
import json
import os
import re
import unicodedata
from json.encoder import INFINITY
from typing import Any, Dict, List, Optional, Tuple, Union

import numpy as np
import regex

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, is_flax_available, is_tf_available, is_torch_available, logging
from ...utils.generic import _is_jax, _is_numpy


_lowerCamelCase	:    Tuple          =      logging.get_logger(__name__)

_lowerCamelCase	:    Any          =      {
    '''artists_file''': '''artists.json''',
    '''lyrics_file''': '''lyrics.json''',
    '''genres_file''': '''genres.json''',
}

_lowerCamelCase	:    List[str]          =      {
    '''artists_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/artists.json''',
    },
    '''genres_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/genres.json''',
    },
    '''lyrics_file''': {
        '''jukebox''': '''https://huggingface.co/ArthurZ/jukebox/blob/main/lyrics.json''',
    },
}

_lowerCamelCase	:    Dict          =      {
    '''jukebox''': 512,
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		int          = VOCAB_FILES_NAMES
	_UpperCAmelCase	:		List[str]          = PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase	:		int          = PRETRAINED_LYRIC_TOKENS_SIZES
	_UpperCAmelCase	:		str          = ["input_ids", "attention_mask"]



	def __init__(  self     :   Optional[Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   str      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Tuple=["v3", "v2", "v2"]      ,  lowercase     :   Optional[int]=512      ,  lowercase     :   str=5      ,  lowercase     :   Any="<|endoftext|>"      ,  **lowercase     :   List[Any]      ,  ):


				'''simple docstring'''


				_snake_case          =      AddedToken(lowercase      ,  lstrip=lowercase      ,  rstrip=lowercase     ) if isinstance(lowercase      ,  lowercase     ) else unk_token
				super().__init__(
				    unk_token=lowercase      ,  n_genres=lowercase      ,  version=lowercase      ,  max_n_lyric_tokens=lowercase      ,  **lowercase      ,  )
				_snake_case          =      version
				_snake_case          =      max_n_lyric_tokens
				_snake_case          =      n_genres

				with open(lowercase      ,  encoding='utf-8'     ) as vocab_handle:
							_snake_case          =      json.load(lowercase     )

				with open(lowercase      ,  encoding='utf-8'     ) as vocab_handle:
							_snake_case          =      json.load(lowercase     )

				with open(lowercase      ,  encoding='utf-8'     ) as vocab_handle:
							_snake_case          =      json.load(lowercase     )

				_snake_case          =      R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'
				# In v2, we had a n_vocab=80 and in v3 we missed + and so n_vocab=79 of characters.
				if len(self.lyrics_encoder     ) == 79:
							_snake_case          =      oov.replace(R'\-\''      ,  R'\-+\''     )

				_snake_case          =      regex.compile(lowercase     )
				_snake_case          =      {v: k for k, v in self.artists_encoder.items()}
				_snake_case          =      {v: k for k, v in self.genres_encoder.items()}
				_snake_case          =      {v: k for k, v in self.lyrics_encoder.items()}



	@property
	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				return len(self.artists_encoder     ) + len(self.genres_encoder     ) + len(self.lyrics_encoder     )



	def 			A							(  self     :   Union[str, Any]     ):


				'''simple docstring'''


				return dict(self.artists_encoder      ,  self.genres_encoder      ,  self.lyrics_encoder     )



	def 			A							(  self     :   int      ,  lowercase     :   Dict      ,  lowercase     :   int      ,  lowercase     :   int     ):


				'''simple docstring'''


				_snake_case          =      [self.artists_encoder.get(lowercase      ,  0     ) for artist in list_artists]
				for genres in range(len(lowercase     )     ):
							_snake_case          =      [self.genres_encoder.get(lowercase      ,  0     ) for genre in list_genres[genres]]
							_snake_case          =      list_genres[genres] + [-1] * (self.n_genres - len(list_genres[genres]     ))

				_snake_case          =      [[self.lyrics_encoder.get(lowercase      ,  0     ) for character in list_lyrics[0]], [], []]
				return artists_id, list_genres, lyric_ids



	def 			A							(  self     :   Dict      ,  lowercase     :   List[Any]     ):


				'''simple docstring'''


				return list(lowercase     )



	def 			A							(  self     :   str      ,  lowercase     :   List[Any]      ,  lowercase     :   int      ,  lowercase     :   Any      ,  **lowercase     :   Optional[int]     ):


				'''simple docstring'''


				_snake_case     , _snake_case     , _snake_case          =      self.prepare_for_tokenization(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case          =      self._tokenize(lowercase     )
				return artist, genre, lyrics



	def 			A							(  self     :   List[str]      ,  lowercase     :   str      ,  lowercase     :   str      ,  lowercase     :   str      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				for idx in range(len(self.version     )     ):
							if self.version[idx] == "v3":
										_snake_case          =      artists[idx].lower()
										_snake_case          =      [genres[idx].lower()]
							else:
										_snake_case          =      self._normalize(artists[idx]     ) + '.v2'
										_snake_case          =      [
										    self._normalize(lowercase     ) + '.v2' for genre in genres[idx].split('_'     )
										]  # split is for the full dictionary with combined genres

				if self.version[0] == "v2":
							_snake_case          =      regex.compile(R'[^A-Za-z0-9.,:;!?\-\'\"()\[\] \t\n]+'     )
							_snake_case          =      'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789.,:;!?-+\'\"()[] \t\n'
							_snake_case          =      {vocab[index]: index + 1 for index in range(len(lowercase     )     )}
							_snake_case          =      0
							_snake_case          =      len(lowercase     ) + 1
							_snake_case          =      self.vocab
							_snake_case          =      {v: k for k, v in self.vocab.items()}
							_snake_case          =      ''
				else:
							_snake_case          =      regex.compile(R'[^A-Za-z0-9.,:;!?\-+\'\"()\[\] \t\n]+'     )

				_snake_case          =      self._run_strip_accents(lowercase     )
				_snake_case          =      lyrics.replace('\\'      ,  '\n'     )
				_snake_case          =      self.out_of_vocab.sub(''      ,  lowercase     ), [], []
				return artists, genres, lyrics



	def 			A							(  self     :   Tuple      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				_snake_case          =      unicodedata.normalize('NFD'      ,  lowercase     )
				_snake_case          =      []
				for char in text:
							_snake_case          =      unicodedata.category(lowercase     )
							if cat == "Mn":
										continue
							output.append(lowercase     )
				return "".join(lowercase     )



	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      (
				    [chr(lowercase     ) for i in range(ord('a'     )      ,  ord('z'     ) + 1     )]
				    + [chr(lowercase     ) for i in range(ord('A'     )      ,  ord('Z'     ) + 1     )]
				    + [chr(lowercase     ) for i in range(ord('0'     )      ,  ord('9'     ) + 1     )]
				    + ['.']
				)
				_snake_case          =      frozenset(lowercase     )
				_snake_case          =      re.compile(R'_+'     )
				_snake_case          =      ''.join([c if c in accepted else '_' for c in text.lower()]     )
				_snake_case          =      pattern.sub('_'      ,  lowercase     ).strip('_'     )
				return text



	def 			A							(  self     :   Any      ,  lowercase     :   List[str]     ):


				'''simple docstring'''


				return " ".join(lowercase     )



	def 			A							(  self     :   List[Any]      ,  lowercase     :   str      ,  lowercase     :   Optional[Union[str, TensorType]] = None      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				if not isinstance(lowercase      ,  lowercase     ):
							_snake_case          =      TensorType(lowercase     )

				# Get a function reference for the correct framework
				if tensor_type == TensorType.TENSORFLOW:
							if not is_tf_available():
										raise ImportError(
										    'Unable to convert output to TensorFlow tensors format, TensorFlow is not installed.'     )
							import tensorflow as tf

							_snake_case          =      tf.constant
							_snake_case          =      tf.is_tensor
				elif tensor_type == TensorType.PYTORCH:
							if not is_torch_available():
										raise ImportError('Unable to convert output to PyTorch tensors format, PyTorch is not installed.'     )
							import torch

							_snake_case          =      torch.tensor
							_snake_case          =      torch.is_tensor
				elif tensor_type == TensorType.JAX:
							if not is_flax_available():
										raise ImportError('Unable to convert output to JAX tensors format, JAX is not installed.'     )
							import jax.numpy as jnp  # noqa: F811

							_snake_case          =      jnp.array
							_snake_case          =      _is_jax
				else:
							_snake_case          =      np.asarray
							_snake_case          =      _is_numpy

				# Do the tensor conversion in batch

				try:
							if prepend_batch_axis:
										_snake_case          =      [inputs]

							if not is_tensor(lowercase     ):
										_snake_case          =      as_tensor(lowercase     )
				except:  # noqa E722
							raise ValueError(
							    'Unable to create tensor, you should probably activate truncation and/or padding '
							    'with \'padding=True\' \'truncation=True\' to have batched tensors with the same length.'     )

				return inputs



	def __call__(  self     :   Dict      ,  lowercase     :   Dict      ,  lowercase     :   Optional[int]      ,  lowercase     :   str=""      ,  lowercase     :   Optional[int]="pt"     ):


				'''simple docstring'''


				_snake_case          =      [0, 0, 0]
				_snake_case          =      [artist] * len(self.version     )
				_snake_case          =      [genres] * len(self.version     )

				_snake_case     , _snake_case     , _snake_case          =      self.tokenize(lowercase      ,  lowercase      ,  lowercase     )
				_snake_case     , _snake_case     , _snake_case          =      self._convert_token_to_id(lowercase      ,  lowercase      ,  lowercase     )

				_snake_case          =      [-INFINITY] * len(full_tokens[-1]     )
				_snake_case          =      [
				    self.convert_to_tensors(
				        [input_ids + [artists_id[i]] + genres_ids[i] + full_tokens[i]]      ,  tensor_type=lowercase     )
				    for i in range(len(self.version     )     )
				]
				return BatchEncoding({'input_ids': input_ids, 'attention_masks': attention_masks}     )



	def 			A							(  self     :   Union[str, Any]      ,  lowercase     :   str      ,  lowercase     :   Optional[str] = None     ):


				'''simple docstring'''


				if not os.path.isdir(lowercase     ):
							logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''     )
							return

				_snake_case          =      os.path.join(
				    lowercase      ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['artists_file']     )
				with open(lowercase      ,  'w'      ,  encoding='utf-8'     ) as f:
							f.write(json.dumps(self.artists_encoder      ,  ensure_ascii=lowercase     )     )

				_snake_case          =      os.path.join(
				    lowercase      ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['genres_file']     )
				with open(lowercase      ,  'w'      ,  encoding='utf-8'     ) as f:
							f.write(json.dumps(self.genres_encoder      ,  ensure_ascii=lowercase     )     )

				_snake_case          =      os.path.join(
				    lowercase      ,  (filename_prefix + '-' if filename_prefix else '') + VOCAB_FILES_NAMES['lyrics_file']     )
				with open(lowercase      ,  'w'      ,  encoding='utf-8'     ) as f:
							f.write(json.dumps(self.lyrics_encoder      ,  ensure_ascii=lowercase     )     )

				return (artists_file, genres_file, lyrics_file)





	def 			A							(  self     :   int      ,  lowercase     :   List[Any]      ,  lowercase     :   List[Any]      ,  lowercase     :   Optional[Any]     ):


				'''simple docstring'''


				_snake_case          =      self.artists_decoder.get(lowercase     )
				_snake_case          =      [self.genres_decoder.get(lowercase     ) for genre in genres_index]
				_snake_case          =      [self.lyrics_decoder.get(lowercase     ) for character in lyric_index]
				return artist, genres, lyrics

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	:    int          =      logging.get_logger(__name__)

_lowerCamelCase	:    Tuple          =      {
    '''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	:    List[str]          =      {
    '''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 	a_		(	__lowercase       :     int			)			->  Optional[int]:
			_snake_case          =      EfficientNetConfig()
			_snake_case          =      CONFIG_MAP[model_name]['hidden_dim']
			_snake_case          =      CONFIG_MAP[model_name]['width_coef']
			_snake_case          =      CONFIG_MAP[model_name]['depth_coef']
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      CONFIG_MAP[model_name]['dropout_rate']
			_snake_case          =      CONFIG_MAP[model_name]['dw_padding']

			_snake_case          =      'huggingface/label-files'
			_snake_case          =      'imagenet-1k-id2label.json'
			_snake_case          =      1_000
			_snake_case          =      json.load(open(hf_hub_download(__lowercase	, __lowercase	, repo_type='dataset'			)	, 'r'			)			)
			_snake_case          =      {int(__lowercase			): v for k, v in idalabel.items()}

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






def 	a_		(	)			->  Any:
			_snake_case          =      'http://images.cocodataset.org/val2017/000000039769.jpg'
			_snake_case          =      Image.open(requests.get(__lowercase	, stream=__lowercase			).raw			)
			return im






def 	a_		(	__lowercase       :     Union[str, Any]			)			->  Tuple:
			_snake_case          =      CONFIG_MAP[model_name]['image_size']
			_snake_case          =      EfficientNetImageProcessor(
			    size={'height': size, 'width': size}	, image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]	, image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]	, do_center_crop=__lowercase	, )
			return preprocessor






def 	a_		(	__lowercase       :     str			)			->  List[Any]:
			_snake_case          =      [v.split('_'			)[0].split('block'			)[1] for v in original_param_names if v.startswith('block'			)]
			_snake_case          =      sorted(set(__lowercase			)			)
			_snake_case          =      len(__lowercase			)
			_snake_case          =      {b: str(__lowercase			) for b, i in zip(__lowercase	, range(__lowercase			)			)}

			_snake_case          =      []
			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:
						_snake_case          =      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')			)

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

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






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

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

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






@torch.no_grad()
def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     Any	, __lowercase       :     int	, __lowercase       :     str			)			->  Dict:
			_snake_case          =      model_classes[model_name](
			    include_top=__lowercase	, weights='imagenet'	, input_tensor=__lowercase	, input_shape=__lowercase	, pooling=__lowercase	, classes=1_000	, classifier_activation='softmax'	, )

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

			# Load HuggingFace model
			_snake_case          =      get_efficientnet_config(__lowercase			)
			_snake_case          =      EfficientNetForImageClassification(__lowercase			).eval()
			_snake_case          =      hf_model.state_dict()

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

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

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

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

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

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


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

			_lowerCamelCase	:    List[str]          =      parser.parse_args()
			convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)