


'''simple docstring'''
import unittest

from transformers import SPIECE_UNDERLINE, ReformerTokenizer, ReformerTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, require_torch, slow
from transformers.utils import cached_property

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase   : str             =				get_tests_dir("""fixtures/test_sentencepiece.model""")




@require_sentencepiece
@require_tokenizers
class        _UpperCamelCase	(    __snake_case						,			unittest.TestCase):


				'''simple docstring'''


				_snake_case						       =				ReformerTokenizer
				_snake_case						       =				ReformerTokenizerFast
				_snake_case						       =				True
				_snake_case						       =				False
				_snake_case						       =				True

				def 	a__		(     self							)    ->  List[Any]:
					super().setUp()

					lowercase						:		str   					=		ReformerTokenizer(a_							,      keep_accents=a_							)
					tokenizer.save_pretrained(self.tmpdirname							)

				def 	a__		(     self							)    ->  List[str]:
					lowercase						:		Dict   					=		"""<s>"""
					lowercase						:		Tuple   					=		1

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

				def 	a__		(     self							)    ->  int:
					lowercase						:		Any   					=		list(self.get_tokenizer().get_vocab().keys()							)

					self.assertEqual(vocab_keys[0]							,      "<unk>"							)
					self.assertEqual(vocab_keys[1]							,      "<s>"							)
					self.assertEqual(vocab_keys[-1]							,      "j"							)
					self.assertEqual(len(a_							)							,      1_0_0_0							)

				def 	a__		(     self							)    ->  List[Any]:
					self.assertEqual(self.get_tokenizer().vocab_size							,      1_0_0_0							)

				def 	a__		(     self							)    ->  Dict:
					if not self.test_rust_tokenizer:
						return

					lowercase						:		int   					=		self.get_tokenizer()
					lowercase						:		Any   					=		self.get_rust_tokenizer()

					lowercase						:		Any   					=		"""I was born in 92000, and this is falsé."""

					lowercase						:		Union[str, Any]   					=		tokenizer.tokenize(a_							)
					lowercase						:		List[str]   					=		rust_tokenizer.tokenize(a_							)
					self.assertListEqual(a_							,      a_							)

					lowercase						:		Optional[Any]   					=		tokenizer.encode(a_							,      add_special_tokens=a_							)
					lowercase						:		Any   					=		rust_tokenizer.encode(a_							,      add_special_tokens=a_							)
					self.assertListEqual(a_							,      a_							)

					lowercase						:		str   					=		self.get_rust_tokenizer()
					lowercase						:		int   					=		tokenizer.encode(a_							)
					lowercase						:		Any   					=		rust_tokenizer.encode(a_							)
					self.assertListEqual(a_							,      a_							)

				def 	a__		(     self							,      a_=1_5							)    ->  List[str]:
					for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
						with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''							):
							lowercase						:		List[str]   					=		self.rust_tokenizer_class.from_pretrained(a_							,      **a_							)

							# Simple input
							lowercase						:		str   					=		"""This is a simple input"""
							lowercase						:		Union[str, Any]   					=		["""This is a simple input 1""", """This is a simple input 2"""]
							lowercase						:		Any   					=		("""This is a simple input""", """This is a pair""")
							lowercase						:		Optional[int]   					=		[
							    ("""This is a simple input 1""", """This is a simple input 2"""),
							    ("""This is a simple pair 1""", """This is a simple pair 2"""),
							]

							# Simple input tests
							self.assertRaises(a_							,      tokenizer_r.encode							,      a_							,      max_length=a_							,      padding="max_length"							)

							# Simple input
							self.assertRaises(a_							,      tokenizer_r.encode_plus							,      a_							,      max_length=a_							,      padding="max_length"							)

							# Simple input
							self.assertRaises(
							    a_							,      tokenizer_r.batch_encode_plus							,      a_							,      max_length=a_							,      padding="max_length"							,      )

							# Pair input
							self.assertRaises(a_							,      tokenizer_r.encode							,      a_							,      max_length=a_							,      padding="max_length"							)

							# Pair input
							self.assertRaises(a_							,      tokenizer_r.encode_plus							,      a_							,      max_length=a_							,      padding="max_length"							)

							# Pair input
							self.assertRaises(
							    a_							,      tokenizer_r.batch_encode_plus							,      a_							,      max_length=a_							,      padding="max_length"							,      )

				def 	a__		(     self							)    ->  List[str]:
					pass

				def 	a__		(     self							)    ->  Union[str, Any]:
					lowercase						:		int   					=		ReformerTokenizer(a_							,      keep_accents=a_							)

					lowercase						:		Optional[int]   					=		tokenizer.tokenize("This is a test"							)
					self.assertListEqual(a_							,      ["▁This", "▁is", "▁a", "▁t", "est"]							)

					self.assertListEqual(
					    tokenizer.convert_tokens_to_ids(a_							)							,      [2_8_5, 4_6, 1_0, 1_7_0, 3_8_2]							,      )

					lowercase						:		Tuple   					=		tokenizer.tokenize("I was born in 92000, and this is falsé."							)
					self.assertListEqual(
					    a_							,      [
					        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",
					        ".",
					    ]							,      )
					lowercase						:		int   					=		tokenizer.convert_tokens_to_ids(a_							)
					self.assertListEqual(
					    a_							,      [8, 2_1, 8_4, 5_5, 2_4, 1_9, 7, 0, 6_0_2, 3_4_7, 3_4_7, 3_4_7, 3, 1_2, 6_6, 4_6, 7_2, 8_0, 6, 0, 4]							,      )

					lowercase						:		int   					=		tokenizer.convert_ids_to_tokens(a_							)
					self.assertListEqual(
					    a_							,      [
					        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 	a__		(     self							)    ->  Any:
					return ReformerTokenizer.from_pretrained("google/reformer-crime-and-punishment"							)

				@slow
				def 	a__		(     self							)    ->  Any:
					lowercase						:		Optional[Any]   					=		"""Hello World!"""
					lowercase						:		Dict   					=		[1_2_6, 3_2, 2_6_2, 1_5_2, 3_8, 7_2, 2_8_7]

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

				@slow
				def 	a__		(     self							)    ->  Optional[Any]:
					lowercase						:		Any   					=		(
					    """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"""
					)
					lowercase						:		Dict   					=		[
					    1_0_8,
					    2_6_5,
					    2_4,
					    1_1_1,
					    4,
					    2_5_8,
					    1_5_6,
					    3_5,
					    2_8,
					    2_7_5,
					    3,
					    2_5_9,
					    2_9_7,
					    2_6_0,
					    8_4,
					    4,
					    3_5,
					    1_1_0,
					    4_4,
					    8,
					    2_5_9,
					    9_1,
					    2_6_8,
					    2_1,
					    1_1,
					    2_0_9,
					    2_7_4,
					    1_0_9,
					    2_6_6,
					    2_7_7,
					    1_1_7,
					    8_6,
					    9_3,
					    3_1_5,
					    2_5_8,
					    2_7_8,
					    2_5_8,
					    2_7_7,
					    2_5_8,
					    0,
					    2_5_8,
					    2_8_8,
					    2_5_8,
					    3_1_9,
					    2_5_8,
					    0,
					    2_5_8,
					    0,
					    2_5_8,
					    0,
					    2_5_8,
					    0,
					    2_5_8,
					    2_8_7,
					    2_5_8,
					    3_1_5,
					    2_5_8,
					    2_8_9,
					    2_5_8,
					    2_7_8,
					    9_9,
					    2_6_9,
					    2_6_6,
					    2_6_2,
					    8,
					    2_5_9,
					    2_4_1,
					    4,
					    2_1_7,
					    2_3_0,
					    2_6_8,
					    2_6_6,
					    5_5,
					    1_6_8,
					    1_0_6,
					    7_5,
					    1_9_3,
					    2_6_6,
					    2_2_3,
					    2_7,
					    4_9,
					    2_6,
					    2_8_2,
					    2_5,
					    2_6_4,
					    2_9_9,
					    1_9,
					    2_6,
					    0,
					    2_5_8,
					    2_7_7,
					    1_1_7,
					    8_6,
					    9_3,
					    1_7_6,
					    1_8_3,
					    2_7_0,
					    1_1,
					    2_6_2,
					    4_2,
					    6_1,
					    2_6_5,
					]

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

				@require_torch
				@slow
				def 	a__		(     self							)    ->  Optional[Any]:
					import torch

					from transformers import ReformerConfig, ReformerModel

					# Build sequence
					lowercase						:		Union[str, Any]   					=		list(self.big_tokenizer.get_vocab().keys()							)[:1_0]
					lowercase						:		List[str]   					=		""" """.join(a_							)
					lowercase						:		str   					=		self.big_tokenizer.encode_plus(a_							,      return_tensors="pt"							)
					lowercase						:		Optional[Any]   					=		self.big_tokenizer.batch_encode_plus([sequence, sequence]							,      return_tensors="pt"							)

					lowercase						:		Dict   					=		ReformerConfig()
					# The input gets padded during training so adjust the axial position encodings from the pretrained model value of (512, 1024)
					lowercase						:		Optional[int]   					=		encoded_sequence["""input_ids"""].shape
					lowercase						:		Optional[Any]   					=		ReformerModel(a_							)

					# Reformer has config.vocab_size == tokenizer.vocab_size == len(tokenizer) - 1 = 320; len(tokenizer) is 321 (including a pad token with id 320)
					assert model.get_input_embeddings().weight.shape[0] >= self.big_tokenizer.vocab_size

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


				@slow
				def 	a__		(     self							)    ->  int:
					# fmt: off
					lowercase						:		Optional[int]   					=		{"""input_ids""": [[1_0_8, 2_6_5, 2_4, 1_1_1, 4, 2_5_8, 1_5_6, 7, 5_1, 2_7_9, 5_8, 7, 7_6, 2_5, 6_9, 2_7_8], [1_4_0, 2_4_3, 2_6_4, 1_3_4, 1_7, 2_6_7, 7_7, 2_6_3, 2_2, 2_6_2, 2_9_7, 2_5_8, 3_0_4, 1_7_7, 2_7_9, 2_6_6, 1_4, 8_9, 1_3, 3_5, 2_6_1, 2_9_9, 2_7_2, 1_3_7, 2_7_5, 2_7_8]], """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]]}  # noqa: E501
					# fmt: on

					# This tokenizer does not know some characters like ")".
					# That is the reason why we use very simple texts here.
					# Also see https://github.com/huggingface/transformers/pull/11737#issuecomment-850769064
					lowercase						:		Optional[int]   					=		[
					    """This is a very simple sentence.""",
					    """The quick brown fox jumps over the lazy dog.""",
					]

					self.tokenizer_integration_test_util(
					    expected_encoding=a_							,      model_name="google/reformer-crime-and-punishment"							,      revision="0e6c3decb8211d49bf881013425dc8b0448b3f5a"							,      padding=a_							,      sequences=a_							,      )










"""simple docstring"""







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()
_lowerCAmelCase            =	logging.get_logger(__name__)

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

_lowerCAmelCase            =	{
    'b0': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.0,
        'image_size': 2_24,
        'dropout_rate': 0.2,
        'dw_padding': [],
    },
    'b1': {
        'hidden_dim': 12_80,
        'width_coef': 1.0,
        'depth_coef': 1.1,
        'image_size': 2_40,
        'dropout_rate': 0.2,
        'dw_padding': [16],
    },
    'b2': {
        'hidden_dim': 14_08,
        'width_coef': 1.1,
        'depth_coef': 1.2,
        'image_size': 2_60,
        'dropout_rate': 0.3,
        'dw_padding': [5, 8, 16],
    },
    'b3': {
        'hidden_dim': 15_36,
        'width_coef': 1.2,
        'depth_coef': 1.4,
        'image_size': 3_00,
        'dropout_rate': 0.3,
        'dw_padding': [5, 18],
    },
    'b4': {
        'hidden_dim': 17_92,
        'width_coef': 1.4,
        'depth_coef': 1.8,
        'image_size': 3_80,
        'dropout_rate': 0.4,
        'dw_padding': [6],
    },
    'b5': {
        'hidden_dim': 20_48,
        'width_coef': 1.6,
        'depth_coef': 2.2,
        'image_size': 4_56,
        'dropout_rate': 0.4,
        'dw_padding': [13, 27],
    },
    'b6': {
        'hidden_dim': 23_04,
        'width_coef': 1.8,
        'depth_coef': 2.6,
        'image_size': 5_28,
        'dropout_rate': 0.5,
        'dw_padding': [31],
    },
    'b7': {
        'hidden_dim': 25_60,
        'width_coef': 2.0,
        'depth_coef': 3.1,
        'image_size': 6_00,
        'dropout_rate': 0.5,
        'dw_padding': [18],
    },
}


def      UpperCamelCase       ( _A )     ->       List[str]:
				lowercase    :      List[str]       = EfficientNetConfig()
				lowercase    :      Any       = CONFIG_MAP[model_name]["""hidden_dim"""]
				lowercase    :      List[str]       = CONFIG_MAP[model_name]["""width_coef"""]
				lowercase    :      str       = CONFIG_MAP[model_name]["""depth_coef"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      List[Any]       = CONFIG_MAP[model_name]["""dropout_rate"""]
				lowercase    :      int       = CONFIG_MAP[model_name]["""dw_padding"""]

				lowercase    :      Optional[int]       = """huggingface/label-files"""
				lowercase    :      int       = """imagenet-1k-id2label.json"""
				lowercase    :      Any       = 1_000
				lowercase    :      Any       = json.load(open(hf_hub_download(_A  ,				_A  ,				repo_type="""dataset""" )  ,				"""r""" ) )
				lowercase    :      Optional[int]       = {int(_A ): v for k, v in idalabel.items()}

				lowercase    :      int       = idalabel
				lowercase    :      Optional[Any]       = {v: k for k, v in idalabel.items()}
				return config


def      UpperCamelCase       ( )     ->       Tuple:
				lowercase    :      Any       = """http://images.cocodataset.org/val2017/000000039769.jpg"""
				lowercase    :      Optional[int]       = Image.open(requests.get(_A  ,				stream=_A ).raw )
				return im


def      UpperCamelCase       ( _A )     ->       Optional[Any]:
				lowercase    :      str       = CONFIG_MAP[model_name]["""image_size"""]
				lowercase    :      Optional[int]       = EfficientNetImageProcessor(
				    size={"""height""": size, """width""": size}  ,				image_mean=[0.485, 0.456, 0.406]  ,				image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]  ,				do_center_crop=_A  ,				)
				return preprocessor


def      UpperCamelCase       ( _A )     ->       Optional[int]:
				lowercase    :      Union[str, Any]       = [v.split("""_""" )[0].split("""block""" )[1] for v in original_param_names if v.startswith("""block""" )]
				lowercase    :      Optional[Any]       = sorted(set(_A ) )
				lowercase    :      Dict       = len(_A )
				lowercase    :      List[str]       = {b: str(_A ) for b, i in zip(_A  ,				range(_A ) )}

				lowercase    :      Union[str, Any]       = []
				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:
								lowercase    :      str       = 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""") )

				lowercase    :      Union[str, Any]       = {}
				for item in rename_keys:
								if item[0] in original_param_names:
												lowercase    :      Optional[int]       = """efficientnet.""" + item[1]

				lowercase    :      Any       = """classifier.weight"""
				lowercase    :      Tuple       = """classifier.bias"""
				return key_mapping


def      UpperCamelCase       ( _A  ,				_A  ,				_A )     ->       Optional[Any]:
				for key, value in tf_params.items():
								if "normalization" in key:
												continue

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

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


@torch.no_grad()
def      UpperCamelCase       ( _A  ,				_A  ,				_A  ,				_A )     ->       str:
				lowercase    :      Any       = model_classes[model_name](
				    include_top=_A  ,				weights="""imagenet"""  ,				input_tensor=_A  ,				input_shape=_A  ,				pooling=_A  ,				classes=1_000  ,				classifier_activation="""softmax"""  ,				)

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

				# Load HuggingFace model
				lowercase    :      str       = get_efficientnet_config(_A )
				lowercase    :      List[Any]       = EfficientNetForImageClassification(_A ).eval()
				lowercase    :      Optional[int]       = hf_model.state_dict()

				# Create src-to-dst parameter name mapping dictionary
				print("""Converting parameters...""" )
				lowercase    :      int       = rename_keys(_A )
				replace_params(_A  ,				_A  ,				_A )

				# Initialize preprocessor and preprocess input image
				lowercase    :      Optional[int]       = convert_image_processor(_A )
				lowercase    :      Any       = preprocessor(images=prepare_img()  ,				return_tensors="""pt""" )

				# HF model inference
				hf_model.eval()
				with torch.no_grad():
								lowercase    :      Union[str, Any]       = hf_model(**_A )
				lowercase    :      List[Any]       = outputs.logits.detach().numpy()

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

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

				if push_to_hub:
								# Push model and image processor to hub
								print(F"""Pushing converted {model_name} to the hub...""" )
								lowercase    :      Dict       = F"""efficientnet-{model_name}"""
								preprocessor.push_to_hub(_A )
								hf_model.push_to_hub(_A )


if __name__ == "__main__":
				_lowerCAmelCase            =	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')

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