





import itertools
import random
import unittest

import numpy as np

from transformers import is_speech_available
from transformers.testing_utils import require_torch, require_torchaudio

from ...test_sequence_feature_extraction_common import SequenceFeatureExtractionTestMixin


if is_speech_available():
		from transformers import SpeechaTextFeatureExtractor

UpperCAmelCase_  							=				random.Random()




def 	lowerCAmelCase_					(						__UpperCAmelCase:    Dict	,     __UpperCAmelCase:    Optional[Any]=1.0	,     __UpperCAmelCase:    Tuple=None	,     __UpperCAmelCase:    str=None   )   ->			List[Any]:

				if rng is None:
								UpperCamelCase__							:       Optional[Any]      =   global_rng

				UpperCamelCase__							:       str      =   []
				for batch_idx in range(shape[0]   ):
								values.append([]   )
								for _ in range(shape[1]   ):
												values[-1].append(rng.random() * scale   )

				return values






@require_torch
@require_torchaudio
class 					lowercase__ (			unittest.TestCase       ):

			'''simple docstring'''


			def __init__(					self,	__magic_name__,	__magic_name__=7,	__magic_name__=400,	__magic_name__=2000,	__magic_name__=24,	__magic_name__=24,	__magic_name__=0.0,	__magic_name__=16000,	__magic_name__=True,	__magic_name__=True,	)				->   int:






							"""simple docstring"""
							UpperCamelCase__							:       int      =   parent
							UpperCamelCase__							:       Dict      =   batch_size
							UpperCamelCase__							:       Optional[Any]      =   min_seq_length
							UpperCamelCase__							:       str      =   max_seq_length
							UpperCamelCase__							:       Optional[int]      =   (self.max_seq_length - self.min_seq_length) // (self.batch_size - 1)
							UpperCamelCase__							:       Any      =   feature_size
							UpperCamelCase__							:       Dict      =   num_mel_bins
							UpperCamelCase__							:       Union[str, Any]      =   padding_value
							UpperCamelCase__							:       str      =   sampling_rate
							UpperCamelCase__							:       List[str]      =   return_attention_mask
							UpperCamelCase__							:       Dict      =   do_normalize




			def    UpperCamelCase__						(					self							)				->   Optional[Any]:






							"""simple docstring"""
							return {
							    "feature_size": self.feature_size,
							    "num_mel_bins": self.num_mel_bins,
							    "padding_value": self.padding_value,
							    "sampling_rate": self.sampling_rate,
							    "return_attention_mask": self.return_attention_mask,
							    "do_normalize": self.do_normalize,
							}




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






							"""simple docstring"""
							def _flatten(__magic_name__							):
											return list(itertools.chain(*A_							)							)

							if equal_length:
											UpperCamelCase__							:       Optional[int]      =   [floats_list((self.max_seq_length, self.feature_size)							) for _ in range(self.batch_size							)]
							else:
											# make sure that inputs increase in size
											UpperCamelCase__							:       Dict      =   [
											    floats_list((x, self.feature_size)							)
											    for x in range(self.min_seq_length,	self.max_seq_length,	self.seq_length_diff							)
											]
							if numpify:
											UpperCamelCase__							:       Union[str, Any]      =   [np.asarray(A_							) for x in speech_inputs]
							return speech_inputs






@require_torch
@require_torchaudio
class 					lowercase__ (			_SCREAMING_SNAKE_CASE    ,       unittest.TestCase       ):

			'''simple docstring'''


			a		:							Optional[int]     	=	SpeechaTextFeatureExtractor if is_speech_available() else None




			def    UpperCamelCase__						(					self							)				->   int:






							"""simple docstring"""
							UpperCamelCase__							:       Optional[int]      =   SpeechaTextFeatureExtractionTester(self							)




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






							"""simple docstring"""
							self.assertTrue(np.all(np.mean(A_,	axis=0							) < 1E-3							)							)
							self.assertTrue(np.all(np.abs(np.var(A_,	axis=0							) - 1							) < 1E-3							)							)




			def    UpperCamelCase__						(					self							)				->   Tuple:






							"""simple docstring"""
							UpperCamelCase__							:       Union[str, Any]      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							# create three inputs of length 800, 1000, and 1200
							UpperCamelCase__							:       Union[str, Any]      =   [floats_list((1, x)							)[0] for x in range(800,	1400,	200							)]
							UpperCamelCase__							:       List[Any]      =   [np.asarray(A_							) for speech_input in speech_inputs]

							# Test feature size
							UpperCamelCase__							:       Dict      =   feature_extractor(A_,	padding=A_,	return_tensors='''np'''							).input_features
							self.assertTrue(input_features.ndim == 3							)
							self.assertTrue(input_features.shape[-1] == feature_extractor.feature_size							)

							# Test not batched input
							UpperCamelCase__							:       List[str]      =   feature_extractor(speech_inputs[0],	return_tensors='''np'''							).input_features
							UpperCamelCase__							:       List[str]      =   feature_extractor(np_speech_inputs[0],	return_tensors='''np'''							).input_features
							self.assertTrue(np.allclose(A_,	A_,	atol=1E-3							)							)

							# Test batched
							UpperCamelCase__							:       List[str]      =   feature_extractor(A_,	return_tensors='''np'''							).input_features
							UpperCamelCase__							:       Tuple      =   feature_extractor(A_,	return_tensors='''np'''							).input_features
							for enc_seq_a, enc_seq_a in zip(A_,	A_							):
											self.assertTrue(np.allclose(A_,	A_,	atol=1E-3							)							)

							# Test 2-D numpy arrays are batched.
							UpperCamelCase__							:       Any      =   [floats_list((1, x)							)[0] for x in (800, 800, 800)]
							UpperCamelCase__							:       str      =   np.asarray(A_							)
							UpperCamelCase__							:       str      =   feature_extractor(A_,	return_tensors='''np'''							).input_features
							UpperCamelCase__							:       Dict      =   feature_extractor(A_,	return_tensors='''np'''							).input_features
							for enc_seq_a, enc_seq_a in zip(A_,	A_							):
											self.assertTrue(np.allclose(A_,	A_,	atol=1E-3							)							)




			def    UpperCamelCase__						(					self							)				->   List[Any]:






							"""simple docstring"""
							UpperCamelCase__							:       Tuple      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__							:       int      =   [floats_list((1, x)							)[0] for x in range(800,	1400,	200							)]

							UpperCamelCase__							:       Union[str, Any]      =   ['''longest''', '''max_length''', '''do_not_pad''']
							UpperCamelCase__							:       List[Any]      =   [None, 16, None]
							for max_length, padding in zip(A_,	A_							):
											UpperCamelCase__							:       Optional[int]      =   feature_extractor(
											    A_,	padding=A_,	max_length=A_,	return_attention_mask=A_							)
											UpperCamelCase__							:       List[str]      =   inputs.input_features
											UpperCamelCase__							:       List[str]      =   inputs.attention_mask
											UpperCamelCase__							:       str      =   [np.sum(A_							) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)




			def    UpperCamelCase__						(					self							)				->   Dict:






							"""simple docstring"""
							UpperCamelCase__							:       Dict      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__							:       List[Any]      =   [floats_list((1, x)							)[0] for x in range(800,	1400,	200							)]

							UpperCamelCase__							:       Union[str, Any]      =   ['''longest''', '''max_length''', '''do_not_pad''']
							UpperCamelCase__							:       List[str]      =   [None, 16, None]
							for max_length, padding in zip(A_,	A_							):
											UpperCamelCase__							:       List[Any]      =   feature_extractor(
											    A_,	max_length=A_,	padding=A_,	return_tensors='''np''',	return_attention_mask=A_							)
											UpperCamelCase__							:       str      =   inputs.input_features
											UpperCamelCase__							:       Tuple      =   inputs.attention_mask
											UpperCamelCase__							:       str      =   [np.sum(A_							) for x in attention_mask]

											self._check_zero_mean_unit_variance(input_features[0][: fbank_feat_lengths[0]]							)
											self.assertTrue(input_features[0][fbank_feat_lengths[0] :].sum() < 1E-6							)
											self._check_zero_mean_unit_variance(input_features[1][: fbank_feat_lengths[1]]							)
											self.assertTrue(input_features[0][fbank_feat_lengths[1] :].sum() < 1E-6							)
											self._check_zero_mean_unit_variance(input_features[2][: fbank_feat_lengths[2]]							)




			def    UpperCamelCase__						(					self							)				->   Dict:






							"""simple docstring"""
							UpperCamelCase__							:       List[Any]      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__							:       Optional[Any]      =   [floats_list((1, x)							)[0] for x in range(800,	1400,	200							)]
							UpperCamelCase__							:       List[Any]      =   feature_extractor(
							    A_,	padding='''max_length''',	max_length=4,	truncation=A_,	return_tensors='''np''',	return_attention_mask=A_,	)
							UpperCamelCase__							:       Union[str, Any]      =   inputs.input_features
							UpperCamelCase__							:       Dict      =   inputs.attention_mask
							UpperCamelCase__							:       Tuple      =   np.sum(attention_mask == 1,	axis=1							)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
							self._check_zero_mean_unit_variance(input_features[1]							)
							self._check_zero_mean_unit_variance(input_features[2]							)




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






							"""simple docstring"""
							UpperCamelCase__							:       List[str]      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__							:       Any      =   [floats_list((1, x)							)[0] for x in range(800,	1400,	200							)]
							UpperCamelCase__							:       Any      =   feature_extractor(
							    A_,	padding='''longest''',	max_length=4,	truncation=A_,	return_tensors='''np''',	return_attention_mask=A_,	)
							UpperCamelCase__							:       List[Any]      =   inputs.input_features
							UpperCamelCase__							:       Dict      =   inputs.attention_mask
							UpperCamelCase__							:       str      =   np.sum(attention_mask == 1,	axis=1							)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
							self._check_zero_mean_unit_variance(input_features[2]							)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape,	(3, 4, 24)							)

							UpperCamelCase__							:       List[Any]      =   [floats_list((1, x)							)[0] for x in range(800,	1400,	200							)]
							UpperCamelCase__							:       List[Any]      =   feature_extractor(
							    A_,	padding='''longest''',	max_length=16,	truncation=A_,	return_tensors='''np''',	return_attention_mask=A_,	)
							UpperCamelCase__							:       str      =   inputs.input_features
							UpperCamelCase__							:       str      =   inputs.attention_mask
							UpperCamelCase__							:       Optional[Any]      =   np.sum(attention_mask == 1,	axis=1							)

							self._check_zero_mean_unit_variance(input_features[0, : fbank_feat_lengths[0]]							)
							self._check_zero_mean_unit_variance(input_features[1, : fbank_feat_lengths[1]]							)
							self._check_zero_mean_unit_variance(input_features[2]							)

							# make sure that if max_length < longest -> then pad to max_length
							self.assertEqual(input_features.shape,	(3, 6, 24)							)




			def    UpperCamelCase__						(					self							)				->   Dict:






							"""simple docstring"""
							import torch

							UpperCamelCase__							:       Dict      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__							:       Tuple      =   np.random.rand(100,	32							).astype(np.floataa							)
							UpperCamelCase__							:       Any      =   np_speech_inputs.tolist()

							for inputs in [py_speech_inputs, np_speech_inputs]:
											UpperCamelCase__							:       str      =   feature_extractor.pad([{'''input_features''': inputs}],	return_tensors='''np'''							)
											self.assertTrue(np_processed.input_features.dtype == np.floataa							)
											UpperCamelCase__							:       List[str]      =   feature_extractor.pad([{'''input_features''': inputs}],	return_tensors='''pt'''							)
											self.assertTrue(pt_processed.input_features.dtype == torch.floataa							)




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






							"""simple docstring"""
							from datasets import load_dataset

							UpperCamelCase__							:       int      =   load_dataset('''hf-internal-testing/librispeech_asr_dummy''',	'''clean''',	split='''validation'''							)
							# automatic decoding with librispeech
							UpperCamelCase__							:       Union[str, Any]      =   ds.sort('''id'''							).select(range(A_							)							)[:num_samples]['''audio''']

							return [x["array"] for x in speech_samples]




			def    UpperCamelCase__						(					self							)				->   int:






							"""simple docstring"""
							UpperCamelCase__							:       Tuple      =   np.array([
							    -1.5745, -1.7713, -1.7020, -1.6069, -1.2250, -1.1105, -0.9072, -0.8241,
							    -1.2310, -0.8098, -0.3320, -0.4101, -0.7985, -0.4996, -0.8213, -0.9128,
							    -1.0420, -1.1286, -1.0440, -0.7999, -0.8405, -1.2275, -1.5443, -1.4625,
							]							)
							# fmt: on

							UpperCamelCase__							:       Union[str, Any]      =   self._load_datasamples(1							)
							UpperCamelCase__							:       Any      =   self.feature_extraction_class(**self.feat_extract_tester.prepare_feat_extract_dict()							)
							UpperCamelCase__							:       Optional[Any]      =   feature_extractor(A_,	return_tensors='''pt'''							).input_features
							self.assertEquals(input_features.shape,	(1, 584, 24)							)
							self.assertTrue(np.allclose(input_features[0, 0, :30],	A_,	atol=1E-4							)							)








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)

