






'''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


_A			:    Any					  =logging.get_logger(__name__)

# General docstring
_A			:    int					  ='''PoolFormerConfig'''

# Base docstring
_A			:    int					  ='''sail/poolformer_s12'''
_A			:    Optional[int]					  =[1, 512, 7, 7]

# Image classification docstring
_A			:    List[str]					  ='''sail/poolformer_s12'''
_A			:    Optional[Any]					  ='''tabby, tabby cat'''

_A			:    Union[str, Any]					  =[
    '''sail/poolformer_s12''',
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
]





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

class _lowercase			(       nn.Module	):




							def __init__(							self:       List[Any]    ,							UpperCamelCase__:       Optional[float] = None  ):
														super().__init__()
														lowerCamelCase__      :			Tuple 						= drop_prob




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       torch.Tensor  ):
														return drop_path(UpperCamelCase__    ,							self.drop_prob    ,							self.training  )




							def 	lowerCamelCase_       (							self:       Tuple  ):
														return "p={}".format(self.drop_prob  )



class _lowercase			(       nn.Module	):




							def __init__(							self:       Optional[Any]    ,							UpperCamelCase__:       Optional[int]    ,							UpperCamelCase__:       Union[str, Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       List[str]    ,							UpperCamelCase__:       Any=None  ):
														super().__init__()
														lowerCamelCase__      :			str 						= patch_size if isinstance(UpperCamelCase__    ,							collections.abc.Iterable  ) else (patch_size, patch_size)
														lowerCamelCase__      :			str 						= stride if isinstance(UpperCamelCase__    ,							collections.abc.Iterable  ) else (stride, stride)
														lowerCamelCase__      :			List[str] 						= padding if isinstance(UpperCamelCase__    ,							collections.abc.Iterable  ) else (padding, padding)

														lowerCamelCase__      :			Any 						= nn.Convad(UpperCamelCase__    ,							UpperCamelCase__    ,							kernel_size=UpperCamelCase__    ,							stride=UpperCamelCase__    ,							padding=UpperCamelCase__  )
														lowerCamelCase__      :			Dict 						= norm_layer(UpperCamelCase__  ) if norm_layer else nn.Identity()




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Any  ):
														lowerCamelCase__      :			List[str] 						= self.projection(UpperCamelCase__  )
														lowerCamelCase__      :			List[Any] 						= self.norm(UpperCamelCase__  )
														return embeddings



class _lowercase			(       nn.GroupNorm	):




							def __init__(							self:       Optional[int]    ,							UpperCamelCase__:       Dict    ,							**UpperCamelCase__:       Optional[int]  ):
														super().__init__(1    ,							UpperCamelCase__    ,							**UpperCamelCase__  )



class _lowercase			(       nn.Module	):




							def __init__(							self:       Any    ,							UpperCamelCase__:       Optional[int]  ):
														super().__init__()
														lowerCamelCase__      :			Tuple 						= nn.AvgPoolad(UpperCamelCase__    ,							stride=1    ,							padding=pool_size // 2    ,							count_include_pad=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       Tuple  ):
														return self.pool(UpperCamelCase__  ) - hidden_states



class _lowercase			(       nn.Module	):




							def __init__(							self:       int    ,							UpperCamelCase__:       str    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       List[Any]  ):
														super().__init__()
														lowerCamelCase__      :			str 						= nn.Convad(UpperCamelCase__    ,							UpperCamelCase__    ,							1  )
														lowerCamelCase__      :			Optional[int] 						= nn.Convad(UpperCamelCase__    ,							UpperCamelCase__    ,							1  )
														lowerCamelCase__      :			Optional[Any] 						= PoolFormerDropPath(UpperCamelCase__  )
														if isinstance(config.hidden_act    ,							UpperCamelCase__  ):
																					lowerCamelCase__      :			Tuple 						= ACTaFN[config.hidden_act]
														else:
																					lowerCamelCase__      :			int 						= config.hidden_act




							def 	lowerCamelCase_       (							self:       Union[str, Any]    ,							UpperCamelCase__:       Optional[int]  ):
														lowerCamelCase__      :			Tuple 						= self.conva(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= self.act_fn(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= self.drop(UpperCamelCase__  )
														lowerCamelCase__      :			str 						= self.conva(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= self.drop(UpperCamelCase__  )

														return hidden_states



class _lowercase			(       nn.Module	):




							def __init__(							self:       str    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Dict    ,							UpperCamelCase__:       Tuple    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]  ):
														super().__init__()
														lowerCamelCase__      :			List[Any] 						= PoolFormerPooling(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= PoolFormerOutput(UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__    ,							UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= PoolFormerGroupNorm(UpperCamelCase__  )
														lowerCamelCase__      :			Tuple 						= PoolFormerGroupNorm(UpperCamelCase__  )

														# Useful for training neural nets
														lowerCamelCase__      :			Dict 						= PoolFormerDropPath(UpperCamelCase__  ) if drop_path > 0.0 else nn.Identity()
														lowerCamelCase__      :			Dict 						= config.use_layer_scale
														if config.use_layer_scale:
																					lowerCamelCase__      :			Dict 						= nn.Parameter(
																					    config.layer_scale_init_value * torch.ones((UpperCamelCase__)  )    ,							requires_grad=UpperCamelCase__  )
																					lowerCamelCase__      :			Optional[int] 						= nn.Parameter(
																					    config.layer_scale_init_value * torch.ones((UpperCamelCase__)  )    ,							requires_grad=UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Any    ,							UpperCamelCase__:       int  ):
														if self.use_layer_scale:
																					lowerCamelCase__      :			Any 						= self.pooling(self.before_norm(UpperCamelCase__  )  )
																					lowerCamelCase__      :			Any 						= self.layer_scale_a.unsqueeze(-1  ).unsqueeze(-1  ) * pooling_output
																					# First residual connection
																					lowerCamelCase__      :			Tuple 						= hidden_states + self.drop_path(UpperCamelCase__  )
																					lowerCamelCase__      :			int 						= ()

																					lowerCamelCase__      :			Any 						= self.output(self.after_norm(UpperCamelCase__  )  )
																					lowerCamelCase__      :			List[Any] 						= self.layer_scale_a.unsqueeze(-1  ).unsqueeze(-1  ) * layer_output
																					# Second residual connection
																					lowerCamelCase__      :			Dict 						= hidden_states + self.drop_path(UpperCamelCase__  )

																					lowerCamelCase__      :			Optional[Any] 						= (output,) + outputs
																					return outputs

														else:
																					lowerCamelCase__      :			List[str] 						= self.drop_path(self.pooling(self.before_norm(UpperCamelCase__  )  )  )
																					# First residual connection
																					lowerCamelCase__      :			int 						= pooling_output + hidden_states
																					lowerCamelCase__      :			int 						= ()

																					# Second residual connection inside the PoolFormerOutput block
																					lowerCamelCase__      :			Optional[Any] 						= self.drop_path(self.output(self.after_norm(UpperCamelCase__  )  )  )
																					lowerCamelCase__      :			Tuple 						= hidden_states + layer_output

																					lowerCamelCase__      :			Union[str, Any] 						= (output,) + outputs
																					return outputs



class _lowercase			(       nn.Module	):




							def __init__(							self:       Union[str, Any]    ,							UpperCamelCase__:       int  ):
														super().__init__()
														lowerCamelCase__      :			Tuple 						= config
														# stochastic depth decay rule
														lowerCamelCase__      :			Optional[int] 						= [x.item() for x in torch.linspace(0    ,							config.drop_path_rate    ,							sum(config.depths  )  )]

														# patch embeddings
														lowerCamelCase__      :			Dict 						= []
														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]    ,							)  )
														lowerCamelCase__      :			Optional[Any] 						= nn.ModuleList(UpperCamelCase__  )

														# Transformer blocks
														lowerCamelCase__      :			List[str] 						= []
														lowerCamelCase__      :			Any 						= 0
														for i in range(config.num_encoder_blocks  ):
																					# each block consists of layers
																					lowerCamelCase__      :			str 						= []
																					if i != 0:
																												cur += config.depths[i - 1]
																					for j in range(config.depths[i]  ):
																												layers.append(
																												    PoolFormerLayer(
																												        UpperCamelCase__    ,							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(UpperCamelCase__  )  )

														lowerCamelCase__      :			List[str] 						= nn.ModuleList(UpperCamelCase__  )




							def 	lowerCamelCase_       (							self:       Optional[int]    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Union[str, Any]=False    ,							UpperCamelCase__:       Optional[Any]=True  ):
														lowerCamelCase__      :			List[Any] 						= () if output_hidden_states else None

														lowerCamelCase__      :			List[str] 						= pixel_values
														for idx, layers in enumerate(zip(self.patch_embeddings    ,							self.block  )  ):
																					lowerCamelCase__						,	lowerCamelCase__      :			Union[str, Any] 						= layers
																					# Get patch embeddings from hidden_states
																					lowerCamelCase__      :			List[Any] 						= embedding_layer(UpperCamelCase__  )
																					# Send the embeddings through the blocks
																					for _, blk in enumerate(UpperCamelCase__  ):
																												lowerCamelCase__      :			Any 						= blk(UpperCamelCase__  )
																												lowerCamelCase__      :			Tuple 						= layer_outputs[0]

																					if output_hidden_states:
																												lowerCamelCase__      :			Tuple 						= 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=UpperCamelCase__    ,							hidden_states=UpperCamelCase__  )



class _lowercase			(       _lowercase	):
							a          = PoolFormerConfig
							a          = """poolformer"""
							a          = """pixel_values"""
							a          = True




							def 	lowerCamelCase_       (							self:       List[Any]    ,							UpperCamelCase__:       Union[str, Any]  ):
														if isinstance(UpperCamelCase__    ,							(nn.Linear, nn.Convad)  ):
																					module.weight.data.normal_(mean=0.0    ,							std=self.config.initializer_range  )
																					if module.bias is not None:
																												module.bias.data.zero_()
														elif isinstance(UpperCamelCase__    ,							nn.LayerNorm  ):
																					module.bias.data.zero_()
																					module.weight.data.fill_(1.0  )




							def 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       int    ,							UpperCamelCase__:       Union[str, Any]=False  ):
														if isinstance(UpperCamelCase__    ,							UpperCamelCase__  ):
																					lowerCamelCase__      :			Optional[Any] 						= value



_A			:    Any					  =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.
'''

_A			:    List[Any]					  =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."""       ,   _lowercase       ,   )
class _lowercase			(       _lowercase	):




							def __init__(							self:       str    ,							UpperCamelCase__:       str  ):
														super().__init__(UpperCamelCase__  )
														lowerCamelCase__      :			Optional[Any] 						= config

														lowerCamelCase__      :			List[str] 						= PoolFormerEncoder(UpperCamelCase__  )

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




							def 	lowerCamelCase_       (							self:       List[str]  ):
														return self.embeddings.patch_embeddings




							@add_start_docstrings_to_model_forward(UpperCamelCase__  )
							@add_code_sample_docstrings(
							    checkpoint=_CHECKPOINT_FOR_DOC    ,							output_type=UpperCamelCase__    ,							config_class=_CONFIG_FOR_DOC    ,							modality="""vision"""    ,							expected_output=_EXPECTED_OUTPUT_SHAPE    ,							)
							def 	lowerCamelCase_       (							self:       str    ,							UpperCamelCase__:       Optional[torch.FloatTensor] = None    ,							UpperCamelCase__:       Optional[bool] = None    ,							UpperCamelCase__:       Optional[bool] = None    ,							):
														lowerCamelCase__      :			Dict 						= (
														    output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
														)
														lowerCamelCase__      :			List[str] 						= 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"""  )

														lowerCamelCase__      :			List[Any] 						= self.encoder(
														    UpperCamelCase__    ,							output_hidden_states=UpperCamelCase__    ,							return_dict=UpperCamelCase__    ,							)
														lowerCamelCase__      :			Any 						= encoder_outputs[0]

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

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



class _lowercase			(       nn.Module	):




							def __init__(							self:       Tuple    ,							UpperCamelCase__:       Optional[int]  ):
														super().__init__()
														lowerCamelCase__      :			Any 						= nn.Linear(config.hidden_size    ,							config.hidden_size  )




							def 	lowerCamelCase_       (							self:       Dict    ,							UpperCamelCase__:       Optional[Any]  ):
														lowerCamelCase__      :			Union[str, Any] 						= self.dense(UpperCamelCase__  )
														return output



@add_start_docstrings(
    """
    PoolFormer Model transformer with an image classification head on top
    """       ,   _lowercase       ,   )
class _lowercase			(       _lowercase	):




							def __init__(							self:       Any    ,							UpperCamelCase__:       Optional[int]  ):
														super().__init__(UpperCamelCase__  )
														lowerCamelCase__      :			str 						= config.num_labels
														lowerCamelCase__      :			Dict 						= PoolFormerModel(UpperCamelCase__  )

														# Final norm
														lowerCamelCase__      :			Optional[int] 						= PoolFormerGroupNorm(config.hidden_sizes[-1]  )
														# Classifier head
														lowerCamelCase__      :			Dict 						= (
														    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 	lowerCamelCase_       (							self:       Tuple    ,							UpperCamelCase__:       Optional[torch.FloatTensor] = None    ,							UpperCamelCase__:       Optional[torch.LongTensor] = None    ,							UpperCamelCase__:       Optional[bool] = None    ,							UpperCamelCase__:       Optional[bool] = None    ,							):
														lowerCamelCase__      :			Union[str, Any] 						= return_dict if return_dict is not None else self.config.use_return_dict

														lowerCamelCase__      :			List[Any] 						= self.poolformer(
														    UpperCamelCase__    ,							output_hidden_states=UpperCamelCase__    ,							return_dict=UpperCamelCase__    ,							)

														lowerCamelCase__      :			int 						= outputs[0]

														lowerCamelCase__      :			str 						= self.classifier(self.norm(UpperCamelCase__  ).mean([-2, -1]  )  )

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

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

														if not return_dict:
																					lowerCamelCase__      :			Optional[Any] 						= (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 argparse
import collections
import json
from pathlib import Path

import requests
import torch
import yaml
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTImageProcessor,
    MobileViTVaConfig,
    MobileViTVaForImageClassification,
    MobileViTVaForSemanticSegmentation,
)
from transformers.utils import logging


logging.set_verbosity_info()
_A			:    int					  =logging.get_logger(__name__)





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> str:
							print("""Loading config file..."""   )

							def flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase=""       ,    UpperCamelCase="."   ):
														lowerCamelCase__      :			Optional[int] 						= []
														for k, v in d.items():
																					lowerCamelCase__      :			Optional[int] 						= parent_key + sep + k if parent_key else k
																					if isinstance(UpperCamelCase       ,    collections.abc.MutableMapping   ):
																												items.extend(flatten_yaml_as_dict(UpperCamelCase       ,    UpperCamelCase       ,    sep=UpperCamelCase   ).items()   )
																					else:
																												items.append((new_key, v)   )
														return dict(UpperCamelCase   )

							lowerCamelCase__      :			Any 						= argparse.Namespace()
							with open(UpperCamelCase       ,    """r"""   ) as yaml_file:
														try:
																					lowerCamelCase__      :			int 						= yaml.load(UpperCamelCase       ,    Loader=yaml.FullLoader   )

																					lowerCamelCase__      :			Tuple 						= flatten_yaml_as_dict(UpperCamelCase   )
																					for k, v in flat_cfg.items():
																												setattr(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )
														except yaml.YAMLError as exc:
																					logger.error("""Error while loading config file: {}. Error message: {}""".format(UpperCamelCase       ,    str(UpperCamelCase   )   )   )
							return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase   )   -> Optional[Any]:
							lowerCamelCase__      :			Union[str, Any] 						= MobileViTVaConfig()

							lowerCamelCase__      :			str 						= False

							# dataset
							if task_name.startswith("""imagenet1k_"""   ):
														lowerCamelCase__      :			Optional[Any] 						= 1000
														if int(task_name.strip().split("""_"""   )[-1]   ) == 384:
																					lowerCamelCase__      :			int 						= 384
														else:
																					lowerCamelCase__      :			Optional[int] 						= 256
														lowerCamelCase__      :			str 						= """imagenet-1k-id2label.json"""
							elif task_name.startswith("""imagenet21k_to_1k_"""   ):
														lowerCamelCase__      :			Tuple 						= 21000
														if int(task_name.strip().split("""_"""   )[-1]   ) == 384:
																					lowerCamelCase__      :			str 						= 384
														else:
																					lowerCamelCase__      :			Any 						= 256
														lowerCamelCase__      :			int 						= """imagenet-22k-id2label.json"""
							elif task_name.startswith("""ade20k_"""   ):
														lowerCamelCase__      :			Dict 						= 151
														lowerCamelCase__      :			str 						= 512
														lowerCamelCase__      :			List[Any] 						= """ade20k-id2label.json"""
														lowerCamelCase__      :			Union[str, Any] 						= True
							elif task_name.startswith("""voc_"""   ):
														lowerCamelCase__      :			Tuple 						= 21
														lowerCamelCase__      :			Optional[int] 						= 512
														lowerCamelCase__      :			List[Any] 						= """pascal-voc-id2label.json"""
														lowerCamelCase__      :			Tuple 						= True

							# orig_config
							lowerCamelCase__      :			Optional[int] 						= load_orig_config_file(UpperCamelCase   )
							assert getattr(UpperCamelCase       ,    """model.classification.name"""       ,    -1   ) == "mobilevit_v2", "Invalid model"
							lowerCamelCase__      :			int 						= getattr(UpperCamelCase       ,    """model.classification.mitv2.width_multiplier"""       ,    1.0   )
							assert (
							    getattr(UpperCamelCase       ,    """model.classification.mitv2.attn_norm_layer"""       ,    -1   ) == "layer_norm_2d"
							), "Norm layers other than layer_norm_2d is not supported"
							lowerCamelCase__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.classification.activation.name"""       ,    """swish"""   )
							# config.image_size == getattr(orig_config,  'sampler.bs.crop_size_width', 256)

							if is_segmentation_model:
														lowerCamelCase__      :			Any 						= getattr(UpperCamelCase       ,    """model.segmentation.output_stride"""       ,    16   )
														if "_deeplabv3" in task_name:
																					lowerCamelCase__      :			str 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_rates"""       ,    [12, 24, 36]   )
																					lowerCamelCase__      :			Tuple 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_out_channels"""       ,    512   )
																					lowerCamelCase__      :			List[Any] 						= getattr(UpperCamelCase       ,    """model.segmentation.deeplabv3.aspp_dropout"""       ,    0.1   )

    # id2label
							lowerCamelCase__      :			Tuple 						= """huggingface/label-files"""
							lowerCamelCase__      :			Optional[Any] 						= json.load(open(hf_hub_download(UpperCamelCase       ,    UpperCamelCase       ,    repo_type="""dataset"""   )       ,    """r"""   )   )
							lowerCamelCase__      :			Union[str, Any] 						= {int(UpperCamelCase   ): v for k, v in idalabel.items()}
							lowerCamelCase__      :			int 						= idalabel
							lowerCamelCase__      :			Optional[Any] 						= {v: k for k, v in idalabel.items()}

							return config





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Any:
							lowerCamelCase__      :			List[Any] 						= dct.pop(UpperCamelCase   )
							lowerCamelCase__      :			Dict 						= val





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase=False   )   -> Tuple:
							if base_model:
														lowerCamelCase__      :			Optional[int] 						= """"""
							else:
														lowerCamelCase__      :			Optional[Any] 						= """mobilevitv2."""

							lowerCamelCase__      :			List[str] 						= []
							for k in state_dict.keys():
														if k[:8] == "encoder.":
																					lowerCamelCase__      :			Optional[Any] 						= k[8:]
														else:
																					lowerCamelCase__      :			Optional[Any] 						= k

														if ".block." in k:
																					lowerCamelCase__      :			Dict 						= k_new.replace(""".block."""       ,    """."""   )
														if ".conv." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace(""".conv."""       ,    """.convolution."""   )
														if ".norm." in k:
																					lowerCamelCase__      :			str 						= k_new.replace(""".norm."""       ,    """.normalization."""   )

														if "conv_1." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace("""conv_1."""       ,    f'''{model_prefix}conv_stem.'''   )
														for i in [1, 2]:
																					if f'''layer_{i}.''' in k:
																												lowerCamelCase__      :			Optional[Any] 						= k_new.replace(f'''layer_{i}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.layer.'''   )
														if ".exp_1x1." in k:
																					lowerCamelCase__      :			Dict 						= k_new.replace(""".exp_1x1."""       ,    """.expand_1x1."""   )
														if ".red_1x1." in k:
																					lowerCamelCase__      :			str 						= k_new.replace(""".red_1x1."""       ,    """.reduce_1x1."""   )

														for i in [3, 4, 5]:
																					if f'''layer_{i}.0.''' in k:
																												lowerCamelCase__      :			List[str] 						= k_new.replace(f'''layer_{i}.0.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.downsampling_layer.'''   )
																					if f'''layer_{i}.1.local_rep.0.''' in k:
																												lowerCamelCase__      :			Optional[Any] 						= k_new.replace(f'''layer_{i}.1.local_rep.0.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_kxk.'''   )
																					if f'''layer_{i}.1.local_rep.1.''' in k:
																												lowerCamelCase__      :			Dict 						= k_new.replace(f'''layer_{i}.1.local_rep.1.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_1x1.'''   )

														for i in [3, 4, 5]:
																					if i == 3:
																												lowerCamelCase__      :			int 						= [0, 1]
																					elif i == 4:
																												lowerCamelCase__      :			str 						= [0, 1, 2, 3]
																					elif i == 5:
																												lowerCamelCase__      :			Dict 						= [0, 1, 2]

																					for j in j_in:
																												if f'''layer_{i}.1.global_rep.{j}.''' in k:
																																			lowerCamelCase__      :			List[Any] 						= k_new.replace(
																																			    f'''layer_{i}.1.global_rep.{j}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.transformer.layer.{j}.'''   )
																					if f'''layer_{i}.1.global_rep.{j+1}.''' in k:
																												lowerCamelCase__      :			Optional[int] 						= k_new.replace(
																												    f'''layer_{i}.1.global_rep.{j+1}.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.layernorm.'''   )

																					if f'''layer_{i}.1.conv_proj.''' in k:
																												lowerCamelCase__      :			Optional[int] 						= k_new.replace(f'''layer_{i}.1.conv_proj.'''       ,    f'''{model_prefix}encoder.layer.{i-1}.conv_projection.'''   )

														if "pre_norm_attn.0." in k:
																					lowerCamelCase__      :			str 						= k_new.replace("""pre_norm_attn.0."""       ,    """layernorm_before."""   )
														if "pre_norm_attn.1." in k:
																					lowerCamelCase__      :			str 						= k_new.replace("""pre_norm_attn.1."""       ,    """attention."""   )
														if "pre_norm_ffn.0." in k:
																					lowerCamelCase__      :			Optional[Any] 						= k_new.replace("""pre_norm_ffn.0."""       ,    """layernorm_after."""   )
														if "pre_norm_ffn.1." in k:
																					lowerCamelCase__      :			Union[str, Any] 						= k_new.replace("""pre_norm_ffn.1."""       ,    """ffn.conv1."""   )
														if "pre_norm_ffn.3." in k:
																					lowerCamelCase__      :			List[Any] 						= k_new.replace("""pre_norm_ffn.3."""       ,    """ffn.conv2."""   )

														if "classifier.1." in k:
																					lowerCamelCase__      :			Tuple 						= k_new.replace("""classifier.1."""       ,    """classifier."""   )

														if "seg_head." in k:
																					lowerCamelCase__      :			Optional[int] 						= k_new.replace("""seg_head."""       ,    """segmentation_head."""   )
														if ".aspp_layer." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_layer."""       ,    """."""   )
														if ".aspp_pool." in k:
																					lowerCamelCase__      :			Any 						= k_new.replace(""".aspp_pool."""       ,    """."""   )

														rename_keys.append((k, k_new)   )
							return rename_keys





def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase   )   -> List[str]:
							lowerCamelCase__      :			Union[str, Any] 						= []
							for k in state_dict.keys():
														if k.startswith("""seg_head.aux_head."""   ):
																					keys_to_ignore.append(UpperCamelCase   )
							for k in keys_to_ignore:
														state_dict.pop(UpperCamelCase       ,    UpperCamelCase   )





def 				SCREAMING_SNAKE_CASE_ ()   -> Dict:
							lowerCamelCase__      :			Union[str, Any] 						= """http://images.cocodataset.org/val2017/000000039769.jpg"""
							# url = "https://cdn.britannica.com/86/141086-050-9D7C75EE/Gulfstream-G450-business-jet-passengers.jpg"
							lowerCamelCase__      :			Tuple 						= Image.open(requests.get(UpperCamelCase       ,    stream=UpperCamelCase   ).raw   )
							return im





@torch.no_grad()
def 				SCREAMING_SNAKE_CASE_ (UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )   -> Dict:
							lowerCamelCase__      :			str 						= get_mobilevitva_config(UpperCamelCase       ,    UpperCamelCase   )

							# load original state_dict
							lowerCamelCase__      :			List[str] 						= torch.load(UpperCamelCase       ,    map_location="""cpu"""   )

							# load huggingface model
							if task_name.startswith("""ade20k_"""   ) or task_name.startswith("""voc_"""   ):
														lowerCamelCase__      :			int 						= MobileViTVaForSemanticSegmentation(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Tuple 						= False
							else:
														lowerCamelCase__      :			int 						= MobileViTVaForImageClassification(UpperCamelCase   ).eval()
														lowerCamelCase__      :			Optional[Any] 						= False

							# remove and rename some keys of load the original model
							lowerCamelCase__      :			Tuple 						= checkpoint
							remove_unused_keys(UpperCamelCase   )
							lowerCamelCase__      :			Union[str, Any] 						= create_rename_keys(UpperCamelCase       ,    base_model=UpperCamelCase   )
							for rename_key_src, rename_key_dest in rename_keys:
														rename_key(UpperCamelCase       ,    UpperCamelCase       ,    UpperCamelCase   )

							# load modified state_dict
							model.load_state_dict(UpperCamelCase   )

							# Check outputs on an image, prepared by MobileViTImageProcessor
							lowerCamelCase__      :			int 						= MobileViTImageProcessor(crop_size=config.image_size       ,    size=config.image_size + 32   )
							lowerCamelCase__      :			Dict 						= image_processor(images=prepare_img()       ,    return_tensors="""pt"""   )
							lowerCamelCase__      :			str 						= model(**UpperCamelCase   )

							# verify classification model
							if task_name.startswith("""imagenet"""   ):
														lowerCamelCase__      :			Dict 						= outputs.logits
														lowerCamelCase__      :			Optional[Any] 						= logits.argmax(-1   ).item()
														print("""Predicted class:"""       ,    model.config.idalabel[predicted_class_idx]   )
														if task_name.startswith("""imagenet1k_256"""   ) and config.width_multiplier == 1.0:
																					# expected_logits for base variant
																					lowerCamelCase__      :			Optional[Any] 						= torch.tensor([-1.63_36E00, -7.32_04E-02, -5.18_83E-01]   )
																					assert torch.allclose(logits[0, :3]       ,    UpperCamelCase       ,    atol=1E-4   )

							Path(UpperCamelCase   ).mkdir(exist_ok=UpperCamelCase   )
							print(f'''Saving model {task_name} to {pytorch_dump_folder_path}'''   )
							model.save_pretrained(UpperCamelCase   )
							print(f'''Saving image processor to {pytorch_dump_folder_path}'''   )
							image_processor.save_pretrained(UpperCamelCase   )


if __name__ == "__main__":
			_A			:    Optional[int]					  =argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--task''',
			    default='''imagenet1k_256''',
			    type=str,
			    help=(
			        '''Name of the task for which the MobileViTV2 model you\'d like to convert is trained on . '''
			        '''
                Classification (ImageNet-1k)
                    - MobileViTV2 (256x256) : imagenet1k_256
                    - MobileViTV2 (Trained on 256x256 and Finetuned on 384x384) : imagenet1k_384
                    - MobileViTV2 (Trained on ImageNet-21k and Finetuned on ImageNet-1k 256x256) :
                      imagenet21k_to_1k_256
                    - MobileViTV2 (Trained on ImageNet-21k, Finetuned on ImageNet-1k 256x256, and Finetuned on
                      ImageNet-1k 384x384) : imagenet21k_to_1k_384
                Segmentation
                    - ADE20K Dataset : ade20k_deeplabv3
                    - Pascal VOC 2012 Dataset: voc_deeplabv3
            '''
			    ),
			    choices=[
			        '''imagenet1k_256''',
			        '''imagenet1k_384''',
			        '''imagenet21k_to_1k_256''',
			        '''imagenet21k_to_1k_384''',
			        '''ade20k_deeplabv3''',
			        '''voc_deeplabv3''',
			    ],
			)

			parser.add_argument(
			    '''--orig_checkpoint_path''', required=True, type=str, help='''Path to the original state dict (.pt file).'''
			)
			parser.add_argument('''--orig_config_path''', required=True, type=str, help='''Path to the original config file.''')
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
			)

			_A			:    Dict					  =parser.parse_args()
			convert_mobilevitva_checkpoint(
			    args.task, args.orig_checkpoint_path, args.orig_config_path, args.pytorch_dump_folder_path
			)

