

"""simple docstring"""






import collections
import inspect
import unittest

from transformers import SwinConfig
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_backbone_common import BackboneTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch
							from torch import nn

							from transformers import SwinBackbone, SwinForImageClassification, SwinForMaskedImageModeling, SwinModel
							from transformers.models.swin.modeling_swin import SWIN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image

							from transformers import AutoImageProcessor
class 	_lowerCAmelCase    :

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

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

							def 					_lowerCAmelCase  ( self       :    List[Any]    )   ->    str:

									"""simple docstring"""
									lowercase										=							floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

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

									lowercase										=							self.get_config()

									return config, pixel_values, labels

							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    List[str]:

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

							def 					_lowerCAmelCase  ( self       :    Dict ,		a       :    str ,		a       :    int ,		a       :    Dict    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							SwinModel(config=__a    )
									model.to(__a    )
									model.eval()
									lowercase										=							model(__a    )

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

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

							def 					_lowerCAmelCase  ( self       :    Optional[int] ,		a       :    Optional[int] ,		a       :    Union[str, Any] ,		a       :    Dict    )   ->    Dict:

									"""simple docstring"""
									lowercase										=							SwinBackbone(config=__a    )
									model.to(__a    )
									model.eval()
									lowercase										=							model(__a    )

									# verify hidden states
									self.parent.assertEqual(len(result.feature_maps    ) ,		len(config.out_features    )    )
									self.parent.assertListEqual(list(result.feature_maps[0].shape    ) ,		[self.batch_size, model.channels[0], 16, 16]    )

									# verify channels
									self.parent.assertEqual(len(model.channels    ) ,		len(config.out_features    )    )

									# verify backbone works with out_features=None
									lowercase										=							None
									lowercase										=							SwinBackbone(config=__a    )
									model.to(__a    )
									model.eval()
									lowercase										=							model(__a    )

									# verify feature maps
									self.parent.assertEqual(len(result.feature_maps    ) ,		1    )
									self.parent.assertListEqual(list(result.feature_maps[0].shape    ) ,		[self.batch_size, model.channels[-1], 4, 4]    )

									# verify channels
									self.parent.assertEqual(len(model.channels    ) ,		1    )

							def 					_lowerCAmelCase  ( self       :    Tuple ,		a       :    Union[str, Any] ,		a       :    List[str] ,		a       :    Tuple    )   ->    Any:

									"""simple docstring"""
									lowercase										=							SwinForMaskedImageModeling(config=__a    )
									model.to(__a    )
									model.eval()
									lowercase										=							model(__a    )
									self.parent.assertEqual(
									    result.logits.shape ,		(self.batch_size, self.num_channels, self.image_size, self.image_size)    )

									# test greyscale images
									lowercase										=							1
									lowercase										=							SwinForMaskedImageModeling(__a    )
									model.to(__a    )
									model.eval()

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

							def 					_lowerCAmelCase  ( self       :    Optional[Any] ,		a       :    Optional[int] ,		a       :    int ,		a       :    Any    )   ->    int:

									"""simple docstring"""
									lowercase										=							self.type_sequence_label_size
									lowercase										=							SwinForImageClassification(__a    )
									model.to(__a    )
									model.eval()
									lowercase										=							model(__a ,		labels=__a    )
									self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)    )

									# test greyscale images
									lowercase										=							1
									lowercase										=							SwinForImageClassification(__a    )
									model.to(__a    )
									model.eval()

									lowercase										=							floats_tensor([self.batch_size, 1, self.image_size, self.image_size]    )
									lowercase										=							model(__a    )
									self.parent.assertEqual(result.logits.shape ,		(self.batch_size, self.type_sequence_label_size)    )

							def 					_lowerCAmelCase  ( self       :    Tuple    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							self.prepare_config_and_inputs()
									(
									    lowercase
									)										=							config_and_inputs
									lowercase										=							{'pixel_values': pixel_values}
									return config, inputs_dict




@require_torch
class 	_lowerCAmelCase    (      UpperCamelCase_       ,       UpperCamelCase_       ,       unittest.TestCase       ):
							__lowerCAmelCase					:    Any     =  (
							    (
							        SwinModel,
							        SwinBackbone,
							        SwinForImageClassification,
							        SwinForMaskedImageModeling,
							    )
							    if is_torch_available()
							    else ()
							)
							__lowerCAmelCase					:    List[Any]     =  (
							    {'''feature-extraction''': SwinModel, '''image-classification''': SwinForImageClassification}
							    if is_torch_available()
							    else {}
							)
							__lowerCAmelCase					:    str     =  True

							__lowerCAmelCase					:    List[str]     =  False
							__lowerCAmelCase					:    List[Any]     =  False
							__lowerCAmelCase					:    List[str]     =  False

							def 					_lowerCAmelCase  ( self       :    str    )   ->    str:

									"""simple docstring"""
									lowercase										=							SwinModelTester(self    )
									lowercase										=							ConfigTester(self ,		config_class=__a ,		embed_dim=37    )

							def 					_lowerCAmelCase  ( self       :    Dict    )   ->    List[str]:

									"""simple docstring"""
									self.create_and_test_config_common_properties()
									self.config_tester.create_and_test_config_to_json_string()
									self.config_tester.create_and_test_config_to_json_file()
									self.config_tester.create_and_test_config_from_and_save_pretrained()
									self.config_tester.create_and_test_config_with_num_labels()
									self.config_tester.check_config_can_be_init_without_params()
									self.config_tester.check_config_arguments_init()

							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    List[Any]:

									"""simple docstring"""
									return

							def 					_lowerCAmelCase  ( self       :    Union[str, Any]    )   ->    Dict:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*__a    )

							@unittest.skip(reason='''Got `CUDA error: misaligned address` with PyTorch 2.0.0.'''    )
							def 					_lowerCAmelCase  ( self       :    Any    )   ->    List[Any]:

									"""simple docstring"""
									pass

							def 					_lowerCAmelCase  ( self       :    int    )   ->    Optional[int]:

									"""simple docstring"""
									super().test_training_gradient_checkpointing()

							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    int:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_backbone(*__a    )

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    Union[str, Any]:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_image_modeling(*__a    )

							def 					_lowerCAmelCase  ( self       :    Tuple    )   ->    List[str]:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*__a    )

							@unittest.skip(reason='''Swin does not use inputs_embeds'''    )
							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    str:

									"""simple docstring"""
									pass

							@unittest.skip(reason='''Swin Transformer does not use feedforward chunking'''    )
							def 					_lowerCAmelCase  ( self       :    int    )   ->    Any:

									"""simple docstring"""
									pass

							def 					_lowerCAmelCase  ( self       :    int    )   ->    Any:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()

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

							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    str:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()

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

											lowercase										=							['pixel_values']
											self.assertListEqual(arg_names[:1] ,		__a    )

							def 					_lowerCAmelCase  ( self       :    List[Any]    )   ->    List[str]:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()
									lowercase										=							True

									for model_class in self.all_model_classes:
											lowercase										=							True
											lowercase										=							False
											lowercase										=							True
											lowercase										=							model_class(__a    )
											model.to(__a    )
											model.eval()
											with torch.no_grad():
													lowercase										=							model(**self._prepare_for_class(__a ,		__a    )    )
											lowercase										=							outputs.attentions
											lowercase										=							len(self.model_tester.depths    )
											self.assertEqual(len(__a    ) ,		__a    )

											# check that output_attentions also work using config
											del inputs_dict["output_attentions"]
											lowercase										=							True
											lowercase										=							config.window_size**2
											lowercase										=							model_class(__a    )
											model.to(__a    )
											model.eval()
											with torch.no_grad():
													lowercase										=							model(**self._prepare_for_class(__a ,		__a    )    )
											lowercase										=							outputs.attentions
											self.assertEqual(len(__a    ) ,		__a    )

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

											# Check attention is always last and order is fine
											lowercase										=							True
											lowercase										=							True
											lowercase										=							model_class(__a    )
											model.to(__a    )
											model.eval()
											with torch.no_grad():
													lowercase										=							model(**self._prepare_for_class(__a ,		__a    )    )

											# also another +1 for reshaped_hidden_states
											lowercase										=							1 if model_class.__name__ == 'SwinBackbone' else 2
											self.assertEqual(out_len + added_hidden_states ,		len(__a    )    )

											lowercase										=							outputs.attentions

											self.assertEqual(len(__a    ) ,		__a    )

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

							def 					_lowerCAmelCase  ( self       :    List[Any] ,		a       :    str ,		a       :    str ,		a       :    List[Any] ,		a       :    Union[str, Any]    )   ->    Any:

									"""simple docstring"""
									lowercase										=							model_class(__a    )
									model.to(__a    )
									model.eval()

									with torch.no_grad():
											lowercase										=							model(**self._prepare_for_class(__a ,		__a    )    )

									lowercase										=							outputs.hidden_states

									lowercase										=							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
									lowercase										=							(
									    config.patch_size
									    if isinstance(config.patch_size ,		collections.abc.Iterable    )
									    else (config.patch_size, config.patch_size)
									)

									lowercase										=							(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] ,		)

									if not model_class.__name__ == "SwinBackbone":
											lowercase										=							outputs.reshaped_hidden_states
											self.assertEqual(len(__a    ) ,		__a    )

											lowercase										=							reshaped_hidden_states[0].shape
											lowercase										=							(
											    reshaped_hidden_states[0].view(__a ,		__a ,		height * width    ).permute(0 ,		2 ,		1    )
											)
											self.assertListEqual(
											    list(reshaped_hidden_states.shape[-2:]    ) ,		[num_patches, self.model_tester.embed_dim] ,		)

							def 					_lowerCAmelCase  ( self       :    Any    )   ->    Dict:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()

									lowercase										=							(
									    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:
											lowercase										=							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"]
											lowercase										=							True

											self.check_hidden_states_output(__a ,		__a ,		__a ,		__a    )

							def 					_lowerCAmelCase  ( self       :    List[str]    )   ->    Tuple:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()
									lowercase										=							3

									lowercase										=							(
									    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)
									)
									lowercase										=							(
									    config.patch_size
									    if isinstance(config.patch_size ,		collections.abc.Iterable    )
									    else (config.patch_size, config.patch_size)
									)

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

									for model_class in self.all_model_classes:
											lowercase										=							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"]
											lowercase										=							True
											self.check_hidden_states_output(__a ,		__a ,		__a ,		(padded_height, padded_width)    )

							@slow
							def 					_lowerCAmelCase  ( self       :    List[Any]    )   ->    List[str]:

									"""simple docstring"""






									for model_name in SWIN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowercase										=							SwinModel.from_pretrained(__a    )
											self.assertIsNotNone(__a    )

							def 					_lowerCAmelCase  ( self       :    Optional[int]    )   ->    Union[str, Any]:

									"""simple docstring"""
									lowercase										=							self.model_tester.prepare_config_and_inputs_for_common()

									lowercase										=							_config_zero_init(__a    )
									for model_class in self.all_model_classes:
											lowercase										=							model_class(config=__a    )
											for name, param in model.named_parameters():
													if "embeddings" not in name and param.requires_grad:
															self.assertIn(
															    ((param.data.mean() * 1E9).round() / 1E9).item() ,		[0.0, 1.0] ,		msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,		)




@require_vision
@require_torch
class 	_lowerCAmelCase    (      unittest.TestCase       ):

							@cached_property
							def 					_lowerCAmelCase  ( self       :    List[Any]    )   ->    List[str]:

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

							@slow
							def 					_lowerCAmelCase  ( self       :    Optional[Any]    )   ->    str:

									"""simple docstring"""
									lowercase										=							SwinForImageClassification.from_pretrained('''microsoft/swin-tiny-patch4-window7-224'''    ).to(__a    )
									lowercase										=							self.default_image_processor

									lowercase										=							Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
									lowercase										=							image_processor(images=__a ,		return_tensors='''pt'''    ).to(__a    )

									# forward pass
									with torch.no_grad():
											lowercase										=							model(**__a    )

									# verify the logits
									lowercase										=							torch.Size((1, 1000)    )
									self.assertEqual(outputs.logits.shape ,		__a    )
									lowercase										=							torch.tensor([-0.0_948, -0.6_454, -0.0_921]    ).to(__a    )
									self.assertTrue(torch.allclose(outputs.logits[0, :3] ,		__a ,		atol=1E-4    )    )




@require_torch
class 	_lowerCAmelCase    (      unittest.TestCase       ,       UpperCamelCase_       ):
							__lowerCAmelCase					:    str     =  (SwinBackbone,) if is_torch_available() else ()
							__lowerCAmelCase					:    Dict     =  SwinConfig

							def 					_lowerCAmelCase  ( self       :    Union[str, Any]    )   ->    Optional[Any]:

									"""simple docstring"""
									lowercase										=							SwinModelTester(self    )







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


__A     						= {
    '''configuration_prophetnet''': ['''PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ProphetNetConfig'''],
    '''tokenization_prophetnet''': ['''ProphetNetTokenizer'''],
}

try:
      if not is_torch_available():
            raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
      pass
else:
      __A     						= [
          '''PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST''',
          '''ProphetNetDecoder''',
          '''ProphetNetEncoder''',
          '''ProphetNetForCausalLM''',
          '''ProphetNetForConditionalGeneration''',
          '''ProphetNetModel''',
          '''ProphetNetPreTrainedModel''',
      ]


if TYPE_CHECKING:
      from .configuration_prophetnet import PROPHETNET_PRETRAINED_CONFIG_ARCHIVE_MAP, ProphetNetConfig
      from .tokenization_prophetnet import ProphetNetTokenizer

      try:
            if not is_torch_available():
                  raise OptionalDependencyNotAvailable()
      except OptionalDependencyNotAvailable:
            pass
      else:
            from .modeling_prophetnet import (
                PROPHETNET_PRETRAINED_MODEL_ARCHIVE_LIST,
                ProphetNetDecoder,
                ProphetNetEncoder,
                ProphetNetForCausalLM,
                ProphetNetForConditionalGeneration,
                ProphetNetModel,
                ProphetNetPreTrainedModel,
            )

else:
      import sys

      __A     						= _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)