


import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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_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 SwiftFormerForImageClassification, SwiftFormerModel
						from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import ViTImageProcessor







class        snake_case_   :
					def __init__(      self   :Optional[int]			,__snake_case   :Union[str, Any]			,__snake_case   :Optional[Any]=13			,__snake_case   :Tuple=3			,__snake_case   :List[Any]=True			,__snake_case   :Dict=True			,__snake_case   :Tuple=0.1			,__snake_case   :List[Any]=0.1			,__snake_case   :Optional[int]=2_24			,__snake_case   :int=10_00			,__snake_case   :Optional[Any]=[3, 3, 6, 4]			,__snake_case   :List[str]=[48, 56, 1_12, 2_20]			,)					->    Union[str, Any]:
												a__             =     parent
												a__             =     batch_size
												a__             =     num_channels
												a__             =     is_training
												a__             =     use_labels
												a__             =     hidden_dropout_prob
												a__             =     attention_probs_dropout_prob
												a__             =     num_labels
												a__             =     image_size
												a__             =     layer_depths
												a__             =     embed_dims
					def  lowerCamelCase__(      self   :str      )					->    Dict:
												a__             =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )

												a__             =     None
												if self.use_labels:
																			a__             =     ids_tensor([self.batch_size]			,self.num_labels      )

												a__             =     self.get_config()

												return config, pixel_values, labels
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												return SwiftFormerConfig(
												    depths=self.layer_depths			,embed_dims=self.embed_dims			,mlp_ratio=4			,downsamples=[True, True, True, True]			,hidden_act='gelu'			,num_labels=self.num_labels			,down_patch_size=3			,down_stride=2			,down_pad=1			,drop_rate=0.0			,drop_path_rate=0.0			,use_layer_scale=__snake_case			,layer_scale_init_value=1E-5			,)
					def  lowerCamelCase__(      self   :Dict			,__snake_case   :int			,__snake_case   :Union[str, Any]			,__snake_case   :Tuple      )					->    str:
												a__             =     SwiftFormerModel(config=__snake_case      )
												model.to(__snake_case      )
												model.eval()
												a__             =     model(__snake_case      )
												self.parent.assertEqual(result.last_hidden_state.shape			,(self.batch_size, self.embed_dims[-1], 7, 7)      )
					def  lowerCamelCase__(      self   :Any			,__snake_case   :List[Any]			,__snake_case   :Tuple			,__snake_case   :Union[str, Any]      )					->    int:
												a__             =     self.num_labels
												a__             =     SwiftFormerForImageClassification(__snake_case      )
												model.to(__snake_case      )
												model.eval()
												a__             =     model(__snake_case			,labels=__snake_case      )
												self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)      )

												a__             =     SwiftFormerForImageClassification(__snake_case      )
												model.to(__snake_case      )
												model.eval()

												a__             =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]      )
												a__             =     model(__snake_case      )
												self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)      )



					def  lowerCamelCase__(      self   :List[Any]      )					->    Dict:
												((a__)      ,					(a__)      ,					(a__))             =     self.prepare_config_and_inputs()
												a__             =     {'pixel_values': pixel_values}
												return config, inputs_dict







@require_torch
class        snake_case_   (lowerCamelCase_	,						lowerCamelCase_	,						unittest.TestCase      ):
					UpperCAmelCase__					:    Any          =			(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
					UpperCAmelCase__					:    int          =			(
					    {'''feature-extraction''': SwiftFormerModel, '''image-classification''': SwiftFormerForImageClassification}
					    if is_torch_available()
					    else {}
					)

					UpperCAmelCase__					:    Optional[int]          =			False
					UpperCAmelCase__					:    Union[str, Any]          =			False
					UpperCAmelCase__					:    Tuple          =			False
					UpperCAmelCase__					:    Optional[int]          =			False
					UpperCAmelCase__					:    Union[str, Any]          =			False
					def  lowerCamelCase__(      self   :List[str]      )					->    int:
												a__             =     SwiftFormerModelTester(self      )
												a__             =     ConfigTester(
												    self			,config_class=__snake_case			,has_text_modality=__snake_case			,hidden_size=37			,num_attention_heads=12			,num_hidden_layers=12			,)
					def  lowerCamelCase__(      self   :Any      )					->    Optional[Any]:
												self.config_tester.run_common_tests()
					@unittest.skip(reason='SwiftFormer does not use inputs_embeds'      )
					def  lowerCamelCase__(      self   :List[str]      )					->    Any:
												pass
					def  lowerCamelCase__(      self   :Tuple      )					->    List[Any]:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__             =     model_class(__snake_case      )
																			a__             =     model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(__snake_case			,nn.Linear      )      )
					def  lowerCamelCase__(      self   :Any      )					->    int:
												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

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

																			a__             =     ['pixel_values']
																			self.assertListEqual(arg_names[:1]			,__snake_case      )
					def  lowerCamelCase__(      self   :List[str]      )					->    int:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__snake_case      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[str]:
												a__             =     self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__snake_case      )
					@slow
					def  lowerCamelCase__(      self   :Dict      )					->    Union[str, Any]:
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			a__             =     SwiftFormerModel.from_pretrained(__snake_case      )
																			self.assertIsNotNone(__snake_case      )
					@unittest.skip(reason='SwiftFormer does not output attentions'      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    List[Any]:
												pass
					def  lowerCamelCase__(      self   :Dict      )					->    Optional[int]:
												def check_hidden_states_output(__snake_case   :Optional[int]			,__snake_case   :Optional[Any]			,__snake_case   :int      ):
																			a__             =     model_class(__snake_case      )
																			model.to(__snake_case      )
																			model.eval()

																			with torch.no_grad():
																										a__             =     model(**self._prepare_for_class(__snake_case			,__snake_case      )      )

																			a__             =     outputs.hidden_states

																			a__             =     8
																			self.assertEqual(len(__snake_case      )			,__snake_case      )  # TODO

																			# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																			# with the width and height being successively divided by 2, after every 2 blocks
																			for i in range(len(__snake_case      )      ):
																										self.assertEqual(
																										    hidden_states[i].shape			,torch.Size(
																										        [
																										            self.model_tester.batch_size,
																										            self.model_tester.embed_dims[i // 2],
																										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																										            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																										        ]      )			,)

												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																			a__             =     True
																			check_hidden_states_output(__snake_case			,__snake_case			,__snake_case      )

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

																			check_hidden_states_output(__snake_case			,__snake_case			,__snake_case      )
					def  lowerCamelCase__(      self   :Union[str, Any]      )					->    int:
												def _config_zero_init(__snake_case   :int      ):
																			a__             =     copy.deepcopy(__snake_case      )
																			for key in configs_no_init.__dict__.keys():
																										if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																																	setattr(__snake_case			,__snake_case			,1E-10      )
																										if isinstance(getattr(__snake_case			,__snake_case			,__snake_case      )			,__snake_case      ):
																																	a__             =     _config_zero_init(getattr(__snake_case			,__snake_case      )      )
																																	setattr(__snake_case			,__snake_case			,__snake_case      )
																			return configs_no_init

												a__      ,					a__             =     self.model_tester.prepare_config_and_inputs_for_common()

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



					@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'      )
					def  lowerCamelCase__(      self   :Dict      )					->    Tuple:
												pass






def 							__lowercase   ( ):
							a__             =     Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							return image







@require_torch
@require_vision
class        snake_case_   (unittest.TestCase      ):
					@cached_property
					def  lowerCamelCase__(      self   :Optional[Any]      )					->    int:
												return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'      ) if is_vision_available() else None



					@slow
					def  lowerCamelCase__(      self   :int      )					->    Union[str, Any]:
												a__             =     SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'      ).to(__snake_case      )

												a__             =     self.default_image_processor
												a__             =     prepare_img()
												a__             =     image_processor(images=__snake_case			,return_tensors='pt'      ).to(__snake_case      )

												# forward pass
												with torch.no_grad():
																			a__             =     model(**__snake_case      )

												# verify the logits
												a__             =     torch.Size((1, 10_00)      )
												self.assertEqual(outputs.logits.shape			,__snake_case      )

												a__             =     torch.tensor([[-2.1703E00, 2.1107E00, -2.0811E00]]      ).to(__snake_case      )
												self.assertTrue(torch.allclose(outputs.logits[0, :3]			,__snake_case			,atol=1E-4      )      )








from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available


snake_case							:       str     			=      {
    '''configuration_rag''': ['''RagConfig'''],
    '''retrieval_rag''': ['''RagRetriever'''],
    '''tokenization_rag''': ['''RagTokenizer'''],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Any     			=      [
						    '''RagModel''',
						    '''RagPreTrainedModel''',
						    '''RagSequenceForGeneration''',
						    '''RagTokenForGeneration''',
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						snake_case							:       Optional[int]     			=      [
						    '''TFRagModel''',
						    '''TFRagPreTrainedModel''',
						    '''TFRagSequenceForGeneration''',
						    '''TFRagTokenForGeneration''',
						]


if TYPE_CHECKING:
						from .configuration_rag import RagConfig
						from .retrieval_rag import RagRetriever
						from .tokenization_rag import RagTokenizer

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_rag import RagModel, RagPreTrainedModel, RagSequenceForGeneration, RagTokenForGeneration

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_rag import (
												    TFRagModel,
												    TFRagPreTrainedModel,
												    TFRagSequenceForGeneration,
												    TFRagTokenForGeneration,
												)

else:
						import sys

						snake_case							:       int     			=      _LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




