


import argparse
import json
from dataclasses import dataclass, field
from functools import partial
from pathlib import Path
from typing import List

import timm
import torch
import torch.nn as nn
from huggingface_hub import hf_hub_download
from torch import Tensor

from transformers import AutoImageProcessor, ResNetConfig, ResNetForImageClassification
from transformers.utils import logging


logging.set_verbosity_info()
_A		:				str									=       logging.get_logger()



@dataclass
class        a					:
					UpperCAmelCase__ :  nn.Module
					UpperCAmelCase__ :  List[nn.Module]						      =	field(default_factory=UpperCAmelCase_   )
					UpperCAmelCase__ :  list						      =	field(default_factory=UpperCAmelCase_   )





					def  SCREAMING_SNAKE_CASE__       (    self					:				Any    ,	SCREAMING_SNAKE_CASE_					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]    ,	SCREAMING_SNAKE_CASE_					:				int							):
								__lowerCamelCase:				Union[str, Any]     =	len(list(m.modules()							)							) == 1 or isinstance(_lowercase    ,	nn.Convad							) or isinstance(_lowercase    ,	nn.BatchNormad							)
								if has_not_submodules:
											self.traced.append(_lowercase							)





					def __call__(    self					:				Tuple    ,	SCREAMING_SNAKE_CASE_					:				List[str]							):
								for m in self.module.modules():
											self.handles.append(m.register_forward_hook(self._forward_hook							)							)
								self.module(_lowercase							)
								[x.remove() for x in self.handles]
								return self







					@property
					def  SCREAMING_SNAKE_CASE__       (    self					:				Tuple							):
								# check the len of the state_dict keys to see if we have learnable params
								return list(filter(lambda SCREAMING_SNAKE_CASE_							: len(list(x.state_dict().keys()							)							) > 0    ,	self.traced							)							)



@dataclass
class        a					:
					UpperCAmelCase__ :  nn.Module
					UpperCAmelCase__ :  nn.Module
					UpperCAmelCase__ :  int						      =	0
					UpperCAmelCase__ :  List						      =	field(default_factory=UpperCAmelCase_   )
					UpperCAmelCase__ :  List						      =	field(default_factory=UpperCAmelCase_   )





					def __call__(    self					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				Any							):
								__lowerCamelCase:				List[str]     =	Tracker(self.dest							)(_lowercase							).parametrized
								__lowerCamelCase:				Any     =	Tracker(self.src							)(_lowercase							).parametrized

								__lowerCamelCase:				Optional[Any]     =	list(filter(lambda SCREAMING_SNAKE_CASE_							: type(_lowercase							) not in self.src_skip    ,	_lowercase							)							)
								__lowerCamelCase:				Tuple     =	list(filter(lambda SCREAMING_SNAKE_CASE_							: type(_lowercase							) not in self.dest_skip    ,	_lowercase							)							)

								if len(_lowercase							) != len(_lowercase							):
											raise Exception(
											    F'Numbers of operations are different. Source module has {len(_lowercase							)} operations while'
											    F' destination module has {len(_lowercase							)}.'							)

								for dest_m, src_m in zip(_lowercase    ,	_lowercase							):
											dest_m.load_state_dict(src_m.state_dict()							)
											if self.verbose == 1:
														print(F'Transfered from={src_m} to={dest_m}'							)



def     __lowerCAmelCase						(							snake_case	:    str						,      snake_case	:    ResNetConfig						,      snake_case	:    Path						,      snake_case	:    bool = True )       ->							Any:
			print(f'Converting {name}...' )
			with torch.no_grad():
						__lowerCamelCase:				List[str]     =	timm.create_model(snake_case						,      pretrained=snake_case ).eval()
						__lowerCamelCase:				List[str]     =	ResNetForImageClassification(snake_case ).eval()
						__lowerCamelCase:				Tuple     =	ModuleTransfer(src=snake_case						,      dest=snake_case )
						__lowerCamelCase:				Optional[int]     =	torch.randn((1, 3, 224, 224) )
						module_transfer(snake_case )

			assert torch.allclose(from_model(snake_case )						,      our_model(snake_case ).logits ), "The model logits don't match the original one."

			__lowerCamelCase:				Optional[Any]     =	f'resnet{"-".join(name.split("resnet" ) )}'
			print(snake_case )

			if push_to_hub:
						our_model.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name						,      commit_message="""Add model"""						,      use_temp_dir=snake_case						,      )

						# we can use the convnext one
						__lowerCamelCase:				Dict     =	AutoImageProcessor.from_pretrained("""facebook/convnext-base-224-22k-1k""" )
						image_processor.push_to_hub(
						    repo_path_or_name=save_directory / checkpoint_name						,      commit_message="""Add image processor"""						,      use_temp_dir=snake_case						,      )

						print(f'Pushed {checkpoint_name}' )



def     __lowerCAmelCase						(							snake_case	:    Path						,      snake_case	:    str = None						,      snake_case	:    bool = True )       ->							Any:
			__lowerCamelCase:				List[Any]     =	'imagenet-1k-id2label.json'
			__lowerCamelCase:				Union[str, Any]     =	1000
			__lowerCamelCase:				str     =	(1, num_labels)

			__lowerCamelCase:				List[str]     =	'huggingface/label-files'
			__lowerCamelCase:				Optional[Any]     =	num_labels
			__lowerCamelCase:				Optional[Any]     =	json.load(open(hf_hub_download(snake_case						,      snake_case						,      repo_type="""dataset""" )						,      """r""" ) )
			__lowerCamelCase:				List[str]     =	{int(snake_case ): v for k, v in idalabel.items()}

			__lowerCamelCase:				Any     =	idalabel
			__lowerCamelCase:				int     =	{v: k for k, v in idalabel.items()}

			__lowerCamelCase:				List[str]     =	partial(snake_case						,      num_labels=snake_case						,      idalabel=snake_case						,      labelaid=snake_case )

			__lowerCamelCase:				List[str]     =	{
			    'resnet18': ImageNetPreTrainedConfig(
			        depths=[2, 2, 2, 2]						,      hidden_sizes=[64, 128, 256, 512]						,      layer_type="""basic""" ),
			    'resnet26': ImageNetPreTrainedConfig(
			        depths=[2, 2, 2, 2]						,      hidden_sizes=[256, 512, 1024, 2048]						,      layer_type="""bottleneck""" ),
			    'resnet34': ImageNetPreTrainedConfig(
			        depths=[3, 4, 6, 3]						,      hidden_sizes=[64, 128, 256, 512]						,      layer_type="""basic""" ),
			    'resnet50': ImageNetPreTrainedConfig(
			        depths=[3, 4, 6, 3]						,      hidden_sizes=[256, 512, 1024, 2048]						,      layer_type="""bottleneck""" ),
			    'resnet101': ImageNetPreTrainedConfig(
			        depths=[3, 4, 23, 3]						,      hidden_sizes=[256, 512, 1024, 2048]						,      layer_type="""bottleneck""" ),
			    'resnet152': ImageNetPreTrainedConfig(
			        depths=[3, 8, 36, 3]						,      hidden_sizes=[256, 512, 1024, 2048]						,      layer_type="""bottleneck""" ),
			}

			if model_name:
						convert_weight_and_push(snake_case						,      names_to_config[model_name]						,      snake_case						,      snake_case )
			else:
						for model_name, config in names_to_config.items():
									convert_weight_and_push(snake_case						,      snake_case						,      snake_case						,      snake_case )
			return config, expected_shape


if __name__ == "__main__":
			_A		:				Tuple									=       argparse.ArgumentParser()
			# Required parameters
			parser.add_argument(
			    '''--model_name''',
			    default=None,
			    type=str,
			    help=(
			        '''The name of the model you wish to convert, it must be one of the supported resnet* architecture,'''
			        ''' currently: resnet18,26,34,50,101,152. If `None`, all of them will the converted.'''
			    ),
			)
			parser.add_argument(
			    '''--pytorch_dump_folder_path''',
			    default=None,
			    type=Path,
			    required=True,
			    help='''Path to the output PyTorch model directory.''',
			)
			parser.add_argument(
			    '''--push_to_hub''',
			    default=True,
			    type=bool,
			    required=False,
			    help='''If True, push model and image processor to the hub.''',
			)

			_A		:				Union[str, Any]									=       parser.parse_args()
			_A		:				Path									=       args.pytorch_dump_folder_path
			pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
			convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)






import inspect
import unittest

from transformers import YolosConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
   import torch
   from torch import nn

   from transformers import YolosForObjectDetection, YolosModel
   from transformers.models.yolos.modeling_yolos import YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import AutoImageProcessor



class        a					:





     def __init__(    self					:				Dict    ,	SCREAMING_SNAKE_CASE_					:				List[str]    ,	SCREAMING_SNAKE_CASE_					:				int=13    ,	SCREAMING_SNAKE_CASE_					:				str=[30, 30]    ,	SCREAMING_SNAKE_CASE_					:				str=2    ,	SCREAMING_SNAKE_CASE_					:				List[Any]=3    ,	SCREAMING_SNAKE_CASE_					:				Dict=True    ,	SCREAMING_SNAKE_CASE_					:				Any=True    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]=32    ,	SCREAMING_SNAKE_CASE_					:				Tuple=5    ,	SCREAMING_SNAKE_CASE_					:				Tuple=4    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]=37    ,	SCREAMING_SNAKE_CASE_					:				Dict="gelu"    ,	SCREAMING_SNAKE_CASE_					:				Any=0.1    ,	SCREAMING_SNAKE_CASE_					:				Tuple=0.1    ,	SCREAMING_SNAKE_CASE_					:				Tuple=10    ,	SCREAMING_SNAKE_CASE_					:				List[str]=0.02    ,	SCREAMING_SNAKE_CASE_					:				Dict=3    ,	SCREAMING_SNAKE_CASE_					:				List[str]=None    ,	SCREAMING_SNAKE_CASE_					:				int=8    ,	SCREAMING_SNAKE_CASE_					:				Dict=10    ,	):
        __lowerCamelCase:				Optional[int]     =	parent
        __lowerCamelCase:				Tuple     =	batch_size
        __lowerCamelCase:				Optional[Any]     =	image_size
        __lowerCamelCase:				str     =	patch_size
        __lowerCamelCase:				Dict     =	num_channels
        __lowerCamelCase:				Optional[int]     =	is_training
        __lowerCamelCase:				Union[str, Any]     =	use_labels
        __lowerCamelCase:				Union[str, Any]     =	hidden_size
        __lowerCamelCase:				Dict     =	num_hidden_layers
        __lowerCamelCase:				int     =	num_attention_heads
        __lowerCamelCase:				Optional[int]     =	intermediate_size
        __lowerCamelCase:				Union[str, Any]     =	hidden_act
        __lowerCamelCase:				List[str]     =	hidden_dropout_prob
        __lowerCamelCase:				Optional[int]     =	attention_probs_dropout_prob
        __lowerCamelCase:				List[str]     =	type_sequence_label_size
        __lowerCamelCase:				Dict     =	initializer_range
        __lowerCamelCase:				Optional[Any]     =	num_labels
        __lowerCamelCase:				Tuple     =	scope
        __lowerCamelCase:				Tuple     =	n_targets
        __lowerCamelCase:				Tuple     =	num_detection_tokens
        # we set the expected sequence length (which is used in several tests)
        # expected sequence length = num_patches + 1 (we add 1 for the [CLS] token) + num_detection_tokens
        __lowerCamelCase:				Any     =	(image_size[1] // patch_size) * (image_size[0] // patch_size)
        __lowerCamelCase:				str     =	num_patches + 1 + self.num_detection_tokens





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        __lowerCamelCase:				List[str]     =	floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]]							)

        __lowerCamelCase:				Optional[int]     =	None
        if self.use_labels:
           # labels is a list of Dict (each Dict being the labels for a given example in the batch)
           __lowerCamelCase:				Optional[int]     =	[]
           for i in range(self.batch_size							):
              __lowerCamelCase:				Optional[int]     =	{}
              __lowerCamelCase:				Optional[int]     =	torch.randint(
                  high=self.num_labels    ,	size=(self.n_targets,)    ,	device=SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				List[str]     =	torch.rand(self.n_targets    ,	4    ,	device=SCREAMING_SNAKE_CASE_							)
              labels.append(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				int     =	self.get_config()

        return config, pixel_values, labels





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        return YolosConfig(
            image_size=self.image_size    ,	patch_size=self.patch_size    ,	num_channels=self.num_channels    ,	hidden_size=self.hidden_size    ,	num_hidden_layers=self.num_hidden_layers    ,	num_attention_heads=self.num_attention_heads    ,	intermediate_size=self.intermediate_size    ,	hidden_act=self.hidden_act    ,	hidden_dropout_prob=self.hidden_dropout_prob    ,	attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,	is_decoder=SCREAMING_SNAKE_CASE_    ,	initializer_range=self.initializer_range    ,	num_detection_tokens=self.num_detection_tokens    ,	num_labels=self.num_labels    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				int    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				Dict							):
        __lowerCamelCase:				List[Any]     =	YolosModel(config=SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()
        __lowerCamelCase:				Optional[int]     =	model(SCREAMING_SNAKE_CASE_							)
        self.parent.assertEqual(
            result.last_hidden_state.shape    ,	(self.batch_size, self.expected_seq_len, self.hidden_size)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				List[Any]    ,	SCREAMING_SNAKE_CASE_					:				str							):
        __lowerCamelCase:				int     =	YolosForObjectDetection(SCREAMING_SNAKE_CASE_							)
        model.to(SCREAMING_SNAKE_CASE_							)
        model.eval()

        __lowerCamelCase:				List[Any]     =	model(pixel_values=SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				str     =	model(SCREAMING_SNAKE_CASE_							)

        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
        self.parent.assertEqual(result.pred_boxes.shape    ,	(self.batch_size, self.num_detection_tokens, 4)							)

        __lowerCamelCase:				Any     =	model(pixel_values=SCREAMING_SNAKE_CASE_    ,	labels=SCREAMING_SNAKE_CASE_							)

        self.parent.assertEqual(result.loss.shape    ,	()							)
        self.parent.assertEqual(result.logits.shape    ,	(self.batch_size, self.num_detection_tokens, self.num_labels + 1)							)
        self.parent.assertEqual(result.pred_boxes.shape    ,	(self.batch_size, self.num_detection_tokens, 4)							)







     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				Union[str, Any]     =	self.prepare_config_and_inputs()
        __lowerCamelCase   ,     __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	config_and_inputs
        __lowerCamelCase:				Optional[Any]     =	{"""pixel_values""": pixel_values}
        return config, inputs_dict



@require_torch
class        a					(			_UpperCAmelCase     ,_UpperCAmelCase     ,unittest.TestCase   ):
     UpperCAmelCase__ :  Any						      =	(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     UpperCAmelCase__ :  Optional[int]						      =	(
         {"feature-extraction": YolosModel, "object-detection": YolosForObjectDetection} if is_torch_available() else {}
     )

     UpperCAmelCase__ :  Dict						      =	False
     UpperCAmelCase__ :  Tuple						      =	False
     UpperCAmelCase__ :  str						      =	False
     UpperCAmelCase__ :  List[str]						      =	False





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				Optional[int]    ,	SCREAMING_SNAKE_CASE_					:				Any    ,	SCREAMING_SNAKE_CASE_					:				List[str]=False							):
        __lowerCamelCase:				Any     =	super()._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	return_labels=SCREAMING_SNAKE_CASE_							)

        if return_labels:
           if model_class.__name__ == "YolosForObjectDetection":
              __lowerCamelCase:				str     =	[]
              for i in range(self.model_tester.batch_size							):
                 __lowerCamelCase:				List[Any]     =	{}
                 __lowerCamelCase:				List[Any]     =	torch.ones(
                     size=(self.model_tester.n_targets,)    ,	device=SCREAMING_SNAKE_CASE_    ,	dtype=torch.long							)
                 __lowerCamelCase:				Optional[int]     =	torch.ones(
                     self.model_tester.n_targets    ,	4    ,	device=SCREAMING_SNAKE_CASE_    ,	dtype=torch.float							)
                 labels.append(SCREAMING_SNAKE_CASE_							)
              __lowerCamelCase:				Optional[Any]     =	labels

        return inputs_dict





     def  SCREAMING_SNAKE_CASE__       (    self					:				Dict							):
        __lowerCamelCase:				Dict     =	YolosModelTester(self							)
        __lowerCamelCase:				str     =	ConfigTester(self    ,	config_class=SCREAMING_SNAKE_CASE_    ,	has_text_modality=SCREAMING_SNAKE_CASE_    ,	hidden_size=37							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Optional[Any]							):
        self.config_tester.run_common_tests()





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        # YOLOS does not use inputs_embeds
        pass





     def  SCREAMING_SNAKE_CASE__       (    self					:				int							):
        __lowerCamelCase   ,     __lowerCamelCase:				Optional[int]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				int     =	model_class(SCREAMING_SNAKE_CASE_							)
           self.assertIsInstance(model.get_input_embeddings()    ,	(nn.Module)							)
           __lowerCamelCase:				Optional[Any]     =	model.get_output_embeddings()
           self.assertTrue(x is None or isinstance(SCREAMING_SNAKE_CASE_    ,	nn.Linear							)							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase   ,     __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Optional[int]     =	model_class(SCREAMING_SNAKE_CASE_							)
           __lowerCamelCase:				Optional[Any]     =	inspect.signature(model.forward							)
           # signature.parameters is an OrderedDict => so arg_names order is deterministic
           __lowerCamelCase:				Any     =	[*signature.parameters.keys()]

           __lowerCamelCase:				Optional[int]     =	["""pixel_values"""]
           self.assertListEqual(arg_names[:1]    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase:				str     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        __lowerCamelCase   ,     __lowerCamelCase:				Any     =	self.model_tester.prepare_config_and_inputs_for_common()
        __lowerCamelCase:				str     =	True

        # in YOLOS, the seq_len is different
        __lowerCamelCase:				List[Any]     =	self.model_tester.expected_seq_len
        for model_class in self.all_model_classes:
           __lowerCamelCase:				Dict     =	True
           __lowerCamelCase:				Union[str, Any]     =	False
           __lowerCamelCase:				Any     =	True
           __lowerCamelCase:				str     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()
           with torch.no_grad():
              __lowerCamelCase:				Tuple     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
           __lowerCamelCase:				str     =	outputs.attentions
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	self.model_tester.num_hidden_layers							)

           # check that output_attentions also work using config
           del inputs_dict["output_attentions"]
           __lowerCamelCase:				Tuple     =	True
           __lowerCamelCase:				Optional[int]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()
           with torch.no_grad():
              __lowerCamelCase:				str     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)
           __lowerCamelCase:				str     =	outputs.attentions
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	self.model_tester.num_hidden_layers							)

           self.assertListEqual(
               list(attentions[0].shape[-3:]							)    ,	[self.model_tester.num_attention_heads, seq_len, seq_len]    ,	)
           __lowerCamelCase:				List[str]     =	len(SCREAMING_SNAKE_CASE_							)

           # Check attention is always last and order is fine
           __lowerCamelCase:				Any     =	True
           __lowerCamelCase:				Optional[Any]     =	True
           __lowerCamelCase:				List[str]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()
           with torch.no_grad():
              __lowerCamelCase:				Optional[Any]     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Tuple     =	1
           self.assertEqual(out_len + added_hidden_states    ,	len(SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Any     =	outputs.attentions

           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	self.model_tester.num_hidden_layers							)
           self.assertListEqual(
               list(self_attentions[0].shape[-3:]							)    ,	[self.model_tester.num_attention_heads, seq_len, seq_len]    ,	)





     def  SCREAMING_SNAKE_CASE__       (    self					:				Union[str, Any]							):
        def check_hidden_states_output(SCREAMING_SNAKE_CASE_					:				Optional[Any]    ,	SCREAMING_SNAKE_CASE_					:				str    ,	SCREAMING_SNAKE_CASE_					:				Union[str, Any]							):
           __lowerCamelCase:				List[Any]     =	model_class(SCREAMING_SNAKE_CASE_							)
           model.to(SCREAMING_SNAKE_CASE_							)
           model.eval()

           with torch.no_grad():
              __lowerCamelCase:				str     =	model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)							)

           __lowerCamelCase:				Any     =	outputs.hidden_states

           __lowerCamelCase:				int     =	getattr(
               self.model_tester    ,	"""expected_num_hidden_layers"""    ,	self.model_tester.num_hidden_layers + 1							)
           self.assertEqual(len(SCREAMING_SNAKE_CASE_							)    ,	SCREAMING_SNAKE_CASE_							)

           # YOLOS has a different seq_length
           __lowerCamelCase:				Optional[int]     =	self.model_tester.expected_seq_len

           self.assertListEqual(
               list(hidden_states[0].shape[-2:]							)    ,	[seq_length, self.model_tester.hidden_size]    ,	)

        __lowerCamelCase   ,     __lowerCamelCase:				Union[str, Any]     =	self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
           __lowerCamelCase:				Any     =	True
           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)

           # check that output_hidden_states also work using config
           del inputs_dict["output_hidden_states"]
           __lowerCamelCase:				int     =	True

           check_hidden_states_output(SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_    ,	SCREAMING_SNAKE_CASE_							)





     def  SCREAMING_SNAKE_CASE__       (    self					:				List[str]							):
        __lowerCamelCase:				int     =	self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_object_detection(*SCREAMING_SNAKE_CASE_							)







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				Any							):
        for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
           __lowerCamelCase:				str     =	YolosModel.from_pretrained(SCREAMING_SNAKE_CASE_							)
           self.assertIsNotNone(SCREAMING_SNAKE_CASE_							)



def     __lowerCAmelCase						(							)       ->							Optional[Any]:
   __lowerCamelCase:				Optional[int]     =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png""" )
   return image







@require_torch
@require_vision
class        a					(			unittest.TestCase   ):





     @cached_property
     def  SCREAMING_SNAKE_CASE__       (    self					:				str							):
        return AutoImageProcessor.from_pretrained("""hustvl/yolos-small"""							) if is_vision_available() else None







     @slow
     def  SCREAMING_SNAKE_CASE__       (    self					:				List[Any]							):
        __lowerCamelCase:				int     =	YolosForObjectDetection.from_pretrained("""hustvl/yolos-small"""							).to(SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[Any]     =	self.default_image_processor
        __lowerCamelCase:				Optional[int]     =	prepare_img()
        __lowerCamelCase:				Optional[Any]     =	image_processor(images=SCREAMING_SNAKE_CASE_    ,	return_tensors="""pt"""							).to(SCREAMING_SNAKE_CASE_							)

        # forward pass
        with torch.no_grad():
           __lowerCamelCase:				str     =	model(inputs.pixel_values							)

        # verify outputs
        __lowerCamelCase:				Optional[int]     =	torch.Size((1, 100, 92)							)
        self.assertEqual(outputs.logits.shape    ,	SCREAMING_SNAKE_CASE_							)

        __lowerCamelCase:				List[str]     =	torch.tensor(
            [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]    ,	device=SCREAMING_SNAKE_CASE_    ,	)
        __lowerCamelCase:				Optional[Any]     =	torch.tensor(
            [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]    ,	device=SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)
        self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)

        # verify postprocessing
        __lowerCamelCase:				Union[str, Any]     =	image_processor.post_process_object_detection(
            SCREAMING_SNAKE_CASE_    ,	threshold=0.3    ,	target_sizes=[image.size[::-1]]							)[0]
        __lowerCamelCase:				Optional[Any]     =	torch.tensor([0.9994, 0.9790, 0.9964, 0.9972, 0.9861]							).to(SCREAMING_SNAKE_CASE_							)
        __lowerCamelCase:				Any     =	[75, 75, 17, 63, 17]
        __lowerCamelCase:				Optional[Any]     =	torch.tensor([335.0609, 79.3848, 375.4216, 187.2495]							).to(SCREAMING_SNAKE_CASE_							)

        self.assertEqual(len(results["""scores"""]							)    ,	5							)
        self.assertTrue(torch.allclose(results["""scores"""]    ,	SCREAMING_SNAKE_CASE_    ,	atol=1E-4							)							)
        self.assertSequenceEqual(results["""labels"""].tolist()    ,	SCREAMING_SNAKE_CASE_							)
        self.assertTrue(torch.allclose(results["""boxes"""][0, :]    ,	SCREAMING_SNAKE_CASE_							)							)


