




import copy
import inspect
import unittest

from transformers import PretrainedConfig, SwiftFormerConfig
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 SwiftFormerForImageClassification, SwiftFormerModel
					from transformers.models.swiftformer.modeling_swiftformer import SWIFTFORMER_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		,			_A=True		,			_A=True		,			_A=0.1		,			_A=0.1		,			_A=2_2_4		,			_A=1_0_0_0		,			_A=[3, 3, 6, 4]		,			_A=[4_8, 5_6, 1_1_2, 2_2_0]		,			):




											"""simple docstring"""



											__lowerCAmelCase				   =       parent
											__lowerCAmelCase				   =       batch_size
											__lowerCAmelCase				   =       num_channels
											__lowerCAmelCase				   =       is_training
											__lowerCAmelCase				   =       use_labels
											__lowerCAmelCase				   =       hidden_dropout_prob
											__lowerCAmelCase				   =       attention_probs_dropout_prob
											__lowerCAmelCase				   =       num_labels
											__lowerCAmelCase				   =       image_size
											__lowerCAmelCase				   =       layer_depths
											__lowerCAmelCase				   =       embed_dims






						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.num_labels			)

											__lowerCAmelCase				   =       self.get_config()

											return config, pixel_values, labels






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											return SwiftFormerConfig(
											    depths=self.layer_depths		,			embed_dims=self.embed_dims		,			mlp_ratio=4		,			downsamples=[True, True, True, True]		,			hidden_act="gelu"		,			num_labels=self.num_labels		,			down_patch_size=3		,			down_stride=2		,			down_pad=1		,			drop_rate=0.0		,			drop_path_rate=0.0		,			use_layer_scale=SCREAMING_SNAKE_CASE_		,			layer_scale_init_value=1E-5		,			)






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




											"""simple docstring"""



											__lowerCAmelCase				   =       SwiftFormerModel(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.embed_dims[-1], 7, 7)			)






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




											"""simple docstring"""



											__lowerCAmelCase				   =       self.num_labels
											__lowerCAmelCase				   =       SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_			)
											model.to(SCREAMING_SNAKE_CASE_			)
											model.eval()
											__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_		,			labels=SCREAMING_SNAKE_CASE_			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)			)

											__lowerCAmelCase				   =       SwiftFormerForImageClassification(SCREAMING_SNAKE_CASE_			)
											model.to(SCREAMING_SNAKE_CASE_			)
											model.eval()

											__lowerCAmelCase				   =       floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]			)
											__lowerCAmelCase				   =       model(SCREAMING_SNAKE_CASE_			)
											self.parent.assertEqual(result.logits.shape		,			(self.batch_size, self.num_labels)			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






@require_torch
class 			a__    (					a__ ,				a__ ,				unittest.TestCase      ):
						_a							:    Optional[int]					=    (SwiftFormerModel, SwiftFormerForImageClassification) if is_torch_available() else ()
						_a							:    List[str]					=    (
						    {"feature-extraction": SwiftFormerModel, "image-classification": SwiftFormerForImageClassification}
						    if is_torch_available()
						    else {}
						)

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






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       SwiftFormerModelTester(self			)
											__lowerCAmelCase				   =       ConfigTester(
											    self		,			config_class=SCREAMING_SNAKE_CASE_		,			has_text_modality=SCREAMING_SNAKE_CASE_		,			hidden_size=3_7		,			num_attention_heads=1_2		,			num_hidden_layers=1_2		,			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											self.config_tester.run_common_tests()






						@unittest.skip(reason="SwiftFormer 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_			)
																__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_image_classification(*SCREAMING_SNAKE_CASE_			)






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



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






						@unittest.skip(reason="SwiftFormer does not output attentions"			)
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											pass






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											def check_hidden_states_output(_A		,			_A		,			_A			):
																__lowerCAmelCase				   =       model_class(SCREAMING_SNAKE_CASE_			)
																model.to(SCREAMING_SNAKE_CASE_			)
																model.eval()

																with torch.no_grad():
																					__lowerCAmelCase				   =       model(**self._prepare_for_class(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)			)

																__lowerCAmelCase				   =       outputs.hidden_states

																__lowerCAmelCase				   =       8
																self.assertEqual(len(SCREAMING_SNAKE_CASE_			)		,			SCREAMING_SNAKE_CASE_			)  # TODO

																# SwiftFormer's feature maps are of shape (batch_size, embed_dims, height, width)
																# with the width and height being successively divided by 2, after every 2 blocks
																for i in range(len(SCREAMING_SNAKE_CASE_			)			):
																					self.assertEqual(
																					    hidden_states[i].shape		,			torch.Size(
																					        [
																					            self.model_tester.batch_size,
																					            self.model_tester.embed_dims[i // 2],
																					            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																					            (self.model_tester.image_size // 4) // 2 ** (i // 2),
																					        ]			)		,			)

											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											for model_class in self.all_model_classes:
																__lowerCAmelCase				   =       True
																check_hidden_states_output(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)

																# check that output_hidden_states also work using config
																del inputs_dict["output_hidden_states"]
																__lowerCAmelCase				   =       True

																check_hidden_states_output(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)






						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											def _config_zero_init(_A			):
																__lowerCAmelCase				   =       copy.deepcopy(SCREAMING_SNAKE_CASE_			)
																for key in configs_no_init.__dict__.keys():
																					if "_range" in key or "_std" in key or "initializer_factor" in key or "layer_scale" in key:
																										setattr(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			1E-1_0			)
																					if isinstance(getattr(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)		,			SCREAMING_SNAKE_CASE_			):
																										__lowerCAmelCase				   =       _config_zero_init(getattr(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)			)
																										setattr(SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_		,			SCREAMING_SNAKE_CASE_			)
																return configs_no_init

											__lowerCAmelCase				   =       self.model_tester.prepare_config_and_inputs_for_common()

											__lowerCAmelCase				   =       _config_zero_init(SCREAMING_SNAKE_CASE_			)
											for model_class in self.all_model_classes:
																__lowerCAmelCase				   =       model_class(config=SCREAMING_SNAKE_CASE_			)
																for name, param in model.named_parameters():
																					if param.requires_grad:
																										self.assertIn(
																										    ((param.data.mean() * 1E9) / 1E9).round().item()		,			[0.0, 1.0]		,			msg=f"""Parameter {name} of model {model_class} seems not properly initialized"""		,			)






						@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







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("MBZUAI/swiftformer-xs"			) if is_vision_available() else None






						@slow
						def 				__SCREAMING_SNAKE_CASE(   self			):




											"""simple docstring"""



											__lowerCAmelCase				   =       SwiftFormerForImageClassification.from_pretrained("MBZUAI/swiftformer-xs"			).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_			)

											# forward pass
											with torch.no_grad():
																__lowerCAmelCase				   =       model(**SCREAMING_SNAKE_CASE_			)

											# verify the logits
											__lowerCAmelCase				   =       torch.Size((1, 1_0_0_0)			)
											self.assertEqual(outputs.logits.shape		,			SCREAMING_SNAKE_CASE_			)

											__lowerCAmelCase				   =       torch.tensor([[-2.1_7_0_3E0_0, 2.1_1_0_7E0_0, -2.0_8_1_1E0_0]]			).to(SCREAMING_SNAKE_CASE_			)
											self.assertTrue(torch.allclose(outputs.logits[0, :3]		,			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)



