


'''simple docstring'''
import torch
from torch import nn
from torch.nn import CrossEntropyLoss, MSELoss

from transformers.file_utils import add_start_docstrings, add_start_docstrings_to_model_forward
from transformers.models.bert.modeling_bert import (
    BERT_INPUTS_DOCSTRING,
    BERT_START_DOCSTRING,
    BertEmbeddings,
    BertLayer,
    BertPooler,
    BertPreTrainedModel,
)




def 	_A  (       A							)  ->				Optional[Any]:
	lowercase						:		Union[str, Any]   					=		torch.exp(A							)
	lowercase						:		List[str]   					=		torch.sum(A							,dim=1							)  # sum of exp(x_i)
	lowercase						:		Optional[int]   					=		torch.sum(x * exp_x							,dim=1							)  # sum of x_i * exp(x_i)
	return torch.log(A							) - B / A




class        _UpperCamelCase	(    nn.Module):


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  Tuple:
					super().__init__()
					lowercase						:		Dict   					=		config.output_attentions
					lowercase						:		List[Any]   					=		config.output_hidden_states
					lowercase						:		Any   					=		nn.ModuleList([BertLayer(a_							) for _ in range(config.num_hidden_layers							)]							)
					lowercase						:		Union[str, Any]   					=		nn.ModuleList([BertHighway(a_							) for _ in range(config.num_hidden_layers							)]							)

					lowercase						:		Union[str, Any]   					=		[-1 for _ in range(config.num_hidden_layers							)]

				def 	a__		(     self							,      a_							)    ->  Tuple:
					if (type(a_							) is float) or (type(a_							) is int):
						for i in range(len(self.early_exit_entropy							)							):
							lowercase						:		List[str]   					=		x
					else:
						lowercase						:		List[Any]   					=		x

				def 	a__		(     self							,      a_							)    ->  str:
					lowercase						:		str   					=		pooler.state_dict()
					for highway in self.highway:
						for name, param in highway.pooler.state_dict().items():
							param.copy_(loaded_model[name]							)


				def 	a__		(     self							,      a_							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      )    ->  List[Any]:
					lowercase						:		Optional[int]   					=		()
					lowercase						:		Union[str, Any]   					=		()
					lowercase						:		Tuple   					=		()
					for i, layer_module in enumerate(self.layer							):
						if self.output_hidden_states:
							lowercase						:		List[str]   					=		all_hidden_states + (hidden_states,)

						lowercase						:		List[Any]   					=		layer_module(
						    a_							,      a_							,      head_mask[i]							,      a_							,      a_							)
						lowercase						:		List[Any]   					=		layer_outputs[0]

						if self.output_attentions:
							lowercase						:		Any   					=		all_attentions + (layer_outputs[1],)

						lowercase						:		Tuple   					=		(hidden_states,)
						if self.output_hidden_states:
							lowercase						:		Any   					=		current_outputs + (all_hidden_states,)
						if self.output_attentions:
							lowercase						:		Optional[Any]   					=		current_outputs + (all_attentions,)

						lowercase						:		int   					=		self.highway[i](a_							)
						# logits, pooled_output

						if not self.training:
							lowercase						:		str   					=		highway_exit[0]
							lowercase						:		List[str]   					=		entropy(a_							)
							lowercase						:		List[str]   					=		highway_exit + (highway_entropy,)  # logits, hidden_states(?), entropy
							lowercase						:		List[str]   					=		all_highway_exits + (highway_exit,)

							if highway_entropy < self.early_exit_entropy[i]:
								lowercase						:		List[Any]   					=		(highway_logits,) + current_outputs[1:] + (all_highway_exits,)
								raise HighwayException(a_							,      i + 1							)
						else:
							lowercase						:		Optional[int]   					=		all_highway_exits + (highway_exit,)

        # Add last layer
					if self.output_hidden_states:
						lowercase						:		Optional[int]   					=		all_hidden_states + (hidden_states,)

					lowercase						:		Any   					=		(hidden_states,)
					if self.output_hidden_states:
						lowercase						:		Optional[Any]   					=		outputs + (all_hidden_states,)
					if self.output_attentions:
						lowercase						:		Dict   					=		outputs + (all_attentions,)

					lowercase						:		Union[str, Any]   					=		outputs + (all_highway_exits,)
					return outputs  # last-layer hidden state, (all hidden states), (all attentions), all highway exits




@add_start_docstrings(
    '''The Bert Model transformer with early exiting (DeeBERT). '''						,			SCREAMING_SNAKE_CASE						,			)
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  List[str]:
					super().__init__(a_							)
					lowercase						:		List[Any]   					=		config

					lowercase						:		Tuple   					=		BertEmbeddings(a_							)
					lowercase						:		Optional[Any]   					=		DeeBertEncoder(a_							)
					lowercase						:		int   					=		BertPooler(a_							)

					self.init_weights()

				def 	a__		(     self							)    ->  int:
					self.encoder.init_highway_pooler(self.pooler							)

				def 	a__		(     self							)    ->  Dict:
					return self.embeddings.word_embeddings

				def 	a__		(     self							,      a_							)    ->  Dict:
					lowercase						:		Optional[Any]   					=		value

				def 	a__		(     self							,      a_							)    ->  Tuple:
					for layer, heads in heads_to_prune.items():
						self.encoder.layer[layer].attention.prune_heads(a_							)


				@add_start_docstrings_to_model_forward(a_							)
				def 	a__		(     self							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      )    ->  List[Any]:
					if input_ids is not None and inputs_embeds is not None:
						raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time"							)
					elif input_ids is not None:
						lowercase						:		List[str]   					=		input_ids.size()
					elif inputs_embeds is not None:
						lowercase						:		str   					=		inputs_embeds.size()[:-1]
					else:
						raise ValueError("You have to specify either input_ids or inputs_embeds"							)

					lowercase						:		str   					=		input_ids.device if input_ids is not None else inputs_embeds.device

					if attention_mask is None:
						lowercase						:		Any   					=		torch.ones(a_							,      device=a_							)
					if encoder_attention_mask is None:
						lowercase						:		Any   					=		torch.ones(a_							,      device=a_							)
					if token_type_ids is None:
						lowercase						:		Optional[Any]   					=		torch.zeros(a_							,      dtype=torch.long							,      device=a_							)

					# We can provide a self-attention mask of dimensions [batch_size, from_seq_length, to_seq_length]
					# ourselves in which case we just need to make it broadcastable to all heads.
					lowercase						:		torch.Tensor   					=		self.get_extended_attention_mask(a_							,      a_							,      a_							)

					# If a 2D ou 3D attention mask is provided for the cross-attention
					# we need to make broadcastable to [batch_size, num_heads, seq_length, seq_length]
					if encoder_attention_mask.dim() == 3:
						lowercase						:		Any   					=		encoder_attention_mask[:, None, :, :]
					if encoder_attention_mask.dim() == 2:
						lowercase						:		Tuple   					=		encoder_attention_mask[:, None, None, :]

					lowercase						:		Optional[Any]   					=		encoder_extended_attention_mask.to(
					    dtype=next(self.parameters()							).dtype							)  # fp16 compatibility
					lowercase						:		List[str]   					=		(1.0 - encoder_extended_attention_mask) * -1_00_00.0

					# Prepare head mask if needed
					# 1.0 in head_mask indicate we keep the head
					# attention_probs has shape bsz x n_heads x N x N
					# input head_mask has shape [num_heads] or [num_hidden_layers x num_heads]
					# and head_mask is converted to shape [num_hidden_layers x batch x num_heads x seq_length x seq_length]
					lowercase						:		int   					=		self.get_head_mask(a_							,      self.config.num_hidden_layers							)

					lowercase						:		Union[str, Any]   					=		self.embeddings(
					    input_ids=a_							,      position_ids=a_							,      token_type_ids=a_							,      inputs_embeds=a_							)
					lowercase						:		Optional[Any]   					=		self.encoder(
					    a_							,      attention_mask=a_							,      head_mask=a_							,      encoder_hidden_states=a_							,      encoder_attention_mask=a_							,      )
					lowercase						:		Optional[Any]   					=		encoder_outputs[0]
					lowercase						:		List[Any]   					=		self.pooler(a_							)

					lowercase						:		List[Any]   					=		(
					    sequence_output,
					    pooled_output,
					) + encoder_outputs[
					    1:
					]  # add hidden_states and attentions if they are here
					return outputs  # sequence_output, pooled_output, (hidden_states), (attentions), highway exits




class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							,      a_							)    ->  Union[str, Any]:
					lowercase						:		Union[str, Any]   					=		message
					lowercase						:		List[Any]   					=		exit_layer  # start from 1!




class        _UpperCamelCase	(    nn.Module):


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  int:
					super().__init__()
					lowercase						:		str   					=		BertPooler(a_							)
					lowercase						:		Dict   					=		nn.Dropout(config.hidden_dropout_prob							)
					lowercase						:		Optional[Any]   					=		nn.Linear(config.hidden_size							,      config.num_labels							)


				def 	a__		(     self							,      a_							)    ->  Union[str, Any]:
					# Pooler
					lowercase						:		Union[str, Any]   					=		encoder_outputs[0]
					lowercase						:		Tuple   					=		self.pooler(a_							)
					# "return" pooler_output

					# BertModel
					lowercase						:		Tuple   					=		(pooler_input, pooler_output) + encoder_outputs[1:]
					# "return" bmodel_output

					# Dropout and classification
					lowercase						:		List[str]   					=		bmodel_output[1]

					lowercase						:		Union[str, Any]   					=		self.dropout(a_							)
					lowercase						:		Any   					=		self.classifier(a_							)

					return logits, pooled_output




@add_start_docstrings(
    '''Bert Model (with early exiting - DeeBERT) with a classifier on top,
    also takes care of multi-layer training. '''						,			SCREAMING_SNAKE_CASE						,			)
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE):


				'''simple docstring'''


				def __init__(     self							,      a_							)    ->  Dict:
					super().__init__(a_							)
					lowercase						:		Dict   					=		config.num_labels
					lowercase						:		Dict   					=		config.num_hidden_layers

					lowercase						:		Any   					=		DeeBertModel(a_							)
					lowercase						:		List[Any]   					=		nn.Dropout(config.hidden_dropout_prob							)
					lowercase						:		Optional[Any]   					=		nn.Linear(config.hidden_size							,      self.config.num_labels							)

					self.init_weights()


				@add_start_docstrings_to_model_forward(a_							)
				def 	a__		(     self							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=None							,      a_=-1							,      a_=False							,      )    ->  Optional[Any]:
					lowercase						:		Optional[Any]   					=		self.num_layers
					try:
						lowercase						:		List[Any]   					=		self.bert(
						    a_							,      attention_mask=a_							,      token_type_ids=a_							,      position_ids=a_							,      head_mask=a_							,      inputs_embeds=a_							,      )
						# sequence_output, pooled_output, (hidden_states), (attentions), highway exits

						lowercase						:		Any   					=		outputs[1]

						lowercase						:		Union[str, Any]   					=		self.dropout(a_							)
						lowercase						:		List[Any]   					=		self.classifier(a_							)
						lowercase						:		Tuple   					=		(logits,) + outputs[2:]  # add hidden states and attention if they are here
					except HighwayException as e:
						lowercase						:		Tuple   					=		e.message
						lowercase						:		Dict   					=		e.exit_layer
						lowercase						:		List[Any]   					=		outputs[0]

					if not self.training:
						lowercase						:		Any   					=		entropy(a_							)
						lowercase						:		Tuple   					=		[]
						lowercase						:		Dict   					=		[]
					if labels is not None:
						if self.num_labels == 1:
							#  We are doing regression
							lowercase						:		str   					=		MSELoss()
							lowercase						:		List[str]   					=		loss_fct(logits.view(-1							)							,      labels.view(-1							)							)
						else:
							lowercase						:		int   					=		CrossEntropyLoss()
							lowercase						:		List[str]   					=		loss_fct(logits.view(-1							,      self.num_labels							)							,      labels.view(-1							)							)

						# work with highway exits
						lowercase						:		Dict   					=		[]
						for highway_exit in outputs[-1]:
							lowercase						:		str   					=		highway_exit[0]
							if not self.training:
								highway_logits_all.append(a_							)
								highway_entropy.append(highway_exit[2]							)
							if self.num_labels == 1:
								#  We are doing regression
								lowercase						:		int   					=		MSELoss()
								lowercase						:		int   					=		loss_fct(highway_logits.view(-1							)							,      labels.view(-1							)							)
							else:
								lowercase						:		Optional[int]   					=		CrossEntropyLoss()
								lowercase						:		Optional[int]   					=		loss_fct(highway_logits.view(-1							,      self.num_labels							)							,      labels.view(-1							)							)
							highway_losses.append(a_							)

						if train_highway:
							lowercase						:		Any   					=		(sum(highway_losses[:-1]							),) + outputs
							# exclude the final highway, of course
						else:
							lowercase						:		Union[str, Any]   					=		(loss,) + outputs
					if not self.training:
						lowercase						:		Dict   					=		outputs + ((original_entropy, highway_entropy), exit_layer)
						if output_layer >= 0:
							lowercase						:		str   					=		(
							    (outputs[0],) + (highway_logits_all[output_layer],) + outputs[2:]
							)  # use the highway of the last layer

					return outputs  # (loss), logits, (hidden_states), (attentions), (highway_exits)






'''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	:


				'''simple docstring'''


				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]							,      )    ->  Union[str, Any]:
					lowercase						:		Optional[Any]   					=		parent
					lowercase						:		List[Any]   					=		batch_size
					lowercase						:		Union[str, Any]   					=		num_channels
					lowercase						:		str   					=		is_training
					lowercase						:		Any   					=		use_labels
					lowercase						:		Optional[Any]   					=		hidden_dropout_prob
					lowercase						:		List[str]   					=		attention_probs_dropout_prob
					lowercase						:		Optional[Any]   					=		num_labels
					lowercase						:		str   					=		image_size
					lowercase						:		Dict   					=		layer_depths
					lowercase						:		List[str]   					=		embed_dims

				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		int   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)

					lowercase						:		int   					=		None
					if self.use_labels:
						lowercase						:		Optional[Any]   					=		ids_tensor([self.batch_size]							,      self.num_labels							)

					lowercase						:		List[Any]   					=		self.get_config()

					return config, pixel_values, labels

				def 	a__		(     self							)    ->  List[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=a_							,      layer_scale_init_value=1e-5							,      )

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Optional[Any]:
					lowercase						:		Tuple   					=		SwiftFormerModel(config=a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Union[str, Any]   					=		model(a_							)
					self.parent.assertEqual(result.last_hidden_state.shape							,      (self.batch_size, self.embed_dims[-1], 7, 7)							)

				def 	a__		(     self							,      a_							,      a_							,      a_							)    ->  Any:
					lowercase						:		Dict   					=		self.num_labels
					lowercase						:		Optional[int]   					=		SwiftFormerForImageClassification(a_							)
					model.to(a_							)
					model.eval()
					lowercase						:		Tuple   					=		model(a_							,      labels=a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)

					lowercase						:		Tuple   					=		SwiftFormerForImageClassification(a_							)
					model.to(a_							)
					model.eval()

					lowercase						:		List[str]   					=		floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]							)
					lowercase						:		str   					=		model(a_							)
					self.parent.assertEqual(result.logits.shape							,      (self.batch_size, self.num_labels)							)


				def 	a__		(     self							)    ->  Dict:
					((lowercase)					,		(lowercase)					,		(lowercase))						:		Dict   					=		self.prepare_config_and_inputs()
					lowercase						:		Dict   					=		{"pixel_values": pixel_values}
					return config, inputs_dict




@require_torch
class        _UpperCamelCase	(    SCREAMING_SNAKE_CASE						,			SCREAMING_SNAKE_CASE						,			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 	a__		(     self							)    ->  str:
					lowercase						:		Optional[int]   					=		SwiftFormerModelTester(self							)
					lowercase						:		int   					=		ConfigTester(
					    self							,      config_class=a_							,      has_text_modality=a_							,      hidden_size=3_7							,      num_attention_heads=1_2							,      num_hidden_layers=1_2							,      )

				def 	a__		(     self							)    ->  Any:
					self.config_tester.run_common_tests()

				@unittest.skip(reason="SwiftFormer does not use inputs_embeds"							)
				def 	a__		(     self							)    ->  int:
					pass

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase					,		lowercase						:		Optional[int]   					=		self.model_tester.prepare_config_and_inputs_for_common()

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

				def 	a__		(     self							)    ->  List[Any]:
					lowercase					,		lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

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

						lowercase						:		int   					=		["pixel_values"]
						self.assertListEqual(arg_names[:1]							,      a_							)

				def 	a__		(     self							)    ->  int:
					lowercase						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*a_							)

				def 	a__		(     self							)    ->  Optional[int]:
					lowercase						:		List[Any]   					=		self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*a_							)

				@slow
				def 	a__		(     self							)    ->  Dict:
					for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase						:		Optional[int]   					=		SwiftFormerModel.from_pretrained(a_							)
						self.assertIsNotNone(a_							)

				@unittest.skip(reason="SwiftFormer does not output attentions"							)
				def 	a__		(     self							)    ->  Optional[Any]:
					pass

				def 	a__		(     self							)    ->  str:
					def check_hidden_states_output(a_							,      a_							,      a_							):
						lowercase						:		Union[str, Any]   					=		model_class(a_							)
						model.to(a_							)
						model.eval()

						with torch.no_grad():
							lowercase						:		List[str]   					=		model(**self._prepare_for_class(a_							,      a_							)							)

						lowercase						:		str   					=		outputs.hidden_states

						lowercase						:		str   					=		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						:		Optional[Any]   					=		self.model_tester.prepare_config_and_inputs_for_common()

					for model_class in self.all_model_classes:
						lowercase						:		Dict   					=		True
						check_hidden_states_output(a_							,      a_							,      a_							)

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

						check_hidden_states_output(a_							,      a_							,      a_							)

				def 	a__		(     self							)    ->  Optional[Any]:
					def _config_zero_init(a_							):
						lowercase						:		str   					=		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-1_0							)
							if isinstance(getattr(a_							,      a_							,      a_							)							,      a_							):
								lowercase						:		List[Any]   					=		_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						:		List[str]   					=		_config_zero_init(a_							)
					for model_class in self.all_model_classes:
						lowercase						:		Tuple   					=		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 	a__		(     self							)    ->  Tuple:
					pass




def 	_A  (       )  ->				List[str]:
	lowercase						:		Optional[int]   					=		Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"							)
	return image






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


				'''simple docstring'''


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


				@slow
				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Tuple   					=		SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"							).to(a_							)

					lowercase						:		Any   					=		self.default_image_processor
					lowercase						:		Optional[int]   					=		prepare_img()
					lowercase						:		Any   					=		image_processor(images=a_							,      return_tensors="pt"							).to(a_							)

					# forward pass
					with torch.no_grad():
						lowercase						:		List[str]   					=		model(**a_							)

					# verify the logits
					lowercase						:		str   					=		torch.Size((1, 1_0_0_0)							)
					self.assertEqual(outputs.logits.shape							,      a_							)

					lowercase						:		List[Any]   					=		torch.tensor([[-2.1_7_0_3e0_0, 2.1_1_0_7e0_0, -2.0_8_1_1e0_0]]							).to(a_							)
					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,      a_							,      atol=1e-4							)							)


