"""simple docstring"""







from __future__ import annotations

import inspect
import unittest

import numpy as np

from transformers import SwinConfig
from transformers.testing_utils import require_tf, require_vision, slow, to_atuple
from transformers.utils import cached_property, is_tf_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_tf_common import TFModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_tf_available():
     import tensorflow as tf

     from transformers.models.swin.modeling_tf_swin import (
         TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST,
         TFSwinForImageClassification,
         TFSwinForMaskedImageModeling,
         TFSwinModel,
     )


if is_vision_available():
     from PIL import Image

     from transformers import AutoImageProcessor






class     _SCREAMING_SNAKE_CASE       :



   '''simple docstring'''



   def __init__(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=13   ,							_SCREAMING_SNAKE_CASE=32   ,							_SCREAMING_SNAKE_CASE=2   ,							_SCREAMING_SNAKE_CASE=3   ,							_SCREAMING_SNAKE_CASE=16   ,							_SCREAMING_SNAKE_CASE=[1, 2, 1]   ,							_SCREAMING_SNAKE_CASE=[2, 2, 4]   ,							_SCREAMING_SNAKE_CASE=2   ,							_SCREAMING_SNAKE_CASE=2.0   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=0.0   ,							_SCREAMING_SNAKE_CASE=0.0   ,							_SCREAMING_SNAKE_CASE=0.1   ,							_SCREAMING_SNAKE_CASE="gelu"   ,							_SCREAMING_SNAKE_CASE=False   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=0.02   ,							_SCREAMING_SNAKE_CASE=1E-5   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=None   ,							_SCREAMING_SNAKE_CASE=True   ,							_SCREAMING_SNAKE_CASE=10   ,							_SCREAMING_SNAKE_CASE=8   ,							):


      '''simple docstring'''

      __lowerCamelCase								=				parent
      __lowerCamelCase								=				batch_size
      __lowerCamelCase								=				image_size
      __lowerCamelCase								=				patch_size
      __lowerCamelCase								=				num_channels
      __lowerCamelCase								=				embed_dim
      __lowerCamelCase								=				depths
      __lowerCamelCase								=				num_heads
      __lowerCamelCase								=				window_size
      __lowerCamelCase								=				mlp_ratio
      __lowerCamelCase								=				qkv_bias
      __lowerCamelCase								=				hidden_dropout_prob
      __lowerCamelCase								=				attention_probs_dropout_prob
      __lowerCamelCase								=				drop_path_rate
      __lowerCamelCase								=				hidden_act
      __lowerCamelCase								=				use_absolute_embeddings
      __lowerCamelCase								=				patch_norm
      __lowerCamelCase								=				layer_norm_eps
      __lowerCamelCase								=				initializer_range
      __lowerCamelCase								=				is_training
      __lowerCamelCase								=				scope
      __lowerCamelCase								=				use_labels
      __lowerCamelCase								=				type_sequence_label_size
      __lowerCamelCase								=				encoder_stride







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

      __lowerCamelCase								=				None
      if self.use_labels:
         __lowerCamelCase								=				ids_tensor([self.batch_size]   ,							self.type_sequence_label_size  )

      __lowerCamelCase								=				self.get_config()

      return config, pixel_values, labels







   def       _A							(  self  ):


      '''simple docstring'''

      return SwinConfig(
          image_size=self.image_size   ,							patch_size=self.patch_size   ,							num_channels=self.num_channels   ,							embed_dim=self.embed_dim   ,							depths=self.depths   ,							num_heads=self.num_heads   ,							window_size=self.window_size   ,							mlp_ratio=self.mlp_ratio   ,							qkv_bias=self.qkv_bias   ,							hidden_dropout_prob=self.hidden_dropout_prob   ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob   ,							drop_path_rate=self.drop_path_rate   ,							hidden_act=self.hidden_act   ,							use_absolute_embeddings=self.use_absolute_embeddings   ,							path_norm=self.patch_norm   ,							layer_norm_eps=self.layer_norm_eps   ,							initializer_range=self.initializer_range   ,							encoder_stride=self.encoder_stride   ,							)







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				TFSwinModel(config=_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				model(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
      __lowerCamelCase								=				int(config.embed_dim * 2 ** (len(config.depths  ) - 1)  )

      self.parent.assertEqual(result.last_hidden_state.shape   ,							(self.batch_size, expected_seq_len, expected_dim)  )







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				TFSwinForMaskedImageModeling(config=_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				model(_SCREAMING_SNAKE_CASE  )
      self.parent.assertEqual(
          result.logits.shape   ,							(self.batch_size, self.num_channels, self.image_size, self.image_size)  )

      # test greyscale images
      __lowerCamelCase								=				1
      __lowerCamelCase								=				TFSwinForMaskedImageModeling(_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase								=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
      __lowerCamelCase								=				model(_SCREAMING_SNAKE_CASE  )
      self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, 1, self.image_size, self.image_size)  )







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.type_sequence_label_size
      __lowerCamelCase								=				TFSwinForImageClassification(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				model(_SCREAMING_SNAKE_CASE   ,							labels=_SCREAMING_SNAKE_CASE  )
      self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size)  )

      # test greyscale images
      __lowerCamelCase								=				1
      __lowerCamelCase								=				TFSwinForImageClassification(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				floats_tensor([self.batch_size, 1, self.image_size, self.image_size]  )
      __lowerCamelCase								=				model(_SCREAMING_SNAKE_CASE  )
      self.parent.assertEqual(result.logits.shape   ,							(self.batch_size, self.type_sequence_label_size)  )







   def       _A							(  self  ):


      '''simple docstring'''

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






@require_tf
class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase						,							UpperCAmelCase						,							unittest.TestCase							):



   '''simple docstring'''



   __UpperCAmelCase  :       Optional[Any]						=						(
       (
           TFSwinModel,
           TFSwinForImageClassification,
           TFSwinForMaskedImageModeling,
       )
       if is_tf_available()
       else ()
   )
   __UpperCAmelCase  :       List[str]						=						(
       {"feature-extraction": TFSwinModel, "image-classification": TFSwinForImageClassification}
       if is_tf_available()
       else {}
   )

   __UpperCAmelCase  :       List[Any]						=						False
   __UpperCAmelCase  :       Union[str, Any]						=						False
   __UpperCAmelCase  :       Optional[Any]						=						False
   __UpperCAmelCase  :       Union[str, Any]						=						False







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				TFSwinModelTester(self  )
      __lowerCamelCase								=				ConfigTester(self   ,							config_class=_SCREAMING_SNAKE_CASE   ,							embed_dim=37  )







   def       _A							(  self  ):


      '''simple docstring'''

      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       _A							(  self  ):


      '''simple docstring'''

      return







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_SCREAMING_SNAKE_CASE  )







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_masked_image_modeling(*_SCREAMING_SNAKE_CASE  )







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_for_image_classification(*_SCREAMING_SNAKE_CASE  )







   @unittest.skip(reason='Swin does not use inputs_embeds'  )
   def       _A							(  self  ):


      '''simple docstring'''

      pass







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
         __lowerCamelCase								=				model_class(_SCREAMING_SNAKE_CASE  )
         self.assertIsInstance(model.get_input_embeddings()   ,							tf.keras.layers.Layer  )
         __lowerCamelCase								=				model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(_SCREAMING_SNAKE_CASE   ,							tf.keras.layers.Dense  )  )







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				self.model_tester.prepare_config_and_inputs_for_common()

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

         __lowerCamelCase								=				['pixel_values']
         self.assertListEqual(arg_names[:1]   ,							_SCREAMING_SNAKE_CASE  )







   def       _A							(  self  ):


      '''simple docstring'''

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

      for model_class in self.all_model_classes:
         __lowerCamelCase								=				True
         __lowerCamelCase								=				False
         __lowerCamelCase								=				True
         __lowerCamelCase								=				model_class(_SCREAMING_SNAKE_CASE  )
         __lowerCamelCase								=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )  )
         __lowerCamelCase								=				outputs.attentions
         __lowerCamelCase								=				len(self.model_tester.depths  )
         self.assertEqual(len(_SCREAMING_SNAKE_CASE  )   ,							_SCREAMING_SNAKE_CASE  )

         # check that output_attentions also work using config
         del inputs_dict["output_attentions"]
         __lowerCamelCase								=				True
         __lowerCamelCase								=				config.window_size**2
         __lowerCamelCase								=				model_class(_SCREAMING_SNAKE_CASE  )
         __lowerCamelCase								=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )  )
         __lowerCamelCase								=				outputs.attentions
         self.assertEqual(len(_SCREAMING_SNAKE_CASE  )   ,							_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(
             list(attentions[0].shape[-3:]  )   ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,							)
         __lowerCamelCase								=				len(_SCREAMING_SNAKE_CASE  )

         # Check attention is always last and order is fine
         __lowerCamelCase								=				True
         __lowerCamelCase								=				True
         __lowerCamelCase								=				model_class(_SCREAMING_SNAKE_CASE  )
         __lowerCamelCase								=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )  )

         if hasattr(self.model_tester   ,							'num_hidden_states_types'  ):
            __lowerCamelCase								=				self.model_tester.num_hidden_states_types
         else:
            # also another +1 for reshaped_hidden_states
            __lowerCamelCase								=				2
         self.assertEqual(out_len + added_hidden_states   ,							len(_SCREAMING_SNAKE_CASE  )  )

         __lowerCamelCase								=				outputs.attentions

         self.assertEqual(len(_SCREAMING_SNAKE_CASE  )   ,							_SCREAMING_SNAKE_CASE  )

         self.assertListEqual(
             list(self_attentions[0].shape[-3:]  )   ,							[self.model_tester.num_heads[0], window_size_squared, window_size_squared]   ,							)







   def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  ):


      '''simple docstring'''

      __lowerCamelCase								=				model_class(_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				model(**self._prepare_for_class(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )  )
      __lowerCamelCase								=				outputs.hidden_states

      __lowerCamelCase								=				getattr(
          self.model_tester   ,							'expected_num_hidden_layers'   ,							len(self.model_tester.depths  ) + 1  )
      self.assertEqual(len(_SCREAMING_SNAKE_CASE  )   ,							_SCREAMING_SNAKE_CASE  )

      # Swin has a different seq_length
      __lowerCamelCase								=				to_atuple(config.patch_size  )

      __lowerCamelCase								=				(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])

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

      __lowerCamelCase								=				outputs.reshaped_hidden_states
      self.assertEqual(len(_SCREAMING_SNAKE_CASE  )   ,							_SCREAMING_SNAKE_CASE  )

      __lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase		,__lowerCamelCase								=				reshaped_hidden_states[0].shape

      __lowerCamelCase								=				tf.reshape(reshaped_hidden_states[0]   ,							(batch_size, num_channels, height * width)  )
      __lowerCamelCase								=				tf.transpose(_SCREAMING_SNAKE_CASE   ,							(0, 2, 1)  )

      self.assertListEqual(
          list(reshaped_hidden_states.shape[-2:]  )   ,							[num_patches, self.model_tester.embed_dim]   ,							)







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				self.model_tester.prepare_config_and_inputs_for_common()

      __lowerCamelCase								=				to_atuple(self.model_tester.image_size  )

      for model_class in self.all_model_classes:
         __lowerCamelCase								=				True
         self.check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,							_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								=				True

         self.check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE  )







   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase		,__lowerCamelCase								=				self.model_tester.prepare_config_and_inputs_for_common()
      __lowerCamelCase								=				3

      __lowerCamelCase								=				to_atuple(self.model_tester.image_size  )
      __lowerCamelCase								=				to_atuple(config.patch_size  )

      __lowerCamelCase								=				image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
      __lowerCamelCase								=				image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

      for model_class in self.all_model_classes:
         __lowerCamelCase								=				True
         self.check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							(padded_height, padded_width)  )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         __lowerCamelCase								=				True
         self.check_hidden_states_output(_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							(padded_height, padded_width)  )







   @slow
   def       _A							(  self  ):


      '''simple docstring'''

      for model_name in TF_SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
         __lowerCamelCase								=				TFSwinModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
         self.assertIsNotNone(_SCREAMING_SNAKE_CASE  )






@require_vision
@require_tf
class     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



   '''simple docstring'''



   @cached_property
   def       _A							(  self  ):


      '''simple docstring'''

      return (
          AutoImageProcessor.from_pretrained('microsoft/swin-tiny-patch4-window7-224'  )
          if is_vision_available()
          else None
      )







   @slow
   def       _A							(  self  ):


      '''simple docstring'''

      __lowerCamelCase								=				TFSwinForImageClassification.from_pretrained('microsoft/swin-tiny-patch4-window7-224'  )
      __lowerCamelCase								=				self.default_image_processor

      __lowerCamelCase								=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'  )
      __lowerCamelCase								=				image_processor(images=_SCREAMING_SNAKE_CASE   ,							return_tensors='tf'  )

      # forward pass
      __lowerCamelCase								=				model(_SCREAMING_SNAKE_CASE  )

      # verify the logits
      __lowerCamelCase								=				tf.TensorShape((1, 1000)  )
      self.assertEqual(outputs.logits.shape   ,							_SCREAMING_SNAKE_CASE  )
      __lowerCamelCase								=				tf.constant([-0.0948, -0.6454, -0.0921]  )
      self.assertTrue(np.allclose(outputs.logits[0, :3]   ,							_SCREAMING_SNAKE_CASE   ,							atol=1E-4  )  )







"""simple docstring"""







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_SCREAMING_SNAKE_CASE  :		List[Any]        =      {
    '''configuration_deformable_detr''': ['''DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DeformableDetrConfig'''],
}

try:
     if not is_vision_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Optional[Any]        =      ['''DeformableDetrFeatureExtractor''']
     _SCREAMING_SNAKE_CASE  :		List[str]        =      ['''DeformableDetrImageProcessor''']

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      [
         '''DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST''',
         '''DeformableDetrForObjectDetection''',
         '''DeformableDetrModel''',
         '''DeformableDetrPreTrainedModel''',
     ]


if TYPE_CHECKING:
     from .configuration_deformable_detr import DEFORMABLE_DETR_PRETRAINED_CONFIG_ARCHIVE_MAP, DeformableDetrConfig

     try:
          if not is_vision_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .feature_extraction_deformable_detr import DeformableDetrFeatureExtractor
          from .image_processing_deformable_detr import DeformableDetrImageProcessor

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_deformable_detr import (
              DEFORMABLE_DETR_PRETRAINED_MODEL_ARCHIVE_LIST,
              DeformableDetrForObjectDetection,
              DeformableDetrModel,
              DeformableDetrPreTrainedModel,
          )

else:
     import sys

     _SCREAMING_SNAKE_CASE  :		Tuple        =      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)






