



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  lowerCamelCase__    :




		'''simple docstring'''



		def __init__(self	,__lowerCamelCase	,__lowerCamelCase=13	,__lowerCamelCase=3	,__lowerCamelCase=True	,__lowerCamelCase=True	,__lowerCamelCase=0.1	,__lowerCamelCase=0.1	,__lowerCamelCase=2_24	,__lowerCamelCase=10_00	,__lowerCamelCase=[3, 3, 6, 4]	,__lowerCamelCase=[48, 56, 1_12, 2_20]	,)  ->    str:

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  parent
						lowerCAmelCase__  :       List[Any]				=  batch_size
						lowerCAmelCase__  :       List[str]				=  num_channels
						lowerCAmelCase__  :       int				=  is_training
						lowerCAmelCase__  :       str				=  use_labels
						lowerCAmelCase__  :       Tuple				=  hidden_dropout_prob
						lowerCAmelCase__  :       Union[str, Any]				=  attention_probs_dropout_prob
						lowerCAmelCase__  :       Optional[Any]				=  num_labels
						lowerCAmelCase__  :       str				=  image_size
						lowerCAmelCase__  :       Dict				=  layer_depths
						lowerCAmelCase__  :       Optional[int]				=  embed_dims



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

						"""simple docstring"""






						lowerCAmelCase__  :       Tuple				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

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

						lowerCAmelCase__  :       List[str]				=  self.get_config()

						return config, pixel_values, labels



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						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=__lowerCamelCase	,layer_scale_init_value=1e-5	,)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       List[Any]				=  SwiftFormerModel(config=__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       List[Any]				=  model(__lowerCamelCase				)
						self.parent.assertEqual(result.last_hidden_state.shape	,(self.batch_size, self.embed_dims[-1], 7, 7)				)



		def 	lowerCAmelCase__   (self	,__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.num_labels
						lowerCAmelCase__  :       Optional[Any]				=  SwiftFormerForImageClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()
						lowerCAmelCase__  :       Dict				=  model(__lowerCamelCase	,labels=__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)				)

						lowerCAmelCase__  :       Dict				=  SwiftFormerForImageClassification(__lowerCamelCase				)
						model.to(__lowerCamelCase				)
						model.eval()

						lowerCAmelCase__  :       int				=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)
						lowerCAmelCase__  :       Tuple				=  model(__lowerCamelCase				)
						self.parent.assertEqual(result.logits.shape	,(self.batch_size, self.num_labels)				)



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						((lowerCAmelCase__)		,	(lowerCAmelCase__)		,	(lowerCAmelCase__))  :       Optional[Any]				=  self.prepare_config_and_inputs()
						lowerCAmelCase__  :       Union[str, Any]				=  {'''pixel_values''': pixel_values}
						return config, inputs_dict



@require_torch
class  lowerCamelCase__    (							lowerCamelCase__   ,       lowerCamelCase__   ,       unittest.TestCase):




		'''simple docstring'''



		snake_case_			=(SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
		snake_case_			=(
		    {"""feature-extraction""": SwiftFormerModel, """image-classification""": SwiftFormerForImageClassification}
		    if is_torch_available()
		    else {}
		)

		snake_case_			=False
		snake_case_			=False
		snake_case_			=False
		snake_case_			=False
		snake_case_			=False



		def 	lowerCAmelCase__   (self				)  ->    Any:

						"""simple docstring"""






						lowerCAmelCase__  :       int				=  SwiftFormerModelTester(self				)
						lowerCAmelCase__  :       List[str]				=  ConfigTester(
						    self	,config_class=__lowerCamelCase	,has_text_modality=__lowerCamelCase	,hidden_size=37	,num_attention_heads=12	,num_hidden_layers=12	,)



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						self.config_tester.run_common_tests()



		@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''				)
		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       Tuple				=  self.model_tester.prepare_config_and_inputs_for_common()

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Tuple				=  model_class(__lowerCamelCase				)
										lowerCAmelCase__  :       Tuple				=  model.get_output_embeddings()
										self.assertTrue(x is None or isinstance(__lowerCamelCase	,nn.Linear				)				)



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

						"""simple docstring"""






						lowerCAmelCase__		,	lowerCAmelCase__  :       Optional[int]				=  self.model_tester.prepare_config_and_inputs_for_common()

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

										lowerCAmelCase__  :       int				=  ['''pixel_values''']
										self.assertListEqual(arg_names[:1]	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    int:

						"""simple docstring"""






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



		def 	lowerCAmelCase__   (self				)  ->    List[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       List[str]				=  self.model_tester.prepare_config_and_inputs()
						self.model_tester.create_and_check_for_image_classification(*__lowerCamelCase				)



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

						"""simple docstring"""






						for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
										lowerCAmelCase__  :       str				=  SwiftFormerModel.from_pretrained(__lowerCamelCase				)
										self.assertIsNotNone(__lowerCamelCase				)



		@unittest.skip(reason='''SwiftFormer does not output attentions'''				)
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						pass



		def 	lowerCAmelCase__   (self				)  ->    Tuple:

						"""simple docstring"""






						def check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				):
										lowerCAmelCase__  :       Optional[Any]				=  model_class(__lowerCamelCase				)
										model.to(__lowerCamelCase				)
										model.eval()

										with torch.no_grad():
														lowerCAmelCase__  :       str				=  model(**self._prepare_for_class(__lowerCamelCase	,__lowerCamelCase				)				)

										lowerCAmelCase__  :       Dict				=  outputs.hidden_states

										lowerCAmelCase__  :       Dict				=  8
										self.assertEqual(len(__lowerCamelCase				)	,__lowerCamelCase				)  # 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(__lowerCamelCase				)				):
														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),
														        ]				)	,)

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

						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Dict				=  True
										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)

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

										check_hidden_states_output(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)



		def 	lowerCAmelCase__   (self				)  ->    List[str]:

						"""simple docstring"""






						def _config_zero_init(__lowerCamelCase				):
										lowerCAmelCase__  :       List[Any]				=  copy.deepcopy(__lowerCamelCase				)
										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(__lowerCamelCase	,__lowerCamelCase	,1e-10				)
														if isinstance(getattr(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)	,__lowerCamelCase				):
																		lowerCAmelCase__  :       List[str]				=  _config_zero_init(getattr(__lowerCamelCase	,__lowerCamelCase				)				)
																		setattr(__lowerCamelCase	,__lowerCamelCase	,__lowerCamelCase				)
										return configs_no_init

						lowerCAmelCase__		,	lowerCAmelCase__  :       Dict				=  self.model_tester.prepare_config_and_inputs_for_common()

						lowerCAmelCase__  :       Union[str, Any]				=  _config_zero_init(__lowerCamelCase				)
						for model_class in self.all_model_classes:
										lowerCAmelCase__  :       Tuple				=  model_class(config=__lowerCamelCase				)
										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				)  ->    Tuple:

						"""simple docstring"""






						pass






def 			lowerCAmelCase__	(					):





				'''simple docstring'''

				lowerCAmelCase__  :       Union[str, Any]				=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png''')
				return image



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




		'''simple docstring'''



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

						"""simple docstring"""






						return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''				) if is_vision_available() else None



		@slow
		def 	lowerCAmelCase__   (self				)  ->    Union[str, Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Optional[int]				=  SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''				).to(__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  self.default_image_processor
						lowerCAmelCase__  :       int				=  prepare_img()
						lowerCAmelCase__  :       Optional[int]				=  image_processor(images=__lowerCamelCase	,return_tensors='''pt'''				).to(__lowerCamelCase				)

						# forward pass
						with torch.no_grad():
										lowerCAmelCase__  :       Optional[int]				=  model(**__lowerCamelCase				)

						# verify the logits
						lowerCAmelCase__  :       Optional[int]				=  torch.Size((1, 10_00)				)
						self.assertEqual(outputs.logits.shape	,__lowerCamelCase				)

						lowerCAmelCase__  :       int				=  torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]				).to(__lowerCamelCase				)
						self.assertTrue(torch.allclose(outputs.logits[0, :3]	,__lowerCamelCase	,atol=1e-4				)				)






import warnings

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__snake_case       :	List[Any]            =logging.get_logger(__name__)

__snake_case       :	str            ={
    'RUCAIBox/mvp': 'https://huggingface.co/RUCAIBox/mvp/resolve/main/config.json',
}



class  lowerCamelCase__    (							lowerCamelCase__):




		'''simple docstring'''



		snake_case_			="""mvp"""
		snake_case_			=["""past_key_values"""]
		snake_case_			={"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}



		def __init__(self	,__lowerCamelCase=5_02_67	,__lowerCamelCase=10_24	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=12	,__lowerCamelCase=40_96	,__lowerCamelCase=16	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase="gelu"	,__lowerCamelCase=10_24	,__lowerCamelCase=0.1	,__lowerCamelCase=0.0	,__lowerCamelCase=0.0	,__lowerCamelCase=0.02	,__lowerCamelCase=0.0	,__lowerCamelCase=False	,__lowerCamelCase=True	,__lowerCamelCase=1	,__lowerCamelCase=0	,__lowerCamelCase=2	,__lowerCamelCase=True	,__lowerCamelCase=2	,__lowerCamelCase=2	,__lowerCamelCase=False	,__lowerCamelCase=1_00	,__lowerCamelCase=8_00	,**__lowerCamelCase	,)  ->    Optional[Any]:

						"""simple docstring"""






						lowerCAmelCase__  :       Union[str, Any]				=  vocab_size
						lowerCAmelCase__  :       Dict				=  max_position_embeddings
						lowerCAmelCase__  :       Optional[int]				=  d_model
						lowerCAmelCase__  :       Any				=  encoder_ffn_dim
						lowerCAmelCase__  :       str				=  encoder_layers
						lowerCAmelCase__  :       Union[str, Any]				=  encoder_attention_heads
						lowerCAmelCase__  :       Dict				=  decoder_ffn_dim
						lowerCAmelCase__  :       Optional[int]				=  decoder_layers
						lowerCAmelCase__  :       int				=  decoder_attention_heads
						lowerCAmelCase__  :       str				=  dropout
						lowerCAmelCase__  :       int				=  attention_dropout
						lowerCAmelCase__  :       Optional[Any]				=  activation_dropout
						lowerCAmelCase__  :       Optional[int]				=  activation_function
						lowerCAmelCase__  :       int				=  init_std
						lowerCAmelCase__  :       List[Any]				=  encoder_layerdrop
						lowerCAmelCase__  :       Union[str, Any]				=  decoder_layerdrop
						lowerCAmelCase__  :       Tuple				=  classifier_dropout
						lowerCAmelCase__  :       Optional[int]				=  use_cache
						lowerCAmelCase__  :       List[Any]				=  encoder_layers
						lowerCAmelCase__  :       List[str]				=  scale_embedding  # scale factor will be sqrt(d_model) if True
						lowerCAmelCase__  :       Any				=  use_prompt
						lowerCAmelCase__  :       Optional[Any]				=  prompt_length
						lowerCAmelCase__  :       int				=  prompt_mid_dim

						super().__init__(
						    pad_token_id=__lowerCamelCase	,bos_token_id=__lowerCamelCase	,eos_token_id=__lowerCamelCase	,is_encoder_decoder=__lowerCamelCase	,decoder_start_token_id=__lowerCamelCase	,forced_eos_token_id=__lowerCamelCase	,**__lowerCamelCase	,)

						if self.forced_bos_token_id is None and kwargs.get('''force_bos_token_to_be_generated'''	,__lowerCamelCase				):
										lowerCAmelCase__  :       Union[str, Any]				=  self.bos_token_id
										warnings.warn(
										    f"""Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions. """
										    '''The config can simply be saved and uploaded again to be fixed.'''				)

