






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:
							def __init__(     self:      List[Any]       ,	UpperCamelCase:      int       ,	UpperCamelCase:      List[str]=13       ,	UpperCamelCase:      List[Any]=3       ,	UpperCamelCase:      str=True       ,	UpperCamelCase:      List[Any]=True       ,	UpperCamelCase:      Dict=0.1       ,	UpperCamelCase:      int=0.1       ,	UpperCamelCase:      Dict=2_24       ,	UpperCamelCase:      List[Any]=10_00       ,	UpperCamelCase:      Union[str, Any]=[3, 3, 6, 4]       ,	UpperCamelCase:      Optional[Any]=[48, 56, 1_12, 2_20]       ,	)		->		List[str]:
											snake_case__									=     parent
											snake_case__									=     batch_size
											snake_case__									=     num_channels
											snake_case__									=     is_training
											snake_case__									=     use_labels
											snake_case__									=     hidden_dropout_prob
											snake_case__									=     attention_probs_dropout_prob
											snake_case__									=     num_labels
											snake_case__									=     image_size
											snake_case__									=     layer_depths
											snake_case__									=     embed_dims
							def lowerCAmelCase_ (     self:      int						)		->		Any:
											snake_case__									=     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]						)

											snake_case__									=     None
											if self.use_labels:
															snake_case__									=     ids_tensor([self.batch_size]       ,	self.num_labels						)

											snake_case__									=     self.get_config()

											return config, pixel_values, labels
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											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 lowerCAmelCase_ (     self:      Tuple       ,	UpperCamelCase:      Optional[Any]       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      int						)		->		Union[str, Any]:
											snake_case__									=     SwiftFormerModel(config=_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.eval()
											snake_case__									=     model(_UpperCAmelCase						)
											self.parent.assertEqual(result.last_hidden_state.shape       ,	(self.batch_size, self.embed_dims[-1], 7, 7)						)
							def lowerCAmelCase_ (     self:      Optional[int]       ,	UpperCamelCase:      int       ,	UpperCamelCase:      Any       ,	UpperCamelCase:      int						)		->		Union[str, Any]:
											snake_case__									=     self.num_labels
											snake_case__									=     SwiftFormerForImageClassification(_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.eval()
											snake_case__									=     model(_UpperCAmelCase       ,	labels=_UpperCAmelCase						)
											self.parent.assertEqual(result.logits.shape       ,	(self.batch_size, self.num_labels)						)

											snake_case__									=     SwiftFormerForImageClassification(_UpperCAmelCase						)
											model.to(_UpperCAmelCase						)
											model.eval()

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


							def lowerCAmelCase_ (     self:      Any						)		->		Optional[int]:
											((snake_case__)							,						(snake_case__)							,						(snake_case__))									=     self.prepare_config_and_inputs()
											snake_case__									=     {'pixel_values': pixel_values}
											return config, inputs_dict


@require_torch
class   __SCREAMING_SNAKE_CASE(	a_    ,  a_    ,  unittest.TestCase	):
							_UpperCAmelCase     =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							_UpperCAmelCase     =       (
							    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							_UpperCAmelCase     =       False
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		int:
											snake_case__									=     SwiftFormerModelTester(self						)
											snake_case__									=     ConfigTester(
											    self       ,	config_class=_UpperCAmelCase       ,	has_text_modality=_UpperCAmelCase       ,	hidden_size=37       ,	num_attention_heads=12       ,	num_hidden_layers=12       ,	)
							def lowerCAmelCase_ (     self:      Tuple						)		->		int:
											self.config_tester.run_common_tests()
							@unittest.skip(reason='SwiftFormer does not use inputs_embeds'						)
							def lowerCAmelCase_ (     self:      Union[str, Any]						)		->		Any:
											pass
							def lowerCAmelCase_ (     self:      str						)		->		Union[str, Any]:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     model_class(_UpperCAmelCase						)
															snake_case__									=     model.get_output_embeddings()
															self.assertTrue(x is None or isinstance(_UpperCAmelCase       ,	nn.Linear						)						)
							def lowerCAmelCase_ (     self:      Optional[int]						)		->		Tuple:
											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

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

															snake_case__									=     ['pixel_values']
															self.assertListEqual(arg_names[:1]       ,	_UpperCAmelCase						)
							def lowerCAmelCase_ (     self:      Dict						)		->		int:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*_UpperCAmelCase						)
							def lowerCAmelCase_ (     self:      Tuple						)		->		Union[str, Any]:
											snake_case__									=     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*_UpperCAmelCase						)
							@slow
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:




											for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
															snake_case__									=     SwiftFormerModel.from_pretrained(_UpperCAmelCase						)
															self.assertIsNotNone(_UpperCAmelCase						)
							@unittest.skip(reason='SwiftFormer does not output attentions'						)
							def lowerCAmelCase_ (     self:      str						)		->		str:
											pass
							def lowerCAmelCase_ (     self:      List[Any]						)		->		Tuple:




											def check_hidden_states_output(UpperCamelCase:      Dict       ,	UpperCamelCase:      List[str]       ,	UpperCamelCase:      Tuple						):
															snake_case__									=     model_class(_UpperCAmelCase						)
															model.to(_UpperCAmelCase						)
															model.eval()

															with torch.no_grad():
																			snake_case__									=     model(**self._prepare_for_class(_UpperCAmelCase       ,	_UpperCAmelCase						)						)

															snake_case__									=     outputs.hidden_states

															snake_case__									=     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),
																			        ]						)       ,	)

											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
															snake_case__									=     True
															check_hidden_states_output(_UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase						)

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

															check_hidden_states_output(_UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase						)
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Optional[Any]:




											def _config_zero_init(UpperCamelCase:      List[Any]						):
															snake_case__									=     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						):
																							snake_case__									=     _config_zero_init(getattr(_UpperCAmelCase       ,	_UpperCAmelCase						)						)
																							setattr(_UpperCAmelCase       ,	_UpperCAmelCase       ,	_UpperCAmelCase						)
															return configs_no_init

											snake_case__							,						snake_case__									=     self.model_tester.prepare_config_and_inputs_for_common()

											snake_case__									=     _config_zero_init(_UpperCAmelCase						)
											for model_class in self.all_model_classes:
															snake_case__									=     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 lowerCAmelCase_ (     self:      int						)		->		Tuple:
											pass


def      a_ (      )			->			Optional[int]:







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


@require_torch
@require_vision
class   __SCREAMING_SNAKE_CASE(	unittest.TestCase	):
							@cached_property
							def lowerCAmelCase_ (     self:      Optional[Any]						)		->		Optional[int]:
											return ViTImageProcessor.from_pretrained('MBZUAI/swiftformer-xs'						) if is_vision_available() else None


							@slow
							def lowerCAmelCase_ (     self:      List[Any]						)		->		str:
											snake_case__									=     SwiftFormerForImageClassification.from_pretrained('MBZUAI/swiftformer-xs'						).to(_UpperCAmelCase						)

											snake_case__									=     self.default_image_processor
											snake_case__									=     prepare_img()
											snake_case__									=     image_processor(images=_UpperCAmelCase       ,	return_tensors='pt'						).to(_UpperCAmelCase						)

											# forward pass
											with torch.no_grad():
															snake_case__									=     model(**_UpperCAmelCase						)

											# verify the logits
											snake_case__									=     torch.Size((1, 10_00)						)
											self.assertEqual(outputs.logits.shape       ,	_UpperCAmelCase						)

											snake_case__									=     torch.tensor([[-2.1703e00, 2.1107e00, -2.0811e00]]						).to(_UpperCAmelCase						)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]       ,	_UpperCAmelCase       ,	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					:		int	  =						{"""configuration_xglm""": ["""XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP""", """XGLMConfig"""]}

try:
							if not is_sentencepiece_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Dict	  =						["""XGLMTokenizer"""]

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Optional[int]	  =						["""XGLMTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		List[str]	  =						[
							    """XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """XGLMForCausalLM""",
							    """XGLMModel""",
							    """XGLMPreTrainedModel""",
							]


try:
							if not is_flax_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		Any	  =						[
							    """FlaxXGLMForCausalLM""",
							    """FlaxXGLMModel""",
							    """FlaxXGLMPreTrainedModel""",
							]


try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase					:		str	  =						[
							    """TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFXGLMForCausalLM""",
							    """TFXGLMModel""",
							    """TFXGLMPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_xglm import XGLM_PRETRAINED_CONFIG_ARCHIVE_MAP, XGLMConfig

							try:
														if not is_sentencepiece_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm import XGLMTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_xglm_fast import XGLMTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_xglm import XGLM_PRETRAINED_MODEL_ARCHIVE_LIST, XGLMForCausalLM, XGLMModel, XGLMPreTrainedModel

							try:
														if not is_flax_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_flax_xglm import FlaxXGLMForCausalLM, FlaxXGLMModel, FlaxXGLMPreTrainedModel

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_xglm import (
														    TF_XGLM_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFXGLMForCausalLM,
														    TFXGLMModel,
														    TFXGLMPreTrainedModel,
														)


else:
							import sys

							__UpperCamelCase					:		Union[str, Any]	  =						_LazyModule(__name__, globals()["""__file__"""], _import_structure)




