



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  __snake_case      :







							'''simple docstring'''






							def __init__(							self  ,  a_  ,  a_=13  ,  a_=3  ,  a_=True  ,  a_=True  ,  a_=0.1  ,  a_=0.1  ,  a_=224  ,  a_=1_000  ,  a_=[3, 3, 6, 4]  ,  a_=[48, 56, 112, 220]  ,  ):
											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 _a      (							self		):
											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 _a      (							self		):
											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_		):
											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 _a      (							self  ,  a_  ,  a_  ,  a_		):
											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 _a      (							self		):
											((a__)      ,			(a__)      ,			(a__))				      =		self.prepare_config_and_inputs()
											a__				      =		{"""pixel_values""": pixel_values}
											return config, inputs_dict




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







							'''simple docstring'''






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

							UpperCamelCase__				:      Dict         =					False
							UpperCamelCase__				:      Any         =					False
							UpperCamelCase__				:      Tuple         =					False
							UpperCamelCase__				:      Tuple         =					False
							UpperCamelCase__				:      Optional[int]         =					False


							def _a      (							self		):
											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 _a      (							self		):
											self.config_tester.run_common_tests()


							@unittest.skip(reason="""SwiftFormer does not use inputs_embeds"""		)
							def _a      (							self		):
											pass


							def _a      (							self		):
											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 _a      (							self		):
											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 _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*a_		)


							def _a      (							self		):
											a__				      =		self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*a_		)


							@slow
							def _a      (							self		):
											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 _a      (							self		):
											pass


							def _a      (							self		):
											def check_hidden_states_output(a_  ,  a_  ,  a_		):
															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 _a      (							self		):
											def _config_zero_init(a_		):
															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-1_0		)
																			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 _a      (							self		):
											pass




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

				"""simple docstring"""
				a__				      =		Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
				return image




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







							'''simple docstring'''






							@cached_property
							def _a      (							self		):
											return ViTImageProcessor.from_pretrained("""MBZUAI/swiftformer-xs"""		) if is_vision_available() else None







							@slow
							def _a      (							self		):
											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, 1_000)		)
											self.assertEqual(outputs.logits.shape  ,  a_		)

											a__				      =		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		)		)






from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_sentencepiece_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


UpperCAmelCase							 =					{
    """configuration_xlm_roberta""": [
        """XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP""",
        """XLMRobertaConfig""",
        """XLMRobertaOnnxConfig""",
    ],
}

try:
	if not is_sentencepiece_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					["""XLMRobertaTokenizer"""]

try:
	if not is_tokenizers_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					["""XLMRobertaTokenizerFast"""]

try:
	if not is_torch_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """XLMRobertaForCausalLM""",
	    """XLMRobertaForMaskedLM""",
	    """XLMRobertaForMultipleChoice""",
	    """XLMRobertaForQuestionAnswering""",
	    """XLMRobertaForSequenceClassification""",
	    """XLMRobertaForTokenClassification""",
	    """XLMRobertaModel""",
	    """XLMRobertaPreTrainedModel""",
	]

try:
	if not is_tf_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """TFXLMRobertaForCausalLM""",
	    """TFXLMRobertaForMaskedLM""",
	    """TFXLMRobertaForMultipleChoice""",
	    """TFXLMRobertaForQuestionAnswering""",
	    """TFXLMRobertaForSequenceClassification""",
	    """TFXLMRobertaForTokenClassification""",
	    """TFXLMRobertaModel""",
	    """TFXLMRobertaPreTrainedModel""",
	]

try:
	if not is_flax_available():
		raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
	pass
else:
	UpperCAmelCase							 =					[
	    """FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST""",
	    """FlaxXLMRobertaForMaskedLM""",
	    """FlaxXLMRobertaForCausalLM""",
	    """FlaxXLMRobertaForMultipleChoice""",
	    """FlaxXLMRobertaForQuestionAnswering""",
	    """FlaxXLMRobertaForSequenceClassification""",
	    """FlaxXLMRobertaForTokenClassification""",
	    """FlaxXLMRobertaModel""",
	    """FlaxXLMRobertaPreTrainedModel""",
	]

if TYPE_CHECKING:
	from .configuration_xlm_roberta import (
	    XLM_ROBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP,
	    XLMRobertaConfig,
	    XLMRobertaOnnxConfig,
	)

	try:
		if not is_sentencepiece_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta import XLMRobertaTokenizer

	try:
		if not is_tokenizers_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .tokenization_xlm_roberta_fast import XLMRobertaTokenizerFast

	try:
		if not is_torch_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_xlm_roberta import (
		    XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    XLMRobertaForCausalLM,
		    XLMRobertaForMaskedLM,
		    XLMRobertaForMultipleChoice,
		    XLMRobertaForQuestionAnswering,
		    XLMRobertaForSequenceClassification,
		    XLMRobertaForTokenClassification,
		    XLMRobertaModel,
		    XLMRobertaPreTrainedModel,
		)

	try:
		if not is_tf_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_tf_xlm_roberta import (
		    TF_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    TFXLMRobertaForCausalLM,
		    TFXLMRobertaForMaskedLM,
		    TFXLMRobertaForMultipleChoice,
		    TFXLMRobertaForQuestionAnswering,
		    TFXLMRobertaForSequenceClassification,
		    TFXLMRobertaForTokenClassification,
		    TFXLMRobertaModel,
		    TFXLMRobertaPreTrainedModel,
		)

	try:
		if not is_flax_available():
			raise OptionalDependencyNotAvailable()
	except OptionalDependencyNotAvailable:
		pass
	else:
		from .modeling_flax_xlm_roberta import (
		    FLAX_XLM_ROBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
		    FlaxXLMRobertaForCausalLM,
		    FlaxXLMRobertaForMaskedLM,
		    FlaxXLMRobertaForMultipleChoice,
		    FlaxXLMRobertaForQuestionAnswering,
		    FlaxXLMRobertaForSequenceClassification,
		    FlaxXLMRobertaForTokenClassification,
		    FlaxXLMRobertaModel,
		    FlaxXLMRobertaPreTrainedModel,
		)

else:
	import sys

	UpperCAmelCase							 =					_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)

