






"""simple docstring"""




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        UpperCamelCase			:


 def __init__(     self		:						Optional[Any]							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						Union[str, Any]=13							,						UpperCAmelCase__		:						int=3							,						UpperCAmelCase__		:						List[Any]=True							,						UpperCAmelCase__		:						Optional[Any]=True							,						UpperCAmelCase__		:						Any=0.1							,						UpperCAmelCase__		:						Union[str, Any]=0.1							,						UpperCAmelCase__		:						Union[str, Any]=224							,						UpperCAmelCase__		:						Optional[int]=1000							,						UpperCAmelCase__		:						Tuple=[3, 3, 6, 4]							,						UpperCAmelCase__		:						str=[48, 56, 112, 220]							,						)					->       int:
   _a  :       Optional[Any]             =	parent
   _a  :       Dict             =	batch_size
   _a  :       Union[str, Any]             =	num_channels
   _a  :       Any             =	is_training
   _a  :       Optional[Any]             =	use_labels
   _a  :       Union[str, Any]             =	hidden_dropout_prob
   _a  :       Dict             =	attention_probs_dropout_prob
   _a  :       Any             =	num_labels
   _a  :       Optional[Any]             =	image_size
   _a  :       str             =	layer_depths
   _a  :       int             =	embed_dims


 def _lowercase	(     self		:						int							)					->       Optional[Any]:
   _a  :       Dict             =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

   _a  :       Dict             =	None
   if self.use_labels:
     _a  :       Union[str, Any]             =	ids_tensor([self.batch_size]							,						self.num_labels							)

   _a  :       Any             =	self.get_config()

   return config, pixel_values, labels


 def _lowercase	(     self		:						Optional[int]							)					->       List[str]:
   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=UpperCAmelCase__							,						layer_scale_init_value=1E-5							,						)


 def _lowercase	(     self		:						str							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						str							,						UpperCAmelCase__		:						int							)					->       List[Any]:
   _a  :       List[str]             =	SwiftFormerModel(config=UpperCAmelCase__							)
   model.to(UpperCAmelCase__							)
   model.eval()
   _a  :       Tuple             =	model(UpperCAmelCase__							)
   self.parent.assertEqual(result.last_hidden_state.shape							,						(self.batch_size, self.embed_dims[-1], 7, 7)							)


 def _lowercase	(     self		:						Union[str, Any]							,						UpperCAmelCase__		:						Optional[Any]							,						UpperCAmelCase__		:						Any							,						UpperCAmelCase__		:						Any							)					->       List[str]:
   _a  :       Tuple             =	self.num_labels
   _a  :       Optional[Any]             =	SwiftFormerForImageClassification(UpperCAmelCase__							)
   model.to(UpperCAmelCase__							)
   model.eval()
   _a  :       Union[str, Any]             =	model(UpperCAmelCase__							,						labels=UpperCAmelCase__							)
   self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)							)

   _a  :       List[str]             =	SwiftFormerForImageClassification(UpperCAmelCase__							)
   model.to(UpperCAmelCase__							)
   model.eval()

   _a  :       Optional[int]             =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
   _a  :       List[str]             =	model(UpperCAmelCase__							)
   self.parent.assertEqual(result.logits.shape							,						(self.batch_size, self.num_labels)							)







 def _lowercase	(     self		:						List[Any]							)					->       Dict:
   ((_a)      ,  (_a)      ,  (_a))  :       Any             =	self.prepare_config_and_inputs()
   _a  :       str             =	{"""pixel_values""": pixel_values}
   return config, inputs_dict







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

 UpperCamelCase					:       Union[str, Any]			=					False
 UpperCamelCase					:       Dict			=					False
 UpperCamelCase					:       Dict			=					False
 UpperCamelCase					:       str			=					False
 UpperCamelCase					:       Union[str, Any]			=					False


 def _lowercase	(     self		:						str							)					->       Union[str, Any]:
   _a  :       int             =	SwiftFormerModelTester(self							)
   _a  :       int             =	ConfigTester(
       self							,						config_class=UpperCAmelCase__							,						has_text_modality=UpperCAmelCase__							,						hidden_size=37							,						num_attention_heads=12							,						num_hidden_layers=12							,						)


 def _lowercase	(     self		:						List[Any]							)					->       Optional[int]:
   self.config_tester.run_common_tests()


 @unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""							)
 def _lowercase	(     self		:						Union[str, Any]							)					->       List[str]:
   pass


 def _lowercase	(     self		:						Optional[Any]							)					->       List[str]:
   _a      ,  _a  :       Any             =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _a  :       Optional[Any]             =	model_class(UpperCAmelCase__							)
     _a  :       Tuple             =	model.get_output_embeddings()
     self.assertTrue(x is None or isinstance(UpperCAmelCase__							,						nn.Linear							)							)


 def _lowercase	(     self		:						List[Any]							)					->       Dict:
   _a      ,  _a  :       List[Any]             =	self.model_tester.prepare_config_and_inputs_for_common()

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

     _a  :       List[str]             =	["""pixel_values"""]
     self.assertListEqual(arg_names[:1]							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						List[str]							)					->       str:
   _a  :       Union[str, Any]             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_model(*UpperCAmelCase__							)


 def _lowercase	(     self		:						List[Any]							)					->       str:
   _a  :       Any             =	self.model_tester.prepare_config_and_inputs()
   self.model_tester.create_and_check_for_image_classification(*UpperCAmelCase__							)


 @slow
 def _lowercase	(     self		:						Dict							)					->       Any:
   for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
     _a  :       List[Any]             =	SwiftFormerModel.from_pretrained(UpperCAmelCase__							)
     self.assertIsNotNone(UpperCAmelCase__							)


 @unittest.skip(reason="""SwiftFormer does not output attentions"""							)
 def _lowercase	(     self		:						List[str]							)					->       List[str]:
   pass


 def _lowercase	(     self		:						str							)					->       int:
   def check_hidden_states_output(UpperCAmelCase__		:						List[str]							,						UpperCAmelCase__		:						int							,						UpperCAmelCase__		:						Any							):
     _a  :       Tuple             =	model_class(UpperCAmelCase__							)
     model.to(UpperCAmelCase__							)
     model.eval()

     with torch.no_grad():
       _a  :       List[Any]             =	model(**self._prepare_for_class(UpperCAmelCase__							,						UpperCAmelCase__							)							)

     _a  :       List[Any]             =	outputs.hidden_states

     _a  :       Optional[int]             =	8
     self.assertEqual(len(UpperCAmelCase__							)							,						UpperCAmelCase__							)  # 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(UpperCAmelCase__							)							):
       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  :       str             =	self.model_tester.prepare_config_and_inputs_for_common()

   for model_class in self.all_model_classes:
     _a  :       str             =	True
     check_hidden_states_output(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)

     # check that output_hidden_states also work using config
     del inputs_dict["output_hidden_states"]
     _a  :       Tuple             =	True

     check_hidden_states_output(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)


 def _lowercase	(     self		:						Optional[Any]							)					->       Union[str, Any]:
   def _config_zero_init(UpperCAmelCase__		:						List[str]							):
     _a  :       Union[str, Any]             =	copy.deepcopy(UpperCAmelCase__							)
     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(UpperCAmelCase__							,						UpperCAmelCase__							,						1E-10							)
       if isinstance(getattr(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)							,						UpperCAmelCase__							):
         _a  :       Tuple             =	_config_zero_init(getattr(UpperCAmelCase__							,						UpperCAmelCase__							)							)
         setattr(UpperCAmelCase__							,						UpperCAmelCase__							,						UpperCAmelCase__							)
     return configs_no_init

   _a      ,  _a  :       Any             =	self.model_tester.prepare_config_and_inputs_for_common()

   _a  :       Any             =	_config_zero_init(UpperCAmelCase__							)
   for model_class in self.all_model_classes:
     _a  :       str             =	model_class(config=UpperCAmelCase__							)
     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 _lowercase	(     self		:						Optional[int]							)					->       Union[str, Any]:
   pass







def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       Optional[int]             =	Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""				)
  return image






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


 @cached_property
 def _lowercase	(     self		:						Optional[Any]							)					->       List[Any]:
   return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""							) if is_vision_available() else None







 @slow
 def _lowercase	(     self		:						int							)					->       Optional[int]:
   _a  :       Dict             =	SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""							).to(UpperCAmelCase__							)

   _a  :       int             =	self.default_image_processor
   _a  :       Optional[int]             =	prepare_img()
   _a  :       Tuple             =	image_processor(images=UpperCAmelCase__							,						return_tensors="""pt"""							).to(UpperCAmelCase__							)

   # forward pass
   with torch.no_grad():
     _a  :       Any             =	model(**UpperCAmelCase__							)

   # verify the logits
   _a  :       str             =	torch.Size((1, 1000)							)
   self.assertEqual(outputs.logits.shape							,						UpperCAmelCase__							)

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









"""simple docstring"""




import warnings

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


_snake_case          =   logging.get_logger(__name__)

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






class        UpperCamelCase			(	snake_case_      ):
 UpperCamelCase					:       int			=					'''mvp'''
 UpperCamelCase					:       Union[str, Any]			=					['''past_key_values''']
 UpperCamelCase					:       Any			=					{'''num_attention_heads''': '''encoder_attention_heads''', '''hidden_size''': '''d_model'''}


 def __init__(     self		:						List[str]							,						UpperCAmelCase__		:						List[str]=50267							,						UpperCAmelCase__		:						Optional[Any]=1024							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						Optional[Any]=4096							,						UpperCAmelCase__		:						int=16							,						UpperCAmelCase__		:						Tuple=12							,						UpperCAmelCase__		:						int=4096							,						UpperCAmelCase__		:						List[Any]=16							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Tuple="gelu"							,						UpperCAmelCase__		:						Union[str, Any]=1024							,						UpperCAmelCase__		:						List[str]=0.1							,						UpperCAmelCase__		:						Any=0.0							,						UpperCAmelCase__		:						Dict=0.0							,						UpperCAmelCase__		:						Tuple=0.0_2							,						UpperCAmelCase__		:						Tuple=0.0							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						int=True							,						UpperCAmelCase__		:						Tuple=1							,						UpperCAmelCase__		:						Dict=0							,						UpperCAmelCase__		:						Union[str, Any]=2							,						UpperCAmelCase__		:						Optional[int]=True							,						UpperCAmelCase__		:						Tuple=2							,						UpperCAmelCase__		:						Any=2							,						UpperCAmelCase__		:						Optional[Any]=False							,						UpperCAmelCase__		:						Dict=100							,						UpperCAmelCase__		:						Union[str, Any]=800							,						**UpperCAmelCase__		:						Dict							,						)					->       List[Any]:
   _a  :       Any             =	vocab_size
   _a  :       Any             =	max_position_embeddings
   _a  :       Union[str, Any]             =	d_model
   _a  :       List[str]             =	encoder_ffn_dim
   _a  :       List[Any]             =	encoder_layers
   _a  :       Dict             =	encoder_attention_heads
   _a  :       Tuple             =	decoder_ffn_dim
   _a  :       List[Any]             =	decoder_layers
   _a  :       Optional[Any]             =	decoder_attention_heads
   _a  :       Optional[Any]             =	dropout
   _a  :       str             =	attention_dropout
   _a  :       Dict             =	activation_dropout
   _a  :       Any             =	activation_function
   _a  :       Tuple             =	init_std
   _a  :       Dict             =	encoder_layerdrop
   _a  :       Optional[int]             =	decoder_layerdrop
   _a  :       Optional[Any]             =	classifier_dropout
   _a  :       List[Any]             =	use_cache
   _a  :       Dict             =	encoder_layers
   _a  :       str             =	scale_embedding  # scale factor will be sqrt(d_model) if True
   _a  :       int             =	use_prompt
   _a  :       Dict             =	prompt_length
   _a  :       Dict             =	prompt_mid_dim

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

   if self.forced_bos_token_id is None and kwargs.get("""force_bos_token_to_be_generated"""							,						UpperCAmelCase__							):
     _a  :       List[str]             =	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."""							)

