



import unittest

from transformers import BertGenerationTokenizer
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


__snake_case						:Dict      		=     '''▁'''

__snake_case						:Tuple      		=     get_tests_dir('''fixtures/test_sentencepiece.model''')

@require_sentencepiece
class     _A       (	__UpperCAmelCase      ,unittest.TestCase    ):
       UpperCamelCase__  :    Dict											=						BertGenerationTokenizer
       UpperCamelCase__  :    Optional[Any]											=						False
       UpperCamelCase__  :    List[str]											=						True



       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           super().setUp()

           __a						       =				BertGenerationTokenizer(__SCREAMING_SNAKE_CASE     ,		keep_accents=__SCREAMING_SNAKE_CASE)
           tokenizer.save_pretrained(self.tmpdirname)



       def 		_lowerCamelCase			(		self							:     int):


           '''simple docstring'''

           __a						       =				'''<s>'''
           __a						       =				1

           self.assertEqual(self.get_tokenizer()._convert_token_to_id(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)
           self.assertEqual(self.get_tokenizer()._convert_id_to_token(__SCREAMING_SNAKE_CASE)     ,		__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Tuple):


           '''simple docstring'''

           __a						       =				list(self.get_tokenizer().get_vocab().keys())

           self.assertEqual(vocab_keys[0]     ,		'''<unk>''')
           self.assertEqual(vocab_keys[1]     ,		'''<s>''')
           self.assertEqual(vocab_keys[-1]     ,		'''<pad>''')
           self.assertEqual(len(__SCREAMING_SNAKE_CASE)     ,		1_002)



       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           self.assertEqual(self.get_tokenizer().vocab_size     ,		1_000)



       def 		_lowerCamelCase			(		self							:     str):


           '''simple docstring'''

           __a						       =				BertGenerationTokenizer(__SCREAMING_SNAKE_CASE     ,		keep_accents=__SCREAMING_SNAKE_CASE)

           __a						       =				tokenizer.tokenize('''This is a test''')
           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		['''▁This''', '''▁is''', '''▁a''', '''▁t''', '''est'''])

           self.assertListEqual(
               tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)     ,		[285, 46, 10, 170, 382]     ,		)

           __a						       =				tokenizer.tokenize('''I was born in 92000, and this is falsé.''')
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE     ,		[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''9''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''é''',
                   '''.''',
               ]     ,		)
           __a						       =				tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE     ,		[8, 21, 84, 55, 24, 19, 7, 0, 602, 347, 347, 347, 3, 12, 66, 46, 72, 80, 6, 0, 4]     ,		)

           __a						       =				tokenizer.convert_ids_to_tokens(__SCREAMING_SNAKE_CASE)
           self.assertListEqual(
               __SCREAMING_SNAKE_CASE     ,		[
                   SPIECE_UNDERLINE + '''I''',
                   SPIECE_UNDERLINE + '''was''',
                   SPIECE_UNDERLINE + '''b''',
                   '''or''',
                   '''n''',
                   SPIECE_UNDERLINE + '''in''',
                   SPIECE_UNDERLINE + '''''',
                   '''<unk>''',
                   '''2''',
                   '''0''',
                   '''0''',
                   '''0''',
                   ''',''',
                   SPIECE_UNDERLINE + '''and''',
                   SPIECE_UNDERLINE + '''this''',
                   SPIECE_UNDERLINE + '''is''',
                   SPIECE_UNDERLINE + '''f''',
                   '''al''',
                   '''s''',
                   '''<unk>''',
                   '''.''',
               ]     ,		)



       @cached_property
       def 		_lowerCamelCase			(		self							:     Optional[int]):


           '''simple docstring'''

           return BertGenerationTokenizer.from_pretrained('''google/bert_for_seq_generation_L-24_bbc_encoder''')



       @slow
       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           __a						       =				'''Hello World!'''
           __a						       =				[18_536, 2_260, 101]

           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE))



       @slow
       def 		_lowerCamelCase			(		self							:     Optional[Any]):


           '''simple docstring'''

           __a						       =				(
               '''This is a very long text with a lot of weird characters, such as: . , ~ ? ( ) " [ ] ! : - . Also we will'''
               ''' add words that should not exsist and be tokenized to <unk>, such as saoneuhaoesuth'''
           )
           __a						       =				[
               871,
               419,
               358,
               946,
               991,
               2_521,
               452,
               358,
               1_357,
               387,
               7_751,
               3_536,
               112,
               985,
               456,
               126,
               865,
               938,
               5_400,
               5_734,
               458,
               1_368,
               467,
               786,
               2_462,
               5_246,
               1_159,
               633,
               865,
               4_519,
               457,
               582,
               852,
               2_557,
               427,
               916,
               508,
               405,
               34_324,
               497,
               391,
               408,
               11_342,
               1_244,
               385,
               100,
               938,
               985,
               456,
               574,
               362,
               12_597,
               3_200,
               3_129,
               1_172,
           ]

           self.assertListEqual(__SCREAMING_SNAKE_CASE     ,		self.big_tokenizer.encode(__SCREAMING_SNAKE_CASE))



       @require_torch
       @slow
       def 		_lowerCamelCase			(		self							:     Dict):


           '''simple docstring'''

           import torch

           from transformers import BertGenerationConfig, BertGenerationEncoder

           # Build sequence
           __a						       =				list(self.big_tokenizer.get_vocab().keys())[:10]
           __a						       =				''' '''.join(__SCREAMING_SNAKE_CASE)
           __a						       =				self.big_tokenizer.encode_plus(__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt'''     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)
           __a						       =				self.big_tokenizer.batch_encode_plus(
               [sequence + ''' ''' + sequence]     ,		return_tensors='''pt'''     ,		return_token_type_ids=__SCREAMING_SNAKE_CASE)

           __a						       =				BertGenerationConfig()
           __a						       =				BertGenerationEncoder(__SCREAMING_SNAKE_CASE)

           assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

           with torch.no_grad():
               model(**__SCREAMING_SNAKE_CASE)
               model(**__SCREAMING_SNAKE_CASE)





       @slow
       def 		_lowerCamelCase			(		self							:     Any):


           '''simple docstring'''

           __a						       =				{'''input_ids''': [[39_286, 458, 36_335, 2_001, 456, 13_073, 13_266, 455, 113, 7_746, 1_741, 11_157, 391, 13_073, 13_266, 455, 113, 3_967, 35_412, 113, 4_936, 109, 3_870, 2_377, 113, 30_084, 45_720, 458, 134, 17_496, 112, 503, 11_672, 113, 118, 112, 5_665, 13_347, 38_687, 112, 1_496, 31_389, 112, 3_268, 47_264, 134, 962, 112, 16_377, 8_035, 23_130, 430, 12_169, 15_518, 28_592, 458, 146, 41_697, 109, 391, 12_169, 15_518, 16_689, 458, 146, 41_358, 109, 452, 726, 4_034, 111, 763, 35_412, 5_082, 388, 1_903, 111, 9_051, 391, 2_870, 48_918, 1_900, 1_123, 550, 998, 112, 9_586, 15_985, 455, 391, 410, 22_955, 37_636, 114], [448, 17_496, 419, 3_663, 385, 763, 113, 27_533, 2_870, 3_283, 13_043, 1_639, 24_713, 523, 656, 24_013, 18_550, 2_521, 517, 27_014, 21_244, 420, 1_212, 1_465, 391, 927, 4_833, 388, 578, 11_786, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [484, 2_169, 7_687, 21_932, 18_146, 726, 363, 17_032, 3_391, 114, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]], '''attention_mask''': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]}  # noqa: E501
           # fmt: on

           self.tokenizer_integration_test_util(
               expected_encoding=__SCREAMING_SNAKE_CASE     ,		model_name='''google/bert_for_seq_generation_L-24_bbc_encoder'''     ,		revision='''c817d1fd1be2ffa69431227a1fe320544943d4db'''     ,		)










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()
__snake_case						:Any      		=     logging.get_logger(__name__)

__snake_case						:Optional[Any]      		=     {
    '''b0''': efficientnet.EfficientNetBa,
    '''b1''': efficientnet.EfficientNetBa,
    '''b2''': efficientnet.EfficientNetBa,
    '''b3''': efficientnet.EfficientNetBa,
    '''b4''': efficientnet.EfficientNetBa,
    '''b5''': efficientnet.EfficientNetBa,
    '''b6''': efficientnet.EfficientNetBa,
    '''b7''': efficientnet.EfficientNetBa,
}

__snake_case						:List[Any]      		=     {
    '''b0''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				EfficientNetConfig()
    __a						       =				CONFIG_MAP[model_name]['''hidden_dim''']
    __a						       =				CONFIG_MAP[model_name]['''width_coef''']
    __a						       =				CONFIG_MAP[model_name]['''depth_coef''']
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				CONFIG_MAP[model_name]['''dropout_rate''']
    __a						       =				CONFIG_MAP[model_name]['''dw_padding''']

    __a						       =				'''huggingface/label-files'''
    __a						       =				'''imagenet-1k-id2label.json'''
    __a						       =				1000
    __a						       =				json.load(open(hf_hub_download(_UpperCAmelCase   , _UpperCAmelCase   , repo_type='''dataset'''				)   , '''r'''				)				)
    __a						       =				{int(_UpperCAmelCase				): v for k, v in idalabel.items()}

    __a						       =				idalabel
    __a						       =				{v: k for k, v in idalabel.items()}
    return config





def      __snake_case	(			):
    __a						       =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    __a						       =				Image.open(requests.get(_UpperCAmelCase   , stream=_UpperCAmelCase				).raw				)
    return im





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				EfficientNetImageProcessor(
        size={'''height''': size, '''width''': size}   , image_mean=[0.4_85, 0.4_56, 0.4_06]   , image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]   , do_center_crop=_UpperCAmelCase   , )
    return preprocessor





def      __snake_case	(			_UpperCAmelCase				):
    __a						       =				[v.split('''_'''				)[0].split('''block'''				)[1] for v in original_param_names if v.startswith('''block'''				)]
    __a						       =				sorted(set(_UpperCAmelCase				)				)
    __a						       =				len(_UpperCAmelCase				)
    __a						       =				{b: str(_UpperCAmelCase				) for b, i in zip(_UpperCAmelCase   , range(_UpperCAmelCase				)				)}

    __a						       =				[]
    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:
        __a						       =				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''')				)

    __a						       =				{}
    for item in rename_keys:
        if item[0] in original_param_names:
            __a						       =				'''efficientnet.''' + item[1]

    __a						       =				'''classifier.weight'''
    __a						       =				'''classifier.bias'''
    return key_mapping





def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

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

        # Replace HF parameters with original TF model parameters
        assert hf_params[hf_key].shape == new_hf_value.shape
        hf_params[hf_key].copy_(_UpperCAmelCase				)





@torch.no_grad()
def      __snake_case	(			_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				):
    __a						       =				model_classes[model_name](
        include_top=_UpperCAmelCase   , weights='''imagenet'''   , input_tensor=_UpperCAmelCase   , input_shape=_UpperCAmelCase   , pooling=_UpperCAmelCase   , classes=1000   , classifier_activation='''softmax'''   , )

    __a						       =				original_model.trainable_variables
    __a						       =				original_model.non_trainable_variables
    __a						       =				{param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        __a						       =				param.numpy()
    __a						       =				list(tf_params.keys()				)

    # Load HuggingFace model
    __a						       =				get_efficientnet_config(_UpperCAmelCase				)
    __a						       =				EfficientNetForImageClassification(_UpperCAmelCase				).eval()
    __a						       =				hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print('''Converting parameters...'''				)
    __a						       =				rename_keys(_UpperCAmelCase				)
    replace_params(_UpperCAmelCase   , _UpperCAmelCase   , _UpperCAmelCase				)

    # Initialize preprocessor and preprocess input image
    __a						       =				convert_image_processor(_UpperCAmelCase				)
    __a						       =				preprocessor(images=prepare_img()   , return_tensors='''pt'''				)

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        __a						       =				hf_model(**_UpperCAmelCase				)
    __a						       =				outputs.logits.detach().numpy()

    # Original model inference
    __a						       =				False
    __a						       =				CONFIG_MAP[model_name]['''image_size''']
    __a						       =				prepare_img().resize((image_size, image_size)   , resample=PIL.Image.NEAREST				)
    __a						       =				image.img_to_array(_UpperCAmelCase				)
    __a						       =				np.expand_dims(_UpperCAmelCase   , axis=0				)
    __a						       =				original_model.predict(_UpperCAmelCase				)

    # Check whether original and HF model outputs match  -> np.allclose
    assert np.allclose(_UpperCAmelCase   , _UpperCAmelCase   , 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(_UpperCAmelCase				):
            os.mkdir(_UpperCAmelCase				)
        # Save converted model and image processor
        hf_model.save_pretrained(_UpperCAmelCase				)
        preprocessor.save_pretrained(_UpperCAmelCase				)

    if push_to_hub:
        # Push model and image processor to hub
        print(f'Pushing converted {model_name} to the hub...'				)
        __a						       =				f'efficientnet-{model_name}'
        preprocessor.push_to_hub(_UpperCAmelCase				)
        hf_model.push_to_hub(_UpperCAmelCase				)


if __name__ == "__main__":
 __snake_case						:int      		=     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''')

 __snake_case						:Optional[int]      		=     parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





