"""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 						__A							:






     def __init__(		self    ,					a__    ,					a__=13    ,					a__=32    ,					a__=2    ,					a__=3    ,					a__=16    ,					a__=[1, 2, 1]    ,					a__=[2, 2, 4]    ,					a__=2    ,					a__=2.0    ,					a__=True    ,					a__=0.0    ,					a__=0.0    ,					a__=0.1    ,					a__="gelu"    ,					a__=False    ,					a__=True    ,					a__=0.0_2    ,					a__=1e-5    ,					a__=True    ,					a__=None    ,					a__=True    ,					a__=10    ,					a__=8    ,					a__=["stage1", "stage2", "stage3"]    ,					a__=[1, 2, 3]    ,					):
       _lowerCAmelCase						:	Union[str, Any]        =					parent
       _lowerCAmelCase						:	List[Any]        =					batch_size
       _lowerCAmelCase						:	int        =					image_size
       _lowerCAmelCase						:	List[str]        =					patch_size
       _lowerCAmelCase						:	int        =					num_channels
       _lowerCAmelCase						:	Optional[int]        =					embed_dim
       _lowerCAmelCase						:	int        =					depths
       _lowerCAmelCase						:	Tuple        =					num_heads
       _lowerCAmelCase						:	Optional[Any]        =					window_size
       _lowerCAmelCase						:	str        =					mlp_ratio
       _lowerCAmelCase						:	Tuple        =					qkv_bias
       _lowerCAmelCase						:	Any        =					hidden_dropout_prob
       _lowerCAmelCase						:	List[str]        =					attention_probs_dropout_prob
       _lowerCAmelCase						:	Optional[int]        =					drop_path_rate
       _lowerCAmelCase						:	Optional[int]        =					hidden_act
       _lowerCAmelCase						:	Union[str, Any]        =					use_absolute_embeddings
       _lowerCAmelCase						:	List[str]        =					patch_norm
       _lowerCAmelCase						:	Union[str, Any]        =					layer_norm_eps
       _lowerCAmelCase						:	Union[str, Any]        =					initializer_range
       _lowerCAmelCase						:	Optional[int]        =					is_training
       _lowerCAmelCase						:	Dict        =					scope
       _lowerCAmelCase						:	List[Any]        =					use_labels
       _lowerCAmelCase						:	List[str]        =					type_sequence_label_size
       _lowerCAmelCase						:	List[Any]        =					encoder_stride
       _lowerCAmelCase						:	Dict        =					out_features
       _lowerCAmelCase						:	List[Any]        =					out_indices






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]  )

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

       _lowerCAmelCase						:	List[Any]        =					self.get_config()

       return config, pixel_values, labels






     def    __A  (		self  ):
       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    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	Dict        =					MaskFormerSwinModel(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Optional[Any]        =					model(a__  )

       _lowerCAmelCase						:	Optional[int]        =					((config.image_size // config.patch_size) ** 2) // (4 ** (len(config.depths  ) - 1))
       _lowerCAmelCase						:	Union[str, 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    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	List[Any]        =					MaskFormerSwinBackbone(config=a__  )
       model.to(a__  )
       model.eval()
       _lowerCAmelCase						:	Optional[int]        =					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__  ):
         _lowerCAmelCase						:	Any        =					["""stem"""]
         _lowerCAmelCase						:	Optional[Any]        =					MaskFormerSwinBackbone(config=a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					self.prepare_config_and_inputs()
       _lowerCAmelCase						:	Union[str, Any]        =					config_and_inputs
       _lowerCAmelCase						:	str        =					{"""pixel_values""": pixel_values}
       return config, inputs_dict







@require_torch
class 						__A							(			SCREAMING_SNAKE_CASE_   ,     SCREAMING_SNAKE_CASE_   ,     unittest.TestCase		):
     _UpperCamelCase							:      List[Any]		 =		(
         (
             MaskFormerSwinModel,
             MaskFormerSwinBackbone,
         )
         if is_torch_available()
         else ()
     )
     _UpperCamelCase							:      str		 =		{"feature-extraction": MaskFormerSwinModel} if is_torch_available() else {}
     _UpperCamelCase							:      Dict		 =		False
     _UpperCamelCase							:      Optional[int]		 =		False
     _UpperCamelCase							:      Tuple		 =		False
     _UpperCamelCase							:      Any		 =		False
     _UpperCamelCase							:      List[str]		 =		False






     def    __A  (		self  ):
       _lowerCAmelCase						:	str        =					MaskFormerSwinModelTester(self  )
       _lowerCAmelCase						:	List[str]        =					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  ):
       pass






     def    __A  (		self  ):
       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  ):
       return






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.model_tester.prepare_config_and_inputs()
       self.model_tester.create_and_check_model(*a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Optional[int]        =					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  ):
       pass






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






     def    __A  (		self  ):
       _lowerCAmelCase						:	List[Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					self.model_tester.prepare_config_and_inputs_for_common()

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

         _lowerCAmelCase						:	Union[str, 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  ):
       pass






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






     def    __A  (		self    ,					a__    ,					a__    ,					a__    ,					a__  ):
       _lowerCAmelCase						:	str        =					model_class(a__  )
       model.to(a__  )
       model.eval()

       with torch.no_grad():
         _lowerCAmelCase						:	List[str]        =					model(**self._prepare_for_class(a__    ,					a__  )  )

       _lowerCAmelCase						:	Tuple        =					outputs.hidden_states

       _lowerCAmelCase						:	List[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
       _lowerCAmelCase						:	int        =					(
           config.patch_size
           if isinstance(config.patch_size    ,					collections.abc.Iterable  )
           else (config.patch_size, config.patch_size)
       )

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

       _lowerCAmelCase						:	Tuple        =					(
           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:
         _lowerCAmelCase						:	List[str]        =					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"]
         _lowerCAmelCase						:	Union[str, Any]        =					True

         self.check_hidden_states_output(a__    ,					a__    ,					a__    ,					a__  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase						:	str        =					3

       _lowerCAmelCase						:	Optional[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)
       )
       _lowerCAmelCase						:	List[Any]        =					(
           config.patch_size
           if isinstance(config.patch_size    ,					collections.abc.Iterable  )
           else (config.patch_size, config.patch_size)
       )

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

       for model_class in self.all_model_classes:
         _lowerCAmelCase						:	Optional[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"]
         _lowerCAmelCase						:	Dict        =					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  ):
       pass






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






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






     def    __A  (		self  ):
       _lowerCAmelCase						:	Union[str, Any]        =					self.model_tester.prepare_config_and_inputs_for_common()

       def set_nan_tensor_to_zero(a__  ):
         _lowerCAmelCase						:	Tuple        =					0
         return t

       def check_equivalence(a__    ,					a__    ,					a__    ,					a__={}  ):
         with torch.no_grad():
           _lowerCAmelCase						:	Optional[Any]        =					model(**a__    ,					return_dict=a__    ,					**a__  )
           _lowerCAmelCase						:	Union[str, Any]        =					model(**a__    ,					return_dict=a__    ,					**a__  ).to_tuple()

           def recursive_check(a__    ,					a__  ):
             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:
         _lowerCAmelCase						:	Any        =					model_class(a__  )
         model.to(a__  )
         model.eval()

         _lowerCAmelCase						:	str        =					self._prepare_for_class(a__    ,					a__  )
         _lowerCAmelCase						:	Optional[Any]        =					self._prepare_for_class(a__    ,					a__  )
         check_equivalence(a__    ,					a__    ,					a__  )

         _lowerCAmelCase						:	Dict        =					self._prepare_for_class(a__    ,					a__    ,					return_labels=a__  )
         _lowerCAmelCase						:	int        =					self._prepare_for_class(a__    ,					a__    ,					return_labels=a__  )
         check_equivalence(a__    ,					a__    ,					a__  )

         _lowerCAmelCase						:	Any        =					self._prepare_for_class(a__    ,					a__  )
         _lowerCAmelCase						:	Any        =					self._prepare_for_class(a__    ,					a__  )
         check_equivalence(a__    ,					a__    ,					a__    ,					{"""output_hidden_states""": True}  )

         _lowerCAmelCase						:	Any        =					self._prepare_for_class(a__    ,					a__    ,					return_labels=a__  )
         _lowerCAmelCase						:	Any        =					self._prepare_for_class(a__    ,					a__    ,					return_labels=a__  )
         check_equivalence(a__    ,					a__    ,					a__    ,					{"""output_hidden_states""": True}  )







@require_torch
class 						__A							(			unittest.TestCase   ,     SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Union[str, Any]		 =		(MaskFormerSwinBackbone,) if is_torch_available() else ()
     _UpperCamelCase							:      List[Any]		 =		MaskFormerSwinConfig






     def    __A  (		self  ):
       _lowerCAmelCase						:	Dict        =					MaskFormerSwinModelTester(self  )






     def    __A  (		self  ):
       _lowerCAmelCase						:	Tuple        =					self.model_tester.prepare_config_and_inputs_for_common()
       _lowerCAmelCase						:	Any        =					inputs_dict["""pixel_values"""].shape[0]

       for backbone_class in self.all_model_classes:
         _lowerCAmelCase						:	int        =					backbone_class(a__  )
         backbone.to(a__  )
         backbone.eval()

         _lowerCAmelCase						:	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
         _lowerCAmelCase						:	Optional[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)
             _lowerCAmelCase						:	Optional[Any]        =					hidden_state.shape
             self.assertTrue((h_batch_size, h_n_channels)    ,					(batch_size, n_channels)  )

            # Test output_attentions=True
         if self.has_attentions:
           _lowerCAmelCase						:	Tuple        =					backbone(**a__    ,					output_attentions=a__  )
           self.assertIsNotNone(outputs.attentions  )





"""simple docstring"""




from dataclasses import dataclass
from typing import Optional, Tuple

import torch
from torch import nn
from transformers import RobertaPreTrainedModel, XLMRobertaConfig, XLMRobertaModel
from transformers.utils import ModelOutput
@dataclass
class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[torch.FloatTensor]		 =		None
     _UpperCamelCase							:      torch.FloatTensor		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None
     _UpperCamelCase							:      Optional[Tuple[torch.FloatTensor]]		 =		None







class 						__A							(			SCREAMING_SNAKE_CASE_		):






     def __init__(		self    ,					a__=1    ,					a__=0    ,					a__=2    ,					a__=512    ,					a__="cls"    ,					a__=False    ,					a__=True    ,					**a__    ,					):
       super().__init__(pad_token_id=a__    ,					bos_token_id=a__    ,					eos_token_id=a__    ,					**a__  )
       _lowerCAmelCase						:	Optional[Any]        =					project_dim
       _lowerCAmelCase						:	List[str]        =					pooler_fn
       _lowerCAmelCase						:	Any        =					learn_encoder
       _lowerCAmelCase						:	Optional[int]        =					use_attention_mask







class 						__A							(			SCREAMING_SNAKE_CASE_		):
     _UpperCamelCase							:      Optional[int]		 =		[R"pooler", R"logit_scale"]
     _UpperCamelCase							:      List[Any]		 =		[R"position_ids", R"predictions.decoder.bias"]
     _UpperCamelCase							:      List[Any]		 =		"roberta"
     _UpperCamelCase							:      Optional[int]		 =		RobertaSeriesConfig






     def __init__(		self    ,					a__  ):
       super().__init__(a__  )
       _lowerCAmelCase						:	str        =					XLMRobertaModel(a__  )
       _lowerCAmelCase						:	Optional[Any]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
       _lowerCAmelCase						:	List[Any]        =					getattr(a__    ,					"""has_pre_transformation"""    ,					a__  )
       if self.has_pre_transformation:
         _lowerCAmelCase						:	List[str]        =					nn.Linear(config.hidden_size    ,					config.project_dim  )
         _lowerCAmelCase						:	Optional[int]        =					nn.LayerNorm(config.hidden_size    ,					eps=config.layer_norm_eps  )
       self.post_init()






     def    __A  (		self    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					a__ = None    ,					):
       _lowerCAmelCase						:	List[str]        =					return_dict if return_dict is not None else self.config.use_return_dict

       _lowerCAmelCase						:	Optional[int]        =					self.base_model(
           input_ids=a__    ,					attention_mask=a__    ,					token_type_ids=a__    ,					position_ids=a__    ,					head_mask=a__    ,					inputs_embeds=a__    ,					encoder_hidden_states=a__    ,					encoder_attention_mask=a__    ,					output_attentions=a__    ,					output_hidden_states=True if self.has_pre_transformation else output_hidden_states    ,					return_dict=a__    ,					)

       if self.has_pre_transformation:
         _lowerCAmelCase						:	Optional[Any]        =					outputs["""hidden_states"""][-2]
         _lowerCAmelCase						:	Optional[Any]        =					self.pre_LN(a__  )
         _lowerCAmelCase						:	int        =					self.transformation_pre(a__  )

         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)
       else:
         _lowerCAmelCase						:	Union[str, Any]        =					self.transformation(outputs.last_hidden_state  )
         return TransformationModelOutput(
             projection_state=a__    ,					last_hidden_state=outputs.last_hidden_state    ,					hidden_states=outputs.hidden_states    ,					attentions=outputs.attentions    ,					)




