelCase,  )
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Any       ,    lowercase_       :						Any					):
										super().__init__(lowercase_					)
										lowercase_      :    List[str]       		= config
										lowercase_      :    Union[str, Any]       		= RegNetEmbeddings(lowercase_					)
										lowercase_      :    Union[str, Any]       		= RegNetEncoder(lowercase_					)
										lowercase_      :    str       		= nn.AdaptiveAvgPoolad((1, 1)					)
										# Initialize weights and apply final processing
										self.post_init()







			@add_start_docstrings_to_model_forward(lowercase_					)
			@add_code_sample_docstrings(
			    checkpoint=_CHECKPOINT_FOR_DOC       ,    output_type=lowercase_       ,    config_class=_CONFIG_FOR_DOC       ,    modality="""vision"""       ,    expected_output=_EXPECTED_OUTPUT_SHAPE       ,    )
			def 			SCREAMING_SNAKE_CASE_  (			self       :						str       ,    lowercase_       :						Tensor       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None					):
										lowercase_      :    List[Any]       		= (
										    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
										)
										lowercase_      :    Tuple       		= return_dict if return_dict is not None else self.config.use_return_dict

										lowercase_      :    str       		= self.embedder(lowercase_					)

										lowercase_      :    Optional[Any]       		= self.encoder(
										    lowercase_       ,    output_hidden_states=lowercase_       ,    return_dict=lowercase_					)

										lowercase_      :    List[Any]       		= encoder_outputs[0]

										lowercase_      :    str       		= self.pooler(lowercase_					)

										if not return_dict:
																	return (last_hidden_state, pooled_output) + encoder_outputs[1:]

										return BaseModelOutputWithPoolingAndNoAttention(
										    last_hidden_state=lowercase_       ,    pooler_output=lowercase_       ,    hidden_states=encoder_outputs.hidden_states       ,    )





@add_start_docstrings(
    '''
    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for
    ImageNet.
    ''',  _UpperCAmelCase,  )
# Copied from transformers.models.resnet.modeling_resnet.ResNetForImageClassification with RESNET->REGNET,ResNet->RegNet,resnet->regnet
class 					__magic_name__		(					_UpperCAmelCase):







			def __init__(			self       :						Dict       ,    lowercase_       :						str					):
										super().__init__(lowercase_					)
										lowercase_      :    Any       		= config.num_labels
										lowercase_      :    List[str]       		= RegNetModel(lowercase_					)
										# classification head
										lowercase_      :    Any       		= nn.Sequential(
										    nn.Flatten()       ,    nn.Linear(config.hidden_sizes[-1]       ,    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(lowercase_					)
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT       ,    output_type=lowercase_       ,    config_class=_CONFIG_FOR_DOC       ,    expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT       ,    )
			def 			SCREAMING_SNAKE_CASE_  (			self       :						List[str]       ,    lowercase_       :						Optional[torch.FloatTensor] = None       ,    lowercase_       :						Optional[torch.LongTensor] = None       ,    lowercase_       :						Optional[bool] = None       ,    lowercase_       :						Optional[bool] = None       ,    ):
										lowercase_      :    Union[str, Any]       		= return_dict if return_dict is not None else self.config.use_return_dict

										lowercase_      :    Optional[int]       		= self.regnet(lowercase_       ,    output_hidden_states=lowercase_       ,    return_dict=lowercase_					)

										lowercase_      :    Optional[int]       		= outputs.pooler_output if return_dict else outputs[1]

										lowercase_      :    List[Any]       		= self.classifier(lowercase_					)

										lowercase_      :    Optional[int]       		= None

										if labels is not None:
																	if self.config.problem_type is None:
																								if self.num_labels == 1:
																															lowercase_      :    Optional[int]       		= """regression"""
																								elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
																															lowercase_      :    str       		= """single_label_classification"""
																								else:
																															lowercase_      :    str       		= """multi_label_classification"""
																	if self.config.problem_type == "regression":
																								lowercase_      :    str       		= MSELoss()
																								if self.num_labels == 1:
																															lowercase_      :    List[Any]       		= loss_fct(logits.squeeze()       ,    labels.squeeze()					)
																								else:
																															lowercase_      :    List[str]       		= loss_fct(lowercase_       ,    lowercase_					)
																	elif self.config.problem_type == "single_label_classification":
																								lowercase_      :    Optional[int]       		= CrossEntropyLoss()
																								lowercase_      :    Union[str, Any]       		= loss_fct(logits.view(-1       ,    self.num_labels					)       ,    labels.view(-1					)					)
																	elif self.config.problem_type == "multi_label_classification":
																								lowercase_      :    Dict       		= BCEWithLogitsLoss()
																								lowercase_      :    Tuple       		= loss_fct(lowercase_       ,    lowercase_					)

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

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





