





'''simple docstring'''




import inspect
import unittest

from transformers import ViTMSNConfig
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 ViTMSNForImageClassification, ViTMSNModel
			from transformers.models.vit_msn.modeling_vit_msn import VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
			from PIL import Image

			from transformers import ViTImageProcessor






class 				__snake_case     :





	def __init__(		self      ,       UpperCamelCase_      ,       UpperCamelCase_=13      ,       UpperCamelCase_=30      ,       UpperCamelCase_=2      ,       UpperCamelCase_=3      ,       UpperCamelCase_=True      ,       UpperCamelCase_=True      ,       UpperCamelCase_=32      ,       UpperCamelCase_=5      ,       UpperCamelCase_=4      ,       UpperCamelCase_=37      ,       UpperCamelCase_="gelu"      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=0.1      ,       UpperCamelCase_=10      ,       UpperCamelCase_=0.0_2      ,       UpperCamelCase_=None      ,       )      ->		Union[str, Any]:
		snake_case__	       =   parent
		snake_case__	       =   batch_size
		snake_case__	       =   image_size
		snake_case__	       =   patch_size
		snake_case__	       =   num_channels
		snake_case__	       =   is_training
		snake_case__	       =   use_labels
		snake_case__	       =   hidden_size
		snake_case__	       =   num_hidden_layers
		snake_case__	       =   num_attention_heads
		snake_case__	       =   intermediate_size
		snake_case__	       =   hidden_act
		snake_case__	       =   hidden_dropout_prob
		snake_case__	       =   attention_probs_dropout_prob
		snake_case__	       =   type_sequence_label_size
		snake_case__	       =   initializer_range
		snake_case__	       =   scope

		# in ViT MSN, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
		snake_case__	       =   (image_size // patch_size) ** 2
		snake_case__	       =   num_patches + 1





	def   _snake_case   (		self		)      ->		Tuple:
		snake_case__	       =   floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]		)

		snake_case__	       =   None
		if self.use_labels:
			snake_case__	       =   ids_tensor([self.batch_size]      ,       self.type_sequence_label_size		)

		snake_case__	       =   self.get_config()

		return config, pixel_values, labels





	def   _snake_case   (		self		)      ->		Any:
		return ViTMSNConfig(
		    image_size=self.image_size      ,       patch_size=self.patch_size      ,       num_channels=self.num_channels      ,       hidden_size=self.hidden_size      ,       num_hidden_layers=self.num_hidden_layers      ,       num_attention_heads=self.num_attention_heads      ,       intermediate_size=self.intermediate_size      ,       hidden_act=self.hidden_act      ,       hidden_dropout_prob=self.hidden_dropout_prob      ,       attention_probs_dropout_prob=self.attention_probs_dropout_prob      ,       initializer_range=self.initializer_range      ,       )





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[int]:
		snake_case__	       =   ViTMSNModel(config=UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_		)
		self.parent.assertEqual(result.last_hidden_state.shape      ,       (self.batch_size, self.seq_length, self.hidden_size)		)





	def   _snake_case   (		self      ,       UpperCamelCase_      ,       UpperCamelCase_      ,       UpperCamelCase_		)      ->		Optional[Any]:
		snake_case__	       =   self.type_sequence_label_size
		snake_case__	       =   ViTMSNForImageClassification(UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()
		snake_case__	       =   model(UpperCamelCase_      ,       labels=UpperCamelCase_		)
		print('Pixel and labels shape: {pixel_values.shape}, {labels.shape}'		)
		print('Labels: {labels}'		)
		self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)		)

		# test greyscale images
		snake_case__	       =   1
		snake_case__	       =   ViTMSNForImageClassification(UpperCamelCase_		)
		model.to(UpperCamelCase_		)
		model.eval()

		snake_case__	       =   floats_tensor([self.batch_size, 1, self.image_size, self.image_size]		)
		snake_case__	       =   model(UpperCamelCase_		)
		self.parent.assertEqual(result.logits.shape      ,       (self.batch_size, self.type_sequence_label_size)		)





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__	       =   self.prepare_config_and_inputs()
		snake_case__       ,  snake_case__       ,  snake_case__	       =   config_and_inputs
		snake_case__	       =   {'pixel_values': pixel_values}
		return config, inputs_dict






@require_torch
class 				__snake_case     (					__magic_name__       ,       __magic_name__       ,       unittest.TestCase							):
	__lowerCAmelCase       						=  (ViTMSNModel, ViTMSNForImageClassification) if is_torch_available() else ()
	__lowerCAmelCase       						=  (
	    {'''feature-extraction''': ViTMSNModel, '''image-classification''': ViTMSNForImageClassification}
	    if is_torch_available()
	    else {}
	)

	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False
	__lowerCAmelCase       						=  False





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__	       =   ViTMSNModelTester(self		)
		snake_case__	       =   ConfigTester(self      ,       config_class=UpperCamelCase_      ,       has_text_modality=UpperCamelCase_      ,       hidden_size=37		)





	def   _snake_case   (		self		)      ->		List[str]:
		self.config_tester.run_common_tests()





	@unittest.skip(reason='ViTMSN does not use inputs_embeds'		)
	def   _snake_case   (		self		)      ->		Tuple:
		pass





	def   _snake_case   (		self		)      ->		Optional[int]:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

		for model_class in self.all_model_classes:
			snake_case__	       =   model_class(UpperCamelCase_		)
			self.assertIsInstance(model.get_input_embeddings()      ,       (nn.Module)		)
			snake_case__	       =   model.get_output_embeddings()
			self.assertTrue(x is None or isinstance(UpperCamelCase_      ,       nn.Linear		)		)





	def   _snake_case   (		self		)      ->		Dict:
		snake_case__       ,  snake_case__	       =   self.model_tester.prepare_config_and_inputs_for_common()

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

			snake_case__	       =   ['pixel_values']
			self.assertListEqual(arg_names[:1]      ,       UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		Any:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_model(*UpperCamelCase_		)





	def   _snake_case   (		self		)      ->		List[Any]:
		snake_case__	       =   self.model_tester.prepare_config_and_inputs()
		self.model_tester.create_and_check_for_image_classification(*UpperCamelCase_		)





	@slow
	def   _snake_case   (		self		)      ->		Dict:
		for model_name in VIT_MSN_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
			snake_case__	       =   ViTMSNModel.from_pretrained(UpperCamelCase_		)
			self.assertIsNotNone(UpperCamelCase_		)





def 						__lowerCamelCase			(	)   ->Union[str, Any]:
	snake_case__	       =   Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'							)
	return image







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





	@cached_property
	def   _snake_case   (		self		)      ->		int:
		return ViTImageProcessor.from_pretrained('facebook/vit-msn-small'		) if is_vision_available() else None





	@slow
	def   _snake_case   (		self		)      ->		List[str]:
		torch.manual_seed(2		)
		snake_case__	       =   ViTMSNForImageClassification.from_pretrained('facebook/vit-msn-small'		).to(UpperCamelCase_		)

		snake_case__	       =   self.default_image_processor
		snake_case__	       =   prepare_img()
		snake_case__	       =   image_processor(images=UpperCamelCase_      ,       return_tensors='pt'		).to(UpperCamelCase_		)

		# forward pass
		with torch.no_grad():
			snake_case__	       =   model(**UpperCamelCase_		)

		# verify the logits
		snake_case__	       =   torch.Size((1, 1000)		)
		self.assertEqual(outputs.logits.shape      ,       UpperCamelCase_		)

		snake_case__	       =   torch.tensor([-0.0_8_0_3, -0.4_4_5_4, -0.2_3_7_5]		).to(UpperCamelCase_		)

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










'''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()
a__					:			Any          =     logging.get_logger()




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_ = True							)   ->Dict:
	print(f'''Converting {name}...'''							)

	with torch.no_grad():
		if hidden_sizes == 1_28:
			if name[-1] == "S":
				snake_case__	       =   timm.create_model('levit_128s'					,				pretrained=UpperCAmelCase_							)
			else:
				snake_case__	       =   timm.create_model('levit_128'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 1_92:
			snake_case__	       =   timm.create_model('levit_192'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 2_56:
			snake_case__	       =   timm.create_model('levit_256'					,				pretrained=UpperCAmelCase_							)
		if hidden_sizes == 3_84:
			snake_case__	       =   timm.create_model('levit_384'					,				pretrained=UpperCAmelCase_							)

		from_model.eval()
		snake_case__	       =   LevitForImageClassificationWithTeacher(UpperCAmelCase_							).eval()
		snake_case__	       =   OrderedDict()

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

		snake_case__	       =   torch.randn((2, 3, 2_24, 2_24)							)
		snake_case__	       =   from_model(UpperCAmelCase_							)
		snake_case__	       =   our_model(UpperCAmelCase_							).logits

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

	snake_case__	       =   name
	print(UpperCAmelCase_							)

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

		print(f'''Pushed {checkpoint_name}'''							)




def 						__lowerCamelCase			(	UpperCAmelCase_					,				UpperCAmelCase_ = None					,				UpperCAmelCase_ = True							)   ->Any:
	snake_case__	       =   'imagenet-1k-id2label.json'
	snake_case__	       =   10_00
	snake_case__	       =   (1, num_labels)

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

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

	snake_case__	       =   partial(UpperCAmelCase_					,				num_labels=UpperCAmelCase_					,				idalabel=UpperCAmelCase_					,				labelaid=UpperCAmelCase_							)

	snake_case__	       =   {
	    'levit-128S': 1_28,
	    'levit-128': 1_28,
	    'levit-192': 1_92,
	    'levit-256': 2_56,
	    'levit-384': 3_84,
	}

	snake_case__	       =   {
	    'levit-128S': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 6, 8]					,				depths=[2, 3, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-128': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_28, 2_56, 3_84]					,				num_attention_heads=[4, 8, 12]					,				depths=[4, 4, 4]					,				key_dim=[16, 16, 16]					,				drop_path_rate=0					,				),
	    'levit-192': ImageNetPreTrainedConfig(
	        hidden_sizes=[1_92, 2_88, 3_84]					,				num_attention_heads=[3, 5, 6]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-256': ImageNetPreTrainedConfig(
	        hidden_sizes=[2_56, 3_84, 5_12]					,				num_attention_heads=[4, 6, 8]					,				depths=[4, 4, 4]					,				key_dim=[32, 32, 32]					,				drop_path_rate=0					,				),
	    'levit-384': ImageNetPreTrainedConfig(
	        hidden_sizes=[3_84, 5_12, 7_68]					,				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]					,				UpperCAmelCase_					,				names_to_config[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	else:
		for model_name, config in names_to_config.items():
			convert_weight_and_push(names_to_hidden_sizes[model_name]					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_					,				UpperCAmelCase_							)
	return config, expected_shape


if __name__ == "__main__":
			a__					:			Optional[int]          =     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''',
			)

			a__					:			Optional[Any]          =     parser.parse_args()
			a__					:			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)



