
import json
import os
from pathlib import Path
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple, Union

import sentencepiece

from ...tokenization_utils import BatchEncoding, PreTrainedTokenizer
from ...utils import logging


_lowerCamelCase	:    List[Any]          =      logging.get_logger(__name__)

_lowerCamelCase	:    str          =      '''▁'''

_lowerCamelCase	:    Optional[int]          =      {
    '''vocab_file''': '''vocab.json''',
    '''spm_file''': '''sentencepiece.bpe.model''',
    '''tokenizer_config_file''': '''tokenizer_config.json''',
}

_lowerCamelCase	:    Dict          =      {
    '''vocab_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/vocab.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/vocab.json''',
    },
    '''spm_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/sentencepiece.bpe.model''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/sentencepiece.bpe.model''',
    },
    '''tokenizer_config_file''': {
        '''facebook/m2m100_418M''': '''https://huggingface.co/facebook/m2m100_418M/resolve/main/tokenizer_config.json''',
        '''facebook/m2m100_1.2B''': '''https://huggingface.co/facebook/m2m100_1.2B/resolve/main/tokenizer_config.json''',
    },
}

_lowerCamelCase	:    Dict          =      {
    '''facebook/m2m100_418M''': 1_024,
}

# fmt: off
_lowerCamelCase	:    int          =      {
    '''m2m100''': ['''af''', '''am''', '''ar''', '''ast''', '''az''', '''ba''', '''be''', '''bg''', '''bn''', '''br''', '''bs''', '''ca''', '''ceb''', '''cs''', '''cy''', '''da''', '''de''', '''el''', '''en''', '''es''', '''et''', '''fa''', '''ff''', '''fi''', '''fr''', '''fy''', '''ga''', '''gd''', '''gl''', '''gu''', '''ha''', '''he''', '''hi''', '''hr''', '''ht''', '''hu''', '''hy''', '''id''', '''ig''', '''ilo''', '''is''', '''it''', '''ja''', '''jv''', '''ka''', '''kk''', '''km''', '''kn''', '''ko''', '''lb''', '''lg''', '''ln''', '''lo''', '''lt''', '''lv''', '''mg''', '''mk''', '''ml''', '''mn''', '''mr''', '''ms''', '''my''', '''ne''', '''nl''', '''no''', '''ns''', '''oc''', '''or''', '''pa''', '''pl''', '''ps''', '''pt''', '''ro''', '''ru''', '''sd''', '''si''', '''sk''', '''sl''', '''so''', '''sq''', '''sr''', '''ss''', '''su''', '''sv''', '''sw''', '''ta''', '''th''', '''tl''', '''tn''', '''tr''', '''uk''', '''ur''', '''uz''', '''vi''', '''wo''', '''xh''', '''yi''', '''yo''', '''zh''', '''zu'''],
    '''wmt21''': ['''en''', '''ha''', '''is''', '''ja''', '''cs''', '''ru''', '''zh''', '''de''']
}




class        SCREAMING_SNAKE_CASE__					(     UpperCAmelCase ):





	'''simple docstring'''



	_UpperCAmelCase	:		Tuple          = VOCAB_FILES_NAMES
	_UpperCAmelCase	:		List[str]          = PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	_UpperCAmelCase	:		Union[str, Any]          = PRETRAINED_VOCAB_FILES_MAP
	_UpperCAmelCase	:		Optional[Any]          = ["input_ids", "attention_mask"]

	_UpperCAmelCase	:		List[int]          = []
	_UpperCAmelCase	:		List[int]          = []



	def __init__(  self     :   int      ,  lowercase     :   Optional[Any]      ,  lowercase     :   Union[str, Any]      ,  lowercase     :   Dict=None      ,  lowercase     :   str=None      ,  lowercase     :   List[Any]="<s>"      ,  lowercase     :   Union[str, Any]="</s>"      ,  lowercase     :   int="</s>"      ,  lowercase     :   Tuple="<pad>"      ,  lowercase     :   List[Any]="<unk>"      ,  lowercase     :   int="m2m100"      ,  lowercase     :   Optional[Dict[str, Any]] = None      ,  lowercase     :   Dict=8      ,  **lowercase     :   Tuple      ,  ):


				'''simple docstring'''


				_snake_case          =      {} if sp_model_kwargs is None else sp_model_kwargs

				_snake_case          =      language_codes
				_snake_case          =      FAIRSEQ_LANGUAGE_CODES[language_codes]
				_snake_case          =      {lang_code: f'''__{lang_code}__''' for lang_code in fairseq_language_code}

				_snake_case          =      kwargs.get('additional_special_tokens'      ,  []     )
				kwargs["additional_special_tokens"] += [
				    self.get_lang_token(lowercase     )
				    for lang_code in fairseq_language_code
				    if self.get_lang_token(lowercase     ) not in kwargs["additional_special_tokens"]
				]

				super().__init__(
				    src_lang=lowercase      ,  tgt_lang=lowercase      ,  bos_token=lowercase      ,  eos_token=lowercase      ,  sep_token=lowercase      ,  unk_token=lowercase      ,  pad_token=lowercase      ,  language_codes=lowercase      ,  sp_model_kwargs=self.sp_model_kwargs      ,  num_madeup_words=lowercase      ,  **lowercase      ,  )

				_snake_case          =      vocab_file
				_snake_case          =      load_json(lowercase     )
				_snake_case          =      {v: k for k, v in self.encoder.items()}
				_snake_case          =      spm_file
				_snake_case          =      load_spm(lowercase      ,  self.sp_model_kwargs     )

				_snake_case          =      len(self.encoder     )

				_snake_case          =      {
				    self.get_lang_token(lowercase     ): self.encoder_size + i for i, lang_code in enumerate(lowercase     )
				}
				_snake_case          =      {lang_code: self.encoder_size + i for i, lang_code in enumerate(lowercase     )}
				_snake_case          =      {v: k for k, v in self.lang_token_to_id.items()}

				_snake_case          =      src_lang if src_lang is not None else 'en'
				_snake_case          =      tgt_lang
				_snake_case          =      self.get_lang_id(self._src_lang     )
				self.set_src_lang_special_tokens(self._src_lang     )

				_snake_case          =      num_madeup_words



	@property
	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				return len(self.encoder     ) + len(self.lang_token_to_id     )



	@property
	def 			A							(  self     :   int     ):


				'''simple docstring'''


				return self._src_lang



	@src_lang.setter
	def 			A							(  self     :   Any      ,  lowercase     :   str     ):


				'''simple docstring'''


				_snake_case          =      new_src_lang
				self.set_src_lang_special_tokens(self._src_lang     )



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


				'''simple docstring'''


				return self.sp_model.encode(lowercase      ,  out_type=lowercase     )



	def 			A							(  self     :   Tuple      ,  lowercase     :   Tuple     ):


				'''simple docstring'''


				if token in self.lang_token_to_id:
							return self.lang_token_to_id[token]
				return self.encoder.get(lowercase      ,  self.encoder[self.unk_token]     )



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


				'''simple docstring'''


				if index in self.id_to_lang_token:
							return self.id_to_lang_token[index]
				return self.decoder.get(lowercase      ,  self.unk_token     )



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


				'''simple docstring'''


				_snake_case          =      []
				_snake_case          =      ''
				for token in tokens:
							# make sure that special tokens are not decoded using sentencepiece model
							if token in self.all_special_tokens:
										out_string += self.sp_model.decode(lowercase     ) + token
										_snake_case          =      []
							else:
										current_sub_tokens.append(lowercase     )
				out_string += self.sp_model.decode(lowercase     )
				return out_string.strip()



	def 			A							(  self     :   Optional[int]      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None      ,  lowercase     :   bool = False     ):


				'''simple docstring'''


				if already_has_special_tokens:
							return super().get_special_tokens_mask(
							    token_ids_a=lowercase      ,  token_ids_a=lowercase      ,  already_has_special_tokens=lowercase     )

				_snake_case          =      [1] * len(self.prefix_tokens     )
				_snake_case          =      [1] * len(self.suffix_tokens     )
				if token_ids_a is None:
							return prefix_ones + ([0] * len(lowercase     )) + suffix_ones
				return prefix_ones + ([0] * len(lowercase     )) + ([0] * len(lowercase     )) + suffix_ones



	def 			A							(  self     :   Any      ,  lowercase     :   List[int]      ,  lowercase     :   Optional[List[int]] = None     ):


				'''simple docstring'''


				if token_ids_a is None:
							return self.prefix_tokens + token_ids_a + self.suffix_tokens
				# We don't expect to process pairs, but leave the pair logic for API consistency
				return self.prefix_tokens + token_ids_a + token_ids_a + self.suffix_tokens



	def 			A							(  self     :   int     ):


				'''simple docstring'''


				_snake_case          =      {self.convert_ids_to_tokens(lowercase     ): i for i in range(self.vocab_size     )}
				vocab.update(self.added_tokens_encoder     )
				return vocab



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


				'''simple docstring'''


				_snake_case          =      self.__dict__.copy()
				_snake_case          =      None
				return state



	def __setstate__(  self     :   Dict      ,  lowercase     :   Dict     ):


				'''simple docstring'''


				_snake_case          =      d

				# for backward compatibility
				if not hasattr(self      ,  'sp_model_kwargs'     ):
							_snake_case          =      {}

				_snake_case          =      load_spm(self.spm_file      ,  self.sp_model_kwargs     )



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


				'''simple docstring'''


				_snake_case          =      Path(lowercase     )
				if not save_dir.is_dir():
							raise OSError(f'''{save_directory} should be a directory'''     )
				_snake_case          =      save_dir / (
				    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['vocab_file']
				)
				_snake_case          =      save_dir / (
				    (filename_prefix + '-' if filename_prefix else '') + self.vocab_files_names['spm_file']
				)

				save_json(self.encoder      ,  lowercase     )

				if os.path.abspath(self.spm_file     ) != os.path.abspath(lowercase     ) and os.path.isfile(self.spm_file     ):
							copyfile(self.spm_file      ,  lowercase     )
				elif not os.path.isfile(self.spm_file     ):
							with open(lowercase      ,  'wb'     ) as fi:
										_snake_case          =      self.sp_model.serialized_model_proto()
										fi.write(lowercase     )

				return (str(lowercase     ), str(lowercase     ))



	def 			A							(  self     :   List[str]      ,  lowercase     :   List[str]      ,  lowercase     :   str = "en"      ,  lowercase     :   Optional[List[str]] = None      ,  lowercase     :   str = "ro"      ,  **lowercase     :   int      ,  ):


				'''simple docstring'''


				_snake_case          =      src_lang
				_snake_case          =      tgt_lang
				self.set_src_lang_special_tokens(self.src_lang     )
				return super().prepare_seqaseq_batch(lowercase      ,  lowercase      ,  **lowercase     )



	def 			A							(  self     :   Dict      ,  lowercase     :   str      ,  lowercase     :   Optional[str]      ,  lowercase     :   Optional[str]      ,  **lowercase     :   Union[str, Any]     ):


				'''simple docstring'''


				if src_lang is None or tgt_lang is None:
							raise ValueError('Translation requires a `src_lang` and a `tgt_lang` for this model'     )
				_snake_case          =      src_lang
				_snake_case          =      self(lowercase      ,  add_special_tokens=lowercase      ,  **lowercase     )
				_snake_case          =      self.get_lang_id(lowercase     )
				_snake_case          =      tgt_lang_id
				return inputs



	def 			A							(  self     :   Optional[int]     ):


				'''simple docstring'''


				self.set_src_lang_special_tokens(self.src_lang     )



	def 			A							(  self     :   Any     ):


				'''simple docstring'''


				self.set_tgt_lang_special_tokens(self.tgt_lang     )



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


				'''simple docstring'''


				_snake_case          =      self.get_lang_token(lowercase     )
				_snake_case          =      self.lang_token_to_id[lang_token]
				_snake_case          =      [self.cur_lang_id]
				_snake_case          =      [self.eos_token_id]



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


				'''simple docstring'''


				_snake_case          =      self.get_lang_token(lowercase     )
				_snake_case          =      self.lang_token_to_id[lang_token]
				_snake_case          =      [self.cur_lang_id]
				_snake_case          =      [self.eos_token_id]



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


				'''simple docstring'''


				return self.lang_code_to_token[lang]





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


				'''simple docstring'''


				_snake_case          =      self.get_lang_token(lowercase     )
				return self.lang_token_to_id[lang_token]






def 	a_		(	__lowercase       :     str	, __lowercase       :     Dict[str, Any]			)			->  sentencepiece.SentencePieceProcessor:
			_snake_case          =      sentencepiece.SentencePieceProcessor(**__lowercase			)
			spm.Load(str(__lowercase			)			)
			return spm






def 	a_		(	__lowercase       :     str			)			->  Union[Dict, List]:
			with open(__lowercase	, 'r'			) as f:
						return json.load(__lowercase			)






def 	a_		(	__lowercase       :     List[Any]	, __lowercase       :     str			)			->  None:
			with open(__lowercase	, 'w'			) as f:
						json.dump(__lowercase	, __lowercase	, indent=2			)

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)