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


_A  =	logging.get_logger(__name__)

# TODO: upload to AWS
_A  =	{
    "yjernite/retribert-base-uncased": (
        "https://huggingface.co/yjernite/retribert-base-uncased/resolve/main/config.json"
    ),
}





class       _lowerCAmelCase   (      __lowercase			):
				_lowercase     						="retribert"




				def __init__(   self							,							_UpperCamelCase=30_522							,							_UpperCamelCase=768							,							_UpperCamelCase=8							,							_UpperCamelCase=12							,							_UpperCamelCase=3_072							,							_UpperCamelCase="gelu"							,							_UpperCamelCase=0.1							,							_UpperCamelCase=0.1							,							_UpperCamelCase=512							,							_UpperCamelCase=2							,							_UpperCamelCase=0.02							,							_UpperCamelCase=1e-1_2							,							_UpperCamelCase=True							,							_UpperCamelCase=128							,							_UpperCamelCase=0							,							**_UpperCamelCase							,							)							-> Optional[int]:
							super().__init__(pad_token_id=_a							,							**_a	)

							lowerCAmelCase_         =      vocab_size
							lowerCAmelCase_         =      hidden_size
							lowerCAmelCase_         =      num_hidden_layers
							lowerCAmelCase_         =      num_attention_heads
							lowerCAmelCase_         =      hidden_act
							lowerCAmelCase_         =      intermediate_size
							lowerCAmelCase_         =      hidden_dropout_prob
							lowerCAmelCase_         =      attention_probs_dropout_prob
							lowerCAmelCase_         =      max_position_embeddings
							lowerCAmelCase_         =      type_vocab_size
							lowerCAmelCase_         =      initializer_range
							lowerCAmelCase_         =      layer_norm_eps
							lowerCAmelCase_         =      share_encoders
							lowerCAmelCase_         =      projection_dim






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


							def 							__lowercase							(     self			)       ->       Optional[Any]:
												_a      :       Dict   =						floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

												_a      :       Dict   =						self.get_config()

												return config, pixel_values, labels


							def 							__lowercase							(     self			)       ->       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=_a			,				layer_scale_init_value=1e-5			,				)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       str:
												_a      :       List[Any]   =						SwiftFormerModel(config=_a			)
												model.to(_a			)
												model.eval()
												_a      :       Optional[int]   =						model(_a			)
												self.parent.assertEqual(result.last_hidden_state.shape			,				(self.batch_size, self.embed_dims[-1], 7, 7)			)


							def 							__lowercase							(     self			,				_a			,				_a			,				_a			)       ->       Optional[Any]:
												_a      :       List[str]   =						self.num_labels
												_a      :       Optional[int]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()
												_a      :       List[str]   =						model(_a			,				labels=_a			)
												self.parent.assertEqual(result.logits.shape			,				(self.batch_size, self.num_labels)			)

												_a      :       Union[str, Any]   =						SwiftFormerForImageClassification(_a			)
												model.to(_a			)
												model.eval()

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




							def 							__lowercase							(     self			)       ->       Tuple:
												((_a) ,   (_a) ,   (_a))      :       Optional[int]   =						self.prepare_config_and_inputs()
												_a      :       List[Any]   =						{'''pixel_values''': pixel_values}
												return config, inputs_dict



@require_torch
class 							UpperCAmelCase_						(     __lowercase							,			__lowercase							,			unittest.TestCase  ):
							"""simple docstring"""







							UpperCAmelCase__			:		List[str]             =       (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
							UpperCAmelCase__			:		Optional[int]             =       (
							    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
							    if is_torch_available()
							    else {}
							)

							UpperCAmelCase__			:		Optional[Any]             =       False
							UpperCAmelCase__			:		str             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		Tuple             =       False
							UpperCAmelCase__			:		str             =       False


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       Union[str, Any]   =						SwiftFormerModelTester(self			)
												_a      :       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 							__lowercase							(     self			)       ->       int:
												self.config_tester.run_common_tests()


							@unittest.skip(reason='''SwiftFormer does not use inputs_embeds'''			)
							def 							__lowercase							(     self			)       ->       Union[str, Any]:
												pass


							def 							__lowercase							(     self			)       ->       Dict:
												_a ,   _a      :       int   =						self.model_tester.prepare_config_and_inputs_for_common()

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


							def 							__lowercase							(     self			)       ->       str:
												_a ,   _a      :       str   =						self.model_tester.prepare_config_and_inputs_for_common()

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

																	_a      :       List[str]   =						['''pixel_values''']
																	self.assertListEqual(arg_names[:1]			,				_a			)


							def 							__lowercase							(     self			)       ->       int:
												_a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*_a			)


							def 							__lowercase							(     self			)       ->       Optional[int]:
												_a      :       int   =						self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_for_image_classification(*_a			)


							@slow
							def 							__lowercase							(     self			)       ->       Optional[Any]:
												for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																	_a      :       Any   =						SwiftFormerModel.from_pretrained(_a			)
																	self.assertIsNotNone(_a			)


							@unittest.skip(reason='''SwiftFormer does not output attentions'''			)
							def 							__lowercase							(     self			)       ->       List[Any]:
												pass


							def 							__lowercase							(     self			)       ->       int:
												def check_hidden_states_output(_a			,				_a			,				_a			):
																	_a      :       Optional[int]   =						model_class(_a			)
																	model.to(_a			)
																	model.eval()

																	with torch.no_grad():
																						_a      :       Union[str, Any]   =						model(**self._prepare_for_class(_a			,				_a			)			)

																	_a      :       Optional[Any]   =						outputs.hidden_states

																	_a      :       Union[str, 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),
																						        ]			)			,				)

												_a ,   _a      :       Optional[int]   =						self.model_tester.prepare_config_and_inputs_for_common()

												for model_class in self.all_model_classes:
																	_a      :       str   =						True
																	check_hidden_states_output(_a			,				_a			,				_a			)

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

																	check_hidden_states_output(_a			,				_a			,				_a			)


							def 							__lowercase							(     self			)       ->       str:
												def _config_zero_init(_a			):
																	_a      :       List[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-1_0			)
																						if isinstance(getattr(_a			,				_a			,				_a			)			,				_a			):
																											_a      :       int   =						_config_zero_init(getattr(_a			,				_a			)			)
																											setattr(_a			,				_a			,				_a			)
																	return configs_no_init

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

												_a      :       Dict   =						_config_zero_init(_a			)
												for model_class in self.all_model_classes:
																	_a      :       Dict   =						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			)       ->       Optional[Any]:
												pass



def   __UpperCAmelCase					(      )     ->	Optional[Any]:

					"""simple docstring"""

					_a      :       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 							__lowercase							(     self			)       ->       str:
												return ViTImageProcessor.from_pretrained('''MBZUAI/swiftformer-xs'''			) if is_vision_available() else None




							@slow
							def 							__lowercase							(     self			)       ->       Dict:
												_a      :       Any   =						SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''			).to(_a			)

												_a      :       Any   =						self.default_image_processor
												_a      :       Any   =						prepare_img()
												_a      :       Any   =						image_processor(images=_a			,				return_tensors='''pt'''			).to(_a			)

												# forward pass
												with torch.no_grad():
																	_a      :       Optional[Any]   =						model(**_a			)

												# verify the logits
												_a      :       List[str]   =						torch.Size((1, 1_0_0_0)			)
												self.assertEqual(outputs.logits.shape			,				_a			)

												_a      :       int   =						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			)			)



