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 	__snake_case	:





      '''simple docstring'''






      def __init__(					self     :				Dict    ,  _UpperCamelCase     :				List[Any]    ,  _UpperCamelCase     :				Any=13    ,  _UpperCamelCase     :				Union[str, Any]=[30, 30]    ,  _UpperCamelCase     :				str=2    ,  _UpperCamelCase     :				Tuple=3    ,  _UpperCamelCase     :				int=True    ,  _UpperCamelCase     :				Tuple=True    ,  _UpperCamelCase     :				Union[str, Any]=32    ,  _UpperCamelCase     :				str=5    ,  _UpperCamelCase     :				List[Any]=4    ,  _UpperCamelCase     :				Optional[int]=37    ,  _UpperCamelCase     :				List[Any]="gelu"    ,  _UpperCamelCase     :				int=0.1    ,  _UpperCamelCase     :				List[Any]=0.1    ,  _UpperCamelCase     :				Optional[int]=10    ,  _UpperCamelCase     :				List[Any]=0.0_2    ,  _UpperCamelCase     :				List[Any]=3    ,  _UpperCamelCase     :				Dict=None    ,  _UpperCamelCase     :				Optional[Any]=8    ,  _UpperCamelCase     :				List[str]=10    ,  )				->str:


          """simple docstring"""


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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->str:


          """simple docstring"""


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

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

          _lowerCamelCase     :		Optional[Any] 						=						self.get_config()

          return config, pixel_values, labels



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->str:


          """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     :				Dict    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				int    ,  _UpperCamelCase     :				Optional[Any])				->List[str]:


          """simple docstring"""


          _lowerCamelCase     :		Tuple 						=						YolosModel(config=_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()
          _lowerCamelCase     :		Tuple 						=						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     :				Dict    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				List[Any])				->Optional[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						YolosForObjectDetection(_UpperCamelCase)
          model.to(_UpperCamelCase)
          model.eval()

          _lowerCamelCase     :		Dict 						=						model(pixel_values=_UpperCamelCase)
          _lowerCamelCase     :		Tuple 						=						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))

          _lowerCamelCase     :		Dict 						=						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     :				Tuple)				->List[str]:


          """simple docstring"""


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





@require_torch
class 	__snake_case	( __lowerCAmelCase ,  __lowerCAmelCase ,  unittest.TestCase		):





      '''simple docstring'''






      _snake_case    		=       (YolosModel, YolosForObjectDetection) if is_torch_available() else ()
      _snake_case    		=       (
          {'feature-extraction': YolosModel, 'object-detection': YolosForObjectDetection} if is_torch_available() else {}
      )

      _snake_case    		=       False
      _snake_case    		=       False
      _snake_case    		=       False
      _snake_case    		=       False



      def 	_SCREAMING_SNAKE_CASE    (					self     :				str    ,  _UpperCamelCase     :				List[str]    ,  _UpperCamelCase     :				Optional[Any]    ,  _UpperCamelCase     :				Dict=False)				->Optional[int]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						super()._prepare_for_class(_UpperCamelCase    ,  _UpperCamelCase    ,  return_labels=_UpperCamelCase)

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

          return inputs_dict



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		int 						=						YolosModelTester(self)
          _lowerCamelCase     :		Union[str, Any] 						=						ConfigTester(self    ,  config_class=_UpperCamelCase    ,  has_text_modality=_UpperCamelCase    ,  hidden_size=37)



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


          """simple docstring"""


          self.config_tester.run_common_tests()



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[Any])				->str:


          """simple docstring"""


          pass



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Tuple:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase     :		Any 						=						self.model_tester.prepare_config_and_inputs_for_common()

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



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Optional[int])				->Any:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase     :		str 						=						self.model_tester.prepare_config_and_inputs_for_common()

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

              _lowerCamelCase     :		Dict 						=						["""pixel_values"""]
              self.assertListEqual(arg_names[:1]    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[str])				->Any:


          """simple docstring"""


          _lowerCamelCase     :		Optional[Any] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*_UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				Tuple)				->Union[str, Any]:


          """simple docstring"""


          _lowerCamelCase						,							_lowerCamelCase     :		Union[str, Any] 						=						self.model_tester.prepare_config_and_inputs_for_common()
          _lowerCamelCase     :		List[str] 						=						True

          # in YOLOS, the seq_len is different
          _lowerCamelCase     :		Dict 						=						self.model_tester.expected_seq_len
          for model_class in self.all_model_classes:
              _lowerCamelCase     :		int 						=						True
              _lowerCamelCase     :		List[str] 						=						False
              _lowerCamelCase     :		int 						=						True
              _lowerCamelCase     :		Union[str, Any] 						=						model_class(_UpperCamelCase)
              model.to(_UpperCamelCase)
              model.eval()
              with torch.no_grad():
                  _lowerCamelCase     :		str 						=						model(**self._prepare_for_class(_UpperCamelCase    ,  _UpperCamelCase))
              _lowerCamelCase     :		Tuple 						=						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"]
              _lowerCamelCase     :		str 						=						True
              _lowerCamelCase     :		int 						=						model_class(_UpperCamelCase)
              model.to(_UpperCamelCase)
              model.eval()
              with torch.no_grad():
                  _lowerCamelCase     :		int 						=						model(**self._prepare_for_class(_UpperCamelCase    ,  _UpperCamelCase))
              _lowerCamelCase     :		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]    ,  )
              _lowerCamelCase     :		Dict 						=						len(_UpperCamelCase)

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

              _lowerCamelCase     :		int 						=						1
              self.assertEqual(out_len + added_hidden_states    ,  len(_UpperCamelCase))

              _lowerCamelCase     :		str 						=						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     :				Tuple)				->Any:


          """simple docstring"""


          def check_hidden_states_output(_UpperCamelCase     :				Union[str, Any]    ,  _UpperCamelCase     :				Optional[int]    ,  _UpperCamelCase     :				int):
              _lowerCamelCase     :		List[str] 						=						model_class(_UpperCamelCase)
              model.to(_UpperCamelCase)
              model.eval()

              with torch.no_grad():
                  _lowerCamelCase     :		Optional[Any] 						=						model(**self._prepare_for_class(_UpperCamelCase    ,  _UpperCamelCase))

              _lowerCamelCase     :		int 						=						outputs.hidden_states

              _lowerCamelCase     :		Optional[Any] 						=						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
              _lowerCamelCase     :		Any 						=						self.model_tester.expected_seq_len

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

          _lowerCamelCase						,							_lowerCamelCase     :		int 						=						self.model_tester.prepare_config_and_inputs_for_common()

          for model_class in self.all_model_classes:
              _lowerCamelCase     :		List[Any] 						=						True
              check_hidden_states_output(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)

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

              check_hidden_states_output(_UpperCamelCase    ,  _UpperCamelCase    ,  _UpperCamelCase)



      def 	_SCREAMING_SNAKE_CASE    (					self     :				int)				->List[Any]:


          """simple docstring"""


          _lowerCamelCase     :		Optional[int] 						=						self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_for_object_detection(*_UpperCamelCase)




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				List[Any])				->Optional[int]:


          """simple docstring"""


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







def 						A__  (					):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""	)
    return image




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





      '''simple docstring'''






      @cached_property
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Any)				->List[str]:


          """simple docstring"""


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




      @slow
      def 	_SCREAMING_SNAKE_CASE    (					self     :				Union[str, Any])				->Dict:


          """simple docstring"""


          _lowerCamelCase     :		Union[str, Any] 						=						YolosForObjectDetection.from_pretrained("""hustvl/yolos-small""").to(_UpperCamelCase)

          _lowerCamelCase     :		Union[str, Any] 						=						self.default_image_processor
          _lowerCamelCase     :		str 						=						prepare_img()
          _lowerCamelCase     :		List[Any] 						=						image_processor(images=_UpperCamelCase    ,  return_tensors="""pt""").to(_UpperCamelCase)

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

          # verify outputs
          _lowerCamelCase     :		str 						=						torch.Size((1, 100, 92))
          self.assertEqual(outputs.logits.shape    ,  _UpperCamelCase)

          _lowerCamelCase     :		Optional[int] 						=						torch.tensor(
              [[-2_4.0_2_4_8, -1_0.3_0_2_4, -1_4.8_2_9_0], [-4_2.0_3_9_2, -1_6.8_2_0_0, -2_7.4_3_3_4], [-2_7.2_7_4_3, -1_1.8_1_5_4, -1_8.7_1_4_8]]    ,  device=_UpperCamelCase    ,  )
          _lowerCamelCase     :		List[Any] 						=						torch.tensor(
              [[0.2_5_5_9, 0.5_4_5_5, 0.4_7_0_6], [0.2_9_8_9, 0.7_2_7_9, 0.1_8_7_5], [0.7_7_3_2, 0.4_0_1_7, 0.4_4_6_2]]    ,  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
          _lowerCamelCase     :		List[str] 						=						image_processor.post_process_object_detection(
              _UpperCamelCase    ,  threshold=0.3    ,  target_sizes=[image.size[::-1]])[0]
          _lowerCamelCase     :		List[str] 						=						torch.tensor([0.9_9_9_4, 0.9_7_9_0, 0.9_9_6_4, 0.9_9_7_2, 0.9_8_6_1]).to(_UpperCamelCase)
          _lowerCamelCase     :		str 						=						[75, 75, 17, 63, 17]
          _lowerCamelCase     :		Union[str, Any] 						=						torch.tensor([3_3_5.0_6_0_9, 7_9.3_8_4_8, 3_7_5.4_2_1_6, 1_8_7.2_4_9_5]).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))





import argparse
from pathlib import Path

import torch
from packaging import version
from torch.onnx import export

from diffusers import AutoencoderKL


lowerCAmelCase			:							Tuple     =version.parse(version.parse(torch.__version__).base_version) < version.parse("1.11")







def 						A__  (					__A   , __A   , __A   , __A   , __A   , __A   , __A   , __A=False   , ):







    '''simple docstring'''
    output_path.parent.mkdir(parents=__A   , exist_ok=__A	)
    # PyTorch deprecated the `enable_onnx_checker` and `use_external_data_format` arguments in v1.11,
    # so we check the torch version for backwards compatibility
    if is_torch_less_than_1_11:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , use_external_data_format=__A   , enable_onnx_checker=__A   , opset_version=__A   , )
    else:
        export(
            __A   , __A   , f=output_path.as_posix()   , input_names=__A   , output_names=__A   , dynamic_axes=__A   , do_constant_folding=__A   , opset_version=__A   , )







@torch.no_grad()
def 						A__  (					__A   , __A   , __A   , __A = False	):







    '''simple docstring'''
    _lowerCamelCase     :		Tuple 						=						torch.floataa if fpaa else torch.floataa
    if fpaa and torch.cuda.is_available():
        _lowerCamelCase     :		str 						=						"""cuda"""
    elif fpaa and not torch.cuda.is_available():
        raise ValueError("""`float16` model export is only supported on GPUs with CUDA"""	)
    else:
        _lowerCamelCase     :		List[str] 						=						"""cpu"""
    _lowerCamelCase     :		Dict 						=						Path(__A	)

    # VAE DECODER
    _lowerCamelCase     :		Optional[Any] 						=						AutoencoderKL.from_pretrained(model_path + """/vae"""	)
    _lowerCamelCase     :		List[str] 						=						vae_decoder.config.latent_channels
    # forward only through the decoder part
    _lowerCamelCase     :		Tuple 						=						vae_decoder.decode
    onnx_export(
        __A   , model_args=(
            torch.randn(1   , __A   , 25   , 25	).to(device=__A   , dtype=__A	),
            False,
        )   , output_path=output_path / """vae_decoder""" / """model.onnx"""   , ordered_input_names=["""latent_sample""", """return_dict"""]   , output_names=["""sample"""]   , dynamic_axes={
            """latent_sample""": {0: """batch""", 1: """channels""", 2: """height""", 3: """width"""},
        }   , opset=__A   , )
    del vae_decoder


if __name__ == "__main__":
    lowerCAmelCase			:							Optional[int]     =argparse.ArgumentParser()

    parser.add_argument(
        "--model_path",
        type=str,
        required=True,
        help="Path to the `diffusers` checkpoint to convert (either a local directory or on the Hub).",
    )

    parser.add_argument("--output_path", type=str, required=True, help="Path to the output model.")
    parser.add_argument(
        "--opset",
        default=14,
        type=int,
        help="The version of the ONNX operator set to use.",
    )
    parser.add_argument("--fp16", action="store_true", default=False, help="Export the models in `float16` mode")

    lowerCAmelCase			:							Optional[Any]     =parser.parse_args()
    print(args.output_path)
    convert_models(args.model_path, args.output_path, args.opset, args.fpaa)
    print("SD: Done: ONNX")




