




import inspect
import math
import tempfile
import unittest

import numpy as np

from transformers import ViTMAEConfig
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 ViTMAEForPreTraining, ViTMAEModel
					from transformers.models.vit.modeling_vit import VIT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
					from PIL import Image

					from transformers import ViTImageProcessor
class 			a__    :






						def __init__(   self		,			_A		,			_A=1_3		,			_A=3_0		,			_A=2		,			_A=3		,			_A=True		,			_A=True		,			_A=3_2		,			_A=5		,			_A=4		,			_A=3_7		,			_A="gelu"		,			_A=0.1		,			_A=0.1		,			_A=1_0		,			_A=0.02		,			_A=3		,			_A=0.6		,			_A=None		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       batch_size
											__lowerCAmelCase				   =       image_size
											__lowerCAmelCase				   =       patch_size
											__lowerCAmelCase				   =       num_channels
											__lowerCAmelCase				   =       is_training
											__lowerCAmelCase				   =       use_labels
											__lowerCAmelCase				   =       hidden_size
											__lowerCAmelCase				   =       num_hidden_layers
											__lowerCAmelCase				   =       num_attention_heads
											__lowerCAmelCase				   =       intermediate_size
											__lowerCAmelCase				   =       hidden_act
											__lowerCAmelCase				   =       hidden_dropout_prob
											__lowerCAmelCase				   =       attention_probs_dropout_prob
											__lowerCAmelCase				   =       type_sequence_label_size
											__lowerCAmelCase				   =       initializer_range
											__lowerCAmelCase				   =       mask_ratio
											__lowerCAmelCase				   =       scope

											# in ViTMAE, the expected sequence length = (num_patches + 1) * (1 - config.mask_ratio), rounded above
											# (we add 1 for the [CLS] token)
											__lowerCAmelCase				   =       (image_size // patch_size) ** 2
											__lowerCAmelCase				   =       int(math.ceil((1 - mask_ratio) * (num_patches + 1)			)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)

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

											__lowerCAmelCase				   =       self.get_config()

											return config, pixel_values, labels






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return ViTMAEConfig(
											    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=SCREAMING_SNAKE_CASE_		,			initializer_range=self.initializer_range		,			mask_ratio=self.mask_ratio		,			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ViTMAEModel(config=SCREAMING_SNAKE_CASE_			)
											model.to(SCREAMING_SNAKE_CASE_			)
											model.eval()
											__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_			)
											self.parent.assertEqual(result.last_hidden_state.shape		,			(self.batch_size, self.seq_length, self.hidden_size)			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_			)
											model.to(SCREAMING_SNAKE_CASE_			)
											model.eval()
											__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_			)
											__lowerCAmelCase				   =       (self.image_size // self.patch_size) ** 2
											__lowerCAmelCase				   =       self.patch_size**2 * self.num_channels
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, num_patches, expected_num_channels)			)

											# test greyscale images
											__lowerCAmelCase				   =       1
											__lowerCAmelCase				   =       ViTMAEForPreTraining(SCREAMING_SNAKE_CASE_			)
											model.to(SCREAMING_SNAKE_CASE_			)
											model.eval()
											__lowerCAmelCase				   =       floats_tensor([self.batch_size, 1, self.image_size, self.image_size]			)
											__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_			)
											__lowerCAmelCase				   =       self.patch_size**2
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, num_patches, expected_num_channels)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.prepare_config_and_inputs()
											__lowerCAmelCase				   =       config_and_inputs
											__lowerCAmelCase				   =       {'pixel_values': pixel_values}
											return config, inputs_dict






@require_torch
class 			a__    (					a__ ,				a__ ,				unittest.TestCase      ):
						_a							:    str					=    (ViTMAEModel, ViTMAEForPreTraining) if is_torch_available() else ()
						_a							:    List[str]					=    {"feature-extraction": ViTMAEModel} if is_torch_available() else {}

						_a							:    str					=    False
						_a							:    List[str]					=    False
						_a							:    str					=    False
						_a							:    List[Any]					=    False






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       ViTMAEModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(self		,			config_class=SCREAMING_SNAKE_CASE_		,			has_text_modality=SCREAMING_SNAKE_CASE_		,			hidden_size=3_7			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="ViTMAE does not use inputs_embeds"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

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

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_model(*SCREAMING_SNAKE_CASE_			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs()
											self.model_tester.create_and_check_for_pretraining(*SCREAMING_SNAKE_CASE_			)






						def 				__SCREAMING_SNAKE_CASE(   self		,			_A		,			_A		,			_A			):




											"""simple docstring"""



											np.random.seed(2			)

											__lowerCAmelCase				   =       int((pt_model.config.image_size // pt_model.config.patch_size) ** 2			)
											__lowerCAmelCase				   =       np.random.uniform(size=(self.model_tester.batch_size, num_patches)			)
											__lowerCAmelCase				   =       torch.from_numpy(SCREAMING_SNAKE_CASE_			)

											# Add `noise` argument.
											# PT inputs will be prepared in `super().check_pt_tf_models()` with this added `noise` argument
											__lowerCAmelCase				   =       pt_noise

											super().check_pt_tf_models(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase				   =       model_class(SCREAMING_SNAKE_CASE_			)
																model.to(SCREAMING_SNAKE_CASE_			)
																model.eval()
																# make random mask reproducible
																torch.manual_seed(2			)
																with torch.no_grad():
																					__lowerCAmelCase				   =       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)			)

																__lowerCAmelCase				   =       outputs[0].cpu().numpy()
																__lowerCAmelCase				   =       0

																with tempfile.TemporaryDirectory() as tmpdirname:
																					model.save_pretrained(SCREAMING_SNAKE_CASE_			)
																					__lowerCAmelCase				   =       model_class.from_pretrained(SCREAMING_SNAKE_CASE_			)
																					model.to(SCREAMING_SNAKE_CASE_			)
																					# make random mask reproducible
																					torch.manual_seed(2			)
																					with torch.no_grad():
																										__lowerCAmelCase				   =       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)			)

																					# Make sure we don't have nans
																					__lowerCAmelCase				   =       after_outputs[0].cpu().numpy()
																					__lowerCAmelCase				   =       0
																					__lowerCAmelCase				   =       np.amax(np.abs(out_a - out_a			)			)
																					self.assertLessEqual(SCREAMING_SNAKE_CASE_		,			1E-5			)






						@unittest.skip(
						    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip(
						    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip(
						    reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load\n    to get deterministic results."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip(reason="ViTMAE returns a random mask + ids_restore in each forward pass. See test_save_load"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											for model_name in VIT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																__lowerCAmelCase				   =       ViTMAEModel.from_pretrained(SCREAMING_SNAKE_CASE_			)
																self.assertIsNotNone(SCREAMING_SNAKE_CASE_			)







def 	_a      (						):
					__lowerCAmelCase				   =       Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"     )
					return image


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






						@cached_property
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return ViTImageProcessor.from_pretrained("facebook/vit-mae-base"			) if is_vision_available() else None






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											np.random.seed(2			)

											__lowerCAmelCase				   =       ViTMAEForPreTraining.from_pretrained("facebook/vit-mae-base"			).to(SCREAMING_SNAKE_CASE_			)

											__lowerCAmelCase				   =       self.default_image_processor
											__lowerCAmelCase				   =       prepare_img()
											__lowerCAmelCase				   =       image_processor(images=SCREAMING_SNAKE_CASE_		,			return_tensors="pt"			).to(SCREAMING_SNAKE_CASE_			)

											# prepare a noise vector that will be also used for testing the TF model
											# (this way we can ensure that the PT and TF models operate on the same inputs)
											__lowerCAmelCase				   =       ViTMAEConfig()
											__lowerCAmelCase				   =       int((vit_mae_config.image_size // vit_mae_config.patch_size) ** 2			)
											__lowerCAmelCase				   =       np.random.uniform(size=(1, num_patches)			)

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase				   =       model(**SCREAMING_SNAKE_CASE_		,			noise=torch.from_numpy(SCREAMING_SNAKE_CASE_			).to(device=SCREAMING_SNAKE_CASE_			)			)

											# verify the logits
											__lowerCAmelCase				   =       torch.Size((1, 1_9_6, 7_6_8)			)
											self.assertEqual(outputs.logits.shape		,			SCREAMING_SNAKE_CASE_			)

											__lowerCAmelCase				   =       torch.tensor(
											    [[-0.05_48, -1.70_23, -0.93_25], [0.37_21, -0.56_70, -0.22_33], [0.82_35, -1.38_78, -0.35_24]]			)

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









import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


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

__UpperCAmelCase          =       {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

__UpperCAmelCase          =       {
    '''b0''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1_408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1_536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1_792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2_304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2_560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}
def 		UpperCamelCase	(   snake_case__       :    int	)	->     Optional[int]:
   UpperCamelCase    :  str							=	EfficientNetConfig()
   UpperCamelCase    :  Union[str, Any]							=	CONFIG_MAP[model_name]['hidden_dim']
   UpperCamelCase    :  Union[str, Any]							=	CONFIG_MAP[model_name]['width_coef']
   UpperCamelCase    :  str							=	CONFIG_MAP[model_name]['depth_coef']
   UpperCamelCase    :  List[str]							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[str]							=	CONFIG_MAP[model_name]['dropout_rate']
   UpperCamelCase    :  str							=	CONFIG_MAP[model_name]['dw_padding']

   UpperCamelCase    :  str							=	'huggingface/label-files'
   UpperCamelCase    :  Optional[Any]							=	'imagenet-1k-id2label.json'
   UpperCamelCase    :  Optional[Any]							=	1000
   UpperCamelCase    :  Dict							=	json.load(open(hf_hub_download(snake_case__       ,     snake_case__       ,     repo_type='dataset'	)       ,     'r'	)	)
   UpperCamelCase    :  Tuple							=	{int(snake_case__	): v for k, v in idalabel.items()}

   UpperCamelCase    :  Optional[int]							=	idalabel
   UpperCamelCase    :  Tuple							=	{v: k for k, v in idalabel.items()}
   return config
def 		UpperCamelCase	(   )	->     Tuple:
   UpperCamelCase    :  Optional[Any]							=	'http://images.cocodataset.org/val2017/000000039769.jpg'
   UpperCamelCase    :  Union[str, Any]							=	Image.open(requests.get(snake_case__       ,     stream=snake_case__	).raw	)
   return im
def 		UpperCamelCase	(   snake_case__       :    List[str]	)	->     List[Any]:
   UpperCamelCase    :  int							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[str]							=	EfficientNetImageProcessor(
       size={'height': size, 'width': size}       ,     image_mean=[0.485, 0.456, 0.406]       ,     image_std=[0.47853944, 0.4732864, 0.47434163]       ,     do_center_crop=snake_case__       ,     )
   return preprocessor
def 		UpperCamelCase	(   snake_case__       :    Optional[int]	)	->     Dict:
   UpperCamelCase    :  int							=	[v.split('_'	)[0].split('block'	)[1] for v in original_param_names if v.startswith('block'	)]
   UpperCamelCase    :  str							=	sorted(set(snake_case__	)	)
   UpperCamelCase    :  int							=	len(snake_case__	)
   UpperCamelCase    :  str							=	{b: str(snake_case__	) for b, i in zip(snake_case__       ,     range(snake_case__	)	)}

   UpperCamelCase    :  Optional[int]							=	[]
   rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')	)
   rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')	)
   rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')	)
   rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')	)
   rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')	)

   for b in block_names:
      UpperCamelCase    :  Union[str, Any]							=	block_name_mapping[b]
      rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""")	)
      rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""")	)
      rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""")	)
      rename_keys.append(
          (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""")	)
      rename_keys.append(
          (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""")	)
      rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""")	)
      rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""")	)
      rename_keys.append(
          (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""")	)

      rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""")	)
      rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""")	)
      rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""")	)
      rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""")	)
      rename_keys.append(
          (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""")	)
      rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""")	)
      rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""")	)
      rename_keys.append(
          (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""")	)
      rename_keys.append(
          (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""")	)

   rename_keys.append(('top_conv/kernel:0', 'encoder.top_conv.weight')	)
   rename_keys.append(('top_bn/gamma:0', 'encoder.top_bn.weight')	)
   rename_keys.append(('top_bn/beta:0', 'encoder.top_bn.bias')	)
   rename_keys.append(('top_bn/moving_mean:0', 'encoder.top_bn.running_mean')	)
   rename_keys.append(('top_bn/moving_variance:0', 'encoder.top_bn.running_var')	)

   UpperCamelCase    :  List[str]							=	{}
   for item in rename_keys:
      if item[0] in original_param_names:
         UpperCamelCase    :  Dict							=	'efficientnet.' + item[1]

   UpperCamelCase    :  Dict							=	'classifier.weight'
   UpperCamelCase    :  Dict							=	'classifier.bias'
   return key_mapping
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    List[Any]       ,     snake_case__       :    int	)	->     Dict:
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      UpperCamelCase    :  Any							=	key_mapping[key]
      if "_conv" in key and "kernel" in key:
         UpperCamelCase    :  str							=	torch.from_numpy(snake_case__	).permute(3       ,     2       ,     0       ,     1	)
      elif "depthwise_kernel" in key:
         UpperCamelCase    :  Any							=	torch.from_numpy(snake_case__	).permute(2       ,     3       ,     0       ,     1	)
      elif "kernel" in key:
         UpperCamelCase    :  str							=	torch.from_numpy(np.transpose(snake_case__	)	)
      else:
         UpperCamelCase    :  str							=	torch.from_numpy(snake_case__	)

      # Replace HF parameters with original TF model parameters
      assert hf_params[hf_key].shape == new_hf_value.shape
      hf_params[hf_key].copy_(snake_case__	)
@torch.no_grad()
def 		UpperCamelCase	(   snake_case__       :    Optional[Any]       ,     snake_case__       :    Dict       ,     snake_case__       :    int       ,     snake_case__       :    Optional[int]	)	->     Any:
   UpperCamelCase    :  Union[str, Any]							=	model_classes[model_name](
       include_top=snake_case__       ,     weights='imagenet'       ,     input_tensor=snake_case__       ,     input_shape=snake_case__       ,     pooling=snake_case__       ,     classes=1000       ,     classifier_activation='softmax'       ,     )

   UpperCamelCase    :  Optional[int]							=	original_model.trainable_variables
   UpperCamelCase    :  Optional[int]							=	original_model.non_trainable_variables
   UpperCamelCase    :  Tuple							=	{param.name: param.numpy() for param in tf_params}
   for param in tf_non_train_params:
      UpperCamelCase    :  List[Any]							=	param.numpy()
   UpperCamelCase    :  List[str]							=	list(tf_params.keys()	)

   # Load HuggingFace model
   UpperCamelCase    :  str							=	get_efficientnet_config(snake_case__	)
   UpperCamelCase    :  Any							=	EfficientNetForImageClassification(snake_case__	).eval()
   UpperCamelCase    :  Tuple							=	hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('Converting parameters...'	)
   UpperCamelCase    :  List[Any]							=	rename_keys(snake_case__	)
   replace_params(snake_case__       ,     snake_case__       ,     snake_case__	)

   # Initialize preprocessor and preprocess input image
   UpperCamelCase    :  List[Any]							=	convert_image_processor(snake_case__	)
   UpperCamelCase    :  Dict							=	preprocessor(images=prepare_img()       ,     return_tensors='pt'	)

   # HF model inference
   hf_model.eval()
   with torch.no_grad():
      UpperCamelCase    :  Optional[int]							=	hf_model(**snake_case__	)
   UpperCamelCase    :  Dict							=	outputs.logits.detach().numpy()

   # Original model inference
   UpperCamelCase    :  Optional[int]							=	False
   UpperCamelCase    :  int							=	CONFIG_MAP[model_name]['image_size']
   UpperCamelCase    :  List[Any]							=	prepare_img().resize((image_size, image_size)       ,     resample=PIL.Image.NEAREST	)
   UpperCamelCase    :  List[Any]							=	image.img_to_array(snake_case__	)
   UpperCamelCase    :  str							=	np.expand_dims(snake_case__       ,     axis=0	)
   UpperCamelCase    :  Any							=	original_model.predict(snake_case__	)

   # Check whether original and HF model outputs match  -> np.allclose
   assert np.allclose(snake_case__       ,     snake_case__       ,     atol=1E-3	), "The predicted logits are not the same."
   print('Model outputs match!'	)

   if save_model:
      # Create folder to save model
      if not os.path.isdir(snake_case__	):
         os.mkdir(snake_case__	)
      # Save converted model and image processor
      hf_model.save_pretrained(snake_case__	)
      preprocessor.save_pretrained(snake_case__	)

   if push_to_hub:
      # Push model and image processor to hub
      print(F"""Pushing converted {model_name} to the hub..."""	)
      UpperCamelCase    :  List[str]							=	F"""efficientnet-{model_name}"""
      preprocessor.push_to_hub(snake_case__	)
      hf_model.push_to_hub(snake_case__	)


if __name__ == "__main__":
    __UpperCAmelCase          =       argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        '''--model_name''',
        default='''b0''',
        type=str,
        help='''Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].''',
    )
    parser.add_argument(
        '''--pytorch_dump_folder_path''',
        default='''hf_model''',
        type=str,
        help='''Path to the output PyTorch model directory.''',
    )
    parser.add_argument('''--save_model''', action='''store_true''', help='''Save model to local''')
    parser.add_argument('''--push_to_hub''', action='''store_true''', help='''Push model and image processor to the hub''')

    __UpperCAmelCase          =       parser.parse_args()
    convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)



