





'''simple docstring'''




import argparse
import json
from pathlib import Path

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

from transformers import DeiTImageProcessor, ViTConfig, ViTForImageClassification, ViTImageProcessor, ViTModel
from transformers.utils import logging


logging.set_verbosity_info()
lowercase__       : Optional[int]         =       logging.get_logger(__name__)






def 			_lowerCAmelCase     (    __snake_case    :					Tuple					,				__snake_case    :					List[str]=False     )	->	Union[str, Any]:
     __A		:					int						=			[]
     for i in range(config.num_hidden_layers     ):
          # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
          rename_keys.append((f'blocks.{i}.norm1.weight', f'vit.encoder.layer.{i}.layernorm_before.weight')     )
          rename_keys.append((f'blocks.{i}.norm1.bias', f'vit.encoder.layer.{i}.layernorm_before.bias')     )
          rename_keys.append((f'blocks.{i}.attn.proj.weight', f'vit.encoder.layer.{i}.attention.output.dense.weight')     )
          rename_keys.append((f'blocks.{i}.attn.proj.bias', f'vit.encoder.layer.{i}.attention.output.dense.bias')     )
          rename_keys.append((f'blocks.{i}.norm2.weight', f'vit.encoder.layer.{i}.layernorm_after.weight')     )
          rename_keys.append((f'blocks.{i}.norm2.bias', f'vit.encoder.layer.{i}.layernorm_after.bias')     )
          rename_keys.append((f'blocks.{i}.mlp.fc1.weight', f'vit.encoder.layer.{i}.intermediate.dense.weight')     )
          rename_keys.append((f'blocks.{i}.mlp.fc1.bias', f'vit.encoder.layer.{i}.intermediate.dense.bias')     )
          rename_keys.append((f'blocks.{i}.mlp.fc2.weight', f'vit.encoder.layer.{i}.output.dense.weight')     )
          rename_keys.append((f'blocks.{i}.mlp.fc2.bias', f'vit.encoder.layer.{i}.output.dense.bias')     )

     # projection layer + position embeddings
     rename_keys.extend(
         [
             ('cls_token', 'vit.embeddings.cls_token'),
             ('patch_embed.proj.weight', 'vit.embeddings.patch_embeddings.projection.weight'),
             ('patch_embed.proj.bias', 'vit.embeddings.patch_embeddings.projection.bias'),
             ('pos_embed', 'vit.embeddings.position_embeddings'),
         ]     )

     if base_model:
          # layernorm + pooler
          rename_keys.extend(
              [
                  ('norm.weight', 'layernorm.weight'),
                  ('norm.bias', 'layernorm.bias'),
                  ('pre_logits.fc.weight', 'pooler.dense.weight'),
                  ('pre_logits.fc.bias', 'pooler.dense.bias'),
              ]     )

          # if just the base model, we should remove "vit" from all keys that start with "vit"
          __A		:					Union[str, Any]						=			[(pair[0], pair[1][4:]) if pair[1].startswith('vit'     ) else pair for pair in rename_keys]
     else:
          # layernorm + classification head
          rename_keys.extend(
              [
                  ('norm.weight', 'vit.layernorm.weight'),
                  ('norm.bias', 'vit.layernorm.bias'),
                  ('head.weight', 'classifier.weight'),
                  ('head.bias', 'classifier.bias'),
              ]     )

     return rename_keys






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






def 			_lowerCAmelCase     (    __snake_case    :					List[Any]     )	->	List[Any]:
     __A		:					Optional[int]						=			['head.weight', 'head.bias']
     for k in ignore_keys:
          state_dict.pop(__snake_case					,				__snake_case     )






def 			_lowerCAmelCase     (    __snake_case    :					Optional[int]					,				__snake_case    :					Optional[Any]					,				__snake_case    :					Union[str, Any]     )	->	Dict:
     __A		:					int						=			dct.pop(__snake_case     )
     __A		:					Optional[Any]						=			val






def 			_lowerCAmelCase     (    )	->	str:
     __A		:					Optional[Any]						=			'http://images.cocodataset.org/val2017/000000039769.jpg'
     __A		:					List[Any]						=			Image.open(requests.get(__snake_case					,				stream=__snake_case     ).raw     )
     return im






@torch.no_grad()
def 			_lowerCAmelCase     (    __snake_case    :					List[Any]					,				__snake_case    :					Tuple     )	->	List[str]:
     __A		:					str						=			ViTConfig()
     __A		:					Optional[int]						=			False
     # dataset (ImageNet-21k only or also fine-tuned on ImageNet 2012), patch_size and image_size
     if vit_name[-5:] == "in21k":
          __A		:					Tuple						=			True
          __A		:					Union[str, Any]						=			int(vit_name[-12:-10]     )
          __A		:					Tuple						=			int(vit_name[-9:-6]     )
     else:
          __A		:					Optional[int]						=			10_00
          __A		:					Tuple						=			'huggingface/label-files'
          __A		:					Dict						=			'imagenet-1k-id2label.json'
          __A		:					Tuple						=			json.load(open(hf_hub_download(__snake_case					,				__snake_case					,				repo_type='dataset'     )					,				'r'     )     )
          __A		:					List[str]						=			{int(__snake_case     ): v for k, v in idalabel.items()}
          __A		:					str						=			idalabel
          __A		:					str						=			{v: k for k, v in idalabel.items()}
          __A		:					List[Any]						=			int(vit_name[-6:-4]     )
          __A		:					Any						=			int(vit_name[-3:]     )
     # size of the architecture
     if "deit" in vit_name:
          if vit_name[9:].startswith('tiny'     ):
               __A		:					Any						=			1_92
               __A		:					Tuple						=			7_68
               __A		:					Any						=			12
               __A		:					Dict						=			3
          elif vit_name[9:].startswith('small'     ):
               __A		:					Any						=			3_84
               __A		:					List[Any]						=			15_36
               __A		:					int						=			12
               __A		:					Union[str, Any]						=			6
          else:
               pass
     else:
          if vit_name[4:].startswith('small'     ):
               __A		:					str						=			7_68
               __A		:					List[str]						=			23_04
               __A		:					Any						=			8
               __A		:					str						=			8
          elif vit_name[4:].startswith('base'     ):
               pass
          elif vit_name[4:].startswith('large'     ):
               __A		:					Any						=			10_24
               __A		:					List[Any]						=			40_96
               __A		:					Optional[int]						=			24
               __A		:					str						=			16
          elif vit_name[4:].startswith('huge'     ):
               __A		:					str						=			12_80
               __A		:					Any						=			51_20
               __A		:					Union[str, Any]						=			32
               __A		:					List[Any]						=			16

    # load original model from timm
     __A		:					Optional[Any]						=			timm.create_model(__snake_case					,				pretrained=__snake_case     )
     timm_model.eval()

     # load state_dict of original model, remove and rename some keys
     __A		:					str						=			timm_model.state_dict()
     if base_model:
          remove_classification_head_(__snake_case     )
     __A		:					Tuple						=			create_rename_keys(__snake_case					,				__snake_case     )
     for src, dest in rename_keys:
          rename_key(__snake_case					,				__snake_case					,				__snake_case     )
     read_in_q_k_v(__snake_case					,				__snake_case					,				__snake_case     )

     # load HuggingFace model
     if vit_name[-5:] == "in21k":
          __A		:					str						=			ViTModel(__snake_case     ).eval()
     else:
          __A		:					Optional[int]						=			ViTForImageClassification(__snake_case     ).eval()
     model.load_state_dict(__snake_case     )

     # Check outputs on an image, prepared by ViTImageProcessor/DeiTImageProcessor
     if "deit" in vit_name:
          __A		:					Optional[int]						=			DeiTImageProcessor(size=config.image_size     )
     else:
          __A		:					Optional[Any]						=			ViTImageProcessor(size=config.image_size     )
     __A		:					List[str]						=			image_processor(images=prepare_img()					,				return_tensors='pt'     )
     __A		:					Tuple						=			encoding['pixel_values']
     __A		:					Any						=			model(__snake_case     )

     if base_model:
          __A		:					str						=			timm_model.forward_features(__snake_case     )
          assert timm_pooled_output.shape == outputs.pooler_output.shape
          assert torch.allclose(__snake_case					,				outputs.pooler_output					,				atol=1e-3     )
     else:
          __A		:					int						=			timm_model(__snake_case     )
          assert timm_logits.shape == outputs.logits.shape
          assert torch.allclose(__snake_case					,				outputs.logits					,				atol=1e-3     )

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


if __name__ == "__main__":
  lowercase__       : Optional[int]         =       argparse.ArgumentParser()
  # Required parameters
  parser.add_argument(
      '''--vit_name''',
      default='''vit_base_patch16_224''',
      type=str,
      help='''Name of the ViT timm model you\'d like to convert.''',
  )
  parser.add_argument(
      '''--pytorch_dump_folder_path''', default=None, type=str, help='''Path to the output PyTorch model directory.'''
  )

  lowercase__       : Tuple         =       parser.parse_args()
  convert_vit_checkpoint(args.vit_name, args.pytorch_dump_folder_path)






'''simple docstring'''




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 					SCREAMING_SNAKE_CASE       :


     def __init__(		self		,      _UpperCAmelCase		,      _UpperCAmelCase=13		,      _UpperCAmelCase=[30, 30]		,      _UpperCAmelCase=2		,      _UpperCAmelCase=3		,      _UpperCAmelCase=True		,      _UpperCAmelCase=True		,      _UpperCAmelCase=32		,      _UpperCAmelCase=5		,      _UpperCAmelCase=4		,      _UpperCAmelCase=37		,      _UpperCAmelCase="gelu"		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=0.1		,      _UpperCAmelCase=10		,      _UpperCAmelCase=0.02		,      _UpperCAmelCase=3		,      _UpperCAmelCase=None		,      _UpperCAmelCase=8		,      _UpperCAmelCase=10		,      ):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			parent
          __A		:					Tuple						=			batch_size
          __A		:					List[str]						=			image_size
          __A		:					Dict						=			patch_size
          __A		:					Optional[Any]						=			num_channels
          __A		:					Tuple						=			is_training
          __A		:					Dict						=			use_labels
          __A		:					List[Any]						=			hidden_size
          __A		:					Tuple						=			num_hidden_layers
          __A		:					int						=			num_attention_heads
          __A		:					Optional[int]						=			intermediate_size
          __A		:					Tuple						=			hidden_act
          __A		:					Any						=			hidden_dropout_prob
          __A		:					Optional[Any]						=			attention_probs_dropout_prob
          __A		:					List[Any]						=			type_sequence_label_size
          __A		:					List[Any]						=			initializer_range
          __A		:					Optional[int]						=			num_labels
          __A		:					List[Any]						=			scope
          __A		:					Any						=			n_targets
          __A		:					Union[str, Any]						=			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
          __A		:					List[str]						=			(image_size[1] // patch_size) * (image_size[0] // patch_size)
          __A		:					int						=			num_patches + 1 + self.num_detection_tokens


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					List[str]						=			floats_tensor([self.batch_size, self.num_channels, self.image_size[0], self.image_size[1]])

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

          __A		:					Any						=			self.get_config()

          return config, pixel_values, labels


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          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=_UpperCAmelCase		,      initializer_range=self.initializer_range		,      num_detection_tokens=self.num_detection_tokens		,      num_labels=self.num_labels		,      )


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosModel(config=_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()
          __A		:					Dict						=			model(_UpperCAmelCase)
          self.parent.assertEqual(
              result.last_hidden_state.shape		,      (self.batch_size, self.expected_seq_len, self.hidden_size))


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection(_UpperCAmelCase)
          model.to(_UpperCAmelCase)
          model.eval()

          __A		:					str						=			model(pixel_values=_UpperCAmelCase)
          __A		:					List[str]						=			model(_UpperCAmelCase)

          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))

          __A		:					Union[str, Any]						=			model(pixel_values=_UpperCAmelCase		,      labels=_UpperCAmelCase)

          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):

          '''simple docstring'''






          __A		:					Any						=			self.prepare_config_and_inputs()
          __A					,__A					,__A		:					Tuple						=			config_and_inputs
          __A		:					Tuple						=			{'pixel_values': pixel_values}
          return config, inputs_dict




@require_torch
class 					SCREAMING_SNAKE_CASE       (a__   ,						a__   ,						unittest.TestCase				):
     lowerCAmelCase						     =			(YolosModel, YolosForObjectDetection) if is_torch_available() else ()
     lowerCAmelCase						     =			(
         {'''feature-extraction''': YolosModel, '''object-detection''': YolosForObjectDetection} if is_torch_available() else {}
     )

     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False
     lowerCAmelCase						     =			False


     def 				SCREAMING_SNAKE_CASE    (		self		,      _UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase=False):

          '''simple docstring'''






          __A		:					Optional[Any]						=			super()._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase		,      return_labels=_UpperCAmelCase)

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

          return inputs_dict


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			YolosModelTester(self)
          __A		:					Dict						=			ConfigTester(self		,      config_class=_UpperCAmelCase		,      has_text_modality=_UpperCAmelCase		,      hidden_size=37)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          self.config_tester.run_common_tests()


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          pass


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[str]						=			self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               self.assertIsInstance(model.get_input_embeddings()		,      (nn.Module))
               __A		:					Any						=			model.get_output_embeddings()
               self.assertTrue(x is None or isinstance(_UpperCAmelCase		,      nn.Linear))


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					List[Any]						=			self.model_tester.prepare_config_and_inputs_for_common()

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

               __A		:					List[str]						=			['pixel_values']
               self.assertListEqual(arg_names[:1]		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A					,__A		:					Dict						=			self.model_tester.prepare_config_and_inputs_for_common()
          __A		:					Optional[int]						=			True

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

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

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

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

               __A		:					Union[str, Any]						=			1
               self.assertEqual(out_len + added_hidden_states		,      len(_UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.attentions

               self.assertEqual(len(_UpperCAmelCase)		,      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):

          '''simple docstring'''






          def check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase):
               __A		:					Tuple						=			model_class(_UpperCAmelCase)
               model.to(_UpperCAmelCase)
               model.eval()

               with torch.no_grad():
                    __A		:					List[str]						=			model(**self._prepare_for_class(_UpperCAmelCase		,      _UpperCAmelCase))

               __A		:					Optional[Any]						=			outputs.hidden_states

               __A		:					List[str]						=			getattr(
                   self.model_tester		,      'expected_num_hidden_layers'		,      self.model_tester.num_hidden_layers + 1)
               self.assertEqual(len(_UpperCAmelCase)		,      _UpperCAmelCase)

               # YOLOS has a different seq_length
               __A		:					Dict						=			self.model_tester.expected_seq_len

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

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

          for model_class in self.all_model_classes:
               __A		:					List[str]						=			True
               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)

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

               check_hidden_states_output(_UpperCAmelCase		,      _UpperCAmelCase		,      _UpperCAmelCase)


     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Union[str, Any]						=			self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*_UpperCAmelCase)


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          for model_name in YOLOS_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
               __A		:					List[Any]						=			YolosModel.from_pretrained(_UpperCAmelCase)
               self.assertIsNotNone(_UpperCAmelCase)






def 			_lowerCAmelCase     (    )	->	int:
     __A		:					Dict						=			Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
     return image







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


     @cached_property
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          return AutoImageProcessor.from_pretrained('hustvl/yolos-small') if is_vision_available() else None


     @slow
     def 				SCREAMING_SNAKE_CASE    (		self):

          '''simple docstring'''






          __A		:					Any						=			YolosForObjectDetection.from_pretrained('hustvl/yolos-small').to(_UpperCAmelCase)

          __A		:					Any						=			self.default_image_processor
          __A		:					str						=			prepare_img()
          __A		:					int						=			image_processor(images=_UpperCAmelCase		,      return_tensors='pt').to(_UpperCAmelCase)

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

          # verify outputs
          __A		:					Tuple						=			torch.Size((1, 100, 92))
          self.assertEqual(outputs.logits.shape		,      _UpperCAmelCase)

          __A		:					Dict						=			torch.tensor(
              [[-24.0248, -10.3024, -14.8290], [-42.0392, -16.8200, -27.4334], [-27.2743, -11.8154, -18.7148]]		,      device=_UpperCAmelCase		,      )
          __A		:					int						=			torch.tensor(
              [[0.2559, 0.5455, 0.4706], [0.2989, 0.7279, 0.1875], [0.7732, 0.4017, 0.4462]]		,      device=_UpperCAmelCase)
          self.assertTrue(torch.allclose(outputs.logits[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))
          self.assertTrue(torch.allclose(outputs.pred_boxes[0, :3, :3]		,      _UpperCAmelCase		,      atol=1e-4))

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

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