





import warnings
from collections import OrderedDict
from typing import Any, Mapping, Optional

from ... import PreTrainedTokenizer
from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig, OnnxConfigWithPast, OnnxSeqaSeqConfigWithPast
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import TensorType, is_torch_available, logging


UpperCAmelCase_  							=				logging.get_logger(__name__)

UpperCAmelCase_  							=				{
    """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/config.json""",
    # See all BART models at https://huggingface.co/models?filter=bart
}






class 					lowercase__ (			UpperCamelCase_       ):

			'''simple docstring'''


			a		:							Optional[Any]     	=	"bart"
			a		:							Any     	=	["past_key_values"]
			a		:							str     	=	{"num_attention_heads": "encoder_attention_heads", "hidden_size": "d_model"}




			def __init__(					self,	__magic_name__=50265,	__magic_name__=1024,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=12,	__magic_name__=4096,	__magic_name__=16,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__="gelu",	__magic_name__=1024,	__magic_name__=0.1,	__magic_name__=0.0,	__magic_name__=0.0,	__magic_name__=0.02,	__magic_name__=0.0,	__magic_name__=False,	__magic_name__=True,	__magic_name__=3,	__magic_name__=1,	__magic_name__=0,	__magic_name__=2,	__magic_name__=True,	__magic_name__=2,	__magic_name__=2,	**__magic_name__,	)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   vocab_size
							UpperCamelCase__							:       List[str]      =   max_position_embeddings
							UpperCamelCase__							:       Tuple      =   d_model
							UpperCamelCase__							:       Any      =   encoder_ffn_dim
							UpperCamelCase__							:       Optional[Any]      =   encoder_layers
							UpperCamelCase__							:       Any      =   encoder_attention_heads
							UpperCamelCase__							:       List[str]      =   decoder_ffn_dim
							UpperCamelCase__							:       Union[str, Any]      =   decoder_layers
							UpperCamelCase__							:       int      =   decoder_attention_heads
							UpperCamelCase__							:       Optional[int]      =   dropout
							UpperCamelCase__							:       Union[str, Any]      =   attention_dropout
							UpperCamelCase__							:       str      =   activation_dropout
							UpperCamelCase__							:       Optional[Any]      =   activation_function
							UpperCamelCase__							:       Any      =   init_std
							UpperCamelCase__							:       Optional[int]      =   encoder_layerdrop
							UpperCamelCase__							:       List[str]      =   decoder_layerdrop
							UpperCamelCase__							:       int      =   classifier_dropout
							UpperCamelCase__							:       Tuple      =   use_cache
							UpperCamelCase__							:       int      =   encoder_layers
							UpperCamelCase__							:       Optional[int]      =   scale_embedding  # scale factor will be sqrt(d_model) if True

							super().__init__(
							    num_labels=__A,	pad_token_id=__A,	bos_token_id=__A,	eos_token_id=__A,	is_encoder_decoder=__A,	decoder_start_token_id=__A,	forced_eos_token_id=__A,	**__A,	)

							# ensure backward compatibility for BART CNN models
							if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated''',	__A							):
											UpperCamelCase__							:       Dict      =   self.bos_token_id
											warnings.warn(
											    f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. "
											    '''The config can simply be saved and uploaded again to be fixed.'''							)






class 					lowercase__ (			UpperCamelCase_       ):

			'''simple docstring'''


			@property
			def    UpperCamelCase__						(					self							)				->   str:






							"""simple docstring"""
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase__							:       List[Any]      =   OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]							)

											if self.use_past:
															UpperCamelCase__							:       Optional[Any]      =   {0: "batch"}
															UpperCamelCase__							:       List[Any]      =   {0: "batch", 1: "past_decoder_sequence + sequence"}
											else:
															UpperCamelCase__							:       Any      =   {0: "batch", 1: "decoder_sequence"}
															UpperCamelCase__							:       Union[str, Any]      =   {0: "batch", 1: "decoder_sequence"}

											if self.use_past:
															self.fill_with_past_key_values_(__A,	direction='''inputs'''							)
							elif self.task == "causal-lm":
											# TODO: figure this case out.
											UpperCamelCase__							:       Any      =   OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											    ]							)
											if self.use_past:
															UpperCamelCase__							:       List[str]      =   self.num_layers
															for i in range(__A							):
																			UpperCamelCase__							:       List[Any]      =   {0: "batch", 2: "past_sequence + sequence"}
																			UpperCamelCase__							:       List[Any]      =   {0: "batch", 2: "past_sequence + sequence"}
							else:
											UpperCamelCase__							:       int      =   OrderedDict(
											    [
											        ('''input_ids''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''attention_mask''', {0: '''batch''', 1: '''encoder_sequence'''}),
											        ('''decoder_input_ids''', {0: '''batch''', 1: '''decoder_sequence'''}),
											        ('''decoder_attention_mask''', {0: '''batch''', 1: '''decoder_sequence'''}),
											    ]							)

							return common_inputs




			@property
			def    UpperCamelCase__						(					self							)				->   Union[str, Any]:






							"""simple docstring"""
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase__							:       List[Any]      =   super().outputs
							else:
											UpperCamelCase__							:       str      =   super(__A,	self							).outputs
											if self.use_past:
															UpperCamelCase__							:       Dict      =   self.num_layers
															for i in range(__A							):
																			UpperCamelCase__							:       Optional[Any]      =   {0: "batch", 2: "past_sequence + sequence"}
																			UpperCamelCase__							:       Tuple      =   {0: "batch", 2: "past_sequence + sequence"}
							return common_outputs




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   List[str]:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __A,	__A,	__A,	__A,	__A							)

							# Generate decoder inputs
							UpperCamelCase__							:       Any      =   seq_length if not self.use_past else 1
							UpperCamelCase__							:       Union[str, Any]      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __A,	__A,	__A,	__A,	__A							)
							UpperCamelCase__							:       List[str]      =   {f"decoder_{name}": tensor for name, tensor in decoder_inputs.items()}
							UpperCamelCase__							:       str      =   dict(**__A,	**__A							)

							if self.use_past:
											if not is_torch_available():
															raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
											else:
															import torch
											UpperCamelCase__							:       List[Any]      =   common_inputs["input_ids"].shape
											UpperCamelCase__							:       Tuple      =   common_inputs["decoder_input_ids"].shape[1]
											UpperCamelCase__							:       Dict      =   self.num_attention_heads
											UpperCamelCase__							:       Dict      =   (
											    batch,
											    num_encoder_attention_heads,
											    encoder_seq_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)
											UpperCamelCase__							:       Optional[Any]      =   decoder_seq_length + 3
											UpperCamelCase__							:       List[Any]      =   (
											    batch,
											    num_decoder_attention_heads,
											    decoder_past_length,
											    self._config.hidden_size // num_decoder_attention_heads,
											)

											UpperCamelCase__							:       Dict      =   torch.cat(
											    [common_inputs['''decoder_attention_mask'''], torch.ones(__A,	__A							)],	dim=1							)

											UpperCamelCase__							:       Optional[int]      =   []
											# If the number of encoder and decoder layers are present in the model configuration, both are considered
											UpperCamelCase__							:       Tuple      =   self.num_layers
											UpperCamelCase__							:       List[str]      =   min(__A,	__A							)
											UpperCamelCase__							:       Any      =   max(__A,	__A							) - min_num_layers
											UpperCamelCase__							:       Optional[Any]      =   "encoder" if num_encoder_layers > num_decoder_layers else "decoder"

											for _ in range(__A							):
															common_inputs["past_key_values"].append(
															    (
															        torch.zeros(__A							),
															        torch.zeros(__A							),
															        torch.zeros(__A							),
															        torch.zeros(__A							),
															    )							)
											# TODO: test this.
											UpperCamelCase__							:       Any      =   encoder_shape if remaining_side_name == "encoder" else decoder_shape
											for _ in range(__A,	__A							):
															common_inputs["past_key_values"].append((torch.zeros(__A							), torch.zeros(__A							))							)
							return common_inputs




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Any      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
							    __A,	__A,	__A,	__A,	__A							)

							if self.use_past:
											if not is_torch_available():
															raise ValueError('''Cannot generate dummy past_keys inputs without PyTorch installed.'''							)
											else:
															import torch
											UpperCamelCase__							:       Union[str, Any]      =   common_inputs["input_ids"].shape
											# Not using the same length for past_key_values
											UpperCamelCase__							:       Any      =   seqlen + 2
											UpperCamelCase__							:       Union[str, Any]      =   self.num_layers
											UpperCamelCase__							:       List[str]      =   self.num_attention_heads
											UpperCamelCase__							:       List[Any]      =   (
											    batch,
											    num_encoder_attention_heads,
											    past_key_values_length,
											    self._config.hidden_size // num_encoder_attention_heads,
											)

											UpperCamelCase__							:       List[str]      =   common_inputs["attention_mask"].dtype
											UpperCamelCase__							:       Optional[int]      =   torch.cat(
											    [common_inputs['''attention_mask'''], torch.ones(__A,	__A,	dtype=__A							)],	dim=1							)
											UpperCamelCase__							:       Optional[Any]      =   [
											    (torch.zeros(__A							), torch.zeros(__A							)) for _ in range(__A							)
											]
							return common_inputs




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   Union[str, Any]:






							"""simple docstring"""
							# Copied from OnnxConfig.generate_dummy_inputs
							# Did not use super(OnnxConfigWithPast, self).generate_dummy_inputs for code clarity.
							# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
							UpperCamelCase__							:       Any      =   compute_effective_axis_dimension(
							    __A,	fixed_dimension=OnnxConfig.default_fixed_batch,	num_token_to_add=0							)

							# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
							UpperCamelCase__							:       int      =   tokenizer.num_special_tokens_to_add(__A							)
							UpperCamelCase__							:       Tuple      =   compute_effective_axis_dimension(
							    __A,	fixed_dimension=OnnxConfig.default_fixed_sequence,	num_token_to_add=__A							)

							# Generate dummy inputs according to compute batch and sequence
							UpperCamelCase__							:       List[str]      =   [" ".join([tokenizer.unk_token]							) * seq_length] * batch_size
							UpperCamelCase__							:       Union[str, Any]      =   dict(tokenizer(__A,	return_tensors=__A							)							)
							return common_inputs




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__ = -1,	__magic_name__ = -1,	__magic_name__ = False,	__magic_name__ = None,	)				->   int:






							"""simple docstring"""
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase__							:       Dict      =   self._generate_dummy_inputs_for_default_and_seqaseq_lm(
											    __A,	batch_size=__A,	seq_length=__A,	is_pair=__A,	framework=__A							)

							elif self.task == "causal-lm":
											UpperCamelCase__							:       Optional[Any]      =   self._generate_dummy_inputs_for_causal_lm(
											    __A,	batch_size=__A,	seq_length=__A,	is_pair=__A,	framework=__A							)
							else:
											UpperCamelCase__							:       Any      =   self._generate_dummy_inputs_for_sequence_classification_and_question_answering(
											    __A,	batch_size=__A,	seq_length=__A,	is_pair=__A,	framework=__A							)

							return common_inputs




			def    UpperCamelCase__						(					self,	__magic_name__,	__magic_name__,	__magic_name__,	__magic_name__							)				->   Optional[int]:






							"""simple docstring"""
							if self.task in ["default", "seq2seq-lm"]:
											UpperCamelCase__							:       Union[str, Any]      =   super()._flatten_past_key_values_(__A,	__A,	__A,	__A							)
							else:
											UpperCamelCase__							:       Union[str, Any]      =   super(__A,	self							)._flatten_past_key_values_(
											    __A,	__A,	__A,	__A							)








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()
UpperCAmelCase_  							=				logging.get_logger(__name__)




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			YolosConfig:
    UpperCamelCase__							:       str      =   YolosConfig()

    # size of the architecture
    if "yolos_ti" in yolos_name:
        UpperCamelCase__							:       Dict      =   192
        UpperCamelCase__							:       Dict      =   768
        UpperCamelCase__							:       Optional[Any]      =   12
        UpperCamelCase__							:       List[Any]      =   3
        UpperCamelCase__							:       Optional[int]      =   [800, 1333]
        UpperCamelCase__							:       Tuple      =   False
    elif yolos_name == "yolos_s_dWr":
        UpperCamelCase__							:       int      =   330
        UpperCamelCase__							:       Tuple      =   14
        UpperCamelCase__							:       str      =   6
        UpperCamelCase__							:       Optional[int]      =   1320
    elif "yolos_s" in yolos_name:
        UpperCamelCase__							:       Optional[int]      =   384
        UpperCamelCase__							:       Any      =   1536
        UpperCamelCase__							:       Union[str, Any]      =   12
        UpperCamelCase__							:       int      =   6
    elif "yolos_b" in yolos_name:
        UpperCamelCase__							:       Dict      =   [800, 1344]

    UpperCamelCase__							:       List[Any]      =   91
    UpperCamelCase__							:       str      =   '''huggingface/label-files'''
    UpperCamelCase__							:       Dict      =   '''coco-detection-id2label.json'''
    UpperCamelCase__							:       List[str]      =   json.load(open(hf_hub_download(__UpperCAmelCase	,     __UpperCAmelCase	,     repo_type='''dataset'''   )	,     '''r'''   )   )
    UpperCamelCase__							:       str      =   {int(__UpperCAmelCase   ): v for k, v in idalabel.items()}
    UpperCamelCase__							:       str      =   idalabel
    UpperCamelCase__							:       Optional[int]      =   {v: k for k, v in idalabel.items()}

    return config




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict	,     __UpperCAmelCase:    YolosConfig	,     __UpperCAmelCase:    bool = False   )   ->			str:
    for i in range(config.num_hidden_layers   ):
        # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
        UpperCamelCase__							:       str      =   state_dict.pop(f"blocks.{i}.attn.qkv.weight"   )
        UpperCamelCase__							:       Dict      =   state_dict.pop(f"blocks.{i}.attn.qkv.bias"   )
        # next, add query, keys and values (in that order) to the state dict
        UpperCamelCase__							:       List[str]      =   in_proj_weight[: config.hidden_size, :]
        UpperCamelCase__							:       Any      =   in_proj_bias[: config.hidden_size]
        UpperCamelCase__							:       Tuple      =   in_proj_weight[
            config.hidden_size : config.hidden_size * 2, :
        ]
        UpperCamelCase__							:       List[str]      =   in_proj_bias[
            config.hidden_size : config.hidden_size * 2
        ]
        UpperCamelCase__							:       Optional[int]      =   in_proj_weight[-config.hidden_size :, :]
        UpperCamelCase__							:       Optional[int]      =   in_proj_bias[-config.hidden_size :]




def 	lowerCAmelCase_					(						__UpperCAmelCase:    str   )   ->			str:
    if "backbone" in name:
        UpperCamelCase__							:       str      =   name.replace('''backbone'''	,     '''vit'''   )
    if "cls_token" in name:
        UpperCamelCase__							:       str      =   name.replace('''cls_token'''	,     '''embeddings.cls_token'''   )
    if "det_token" in name:
        UpperCamelCase__							:       str      =   name.replace('''det_token'''	,     '''embeddings.detection_tokens'''   )
    if "mid_pos_embed" in name:
        UpperCamelCase__							:       List[str]      =   name.replace('''mid_pos_embed'''	,     '''encoder.mid_position_embeddings'''   )
    if "pos_embed" in name:
        UpperCamelCase__							:       List[Any]      =   name.replace('''pos_embed'''	,     '''embeddings.position_embeddings'''   )
    if "patch_embed.proj" in name:
        UpperCamelCase__							:       Any      =   name.replace('''patch_embed.proj'''	,     '''embeddings.patch_embeddings.projection'''   )
    if "blocks" in name:
        UpperCamelCase__							:       Union[str, Any]      =   name.replace('''blocks'''	,     '''encoder.layer'''   )
    if "attn.proj" in name:
        UpperCamelCase__							:       Dict      =   name.replace('''attn.proj'''	,     '''attention.output.dense'''   )
    if "attn" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''attn'''	,     '''attention.self'''   )
    if "norm1" in name:
        UpperCamelCase__							:       int      =   name.replace('''norm1'''	,     '''layernorm_before'''   )
    if "norm2" in name:
        UpperCamelCase__							:       str      =   name.replace('''norm2'''	,     '''layernorm_after'''   )
    if "mlp.fc1" in name:
        UpperCamelCase__							:       Optional[Any]      =   name.replace('''mlp.fc1'''	,     '''intermediate.dense'''   )
    if "mlp.fc2" in name:
        UpperCamelCase__							:       List[str]      =   name.replace('''mlp.fc2'''	,     '''output.dense'''   )
    if "class_embed" in name:
        UpperCamelCase__							:       List[Any]      =   name.replace('''class_embed'''	,     '''class_labels_classifier'''   )
    if "bbox_embed" in name:
        UpperCamelCase__							:       Optional[int]      =   name.replace('''bbox_embed'''	,     '''bbox_predictor'''   )
    if "vit.norm" in name:
        UpperCamelCase__							:       Tuple      =   name.replace('''vit.norm'''	,     '''vit.layernorm'''   )

    return name




def 	lowerCAmelCase_					(						__UpperCAmelCase:    dict	,     __UpperCAmelCase:    YolosForObjectDetection   )   ->			dict:
    for key in orig_state_dict.copy().keys():
        UpperCamelCase__							:       Union[str, Any]      =   orig_state_dict.pop(__UpperCAmelCase   )

        if "qkv" in key:
            UpperCamelCase__							:       Optional[int]      =   key.split('''.'''   )
            UpperCamelCase__							:       Union[str, Any]      =   int(key_split[2]   )
            UpperCamelCase__							:       Tuple      =   model.vit.encoder.layer[layer_num].attention.attention.all_head_size
            if "weight" in key:
                UpperCamelCase__							:       Dict      =   val[:dim, :]
                UpperCamelCase__							:       int      =   val[
                    dim : dim * 2, :
                ]
                UpperCamelCase__							:       Dict      =   val[-dim:, :]
            else:
                UpperCamelCase__							:       List[Any]      =   val[:dim]
                UpperCamelCase__							:       List[str]      =   val[dim : dim * 2]
                UpperCamelCase__							:       int      =   val[-dim:]
        else:
            UpperCamelCase__							:       Union[str, Any]      =   val

    return orig_state_dict




def 	lowerCAmelCase_					(						)   ->			torch.Tensor:
    UpperCamelCase__							:       str      =   '''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCamelCase__							:       Dict      =   Image.open(requests.get(__UpperCAmelCase	,     stream=__UpperCAmelCase   ).raw   )
    return im




@torch.no_grad()
def 	lowerCAmelCase_					(						__UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    str	,     __UpperCAmelCase:    bool = False   )   ->			Optional[int]:
    UpperCamelCase__							:       List[str]      =   get_yolos_config(__UpperCAmelCase   )

    # load original state_dict
    UpperCamelCase__							:       List[Any]      =   torch.load(__UpperCAmelCase	,     map_location='''cpu'''   )['''model''']

    # load 🤗 model
    UpperCamelCase__							:       List[Any]      =   YolosForObjectDetection(__UpperCAmelCase   )
    model.eval()
    UpperCamelCase__							:       Dict      =   convert_state_dict(__UpperCAmelCase	,     __UpperCAmelCase   )
    model.load_state_dict(__UpperCAmelCase   )

    # Check outputs on an image, prepared by YolosImageProcessor
    UpperCamelCase__							:       List[Any]      =   800 if yolos_name != '''yolos_ti''' else 512
    UpperCamelCase__							:       int      =   YolosImageProcessor(format='''coco_detection'''	,     size=__UpperCAmelCase   )
    UpperCamelCase__							:       Union[str, Any]      =   image_processor(images=prepare_img()	,     return_tensors='''pt'''   )
    UpperCamelCase__							:       Dict      =   model(**__UpperCAmelCase   )
    UpperCamelCase__     ,UpperCamelCase__							:       Optional[Any]      =   outputs.logits, outputs.pred_boxes

    UpperCamelCase__     ,UpperCamelCase__							:       int      =   None, None
    if yolos_name == "yolos_ti":
        UpperCamelCase__							:       List[str]      =   torch.tensor(
            [[-39.5022, -11.9820, -17.6888], [-29.9574, -9.9769, -17.7691], [-42.3281, -20.7200, -30.6294]]   )
        UpperCamelCase__							:       Dict      =   torch.tensor(
            [[0.4021, 0.0836, 0.7979], [0.0184, 0.2609, 0.0364], [0.1781, 0.2004, 0.2095]]   )
    elif yolos_name == "yolos_s_200_pre":
        UpperCamelCase__							:       Optional[int]      =   torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]   )
        UpperCamelCase__							:       Union[str, Any]      =   torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]   )
    elif yolos_name == "yolos_s_300_pre":
        UpperCamelCase__							:       Optional[Any]      =   torch.tensor(
            [[-36.2220, -14.4385, -23.5457], [-35.6970, -14.7583, -21.3935], [-31.5939, -13.6042, -16.8049]]   )
        UpperCamelCase__							:       List[Any]      =   torch.tensor(
            [[0.7614, 0.2316, 0.4728], [0.7168, 0.4495, 0.3855], [0.4996, 0.1466, 0.9996]]   )
    elif yolos_name == "yolos_s_dWr":
        UpperCamelCase__							:       Any      =   torch.tensor(
            [[-42.8668, -24.1049, -41.1690], [-34.7456, -14.1274, -24.9194], [-33.7898, -12.1946, -25.6495]]   )
        UpperCamelCase__							:       List[Any]      =   torch.tensor(
            [[0.5587, 0.2773, 0.0605], [0.5004, 0.3014, 0.9994], [0.4999, 0.1548, 0.9994]]   )
    elif yolos_name == "yolos_base":
        UpperCamelCase__							:       Optional[Any]      =   torch.tensor(
            [[-40.6064, -24.3084, -32.6447], [-55.1990, -30.7719, -35.5877], [-51.4311, -33.3507, -35.6462]]   )
        UpperCamelCase__							:       List[Any]      =   torch.tensor(
            [[0.5555, 0.2794, 0.0655], [0.9049, 0.2664, 0.1894], [0.9183, 0.1984, 0.1635]]   )
    else:
        raise ValueError(f"Unknown yolos_name: {yolos_name}"   )

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

    Path(__UpperCAmelCase   ).mkdir(exist_ok=__UpperCAmelCase   )
    print(f"Saving model {yolos_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 push_to_hub:
        UpperCamelCase__							:       Any      =   {
            '''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...'''   )
        UpperCamelCase__							:       Optional[int]      =   model_mapping[yolos_name]
        image_processor.push_to_hub(__UpperCAmelCase	,     organization='''hustvl'''   )
        model.push_to_hub(__UpperCAmelCase	,     organization='''hustvl'''   )


if __name__ == "__main__":
  UpperCAmelCase_  							=				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.'
  )

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

