
'''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 			lowerCAmelCase__       :






     def __init__(       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE=13    ,							__SCREAMING_SNAKE_CASE=3    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=2_24    ,							__SCREAMING_SNAKE_CASE=10_00    ,							__SCREAMING_SNAKE_CASE=[3, 3, 6, 4]    ,							__SCREAMING_SNAKE_CASE=[48, 56, 1_12, 2_20]    ,							):


           """simple docstring"""





           lowercase_     :					str													=	parent
           lowercase_     :					List[Any]													=	batch_size
           lowercase_     :					List[Any]													=	num_channels
           lowercase_     :					Dict													=	is_training
           lowercase_     :					List[str]													=	use_labels
           lowercase_     :					str													=	hidden_dropout_prob
           lowercase_     :					List[Any]													=	attention_probs_dropout_prob
           lowercase_     :					Optional[Any]													=	num_labels
           lowercase_     :					str													=	image_size
           lowercase_     :					str													=	layer_depths
           lowercase_     :					int													=	embed_dims






     def      _snake_case   (       self		):


           """simple docstring"""





           lowercase_     :					int													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

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

           lowercase_     :					List[str]													=	self.get_config()

           return config, pixel_values, labels






     def      _snake_case   (       self		):


           """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      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


           """simple docstring"""





           lowercase_     :					Union[str, Any]													=	SwiftFormerModel(config=_A		)
           model.to(_A		)
           model.eval()
           lowercase_     :					str													=	model(_A		)
           self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.embed_dims[-1], 7, 7)		)






     def      _snake_case   (       self    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):


           """simple docstring"""





           lowercase_     :					Optional[Any]													=	self.num_labels
           lowercase_     :					List[str]													=	SwiftFormerForImageClassification(_A		)
           model.to(_A		)
           model.eval()
           lowercase_     :					List[str]													=	model(_A    ,							labels=_A		)
           self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)		)

           lowercase_     :					List[str]													=	SwiftFormerForImageClassification(_A		)
           model.to(_A		)
           model.eval()

           lowercase_     :					Any													=	floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)
           lowercase_     :					Tuple													=	model(_A		)
           self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.num_labels)		)






     def      _snake_case   (       self		):


           """simple docstring"""





           ((lowercase_)						,			(lowercase_)						,			(lowercase_))     :					str													=	self.prepare_config_and_inputs()
           lowercase_     :					Optional[Any]													=	{'''pixel_values''': pixel_values}
           return config, inputs_dict







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

     lowerCAmelCase_					       =					False
     lowerCAmelCase_					       =					False
     lowerCAmelCase_					       =					False
     lowerCAmelCase_					       =					False
     lowerCAmelCase_					       =					False






     def      _snake_case   (       self		):


           """simple docstring"""





           lowercase_     :					Any													=	SwiftFormerModelTester(self		)
           lowercase_     :					Dict													=	ConfigTester(
               self    ,							config_class=_A    ,							has_text_modality=_A    ,							hidden_size=37    ,							num_attention_heads=12    ,							num_hidden_layers=12    ,							)






     def      _snake_case   (       self		):


           """simple docstring"""





           self.config_tester.run_common_tests()






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


           """simple docstring"""





           pass






     def      _snake_case   (       self		):


           """simple docstring"""





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

           for model_class in self.all_model_classes:
                 lowercase_     :					List[Any]													=	model_class(_A		)
                 lowercase_     :					List[Any]													=	model.get_output_embeddings()
                 self.assertTrue(x is None or isinstance(_A    ,							nn.Linear		)		)






     def      _snake_case   (       self		):


           """simple docstring"""





           lowercase_						,			lowercase_     :					Optional[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

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

                 lowercase_     :					Optional[Any]													=	['''pixel_values''']
                 self.assertListEqual(arg_names[:1]    ,							_A		)






     def      _snake_case   (       self		):


           """simple docstring"""





           lowercase_     :					List[Any]													=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_model(*_A		)






     def      _snake_case   (       self		):


           """simple docstring"""





           lowercase_     :					Optional[Any]													=	self.model_tester.prepare_config_and_inputs()
           self.model_tester.create_and_check_for_image_classification(*_A		)






     @slow
     def      _snake_case   (       self		):


           """simple docstring"""





           for model_name in SWIFTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
                 lowercase_     :					Union[str, Any]													=	SwiftFormerModel.from_pretrained(_A		)
                 self.assertIsNotNone(_A		)






     @unittest.skip(reason='''SwiftFormer does not output attentions'''		)
     def      _snake_case   (       self		):


           """simple docstring"""





           pass






     def      _snake_case   (       self		):


           """simple docstring"""





           def check_hidden_states_output(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		):
                 lowercase_     :					Optional[int]													=	model_class(_A		)
                 model.to(_A		)
                 model.eval()

                 with torch.no_grad():
                       lowercase_     :					Dict													=	model(**self._prepare_for_class(_A    ,							_A		)		)

                 lowercase_     :					int													=	outputs.hidden_states

                 lowercase_     :					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),
                               ]		)    ,							)

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

           for model_class in self.all_model_classes:
                 lowercase_     :					Union[str, Any]													=	True
                 check_hidden_states_output(_A    ,							_A    ,							_A		)

                 # check that output_hidden_states also work using config
                 del inputs_dict["output_hidden_states"]
                 lowercase_     :					Optional[Any]													=	True

                 check_hidden_states_output(_A    ,							_A    ,							_A		)






     def      _snake_case   (       self		):


           """simple docstring"""





           def _config_zero_init(__SCREAMING_SNAKE_CASE		):
                 lowercase_     :					Optional[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		):
                             lowercase_     :					Optional[Any]													=	_config_zero_init(getattr(_A    ,							_A		)		)
                             setattr(_A    ,							_A    ,							_A		)
                 return configs_no_init

           lowercase_						,			lowercase_     :					List[Any]													=	self.model_tester.prepare_config_and_inputs_for_common()

           lowercase_     :					Dict													=	_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      _snake_case   (       self		):


           """simple docstring"""





           pass







def  snake_case_		(    ):



      """simple docstring"""





      lowercase_     :					Optional[int]													=	Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''							)
      return image






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






     @cached_property
     def      _snake_case   (       self		):


           """simple docstring"""





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






     @slow
     def      _snake_case   (       self		):


           """simple docstring"""





           lowercase_     :					Dict													=	SwiftFormerForImageClassification.from_pretrained('''MBZUAI/swiftformer-xs'''		).to(_A		)

           lowercase_     :					Optional[int]													=	self.default_image_processor
           lowercase_     :					Optional[int]													=	prepare_img()
           lowercase_     :					Dict													=	image_processor(images=_A    ,							return_tensors='''pt'''		).to(_A		)

           # forward pass
           with torch.no_grad():
                 lowercase_     :					Tuple													=	model(**_A		)

           # verify the logits
           lowercase_     :					List[str]													=	torch.Size((1, 10_00)		)
           self.assertEqual(outputs.logits.shape    ,							_A		)

           lowercase_     :					Optional[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		)		)





'''simple docstring'''







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


_lowercase			:  Any   							=	logging.get_logger(__name__)

_lowercase			:  Dict   							=	{
    "EleutherAI/gpt-neox-20b": "https://huggingface.co/EleutherAI/gpt-neox-20b/resolve/main/config.json",
    # See all GPTNeoX models at https://huggingface.co/models?filter=gpt_neox
}






class 			lowerCAmelCase__       (  lowerCamelCase_					):
					lowerCAmelCase_					       =					'''gpt_neox'''






					def __init__(       self    ,							__SCREAMING_SNAKE_CASE=5_04_32    ,							__SCREAMING_SNAKE_CASE=61_44    ,							__SCREAMING_SNAKE_CASE=44    ,							__SCREAMING_SNAKE_CASE=64    ,							__SCREAMING_SNAKE_CASE=2_45_76    ,							__SCREAMING_SNAKE_CASE="gelu"    ,							__SCREAMING_SNAKE_CASE=0.25    ,							__SCREAMING_SNAKE_CASE=1_00_00    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.0    ,							__SCREAMING_SNAKE_CASE=0.1    ,							__SCREAMING_SNAKE_CASE=20_48    ,							__SCREAMING_SNAKE_CASE=0.02    ,							__SCREAMING_SNAKE_CASE=1E-5    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=0    ,							__SCREAMING_SNAKE_CASE=2    ,							__SCREAMING_SNAKE_CASE=False    ,							__SCREAMING_SNAKE_CASE=True    ,							__SCREAMING_SNAKE_CASE=None    ,							**__SCREAMING_SNAKE_CASE    ,							):


											"""simple docstring"""





											super().__init__(bos_token_id=__SCREAMING_SNAKE_CASE    ,							eos_token_id=__SCREAMING_SNAKE_CASE    ,							**__SCREAMING_SNAKE_CASE		)
											lowercase_     :					Optional[Any]													=	vocab_size
											lowercase_     :					Optional[Any]													=	max_position_embeddings
											lowercase_     :					Optional[int]													=	hidden_size
											lowercase_     :					Tuple													=	num_hidden_layers
											lowercase_     :					Any													=	num_attention_heads
											lowercase_     :					str													=	intermediate_size
											lowercase_     :					Any													=	hidden_act
											lowercase_     :					Tuple													=	rotary_pct
											lowercase_     :					Optional[Any]													=	rotary_emb_base
											lowercase_     :					Any													=	attention_dropout
											lowercase_     :					str													=	hidden_dropout
											lowercase_     :					Dict													=	classifier_dropout
											lowercase_     :					Tuple													=	initializer_range
											lowercase_     :					List[str]													=	layer_norm_eps
											lowercase_     :					Union[str, Any]													=	use_cache
											lowercase_     :					int													=	tie_word_embeddings
											lowercase_     :					Tuple													=	use_parallel_residual
											lowercase_     :					Optional[Any]													=	rope_scaling
											self._rope_scaling_validation()

											if self.hidden_size % self.num_attention_heads != 0:
																	raise ValueError(
																	    '''The hidden size is not divisble by the number of attention heads! Make sure to update them!'''		)






					def      _snake_case   (       self		):


											"""simple docstring"""





											if self.rope_scaling is None:
																	return

											if not isinstance(self.rope_scaling    ,							__SCREAMING_SNAKE_CASE		) or len(self.rope_scaling		) != 2:
																	raise ValueError(
																	    '''`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '''
																	    F'''got {self.rope_scaling}'''		)
											lowercase_     :					List[Any]													=	self.rope_scaling.get('''type'''    ,							__SCREAMING_SNAKE_CASE		)
											lowercase_     :					int													=	self.rope_scaling.get('''factor'''    ,							__SCREAMING_SNAKE_CASE		)
											if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
																	raise ValueError(
																	    F'''`rope_scaling`\'s name field must be one of [\'linear\', \'dynamic\'], got {rope_scaling_type}'''		)
											if rope_scaling_factor is None or not isinstance(__SCREAMING_SNAKE_CASE    ,							__SCREAMING_SNAKE_CASE		) or rope_scaling_factor <= 1.0:
																	raise ValueError(F'''`rope_scaling`\'s factor field must be an float > 1, got {rope_scaling_factor}'''		)



