
"""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_		:


				"""simple docstring"""



				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]     , )      ->	str:



											"""simple docstring"""




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



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Any     =     floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]					)

											SCREAMING_SNAKE_CASE__		: List[str]     =     None
											if self.use_labels:
																		SCREAMING_SNAKE_CASE__		: Optional[Any]     =     ids_tensor([self.batch_size]     , self.num_labels					)

											SCREAMING_SNAKE_CASE__		: int     =     self.get_config()

											return config, pixel_values, labels



				def 			__magic_name__					(self					)      ->	Dict:



											"""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=SCREAMING_SNAKE_CASE__     , layer_scale_init_value=1E-5     , )



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: List[Any]     =     SwiftFormerModel(config=SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_SNAKE_CASE__					)
											model.eval()
											SCREAMING_SNAKE_CASE__		: Dict     =     model(SCREAMING_SNAKE_CASE__					)
											self.parent.assertEqual(result.last_hidden_state.shape     , (self.batch_size, self.embed_dims[-1], 7, 7)					)



				def 			__magic_name__					(self     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)      ->	Dict:



											"""simple docstring"""




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

											SCREAMING_SNAKE_CASE__		: List[Any]     =     SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE__					)
											model.to(SCREAMING_SNAKE_CASE__					)
											model.eval()

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



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											((SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__)   ,   (SCREAMING_SNAKE_CASE__))		: Optional[int]     =     self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE__		: Optional[int]     =     {"""pixel_values""": pixel_values}
											return config, inputs_dict






@require_torch
class 			lowerCAmelCase_		(a__				,      a__				,      unittest.TestCase						):


				"""simple docstring"""



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

				__UpperCamelCase   :		Tuple       =     False
				__UpperCamelCase   :		Any       =     False
				__UpperCamelCase   :		Optional[Any]       =     False
				__UpperCamelCase   :		List[str]       =     False
				__UpperCamelCase   :		Dict       =     False



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[Any]     =     SwiftFormerModelTester(self					)
											SCREAMING_SNAKE_CASE__		: int     =     ConfigTester(
											    self     , config_class=SCREAMING_SNAKE_CASE__     , has_text_modality=SCREAMING_SNAKE_CASE__     , hidden_size=37     , num_attention_heads=12     , num_hidden_layers=12     , )



				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											self.config_tester.run_common_tests()



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



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.model_tester.prepare_config_and_inputs_for_common()

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



				def 			__magic_name__					(self					)      ->	Optional[Any]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: List[str]     =     self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE__		: int     =     ["""pixel_values"""]
																		self.assertListEqual(arg_names[:1]     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: Optional[int]     =     self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_image_classification(*SCREAMING_SNAKE_CASE__					)



				@slow
				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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



				@unittest.skip(reason="""SwiftFormer does not output attentions"""					)
				def 			__magic_name__					(self					)      ->	int:



											"""simple docstring"""




											pass



				def 			__magic_name__					(self					)      ->	str:



											"""simple docstring"""




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

																		with torch.no_grad():
																									SCREAMING_SNAKE_CASE__		: Tuple     =     model(**self._prepare_for_class(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)

																		SCREAMING_SNAKE_CASE__		: List[str]     =     outputs.hidden_states

																		SCREAMING_SNAKE_CASE__		: Tuple     =     8
																		self.assertEqual(len(SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__					)  # 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(SCREAMING_SNAKE_CASE__					)					):
																									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),
																									        ]					)     , )

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__		: Dict     =     True
																		check_hidden_states_output(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)

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

																		check_hidden_states_output(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)



				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




											def _config_zero_init(SCREAMING_SNAKE_CASE__					):
																		SCREAMING_SNAKE_CASE__		: int     =     copy.deepcopy(SCREAMING_SNAKE_CASE__					)
																		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(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , 1E-10					)
																									if isinstance(getattr(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)     , SCREAMING_SNAKE_CASE__					):
																																SCREAMING_SNAKE_CASE__		: Any     =     _config_zero_init(getattr(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)					)
																																setattr(SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__     , SCREAMING_SNAKE_CASE__					)
																		return configs_no_init

											SCREAMING_SNAKE_CASE__   ,   SCREAMING_SNAKE_CASE__		: Dict     =     self.model_tester.prepare_config_and_inputs_for_common()

											SCREAMING_SNAKE_CASE__		: List[Any]     =     _config_zero_init(SCREAMING_SNAKE_CASE__					)
											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE__		: List[Any]     =     model_class(config=SCREAMING_SNAKE_CASE__					)
																		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 			__magic_name__					(self					)      ->	Union[str, Any]:



											"""simple docstring"""




											pass






def lowercase_						(     ):
							SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""		)
							return image



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


				"""simple docstring"""



				@cached_property
				def 			__magic_name__					(self					)      ->	Tuple:



											"""simple docstring"""




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



				@slow
				def 			__magic_name__					(self					)      ->	List[str]:



											"""simple docstring"""




											SCREAMING_SNAKE_CASE__		: str     =     SwiftFormerForImageClassification.from_pretrained("""MBZUAI/swiftformer-xs"""					).to(SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: Tuple     =     self.default_image_processor
											SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     prepare_img()
											SCREAMING_SNAKE_CASE__		: List[Any]     =     image_processor(images=SCREAMING_SNAKE_CASE__     , return_tensors="""pt"""					).to(SCREAMING_SNAKE_CASE__					)

											# forward pass
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE__		: Any     =     model(**SCREAMING_SNAKE_CASE__					)

											# verify the logits
											SCREAMING_SNAKE_CASE__		: int     =     torch.Size((1, 10_00)					)
											self.assertEqual(outputs.logits.shape     , SCREAMING_SNAKE_CASE__					)

											SCREAMING_SNAKE_CASE__		: str     =     torch.tensor([[-2.17_03E00, 2.11_07E00, -2.08_11E00]]					).to(SCREAMING_SNAKE_CASE__					)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]     , SCREAMING_SNAKE_CASE__     , atol=1E-4					)					)






"""simple docstring"""




import os
import zipfile

import requests
from get_ci_error_statistics import download_artifact, get_artifacts_links

def lowercase_						(     _snake_case     ,_snake_case=7		):
							SCREAMING_SNAKE_CASE__		: Dict     =     None
							if token is not None:
														SCREAMING_SNAKE_CASE__		: str     =     {"""Accept""": """application/vnd.github+json""", """Authorization""": f'''Bearer {token}'''}

							# The id of a workflow (not of a workflow run)
							SCREAMING_SNAKE_CASE__		: List[str]     =     """636036"""

							SCREAMING_SNAKE_CASE__		: Any     =     f'''https://api.github.com/repos/huggingface/transformers/actions/workflows/{workflow_id}/runs'''
							# On `main` branch + event being `schedule` + not returning PRs + only `num_runs` results
							url += f'''?branch=main&event=schedule&exclude_pull_requests=true&per_page={num_runs}'''

							SCREAMING_SNAKE_CASE__		: str     =     requests.get(_snake_case     ,headers=_snake_case		).json()

							return result["workflow_runs"]

def lowercase_						(     _snake_case		):
							SCREAMING_SNAKE_CASE__		: Tuple     =     get_daily_ci_runs(_snake_case		)
							SCREAMING_SNAKE_CASE__		: int     =     None
							for workflow_run in workflow_runs:
														if workflow_run["status"] == "completed":
																					SCREAMING_SNAKE_CASE__		: Union[str, Any]     =     workflow_run["""id"""]
																					break

							return workflow_run_id

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							SCREAMING_SNAKE_CASE__		: Dict     =     get_last_daily_ci_runs(_snake_case		)
							if workflow_run_id is not None:
														SCREAMING_SNAKE_CASE__		: Tuple     =     get_artifacts_links(worflow_run_id=_snake_case     ,token=_snake_case		)
														for artifact_name in artifact_names:
																					if artifact_name in artifacts_links:
																												SCREAMING_SNAKE_CASE__		: Tuple     =     artifacts_links[artifact_name]
																												download_artifact(
																												    artifact_name=_snake_case     ,artifact_url=_snake_case     ,output_dir=_snake_case     ,token=_snake_case		)

def lowercase_						(     _snake_case     ,_snake_case     ,_snake_case		):
							get_last_daily_ci_artifacts(_snake_case     ,_snake_case     ,_snake_case		)

							SCREAMING_SNAKE_CASE__		: List[str]     =     {}
							for artifact_name in artifact_names:
														SCREAMING_SNAKE_CASE__		: Optional[Any]     =     os.path.join(_snake_case     ,f'''{artifact_name}.zip'''		)
														if os.path.isfile(_snake_case		):
																					SCREAMING_SNAKE_CASE__		: List[Any]     =     {}
																					with zipfile.ZipFile(_snake_case		) as z:
																												for filename in z.namelist():
																																			if not os.path.isdir(_snake_case		):
																																										# read the file
																																										with z.open(_snake_case		) as f:
																																																	SCREAMING_SNAKE_CASE__		: List[Any]     =     f.read().decode("""UTF-8"""		)

							return results




