




"""simple docstring"""






import inspect
import unittest

from datasets import load_dataset
from packaging import version

from transformers import BeitConfig
from transformers.models.auto import get_values
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, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    MODEL_MAPPING,
				    BeitForImageClassification,
				    BeitForMaskedImageModeling,
				    BeitForSemanticSegmentation,
				    BeitModel,
				)
				from transformers.models.beit.modeling_beit import BEIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				import PIL
				from PIL import Image

				from transformers import BeitImageProcessor




class   a__			:





						def __init__(							self      :	str     ,a__      :	Optional[Any]     ,a__      :	Union[str, Any]=100     ,a__      :	int=13     ,a__      :	Dict=30     ,a__      :	List[str]=2     ,a__      :	int=3     ,a__      :	Optional[int]=True     ,a__      :	Union[str, Any]=True     ,a__      :	Tuple=32     ,a__      :	Tuple=4     ,a__      :	int=4     ,a__      :	Tuple=37     ,a__      :	Dict="gelu"     ,a__      :	Tuple=0.1     ,a__      :	Optional[Any]=0.1     ,a__      :	int=10     ,a__      :	Dict=0.02     ,a__      :	int=3     ,a__      :	Optional[int]=None     ,a__      :	Dict=[0, 1, 2, 3]     ,)						->       Optional[Any]:




									"""simple docstring"""







									_lowerCAmelCase:Optional[int]									=  parent
									_lowerCAmelCase:str									=  100
									_lowerCAmelCase:str									=  batch_size
									_lowerCAmelCase:Dict									=  image_size
									_lowerCAmelCase:Union[str, Any]									=  patch_size
									_lowerCAmelCase:Any									=  num_channels
									_lowerCAmelCase:List[str]									=  is_training
									_lowerCAmelCase:List[str]									=  use_labels
									_lowerCAmelCase:Any									=  hidden_size
									_lowerCAmelCase:Tuple									=  num_hidden_layers
									_lowerCAmelCase:Dict									=  num_attention_heads
									_lowerCAmelCase:str									=  intermediate_size
									_lowerCAmelCase:List[Any]									=  hidden_act
									_lowerCAmelCase:Union[str, Any]									=  hidden_dropout_prob
									_lowerCAmelCase:int									=  attention_probs_dropout_prob
									_lowerCAmelCase:Any									=  type_sequence_label_size
									_lowerCAmelCase:List[str]									=  initializer_range
									_lowerCAmelCase:Optional[int]									=  scope
									_lowerCAmelCase:Dict									=  out_indices
									_lowerCAmelCase:int									=  num_labels

									# in BeiT, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
									_lowerCAmelCase:List[Any]									=  (image_size // patch_size) ** 2
									_lowerCAmelCase:int									=  num_patches + 1





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




									"""simple docstring"""







									_lowerCAmelCase:str									=  floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

									_lowerCAmelCase:Dict									=  None
									_lowerCAmelCase:Union[str, Any]									=  None
									if self.use_labels:
												_lowerCAmelCase:str									=  ids_tensor([self.batch_size]     ,self.type_sequence_label_size)
												_lowerCAmelCase:Dict									=  ids_tensor([self.batch_size, self.image_size, self.image_size]     ,self.num_labels)

									_lowerCAmelCase:Tuple									=  self.get_config()

									return config, pixel_values, labels, pixel_labels





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




									"""simple docstring"""







									return BeitConfig(
									    vocab_size=self.vocab_size     ,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     ,is_decoder=_lowercase     ,initializer_range=self.initializer_range     ,out_indices=self.out_indices     ,)





						def       __UpperCamelCase     (							self      :	Tuple     ,a__      :	Optional[Any]     ,a__      :	Optional[int]     ,a__      :	Tuple     ,a__      :	Optional[int])						->       List[str]:




									"""simple docstring"""







									_lowerCAmelCase:Any									=  BeitModel(config=_lowercase)
									model.to(_lowercase)
									model.eval()
									_lowerCAmelCase:Optional[int]									=  model(_lowercase)
									self.parent.assertEqual(result.last_hidden_state.shape     ,(self.batch_size, self.seq_length, self.hidden_size))





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	str     ,a__      :	Dict     ,a__      :	Dict     ,a__      :	str)						->       Optional[Any]:




									"""simple docstring"""







									_lowerCAmelCase:Dict									=  BeitForMaskedImageModeling(config=_lowercase)
									model.to(_lowercase)
									model.eval()
									_lowerCAmelCase:Optional[Any]									=  model(_lowercase)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.seq_length - 1, self.vocab_size))





						def       __UpperCamelCase     (							self      :	Any     ,a__      :	Tuple     ,a__      :	Dict     ,a__      :	int     ,a__      :	Tuple)						->       Dict:




									"""simple docstring"""







									_lowerCAmelCase:Union[str, Any]									=  self.type_sequence_label_size
									_lowerCAmelCase:Dict									=  BeitForImageClassification(_lowercase)
									model.to(_lowercase)
									model.eval()
									_lowerCAmelCase:int									=  model(_lowercase     ,labels=_lowercase)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size))

									# test greyscale images
									_lowerCAmelCase:Dict									=  1
									_lowerCAmelCase:Tuple									=  BeitForImageClassification(_lowercase)
									model.to(_lowercase)
									model.eval()

									_lowerCAmelCase:Union[str, Any]									=  floats_tensor([self.batch_size, 1, self.image_size, self.image_size])
									_lowerCAmelCase:Optional[int]									=  model(_lowercase     ,labels=_lowercase)
									self.parent.assertEqual(result.logits.shape     ,(self.batch_size, self.type_sequence_label_size))





						def       __UpperCamelCase     (							self      :	Optional[int]     ,a__      :	List[Any]     ,a__      :	Dict     ,a__      :	Union[str, Any]     ,a__      :	Tuple)						->       Union[str, Any]:




									"""simple docstring"""







									_lowerCAmelCase:List[Any]									=  self.num_labels
									_lowerCAmelCase:Any									=  BeitForSemanticSegmentation(_lowercase)
									model.to(_lowercase)
									model.eval()
									_lowerCAmelCase:Optional[int]									=  model(_lowercase)
									self.parent.assertEqual(
									    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))
									_lowerCAmelCase:List[Any]									=  model(_lowercase     ,labels=_lowercase)
									self.parent.assertEqual(
									    result.logits.shape     ,(self.batch_size, self.num_labels, self.image_size * 2, self.image_size * 2))







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




									"""simple docstring"""







									_lowerCAmelCase:Any									=  self.prepare_config_and_inputs()
									_lowerCAmelCase:int									=  config_and_inputs
									_lowerCAmelCase:int									=  {"""pixel_values""": pixel_values}
									return config, inputs_dict




@require_torch
class   a__			(       SCREAMING_SNAKE_CASE__					,	SCREAMING_SNAKE_CASE__					,	unittest.TestCase  ):
						snake_case__             = (
						    (BeitModel, BeitForImageClassification, BeitForMaskedImageModeling, BeitForSemanticSegmentation)
						    if is_torch_available()
						    else ()
						)
						snake_case__             = (
						    {
						        '''feature-extraction''': BeitModel,
						        '''image-classification''': BeitForImageClassification,
						        '''image-segmentation''': BeitForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						snake_case__             = False
						snake_case__             = False
						snake_case__             = False





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




									"""simple docstring"""







									_lowerCAmelCase:Optional[int]									=  BeitModelTester(self)
									_lowerCAmelCase:str									=  ConfigTester(self     ,config_class=_lowercase     ,has_text_modality=_lowercase     ,hidden_size=37)





						def       __UpperCamelCase     (							self      :	List[Any])						->       Union[str, Any]:




									"""simple docstring"""







									self.config_tester.run_common_tests()





						@unittest.skip(reason='''BEiT does not use inputs_embeds''')
						def       __UpperCamelCase     (							self      :	Tuple)						->       int:




									"""simple docstring"""







									pass





						@require_torch_multi_gpu
						@unittest.skip(reason='''BEiT has some layers using `add_module` which doesn\'t work well with `nn.DataParallel`''')
						def       __UpperCamelCase     (							self      :	int)						->       Union[str, Any]:




									"""simple docstring"""







									pass





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




									"""simple docstring"""







									_lowerCAmelCase:Dict									=  self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
												_lowerCAmelCase:Tuple									=  model_class(_lowercase)
												self.assertIsInstance(model.get_input_embeddings()     ,(nn.Module))
												_lowerCAmelCase:List[str]									=  model.get_output_embeddings()
												self.assertTrue(x is None or isinstance(_lowercase     ,nn.Linear))





						def       __UpperCamelCase     (							self      :	Tuple)						->       List[Any]:




									"""simple docstring"""







									_lowerCAmelCase:Any									=  self.model_tester.prepare_config_and_inputs_for_common()

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

												_lowerCAmelCase:Any									=  ["""pixel_values"""]
												self.assertListEqual(arg_names[:1]     ,_lowercase)





						def       __UpperCamelCase     (							self      :	int)						->       Dict:




									"""simple docstring"""







									_lowerCAmelCase:Any									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*_lowercase)





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




									"""simple docstring"""







									_lowerCAmelCase:Optional[Any]									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_masked_lm(*_lowercase)





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




									"""simple docstring"""







									_lowerCAmelCase:List[str]									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_image_classification(*_lowercase)





						def       __UpperCamelCase     (							self      :	Any)						->       List[Any]:




									"""simple docstring"""







									_lowerCAmelCase:Any									=  self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_for_semantic_segmentation(*_lowercase)





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




									"""simple docstring"""







									if not self.model_tester.is_training:
												return

									_lowerCAmelCase:List[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()
									_lowerCAmelCase:Tuple									=  True

									for model_class in self.all_model_classes:
												# we don't test BeitForMaskedImageModeling
												if model_class in [*get_values(_lowercase), BeitForMaskedImageModeling]:
															continue

												_lowerCAmelCase:Optional[int]									=  model_class(_lowercase)
												model.to(_lowercase)
												model.train()
												_lowerCAmelCase:Tuple									=  self._prepare_for_class(_lowercase     ,_lowercase     ,return_labels=_lowercase)
												_lowerCAmelCase:str									=  model(**_lowercase).loss
												loss.backward()





						def       __UpperCamelCase     (							self      :	List[Any])						->       List[Any]:




									"""simple docstring"""







									_lowerCAmelCase:int									=  self.model_tester.prepare_config_and_inputs_for_common()
									if not self.model_tester.is_training:
												return

									_lowerCAmelCase:Dict									=  False
									_lowerCAmelCase:List[str]									=  True

									for model_class in self.all_model_classes:
												# we don't test BeitForMaskedImageModeling
												if (
												    model_class in [*get_values(_lowercase), BeitForMaskedImageModeling]
												    or not model_class.supports_gradient_checkpointing
												):
															continue

												_lowerCAmelCase:int									=  model_class(_lowercase)
												model.gradient_checkpointing_enable()
												model.to(_lowercase)
												model.train()
												_lowerCAmelCase:List[Any]									=  self._prepare_for_class(_lowercase     ,_lowercase     ,return_labels=_lowercase)
												_lowerCAmelCase:List[Any]									=  model(**_lowercase).loss
												loss.backward()





						def       __UpperCamelCase     (							self      :	int)						->       str:




									"""simple docstring"""







									_lowerCAmelCase:List[Any]									=  self.model_tester.prepare_config_and_inputs_for_common()

									_lowerCAmelCase:int									=  _config_zero_init(_lowercase)
									for model_class in self.all_model_classes:
												_lowerCAmelCase:Union[str, Any]									=  model_class(config=_lowercase)
												for name, param in model.named_parameters():
															# we skip lambda parameters as these require special initial values
															# determined by config.layer_scale_init_value
															if "lambda" in name:
																		continue
															if param.requires_grad:
																		self.assertIn(
																		    ((param.data.mean() * 1E9).round() / 1E9).item()     ,[0.0, 1.0]     ,msg=F'Parameter {name} of model {model_class} seems not properly initialized'     ,)







						@slow
						def       __UpperCamelCase     (							self      :	int)						->       Union[str, Any]:




									"""simple docstring"""







									for model_name in BEIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
												_lowerCAmelCase:Union[str, Any]									=  BeitModel.from_pretrained(_lowercase)
												self.assertIsNotNone(_lowercase)





def 							UpperCAmelCase		(		):
			_lowerCAmelCase:Dict									=  Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''    )
			return image





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





						@cached_property
						def       __UpperCamelCase     (							self      :	Tuple)						->       Optional[int]:




									"""simple docstring"""







									return BeitImageProcessor.from_pretrained('''microsoft/beit-base-patch16-224''') if is_vision_available() else None





						@slow
						def       __UpperCamelCase     (							self      :	Union[str, Any])						->       Optional[Any]:




									"""simple docstring"""







									_lowerCAmelCase:List[str]									=  BeitForMaskedImageModeling.from_pretrained('''microsoft/beit-base-patch16-224-pt22k''').to(_lowercase)

									_lowerCAmelCase:str									=  self.default_image_processor
									_lowerCAmelCase:int									=  prepare_img()
									_lowerCAmelCase:Optional[int]									=  image_processor(images=_lowercase     ,return_tensors='''pt''').pixel_values.to(_lowercase)

									# prepare bool_masked_pos
									_lowerCAmelCase:Optional[Any]									=  torch.ones((1, 196)     ,dtype=torch.bool).to(_lowercase)

									# forward pass
									with torch.no_grad():
												_lowerCAmelCase:Dict									=  model(pixel_values=_lowercase     ,bool_masked_pos=_lowercase)
									_lowerCAmelCase:str									=  outputs.logits

									# verify the logits
									_lowerCAmelCase:str									=  torch.Size((1, 196, 8192))
									self.assertEqual(logits.shape     ,_lowercase)

									_lowerCAmelCase:int									=  torch.tensor(
									    [[-3.2437, 0.5072, -13.9174], [-3.2456, 0.4948, -13.9401], [-3.2033, 0.5121, -13.8550]]).to(_lowercase)

									self.assertTrue(torch.allclose(logits[bool_masked_pos][:3, :3]     ,_lowercase     ,atol=1E-2))





						@slow
						def       __UpperCamelCase     (							self      :	List[Any])						->       Tuple:




									"""simple docstring"""







									_lowerCAmelCase:Tuple									=  BeitForImageClassification.from_pretrained('''microsoft/beit-base-patch16-224''').to(_lowercase)

									_lowerCAmelCase:Tuple									=  self.default_image_processor
									_lowerCAmelCase:Any									=  prepare_img()
									_lowerCAmelCase:Dict									=  image_processor(images=_lowercase     ,return_tensors='''pt''').to(_lowercase)

									# forward pass
									with torch.no_grad():
												_lowerCAmelCase:Union[str, Any]									=  model(**_lowercase)
									_lowerCAmelCase:Dict									=  outputs.logits

									# verify the logits
									_lowerCAmelCase:Optional[Any]									=  torch.Size((1, 1000))
									self.assertEqual(logits.shape     ,_lowercase)

									_lowerCAmelCase:Any									=  torch.tensor([-1.2385, -1.0987, -1.0108]).to(_lowercase)

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

									_lowerCAmelCase:int									=  281
									self.assertEqual(logits.argmax(-1).item()     ,_lowercase)





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




									"""simple docstring"""







									_lowerCAmelCase:str									=  BeitForImageClassification.from_pretrained('''microsoft/beit-large-patch16-224-pt22k-ft22k''').to(
									    _lowercase)

									_lowerCAmelCase:int									=  self.default_image_processor
									_lowerCAmelCase:int									=  prepare_img()
									_lowerCAmelCase:Union[str, Any]									=  image_processor(images=_lowercase     ,return_tensors='''pt''').to(_lowercase)

									# forward pass
									with torch.no_grad():
												_lowerCAmelCase:Any									=  model(**_lowercase)
									_lowerCAmelCase:str									=  outputs.logits

									# verify the logits
									_lowerCAmelCase:List[str]									=  torch.Size((1, 2_1841))
									self.assertEqual(logits.shape     ,_lowercase)

									_lowerCAmelCase:Union[str, Any]									=  torch.tensor([1.6881, -0.2787, 0.5901]).to(_lowercase)

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

									_lowerCAmelCase:Dict									=  2396
									self.assertEqual(logits.argmax(-1).item()     ,_lowercase)





						@slow
						def       __UpperCamelCase     (							self      :	Dict)						->       Dict:




									"""simple docstring"""







									_lowerCAmelCase:str									=  BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''')
									_lowerCAmelCase:int									=  model.to(_lowercase)

									_lowerCAmelCase:Any									=  BeitImageProcessor(do_resize=_lowercase     ,size=640     ,do_center_crop=_lowercase)

									_lowerCAmelCase:List[str]									=  load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,split='''test''')
									_lowerCAmelCase:Tuple									=  Image.open(ds[0]['''file'''])
									_lowerCAmelCase:Any									=  image_processor(images=_lowercase     ,return_tensors='''pt''').to(_lowercase)

									# forward pass
									with torch.no_grad():
												_lowerCAmelCase:List[str]									=  model(**_lowercase)
									_lowerCAmelCase:str									=  outputs.logits

									# verify the logits
									_lowerCAmelCase:Optional[Any]									=  torch.Size((1, 150, 160, 160))
									self.assertEqual(logits.shape     ,_lowercase)

									_lowerCAmelCase:int									=  version.parse(PIL.__version__) < version.parse('''9.0.0''')

									if is_pillow_less_than_a:
												_lowerCAmelCase:Dict									=  torch.tensor(
												    [
												        [[-4.9225, -2.3954, -3.0522], [-2.8822, -1.0046, -1.7561], [-2.9549, -1.3228, -2.1347]],
												        [[-5.8168, -3.4129, -4.0778], [-3.8651, -2.2214, -3.0277], [-3.8356, -2.4643, -3.3535]],
												        [[-0.0078, 3.9952, 4.0754], [2.9856, 4.6944, 5.0035], [3.2413, 4.7813, 4.9969]],
												    ]     ,device=_lowercase     ,)
									else:
												_lowerCAmelCase:List[str]									=  torch.tensor(
												    [
												        [[-4.8960, -2.3688, -3.0355], [-2.8478, -0.9836, -1.7418], [-2.9449, -1.3332, -2.1456]],
												        [[-5.8081, -3.4124, -4.1006], [-3.8561, -2.2081, -3.0323], [-3.8365, -2.4601, -3.3669]],
												        [[-0.0309, 3.9868, 4.0540], [2.9640, 4.6877, 4.9976], [3.2081, 4.7690, 4.9942]],
												    ]     ,device=_lowercase     ,)

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







						@slow
						def       __UpperCamelCase     (							self      :	Optional[int])						->       Any:




									"""simple docstring"""







									_lowerCAmelCase:Dict									=  BeitForSemanticSegmentation.from_pretrained('''microsoft/beit-base-finetuned-ade-640-640''')
									_lowerCAmelCase:Optional[Any]									=  model.to(_lowercase)

									_lowerCAmelCase:List[Any]									=  BeitImageProcessor(do_resize=_lowercase     ,size=640     ,do_center_crop=_lowercase)

									_lowerCAmelCase:Tuple									=  load_dataset('''hf-internal-testing/fixtures_ade20k'''     ,split='''test''')
									_lowerCAmelCase:Any									=  Image.open(ds[0]['''file'''])
									_lowerCAmelCase:Optional[int]									=  image_processor(images=_lowercase     ,return_tensors='''pt''').to(_lowercase)

									# forward pass
									with torch.no_grad():
												_lowerCAmelCase:Optional[Any]									=  model(**_lowercase)

									_lowerCAmelCase:str									=  outputs.logits.detach().cpu()

									_lowerCAmelCase:List[str]									=  image_processor.post_process_semantic_segmentation(outputs=_lowercase     ,target_sizes=[(500, 300)])
									_lowerCAmelCase:int									=  torch.Size((500, 300))
									self.assertEqual(segmentation[0].shape     ,_lowercase)

									_lowerCAmelCase:List[str]									=  image_processor.post_process_semantic_segmentation(outputs=_lowercase)
									_lowerCAmelCase:Optional[Any]									=  torch.Size((160, 160))
									self.assertEqual(segmentation[0].shape     ,_lowercase)












"""simple docstring"""






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__						=			logging.get_logger(__name__)





def 							UpperCAmelCase		(		snake_case			:					Optional[int]    ):
					_lowerCAmelCase:List[Any]									=  MobileNetVaConfig(layer_norm_eps=0.0_01    )

					if "_quant" in model_name:
										raise ValueError('''Quantized models are not supported.'''    )

					_lowerCAmelCase:List[Any]									=  re.match(R'''^mobilenet_v1_([^_]*)_([^_]*)$'''      ,       snake_case    )
					if matches:
										_lowerCAmelCase:str									=  float(matches[1]    )
										_lowerCAmelCase:str									=  int(matches[2]    )

					# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
					# the usual 1000. The first class (index 0) is "background".
					_lowerCAmelCase:int									=  1001
					_lowerCAmelCase:Union[str, Any]									=  '''imagenet-1k-id2label.json'''
					_lowerCAmelCase:Optional[int]									=  '''huggingface/label-files'''
					_lowerCAmelCase:List[str]									=  json.load(open(hf_hub_download(snake_case      ,       snake_case      ,       repo_type='''dataset'''    )      ,       '''r'''    )    )
					_lowerCAmelCase:Union[str, Any]									=  {int(snake_case    ) + 1: v for k, v in idalabel.items()}
					_lowerCAmelCase:Optional[Any]									=  '''background'''
					_lowerCAmelCase:Optional[Any]									=  idalabel
					_lowerCAmelCase:List[Any]									=  {v: k for k, v in idalabel.items()}

					return config





def 							UpperCAmelCase		(		):
					_lowerCAmelCase:Tuple									=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
					_lowerCAmelCase:Optional[Any]									=  Image.open(requests.get(snake_case      ,       stream=snake_case    ).raw    )
					return im





@torch.no_grad()
def 							UpperCAmelCase		(		snake_case			:					Any      ,       snake_case			:					Union[str, Any]      ,       snake_case			:					List[Any]      ,       snake_case			:					Any=False    ):
					_lowerCAmelCase:List[str]									=  get_mobilenet_va_config(snake_case    )

					# Load 🤗 model
					_lowerCAmelCase:Any									=  MobileNetVaForImageClassification(snake_case    ).eval()

					# Load weights from TensorFlow checkpoint
					load_tf_weights_in_mobilenet_va(snake_case      ,       snake_case      ,       snake_case    )

					# Check outputs on an image, prepared by MobileNetV1ImageProcessor
					_lowerCAmelCase:Dict									=  MobileNetVaImageProcessor(
					    crop_size={'''width''': config.image_size, '''height''': config.image_size}      ,       size={'''shortest_edge''': config.image_size + 32}      ,       )
					_lowerCAmelCase:List[str]									=  image_processor(images=prepare_img()      ,       return_tensors='''pt'''    )
					_lowerCAmelCase:List[str]									=  model(**snake_case    )
					_lowerCAmelCase:int									=  outputs.logits

					assert logits.shape == (1, 1001)

					if model_name == "mobilenet_v1_1.0_224":
										_lowerCAmelCase:Union[str, Any]									=  torch.tensor([-4.17_39, -1.12_33, 3.12_05]    )
					elif model_name == "mobilenet_v1_0.75_192":
										_lowerCAmelCase:Any									=  torch.tensor([-3.94_40, -2.31_41, -0.33_33]    )
					else:
										_lowerCAmelCase:Dict									=  None

					if expected_logits is not None:
										assert torch.allclose(logits[0, :3]      ,       snake_case      ,       atol=1e-4    )

					Path(snake_case    ).mkdir(exist_ok=snake_case    )
					print(F'Saving model {model_name} to {pytorch_dump_folder_path}'    )
					model.save_pretrained(snake_case    )
					print(F'Saving image processor to {pytorch_dump_folder_path}'    )
					image_processor.save_pretrained(snake_case    )

					if push_to_hub:
										print('''Pushing to the hub...'''    )
										_lowerCAmelCase:List[str]									=  '''google/''' + model_name
										image_processor.push_to_hub(snake_case    )
										model.push_to_hub(snake_case    )


if __name__ == "__main__":
				UpperCamelCase__						=			argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    '''--model_name''',
				    default='''mobilenet_v1_1.0_224''',
				    type=str,
				    help='''Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.''',
				)
				parser.add_argument(
				    '''--checkpoint_path''', required=True, type=str, help='''Path to the original TensorFlow checkpoint (.ckpt file).'''
				)
				parser.add_argument(
				    '''--pytorch_dump_folder_path''', required=True, type=str, help='''Path to the output PyTorch model directory.'''
				)
				parser.add_argument(
				    '''--push_to_hub''', action='''store_true''', help='''Whether or not to push the converted model to the 🤗 hub.'''
				)

				UpperCamelCase__						=			parser.parse_args()
				convert_movilevit_checkpoint(
				    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
				)






