



"""simple docstring"""







import inspect
import unittest

from transformers import BitConfig
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_backbone_common import BackboneTesterMixin
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 BitBackbone, BitForImageClassification, BitImageProcessor, BitModel
							from transformers.models.bit.modeling_bit import BIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
							from PIL import Image
class        __A       :







		'''simple docstring'''





		def __init__(	self						:  Union[str, Any]			,_snake_case						:  int			,_snake_case						:  Any=3			,_snake_case						:  List[Any]=32			,_snake_case						:  str=3			,_snake_case						:  List[Any]=10			,_snake_case						:  Any=[8, 16, 32, 64]			,_snake_case						:  Optional[int]=[1, 1, 2, 1]			,_snake_case						:  Dict=True			,_snake_case						:  Dict=True			,_snake_case						:  List[Any]="relu"			,_snake_case						:  int=3			,_snake_case						:  Dict=None			,_snake_case						:  List[Any]=["stage2", "stage3", "stage4"]			,_snake_case						:  List[Any]=[2, 3, 4]			,_snake_case						:  int=1			,)					->		Optional[int]:






				"""simple docstring"""



				lowercase__   :    Any				=			parent
				lowercase__   :    Dict				=			batch_size
				lowercase__   :    Any				=			image_size
				lowercase__   :    Dict				=			num_channels
				lowercase__   :    int				=			embeddings_size
				lowercase__   :    str				=			hidden_sizes
				lowercase__   :    Tuple				=			depths
				lowercase__   :    Tuple				=			is_training
				lowercase__   :    str				=			use_labels
				lowercase__   :    int				=			hidden_act
				lowercase__   :    List[Any]				=			num_labels
				lowercase__   :    Dict				=			scope
				lowercase__   :    Dict				=			len(_snake_case						)
				lowercase__   :    Optional[int]				=			out_features
				lowercase__   :    List[str]				=			out_indices
				lowercase__   :    Any				=			num_groups







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






				"""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__   :    str				=			ids_tensor([self.batch_size]			,self.num_labels						)

				lowercase__   :    Optional[int]				=			self.get_config()

				return config, pixel_values, labels







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[int]:






				"""simple docstring"""



				return BitConfig(
				    num_channels=self.num_channels			,embeddings_size=self.embeddings_size			,hidden_sizes=self.hidden_sizes			,depths=self.depths			,hidden_act=self.hidden_act			,num_labels=self.num_labels			,out_features=self.out_features			,out_indices=self.out_indices			,num_groups=self.num_groups			,)







		def 					UpperCAmelCase    (	self						:  Tuple			,_snake_case						:  Optional[Any]			,_snake_case						:  Any			,_snake_case						:  str						)					->		int:






				"""simple docstring"""



				lowercase__   :    Tuple				=			BitModel(config=_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    Union[str, Any]				=			model(_snake_case						)
				self.parent.assertEqual(
				    result.last_hidden_state.shape			,(self.batch_size, self.hidden_sizes[-1], self.image_size // 32, self.image_size // 32)			,)







		def 					UpperCAmelCase    (	self						:  str			,_snake_case						:  List[str]			,_snake_case						:  int			,_snake_case						:  Optional[int]						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    Optional[int]				=			self.num_labels
				lowercase__   :    List[str]				=			BitForImageClassification(_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    int				=			model(_snake_case			,labels=_snake_case						)
				self.parent.assertEqual(result.logits.shape			,(self.batch_size, self.num_labels)						)







		def 					UpperCAmelCase    (	self						:  Optional[Any]			,_snake_case						:  List[Any]			,_snake_case						:  Tuple			,_snake_case						:  str						)					->		Dict:






				"""simple docstring"""



				lowercase__   :    int				=			BitBackbone(config=_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    str				=			model(_snake_case						)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps						)			,len(config.out_features						)						)
				self.parent.assertListEqual(list(result.feature_maps[0].shape						)			,[self.batch_size, self.hidden_sizes[1], 4, 4]						)

				# verify channels
				self.parent.assertEqual(len(model.channels						)			,len(config.out_features						)						)
				self.parent.assertListEqual(model.channels			,config.hidden_sizes[1:]						)

				# verify backbone works with out_features=None
				lowercase__   :    Tuple				=			None
				lowercase__   :    Optional[int]				=			BitBackbone(config=_snake_case						)
				model.to(_snake_case						)
				model.eval()
				lowercase__   :    int				=			model(_snake_case						)

				# verify feature maps
				self.parent.assertEqual(len(result.feature_maps						)			,1						)
				self.parent.assertListEqual(list(result.feature_maps[0].shape						)			,[self.batch_size, self.hidden_sizes[-1], 1, 1]						)

				# verify channels
				self.parent.assertEqual(len(model.channels						)			,1						)
				self.parent.assertListEqual(model.channels			,[config.hidden_sizes[-1]]						)







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






				"""simple docstring"""



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





@require_torch
class        __A       (	A_     ,A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					List[str]   	=				(BitModel, BitForImageClassification, BitBackbone) if is_torch_available() else ()
		lowerCAmelCase			:					List[str]   	=				(
		    {"feature-extraction": BitModel, "image-classification": BitForImageClassification}
		    if is_torch_available()
		    else {}
		)

		lowerCAmelCase			:					Any   	=				False
		lowerCAmelCase			:					str   	=				False
		lowerCAmelCase			:					str   	=				False
		lowerCAmelCase			:					Optional[int]   	=				False
		lowerCAmelCase			:					List[str]   	=				False







		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		int:






				"""simple docstring"""



				lowercase__   :    Optional[Any]				=			BitModelTester(self						)
				lowercase__   :    Tuple				=			ConfigTester(self			,config_class=_snake_case			,has_text_modality=_snake_case						)







		def 					UpperCAmelCase    (	self						:  Dict						)					->		Optional[int]:






				"""simple docstring"""



				self.create_and_test_config_common_properties()
				self.config_tester.create_and_test_config_to_json_string()
				self.config_tester.create_and_test_config_to_json_file()
				self.config_tester.create_and_test_config_from_and_save_pretrained()
				self.config_tester.create_and_test_config_with_num_labels()
				self.config_tester.check_config_can_be_init_without_params()
				self.config_tester.check_config_arguments_init()







		def 					UpperCAmelCase    (	self						:  int						)					->		Tuple:






				"""simple docstring"""



				return







		@unittest.skip(reason='''Bit does not output attentions'''						)
		def 					UpperCAmelCase    (	self						:  Optional[Any]						)					->		Dict:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''Bit does not use inputs_embeds'''						)
		def 					UpperCAmelCase    (	self						:  Tuple						)					->		Optional[Any]:






				"""simple docstring"""



				pass







		@unittest.skip(reason='''Bit does not support input and output embeddings'''						)
		def 					UpperCAmelCase    (	self						:  Union[str, Any]						)					->		Any:






				"""simple docstring"""



				pass







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






				"""simple docstring"""



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

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

						lowercase__   :    List[Any]				=			['''pixel_values''']
						self.assertListEqual(arg_names[:1]			,_snake_case						)







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






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_model(*_snake_case						)







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






				"""simple docstring"""



				lowercase__   :    str				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_backbone(*_snake_case						)







		def 					UpperCAmelCase    (	self						:  Optional[int]						)					->		Optional[int]:






				"""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(config=_snake_case						)
						for name, module in model.named_modules():
								if isinstance(_snake_case			,(nn.BatchNormad, nn.GroupNorm)						):
										self.assertTrue(
										    torch.all(module.weight == 1						)			,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			,)
										self.assertTrue(
										    torch.all(module.bias == 0						)			,msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""			,)







		def 					UpperCAmelCase    (	self						:  List[str]						)					->		Optional[Any]:






				"""simple docstring"""



				def check_hidden_states_output(_snake_case						:  Union[str, Any]			,_snake_case						:  int			,_snake_case						:  Optional[Any]						):
						lowercase__   :    Optional[int]				=			model_class(_snake_case						)
						model.to(_snake_case						)
						model.eval()

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

						lowercase__   :    Optional[Any]				=			outputs.encoder_hidden_states if config.is_encoder_decoder else outputs.hidden_states

						lowercase__   :    int				=			self.model_tester.num_stages
						self.assertEqual(len(_snake_case						)			,expected_num_stages + 1						)

						# Bit's feature maps are of shape (batch_size, num_channels, height, width)
						self.assertListEqual(
						    list(hidden_states[0].shape[-2:]						)			,[self.model_tester.image_size // 4, self.model_tester.image_size // 4]			,)

				lowercase__		,			lowercase__   :    List[str]				=			self.model_tester.prepare_config_and_inputs_for_common()
				lowercase__   :    str				=			['''preactivation''', '''bottleneck''']
				for model_class in self.all_model_classes:
						for layer_type in layers_type:
								lowercase__   :    Dict				=			layer_type
								lowercase__   :    Tuple				=			True
								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)

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

								check_hidden_states_output(_snake_case			,_snake_case			,_snake_case						)







		@unittest.skip(reason='''Bit does not use feedforward chunking'''						)
		def 					UpperCAmelCase    (	self						:  str						)					->		Optional[int]:






				"""simple docstring"""



				pass







		def 					UpperCAmelCase    (	self						:  List[Any]						)					->		List[str]:






				"""simple docstring"""



				lowercase__   :    List[str]				=			self.model_tester.prepare_config_and_inputs()
				self.model_tester.create_and_check_for_image_classification(*_snake_case						)







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






				"""simple docstring"""



				for model_name in BIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
						lowercase__   :    Dict				=			BitModel.from_pretrained(_snake_case						)
						self.assertIsNotNone(_snake_case						)





def      __UpperCAmelCase      (    )							->      List[str]:
		lowercase__   :    int				=			Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''	)
		return image
@require_torch
@require_vision
class        __A       (	unittest.TestCase    ):







		'''simple docstring'''





		@cached_property
		def 					UpperCAmelCase    (	self						:  Dict						)					->		List[str]:






				"""simple docstring"""



				return (
				    BitImageProcessor.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						) if is_vision_available() else None
				)







		@slow
		def 					UpperCAmelCase    (	self						:  Dict						)					->		Union[str, Any]:






				"""simple docstring"""



				lowercase__   :    Union[str, Any]				=			BitForImageClassification.from_pretrained(BIT_PRETRAINED_MODEL_ARCHIVE_LIST[0]						).to(_snake_case						)

				lowercase__   :    List[Any]				=			self.default_image_processor
				lowercase__   :    str				=			prepare_img()
				lowercase__   :    str				=			image_processor(images=_snake_case			,return_tensors='''pt'''						).to(_snake_case						)

				# forward pass
				with torch.no_grad():
						lowercase__   :    Optional[int]				=			model(**_snake_case						)

				# verify the logits
				lowercase__   :    Tuple				=			torch.Size((1, 1_000)						)
				self.assertEqual(outputs.logits.shape			,_snake_case						)

				lowercase__   :    List[str]				=			torch.tensor([[-0.6526, -0.5263, -1.4398]]						).to(_snake_case						)

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





@require_torch
class        __A       (	A_     ,unittest.TestCase    ):







		'''simple docstring'''





		lowerCAmelCase			:					Optional[int]   	=				(BitBackbone,) if is_torch_available() else ()
		lowerCAmelCase			:					Any   	=				BitConfig

		lowerCAmelCase			:					Optional[int]   	=				False







		def 					UpperCAmelCase    (	self						:  Any						)					->		Any:






				"""simple docstring"""



				lowercase__   :    Tuple				=			BitModelTester(self						)





"""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()
lowerCAmelCase_   						=   logging.get_logger()



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase						,     __lowerCamelCase = True	)							->      Union[str, Any]:
		print(f"""Converting {name}..."""	)

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

				from_model.eval()
				lowercase__   :    Optional[int]				=			LevitForImageClassificationWithTeacher(__lowerCamelCase	).eval()
				lowercase__   :    str				=			OrderedDict()

				lowercase__   :    int				=			from_model.state_dict()
				lowercase__   :    Dict				=			list(from_model.state_dict().keys()	)
				lowercase__   :    Any				=			list(our_model.state_dict().keys()	)
				print(len(__lowerCamelCase	)						,     len(__lowerCamelCase	)	)
				for i in range(len(__lowerCamelCase	)	):
						lowercase__   :    str				=			weights[og_keys[i]]
				our_model.load_state_dict(__lowerCamelCase	)

				lowercase__   :    Optional[int]				=			torch.randn((2, 3, 2_24, 2_24)	)
				lowercase__   :    Optional[int]				=			from_model(__lowerCamelCase	)
				lowercase__   :    List[Any]				=			our_model(__lowerCamelCase	).logits

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

		lowercase__   :    Any				=			name
		print(__lowerCamelCase	)

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

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



def      __UpperCAmelCase      (    __lowerCamelCase						,     __lowerCamelCase = None						,     __lowerCamelCase = True	)							->      List[Any]:
		lowercase__   :    Any				=			'''imagenet-1k-id2label.json'''
		lowercase__   :    Tuple				=			10_00
		lowercase__   :    Dict				=			(1, num_labels)

		lowercase__   :    List[str]				=			'''huggingface/label-files'''
		lowercase__   :    str				=			num_labels
		lowercase__   :    List[Any]				=			json.load(open(hf_hub_download(__lowerCamelCase						,     __lowerCamelCase						,     repo_type='''dataset'''	)						,     '''r'''	)	)
		lowercase__   :    Union[str, Any]				=			{int(__lowerCamelCase	): v for k, v in idalabel.items()}

		lowercase__   :    Union[str, Any]				=			idalabel
		lowercase__   :    Optional[int]				=			{v: k for k, v in idalabel.items()}

		lowercase__   :    List[Any]				=			partial(__lowerCamelCase						,     num_labels=__lowerCamelCase						,     idalabel=__lowerCamelCase						,     labelaid=__lowerCamelCase	)

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

		lowercase__   :    Any				=			{
		    '''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]						,     __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__":
							lowerCAmelCase_   						=   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',
							)

							lowerCAmelCase_   						=   parser.parse_args()
							lowerCAmelCase_   						=   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)
