
import collections
import inspect
import unittest
from typing import Dict, List, Tuple

from transformers import MaskFormerSwinConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, torch_device
from transformers.utils import is_torch_available

from ...test_backbone_common import BackboneTesterMixin
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 torch import nn

 from transformers import MaskFormerSwinBackbone
 from transformers.models.maskformer import MaskFormerSwinModel
class       _lowerCAmelCase  :

 """simple docstring"""

 def __init__(						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Optional[int]=13					, SCREAMING_SNAKE_CASE__			:			int=32					, SCREAMING_SNAKE_CASE__			:			int=2					, SCREAMING_SNAKE_CASE__			:			List[Any]=3					, SCREAMING_SNAKE_CASE__			:			List[str]=16					, SCREAMING_SNAKE_CASE__			:			str=[1, 2, 1]					, SCREAMING_SNAKE_CASE__			:			Optional[int]=[2, 2, 4]					, SCREAMING_SNAKE_CASE__			:			Tuple=2					, SCREAMING_SNAKE_CASE__			:			Any=2.0					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=True					, SCREAMING_SNAKE_CASE__			:			Tuple=0.0					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]=0.0					, SCREAMING_SNAKE_CASE__			:			str=0.1					, SCREAMING_SNAKE_CASE__			:			List[Any]="gelu"					, SCREAMING_SNAKE_CASE__			:			Tuple=False					, SCREAMING_SNAKE_CASE__			:			Tuple=True					, SCREAMING_SNAKE_CASE__			:			Tuple=0.02					, SCREAMING_SNAKE_CASE__			:			str=1e-5					, SCREAMING_SNAKE_CASE__			:			List[Any]=True					, SCREAMING_SNAKE_CASE__			:			int=None					, SCREAMING_SNAKE_CASE__			:			int=True					, SCREAMING_SNAKE_CASE__			:			List[Any]=10					, SCREAMING_SNAKE_CASE__			:			Dict=8					, SCREAMING_SNAKE_CASE__			:			int=["stage1", "stage2", "stage3"]					, SCREAMING_SNAKE_CASE__			:			str=[1, 2, 3]					, ):






      """simple docstring"""



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

 def      __lowerCAmelCase  (						self			:			List[Any]   ):






      """simple docstring"""



      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.type_sequence_label_size   )

      UpperCamelCase						    =					self.get_config()

      return config, pixel_values, labels

 def      __lowerCAmelCase  (						self			:			Dict   ):






      """simple docstring"""



      return MaskFormerSwinConfig(
          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					, out_features=self.out_features					, out_indices=self.out_indices					, )

 def      __lowerCAmelCase  (						self			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					MaskFormerSwinModel(config=_lowerCamelCase   )
      model.to(_lowerCamelCase   )
      model.eval()
      UpperCamelCase						    =					model(_lowerCamelCase   )

      UpperCamelCase						    =					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths   ) - 1))
      UpperCamelCase						    =					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      __lowerCAmelCase  (						self			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Tuple					, SCREAMING_SNAKE_CASE__			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					MaskFormerSwinBackbone(config=_lowerCamelCase   )
      model.to(_lowerCamelCase   )
      model.eval()
      UpperCamelCase						    =					model(_lowerCamelCase   )

      # verify feature maps
      self.parent.assertEqual(len(result.feature_maps   )					, len(config.out_features   )   )
      self.parent.assertListEqual(list(result.feature_maps[0].shape   )					, [13, 16, 16, 16]   )

      # verify channels
      self.parent.assertEqual(len(model.channels   )					, len(config.out_features   )   )
      self.parent.assertListEqual(model.channels					, [16, 32, 64]   )

      # verify ValueError
      with self.parent.assertRaises(_lowerCamelCase   ):
           UpperCamelCase						    =					["""stem"""]
           UpperCamelCase						    =					MaskFormerSwinBackbone(config=_lowerCamelCase   )






 def      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      UpperCamelCase						    =					self.prepare_config_and_inputs()
      UpperCamelCase						    =					config_and_inputs
      UpperCamelCase						    =					{"""pixel_values""": pixel_values}
      return config, inputs_dict




@require_torch
class       _lowerCAmelCase  (		lowerCAmelCase__			,	lowerCAmelCase__			,	unittest.TestCase							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					Any       						=(
     (
         MaskFormerSwinModel,
         MaskFormerSwinBackbone,
     )
     if is_torch_available()
     else ()
 )
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						={"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
 SCREAMING_SNAKE_CASE_				:					Tuple       						=False
 SCREAMING_SNAKE_CASE_				:					List[str]       						=False
 SCREAMING_SNAKE_CASE_				:					int       						=False
 SCREAMING_SNAKE_CASE_				:					Optional[int]       						=False
 SCREAMING_SNAKE_CASE_				:					str       						=False

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					MaskFormerSwinModelTester(self   )
      UpperCamelCase						    =					ConfigTester(self					, config_class=_lowerCamelCase					, embed_dim=37   )

 @require_torch_multi_gpu
 @unittest.skip(
     reason=(
         '`MaskFormerSwinModel` outputs `hidden_states_spatial_dimensions` which doesn\'t work well with'
         ' `nn.DataParallel`'
     )   )
 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """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      __lowerCAmelCase  (						self			:			Any   ):






      """simple docstring"""



      return

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_model(*_lowerCamelCase   )

 def      __lowerCAmelCase  (						self			:			Tuple   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs()
      self.model_tester.create_and_check_backbone(*_lowerCamelCase   )

 @unittest.skip('Swin does not use inputs_embeds'   )
 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      pass

 @unittest.skip('Swin does not support feedforward chunking'   )
 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

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

 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(_lowerCamelCase   )
           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]					, _lowerCamelCase   )

 @unittest.skip(reason='MaskFormerSwin is only used as backbone and doesn\'t support output_attentions'   )
 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      pass

 @unittest.skip(reason='MaskFormerSwin is only used as an internal backbone'   )
 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      pass

 def      __lowerCAmelCase  (						self			:			Dict					, SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			int					, SCREAMING_SNAKE_CASE__			:			Optional[int]					, SCREAMING_SNAKE_CASE__			:			Dict   ):






      """simple docstring"""



      UpperCamelCase						    =					model_class(_lowerCamelCase   )
      model.to(_lowerCamelCase   )
      model.eval()

      with torch.no_grad():
           UpperCamelCase						    =					model(**self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase   )   )

      UpperCamelCase						    =					outputs.hidden_states

      UpperCamelCase						    =					getattr(
          self.model_tester					, 'expected_num_hidden_layers'					, len(self.model_tester.depths   ) + 1   )
      self.assertEqual(len(_lowerCamelCase   )					, _lowerCamelCase   )

      # Swin has a different seq_length
      UpperCamelCase						    =					(
          config.patch_size
          if isinstance(config.patch_size					, collections.abc.Iterable   )
          else (config.patch_size, config.patch_size)
      )

      UpperCamelCase						    =					(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]					, )

 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      UpperCamelCase						    =					(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size					, collections.abc.Iterable   )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					True
           self.check_hidden_states_output(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase   )

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

           self.check_hidden_states_output(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase   )

 def      __lowerCAmelCase  (						self			:			List[str]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					3

      UpperCamelCase						    =					(
          self.model_tester.image_size
          if isinstance(self.model_tester.image_size					, collections.abc.Iterable   )
          else (self.model_tester.image_size, self.model_tester.image_size)
      )
      UpperCamelCase						    =					(
          config.patch_size
          if isinstance(config.patch_size					, collections.abc.Iterable   )
          else (config.patch_size, config.patch_size)
      )

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

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					True
           self.check_hidden_states_output(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, (padded_height, padded_width)   )

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

 @unittest.skip(reason='MaskFormerSwin doesn\'t have pretrained checkpoints'   )
 def      __lowerCAmelCase  (						self			:			Union[str, Any]   ):






      """simple docstring"""



      pass

 @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'   )
 def      __lowerCAmelCase  (						self			:			str   ):






      """simple docstring"""



      pass

 @unittest.skip(reason='This will be fixed once MaskFormerSwin is replaced by native Swin'   )
 def      __lowerCAmelCase  (						self			:			int   ):






      """simple docstring"""



      pass






 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()

      def set_nan_tensor_to_zero(SCREAMING_SNAKE_CASE__			:			Union[str, Any]   ):
           UpperCamelCase						    =					0
           return t

      def check_equivalence(SCREAMING_SNAKE_CASE__			:			List[str]					, SCREAMING_SNAKE_CASE__			:			Any					, SCREAMING_SNAKE_CASE__			:			Union[str, Any]					, SCREAMING_SNAKE_CASE__			:			Any={}   ):
           with torch.no_grad():
                UpperCamelCase						    =					model(**_lowerCamelCase					, return_dict=_lowerCamelCase					, **_lowerCamelCase   )
                UpperCamelCase						    =					model(**_lowerCamelCase					, return_dict=_lowerCamelCase					, **_lowerCamelCase   ).to_tuple()

                def recursive_check(SCREAMING_SNAKE_CASE__			:			Optional[Any]					, SCREAMING_SNAKE_CASE__			:			str   ):
                     if isinstance(_lowerCamelCase					, (List, Tuple)   ):
                          for tuple_iterable_value, dict_iterable_value in zip(_lowerCamelCase					, _lowerCamelCase   ):
                               recursive_check(_lowerCamelCase					, _lowerCamelCase   )
                     elif isinstance(_lowerCamelCase					, _lowerCamelCase   ):
                          for tuple_iterable_value, dict_iterable_value in zip(
                              tuple_object.values()					, dict_object.values()   ):
                               recursive_check(_lowerCamelCase					, _lowerCamelCase   )
                     elif tuple_object is None:
                          return
                     else:
                          self.assertTrue(
                              torch.allclose(
                                  set_nan_tensor_to_zero(_lowerCamelCase   )					, set_nan_tensor_to_zero(_lowerCamelCase   )					, atol=1e-5   )					, msg=(
                                  'Tuple and dict output are not equal. Difference:'
                                  F' {torch.max(torch.abs(tuple_object - dict_object   )   )}. Tuple has `nan`:'
                                  F' {torch.isnan(_lowerCamelCase   ).any()} and `inf`: {torch.isinf(_lowerCamelCase   )}. Dict has'
                                  F' `nan`: {torch.isnan(_lowerCamelCase   ).any()} and `inf`: {torch.isinf(_lowerCamelCase   )}.'
                              )					, )

                recursive_check(_lowerCamelCase					, _lowerCamelCase   )

      for model_class in self.all_model_classes:
           UpperCamelCase						    =					model_class(_lowerCamelCase   )
           model.to(_lowerCamelCase   )
           model.eval()

           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase   )
           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase   )
           check_equivalence(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase   )

           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase					, return_labels=_lowerCamelCase   )
           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase					, return_labels=_lowerCamelCase   )
           check_equivalence(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase   )

           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase   )
           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase   )
           check_equivalence(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, {'output_hidden_states': True}   )

           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase					, return_labels=_lowerCamelCase   )
           UpperCamelCase						    =					self._prepare_for_class(_lowerCamelCase					, _lowerCamelCase					, return_labels=_lowerCamelCase   )
           check_equivalence(_lowerCamelCase					, _lowerCamelCase					, _lowerCamelCase					, {'output_hidden_states': True}   )




@require_torch
class       _lowerCAmelCase  (		unittest.TestCase			,	lowerCAmelCase__							):

 """simple docstring"""
 SCREAMING_SNAKE_CASE_				:					int       						=(MaskFormerSwinBackbone,) if is_torch_available() else ()
 SCREAMING_SNAKE_CASE_				:					int       						=MaskFormerSwinConfig

 def      __lowerCAmelCase  (						self			:			Optional[int]   ):






      """simple docstring"""



      UpperCamelCase						    =					MaskFormerSwinModelTester(self   )






 def      __lowerCAmelCase  (						self			:			Optional[Any]   ):






      """simple docstring"""



      UpperCamelCase						    =					self.model_tester.prepare_config_and_inputs_for_common()
      UpperCamelCase						    =					inputs_dict["""pixel_values"""].shape[0]

      for backbone_class in self.all_model_classes:
           UpperCamelCase						    =					backbone_class(_lowerCamelCase   )
           backbone.to(_lowerCamelCase   )
           backbone.eval()

           UpperCamelCase						    =					backbone(**_lowerCamelCase   )

           # Test default outputs and verify feature maps
           self.assertIsInstance(outputs.feature_maps					, _lowerCamelCase   )
           self.assertTrue(len(outputs.feature_maps   ) == len(backbone.channels   )   )
           for feature_map, n_channels in zip(outputs.feature_maps					, backbone.channels   ):
                self.assertTrue(feature_map.shape[:2]					, (batch_size, n_channels)   )
           self.assertIsNone(outputs.hidden_states   )
           self.assertIsNone(outputs.attentions   )

           # Test output_hidden_states=True
           UpperCamelCase						    =					backbone(**_lowerCamelCase					, output_hidden_states=_lowerCamelCase   )
           self.assertIsNotNone(outputs.hidden_states   )
           self.assertTrue(len(outputs.hidden_states   )					, len(backbone.stage_names   )   )
           # We skip the stem layer
           for hidden_states, n_channels in zip(outputs.hidden_states[1:]					, backbone.channels   ):
                for hidden_state in hidden_states:
                     # Hidden states are in the format (batch_size, (height * width), n_channels)
                     UpperCamelCase						    =					hidden_state.shape
                     self.assertTrue((h_batch_size, h_n_channels)					, (batch_size, n_channels)   )

            # Test output_attentions=True
           if self.has_attentions:
                UpperCamelCase						    =					backbone(**_lowerCamelCase					, output_attentions=_lowerCamelCase   )
                self.assertIsNotNone(outputs.attentions   )





"""simple docstring"""
def 					a__						(						)	-> Union[str, Any]:
      UpperCAmelCase__			:							Dict							=    []
      UpperCAmelCase__			:							Tuple							=    1

      while len(lowerCAmelCase    ) < 1E6:
            constant.append(str(lowerCAmelCase    )    )
            i += 1

      UpperCAmelCase__			:							Any							=    """""".join(lowerCAmelCase    )

      return (
          int(constant[0]    )
          * int(constant[9]    )
          * int(constant[99]    )
          * int(constant[9_99]    )
          * int(constant[99_99]    )
          * int(constant[9_99_99]    )
          * int(constant[99_99_99]    )
      )


if __name__ == "__main__":
    print(solution())

