

'''simple docstring'''

import inspect
import unittest

import numpy as np

from tests.test_modeling_common import floats_tensor
from transformers import MaskaFormerConfig, is_torch_available, is_vision_available
from transformers.testing_utils import require_torch, require_torch_multi_gpu, require_vision, slow, torch_device
from transformers.utils import cached_property

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
							import torch

							from transformers import MaskaFormerForUniversalSegmentation, MaskaFormerModel

							if is_vision_available():
														from transformers import MaskaFormerImageProcessor

if is_vision_available():
							from PIL import Image






class    snake_case     :






				"""simple docstring"""



				def __init__(    self,     _lowercase,     _lowercase=2,     _lowercase=True,     _lowercase=False,     _lowercase=10,     _lowercase=3,     _lowercase=32 * 8,     _lowercase=32 * 8,     _lowercase=4,     _lowercase=64,     )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				parent
											SCREAMING_SNAKE_CASE_				=				batch_size
											SCREAMING_SNAKE_CASE_				=				is_training
											SCREAMING_SNAKE_CASE_				=				use_auxiliary_loss
											SCREAMING_SNAKE_CASE_				=				num_queries
											SCREAMING_SNAKE_CASE_				=				num_channels
											SCREAMING_SNAKE_CASE_				=				min_size
											SCREAMING_SNAKE_CASE_				=				max_size
											SCREAMING_SNAKE_CASE_				=				num_labels
											SCREAMING_SNAKE_CASE_				=				hidden_dim
											SCREAMING_SNAKE_CASE_				=				hidden_dim



				def   a__					(    self       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				floats_tensor([self.batch_size, self.num_channels, self.min_size, self.max_size]       ).to(
											    _lowercase       )

											SCREAMING_SNAKE_CASE_				=				torch.ones([self.batch_size, self.min_size, self.max_size],     device=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				(
											    torch.rand([self.batch_size, self.num_labels, self.min_size, self.max_size],     device=_lowercase       ) > 0.5
											).float()
											SCREAMING_SNAKE_CASE_				=				(torch.rand((self.batch_size, self.num_labels),     device=_lowercase       ) > 0.5).long()

											SCREAMING_SNAKE_CASE_				=				self.get_config()
											return config, pixel_values, pixel_mask, mask_labels, class_labels



				def   a__					(    self       )     ->							Dict:
											SCREAMING_SNAKE_CASE_				=				MaskaFormerConfig(
											    hidden_size=self.hidden_dim,     )
											SCREAMING_SNAKE_CASE_				=				self.num_queries
											SCREAMING_SNAKE_CASE_				=				self.num_labels

											SCREAMING_SNAKE_CASE_				=				[1, 1, 1, 1]
											SCREAMING_SNAKE_CASE_				=				self.num_channels

											SCREAMING_SNAKE_CASE_				=				64
											SCREAMING_SNAKE_CASE_				=				128
											SCREAMING_SNAKE_CASE_				=				self.hidden_dim
											SCREAMING_SNAKE_CASE_				=				self.hidden_dim
											SCREAMING_SNAKE_CASE_				=				self.hidden_dim
											return config



				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE_				=				{'pixel_values': pixel_values, 'pixel_mask': pixel_mask}
											return config, inputs_dict



				def   a__					(    self,     _lowercase,     _lowercase       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				output.encoder_hidden_states
											SCREAMING_SNAKE_CASE_				=				output.pixel_decoder_hidden_states
											SCREAMING_SNAKE_CASE_				=				output.transformer_decoder_hidden_states

											self.parent.assertTrue(len(_lowercase       ),     len(config.backbone_config.depths       )       )
											self.parent.assertTrue(len(_lowercase       ),     len(config.backbone_config.depths       )       )
											self.parent.assertTrue(len(_lowercase       ),     config.decoder_layers       )



				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase=False       )     ->							int:
											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_				=				MaskaFormerModel(config=_lowercase       )
																		model.to(_lowercase       )
																		model.eval()

																		SCREAMING_SNAKE_CASE_				=				model(pixel_values=_lowercase,     pixel_mask=_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				model(_lowercase,     output_hidden_states=_lowercase       )

											self.parent.assertEqual(
											    output.transformer_decoder_last_hidden_state.shape,     (self.batch_size, self.num_queries, self.hidden_dim),     )
											# let's ensure the other two hidden state exists
											self.parent.assertTrue(output.pixel_decoder_last_hidden_state is not None       )
											self.parent.assertTrue(output.encoder_last_hidden_state is not None       )

											if output_hidden_states:
																		self.check_output_hidden_state(_lowercase,     _lowercase       )





				def   a__					(    self,     _lowercase,     _lowercase,     _lowercase,     _lowercase,     _lowercase       )     ->							Optional[Any]:
											SCREAMING_SNAKE_CASE_				=				MaskaFormerForUniversalSegmentation(config=_lowercase       )
											model.to(_lowercase       )
											model.eval()

											def comm_check_on_output(_lowercase       ):
																		# let's still check that all the required stuff is there
																		self.parent.assertTrue(result.transformer_decoder_last_hidden_state is not None       )
																		self.parent.assertTrue(result.pixel_decoder_last_hidden_state is not None       )
																		self.parent.assertTrue(result.encoder_last_hidden_state is not None       )
																		# okay, now we need to check the logits shape
																		# due to the encoder compression, masks have a //4 spatial size
																		self.parent.assertEqual(
																		    result.masks_queries_logits.shape,     (self.batch_size, self.num_queries, self.min_size // 4, self.max_size // 4),     )
																		# + 1 for null class
																		self.parent.assertEqual(
																		    result.class_queries_logits.shape,     (self.batch_size, self.num_queries, self.num_labels + 1)       )

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_				=				model(pixel_values=_lowercase,     pixel_mask=_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				model(_lowercase       )

																		comm_check_on_output(_lowercase       )

																		SCREAMING_SNAKE_CASE_				=				model(
																		    pixel_values=_lowercase,     pixel_mask=_lowercase,     mask_labels=_lowercase,     class_labels=_lowercase       )

											comm_check_on_output(_lowercase       )

											self.parent.assertTrue(result.loss is not None       )
											self.parent.assertEqual(result.loss.shape,     torch.Size([1]       )       )






@require_torch
class    snake_case     (      lowercase_,						lowercase_,						unittest.TestCase		):






				"""simple docstring"""
				_a    	=     (MaskaFormerModel, MaskaFormerForUniversalSegmentation) if is_torch_available() else ()
				_a    	=     {"""feature-extraction""": MaskaFormerModel} if is_torch_available() else {}

				_a    	=     False
				_a    	=     False
				_a    	=     False
				_a    	=     False



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				MaskaFormerModelTester(self       )
											SCREAMING_SNAKE_CASE_				=				ConfigTester(self,     config_class=_lowercase,     has_text_modality=_lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											self.config_tester.run_common_tests()



				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(_lowercase,     **_lowercase,     output_hidden_states=_lowercase       )



				def   a__					(    self       )     ->							Tuple:
											SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_maskaformer_instance_segmentation_head_model(*_lowercase       )



				@unittest.skip(reason='Mask2Former does not use inputs_embeds'       )
				def   a__					(    self       )     ->							Dict:
											pass



				@unittest.skip(reason='Mask2Former does not have a get_input_embeddings method'       )
				def   a__					(    self       )     ->							List[str]:
											pass



				@unittest.skip(reason='Mask2Former is not a generative model'       )
				def   a__					(    self       )     ->							Optional[Any]:
											pass



				@unittest.skip(reason='Mask2Former does not use token embeddings'       )
				def   a__					(    self       )     ->							int:
											pass



				@require_torch_multi_gpu
				@unittest.skip(
				    reason='Mask2Former has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`'       )
				def   a__					(    self       )     ->							List[str]:
											pass



				@unittest.skip('Will be fixed soon by reducing the size of the model used for common tests.'       )
				def   a__					(    self       )     ->							Optional[Any]:
											pass



				def   a__					(    self       )     ->							str:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()

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

																		SCREAMING_SNAKE_CASE_				=				['pixel_values']
																		self.assertListEqual(arg_names[:1],     _lowercase       )



				@slow
				def   a__					(    self       )     ->							List[Any]:
											for model_name in ["facebook/mask2former-swin-small-coco-instance"]:
																		SCREAMING_SNAKE_CASE_				=				MaskaFormerModel.from_pretrained(_lowercase       )
																		self.assertIsNotNone(_lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				(self.model_tester.min_size,) * 2
											SCREAMING_SNAKE_CASE_				=				{
											    'pixel_values': torch.randn((2, 3, *size),     device=_lowercase       ),
											    'mask_labels': torch.randn((2, 10, *size),     device=_lowercase       ),
											    'class_labels': torch.zeros(2,     10,     device=_lowercase       ).long(),
											}
											SCREAMING_SNAKE_CASE_				=				self.model_tester.get_config()

											SCREAMING_SNAKE_CASE_				=				MaskaFormerForUniversalSegmentation(_lowercase       ).to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				model(**_lowercase       )
											self.assertTrue(outputs.loss is not None       )



				def   a__					(    self       )     ->							List[str]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()
											self.model_tester.create_and_check_maskaformer_model(_lowercase,     **_lowercase,     output_hidden_states=_lowercase       )



				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																		SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       ).to(_lowercase       )
																		SCREAMING_SNAKE_CASE_				=				model(**_lowercase,     output_attentions=_lowercase       )
																		self.assertTrue(outputs.attentions is not None       )



				def   a__					(    self       )     ->							int:
											if not self.model_tester.is_training:
																		return

											SCREAMING_SNAKE_CASE_				=				self.all_model_classes[1]
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs()

											SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       )
											model.to(_lowercase       )
											model.train()

											SCREAMING_SNAKE_CASE_				=				model(_lowercase,     mask_labels=_lowercase,     class_labels=_lowercase       ).loss
											loss.backward()





				def   a__					(    self       )     ->							Union[str, Any]:
											SCREAMING_SNAKE_CASE_				=				self.all_model_classes[1]
											SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				,    SCREAMING_SNAKE_CASE_				=				self.model_tester.prepare_config_and_inputs()
											SCREAMING_SNAKE_CASE_				=				True
											SCREAMING_SNAKE_CASE_				=				True

											SCREAMING_SNAKE_CASE_				=				model_class(_lowercase       ).to(_lowercase       )
											model.train()

											SCREAMING_SNAKE_CASE_				=				model(_lowercase,     mask_labels=_lowercase,     class_labels=_lowercase       )

											SCREAMING_SNAKE_CASE_				=				outputs.encoder_hidden_states[0]
											encoder_hidden_states.retain_grad()

											SCREAMING_SNAKE_CASE_				=				outputs.pixel_decoder_hidden_states[0]
											pixel_decoder_hidden_states.retain_grad()

											SCREAMING_SNAKE_CASE_				=				outputs.transformer_decoder_hidden_states[0]
											transformer_decoder_hidden_states.retain_grad()

											SCREAMING_SNAKE_CASE_				=				outputs.attentions[0]
											attentions.retain_grad()

											outputs.loss.backward(retain_graph=_lowercase       )

											self.assertIsNotNone(encoder_hidden_states.grad       )
											self.assertIsNotNone(pixel_decoder_hidden_states.grad       )
											self.assertIsNotNone(transformer_decoder_hidden_states.grad       )
											self.assertIsNotNone(attentions.grad       )


SCREAMING_SNAKE_CASE							:				Dict     	=			1E-4




def 							_UpperCamelCase				(   )      -> Any:
							SCREAMING_SNAKE_CASE_				=				Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
							return image






@require_vision
@slow
class    snake_case     (      unittest.TestCase		):






				"""simple docstring"""



				@cached_property
				def   a__					(    self       )     ->							List[Any]:
											return "facebook/mask2former-swin-small-coco-instance"



				@cached_property
				def   a__					(    self       )     ->							str:
											return MaskaFormerImageProcessor.from_pretrained(self.model_checkpoints       ) if is_vision_available() else None



				def   a__					(    self       )     ->							List[Any]:
											SCREAMING_SNAKE_CASE_				=				MaskaFormerModel.from_pretrained(self.model_checkpoints       ).to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				self.default_image_processor
											SCREAMING_SNAKE_CASE_				=				prepare_img()
											SCREAMING_SNAKE_CASE_				=				image_processor(_lowercase,     return_tensors='pt'       ).to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				inputs['pixel_values'].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
											# check size
											self.assertEqual(_lowercase,     (1, 3, 384, 384)       )

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_				=				model(**_lowercase       )

											SCREAMING_SNAKE_CASE_				=				torch.tensor(
											    [[-0.2_790, -1.0_717, -1.1_668], [-0.5_128, -0.3_128, -0.4_987], [-0.5_832, 0.1_971, -0.0_197]]       ).to(_lowercase       )
											self.assertTrue(
											    torch.allclose(
											        outputs.encoder_last_hidden_state[0, 0, :3, :3],     _lowercase,     atol=_lowercase       )       )

											SCREAMING_SNAKE_CASE_				=				torch.tensor(
											    [[0.8_973, 1.1_847, 1.1_776], [1.1_934, 1.5_040, 1.5_128], [1.1_153, 1.4_486, 1.4_951]]       ).to(_lowercase       )
											self.assertTrue(
											    torch.allclose(
											        outputs.pixel_decoder_last_hidden_state[0, 0, :3, :3],     _lowercase,     atol=_lowercase       )       )

											SCREAMING_SNAKE_CASE_				=				torch.tensor(
											    [[2.1_152, 1.7_000, -0.8_603], [1.5_808, 1.8_004, -0.9_353], [1.6_043, 1.7_495, -0.5_999]]       ).to(_lowercase       )
											self.assertTrue(
											    torch.allclose(
											        outputs.transformer_decoder_last_hidden_state[0, :3, :3],     _lowercase,     atol=_lowercase       )       )



				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_lowercase       ).eval()
											SCREAMING_SNAKE_CASE_				=				self.default_image_processor
											SCREAMING_SNAKE_CASE_				=				prepare_img()
											SCREAMING_SNAKE_CASE_				=				image_processor(_lowercase,     return_tensors='pt'       ).to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				inputs['pixel_values'].shape
											# check size is divisible by 32
											self.assertTrue((inputs_shape[-1] % 32) == 0 and (inputs_shape[-2] % 32) == 0       )
											# check size
											self.assertEqual(_lowercase,     (1, 3, 384, 384)       )

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_				=				model(**_lowercase       )
											# masks_queries_logits
											SCREAMING_SNAKE_CASE_				=				outputs.masks_queries_logits
											self.assertEqual(
											    masks_queries_logits.shape,     (1, model.config.num_queries, inputs_shape[-2] // 4, inputs_shape[-1] // 4)       )
											SCREAMING_SNAKE_CASE_				=				[
											    [-8.7_839, -9.0_056, -8.8_121],
											    [-7.4_104, -7.0_313, -6.5_401],
											    [-6.6_105, -6.3_427, -6.4_675],
											]
											SCREAMING_SNAKE_CASE_				=				torch.tensor(_lowercase       ).to(_lowercase       )
											self.assertTrue(torch.allclose(masks_queries_logits[0, 0, :3, :3],     _lowercase,     atol=_lowercase       )       )
											# class_queries_logits
											SCREAMING_SNAKE_CASE_				=				outputs.class_queries_logits
											self.assertEqual(class_queries_logits.shape,     (1, model.config.num_queries, model.config.num_labels + 1)       )
											SCREAMING_SNAKE_CASE_				=				torch.tensor(
											    [
											        [1.8_324, -8.0_835, -4.1_922],
											        [0.8_450, -9.0_050, -3.6_053],
											        [0.3_045, -7.7_293, -3.0_275],
											    ]       ).to(_lowercase       )
											self.assertTrue(torch.allclose(outputs.class_queries_logits[0, :3, :3],     _lowercase,     atol=_lowercase       )       )





				def   a__					(    self       )     ->							Any:
											SCREAMING_SNAKE_CASE_				=				MaskaFormerForUniversalSegmentation.from_pretrained(self.model_checkpoints       ).to(_lowercase       ).eval()
											SCREAMING_SNAKE_CASE_				=				self.default_image_processor

											SCREAMING_SNAKE_CASE_				=				image_processor(
											    [np.zeros((3, 800, 1333)       ), np.zeros((3, 800, 1333)       )],     segmentation_maps=[np.zeros((384, 384)       ).astype(np.floataa       ), np.zeros((384, 384)       ).astype(np.floataa       )],     return_tensors='pt',     )

											SCREAMING_SNAKE_CASE_				=				inputs['pixel_values'].to(_lowercase       )
											SCREAMING_SNAKE_CASE_				=				[el.to(_lowercase       ) for el in inputs['mask_labels']]
											SCREAMING_SNAKE_CASE_				=				[el.to(_lowercase       ) for el in inputs['class_labels']]

											with torch.no_grad():
																		SCREAMING_SNAKE_CASE_				=				model(**_lowercase       )

											self.assertTrue(outputs.loss is not None       )








'''simple docstring'''

import argparse
import json
from collections import OrderedDict
from functools import partial
from pathlib import Path

import timm
import torch
from huggingface_hub import hf_hub_download

from transformers import LevitConfig, LevitForImageClassificationWithTeacher, LevitImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE							:				Dict     	=			logging.get_logger()




def 							_UpperCamelCase				(   lowerCAmelCase__:					int		,lowerCAmelCase__:					str		,lowerCAmelCase__:					LevitConfig		,lowerCAmelCase__:					Path		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							print(F"""Converting {name}..."""   )

							with torch.no_grad():
														if hidden_sizes == 128:
																					if name[-1] == "S":
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128s'		,pretrained=lowerCAmelCase__   )
																					else:
																												SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_128'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 192:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_192'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 256:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_256'		,pretrained=lowerCAmelCase__   )
														if hidden_sizes == 384:
																					SCREAMING_SNAKE_CASE_				=				timm.create_model('levit_384'		,pretrained=lowerCAmelCase__   )

														from_model.eval()
														SCREAMING_SNAKE_CASE_				=				LevitForImageClassificationWithTeacher(lowerCAmelCase__   ).eval()
														SCREAMING_SNAKE_CASE_				=				OrderedDict()

														SCREAMING_SNAKE_CASE_				=				from_model.state_dict()
														SCREAMING_SNAKE_CASE_				=				list(from_model.state_dict().keys()   )
														SCREAMING_SNAKE_CASE_				=				list(our_model.state_dict().keys()   )
														print(len(lowerCAmelCase__   )		,len(lowerCAmelCase__   )   )
														for i in range(len(lowerCAmelCase__   )   ):
																					SCREAMING_SNAKE_CASE_				=				weights[og_keys[i]]
														our_model.load_state_dict(lowerCAmelCase__   )

														SCREAMING_SNAKE_CASE_				=				torch.randn((2, 3, 224, 224)   )
														SCREAMING_SNAKE_CASE_				=				from_model(lowerCAmelCase__   )
														SCREAMING_SNAKE_CASE_				=				our_model(lowerCAmelCase__   ).logits

							assert torch.allclose(lowerCAmelCase__		,lowerCAmelCase__   ), "The model logits don't match the original one."

							SCREAMING_SNAKE_CASE_				=				name
							print(lowerCAmelCase__   )

							if push_to_hub:
														our_model.save_pretrained(save_directory / checkpoint_name   )
														SCREAMING_SNAKE_CASE_				=				LevitImageProcessor()
														image_processor.save_pretrained(save_directory / checkpoint_name   )

														print(F"""Pushed {checkpoint_name}"""   )




def 							_UpperCamelCase				(   lowerCAmelCase__:					Path		,lowerCAmelCase__:					str = None		,lowerCAmelCase__:					bool = True   )      -> Optional[int]:
							SCREAMING_SNAKE_CASE_				=				'imagenet-1k-id2label.json'
							SCREAMING_SNAKE_CASE_				=				1000
							SCREAMING_SNAKE_CASE_				=				(1, num_labels)

							SCREAMING_SNAKE_CASE_				=				'huggingface/label-files'
							SCREAMING_SNAKE_CASE_				=				num_labels
							SCREAMING_SNAKE_CASE_				=				json.load(open(hf_hub_download(lowerCAmelCase__		,lowerCAmelCase__		,repo_type='dataset'   )		,'r'   )   )
							SCREAMING_SNAKE_CASE_				=				{int(lowerCAmelCase__   ): v for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				idalabel
							SCREAMING_SNAKE_CASE_				=				{v: k for k, v in idalabel.items()}

							SCREAMING_SNAKE_CASE_				=				partial(lowerCAmelCase__		,num_labels=lowerCAmelCase__		,idalabel=lowerCAmelCase__		,labelaid=lowerCAmelCase__   )

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': 128,
							    'levit-128': 128,
							    'levit-192': 192,
							    'levit-256': 256,
							    'levit-384': 384,
							}

							SCREAMING_SNAKE_CASE_				=				{
							    'levit-128S': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 6, 8]		,depths=[2, 3, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-128': ImageNetPreTrainedConfig(
							        hidden_sizes=[128, 256, 384]		,num_attention_heads=[4, 8, 12]		,depths=[4, 4, 4]		,key_dim=[16, 16, 16]		,drop_path_rate=0		,),
							    'levit-192': ImageNetPreTrainedConfig(
							        hidden_sizes=[192, 288, 384]		,num_attention_heads=[3, 5, 6]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-256': ImageNetPreTrainedConfig(
							        hidden_sizes=[256, 384, 512]		,num_attention_heads=[4, 6, 8]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0		,),
							    'levit-384': ImageNetPreTrainedConfig(
							        hidden_sizes=[384, 512, 768]		,num_attention_heads=[6, 9, 12]		,depths=[4, 4, 4]		,key_dim=[32, 32, 32]		,drop_path_rate=0.1		,),
							}

							if model_name:
														convert_weight_and_push(
														    names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,names_to_config[model_name]		,lowerCAmelCase__		,lowerCAmelCase__   )
							else:
														for model_name, config in names_to_config.items():
																					convert_weight_and_push(names_to_hidden_sizes[model_name]		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__		,lowerCAmelCase__   )
							return config, expected_shape


if __name__ == "__main__":
							SCREAMING_SNAKE_CASE							:				str     	=			argparse.ArgumentParser()
							# Required parameters
							parser.add_argument(
							    "--model_name",
							    default=None,
							    type=str,
							    help="The name of the model you wish to convert, it must be one of the supported Levit* architecture,",
							)
							parser.add_argument(
							    "--pytorch_dump_folder_path",
							    default="levit-dump-folder/",
							    type=Path,
							    required=False,
							    help="Path to the output PyTorch model directory.",
							)
							parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")
							parser.add_argument(
							    "--no-push_to_hub",
							    dest="push_to_hub",
							    action="store_false",
							    help="Do not push model and image processor to the hub",
							)

							SCREAMING_SNAKE_CASE							:				Optional[Any]     	=			parser.parse_args()
							SCREAMING_SNAKE_CASE							:				Path     	=			args.pytorch_dump_folder_path
							pytorch_dump_folder_path.mkdir(exist_ok=True, parents=True)
							convert_weights_and_push(pytorch_dump_folder_path, args.model_name, args.push_to_hub)





