


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    UpperCAmelCase_	:
       '''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 ,					__SCREAMING_SNAKE_CASE=["stage1", "stage2", "stage3"] ,					__SCREAMING_SNAKE_CASE=[1, 2, 3] ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		parent
              UpperCamelCase    :	Any   	=		batch_size
              UpperCamelCase    :	str   	=		image_size
              UpperCamelCase    :	List[Any]   	=		patch_size
              UpperCamelCase    :	Union[str, Any]   	=		num_channels
              UpperCamelCase    :	Tuple   	=		embed_dim
              UpperCamelCase    :	Optional[Any]   	=		depths
              UpperCamelCase    :	Optional[int]   	=		num_heads
              UpperCamelCase    :	Dict   	=		window_size
              UpperCamelCase    :	Union[str, Any]   	=		mlp_ratio
              UpperCamelCase    :	Tuple   	=		qkv_bias
              UpperCamelCase    :	List[str]   	=		hidden_dropout_prob
              UpperCamelCase    :	Optional[Any]   	=		attention_probs_dropout_prob
              UpperCamelCase    :	Optional[int]   	=		drop_path_rate
              UpperCamelCase    :	Dict   	=		hidden_act
              UpperCamelCase    :	Tuple   	=		use_absolute_embeddings
              UpperCamelCase    :	Optional[Any]   	=		patch_norm
              UpperCamelCase    :	Union[str, Any]   	=		layer_norm_eps
              UpperCamelCase    :	List[str]   	=		initializer_range
              UpperCamelCase    :	Any   	=		is_training
              UpperCamelCase    :	List[Any]   	=		scope
              UpperCamelCase    :	int   	=		use_labels
              UpperCamelCase    :	Tuple   	=		type_sequence_label_size
              UpperCamelCase    :	List[Any]   	=		encoder_stride
              UpperCamelCase    :	Optional[Any]   	=		out_features
              UpperCamelCase    :	str   	=		out_indices






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

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

              UpperCamelCase    :	Optional[int]   	=		self.get_config()

              return config, pixel_values, labels






       def  _lowercase   (     self						):




              """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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		MaskFormerSwinModel(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	List[Any]   	=		model(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	List[str]   	=		((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths						) - 1))
              UpperCamelCase    :	str   	=		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  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		MaskFormerSwinBackbone(config=__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()
              UpperCamelCase    :	Optional[Any]   	=		model(__SCREAMING_SNAKE_CASE						)

              # 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(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	int   	=		['''stem''']
                     UpperCamelCase    :	Dict   	=		MaskFormerSwinBackbone(config=__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		self.prepare_config_and_inputs()
              UpperCamelCase	, UpperCamelCase	, UpperCamelCase    :	List[Any]   	=		config_and_inputs
              UpperCamelCase    :	int   	=		{'''pixel_values''': pixel_values}
              return config, inputs_dict




@require_torch
class    UpperCAmelCase_	(							_a,     _a,     unittest.TestCase):
       '''simple docstring'''
       __UpperCamelCase      :     Dict							=					(
           (
               MaskFormerSwinModel,
               MaskFormerSwinBackbone,
           )
           if is_torch_available()
           else ()
       )
       __UpperCamelCase      :     Any							=					{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
       __UpperCamelCase      :     Any							=					False
       __UpperCamelCase      :     Optional[Any]							=					False
       __UpperCamelCase      :     Optional[int]							=					False
       __UpperCamelCase      :     Tuple							=					False
       __UpperCamelCase      :     Tuple							=					False






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		MaskFormerSwinModelTester(self						)
              UpperCamelCase    :	List[str]   	=		ConfigTester(self ,					config_class=__SCREAMING_SNAKE_CASE ,					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  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     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  _lowercase   (     self						):




              """simple docstring"""




              return






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_model(*__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	List[Any]   	=		self.model_tester.prepare_config_and_inputs()
              self.model_tester.create_and_check_backbone(*__SCREAMING_SNAKE_CASE						)






       @unittest.skip('''Swin does not use inputs_embeds'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       @unittest.skip('''Swin does not support feedforward chunking'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Union[str, Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

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






       def  _lowercase   (     self						):




              """simple docstring"""




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

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Union[str, Any]   	=		model_class(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	List[str]   	=		inspect.signature(model.forward						)
                     # signature.parameters is an OrderedDict => so arg_names order is deterministic
                     UpperCamelCase    :	Optional[int]   	=		[*signature.parameters.keys()]

                     UpperCamelCase    :	Union[str, Any]   	=		['''pixel_values''']
                     self.assertListEqual(arg_names[:1] ,					__SCREAMING_SNAKE_CASE						)






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




              """simple docstring"""




              pass






       @unittest.skip(reason='''MaskFormerSwin is only used as an internal backbone'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	int   	=		model_class(__SCREAMING_SNAKE_CASE						)
              model.to(__SCREAMING_SNAKE_CASE						)
              model.eval()

              with torch.no_grad():
                     UpperCamelCase    :	Optional[int]   	=		model(**self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)						)

              UpperCamelCase    :	List[str]   	=		outputs.hidden_states

              UpperCamelCase    :	int   	=		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
              UpperCamelCase    :	Optional[int]   	=		(
                  config.patch_size
                  if isinstance(config.patch_size ,					collections.abc.Iterable						)
                  else (config.patch_size, config.patch_size)
              )

              UpperCamelCase    :	List[Any]   	=		(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  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	List[Any]   	=		self.model_tester.prepare_config_and_inputs_for_common()

              UpperCamelCase    :	List[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:
                     UpperCamelCase    :	Optional[int]   	=		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"]
                     UpperCamelCase    :	Dict   	=		True

                     self.check_hidden_states_output(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	str   	=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase    :	List[str]   	=		3

              UpperCamelCase    :	int   	=		(
                  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    :	Tuple   	=		(
                  config.patch_size
                  if isinstance(config.patch_size ,					collections.abc.Iterable						)
                  else (config.patch_size, config.patch_size)
              )

              UpperCamelCase    :	List[Any]   	=		image_size[0] + patch_size[0] - (image_size[0] % patch_size[0])
              UpperCamelCase    :	Optional[int]   	=		image_size[1] + patch_size[1] - (image_size[1] % patch_size[1])

              for model_class in self.all_model_classes:
                     UpperCamelCase    :	Any   	=		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"]
                     UpperCamelCase    :	List[str]   	=		True
                     self.check_hidden_states_output(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					(padded_height, padded_width)						)






       @unittest.skip(reason='''MaskFormerSwin doesn\'t have pretrained checkpoints'''						)
       def  _lowercase   (     self						):




              """simple docstring"""




              pass






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




              """simple docstring"""




              pass






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




              """simple docstring"""




              pass






       def  _lowercase   (     self						):




              """simple docstring"""




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

              def set_nan_tensor_to_zero(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	List[str]   	=		0
                     return t

              def check_equivalence(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE={}						):
                     with torch.no_grad():
                            UpperCamelCase    :	Any   	=		model(**__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	int   	=		model(**__SCREAMING_SNAKE_CASE ,					return_dict=__SCREAMING_SNAKE_CASE ,					**__SCREAMING_SNAKE_CASE						).to_tuple()

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

                            recursive_check(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

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

                     UpperCamelCase    :	List[str]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	List[str]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                     check_equivalence(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

                     UpperCamelCase    :	List[Any]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_labels=__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Dict   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_labels=__SCREAMING_SNAKE_CASE						)
                     check_equivalence(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)

                     UpperCamelCase    :	List[Any]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Optional[Any]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                     check_equivalence(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					{'''output_hidden_states''': True}						)

                     UpperCamelCase    :	List[str]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_labels=__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Optional[int]   	=		self._prepare_for_class(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					return_labels=__SCREAMING_SNAKE_CASE						)
                     check_equivalence(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					{'''output_hidden_states''': True}						)




@require_torch
class    UpperCAmelCase_	(							unittest.TestCase,     _a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[int]							=					(MaskFormerSwinBackbone,) if is_torch_available() else ()
       __UpperCamelCase      :     Optional[Any]							=					MaskFormerSwinConfig






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase    :	Optional[Any]   	=		MaskFormerSwinModelTester(self						)






       def  _lowercase   (     self						):




              """simple docstring"""




              UpperCamelCase	, UpperCamelCase    :	Optional[int]   	=		self.model_tester.prepare_config_and_inputs_for_common()
              UpperCamelCase    :	List[str]   	=		inputs_dict['''pixel_values'''].shape[0]

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

                     UpperCamelCase    :	str   	=		backbone(**__SCREAMING_SNAKE_CASE						)

                     # Test default outputs and verify feature maps
                     self.assertIsInstance(outputs.feature_maps ,					__SCREAMING_SNAKE_CASE						)
                     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    :	List[str]   	=		backbone(**__SCREAMING_SNAKE_CASE ,					output_hidden_states=__SCREAMING_SNAKE_CASE						)
                     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	, UpperCamelCase	, UpperCamelCase    :	Tuple   	=		hidden_state.shape
                                   self.assertTrue((h_batch_size, h_n_channels) ,					(batch_size, n_channels)						)

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





from typing import List, Optional, Tuple, Union

import torch
from torch import nn
from torch.nn import CrossEntropyLoss

from ... import AutoBackbone
from ...modeling_outputs import SemanticSegmenterOutput
from ...modeling_utils import PreTrainedModel
from ...utils import add_start_docstrings, add_start_docstrings_to_model_forward, replace_return_docstrings
from ...utils.backbone_utils import BackboneMixin
from .configuration_upernet import UperNetConfig


__UpperCAmelCase : List[Any] 			=				[
    "openmmlab/upernet-convnext-tiny",
    # See all UperNet models at https://huggingface.co/models?filter=upernet
]

# General docstring
__UpperCAmelCase : List[str] 			=				"UperNetConfig"



class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 0 ,					__SCREAMING_SNAKE_CASE = False ,					__SCREAMING_SNAKE_CASE = 1 ,					):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	str   	=		nn.Convad(
                  in_channels=__SCREAMING_SNAKE_CASE ,					out_channels=__SCREAMING_SNAKE_CASE ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					bias=__SCREAMING_SNAKE_CASE ,					dilation=__SCREAMING_SNAKE_CASE ,					)
              UpperCamelCase    :	int   	=		nn.BatchNormad(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		nn.ReLU()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		self.conv(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[Any]   	=		self.batch_norm(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		self.activation(__SCREAMING_SNAKE_CASE						)

              return output




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		[
                  nn.AdaptiveAvgPoolad(__SCREAMING_SNAKE_CASE						),
                  UperNetConvModule(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					kernel_size=1						),
              ]
              for i, layer in enumerate(self.layers						):
                     self.add_module(str(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		input
              for layer in self.layers:
                     UpperCamelCase    :	int   	=		layer(__SCREAMING_SNAKE_CASE						)
              return hidden_state




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()
              UpperCamelCase    :	List[Any]   	=		pool_scales
              UpperCamelCase    :	Dict   	=		align_corners
              UpperCamelCase    :	Optional[int]   	=		in_channels
              UpperCamelCase    :	Union[str, Any]   	=		channels
              UpperCamelCase    :	List[str]   	=		[]
              for i, pool_scale in enumerate(__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	Union[str, Any]   	=		UperNetPyramidPoolingBlock(pool_scale=__SCREAMING_SNAKE_CASE ,					in_channels=__SCREAMING_SNAKE_CASE ,					channels=__SCREAMING_SNAKE_CASE						)
                     self.blocks.append(__SCREAMING_SNAKE_CASE						)
                     self.add_module(str(__SCREAMING_SNAKE_CASE						) ,					__SCREAMING_SNAKE_CASE						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[]
              for ppm in self.blocks:
                     UpperCamelCase    :	List[str]   	=		ppm(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Tuple   	=		nn.functional.interpolate(
                         __SCREAMING_SNAKE_CASE ,					size=x.size()[2:] ,					mode='''bilinear''' ,					align_corners=self.align_corners						)
                     ppm_outs.append(__SCREAMING_SNAKE_CASE						)
              return ppm_outs




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__()

              UpperCamelCase    :	int   	=		config
              UpperCamelCase    :	List[str]   	=		config.pool_scales  # e.g. (1, 2, 3, 6)
              UpperCamelCase    :	Optional[int]   	=		in_channels
              UpperCamelCase    :	str   	=		config.hidden_size
              UpperCamelCase    :	str   	=		False
              UpperCamelCase    :	List[str]   	=		nn.Convad(self.channels ,					config.num_labels ,					kernel_size=1						)

              # PSP Module
              UpperCamelCase    :	Optional[int]   	=		UperNetPyramidPoolingModule(
                  self.pool_scales ,					self.in_channels[-1] ,					self.channels ,					align_corners=self.align_corners ,					)
              UpperCamelCase    :	str   	=		UperNetConvModule(
                  self.in_channels[-1] + len(self.pool_scales						) * self.channels ,					self.channels ,					kernel_size=3 ,					padding=1 ,					)
              # FPN Module
              UpperCamelCase    :	Union[str, Any]   	=		nn.ModuleList()
              UpperCamelCase    :	Union[str, Any]   	=		nn.ModuleList()
              for in_channels in self.in_channels[:-1]:  # skip the top layer
                     UpperCamelCase    :	List[Any]   	=		UperNetConvModule(__SCREAMING_SNAKE_CASE ,					self.channels ,					kernel_size=1						)
                     UpperCamelCase    :	int   	=		UperNetConvModule(self.channels ,					self.channels ,					kernel_size=3 ,					padding=1						)
                     self.lateral_convs.append(__SCREAMING_SNAKE_CASE						)
                     self.fpn_convs.append(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Optional[Any]   	=		UperNetConvModule(
                  len(self.in_channels						) * self.channels ,					self.channels ,					kernel_size=3 ,					padding=1 ,					)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					nn.Convad						):
                     module.weight.data.normal_(mean=0.0 ,					std=self.config.initializer_range						)
                     if module.bias is not None:
                            module.bias.data.zero_()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		inputs[-1]
              UpperCamelCase    :	int   	=		[x]
              psp_outs.extend(self.psp_modules(__SCREAMING_SNAKE_CASE						)						)
              UpperCamelCase    :	Any   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=1						)
              UpperCamelCase    :	Union[str, Any]   	=		self.bottleneck(__SCREAMING_SNAKE_CASE						)

              return output






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	str   	=		[lateral_conv(encoder_hidden_states[i]						) for i, lateral_conv in enumerate(self.lateral_convs						)]

              laterals.append(self.psp_forward(__SCREAMING_SNAKE_CASE						)						)

              # build top-down path
              UpperCamelCase    :	int   	=		len(__SCREAMING_SNAKE_CASE						)
              for i in range(used_backbone_levels - 1 ,					0 ,					-1						):
                     UpperCamelCase    :	Optional[int]   	=		laterals[i - 1].shape[2:]
                     UpperCamelCase    :	Optional[Any]   	=		laterals[i - 1] + nn.functional.interpolate(
                         laterals[i] ,					size=__SCREAMING_SNAKE_CASE ,					mode='''bilinear''' ,					align_corners=self.align_corners						)

              # build outputs
              UpperCamelCase    :	str   	=		[self.fpn_convs[i](laterals[i]						) for i in range(used_backbone_levels - 1						)]
              # append psp feature
              fpn_outs.append(laterals[-1]						)

              for i in range(used_backbone_levels - 1 ,					0 ,					-1						):
                     UpperCamelCase    :	int   	=		nn.functional.interpolate(
                         fpn_outs[i] ,					size=fpn_outs[0].shape[2:] ,					mode='''bilinear''' ,					align_corners=self.align_corners						)
              UpperCamelCase    :	str   	=		torch.cat(__SCREAMING_SNAKE_CASE ,					dim=1						)
              UpperCamelCase    :	Tuple   	=		self.fpn_bottleneck(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Union[str, Any]   	=		self.classifier(__SCREAMING_SNAKE_CASE						)

              return output




class    UpperCAmelCase_	(							nn.Module):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE = 2 ,					__SCREAMING_SNAKE_CASE = 3 ,					__SCREAMING_SNAKE_CASE = 1						):




              """simple docstring"""




              super().__init__()

              UpperCamelCase    :	Dict   	=		config
              UpperCamelCase    :	Optional[Any]   	=		config.auxiliary_in_channels
              UpperCamelCase    :	Union[str, Any]   	=		config.auxiliary_channels
              UpperCamelCase    :	Union[str, Any]   	=		config.auxiliary_num_convs
              UpperCamelCase    :	Optional[Any]   	=		config.auxiliary_concat_input
              UpperCamelCase    :	List[str]   	=		in_index

              UpperCamelCase    :	Any   	=		(kernel_size // 2) * dilation
              UpperCamelCase    :	Optional[Any]   	=		[]
              convs.append(
                  UperNetConvModule(
                      self.in_channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					dilation=__SCREAMING_SNAKE_CASE						)						)
              for i in range(self.num_convs - 1						):
                     convs.append(
                         UperNetConvModule(
                             self.channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=__SCREAMING_SNAKE_CASE ,					dilation=__SCREAMING_SNAKE_CASE						)						)
              if self.num_convs == 0:
                     UpperCamelCase    :	str   	=		nn.Identity()
              else:
                     UpperCamelCase    :	Dict   	=		nn.Sequential(*__SCREAMING_SNAKE_CASE						)
              if self.concat_input:
                     UpperCamelCase    :	Union[str, Any]   	=		UperNetConvModule(
                         self.in_channels + self.channels ,					self.channels ,					kernel_size=__SCREAMING_SNAKE_CASE ,					padding=kernel_size // 2						)

              UpperCamelCase    :	Optional[Any]   	=		nn.Convad(self.channels ,					config.num_labels ,					kernel_size=1						)






       def  _lowercase   (     self						):




              """simple docstring"""




              self.apply(self._init_weights						)






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					nn.Convad						):
                     module.weight.data.normal_(mean=0.0 ,					std=self.config.initializer_range						)
                     if module.bias is not None:
                            module.bias.data.zero_()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              UpperCamelCase    :	Any   	=		encoder_hidden_states[self.in_index]
              UpperCamelCase    :	str   	=		self.convs(__SCREAMING_SNAKE_CASE						)
              if self.concat_input:
                     UpperCamelCase    :	int   	=		self.conv_cat(torch.cat([hidden_states, output] ,					dim=1						)						)
              UpperCamelCase    :	Union[str, Any]   	=		self.classifier(__SCREAMING_SNAKE_CASE						)
              return output




class    UpperCAmelCase_	(							_a):
       '''simple docstring'''
       __UpperCamelCase      :     Optional[Any]							=					UperNetConfig
       __UpperCamelCase      :     Optional[int]							=					"pixel_values"
       __UpperCamelCase      :     Dict							=					True






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     module.backbone.init_weights()
                     module.decode_head.init_weights()
                     module.auxiliary_head.init_weights()






       def  _lowercase   (     self						):




              """simple docstring"""




              self.backbone.init_weights()
              self.decode_head.init_weights()
              self.auxiliary_head.init_weights()






       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE=False						):




              """simple docstring"""




              if isinstance(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						):
                     UpperCamelCase    :	str   	=		value




__UpperCAmelCase : List[Any] 			=				r"\n    Parameters:\n    This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) sub-class. Use\n    it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and\n    behavior.\n        config ([`UperNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights.\n"

__UpperCAmelCase : Union[str, Any] 			=				r"\n    Args:\n        pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Padding will be ignored by default should you provide it. Pixel values can be obtained using\n            [`AutoImageProcessor`]. See [`SegformerImageProcessor.__call__`] for details.\n        output_attentions (`bool`, *optional*):\n            Whether or not to return the attentions tensors of all attention layers in case the backbone has them. See\n            `attentions` under returned tensors for more detail.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers of the backbone. See `hidden_states` under\n            returned tensors for more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n"



@add_start_docstrings(
    "UperNet framework leveraging any vision backbone e.g. for ADE20k, CityScapes.",     _a,     )
class    UpperCAmelCase_	(							_a):
       '''simple docstring'''






       def __init__(     self ,					__SCREAMING_SNAKE_CASE						):




              """simple docstring"""




              super().__init__(__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Dict   	=		AutoBackbone.from_config(config.backbone_config						)

              # Semantic segmentation head(s)
              UpperCamelCase    :	int   	=		UperNetHead(__SCREAMING_SNAKE_CASE ,					in_channels=self.backbone.channels						)
              UpperCamelCase    :	int   	=		UperNetFCNHead(__SCREAMING_SNAKE_CASE						) if config.use_auxiliary_head else None

              # Initialize weights and apply final processing
              self.post_init()






       @add_start_docstrings_to_model_forward(UPERNET_INPUTS_DOCSTRING.format('''batch_size, sequence_length'''						)						)
       @replace_return_docstrings(output_type=__SCREAMING_SNAKE_CASE ,					config_class=_CONFIG_FOR_DOC						)
       def  _lowercase   (     self ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					__SCREAMING_SNAKE_CASE = None ,					):




              """simple docstring"""




              UpperCamelCase    :	List[str]   	=		return_dict if return_dict is not None else self.config.use_return_dict
              UpperCamelCase    :	str   	=		(
                  output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
              )
              UpperCamelCase    :	Tuple   	=		output_attentions if output_attentions is not None else self.config.output_attentions

              UpperCamelCase    :	Tuple   	=		self.backbone.forward_with_filtered_kwargs(
                  __SCREAMING_SNAKE_CASE ,					output_hidden_states=__SCREAMING_SNAKE_CASE ,					output_attentions=__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	List[str]   	=		outputs.feature_maps

              UpperCamelCase    :	Union[str, Any]   	=		self.decode_head(__SCREAMING_SNAKE_CASE						)
              UpperCamelCase    :	Optional[int]   	=		nn.functional.interpolate(__SCREAMING_SNAKE_CASE ,					size=pixel_values.shape[2:] ,					mode='''bilinear''' ,					align_corners=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	int   	=		None
              if self.auxiliary_head is not None:
                     UpperCamelCase    :	int   	=		self.auxiliary_head(__SCREAMING_SNAKE_CASE						)
                     UpperCamelCase    :	Tuple   	=		nn.functional.interpolate(
                         __SCREAMING_SNAKE_CASE ,					size=pixel_values.shape[2:] ,					mode='''bilinear''' ,					align_corners=__SCREAMING_SNAKE_CASE						)

              UpperCamelCase    :	Union[str, Any]   	=		None
              if labels is not None:
                     if self.config.num_labels == 1:
                            raise ValueError('''The number of labels should be greater than one'''						)
                     else:
                            # compute weighted loss
                            UpperCamelCase    :	Optional[int]   	=		CrossEntropyLoss(ignore_index=self.config.loss_ignore_index						)
                            UpperCamelCase    :	Tuple   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Optional[Any]   	=		loss_fct(__SCREAMING_SNAKE_CASE ,					__SCREAMING_SNAKE_CASE						)
                            UpperCamelCase    :	Union[str, Any]   	=		main_loss + self.config.auxiliary_loss_weight * auxiliary_loss

              if not return_dict:
                     if output_hidden_states:
                            UpperCamelCase    :	Optional[Any]   	=		(logits,) + outputs[1:]
                     else:
                            UpperCamelCase    :	int   	=		(logits,) + outputs[2:]
                     return ((loss,) + output) if loss is not None else output

              return SemanticSegmenterOutput(
                  loss=__SCREAMING_SNAKE_CASE ,					logits=__SCREAMING_SNAKE_CASE ,					hidden_states=outputs.hidden_states ,					attentions=outputs.attentions ,					)

