
import inspect
import unittest

from transformers import MobileViTVaConfig
from transformers.testing_utils import require_torch, require_torch_multi_gpu, 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 transformers import MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation, MobileViTVaModel
					from transformers.models.mobilevitva.modeling_mobilevitva import (
					    MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST,
					    make_divisible,
					)


if is_vision_available():
					from PIL import Image

					from transformers import MobileViTImageProcessor





class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE  ):




	'''simple docstring'''





	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					__A   			= self.config_class(**self.inputs_dict )
					self.parent.assertTrue(hasattr(A							,"width_multiplier" ) )





class 							UpperCAmelCase      :




	'''simple docstring'''





	def __init__(       self					: List[str]							,A					: List[Any]							,A					: Optional[int]=13							,A					: Dict=64							,A					: Optional[Any]=2							,A					: Optional[int]=3							,A					: int="swish"							,A					: Tuple=3							,A					: Tuple=32							,A					: int=0.1							,A					: Any=0.02							,A					: Any=True							,A					: Optional[int]=True							,A					: Tuple=10							,A					: Any=None							,A					: Any=0.25							,A					: Tuple=0.0							,A					: Optional[int]=0.0							,):
					__A   			= parent
					__A   			= batch_size
					__A   			= image_size
					__A   			= patch_size
					__A   			= num_channels
					__A   			= make_divisible(5_12 * width_multiplier							,divisor=8 )
					__A   			= hidden_act
					__A   			= conv_kernel_size
					__A   			= output_stride
					__A   			= classifier_dropout_prob
					__A   			= use_labels
					__A   			= is_training
					__A   			= num_labels
					__A   			= initializer_range
					__A   			= scope
					__A   			= width_multiplier
					__A   			= ffn_dropout
					__A   			= attn_dropout






	def 				UpperCamelCase_  (       self					: Tuple ):
					__A   			= floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size] )

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

					__A   			= self.get_config()

					return config, pixel_values, labels, pixel_labels






	def 				UpperCamelCase_  (       self					: List[Any] ):
					return MobileViTVaConfig(
					    image_size=self.image_size							,patch_size=self.patch_size							,num_channels=self.num_channels							,hidden_act=self.hidden_act							,conv_kernel_size=self.conv_kernel_size							,output_stride=self.output_stride							,classifier_dropout_prob=self.classifier_dropout_prob							,initializer_range=self.initializer_range							,width_multiplier=self.width_multiplier							,ffn_dropout=self.ffn_dropout_prob							,attn_dropout=self.attn_dropout_prob							,)






	def 				UpperCamelCase_  (       self					: int							,A					: Any							,A					: Any							,A					: Union[str, Any]							,A					: Optional[int] ):
					__A   			= MobileViTVaModel(config=A )
					model.to(A )
					model.eval()
					__A   			= model(A )
					self.parent.assertEqual(
					    result.last_hidden_state.shape							,(
					        self.batch_size,
					        self.last_hidden_size,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )							,)






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Optional[Any]							,A					: Optional[Any]							,A					: int							,A					: Tuple ):
					__A   			= self.num_labels
					__A   			= MobileViTVaForImageClassification(A )
					model.to(A )
					model.eval()
					__A   			= model(A							,labels=A )
					self.parent.assertEqual(result.logits.shape							,(self.batch_size, self.num_labels) )






	def 				UpperCamelCase_  (       self					: List[Any]							,A					: Dict							,A					: List[str]							,A					: Union[str, Any]							,A					: int ):
					__A   			= self.num_labels
					__A   			= MobileViTVaForSemanticSegmentation(A )
					model.to(A )
					model.eval()
					__A   			= model(A )
					self.parent.assertEqual(
					    result.logits.shape							,(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )							,)
					__A   			= model(A							,labels=A )
					self.parent.assertEqual(
					    result.logits.shape							,(
					        self.batch_size,
					        self.num_labels,
					        self.image_size // self.output_stride,
					        self.image_size // self.output_stride,
					    )							,)







	def 				UpperCamelCase_  (       self					: Dict ):
					__A   			= self.prepare_config_and_inputs()
					__A			,							__A			,							__A			,							__A   			= config_and_inputs
					__A   			= {"pixel_values": pixel_values}
					return config, inputs_dict





@require_torch
class 							UpperCAmelCase      (  __SCREAMING_SNAKE_CASE			,			__SCREAMING_SNAKE_CASE			,			unittest.TestCase  ):




	'''simple docstring'''





	snake_case_					    =     (
	    (MobileViTVaModel, MobileViTVaForImageClassification, MobileViTVaForSemanticSegmentation)
	    if is_torch_available()
	    else ()
	)

	snake_case_					    =     (
	    {
	        "feature-extraction": MobileViTVaModel,
	        "image-classification": MobileViTVaForImageClassification,
	        "image-segmentation": MobileViTVaForSemanticSegmentation,
	    }
	    if is_torch_available()
	    else {}
	)

	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False
	snake_case_					    =     False






	def 				UpperCamelCase_  (       self					: Union[str, Any] ):
					__A   			= MobileViTVaModelTester(self )
					__A   			= MobileViTVaConfigTester(self							,config_class=A							,has_text_modality=A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					self.config_tester.run_common_tests()






	@unittest.skip(reason="MobileViTV2 does not use inputs_embeds" )
	def 				UpperCamelCase_  (       self					: List[str] ):
					pass






	@unittest.skip(reason="MobileViTV2 does not support input and output embeddings" )
	def 				UpperCamelCase_  (       self					: List[Any] ):
					pass






	@unittest.skip(reason="MobileViTV2 does not output attentions" )
	def 				UpperCamelCase_  (       self					: List[Any] ):
					pass






	@require_torch_multi_gpu
	@unittest.skip(reason="Got `CUDA error: misaligned address` for tests after this one being run." )
	def 				UpperCamelCase_  (       self					: int ):
					pass






	@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests." )
	def 				UpperCamelCase_  (       self					: Optional[int] ):
					pass






	def 				UpperCamelCase_  (       self					: Dict ):
					__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 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_model(*A )






	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					def check_hidden_states_output(A					: Dict							,A					: Optional[int]							,A					: Any ):
									__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   			= 5
									self.assertEqual(len(A )							,A )

									# MobileViTV2's feature maps are of shape (batch_size, num_channels, height, width)
									# with the width and height being successively divided by 2.
									__A   			= 2
									for i in range(len(A ) ):
													self.assertListEqual(
													    list(hidden_states[i].shape[-2:] )							,[self.model_tester.image_size // divisor, self.model_tester.image_size // divisor]							,)
													divisor *= 2

									self.assertEqual(self.model_tester.output_stride							,divisor // 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 				UpperCamelCase_  (       self					: Any ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_image_classification(*A )






	def 				UpperCamelCase_  (       self					: Optional[int] ):
					__A   			= self.model_tester.prepare_config_and_inputs()
					self.model_tester.create_and_check_for_semantic_segmentation(*A )







	@slow
	def 				UpperCamelCase_  (       self					: Optional[Any] ):
					for model_name in MOBILEVITV2_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
									__A   			= MobileViTVaModel.from_pretrained(A )
									self.assertIsNotNone(A )





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







				"""simple docstring"""



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





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




	'''simple docstring'''





	@cached_property
	def 				UpperCamelCase_  (       self					: Any ):
					return (
					    MobileViTImageProcessor.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" )
					    if is_vision_available()
					    else None
					)






	@slow
	def 				UpperCamelCase_  (       self					: str ):
					__A   			= MobileViTVaForImageClassification.from_pretrained("apple/mobilevitv2-1.0-imagenet1k-256" ).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, 10_00) )
					self.assertEqual(outputs.logits.shape							,A )

					__A   			= torch.tensor([-1.6336E00, -7.3204E-02, -5.1883E-01] ).to(A )

					self.assertTrue(torch.allclose(outputs.logits[0, :3]							,A							,atol=1E-4 ) )






	@slow
	def 				UpperCamelCase_  (       self					: int ):
					__A   			= MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
					__A   			= model.to(A )

					__A   			= MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )

					__A   			= prepare_img()
					__A   			= image_processor(images=A							,return_tensors="pt" ).to(A )

					# forward pass
					with torch.no_grad():
									__A   			= model(**A )
					__A   			= outputs.logits

					# verify the logits
					__A   			= torch.Size((1, 21, 32, 32) )
					self.assertEqual(logits.shape							,A )

					__A   			= torch.tensor(
					    [
					        [[7.08_63, 7.15_25, 6.82_01], [6.69_31, 6.87_70, 6.89_33], [6.29_78, 7.03_66, 6.96_36]],
					        [[-3.71_34, -3.67_12, -3.66_75], [-3.58_25, -3.35_49, -3.47_77], [-3.34_35, -3.39_79, -3.28_57]],
					        [[-2.93_29, -2.80_03, -2.73_69], [-3.05_64, -2.47_80, -2.02_07], [-2.68_89, -1.92_98, -1.76_40]],
					    ]							,device=A							,)

					self.assertTrue(torch.allclose(logits[0, :3, :3, :3]							,A							,atol=1E-4 ) )







	@slow
	def 				UpperCamelCase_  (       self					: List[Any] ):
					__A   			= MobileViTVaForSemanticSegmentation.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )
					__A   			= model.to(A )

					__A   			= MobileViTImageProcessor.from_pretrained("shehan97/mobilevitv2-1.0-voc-deeplabv3" )

					__A   			= prepare_img()
					__A   			= image_processor(images=A							,return_tensors="pt" ).to(A )

					# forward pass
					with torch.no_grad():
									__A   			= model(**A )

					__A   			= outputs.logits.detach().cpu()

					__A   			= image_processor.post_process_semantic_segmentation(outputs=A							,target_sizes=[(50, 60)] )
					__A   			= torch.Size((50, 60) )
					self.assertEqual(segmentation[0].shape							,A )

					__A   			= image_processor.post_process_semantic_segmentation(outputs=A )
					__A   			= torch.Size((32, 32) )
					self.assertEqual(segmentation[0].shape							,A )


import argparse
import math
import traceback

import dateutil.parser as date_parser
import requests


def 						UpperCAmelCase			(	a_						)		->	str:







				"""simple docstring"""



				__A   			= {}

				__A   			= job["started_at"]
				__A   			= job["completed_at"]

				__A   			= date_parser.parse(a_						)
				__A   			= date_parser.parse(a_						)

				__A   			= round((end_datetime - start_datetime).total_seconds() / 60.0						)

				__A   			= start
				__A   			= end
				__A   			= duration_in_min

				return job_info


def 						UpperCAmelCase			(	a_		,   a_=None						)		->	str:







				"""simple docstring"""



				__A   			= None
				if token is not None:
								__A   			= {"Accept": "application/vnd.github+json", "Authorization": F'''Bearer {token}'''}

				__A   			= F'''https://api.github.com/repos/huggingface/transformers/actions/runs/{workflow_run_id}/jobs?per_page=100'''
				__A   			= requests.get(a_		,   headers=a_						).json()
				__A   			= {}

				try:
								job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)
								__A   			= math.ceil((result["total_count"] - 1_0_0) / 1_0_0						)

								for i in range(a_						):
												__A   			= requests.get(url + F'''&page={i + 2}'''		,   headers=a_						).json()
												job_time.update({job["name"]: extract_time_from_single_job(a_						) for job in result["jobs"]}						)

								return job_time
				except Exception:
								print(F'''Unknown error, could not fetch links:\n{traceback.format_exc()}'''						)

				return {}


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				argparse.ArgumentParser()
					# Required parameters
					parser.add_argument('--workflow_run_id', type=str, required=True, help='A GitHub Actions workflow run id.')
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				parser.parse_args()

					SCREAMING_SNAKE_CASE							:Union[str, Any]							 =				get_job_time(args.workflow_run_id)
					SCREAMING_SNAKE_CASE							:Optional[int]							 =				dict(sorted(job_time.items(), key=lambda item: item[1]["duration"], reverse=True))

					for k, v in job_time.items():
										print(f'''{k}: {v["duration"]}''')
