"""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		,			a		,			a=1_3		,			a=3		,			a=True		,			a=True		,			a=0.1		,			a=0.1		,			a=2_2_4		,			a=1_0_0_0		,			a=[3, 3, 6, 4]		,			a=[4_8, 5_6, 1_1_2, 2_2_0]		,			)       ->					str:
             lowercase__			:		Optional[int]       =	parent
             lowercase__			:		Union[str, Any]       =	batch_size
             lowercase__			:		int       =	num_channels
             lowercase__			:		Union[str, Any]       =	is_training
             lowercase__			:		Dict       =	use_labels
             lowercase__			:		Union[str, Any]       =	hidden_dropout_prob
             lowercase__			:		Union[str, Any]       =	attention_probs_dropout_prob
             lowercase__			:		Dict       =	num_labels
             lowercase__			:		List[Any]       =	image_size
             lowercase__			:		Union[str, Any]       =	layer_depths
             lowercase__			:		Tuple       =	embed_dims


      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             lowercase__			:		str       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

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

             lowercase__			:		Optional[int]       =	self.get_config()

             return config, pixel_values, labels


      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             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=a		,			layer_scale_init_value=1e-5		,			)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Optional[int]:
             lowercase__			:		Dict       =	SwiftFormerModel(config=a							)
             model.to(a							)
             model.eval()
             lowercase__			:		Dict       =	model(a							)
             self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.embed_dims[-1], 7, 7)							)


      def 		_UpperCAmelCase       (   self		,			a		,			a		,			a							)       ->					Union[str, Any]:
             lowercase__			:		Tuple       =	self.num_labels
             lowercase__			:		Dict       =	SwiftFormerForImageClassification(a							)
             model.to(a							)
             model.eval()
             lowercase__			:		List[Any]       =	model(a		,			labels=a							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)							)

             lowercase__			:		str       =	SwiftFormerForImageClassification(a							)
             model.to(a							)
             model.eval()

             lowercase__			:		List[Any]       =	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
             lowercase__			:		Optional[Any]       =	model(a							)
             self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             ((lowercase__)    ,      (lowercase__)    ,      (lowercase__))			:		Union[str, Any]       =	self.prepare_config_and_inputs()
             lowercase__			:		Tuple       =	{'pixel_values': pixel_values}
             return config, inputs_dict





@require_torch
class      UpperCAmelCase_	(    _a ,							_a ,							unittest.TestCase):
      lowerCamelCase__		:    Any   =  (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
      lowerCamelCase__		:    int   =  (
          {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      lowerCamelCase__		:    List[str]   =  False
      lowerCamelCase__		:    Optional[Any]   =  False
      lowerCamelCase__		:    Optional[Any]   =  False
      lowerCamelCase__		:    Union[str, Any]   =  False
      lowerCamelCase__		:    List[str]   =  False


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Any       =	SwiftFormerModelTester(self							)
             lowercase__			:		Dict       =	ConfigTester(
                 self		,			config_class=a		,			has_text_modality=a		,			hidden_size=3_7		,			num_attention_heads=1_2		,			num_hidden_layers=1_2		,			)


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             self.config_tester.run_common_tests()


      @unittest.skip(reason='SwiftFormer does not use inputs_embeds'							)
      def 		_UpperCAmelCase       (   self							)       ->					Any:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__    ,      lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs_for_common()

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


      def 		_UpperCAmelCase       (   self							)       ->					Optional[int]:
             lowercase__    ,      lowercase__			:		str       =	self.model_tester.prepare_config_and_inputs_for_common()

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

                    lowercase__			:		Any       =	['pixel_values']
                    self.assertListEqual(arg_names[:1]		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Tuple       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_model(*a							)


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             lowercase__			:		List[Any]       =	self.model_tester.prepare_config_and_inputs()
             self.model_tester.create_and_check_for_image_classification(*a							)


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                    lowercase__			:		str       =	SwiftFormerModel.from_pretrained(a							)
                    self.assertIsNotNone(a							)


      @unittest.skip(reason='SwiftFormer does not output attentions'							)
      def 		_UpperCAmelCase       (   self							)       ->					Dict:
             pass


      def 		_UpperCAmelCase       (   self							)       ->					Union[str, Any]:
             def check_hidden_states_output(a		,			a		,			a							):
                    lowercase__			:		Optional[Any]       =	model_class(a							)
                    model.to(a							)
                    model.eval()

                    with torch.no_grad():
                           lowercase__			:		Tuple       =	model(**self._prepare_for_class(a		,			a							)							)

                    lowercase__			:		Union[str, Any]       =	outputs.hidden_states

                    lowercase__			:		Optional[Any]       =	8
                    self.assertEqual(len(a							)		,			a							)  # 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(a							)							):
                           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),
                                   ]							)		,			)

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

             for model_class in self.all_model_classes:
                    lowercase__			:		Tuple       =	True
                    check_hidden_states_output(a		,			a		,			a							)

                    # check that output_hidden_states also work using config
                    del inputs_dict["output_hidden_states"]
                    lowercase__			:		Union[str, Any]       =	True

                    check_hidden_states_output(a		,			a		,			a							)


      def 		_UpperCAmelCase       (   self							)       ->					Any:
             def _config_zero_init(a							):
                    lowercase__			:		Any       =	copy.deepcopy(a							)
                    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(a		,			a		,			1e-10							)
                           if isinstance(getattr(a		,			a		,			a							)		,			a							):
                                  lowercase__			:		Optional[int]       =	_config_zero_init(getattr(a		,			a							)							)
                                  setattr(a		,			a		,			a							)
                    return configs_no_init

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

             lowercase__			:		int       =	_config_zero_init(a							)
             for model_class in self.all_model_classes:
                    lowercase__			:		Optional[Any]       =	model_class(config=a							)
                    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 		_UpperCAmelCase       (   self							)       ->					str:
             pass





def 				a_     (      ):





       '''simple docstring'''

       lowercase__			:		Tuple       =	Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'     )
       return image






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


      @cached_property
      def 		_UpperCAmelCase       (   self							)       ->					List[str]:
             return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'							) if is_vision_available() else None


      @slow
      def 		_UpperCAmelCase       (   self							)       ->					int:
             lowercase__			:		Tuple       =	SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'							).to(a							)

             lowercase__			:		Union[str, Any]       =	self.default_image_processor
             lowercase__			:		int       =	prepare_img()
             lowercase__			:		List[Any]       =	image_processor(images=a		,			return_tensors='pt'							).to(a							)

             # forward pass
             with torch.no_grad():
                    lowercase__			:		Union[str, Any]       =	model(**a							)

             # verify the logits
             lowercase__			:		Any       =	torch.Size((1, 1_0_0_0)							)
             self.assertEqual(outputs.logits.shape		,			a							)

             lowercase__			:		Tuple       =	torch.tensor([[-2.1_703e00, 2.1_107e00, -2.0_811e00]]							).to(a							)
             self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			a		,			atol=1e-4							)							)









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


lowerCamelCase       :       Optional[Any]        =				logging.get_logger(__name__)

lowerCamelCase       :       List[str]        =				{
    '''google/canine-s''': '''https://huggingface.co/google/canine-s/resolve/main/config.json''',
    # See all CANINE models at https://huggingface.co/models?filter=canine
}

class 						lowerCAmelCase   (       __a	):

					'''simple docstring'''

					_A  :	List[Any]     =					'''canine'''



					def __init__(     self :		List[str]						,   __a :		Optional[int]=768						,   __a :		Any=12						,   __a :		Any=12						,   __a :		Dict=3072						,   __a :		Dict="gelu"						,   __a :		List[Any]=0.1						,   __a :		List[Any]=0.1						,   __a :		Tuple=16384						,   __a :		List[Any]=16						,   __a :		List[Any]=0.02						,   __a :		Optional[Any]=1E-12						,   __a :		Dict=0						,   __a :		List[Any]=0xe_0_0_0						,   __a :		Optional[int]=0xe_0_0_1						,   __a :		Any=4						,   __a :		Dict=4						,   __a :		Optional[int]=8						,   __a :		Any=16384						,   __a :		Optional[Any]=128						,   **__a :		List[str]						,   )	->     Union[str, Any]:


												"""simple docstring"""

												super().__init__(pad_token_id=__a						,   bos_token_id=__a						,   eos_token_id=__a						,   **__a							)

												__lowercase					:							int			=   max_position_embeddings
												__lowercase					:							List[str]			=   hidden_size
												__lowercase					:							List[Any]			=   num_hidden_layers
												__lowercase					:							Dict			=   num_attention_heads
												__lowercase					:							int			=   intermediate_size
												__lowercase					:							Dict			=   hidden_act
												__lowercase					:							Tuple			=   hidden_dropout_prob
												__lowercase					:							Union[str, Any]			=   attention_probs_dropout_prob
												__lowercase					:							Union[str, Any]			=   initializer_range
												__lowercase					:							Any			=   type_vocab_size
												__lowercase					:							int			=   layer_norm_eps

												# Character config:
												__lowercase					:							int			=   downsampling_rate
												__lowercase					:							str			=   upsampling_kernel_size
												__lowercase					:							Union[str, Any]			=   num_hash_functions
												__lowercase					:							Optional[Any]			=   num_hash_buckets
												__lowercase					:							Optional[int]			=   local_transformer_stride