

"""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


A_							:			int											= logging.get_logger(__name__)

# General docstring
A_							:			Optional[Any]											= "RegNetConfig"

# Base docstring
A_							:			int											= "facebook/regnet-y-040"
A_							:			List[str]											= [1, 1_088, 7, 7]

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

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





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						List[Any]       )   ->    int:
						SCREAMING_SNAKE_CASE__								=      self.convolution(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.normalization(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.activation(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						List[Any]       )   ->    Optional[Any]:
						SCREAMING_SNAKE_CASE__								=      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."""       )
						SCREAMING_SNAKE_CASE__								=      self.embedder(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Tuple    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int = 2       )   ->    Union[str, Any]:
						super().__init__()
						SCREAMING_SNAKE_CASE__								=      nn.Convad(__UpperCAmelCase    ,				__UpperCAmelCase    ,				kernel_size=1    ,				stride=__UpperCAmelCase    ,				bias=__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      nn.BatchNormad(__UpperCAmelCase       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[Any]    ,				__UpperCAmelCase				:						Tensor       )   ->    Tensor:
						SCREAMING_SNAKE_CASE__								=      self.convolution(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.normalization(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Any    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int       )   ->    List[str]:
						super().__init__()

						SCREAMING_SNAKE_CASE__								=      nn.AdaptiveAvgPoolad((1, 1)       )
						SCREAMING_SNAKE_CASE__								=      nn.Sequential(
						    nn.Convad(__UpperCAmelCase    ,				__UpperCAmelCase    ,				kernel_size=1       )    ,				nn.ReLU()    ,				nn.Convad(__UpperCAmelCase    ,				__UpperCAmelCase    ,				kernel_size=1       )    ,				nn.Sigmoid()    ,				)





			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						int       )   ->    Union[str, Any]:
						# b c h w -> b c 1 1
						SCREAMING_SNAKE_CASE__								=      self.pooler(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.attention(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      hidden_state * attention
						return hidden_state





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						int    ,				__UpperCAmelCase				:						List[str]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      hidden_state
						SCREAMING_SNAKE_CASE__								=      self.layer(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.shortcut(__UpperCAmelCase       )
						hidden_state += residual
						SCREAMING_SNAKE_CASE__								=      self.activation(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						List[str]    ,				__UpperCAmelCase				:						List[Any]       )   ->    Union[str, Any]:
						SCREAMING_SNAKE_CASE__								=      hidden_state
						SCREAMING_SNAKE_CASE__								=      self.layer(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      self.shortcut(__UpperCAmelCase       )
						hidden_state += residual
						SCREAMING_SNAKE_CASE__								=      self.activation(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						RegNetConfig    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int    ,				__UpperCAmelCase				:						int = 2    ,				__UpperCAmelCase				:						int = 2    ,				)   ->    Optional[int]:
						super().__init__()

						SCREAMING_SNAKE_CASE__								=      RegNetXLayer if config.layer_type == """x""" else RegNetYLayer

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						int       )   ->    Optional[int]:
						SCREAMING_SNAKE_CASE__								=      self.layers(__UpperCAmelCase       )
						return hidden_state





class    lowerCamelCase						(nn.Module			):

			def __init__(					self				:						Dict    ,				__UpperCAmelCase				:						RegNetConfig       )   ->    Tuple:
						super().__init__()
						SCREAMING_SNAKE_CASE__								=      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(
						        __UpperCAmelCase    ,				config.embedding_size    ,				config.hidden_sizes[0]    ,				stride=2 if config.downsample_in_first_stage else 1    ,				depth=config.depths[0]    ,				)       )
						SCREAMING_SNAKE_CASE__								=      zip(config.hidden_sizes    ,				config.hidden_sizes[1:]       )
						for (in_channels, out_channels), depth in zip(__UpperCAmelCase    ,				config.depths[1:]       ):
									self.stages.append(RegNetStage(__UpperCAmelCase    ,				__UpperCAmelCase    ,				__UpperCAmelCase    ,				depth=__UpperCAmelCase       )       )





			def 					SCREAMING_SNAKE_CASE				(					self				:						Optional[Any]    ,				__UpperCAmelCase				:						Tensor    ,				__UpperCAmelCase				:						bool = False    ,				__UpperCAmelCase				:						bool = True       )   ->    BaseModelOutputWithNoAttention:
						SCREAMING_SNAKE_CASE__								=      () if output_hidden_states else None

						for stage_module in self.stages:
									if output_hidden_states:
												SCREAMING_SNAKE_CASE__								=      hidden_states + (hidden_state,)

									SCREAMING_SNAKE_CASE__								=      stage_module(__UpperCAmelCase       )

						if output_hidden_states:
									SCREAMING_SNAKE_CASE__								=      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=__UpperCAmelCase    ,				hidden_states=__UpperCAmelCase       )





class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		List[str]							=							RegNetConfig
			lowerCamelCase__   :		Union[str, Any]							=							'regnet'
			lowerCamelCase__   :		Optional[Any]							=							'pixel_values'
			lowerCamelCase__   :		Union[str, Any]							=							True

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





			def 					SCREAMING_SNAKE_CASE				(					self				:						Dict    ,				__UpperCAmelCase				:						List[Any]    ,				__UpperCAmelCase				:						Union[str, Any]=False       )   ->    Union[str, Any]:
						if isinstance(__UpperCAmelCase    ,				__UpperCAmelCase       ):
									SCREAMING_SNAKE_CASE__								=      value
A_							:			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"

A_							:			Tuple											= 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.'			,A__			,)
# Copied from transformers.models.resnet.modeling_resnet.ResNetModel with RESNET->REGNET,ResNet->RegNet
class    lowerCamelCase						(A__			):

			def __init__(					self				:						List[str]    ,				__UpperCAmelCase				:						List[str]       )   ->    int:
						super().__init__(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      config
						SCREAMING_SNAKE_CASE__								=      RegNetEmbeddings(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      RegNetEncoder(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      nn.AdaptiveAvgPoolad((1, 1)       )
						# Initialize weights and apply final processing
						self.post_init()





			@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 					SCREAMING_SNAKE_CASE				(					self				:						str    ,				__UpperCAmelCase				:						Tensor    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				__UpperCAmelCase				:						Optional[bool] = None       )   ->    BaseModelOutputWithPoolingAndNoAttention:
						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

						SCREAMING_SNAKE_CASE__								=      self.embedder(__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      self.encoder(
						    __UpperCAmelCase    ,				output_hidden_states=__UpperCAmelCase    ,				return_dict=__UpperCAmelCase       )

						SCREAMING_SNAKE_CASE__								=      encoder_outputs[0]

						SCREAMING_SNAKE_CASE__								=      self.pooler(__UpperCAmelCase       )

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

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





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

			def __init__(					self				:						Optional[int]    ,				__UpperCAmelCase				:						str       )   ->    Dict:
						super().__init__(__UpperCAmelCase       )
						SCREAMING_SNAKE_CASE__								=      config.num_labels
						SCREAMING_SNAKE_CASE__								=      RegNetModel(__UpperCAmelCase       )
						# classification head
						SCREAMING_SNAKE_CASE__								=      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(__UpperCAmelCase       )
			@add_code_sample_docstrings(
			    checkpoint=_IMAGE_CLASS_CHECKPOINT    ,				output_type=__UpperCAmelCase    ,				config_class=_CONFIG_FOR_DOC    ,				expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT    ,				)
			def 					SCREAMING_SNAKE_CASE				(					self				:						Union[str, Any]    ,				__UpperCAmelCase				:						Optional[torch.FloatTensor] = None    ,				__UpperCAmelCase				:						Optional[torch.LongTensor] = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				__UpperCAmelCase				:						Optional[bool] = None    ,				)   ->    ImageClassifierOutputWithNoAttention:
						SCREAMING_SNAKE_CASE__								=      return_dict if return_dict is not None else self.config.use_return_dict

						SCREAMING_SNAKE_CASE__								=      self.regnet(__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(__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"""




import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import torch
from datasets import load_dataset
from torchvision.transforms import Compose, Lambda, Normalize, RandomHorizontalFlip, RandomResizedCrop, ToTensor
from torchvision.transforms.functional import InterpolationMode

import transformers
from transformers import (
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    ViTImageProcessor,
    ViTMAEConfig,
    ViTMAEForPreTraining,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
A_							:			List[Any]											= logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.31.0")

require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/image-pretraining/requirements.txt")





@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		Optional[str]							=							field(
			    default='cifar10'			,metadata={'help': 'Name of a dataset from the datasets package'}			)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'The configuration name of the dataset to use (via the datasets library).'}			)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'The column name of the images in the files.'}			)
			lowerCamelCase__   :		Optional[str]							=							field(default=A__			,metadata={'help': 'A folder containing the training data.'}			)
			lowerCamelCase__   :		Optional[str]							=							field(default=A__			,metadata={'help': 'A folder containing the validation data.'}			)
			lowerCamelCase__   :		Optional[float]							=							field(
			    default=0.1_5			,metadata={'help': 'Percent to split off of train for validation.'}			)
			lowerCamelCase__   :		Optional[int]							=							field(
			    default=A__			,metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of training examples to this '
			            'value if set.'
			        )
			    }			,)
			lowerCamelCase__   :		Optional[int]							=							field(
			    default=A__			,metadata={
			        'help': (
			            'For debugging purposes or quicker training, truncate the number of evaluation examples to this '
			            'value if set.'
			        )
			    }			,)

			def 					SCREAMING_SNAKE_CASE				(					self				:						Any       )   ->    List[str]:
						SCREAMING_SNAKE_CASE__								=      {}
						if self.train_dir is not None:
									SCREAMING_SNAKE_CASE__								=      self.train_dir
						if self.validation_dir is not None:
									SCREAMING_SNAKE_CASE__								=      self.validation_dir
						SCREAMING_SNAKE_CASE__								=      data_files if data_files else None





@dataclass
class    lowerCamelCase						:
			lowerCamelCase__   :		str							=							field(
			    default=A__			,metadata={
			        'help': (
			            'The model checkpoint for weights initialization.Don\'t set if you want to train a model from scratch.'
			        )
			    }			,)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'Pretrained config name or path if not the same as model_name_or_path'}			)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={
			        'help': (
			            'Override some existing default config settings when a model is trained from scratch. Example: '
			            'n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index'
			        )
			    }			,)
			lowerCamelCase__   :		Optional[str]							=							field(
			    default=A__			,metadata={'help': 'Where do you want to store the pretrained models downloaded from s3'}			)
			lowerCamelCase__   :		str							=							field(
			    default='main'			,metadata={'help': 'The specific model version to use (can be a branch name, tag name or commit id).'}			,)
			lowerCamelCase__   :		str							=							field(default=A__			,metadata={'help': 'Name or path of preprocessor config.'}			)
			lowerCamelCase__   :		bool							=							field(
			    default=A__			,metadata={
			        'help': (
			            'Will use the token generated when running `huggingface-cli login` (necessary to use this script '
			            'with private models).'
			        )
			    }			,)
			lowerCamelCase__   :		float							=							field(
			    default=0.7_5			,metadata={'help': 'The ratio of the number of masked tokens in the input sequence.'}			)
			lowerCamelCase__   :		bool							=							field(
			    default=A__			,metadata={'help': 'Whether or not to train with normalized pixel values as target.'}			)





@dataclass
class    lowerCamelCase						(A__			):
			lowerCamelCase__   :		float							=							field(
			    default=1E-3			,metadata={'help': 'Base learning rate: absolute_lr = base_lr * total_batch_size / 256.'}			)

def 	A       (     snake_case__    ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      torch.stack([example["""pixel_values"""] for example in examples]    )
			return {"pixel_values": pixel_values}

def 	A       (     ):




			'''simple docstring'''


			SCREAMING_SNAKE_CASE__								=      HfArgumentParser((ModelArguments, DataTrainingArguments, CustomTrainingArguments)    )
			if len(sys.argv    ) == 2 and sys.argv[1].endswith(""".json"""    ):
						# If we pass only one argument to the script and it's the path to a json file,
						# let's parse it to get our arguments.
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]    )    )
			else:
						SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__							,		SCREAMING_SNAKE_CASE__								=      parser.parse_args_into_dataclasses()

			# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
			# information sent is the one passed as arguments along with your Python/PyTorch versions.
			send_example_telemetry("""run_mae"""		,				snake_case__		,				snake_case__    )

			# Setup logging
			logging.basicConfig(
			    format="""%(asctime)s - %(levelname)s - %(name)s - %(message)s"""		,				datefmt="""%m/%d/%Y %H:%M:%S"""		,				handlers=[logging.StreamHandler(sys.stdout    )]		,				)

			if training_args.should_log:
						# The default of training_args.log_level is passive, so we set log level at info here to have that default.
						transformers.utils.logging.set_verbosity_info()

			SCREAMING_SNAKE_CASE__								=      training_args.get_process_log_level()
			logger.setLevel(snake_case__    )
			transformers.utils.logging.set_verbosity(snake_case__    )
			transformers.utils.logging.enable_default_handler()
			transformers.utils.logging.enable_explicit_format()

			# Log on each process the small summary:
			logger.warning(
			    f"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
			    + f"""distributed training: {bool(training_args.local_rank != -1    )}, 16-bits training: {training_args.fpaa}"""    )
			logger.info(f"""Training/evaluation parameters {training_args}"""    )

			# Detecting last checkpoint.
			SCREAMING_SNAKE_CASE__								=      None
			if os.path.isdir(training_args.output_dir    ) and training_args.do_train and not training_args.overwrite_output_dir:
						SCREAMING_SNAKE_CASE__								=      get_last_checkpoint(training_args.output_dir    )
						if last_checkpoint is None and len(os.listdir(training_args.output_dir    )    ) > 0:
									raise ValueError(
									    f"""Output directory ({training_args.output_dir}) already exists and is not empty. """
									    """Use --overwrite_output_dir to overcome."""    )
						elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
									logger.info(
									    f"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
									    """the `--output_dir` or add `--overwrite_output_dir` to train from scratch."""    )

    # Initialize our dataset.
			SCREAMING_SNAKE_CASE__								=      load_dataset(
			    data_args.dataset_name		,				data_args.dataset_config_name		,				data_files=data_args.data_files		,				cache_dir=model_args.cache_dir		,				use_auth_token=True if model_args.use_auth_token else None		,				)

			# If we don't have a validation split, split off a percentage of train as validation.
			SCREAMING_SNAKE_CASE__								=      None if """validation""" in ds.keys() else data_args.train_val_split
			if isinstance(data_args.train_val_split		,				snake_case__    ) and data_args.train_val_split > 0.0:
						SCREAMING_SNAKE_CASE__								=      ds["""train"""].train_test_split(data_args.train_val_split    )
						SCREAMING_SNAKE_CASE__								=      split["""train"""]
						SCREAMING_SNAKE_CASE__								=      split["""test"""]

			# Load pretrained model and image processor
			#
			# Distributed training:
			# The .from_pretrained methods guarantee that only one local process can concurrently
			# download model & vocab.
			SCREAMING_SNAKE_CASE__								=      {
			    """cache_dir""": model_args.cache_dir,
			    """revision""": model_args.model_revision,
			    """use_auth_token""": True if model_args.use_auth_token else None,
			}
			if model_args.config_name:
						SCREAMING_SNAKE_CASE__								=      ViTMAEConfig.from_pretrained(model_args.config_name		,				**snake_case__    )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      ViTMAEConfig.from_pretrained(model_args.model_name_or_path		,				**snake_case__    )
			else:
						SCREAMING_SNAKE_CASE__								=      ViTMAEConfig()
						logger.warning("""You are instantiating a new config instance from scratch."""    )
						if model_args.config_overrides is not None:
									logger.info(f"""Overriding config: {model_args.config_overrides}"""    )
									config.update_from_string(model_args.config_overrides    )
									logger.info(f"""New config: {config}"""    )

    # adapt config
			config.update(
			    {
			        """mask_ratio""": model_args.mask_ratio,
			        """norm_pix_loss""": model_args.norm_pix_loss,
			    }    )

			# create image processor
			if model_args.image_processor_name:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(model_args.image_processor_name		,				**snake_case__    )
			elif model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor.from_pretrained(model_args.model_name_or_path		,				**snake_case__    )
			else:
						SCREAMING_SNAKE_CASE__								=      ViTImageProcessor()

			# create model
			if model_args.model_name_or_path:
						SCREAMING_SNAKE_CASE__								=      ViTMAEForPreTraining.from_pretrained(
						    model_args.model_name_or_path		,				from_tf=bool(""".ckpt""" in model_args.model_name_or_path    )		,				config=snake_case__		,				cache_dir=model_args.cache_dir		,				revision=model_args.model_revision		,				use_auth_token=True if model_args.use_auth_token else None		,				)
			else:
						logger.info("""Training new model from scratch"""    )
						SCREAMING_SNAKE_CASE__								=      ViTMAEForPreTraining(snake_case__    )

			if training_args.do_train:
						SCREAMING_SNAKE_CASE__								=      ds["""train"""].column_names
			else:
						SCREAMING_SNAKE_CASE__								=      ds["""validation"""].column_names

			if data_args.image_column_name is not None:
						SCREAMING_SNAKE_CASE__								=      data_args.image_column_name
			elif "image" in column_names:
						SCREAMING_SNAKE_CASE__								=      """image"""
			elif "img" in column_names:
						SCREAMING_SNAKE_CASE__								=      """img"""
			else:
						SCREAMING_SNAKE_CASE__								=      column_names[0]

			# transformations as done in original MAE paper
			# source: https://github.com/facebookresearch/mae/blob/main/main_pretrain.py
			if "shortest_edge" in image_processor.size:
						SCREAMING_SNAKE_CASE__								=      image_processor.size["""shortest_edge"""]
			else:
						SCREAMING_SNAKE_CASE__								=      (image_processor.size["""height"""], image_processor.size["""width"""])
			SCREAMING_SNAKE_CASE__								=      Compose(
			    [
			        Lambda(lambda snake_case__    : img.convert("""RGB"""    ) if img.mode != "RGB" else img    ),
			        RandomResizedCrop(snake_case__		,				scale=(0.2, 1.0)		,				interpolation=InterpolationMode.BICUBIC    ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        Normalize(mean=image_processor.image_mean		,				std=image_processor.image_std    ),
			    ]    )

			def preprocess_images(snake_case__    ):
						SCREAMING_SNAKE_CASE__								=      [transforms(snake_case__    ) for image in examples[image_column_name]]
						return examples

			if training_args.do_train:
						if "train" not in ds:
									raise ValueError("""--do_train requires a train dataset"""    )
						if data_args.max_train_samples is not None:
									SCREAMING_SNAKE_CASE__								=      ds["""train"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_train_samples    )    )
						# Set the training transforms
						ds["train"].set_transform(snake_case__    )

			if training_args.do_eval:
						if "validation" not in ds:
									raise ValueError("""--do_eval requires a validation dataset"""    )
						if data_args.max_eval_samples is not None:
									SCREAMING_SNAKE_CASE__								=      (
									    ds["""validation"""].shuffle(seed=training_args.seed    ).select(range(data_args.max_eval_samples    )    )
									)
						# Set the validation transforms
						ds["validation"].set_transform(snake_case__    )

			# Compute absolute learning rate
			SCREAMING_SNAKE_CASE__								=      (
			    training_args.train_batch_size * training_args.gradient_accumulation_steps * training_args.world_size
			)
			if training_args.base_learning_rate is not None:
						SCREAMING_SNAKE_CASE__								=      training_args.base_learning_rate * total_train_batch_size / 2_56

			# Initialize our trainer
			SCREAMING_SNAKE_CASE__								=      Trainer(
			    model=snake_case__		,				args=snake_case__		,				train_dataset=ds["""train"""] if training_args.do_train else None		,				eval_dataset=ds["""validation"""] if training_args.do_eval else None		,				tokenizer=snake_case__		,				data_collator=snake_case__		,				)

			# Training
			if training_args.do_train:
						SCREAMING_SNAKE_CASE__								=      None
						if training_args.resume_from_checkpoint is not None:
									SCREAMING_SNAKE_CASE__								=      training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									SCREAMING_SNAKE_CASE__								=      last_checkpoint
						SCREAMING_SNAKE_CASE__								=      trainer.train(resume_from_checkpoint=snake_case__    )
						trainer.save_model()
						trainer.log_metrics("""train"""		,				train_result.metrics    )
						trainer.save_metrics("""train"""		,				train_result.metrics    )
						trainer.save_state()

			# Evaluation
			if training_args.do_eval:
						SCREAMING_SNAKE_CASE__								=      trainer.evaluate()
						trainer.log_metrics("""eval"""		,				snake_case__    )
						trainer.save_metrics("""eval"""		,				snake_case__    )

			# Write model card and (optionally) push to hub
			SCREAMING_SNAKE_CASE__								=      {
			    """tasks""": """masked-auto-encoding""",
			    """dataset""": data_args.dataset_name,
			    """tags""": ["""masked-auto-encoding"""],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**snake_case__    )
			else:
						trainer.create_model_card(**snake_case__    )

def 	A       (     snake_case__    ):




			'''simple docstring'''


			main()


if __name__ == "__main__":
						main()


