






"""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        _SCREAMING_SNAKE_CASE						:





							"""simple docstring"""





							def __init__(   self:     str				,		__A:     Tuple				,		__A:     Optional[Any]=13				,		__A:     Optional[int]=3				,		__A:     Dict=True				,		__A:     Union[str, Any]=True				,		__A:     Union[str, Any]=0.1				,		__A:     Any=0.1				,		__A:     Union[str, Any]=224				,		__A:     Dict=1000				,		__A:     Optional[int]=[3, 3, 6, 4]				,		__A:     int=[48, 56, 112, 220]				,		):

												'''simple docstring'''

												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  lowercase		(   self:     Any    ):

												'''simple docstring'''

												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  lowercase		(   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=__A				,		layer_scale_init_value=1e-5				,		)




							def  lowercase		(   self:     Tuple				,		__A:     List[str]				,		__A:     List[str]				,		__A:     List[Any]    ):

												'''simple docstring'''

												a__          =  SwiftFormerModel(config=__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A    )
												self.parent.assertEqual(result.last_hidden_state.shape				,		(self.batch_size, self.embed_dims[-1], 7, 7)    )




							def  lowercase		(   self:     List[str]				,		__A:     Dict				,		__A:     Optional[int]				,		__A:     str    ):

												'''simple docstring'''

												a__          =  self.num_labels
												a__          =  SwiftFormerForImageClassification(__A    )
												model.to(__A    )
												model.eval()
												a__          =  model(__A				,		labels=__A    )
												self.parent.assertEqual(result.logits.shape				,		(self.batch_size, self.num_labels)    )

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

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





							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												((a__)       ,(a__)       ,(a__))          =  self.prepare_config_and_inputs()
												a__          =  {'''pixel_values''': pixel_values}
												return config, inputs_dict







@require_torch
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , __snake_case       , unittest.TestCase							):





							"""simple docstring"""





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

							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False
							_SCREAMING_SNAKE_CASE    =False




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  SwiftFormerModelTester(self    )
												a__          =  ConfigTester(
												    self				,		config_class=__A				,		has_text_modality=__A				,		hidden_size=37				,		num_attention_heads=12				,		num_hidden_layers=12				,		)




							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												self.config_tester.run_common_tests()




							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''    )
							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

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




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	a__          =  model_class(__A    )
																	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]				,		__A    )




							def  lowercase		(   self:     Union[str, Any]    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*__A    )




							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												a__          =  self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*__A    )




							@slow
							def  lowercase		(   self:     Any    ):

												'''simple docstring'''

												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	a__          =  SwiftFormerModel.from_pretrained(__A    )
																	self.assertIsNotNone(__A    )




							@unittest.skip(reason='''SwiftFormer does not output attentions'''    )
							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												pass




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												def check_hidden_states_output(__A:     List[Any]				,		__A:     int				,		__A:     List[str]    ):
																	a__          =  model_class(__A    )
																	model.to(__A    )
																	model.eval()

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

																	a__          =  outputs.hidden_states

																	a__          =  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),
																						        ]    )				,		)

												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(__A				,		__A				,		__A    )

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

																	check_hidden_states_output(__A				,		__A				,		__A    )




							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												def _config_zero_init(__A:     List[str]    ):
																	a__          =  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    ):
																											a__          =  _config_zero_init(getattr(__A				,		__A    )    )
																											setattr(__A				,		__A				,		__A    )
																	return configs_no_init

												a__       ,a__          =  self.model_tester.prepare_config_and_inputs_for_common()

												a__          =  _config_zero_init(__A    )
												for model_class in self.all_model_classes:
																	a__          =  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  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												pass





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







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





							"""simple docstring"""





							@cached_property
							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

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





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''    ).to(__A    )

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

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

												# verify the logits
												a__          =  torch.Size((1, 1000)    )
												self.assertEqual(outputs.logits.shape				,		__A    )

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










"""simple docstring"""







import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
	import jax.numpy as jnp

	from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
	    FlaxRobertaPreLayerNormForCausalLM,
	    FlaxRobertaPreLayerNormForMaskedLM,
	    FlaxRobertaPreLayerNormForMultipleChoice,
	    FlaxRobertaPreLayerNormForQuestionAnswering,
	    FlaxRobertaPreLayerNormForSequenceClassification,
	    FlaxRobertaPreLayerNormForTokenClassification,
	    FlaxRobertaPreLayerNormModel,
	)







class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							def __init__(   self:     List[str]				,		__A:     Optional[Any]				,		__A:     List[Any]=13				,		__A:     Optional[int]=7				,		__A:     Any=True				,		__A:     str=True				,		__A:     Any=True				,		__A:     str=True				,		__A:     Optional[Any]=99				,		__A:     Union[str, Any]=32				,		__A:     str=5				,		__A:     Any=4				,		__A:     List[str]=37				,		__A:     Union[str, Any]="gelu"				,		__A:     str=0.1				,		__A:     Tuple=0.1				,		__A:     Optional[Any]=512				,		__A:     Union[str, Any]=16				,		__A:     str=2				,		__A:     Any=0.0_2				,		__A:     Tuple=4				,		):

												'''simple docstring'''

												a__          =  parent
												a__          =  batch_size
												a__          =  seq_length
												a__          =  is_training
												a__          =  use_attention_mask
												a__          =  use_token_type_ids
												a__          =  use_labels
												a__          =  vocab_size
												a__          =  hidden_size
												a__          =  num_hidden_layers
												a__          =  num_attention_heads
												a__          =  intermediate_size
												a__          =  hidden_act
												a__          =  hidden_dropout_prob
												a__          =  attention_probs_dropout_prob
												a__          =  max_position_embeddings
												a__          =  type_vocab_size
												a__          =  type_sequence_label_size
												a__          =  initializer_range
												a__          =  num_choices




							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.vocab_size    )

												a__          =  None
												if self.use_attention_mask:
																	a__          =  random_attention_mask([self.batch_size, self.seq_length]    )

												a__          =  None
												if self.use_token_type_ids:
																	a__          =  ids_tensor([self.batch_size, self.seq_length]				,		self.type_vocab_size    )

												a__          =  RobertaPreLayerNormConfig(
												    vocab_size=self.vocab_size				,		hidden_size=self.hidden_size				,		num_hidden_layers=self.num_hidden_layers				,		num_attention_heads=self.num_attention_heads				,		intermediate_size=self.intermediate_size				,		hidden_act=self.hidden_act				,		hidden_dropout_prob=self.hidden_dropout_prob				,		attention_probs_dropout_prob=self.attention_probs_dropout_prob				,		max_position_embeddings=self.max_position_embeddings				,		type_vocab_size=self.type_vocab_size				,		is_decoder=__A				,		initializer_range=self.initializer_range				,		)

												return config, input_ids, token_type_ids, attention_mask




							def  lowercase		(   self:     Optional[int]    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs
												a__          =  {'''input_ids''': input_ids, '''token_type_ids''': token_type_ids, '''attention_mask''': attention_mask}
												return config, inputs_dict





							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												a__          =  self.prepare_config_and_inputs()
												a__       ,a__       ,a__       ,a__          =  config_and_inputs

												a__          =  True
												a__          =  floats_tensor([self.batch_size, self.seq_length, self.hidden_size]    )
												a__          =  ids_tensor([self.batch_size, self.seq_length]				,		vocab_size=2    )

												return (
												    config,
												    input_ids,
												    token_type_ids,
												    encoder_hidden_states,
												    encoder_attention_mask,
												)







@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        _SCREAMING_SNAKE_CASE						(  __snake_case       , unittest.TestCase							):





							"""simple docstring"""





							_SCREAMING_SNAKE_CASE    =True

							_SCREAMING_SNAKE_CASE    =(
							    (
							        FlaxRobertaPreLayerNormModel,
							        FlaxRobertaPreLayerNormForCausalLM,
							        FlaxRobertaPreLayerNormForMaskedLM,
							        FlaxRobertaPreLayerNormForSequenceClassification,
							        FlaxRobertaPreLayerNormForTokenClassification,
							        FlaxRobertaPreLayerNormForMultipleChoice,
							        FlaxRobertaPreLayerNormForQuestionAnswering,
							    )
							    if is_flax_available()
							    else ()
							)




							def  lowercase		(   self:     Tuple    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModelTester(self    )





							@slow
							def  lowercase		(   self:     int    ):

												'''simple docstring'''

												for model_class_name in self.all_model_classes:
																	a__          =  model_class_name.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )
																	a__          =  model(np.ones((1, 1)    )    )
																	self.assertIsNotNone(__A    )







@require_flax
class        _SCREAMING_SNAKE_CASE						(  unittest.TestCase							):





							"""simple docstring"""





							@slow
							def  lowercase		(   self:     Dict    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												a__          =  [1, 11, 50265]
												self.assertEqual(list(output.shape    )				,		__A    )
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[4_0.4_8_8_0, 1_8.0_1_9_9, -5.2_3_6_7], [-1.8_8_7_7, -4.0_8_8_5, 1_0.7_0_8_5], [-2.2_6_1_3, -5.6_1_1_0, 7.2_6_6_5]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )





							@slow
							def  lowercase		(   self:     str    ):

												'''simple docstring'''

												a__          =  FlaxRobertaPreLayerNormModel.from_pretrained('''andreasmadsen/efficient_mlm_m0.40'''				,		from_pt=__A    )

												a__          =  np.array([[0, 31414, 232, 328, 740, 1140, 12695, 69, 46078, 1588, 2]]				,		dtype=jnp.intaa    )
												a__          =  model(__A    )[0]
												# compare the actual values for a slice.
												a__          =  np.array(
												    [[[0.0_2_0_8, -0.0_3_5_6, 0.0_2_3_7], [-0.1_5_6_9, -0.0_4_1_1, -0.2_6_2_6], [0.1_8_7_9, 0.0_1_2_5, -0.0_0_8_9]]]				,		dtype=np.floataa    )
												self.assertTrue(np.allclose(output[:, :3, :3]				,		__A				,		atol=1e-4    )    )


