






'''simple docstring'''





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  SCREAMING_SNAKE_CASE			:
     '''simple docstring'''


     def __init__(				self       :	str					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	Dict=13					, UpperCAmelCase_       :	Union[str, Any]=32					, UpperCAmelCase_       :	int=2					, UpperCAmelCase_       :	int=3					, UpperCAmelCase_       :	Optional[Any]=16					, UpperCAmelCase_       :	Optional[int]=[1, 2, 1]					, UpperCAmelCase_       :	str=[2, 2, 4]					, UpperCAmelCase_       :	Optional[Any]=2					, UpperCAmelCase_       :	Any=2.0					, UpperCAmelCase_       :	Optional[Any]=True					, UpperCAmelCase_       :	Optional[Any]=0.0					, UpperCAmelCase_       :	Union[str, Any]=0.0					, UpperCAmelCase_       :	List[str]=0.1					, UpperCAmelCase_       :	Optional[int]="gelu"					, UpperCAmelCase_       :	Tuple=False					, UpperCAmelCase_       :	Any=True					, UpperCAmelCase_       :	Tuple=0.02					, UpperCAmelCase_       :	Dict=1E-5					, UpperCAmelCase_       :	str=True					, UpperCAmelCase_       :	int=None					, UpperCAmelCase_       :	Union[str, Any]=True					, UpperCAmelCase_       :	int=10					, UpperCAmelCase_       :	Optional[int]=8					, UpperCAmelCase_       :	Union[str, Any]=["stage1", "stage2", "stage3"]					, UpperCAmelCase_       :	List[Any]=[1, 2, 3]					, ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    parent
       SCREAMING_SNAKE_CASE       :      List[Any]							=    batch_size
       SCREAMING_SNAKE_CASE       :      Dict							=    image_size
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    patch_size
       SCREAMING_SNAKE_CASE       :      List[str]							=    num_channels
       SCREAMING_SNAKE_CASE       :      List[Any]							=    embed_dim
       SCREAMING_SNAKE_CASE       :      List[Any]							=    depths
       SCREAMING_SNAKE_CASE       :      List[Any]							=    num_heads
       SCREAMING_SNAKE_CASE       :      str							=    window_size
       SCREAMING_SNAKE_CASE       :      str							=    mlp_ratio
       SCREAMING_SNAKE_CASE       :      Any							=    qkv_bias
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    hidden_dropout_prob
       SCREAMING_SNAKE_CASE       :      Dict							=    attention_probs_dropout_prob
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    drop_path_rate
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    hidden_act
       SCREAMING_SNAKE_CASE       :      int							=    use_absolute_embeddings
       SCREAMING_SNAKE_CASE       :      Dict							=    patch_norm
       SCREAMING_SNAKE_CASE       :      str							=    layer_norm_eps
       SCREAMING_SNAKE_CASE       :      List[str]							=    initializer_range
       SCREAMING_SNAKE_CASE       :      int							=    is_training
       SCREAMING_SNAKE_CASE       :      Optional[int]							=    scope
       SCREAMING_SNAKE_CASE       :      Dict							=    use_labels
       SCREAMING_SNAKE_CASE       :      Dict							=    type_sequence_label_size
       SCREAMING_SNAKE_CASE       :      List[Any]							=    encoder_stride
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    out_features
       SCREAMING_SNAKE_CASE       :      int							=    out_indices


     def 				_A       (				self       :	List[Any]      ):
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

       SCREAMING_SNAKE_CASE       :      Any							=    None
       if self.use_labels:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    ids_tensor([self.batch_size]					, self.type_sequence_label_size      )

       SCREAMING_SNAKE_CASE       :      Any							=    self.get_config()

       return config, pixel_values, labels


     def 				_A       (				self       :	int      ):
       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 				_A       (				self       :	Optional[int]					, UpperCAmelCase_       :	Optional[Any]					, UpperCAmelCase_       :	List[str]					, UpperCAmelCase_       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    MaskFormerSwinModel(config=A_      )
       model.to(A_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    model(A_      )

       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    ((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths      ) - 1))
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    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       :	Optional[Any]					, UpperCAmelCase_       :	str					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      List[Any]							=    MaskFormerSwinBackbone(config=A_      )
       model.to(A_      )
       model.eval()
       SCREAMING_SNAKE_CASE       :      List[Any]							=    model(A_      )

       # 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(A_      ):
         SCREAMING_SNAKE_CASE       :      List[str]							=    ["stem"]
         SCREAMING_SNAKE_CASE       :      Dict							=    MaskFormerSwinBackbone(config=A_      )






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





@require_torch
class  SCREAMING_SNAKE_CASE			(			_UpperCAmelCase      ,		_UpperCAmelCase      ,		unittest.TestCase					):
     '''simple docstring'''


     UpperCamelCase_							:		str			=	(
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     UpperCamelCase_							:		Union[str, Any]			=	{'''feature-extraction''': MaskFormerSwinModel} if is_torch_available() else {}
     UpperCamelCase_							:		Optional[Any]			=	False
     UpperCamelCase_							:		Union[str, Any]			=	False
     UpperCamelCase_							:		Optional[Any]			=	False
     UpperCamelCase_							:		Dict			=	False
     UpperCamelCase_							:		List[Any]			=	False


     def 				_A       (				self       :	int      ):
       SCREAMING_SNAKE_CASE       :      List[str]							=    MaskFormerSwinModelTester(self      )
       SCREAMING_SNAKE_CASE       :      Dict							=    ConfigTester(self					, config_class=A_					, 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 				_A       (				self       :	Any      ):
       pass


     def 				_A       (				self       :	str      ):
       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       :	int      ):
       return


     def 				_A       (				self       :	Tuple      ):
       SCREAMING_SNAKE_CASE       :      Dict							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*A_      )


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_backbone(*A_      )


     @unittest.skip("Swin does not use inputs_embeds"      )
     def 				_A       (				self       :	List[str]      ):
       pass


     @unittest.skip("Swin does not support feedforward chunking"      )
     def 				_A       (				self       :	str      ):
       pass


     def 				_A       (				self       :	List[str]      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    self.model_tester.prepare_config_and_inputs_for_common()

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    model_class(A_      )
         self.assertIsInstance(model.get_input_embeddings()					, (nn.Module)      )
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model.get_output_embeddings()
         self.assertTrue(x is None or isinstance(A_					, nn.Linear      )      )


     def 				_A       (				self       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Any							=    self.model_tester.prepare_config_and_inputs_for_common()

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

         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    ["pixel_values"]
         self.assertListEqual(arg_names[:1]					, A_      )


     @unittest.skip(reason="MaskFormerSwin is only used as backbone and doesn\'t support output_attentions"      )
     def 				_A       (				self       :	Tuple      ):
       pass


     @unittest.skip(reason="MaskFormerSwin is only used as an internal backbone"      )
     def 				_A       (				self       :	Any      ):
       pass


     def 				_A       (				self       :	Dict					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Tuple					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Union[str, Any]      ):
       SCREAMING_SNAKE_CASE       :      Any							=    model_class(A_      )
       model.to(A_      )
       model.eval()

       with torch.no_grad():
         SCREAMING_SNAKE_CASE       :      Dict							=    model(**self._prepare_for_class(A_					, A_      )      )

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    outputs.hidden_states

       SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    getattr(
           self.model_tester					, "expected_num_hidden_layers"					, len(self.model_tester.depths      ) + 1      )
       self.assertEqual(len(A_      )					, A_      )

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

       SCREAMING_SNAKE_CASE       :      Optional[int]							=    (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 				_A       (				self       :	Dict      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self.model_tester.prepare_config_and_inputs_for_common()

       SCREAMING_SNAKE_CASE       :      str							=    (
           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:
         SCREAMING_SNAKE_CASE       :      int							=    True
         self.check_hidden_states_output(A_					, A_					, A_					, A_      )

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

         self.check_hidden_states_output(A_					, A_					, A_					, A_      )


     def 				_A       (				self       :	Any      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      str							=    self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE       :      Any							=    3

       SCREAMING_SNAKE_CASE       :      Any							=    (
           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)
       )
       SCREAMING_SNAKE_CASE       :      Dict							=    (
           config.patch_size
           if isinstance(config.patch_size					, collections.abc.Iterable      )
           else (config.patch_size, config.patch_size)
       )

       SCREAMING_SNAKE_CASE       :      Any							=    image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
       SCREAMING_SNAKE_CASE       :      Tuple							=    image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    True
         self.check_hidden_states_output(A_					, A_					, A_					, (padded_height, padded_width)      )

         # check that output_hidden_states also work using config
         del inputs_dict["output_hidden_states"]
         SCREAMING_SNAKE_CASE       :      Any							=    True
         self.check_hidden_states_output(A_					, A_					, A_					, (padded_height, padded_width)      )


     @unittest.skip(reason="MaskFormerSwin doesn\'t have pretrained checkpoints"      )
     def 				_A       (				self       :	Optional[Any]      ):
       pass


     @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"      )
     def 				_A       (				self       :	Tuple      ):
       pass


     @unittest.skip(reason="This will be fixed once MaskFormerSwin is replaced by native Swin"      )
     def 				_A       (				self       :	List[str]      ):
       pass






     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      List[str]							=    self.model_tester.prepare_config_and_inputs_for_common()

       def set_nan_tensor_to_zero(UpperCAmelCase_       :	List[str]      ):
         SCREAMING_SNAKE_CASE       :      List[Any]							=    0
         return t

       def check_equivalence(UpperCAmelCase_       :	Union[str, Any]					, UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	Optional[int]					, UpperCAmelCase_       :	List[str]={}      ):
         with torch.no_grad():
           SCREAMING_SNAKE_CASE       :      List[Any]							=    model(**A_					, return_dict=A_					, **A_      )
           SCREAMING_SNAKE_CASE       :      int							=    model(**A_					, return_dict=A_					, **A_      ).to_tuple()

           def recursive_check(UpperCAmelCase_       :	List[Any]					, UpperCAmelCase_       :	List[str]      ):
             if isinstance(A_					, (List, Tuple)      ):
               for tuple_iterable_value, dict_iterable_value in zip(A_					, A_      ):
                 recursive_check(A_					, A_      )
             elif isinstance(A_					, A_      ):
               for tuple_iterable_value, dict_iterable_value in zip(
                   tuple_object.values()					, dict_object.values()      ):
                 recursive_check(A_					, A_      )
             elif tuple_object is None:
               return
             else:
               self.assertTrue(
                   torch.allclose(
                       set_nan_tensor_to_zero(A_      )					, set_nan_tensor_to_zero(A_      )					, 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(A_      ).any()} and `inf`: {torch.isinf(A_      )}. Dict has'''
                       f''' `nan`: {torch.isnan(A_      ).any()} and `inf`: {torch.isinf(A_      )}.'''
                   )					, )

           recursive_check(A_					, A_      )

       for model_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      List[Any]							=    model_class(A_      )
         model.to(A_      )
         model.eval()

         SCREAMING_SNAKE_CASE       :      Optional[Any]							=    self._prepare_for_class(A_					, A_      )
         SCREAMING_SNAKE_CASE       :      Dict							=    self._prepare_for_class(A_					, A_      )
         check_equivalence(A_					, A_					, A_      )

         SCREAMING_SNAKE_CASE       :      List[str]							=    self._prepare_for_class(A_					, A_					, return_labels=A_      )
         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self._prepare_for_class(A_					, A_					, return_labels=A_      )
         check_equivalence(A_					, A_					, A_      )

         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self._prepare_for_class(A_					, A_      )
         SCREAMING_SNAKE_CASE       :      Tuple							=    self._prepare_for_class(A_					, A_      )
         check_equivalence(A_					, A_					, A_					, {"output_hidden_states": True}      )

         SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    self._prepare_for_class(A_					, A_					, return_labels=A_      )
         SCREAMING_SNAKE_CASE       :      str							=    self._prepare_for_class(A_					, A_					, return_labels=A_      )
         check_equivalence(A_					, A_					, A_					, {"output_hidden_states": True}      )





@require_torch
class  SCREAMING_SNAKE_CASE			(			unittest.TestCase      ,		_UpperCAmelCase					):
     '''simple docstring'''


     UpperCamelCase_							:		Tuple			=	(MaskFormerSwinBackbone,) if is_torch_available() else ()
     UpperCamelCase_							:		int			=	MaskFormerSwinConfig


     def 				_A       (				self       :	Optional[Any]      ):
       SCREAMING_SNAKE_CASE       :      Tuple							=    MaskFormerSwinModelTester(self      )






     def 				_A       (				self       :	Optional[int]      ):
       SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      Optional[int]							=    self.model_tester.prepare_config_and_inputs_for_common()
       SCREAMING_SNAKE_CASE       :      Optional[Any]							=    inputs_dict["pixel_values"].shape[0]

       for backbone_class in self.all_model_classes:
         SCREAMING_SNAKE_CASE       :      Dict							=    backbone_class(A_      )
         backbone.to(A_      )
         backbone.eval()

         SCREAMING_SNAKE_CASE       :      Any							=    backbone(**A_      )

         # Test default outputs and verify feature maps
         self.assertIsInstance(outputs.feature_maps					, A_      )
         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
         SCREAMING_SNAKE_CASE       :      int							=    backbone(**A_					, output_hidden_states=A_      )
         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)
             SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE						,  SCREAMING_SNAKE_CASE       :      int							=    hidden_state.shape
             self.assertTrue((h_batch_size, h_n_channels)					, (batch_size, n_channels)      )

            # Test output_attentions=True
         if self.has_attentions:
           SCREAMING_SNAKE_CASE       :      Dict							=    backbone(**A_					, output_attentions=A_      )
           self.assertIsNotNone(outputs.attentions      )














from transformers import HfArgumentParser, TensorFlowBenchmark, TensorFlowBenchmarkArguments


def   lowerCamelCase__			(     ):

  """simple docstring"""






  SCREAMING_SNAKE_CASE       :      List[Any]							=    HfArgumentParser(lowercase				)
  SCREAMING_SNAKE_CASE       :      Any							=    parser.parse_args_into_dataclasses()[0]
  SCREAMING_SNAKE_CASE       :      Optional[Any]							=    TensorFlowBenchmark(args=lowercase				)
  try:
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    parser.parse_args_into_dataclasses()[0]
  except ValueError as e:
    SCREAMING_SNAKE_CASE       :      int							=    "Arg --no_{0} is no longer used, please use --no-{0} instead."
    SCREAMING_SNAKE_CASE       :      Optional[int]							=    " ".join(str(lowercase				).split(" "				)[:-1]				)
    SCREAMING_SNAKE_CASE       :      Union[str, Any]							=    ""
    SCREAMING_SNAKE_CASE       :      Any							=    eval(str(lowercase				).split(" "				)[-1]				)
    SCREAMING_SNAKE_CASE       :      List[str]							=    []
    for arg in depreciated_args:
      # arg[2:] removes '--'
      if arg[2:] in TensorFlowBenchmark.deprecated_args:
        # arg[5:] removes '--no_'
        full_error_msg += arg_error_msg.format(arg[5:]				)
      else:
        wrong_args.append(lowercase				)
    if len(lowercase				) > 0:
      SCREAMING_SNAKE_CASE       :      Optional[int]							=    full_error_msg + begin_error_msg + str(lowercase				)
    raise ValueError(lowercase				)
  benchmark.run()


if __name__ == "__main__":
   main()






