




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

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
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
__lowerCAmelCase		:				str       			=		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-classification/requirements.txt')

__lowerCAmelCase		:				Tuple       			=		list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
__lowerCAmelCase		:				List[str]       			=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)




def 			__magic_name__				( A		:					str   ):






			'''simple docstring'''







			with open(A,  "rb"   ) as f:
						a    =			Image.open(A   )
						return im.convert("RGB"   )




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={
				        """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(default=_UpperCamelCase    ,       metadata={"""help""": """A folder containing the training data."""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(default=_UpperCamelCase    ,       metadata={"""help""": """A folder containing the validation data."""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[float]							 =					field(
				    default=0.15    ,       metadata={"""help""": """Percent to split off of train for validation."""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					field(
				    default=_UpperCamelCase    ,       metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of training examples to this """
				            """value if set."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[int]							 =					field(
				    default=_UpperCamelCase    ,       metadata={
				        """help""": (
				            """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
				            """value if set."""
				        )
				    }    ,       )





				def 							__UpperCAmelCase							(      self				:     Union[str, Any]							)					->     Dict:
							if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
										raise ValueError(
										    "You must specify either a dataset name from the hub or a train and/or validation directory."							)




@dataclass
class     snake_case__							:
				"""simple docstring"""



				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    default="""google/vit-base-patch16-224-in21k"""    ,       metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(_UpperCamelCase   )}    ,       )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Pretrained config name or path if not the same as model_name"""}   )
				SCREAMING_SNAKE_CASE_       :							Optional[str]							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""}   )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(
				    default="""main"""    ,       metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}    ,       )
				SCREAMING_SNAKE_CASE_       :							str							 =					field(default=_UpperCamelCase    ,       metadata={"""help""": """Name or path of preprocessor config."""}   )
				SCREAMING_SNAKE_CASE_       :							bool							 =					field(
				    default=_UpperCamelCase    ,       metadata={
				        """help""": (
				            """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
				            """with private models)."""
				        )
				    }    ,       )
				SCREAMING_SNAKE_CASE_       :							bool							 =					field(
				    default=_UpperCamelCase    ,       metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}    ,       )




def 			__magic_name__				( A		:					Dict   ):






			'''simple docstring'''







			a    =			torch.stack([example["pixel_values"] for example in examples]   )
			a    =			torch.tensor([example["labels"] for example in examples]   )
			return {"pixel_values": pixel_values, "labels": labels}




def 			__magic_name__				( ):






			'''simple docstring'''







			a    =			HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)   )
			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.
						a    ,		a    ,		a    =			parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]   )   )
			else:
						a    ,		a    ,		a    =			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_image_classification",  A,  A   )

			# 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()

			a    =			training_args.get_process_log_level()
			logger.setLevel(A   )
			transformers.utils.logging.set_verbosity(A   )
			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.
			a    =			None
			if os.path.isdir(training_args.output_dir   ) and training_args.do_train and not training_args.overwrite_output_dir:
						a    =			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."   )

    # Set seed before initializing model.
			set_seed(training_args.seed   )

			# Initialize our dataset and prepare it for the 'image-classification' task.
			if data_args.dataset_name is not None:
						a    =			load_dataset(
						    data_args.dataset_name,  data_args.dataset_config_name,  cache_dir=model_args.cache_dir,  task="image-classification",  use_auth_token=True if model_args.use_auth_token else None,  )
			else:
						a    =			{}
						if data_args.train_dir is not None:
									a    =			os.path.join(data_args.train_dir,  "**"   )
						if data_args.validation_dir is not None:
									a    =			os.path.join(data_args.validation_dir,  "**"   )
						a    =			load_dataset(
						    "imagefolder",  data_files=A,  cache_dir=model_args.cache_dir,  task="image-classification",  )

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

			# Prepare label mappings.
			# We'll include these in the model's config to get human readable labels in the Inference API.
			a    =			dataset["train"].features["labels"].names
			a    ,		a    =			{}, {}
			for i, label in enumerate(A   ):
						a    =			str(A   )
						a    =			label

			# Load the accuracy metric from the datasets package
			a    =			evaluate.load("accuracy"   )

			# Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
			# predictions and label_ids field) and has to return a dictionary string to float.
			def compute_metrics(A		:					int   ):
						return metric.compute(predictions=np.argmax(p.predictions,  axis=1   ),  references=p.label_ids   )

			a    =			AutoConfig.from_pretrained(
			    model_args.config_name or model_args.model_name_or_path,  num_labels=len(A   ),  labelaid=A,  idalabel=A,  finetuning_task="image-classification",  cache_dir=model_args.cache_dir,  revision=model_args.model_revision,  use_auth_token=True if model_args.use_auth_token else None,  )
			a    =			AutoModelForImageClassification.from_pretrained(
			    model_args.model_name_or_path,  from_tf=bool(".ckpt" in model_args.model_name_or_path   ),  config=A,  cache_dir=model_args.cache_dir,  revision=model_args.model_revision,  use_auth_token=True if model_args.use_auth_token else None,  ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,  )
			a    =			AutoImageProcessor.from_pretrained(
			    model_args.image_processor_name or model_args.model_name_or_path,  cache_dir=model_args.cache_dir,  revision=model_args.model_revision,  use_auth_token=True if model_args.use_auth_token else None,  )

			# Define torchvision transforms to be applied to each image.
			if "shortest_edge" in image_processor.size:
						a    =			image_processor.size["shortest_edge"]
			else:
						a    =			(image_processor.size["height"], image_processor.size["width"])
			a    =			Normalize(mean=image_processor.image_mean,  std=image_processor.image_std   )
			a    =			Compose(
			    [
			        RandomResizedCrop(A   ),
			        RandomHorizontalFlip(),
			        ToTensor(),
			        normalize,
			    ]   )
			a    =			Compose(
			    [
			        Resize(A   ),
			        CenterCrop(A   ),
			        ToTensor(),
			        normalize,
			    ]   )

			def train_transforms(A		:					Dict   ):
						a    =			[
						    _train_transforms(pil_img.convert("RGB"   )   ) for pil_img in example_batch["image"]
						]
						return example_batch

			def val_transforms(A		:					List[str]   ):
						a    =			[_val_transforms(pil_img.convert("RGB"   )   ) for pil_img in example_batch["image"]]
						return example_batch

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

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

			# Initalize our trainer
			a    =			Trainer(
			    model=A,  args=A,  train_dataset=dataset["train"] if training_args.do_train else None,  eval_dataset=dataset["validation"] if training_args.do_eval else None,  compute_metrics=A,  tokenizer=A,  data_collator=A,  )

			# Training
			if training_args.do_train:
						a    =			None
						if training_args.resume_from_checkpoint is not None:
									a    =			training_args.resume_from_checkpoint
						elif last_checkpoint is not None:
									a    =			last_checkpoint
						a    =			trainer.train(resume_from_checkpoint=A   )
						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:
						a    =			trainer.evaluate()
						trainer.log_metrics("eval",  A   )
						trainer.save_metrics("eval",  A   )

			# Write model card and (optionally) push to hub
			a    =			{
			    "finetuned_from": model_args.model_name_or_path,
			    "tasks": "image-classification",
			    "dataset": data_args.dataset_name,
			    "tags": ["image-classification", "vision"],
			}
			if training_args.push_to_hub:
						trainer.push_to_hub(**A   )
			else:
						trainer.create_model_card(**A   )


if __name__ == "__main__":
		main()













'''simple docstring'''

import collections.abc
from typing import Optional, Tuple, Union

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

from ...activations import ACTaFN
from ...modeling_outputs import BaseModelOutputWithNoAttention, 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_poolformer import PoolFormerConfig


lowercase_					= logging.get_logger(__name__)

# General docstring
lowercase_					= """PoolFormerConfig"""

# Base docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= [1, 512, 7, 7]

# Image classification docstring
lowercase_					= """sail/poolformer_s12"""
lowercase_					= """tabby, tabby cat"""

lowercase_					= [
    """sail/poolformer_s12""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]





def lowerCamelCase						(     __lowerCamelCase :    List[Any]					,       __lowerCamelCase :    float = 0.0					,       __lowerCamelCase :    bool = False    )						->int:
    if drop_prob == 0.0 or not training:
        return input
    _SCREAMING_SNAKE_CASE									=			1 - drop_prob
    _SCREAMING_SNAKE_CASE									=			(input.shape[0],) + (1,) * (input.ndim - 1)  # work with diff dim tensors, not just 2D ConvNets
    _SCREAMING_SNAKE_CASE									=			keep_prob + torch.rand(__lowerCamelCase					,       dtype=input.dtype					,       device=input.device    )
    random_tensor.floor_()  # binarize
    _SCREAMING_SNAKE_CASE									=			input.div(__lowerCamelCase    ) * random_tensor
    return output


class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A = None       )		->   None:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			drop_prob


    def 			snake_case_(		self ,		A       )		->   torch.Tensor:
        return drop_path(A ,		self.drop_prob ,		self.training       )





    def 			snake_case_(		self       )		->   str:
        return "p={}".format(self.drop_prob       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A=None       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			patch_size if isinstance(A ,		collections.abc.Iterable       ) else (patch_size, patch_size)
        _SCREAMING_SNAKE_CASE									=			stride if isinstance(A ,		collections.abc.Iterable       ) else (stride, stride)
        _SCREAMING_SNAKE_CASE									=			padding if isinstance(A ,		collections.abc.Iterable       ) else (padding, padding)

        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		kernel_size=A ,		stride=A ,		padding=A       )
        _SCREAMING_SNAKE_CASE									=			norm_layer(A       ) if norm_layer else nn.Identity()





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        _SCREAMING_SNAKE_CASE									=			self.projection(A       )
        _SCREAMING_SNAKE_CASE									=			self.norm(A       )
        return embeddings




class 					a_     (				nn.GroupNorm       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		**A       )		->   Union[str, Any]:
        super().__init__(1 ,		A ,		**A       )




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.AvgPoolad(A ,		stride=1 ,		padding=pool_size // 2 ,		count_include_pad=A       )





    def 			snake_case_(		self ,		A       )		->   Union[str, Any]:
        return self.pool(A       ) - hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A       )		->   List[Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			nn.Convad(A ,		A ,		1       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerDropPath(A       )
        if isinstance(config.hidden_act ,		A       ):
            _SCREAMING_SNAKE_CASE									=			ACTaFN[config.hidden_act]
        else:
            _SCREAMING_SNAKE_CASE									=			config.hidden_act





    def 			snake_case_(		self ,		A       )		->   Optional[int]:
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.act_fn(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )
        _SCREAMING_SNAKE_CASE									=			self.conva(A       )
        _SCREAMING_SNAKE_CASE									=			self.drop(A       )

        return hidden_states




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A ,		A ,		A ,		A ,		A ,		A       )		->   Union[str, Any]:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			PoolFormerPooling(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerOutput(A ,		A ,		A ,		A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(A       )

        # Useful for training neural nets
        _SCREAMING_SNAKE_CASE									=			PoolFormerDropPath(A       ) if drop_path > 0.0 else nn.Identity()
        _SCREAMING_SNAKE_CASE									=			config.use_layer_scale
        if config.use_layer_scale:
            _SCREAMING_SNAKE_CASE									=			nn.Parameter(
                config.layer_scale_init_value * torch.ones((A)       ) ,		requires_grad=A       )
            _SCREAMING_SNAKE_CASE									=			nn.Parameter(
                config.layer_scale_init_value * torch.ones((A)       ) ,		requires_grad=A       )





    def 			snake_case_(		self ,		A       )		->   Optional[Any]:
        if self.use_layer_scale:
            _SCREAMING_SNAKE_CASE									=			self.pooling(self.before_norm(A       )       )
            _SCREAMING_SNAKE_CASE									=			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * pooling_output
            # First residual connection
            _SCREAMING_SNAKE_CASE									=			hidden_states + self.drop_path(A       )
            _SCREAMING_SNAKE_CASE									=			()

            _SCREAMING_SNAKE_CASE									=			self.output(self.after_norm(A       )       )
            _SCREAMING_SNAKE_CASE									=			self.layer_scale_a.unsqueeze(-1       ).unsqueeze(-1       ) * layer_output
            # Second residual connection
            _SCREAMING_SNAKE_CASE									=			hidden_states + self.drop_path(A       )

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs

        else:
            _SCREAMING_SNAKE_CASE									=			self.drop_path(self.pooling(self.before_norm(A       )       )       )
            # First residual connection
            _SCREAMING_SNAKE_CASE									=			pooling_output + hidden_states
            _SCREAMING_SNAKE_CASE									=			()

            # Second residual connection inside the PoolFormerOutput block
            _SCREAMING_SNAKE_CASE									=			self.drop_path(self.output(self.after_norm(A       )       )       )
            _SCREAMING_SNAKE_CASE									=			hidden_states + layer_output

            _SCREAMING_SNAKE_CASE									=			(output,) + outputs
            return outputs




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Any:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			config
        # stochastic depth decay rule
        _SCREAMING_SNAKE_CASE									=			[x.item() for x in torch.linspace(0 ,		config.drop_path_rate ,		sum(config.depths       )       )]

        # patch embeddings
        _SCREAMING_SNAKE_CASE									=			[]
        for i in range(config.num_encoder_blocks       ):
            embeddings.append(
                PoolFormerEmbeddings(
                    patch_size=config.patch_sizes[i] ,		stride=config.strides[i] ,		padding=config.padding[i] ,		num_channels=config.num_channels if i == 0 else config.hidden_sizes[i - 1] ,		hidden_size=config.hidden_sizes[i] ,		)       )
        _SCREAMING_SNAKE_CASE									=			nn.ModuleList(A       )

        # Transformer blocks
        _SCREAMING_SNAKE_CASE									=			[]
        _SCREAMING_SNAKE_CASE									=			0
        for i in range(config.num_encoder_blocks       ):
            # each block consists of layers
            _SCREAMING_SNAKE_CASE									=			[]
            if i != 0:
                cur += config.depths[i - 1]
            for j in range(config.depths[i]       ):
                layers.append(
                    PoolFormerLayer(
                        A ,		num_channels=config.hidden_sizes[i] ,		pool_size=config.pool_size ,		hidden_size=config.hidden_sizes[i] ,		intermediate_size=int(config.hidden_sizes[i] * config.mlp_ratio       ) ,		drop_path=dpr[cur + j] ,		)       )
            blocks.append(nn.ModuleList(A       )       )

        _SCREAMING_SNAKE_CASE									=			nn.ModuleList(A       )





    def 			snake_case_(		self ,		A ,		A=False ,		A=True       )		->   List[Any]:
        _SCREAMING_SNAKE_CASE									=			() if output_hidden_states else None

        _SCREAMING_SNAKE_CASE									=			pixel_values
        for idx, layers in enumerate(zip(self.patch_embeddings ,		self.block       )       ):
            _SCREAMING_SNAKE_CASE				,       _SCREAMING_SNAKE_CASE									=			layers
            # Get patch embeddings from hidden_states
            _SCREAMING_SNAKE_CASE									=			embedding_layer(A       )
            # Send the embeddings through the blocks
            for _, blk in enumerate(A       ):
                _SCREAMING_SNAKE_CASE									=			blk(A       )
                _SCREAMING_SNAKE_CASE									=			layer_outputs[0]

            if output_hidden_states:
                _SCREAMING_SNAKE_CASE									=			all_hidden_states + (hidden_states,)

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

        return BaseModelOutputWithNoAttention(last_hidden_state=A ,		hidden_states=A       )




class 					a_     (				snake_case_       ):

    '''simple docstring'''
    UpperCamelCase     = PoolFormerConfig
    UpperCamelCase     = '''poolformer'''
    UpperCamelCase     = '''pixel_values'''
    UpperCamelCase     = True


    def 			snake_case_(		self ,		A       )		->   int:
        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.LayerNorm       ):
            module.bias.data.zero_()
            module.weight.data.fill_(1.0       )





    def 			snake_case_(		self ,		A ,		A=False       )		->   Dict:
        if isinstance(A ,		A       ):
            _SCREAMING_SNAKE_CASE									=			value




lowercase_					= R"""
    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use
    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
    behavior.

    Parameters:
        config ([`PoolFormerConfig`]): Model configuration class with all the parameters of the model.
            Initializing with a config file does not load the weights associated with the model, only the
            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.
"""

lowercase_					= R"""
    Args:
        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):
            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See
            [`PoolFormerImageProcessor.__call__`] for details.
"""


@add_start_docstrings(
    '''The bare PoolFormer Model transformer outputting raw hidden-states without any specific head on top.'''      ,    snake_case_      ,    )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   int:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config

        _SCREAMING_SNAKE_CASE									=			PoolFormerEncoder(A       )

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


    def 			snake_case_(		self       )		->   Any:
        return self.embeddings.patch_embeddings





    @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 			snake_case_(		self ,		A = None ,		A = None ,		A = None ,		)		->   Union[Tuple, BaseModelOutputWithNoAttention]:
        _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.encoder(
            A ,		output_hidden_states=A ,		return_dict=A ,		)
        _SCREAMING_SNAKE_CASE									=			encoder_outputs[0]

        if not return_dict:
            return (sequence_output, None) + encoder_outputs[1:]

        return BaseModelOutputWithNoAttention(
            last_hidden_state=A ,		hidden_states=encoder_outputs.hidden_states ,		)




class 					a_     (				nn.Module       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Dict:
        super().__init__()
        _SCREAMING_SNAKE_CASE									=			nn.Linear(config.hidden_size ,		config.hidden_size       )





    def 			snake_case_(		self ,		A       )		->   str:
        _SCREAMING_SNAKE_CASE									=			self.dense(A       )
        return output




@add_start_docstrings(
    '''
    PoolFormer Model transformer with an image classification head on top
    '''      ,    snake_case_      ,    )
class 					a_     (				snake_case_       ):

    '''simple docstring'''


    def __init__(		self ,		A       )		->   Optional[Any]:
        super().__init__(A       )
        _SCREAMING_SNAKE_CASE									=			config.num_labels
        _SCREAMING_SNAKE_CASE									=			PoolFormerModel(A       )

        # Final norm
        _SCREAMING_SNAKE_CASE									=			PoolFormerGroupNorm(config.hidden_sizes[-1]       )
        # Classifier head
        _SCREAMING_SNAKE_CASE									=			(
            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(A       )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT ,		output_type=A ,		config_class=_CONFIG_FOR_DOC ,		expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT ,		)
    def 			snake_case_(		self ,		A = None ,		A = None ,		A = None ,		A = None ,		)		->   Union[Tuple, ImageClassifierOutputWithNoAttention]:
        _SCREAMING_SNAKE_CASE									=			return_dict if return_dict is not None else self.config.use_return_dict

        _SCREAMING_SNAKE_CASE									=			self.poolformer(
            A ,		output_hidden_states=A ,		return_dict=A ,		)

        _SCREAMING_SNAKE_CASE									=			outputs[0]

        _SCREAMING_SNAKE_CASE									=			self.classifier(self.norm(A       ).mean([-2, -1]       )       )

        _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(A ,		A       )
            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(A ,		A       )

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

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



