






from typing import TYPE_CHECKING

# rely on isort to merge the imports
from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


snake_case__      :	Tuple       	=      {
    """configuration_informer""": [
        """INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """InformerConfig""",
    ],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     snake_case__      :	Dict       	=      [
         """INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """InformerForPrediction""",
         """InformerModel""",
         """InformerPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_informer import INFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, InformerConfig

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_informer import (
              INFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
              InformerForPrediction,
              InformerModel,
              InformerPreTrainedModel,
          )

else:
     import sys

     snake_case__      :	str       	=      _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)













import inspect
import unittest
import warnings
from math import ceil, floor

from transformers import LevitConfig
from transformers.file_utils import cached_property, is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

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


if is_torch_available():
 import torch

 from transformers import (
     MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
     MODEL_MAPPING,
     LevitForImageClassification,
     LevitForImageClassificationWithTeacher,
     LevitModel,
 )
 from transformers.models.levit.modeling_levit import LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
 from PIL import Image

 from transformers import LevitImageProcessor


class 						_a       (  UpperCAmelCase__    ):






  """simple docstring"""





  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_									=     self.config_class(**self.inputs_dict )
     self.parent.assertTrue(hasattr(_UpperCAmelCase		,		'hidden_sizes' ) )
     self.parent.assertTrue(hasattr(_UpperCAmelCase		,		'num_attention_heads' ) )




class 						_a       :






  """simple docstring"""





  def __init__(     self		,		_UpperCAmelCase		,		_UpperCAmelCase=13		,		_UpperCAmelCase=64		,		_UpperCAmelCase=3		,		_UpperCAmelCase=3		,		_UpperCAmelCase=2		,		_UpperCAmelCase=1		,		_UpperCAmelCase=16		,		_UpperCAmelCase=[128, 256, 384]		,		_UpperCAmelCase=[4, 6, 8]		,		_UpperCAmelCase=[2, 3, 4]		,		_UpperCAmelCase=[16, 16, 16]		,		_UpperCAmelCase=0		,		_UpperCAmelCase=[2, 2, 2]		,		_UpperCAmelCase=[2, 2, 2]		,		_UpperCAmelCase=0.0_2		,		_UpperCAmelCase=True		,		_UpperCAmelCase=True		,		_UpperCAmelCase=2		,		)			-> int:
     UpperCamelCase_									=     parent
     UpperCamelCase_									=     batch_size
     UpperCamelCase_									=     image_size
     UpperCamelCase_									=     num_channels
     UpperCamelCase_									=     kernel_size
     UpperCamelCase_									=     stride
     UpperCamelCase_									=     padding
     UpperCamelCase_									=     hidden_sizes
     UpperCamelCase_									=     num_attention_heads
     UpperCamelCase_									=     depths
     UpperCamelCase_									=     key_dim
     UpperCamelCase_									=     drop_path_rate
     UpperCamelCase_									=     patch_size
     UpperCamelCase_									=     attention_ratio
     UpperCamelCase_									=     mlp_ratio
     UpperCamelCase_									=     initializer_range
     UpperCamelCase_									=     [
         ['Subsample', key_dim[0], hidden_sizes[0] // key_dim[0], 4, 2, 2],
         ['Subsample', key_dim[0], hidden_sizes[1] // key_dim[0], 4, 2, 2],
     ]
     UpperCamelCase_									=     is_training
     UpperCamelCase_									=     use_labels
     UpperCamelCase_									=     num_labels
     UpperCamelCase_									=     initializer_range




  def    _UpperCAmelCase     (     self )			-> Optional[int]:
     UpperCamelCase_									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

     UpperCamelCase_									=     None
     if self.use_labels:
        UpperCamelCase_									=     ids_tensor([self.batch_size]		,		self.num_labels )

     UpperCamelCase_									=     self.get_config()
     return config, pixel_values, labels




  def    _UpperCAmelCase     (     self )			-> str:
     return LevitConfig(
         image_size=self.image_size		,		num_channels=self.num_channels		,		kernel_size=self.kernel_size		,		stride=self.stride		,		padding=self.padding		,		patch_size=self.patch_size		,		hidden_sizes=self.hidden_sizes		,		num_attention_heads=self.num_attention_heads		,		depths=self.depths		,		key_dim=self.key_dim		,		drop_path_rate=self.drop_path_rate		,		mlp_ratio=self.mlp_ratio		,		attention_ratio=self.attention_ratio		,		initializer_range=self.initializer_range		,		down_ops=self.down_ops		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> int:
     UpperCamelCase_									=     LevitModel(config=_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase )
     UpperCamelCase_									=     (self.image_size, self.image_size)
     UpperCamelCase_		,      UpperCamelCase_									=     image_size[0], image_size[1]
     for _ in range(4 ):
        UpperCamelCase_									=     floor(((height + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
        UpperCamelCase_									=     floor(((width + 2 * self.padding - self.kernel_size) / self.stride) + 1 )
     self.parent.assertEqual(
         result.last_hidden_state.shape		,		(self.batch_size, ceil(height / 4 ) * ceil(width / 4 ), self.hidden_sizes[-1])		,		)




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )			-> Tuple:
     UpperCamelCase_									=     self.num_labels
     UpperCamelCase_									=     LevitForImageClassification(_UpperCAmelCase )
     model.to(_UpperCAmelCase )
     model.eval()
     UpperCamelCase_									=     model(_UpperCAmelCase		,		labels=_UpperCAmelCase )
     self.parent.assertEqual(result.logits.shape		,		(self.batch_size, self.num_labels) )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_									=     self.prepare_config_and_inputs()
     UpperCamelCase_		,      UpperCamelCase_		,      UpperCamelCase_									=     config_and_inputs
     UpperCamelCase_									=     {'pixel_values': pixel_values}
     return config, inputs_dict




@require_torch
class 						_a       (  UpperCAmelCase__						,       UpperCAmelCase__						,       unittest.TestCase    ):






  """simple docstring"""





  A_						=   (
      (LevitModel, LevitForImageClassification, LevitForImageClassificationWithTeacher)
      if is_torch_available()
      else ()
  )
  A_						=   (
      {
          """feature-extraction""": LevitModel,
          """image-classification""": (LevitForImageClassification, LevitForImageClassificationWithTeacher),
      }
      if is_torch_available()
      else {}
  )

  A_						=   False
  A_						=   False
  A_						=   False
  A_						=   False
  A_						=   False




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     LevitModelTester(self )
     UpperCamelCase_									=     ConfigTester(self		,		config_class=_UpperCAmelCase		,		has_text_modality=_UpperCAmelCase		,		hidden_size=37 )




  def    _UpperCAmelCase     (     self )			-> Dict:
     self.create_and_test_config_common_properties()
     self.config_tester.create_and_test_config_to_json_string()
     self.config_tester.create_and_test_config_to_json_file()
     self.config_tester.create_and_test_config_from_and_save_pretrained()
     self.config_tester.create_and_test_config_with_num_labels()
     self.config_tester.check_config_can_be_init_without_params()
     self.config_tester.check_config_arguments_init()




  def    _UpperCAmelCase     (     self )			-> Any:
     return




  @unittest.skip(reason='Levit does not use inputs_embeds' )
  def    _UpperCAmelCase     (     self )			-> str:
     pass




  @unittest.skip(reason='Levit does not support input and output embeddings' )
  def    _UpperCAmelCase     (     self )			-> Dict:
     pass




  @unittest.skip(reason='Levit does not output attentions' )
  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     pass




  def    _UpperCAmelCase     (     self )			-> Optional[Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

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

        UpperCamelCase_									=     ['pixel_values']
        self.assertListEqual(arg_names[:1]		,		_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     def check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase ):
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.eval()

        with torch.no_grad():
           UpperCamelCase_									=     model(**self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase ) )

        UpperCamelCase_									=     outputs.hidden_states

        UpperCamelCase_									=     len(self.model_tester.depths ) + 1
        self.assertEqual(len(_UpperCAmelCase )		,		_UpperCAmelCase )

        UpperCamelCase_									=     (self.model_tester.image_size, self.model_tester.image_size)
        UpperCamelCase_		,      UpperCamelCase_									=     image_size[0], image_size[1]
        for _ in range(4 ):
           UpperCamelCase_									=     floor(
               (
                   (height + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1 )
           UpperCamelCase_									=     floor(
               (
                   (width + 2 * self.model_tester.padding - self.model_tester.kernel_size)
                   / self.model_tester.stride
               )
               + 1 )
        # verify the first hidden states (first block)
        self.assertListEqual(
            list(hidden_states[0].shape[-2:] )		,		[
                height * width,
                self.model_tester.hidden_sizes[0],
            ]		,		)

     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     for model_class in self.all_model_classes:
        UpperCamelCase_									=     True
        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )

        # check that output_hidden_states also work using config
        del inputs_dict["output_hidden_states"]
        UpperCamelCase_									=     True

        check_hidden_states_output(_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase )




  @unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.' )
  def    _UpperCAmelCase     (     self )			-> int:
     pass




  def    _UpperCAmelCase     (     self		,		_UpperCAmelCase		,		_UpperCAmelCase		,		_UpperCAmelCase=False )			-> Tuple:
     UpperCamelCase_									=     super()._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )

     if return_labels:
        if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           del inputs_dict["labels"]

     return inputs_dict




  def    _UpperCAmelCase     (     self )			-> Union[str, Any]:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_model(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs()
     self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase )




  def    _UpperCAmelCase     (     self )			-> Tuple:
     if not self.model_tester.is_training:
        return

     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     UpperCamelCase_									=     True

     for model_class in self.all_model_classes:
        # LevitForImageClassificationWithTeacher supports inference-only
        if (
            model_class in get_values(_UpperCAmelCase )
            or model_class.__name__ == "LevitForImageClassificationWithTeacher"
        ):
           continue
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> int:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()
     if not self.model_tester.is_training:
        return

     UpperCamelCase_									=     False
     UpperCamelCase_									=     True

     for model_class in self.all_model_classes:
        if model_class in get_values(_UpperCAmelCase ) or not model_class.supports_gradient_checkpointing:
           continue
        # LevitForImageClassificationWithTeacher supports inference-only
        if model_class.__name__ == "LevitForImageClassificationWithTeacher":
           continue
        UpperCamelCase_									=     model_class(_UpperCAmelCase )
        model.gradient_checkpointing_enable()
        model.to(_UpperCAmelCase )
        model.train()
        UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )
        UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
        loss.backward()




  def    _UpperCAmelCase     (     self )			-> List[Any]:
     UpperCamelCase_		,      UpperCamelCase_									=     self.model_tester.prepare_config_and_inputs_for_common()

     UpperCamelCase_									=     [
         {'title': 'multi_label_classification', 'num_labels': 2, 'dtype': torch.float},
         {'title': 'single_label_classification', 'num_labels': 1, 'dtype': torch.long},
         {'title': 'regression', 'num_labels': 1, 'dtype': torch.float},
     ]

     for model_class in self.all_model_classes:
        if (
            model_class
            not in [
                *get_values(_UpperCAmelCase ),
            ]
            or model_class.__name__ == "LevitForImageClassificationWithTeacher"
        ):
           continue

        for problem_type in problem_types:
           with self.subTest(msg=f"""Testing {model_class} with {problem_type["title"]}""" ):
              UpperCamelCase_									=     problem_type['title']
              UpperCamelCase_									=     problem_type['num_labels']

              UpperCamelCase_									=     model_class(_UpperCAmelCase )
              model.to(_UpperCAmelCase )
              model.train()

              UpperCamelCase_									=     self._prepare_for_class(_UpperCAmelCase		,		_UpperCAmelCase		,		return_labels=_UpperCAmelCase )

              if problem_type["num_labels"] > 1:
                 UpperCamelCase_									=     inputs['labels'].unsqueeze(1 ).repeat(1		,		problem_type['num_labels'] )

              UpperCamelCase_									=     inputs['labels'].to(problem_type['dtype'] )

              # This tests that we do not trigger the warning form PyTorch "Using a target size that is different
              # to the input size. This will likely lead to incorrect results due to broadcasting. Please ensure
              # they have the same size." which is a symptom something in wrong for the regression problem.
              # See https://github.com/huggingface/transformers/issues/11780
              with warnings.catch_warnings(record=_UpperCAmelCase ) as warning_list:
                 UpperCamelCase_									=     model(**_UpperCAmelCase ).loss
              for w in warning_list:
                 if "Using a target size that is different to the input size" in str(w.message ):
                    raise ValueError(
                        f"""Something is going wrong in the regression problem: intercepted {w.message}""" )

              loss.backward()




  @slow
  def    _UpperCAmelCase     (     self )			-> List[str]:
     for model_name in LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
        UpperCamelCase_									=     LevitModel.from_pretrained(_UpperCAmelCase )
        self.assertIsNotNone(_UpperCAmelCase )






def     _snake_case    ():
   UpperCamelCase_									=     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png')
   return image


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






  """simple docstring"""





  @cached_property
  def    _UpperCAmelCase     (     self )			-> str:
     return LevitImageProcessor.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] )




  @slow
  def    _UpperCAmelCase     (     self )			-> Dict:
     UpperCamelCase_									=     LevitForImageClassificationWithTeacher.from_pretrained(LEVIT_PRETRAINED_MODEL_ARCHIVE_LIST[0] ).to(
         _UpperCAmelCase )

     UpperCamelCase_									=     self.default_image_processor
     UpperCamelCase_									=     prepare_img()
     UpperCamelCase_									=     image_processor(images=_UpperCAmelCase		,		return_tensors='pt' ).to(_UpperCAmelCase )

     # forward pass
     with torch.no_grad():
        UpperCamelCase_									=     model(**_UpperCAmelCase )

     # verify the logits
     UpperCamelCase_									=     torch.Size((1, 1000) )
     self.assertEqual(outputs.logits.shape		,		_UpperCAmelCase )

     UpperCamelCase_									=     torch.tensor([1.0_4_4_8, -0.3_7_4_5, -1.8_3_1_7] ).to(_UpperCAmelCase )

     self.assertTrue(torch.allclose(outputs.logits[0, :3]		,		_UpperCAmelCase		,		atol=1e-4 ) )





