

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 ...modeling_outputs import (
    BackboneOutput,
    BaseModelOutputWithNoAttention,
    BaseModelOutputWithPoolingAndNoAttention,
    ImageClassifierOutputWithNoAttention,
)
from ...modeling_utils import PreTrainedModel
from ...utils import (
    add_code_sample_docstrings,
    add_start_docstrings,
    add_start_docstrings_to_model_forward,
    logging,
    replace_return_docstrings,
)
from ...utils.backbone_utils import BackboneMixin
from .configuration_resnet import ResNetConfig


lowercase_									=   logging.get_logger(__name__)

# General docstring
lowercase_									=   'ResNetConfig'

# Base docstring
lowercase_									=   'microsoft/resnet-50'
lowercase_									=   [1, 2_0_4_8, 7, 7]

# Image classification docstring
lowercase_									=   'microsoft/resnet-50'
lowercase_									=   'tiger cat'

lowercase_									=   [
    'microsoft/resnet-50',
    # See all resnet models at https://huggingface.co/models?filter=resnet
]





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 3							,   lowerCAmelCase = 1							,   lowerCAmelCase = "relu"					)  -> List[Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =nn.Convad(
            lowerCAmelCase							,   lowerCAmelCase							,   kernel_size=lowerCAmelCase							,   stride=lowerCAmelCase							,   padding=kernel_size // 2							,   bias=lowerCAmelCase					)
        _lowercase	       =nn.BatchNormad(lowerCAmelCase					)
        _lowercase	       =ACTaFN[activation] if activation is not None else nn.Identity()




  def   A__     (				self							,   lowerCAmelCase					)  -> Tensor:
        '''simple docstring'''






        _lowercase	       =self.convolution(lowerCAmelCase					)
        _lowercase	       =self.normalization(lowerCAmelCase					)
        _lowercase	       =self.activation(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase					)  -> List[str]:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =ResNetConvLayer(
            config.num_channels							,   config.embedding_size							,   kernel_size=7							,   stride=2							,   activation=config.hidden_act					)
        _lowercase	       =nn.MaxPoolad(kernel_size=3							,   stride=2							,   padding=1					)
        _lowercase	       =config.num_channels




  def   A__     (				self							,   lowerCAmelCase					)  -> Tensor:
        '''simple docstring'''






        _lowercase	       =pixel_values.shape[1]
        if num_channels != self.num_channels:
              raise ValueError(
                  'Make sure that the channel dimension of the pixel values match with the one set in the configuration.'					)
        _lowercase	       =self.embedder(lowerCAmelCase					)
        _lowercase	       =self.pooler(lowerCAmelCase					)
        return embedding





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 2					)  -> Dict:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =nn.Convad(lowerCAmelCase							,   lowerCAmelCase							,   kernel_size=1							,   stride=lowerCAmelCase							,   bias=lowerCAmelCase					)
        _lowercase	       =nn.BatchNormad(lowerCAmelCase					)




  def   A__     (				self							,   lowerCAmelCase					)  -> Tensor:
        '''simple docstring'''






        _lowercase	       =self.convolution(lowerCAmelCase					)
        _lowercase	       =self.normalization(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 1							,   lowerCAmelCase = "relu"					)  -> List[Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =in_channels != out_channels or stride != 1
        _lowercase	       =(
            ResNetShortCut(lowerCAmelCase							,   lowerCAmelCase							,   stride=lowerCAmelCase					) if should_apply_shortcut else nn.Identity()
        )
        _lowercase	       =nn.Sequential(
            ResNetConvLayer(lowerCAmelCase							,   lowerCAmelCase							,   stride=lowerCAmelCase					)							,   ResNetConvLayer(lowerCAmelCase							,   lowerCAmelCase							,   activation=lowerCAmelCase					)							,   )
        _lowercase	       =ACTaFN[activation]




  def   A__     (				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        _lowercase	       =hidden_state
        _lowercase	       =self.layer(lowerCAmelCase					)
        _lowercase	       =self.shortcut(lowerCAmelCase					)
        hidden_state += residual
        _lowercase	       =self.activation(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 1							,   lowerCAmelCase = "relu"							,   lowerCAmelCase = 4					)  -> List[Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =in_channels != out_channels or stride != 1
        _lowercase	       =out_channels // reduction
        _lowercase	       =(
            ResNetShortCut(lowerCAmelCase							,   lowerCAmelCase							,   stride=lowerCAmelCase					) if should_apply_shortcut else nn.Identity()
        )
        _lowercase	       =nn.Sequential(
            ResNetConvLayer(lowerCAmelCase							,   lowerCAmelCase							,   kernel_size=1					)							,   ResNetConvLayer(lowerCAmelCase							,   lowerCAmelCase							,   stride=lowerCAmelCase					)							,   ResNetConvLayer(lowerCAmelCase							,   lowerCAmelCase							,   kernel_size=1							,   activation=lowerCAmelCase					)							,   )
        _lowercase	       =ACTaFN[activation]




  def   A__     (				self							,   lowerCAmelCase					)  -> Optional[Any]:
        '''simple docstring'''






        _lowercase	       =hidden_state
        _lowercase	       =self.layer(lowerCAmelCase					)
        _lowercase	       =self.shortcut(lowerCAmelCase					)
        hidden_state += residual
        _lowercase	       =self.activation(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase = 2							,   lowerCAmelCase = 2							,   )  -> Union[str, Any]:
        '''simple docstring'''






        super().__init__()

        _lowercase	       =ResNetBottleNeckLayer if config.layer_type == 'bottleneck' else ResNetBasicLayer

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




  def   A__     (				self							,   lowerCAmelCase					)  -> Tensor:
        '''simple docstring'''






        _lowercase	       =input
        for layer in self.layers:
              _lowercase	       =layer(lowerCAmelCase					)
        return hidden_state





class  __lowerCAmelCase     (					nn.Module							):




  def __init__(				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        super().__init__()
        _lowercase	       =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(
            ResNetStage(
                lowerCAmelCase							,   config.embedding_size							,   config.hidden_sizes[0]							,   stride=2 if config.downsample_in_first_stage else 1							,   depth=config.depths[0]							,   )					)
        _lowercase	       =zip(config.hidden_sizes							,   config.hidden_sizes[1:]					)
        for (in_channels, out_channels), depth in zip(lowerCAmelCase							,   config.depths[1:]					):
              self.stages.append(ResNetStage(lowerCAmelCase							,   lowerCAmelCase							,   lowerCAmelCase							,   depth=lowerCAmelCase					)					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = False							,   lowerCAmelCase = True					)  -> BaseModelOutputWithNoAttention:
        '''simple docstring'''






        _lowercase	       =() if output_hidden_states else None

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

              _lowercase	       =stage_module(lowerCAmelCase					)

        if output_hidden_states:
              _lowercase	       =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=lowerCAmelCase							,   hidden_states=lowerCAmelCase							,   )





class  __lowerCAmelCase     (					snake_case_							):
  _a									=  ResNetConfig
  _a									=  """resnet"""
  _a									=  """pixel_values"""
  _a									=  True




  def   A__     (				self							,   lowerCAmelCase					)  -> Tuple:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   nn.Convad					):
              nn.init.kaiming_normal_(module.weight							,   mode='fan_out'							,   nonlinearity='relu'					)
        elif isinstance(lowerCAmelCase							,   (nn.BatchNormad, nn.GroupNorm)					):
              nn.init.constant_(module.weight							,   1					)
              nn.init.constant_(module.bias							,   0					)




  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase=False					)  -> Tuple:
        '''simple docstring'''






        if isinstance(lowerCAmelCase							,   lowerCAmelCase					):
              _lowercase	       =value
lowercase_									=   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 ([`ResNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n'

lowercase_									=   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 [`~utils.ModelOutput`] instead of a plain tuple.\n'





@add_start_docstrings(
    """The bare ResNet model outputting raw features without any specific head on top."""			,			snake_case_			,			)
class  __lowerCAmelCase     (					snake_case_							):




  def __init__(				self							,   lowerCAmelCase					)  -> Union[str, Any]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        _lowercase	       =config
        _lowercase	       =ResNetEmbeddings(lowerCAmelCase					)
        _lowercase	       =ResNetEncoder(lowerCAmelCase					)
        _lowercase	       =nn.AdaptiveAvgPoolad((1, 1)					)
        # Initialize weights and apply final processing
        self.post_init()




  @add_start_docstrings_to_model_forward(lowerCAmelCase					)
  @add_code_sample_docstrings(
      checkpoint=_CHECKPOINT_FOR_DOC							,   output_type=lowerCAmelCase							,   config_class=_CONFIG_FOR_DOC							,   modality='vision'							,   expected_output=_EXPECTED_OUTPUT_SHAPE							,   )
  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None					)  -> BaseModelOutputWithPoolingAndNoAttention:
        '''simple docstring'''






        _lowercase	       =(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )
        _lowercase	       =return_dict if return_dict is not None else self.config.use_return_dict

        _lowercase	       =self.embedder(lowerCAmelCase					)

        _lowercase	       =self.encoder(
            lowerCAmelCase							,   output_hidden_states=lowerCAmelCase							,   return_dict=lowerCAmelCase					)

        _lowercase	       =encoder_outputs[0]

        _lowercase	       =self.pooler(lowerCAmelCase					)

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

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





@add_start_docstrings(
    """\n    ResNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    """			,			snake_case_			,			)
class  __lowerCAmelCase     (					snake_case_							):




  def __init__(				self							,   lowerCAmelCase					)  -> str:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        _lowercase	       =config.num_labels
        _lowercase	       =ResNetModel(lowerCAmelCase					)
        # classification head
        _lowercase	       =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(lowerCAmelCase					)
  @add_code_sample_docstrings(
      checkpoint=_IMAGE_CLASS_CHECKPOINT							,   output_type=lowerCAmelCase							,   config_class=_CONFIG_FOR_DOC							,   expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT							,   )
  def   A__     (				self							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   lowerCAmelCase = None							,   )  -> ImageClassifierOutputWithNoAttention:
        '''simple docstring'''






        _lowercase	       =return_dict if return_dict is not None else self.config.use_return_dict

        _lowercase	       =self.resnet(lowerCAmelCase							,   output_hidden_states=lowerCAmelCase							,   return_dict=lowerCAmelCase					)

        _lowercase	       =outputs.pooler_output if return_dict else outputs[1]

        _lowercase	       =self.classifier(lowerCAmelCase					)

        _lowercase	       =None

        if labels is not None:
              if self.config.problem_type is None:
                    if self.num_labels == 1:
                          _lowercase	       ='regression'
                    elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int):
                          _lowercase	       ='single_label_classification'
                    else:
                          _lowercase	       ='multi_label_classification'
              if self.config.problem_type == "regression":
                    _lowercase	       =MSELoss()
                    if self.num_labels == 1:
                          _lowercase	       =loss_fct(logits.squeeze()							,   labels.squeeze()					)
                    else:
                          _lowercase	       =loss_fct(lowerCAmelCase							,   lowerCAmelCase					)
              elif self.config.problem_type == "single_label_classification":
                    _lowercase	       =CrossEntropyLoss()
                    _lowercase	       =loss_fct(logits.view(-1							,   self.num_labels					)							,   labels.view(-1					)					)
              elif self.config.problem_type == "multi_label_classification":
                    _lowercase	       =BCEWithLogitsLoss()
                    _lowercase	       =loss_fct(lowerCAmelCase							,   lowerCAmelCase					)

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

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





@add_start_docstrings(
    """\n    ResNet backbone, to be used with frameworks like DETR and MaskFormer.\n    """			,			snake_case_			,			)
class  __lowerCAmelCase     (					snake_case_			,			snake_case_							):




  def __init__(				self							,   lowerCAmelCase					)  -> Optional[int]:
        '''simple docstring'''






        super().__init__(lowerCAmelCase					)
        super()._init_backbone(lowerCAmelCase					)

        _lowercase	       =[config.embedding_size] + config.hidden_sizes
        _lowercase	       =ResNetEmbeddings(lowerCAmelCase					)
        _lowercase	       =ResNetEncoder(lowerCAmelCase					)

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




  @add_start_docstrings_to_model_forward(lowerCAmelCase					)
  @replace_return_docstrings(output_type=lowerCAmelCase							,   config_class=_CONFIG_FOR_DOC					)
  def   A__     (				self							,   lowerCAmelCase							,   lowerCAmelCase = None							,   lowerCAmelCase = None					)  -> BackboneOutput:
        '''simple docstring'''






        _lowercase	       =return_dict if return_dict is not None else self.config.use_return_dict
        _lowercase	       =(
            output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
        )

        _lowercase	       =self.embedder(lowerCAmelCase					)

        _lowercase	       =self.encoder(lowerCAmelCase							,   output_hidden_states=lowerCAmelCase							,   return_dict=lowerCAmelCase					)

        _lowercase	       =outputs.hidden_states

        _lowercase	       =()
        for idx, stage in enumerate(self.stage_names					):
              if stage in self.out_features:
                    feature_maps += (hidden_states[idx],)

        if not return_dict:
              _lowercase	       =(feature_maps,)
              if output_hidden_states:
                    output += (outputs.hidden_states,)
              return output

        return BackboneOutput(
            feature_maps=lowerCAmelCase							,   hidden_states=outputs.hidden_states if output_hidden_states else None							,   attentions=lowerCAmelCase							,   )




import argparse
import json
from pathlib import Path

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

from transformers import YolosConfig, YolosForObjectDetection, YolosImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__A				=logging.get_logger(__name__)





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				lowerCamelCase_						=			YolosConfig()

				# size of the architecture
				if "yolos_ti" in yolos_name:
								lowerCamelCase_						=			1_9_2
								lowerCamelCase_						=			7_6_8
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			3
								lowerCamelCase_						=			[8_0_0, 1_3_3_3]
								lowerCamelCase_						=			False
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			3_3_0
								lowerCamelCase_						=			1_4
								lowerCamelCase_						=			6
								lowerCamelCase_						=			1_3_2_0
				elif "yolos_s" in yolos_name:
								lowerCamelCase_						=			3_8_4
								lowerCamelCase_						=			1_5_3_6
								lowerCamelCase_						=			1_2
								lowerCamelCase_						=			6
				elif "yolos_b" in yolos_name:
								lowerCamelCase_						=			[8_0_0, 1_3_4_4]

				lowerCamelCase_						=			9_1
				lowerCamelCase_						=			"huggingface/label-files"
				lowerCamelCase_						=			"coco-detection-id2label.json"
				lowerCamelCase_						=			json.load(open(hf_hub_download(lowerCamelCase__	, lowerCamelCase__	, repo_type="dataset"    )	, "r"    )    )
				lowerCamelCase_						=			{int(lowerCamelCase__    ): v for k, v in idalabel.items()}
				lowerCamelCase_						=			idalabel
				lowerCamelCase_						=			{v: k for k, v in idalabel.items()}

				return config





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				for i in range(config.num_hidden_layers    ):
								# read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.weight'    )
								lowerCamelCase_						=			state_dict.pop(F'blocks.{i}.attn.qkv.bias'    )
								# next, add query, keys and values (in that order) to the state dict
								lowerCamelCase_						=			in_proj_weight[: config.hidden_size, :]
								lowerCamelCase_						=			in_proj_bias[: config.hidden_size]
								lowerCamelCase_						=			in_proj_weight[
								    config.hidden_size : config.hidden_size * 2, :
								]
								lowerCamelCase_						=			in_proj_bias[
								    config.hidden_size : config.hidden_size * 2
								]
								lowerCamelCase_						=			in_proj_weight[-config.hidden_size :, :]
								lowerCamelCase_						=			in_proj_bias[-config.hidden_size :]





def 			lowerCamelCase_ (				lowerCamelCase__    ):
				if "backbone" in name:
								lowerCamelCase_						=			name.replace("backbone"	, "vit"    )
				if "cls_token" in name:
								lowerCamelCase_						=			name.replace("cls_token"	, "embeddings.cls_token"    )
				if "det_token" in name:
								lowerCamelCase_						=			name.replace("det_token"	, "embeddings.detection_tokens"    )
				if "mid_pos_embed" in name:
								lowerCamelCase_						=			name.replace("mid_pos_embed"	, "encoder.mid_position_embeddings"    )
				if "pos_embed" in name:
								lowerCamelCase_						=			name.replace("pos_embed"	, "embeddings.position_embeddings"    )
				if "patch_embed.proj" in name:
								lowerCamelCase_						=			name.replace("patch_embed.proj"	, "embeddings.patch_embeddings.projection"    )
				if "blocks" in name:
								lowerCamelCase_						=			name.replace("blocks"	, "encoder.layer"    )
				if "attn.proj" in name:
								lowerCamelCase_						=			name.replace("attn.proj"	, "attention.output.dense"    )
				if "attn" in name:
								lowerCamelCase_						=			name.replace("attn"	, "attention.self"    )
				if "norm1" in name:
								lowerCamelCase_						=			name.replace("norm1"	, "layernorm_before"    )
				if "norm2" in name:
								lowerCamelCase_						=			name.replace("norm2"	, "layernorm_after"    )
				if "mlp.fc1" in name:
								lowerCamelCase_						=			name.replace("mlp.fc1"	, "intermediate.dense"    )
				if "mlp.fc2" in name:
								lowerCamelCase_						=			name.replace("mlp.fc2"	, "output.dense"    )
				if "class_embed" in name:
								lowerCamelCase_						=			name.replace("class_embed"	, "class_labels_classifier"    )
				if "bbox_embed" in name:
								lowerCamelCase_						=			name.replace("bbox_embed"	, "bbox_predictor"    )
				if "vit.norm" in name:
								lowerCamelCase_						=			name.replace("vit.norm"	, "vit.layernorm"    )

				return name





def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__    ):
				for key in orig_state_dict.copy().keys():
								lowerCamelCase_						=			orig_state_dict.pop(lowerCamelCase__    )

								if "qkv" in key:
												lowerCamelCase_						=			key.split("."    )
												lowerCamelCase_						=			int(key_split[2]    )
												lowerCamelCase_						=			model.vit.encoder.layer[layer_num].attention.attention.all_head_size
												if "weight" in key:
																lowerCamelCase_						=			val[:dim, :]
																lowerCamelCase_						=			val[
																    dim : dim * 2, :
																]
																lowerCamelCase_						=			val[-dim:, :]
												else:
																lowerCamelCase_						=			val[:dim]
																lowerCamelCase_						=			val[dim : dim * 2]
																lowerCamelCase_						=			val[-dim:]
								else:
												lowerCamelCase_						=			val

				return orig_state_dict





def 			lowerCamelCase_ (				):
				lowerCamelCase_						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
				lowerCamelCase_						=			Image.open(requests.get(lowerCamelCase__	, stream=lowerCamelCase__    ).raw    )
				return im





@torch.no_grad()
def 			lowerCamelCase_ (				lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__	, lowerCamelCase__ = False    ):
				lowerCamelCase_						=			get_yolos_config(lowerCamelCase__    )

				# load original state_dict
				lowerCamelCase_						=			torch.load(lowerCamelCase__	, map_location="cpu"    )["model"]

				# load 🤗 model
				lowerCamelCase_						=			YolosForObjectDetection(lowerCamelCase__    )
				model.eval()
				lowerCamelCase_						=			convert_state_dict(lowerCamelCase__	, lowerCamelCase__    )
				model.load_state_dict(lowerCamelCase__    )

				# Check outputs on an image, prepared by YolosImageProcessor
				lowerCamelCase_						=			8_0_0 if yolos_name != "yolos_ti" else 5_1_2
				lowerCamelCase_						=			YolosImageProcessor(format="coco_detection"	, size=lowerCamelCase__    )
				lowerCamelCase_						=			image_processor(images=prepare_img()	, return_tensors="pt"    )
				lowerCamelCase_						=			model(**lowerCamelCase__    )
				lowerCamelCase_	,    lowerCamelCase_						=			outputs.logits, outputs.pred_boxes

				lowerCamelCase_	,    lowerCamelCase_						=			None, None
				if yolos_name == "yolos_ti":
								lowerCamelCase_						=			torch.tensor(
								    [[-39.50_22, -11.98_20, -17.68_88], [-29.95_74, -9.97_69, -17.76_91], [-42.32_81, -20.72_00, -30.62_94]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.40_21, 0.08_36, 0.79_79], [0.01_84, 0.26_09, 0.03_64], [0.17_81, 0.20_04, 0.20_95]]    )
				elif yolos_name == "yolos_s_200_pre":
								lowerCamelCase_						=			torch.tensor(
								    [[-24.02_48, -10.30_24, -14.82_90], [-42.03_92, -16.82_00, -27.43_34], [-27.27_43, -11.81_54, -18.71_48]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.25_59, 0.54_55, 0.47_06], [0.29_89, 0.72_79, 0.18_75], [0.77_32, 0.40_17, 0.44_62]]    )
				elif yolos_name == "yolos_s_300_pre":
								lowerCamelCase_						=			torch.tensor(
								    [[-36.22_20, -14.43_85, -23.54_57], [-35.69_70, -14.75_83, -21.39_35], [-31.59_39, -13.60_42, -16.80_49]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.76_14, 0.23_16, 0.47_28], [0.71_68, 0.44_95, 0.38_55], [0.49_96, 0.14_66, 0.99_96]]    )
				elif yolos_name == "yolos_s_dWr":
								lowerCamelCase_						=			torch.tensor(
								    [[-42.86_68, -24.10_49, -41.16_90], [-34.74_56, -14.12_74, -24.91_94], [-33.78_98, -12.19_46, -25.64_95]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.55_87, 0.27_73, 0.06_05], [0.50_04, 0.30_14, 0.99_94], [0.49_99, 0.15_48, 0.99_94]]    )
				elif yolos_name == "yolos_base":
								lowerCamelCase_						=			torch.tensor(
								    [[-40.60_64, -24.30_84, -32.64_47], [-55.19_90, -30.77_19, -35.58_77], [-51.43_11, -33.35_07, -35.64_62]]    )
								lowerCamelCase_						=			torch.tensor(
								    [[0.55_55, 0.27_94, 0.06_55], [0.90_49, 0.26_64, 0.18_94], [0.91_83, 0.19_84, 0.16_35]]    )
				else:
								raise ValueError(F'Unknown yolos_name: {yolos_name}'    )

				assert torch.allclose(logits[0, :3, :3]	, lowerCamelCase__	, atol=1e-4    )
				assert torch.allclose(pred_boxes[0, :3, :3]	, lowerCamelCase__	, atol=1e-4    )

				Path(lowerCamelCase__    ).mkdir(exist_ok=lowerCamelCase__    )
				print(F'Saving model {yolos_name} to {pytorch_dump_folder_path}'    )
				model.save_pretrained(lowerCamelCase__    )
				print(F'Saving image processor to {pytorch_dump_folder_path}'    )
				image_processor.save_pretrained(lowerCamelCase__    )

				if push_to_hub:
								lowerCamelCase_						=			{
								    "yolos_ti": "yolos-tiny",
								    "yolos_s_200_pre": "yolos-small",
								    "yolos_s_300_pre": "yolos-small-300",
								    "yolos_s_dWr": "yolos-small-dwr",
								    "yolos_base": "yolos-base",
								}

								print("Pushing to the hub..."    )
								lowerCamelCase_						=			model_mapping[yolos_name]
								image_processor.push_to_hub(lowerCamelCase__	, organization="hustvl"    )
								model.push_to_hub(lowerCamelCase__	, organization="hustvl"    )


if __name__ == "__main__":
			__A				=argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--yolos_name''',
			    default='''yolos_s_200_pre''',
			    type=str,
			    help=(
			        '''Name of the YOLOS model you\'d like to convert. Should be one of \'yolos_ti\', \'yolos_s_200_pre\','''
			        ''' \'yolos_s_300_pre\', \'yolos_s_dWr\', \'yolos_base\'.'''
			    ),
			)
			parser.add_argument(
			    '''--checkpoint_path''', default=None, type=str, help='''Path to the original state dict (.pth file).'''
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
			)
			parser.add_argument(
			    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
			)

			__A				=parser.parse_args()
			convert_yolos_checkpoint(args.yolos_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)


