






import os
from shutil import copyfile
from typing import Any, Dict, List, Optional, Tuple

import sentencepiece as spm

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


UpperCamelCase							       =    logging.get_logger(__name__)

UpperCamelCase							       =    """▁"""

UpperCamelCase							       =    {"""vocab_file""": """sentencepiece.bpe.model"""}

UpperCamelCase							       =    {
    """vocab_file""": {
        """facebook/nllb-200-distilled-600M""": (
            """https://huggingface.co/facebook/nllb-200-distilled-600M/blob/main/sentencepiece.bpe.model"""
        ),
    }
}

UpperCamelCase							       =    {
    """facebook/nllb-200-distilled-600M""": 1_024,
}

# fmt: off
UpperCamelCase							       =    ["""ace_Arab""", """ace_Latn""", """acm_Arab""", """acq_Arab""", """aeb_Arab""", """afr_Latn""", """ajp_Arab""", """aka_Latn""", """amh_Ethi""", """apc_Arab""", """arb_Arab""", """ars_Arab""", """ary_Arab""", """arz_Arab""", """asm_Beng""", """ast_Latn""", """awa_Deva""", """ayr_Latn""", """azb_Arab""", """azj_Latn""", """bak_Cyrl""", """bam_Latn""", """ban_Latn""", """bel_Cyrl""", """bem_Latn""", """ben_Beng""", """bho_Deva""", """bjn_Arab""", """bjn_Latn""", """bod_Tibt""", """bos_Latn""", """bug_Latn""", """bul_Cyrl""", """cat_Latn""", """ceb_Latn""", """ces_Latn""", """cjk_Latn""", """ckb_Arab""", """crh_Latn""", """cym_Latn""", """dan_Latn""", """deu_Latn""", """dik_Latn""", """dyu_Latn""", """dzo_Tibt""", """ell_Grek""", """eng_Latn""", """epo_Latn""", """est_Latn""", """eus_Latn""", """ewe_Latn""", """fao_Latn""", """pes_Arab""", """fij_Latn""", """fin_Latn""", """fon_Latn""", """fra_Latn""", """fur_Latn""", """fuv_Latn""", """gla_Latn""", """gle_Latn""", """glg_Latn""", """grn_Latn""", """guj_Gujr""", """hat_Latn""", """hau_Latn""", """heb_Hebr""", """hin_Deva""", """hne_Deva""", """hrv_Latn""", """hun_Latn""", """hye_Armn""", """ibo_Latn""", """ilo_Latn""", """ind_Latn""", """isl_Latn""", """ita_Latn""", """jav_Latn""", """jpn_Jpan""", """kab_Latn""", """kac_Latn""", """kam_Latn""", """kan_Knda""", """kas_Arab""", """kas_Deva""", """kat_Geor""", """knc_Arab""", """knc_Latn""", """kaz_Cyrl""", """kbp_Latn""", """kea_Latn""", """khm_Khmr""", """kik_Latn""", """kin_Latn""", """kir_Cyrl""", """kmb_Latn""", """kon_Latn""", """kor_Hang""", """kmr_Latn""", """lao_Laoo""", """lvs_Latn""", """lij_Latn""", """lim_Latn""", """lin_Latn""", """lit_Latn""", """lmo_Latn""", """ltg_Latn""", """ltz_Latn""", """lua_Latn""", """lug_Latn""", """luo_Latn""", """lus_Latn""", """mag_Deva""", """mai_Deva""", """mal_Mlym""", """mar_Deva""", """min_Latn""", """mkd_Cyrl""", """plt_Latn""", """mlt_Latn""", """mni_Beng""", """khk_Cyrl""", """mos_Latn""", """mri_Latn""", """zsm_Latn""", """mya_Mymr""", """nld_Latn""", """nno_Latn""", """nob_Latn""", """npi_Deva""", """nso_Latn""", """nus_Latn""", """nya_Latn""", """oci_Latn""", """gaz_Latn""", """ory_Orya""", """pag_Latn""", """pan_Guru""", """pap_Latn""", """pol_Latn""", """por_Latn""", """prs_Arab""", """pbt_Arab""", """quy_Latn""", """ron_Latn""", """run_Latn""", """rus_Cyrl""", """sag_Latn""", """san_Deva""", """sat_Beng""", """scn_Latn""", """shn_Mymr""", """sin_Sinh""", """slk_Latn""", """slv_Latn""", """smo_Latn""", """sna_Latn""", """snd_Arab""", """som_Latn""", """sot_Latn""", """spa_Latn""", """als_Latn""", """srd_Latn""", """srp_Cyrl""", """ssw_Latn""", """sun_Latn""", """swe_Latn""", """swh_Latn""", """szl_Latn""", """tam_Taml""", """tat_Cyrl""", """tel_Telu""", """tgk_Cyrl""", """tgl_Latn""", """tha_Thai""", """tir_Ethi""", """taq_Latn""", """taq_Tfng""", """tpi_Latn""", """tsn_Latn""", """tso_Latn""", """tuk_Latn""", """tum_Latn""", """tur_Latn""", """twi_Latn""", """tzm_Tfng""", """uig_Arab""", """ukr_Cyrl""", """umb_Latn""", """urd_Arab""", """uzn_Latn""", """vec_Latn""", """vie_Latn""", """war_Latn""", """wol_Latn""", """xho_Latn""", """ydd_Hebr""", """yor_Latn""", """yue_Hant""", """zho_Hans""", """zho_Hant""", """zul_Latn"""]




class    lowercase_    (_UpperCAmelCase     ):
      A__	:		Dict       			=				VOCAB_FILES_NAMES
      A__	:		Union[str, Any]       			=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      A__	:		List[Any]       			=				PRETRAINED_VOCAB_FILES_MAP
      A__	:		Tuple       			=				['''input_ids''', '''attention_mask''']

      A__	:		List[int]       			=				[]
      A__	:		List[int]       			=				[]






      def __init__(    self     ,     a_     ,     a_="<s>"     ,     a_="</s>"     ,     a_="</s>"     ,     a_="<s>"     ,     a_="<unk>"     ,     a_="<pad>"     ,     a_="<mask>"     ,     a_=None     ,     a_=None     ,     a_=None     ,     a_ = None     ,     a_=None     ,     a_=False     ,     **a_     ,     )	->List[str]:







        '''simple docstring'''


        _a							=	AddedToken(a_     ,     lstrip=a_     ,     rstrip=a_	) if isinstance(a_     ,     a_	) else mask_token

        _a							=	{} if sp_model_kwargs is None else sp_model_kwargs
        _a							=	legacy_behaviour

        super().__init__(
            bos_token=a_     ,     eos_token=a_     ,     unk_token=a_     ,     sep_token=a_     ,     cls_token=a_     ,     pad_token=a_     ,     mask_token=a_     ,     tokenizer_file=a_     ,     src_lang=a_     ,     tgt_lang=a_     ,     additional_special_tokens=a_     ,     sp_model_kwargs=self.sp_model_kwargs     ,     legacy_behaviour=a_     ,     **a_     ,     )

        _a							=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.Load(str(a_	)	)
        _a							=	vocab_file
        # Original fairseq vocab and spm vocab must be "aligned":
        # Vocab    |    0    |    1    |   2    |    3    |  4   |  5   |  6   |   7  |   8  |  9
        # -------- | ------- | ------- | ------ | ------- | ---- | ---- | ---- | ---- | ---- | ----
        # fairseq  | '<s>'   | '<pad>' | '</s>' | '<unk>' | 'an' | '▁n' | '▁m' | '▁t' | '▁k' | '▁a'
        # spm      | '<unk>' | '<s>'   | '</s>' | 'an'    | '▁n' | '▁m' | '▁t' | '▁k' | '▁a' | '▁s'

        # Mimic fairseq token-to-id alignment for the first 4 token
        _a							=	{"<s>": 0, "<pad>": 1, "</s>": 2, "<unk>": 3}

        # The first "real" token "," has position 4 in the original fairseq vocab and position 3 in the spm vocab
        _a							=	1

        _a							=	len(self.sp_model	)
        _a							=	{
            code: self.sp_model_size + i + self.fairseq_offset for i, code in enumerate(a_	)
        }
        _a							=	{v: k for k, v in self.lang_code_to_id.items()}
        _a							=	len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset

        self.fairseq_tokens_to_ids.update(self.lang_code_to_id	)
        _a							=	{v: k for k, v in self.fairseq_tokens_to_ids.items()}
        _a							=	list(self.lang_code_to_id.keys()	)

        if additional_special_tokens is not None:
          # Only add those special tokens if they are not already there.
          self._additional_special_tokens.extend(
              [t for t in additional_special_tokens if t not in self._additional_special_tokens]	)

        _a							=	src_lang if src_lang is not None else "eng_Latn"
        _a							=	self.lang_code_to_id[self._src_lang]
        _a							=	tgt_lang
        self.set_src_lang_special_tokens(self._src_lang	)






      def __getstate__(    self	)	->Optional[Any]:







        '''simple docstring'''


        _a							=	self.__dict__.copy()
        _a							=	None
        _a							=	self.sp_model.serialized_model_proto()
        return state






      def __setstate__(    self     ,     a_	)	->Union[str, Any]:







        '''simple docstring'''


        _a							=	d

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

        _a							=	spm.SentencePieceProcessor(**self.sp_model_kwargs	)
        self.sp_model.LoadFromSerializedProto(self.sp_model_proto	)






      @property
      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        return len(self.sp_model	) + len(self.lang_code_to_id	) + self.fairseq_offset + 1  # Plus 1 for the mask token






      @property
      def 			lowerCamelCase__    (    self	)	->str:







        '''simple docstring'''


        return self._src_lang






      @src_lang.setter
      def 			lowerCamelCase__    (    self     ,     a_	)	->None:







        '''simple docstring'''


        _a							=	new_src_lang
        self.set_src_lang_special_tokens(self._src_lang	)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None     ,     a_ = False	)	->List[int]:







        '''simple docstring'''


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

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






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None	)	->List[int]:







        '''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 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None	)	->List[int]:







        '''simple docstring'''


        _a							=	[self.sep_token_id]
        _a							=	[self.cls_token_id]

        if token_ids_a is None:
          return len(cls + token_ids_a + sep	) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_     ,     a_     ,     a_     ,     **a_	)	->Optional[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"	)
        _a							=	src_lang
        _a							=	self(a_     ,     add_special_tokens=a_     ,     return_tensors=a_     ,     **a_	)
        _a							=	self.convert_tokens_to_ids(a_	)
        _a							=	tgt_lang_id
        return inputs






      def 			lowerCamelCase__    (    self	)	->int:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self     ,     a_	)	->List[str]:







        '''simple docstring'''


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






      def 			lowerCamelCase__    (    self     ,     a_	)	->str:







        '''simple docstring'''


        if token in self.fairseq_tokens_to_ids:
          return self.fairseq_tokens_to_ids[token]
        _a							=	self.sp_model.PieceToId(a_	)

        # Need to return unknown token if the SP model returned 0
        return spm_id + self.fairseq_offset if spm_id else self.unk_token_id






      def 			lowerCamelCase__    (    self     ,     a_	)	->Any:







        '''simple docstring'''


        if index in self.fairseq_ids_to_tokens:
          return self.fairseq_ids_to_tokens[index]
        return self.sp_model.IdToPiece(index - self.fairseq_offset	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->Optional[int]:







        '''simple docstring'''


        _a							=	"".join(a_	).replace(a_     ,     " "	).strip()
        return out_string






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = None	)	->Tuple[str]:







        '''simple docstring'''


        if not os.path.isdir(a_	):
          logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''	)
          return
        _a							=	os.path.join(
            a_     ,     (filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]	)

        if os.path.abspath(self.vocab_file	) != os.path.abspath(a_	) and os.path.isfile(self.vocab_file	):
          copyfile(self.vocab_file     ,     a_	)
        elif not os.path.isfile(self.vocab_file	):
          with open(a_     ,     "wb"	) as fi:
            _a							=	self.sp_model.serialized_model_proto()
            fi.write(a_	)

        return (out_vocab_file,)






      def 			lowerCamelCase__    (    self     ,     a_     ,     a_ = "eng_Latn"     ,     a_ = None     ,     a_ = "fra_Latn"     ,     **a_     ,     )	->BatchEncoding:







        '''simple docstring'''


        _a							=	src_lang
        _a							=	tgt_lang
        return super().prepare_seqaseq_batch(a_     ,     a_     ,     **a_	)






      def 			lowerCamelCase__    (    self	)	->List[Any]:







        '''simple docstring'''


        return self.set_src_lang_special_tokens(self.src_lang	)






      def 			lowerCamelCase__    (    self	)	->Optional[Any]:







        '''simple docstring'''


        return self.set_tgt_lang_special_tokens(self.tgt_lang	)






      def 			lowerCamelCase__    (    self     ,     a_	)	->None:







        '''simple docstring'''


        _a							=	self.lang_code_to_id[src_lang]
        if self.legacy_behaviour:
          _a							=	[]
          _a							=	[self.eos_token_id, self.cur_lang_code]
        else:
          _a							=	[self.cur_lang_code]
          _a							=	[self.eos_token_id]






      def 			lowerCamelCase__    (    self     ,     a_	)	->None:







        '''simple docstring'''


        _a							=	self.lang_code_to_id[lang]
        if self.legacy_behaviour:
          _a							=	[]
          _a							=	[self.eos_token_id, self.cur_lang_code]
        else:
          _a							=	[self.cur_lang_code]
          _a							=	[self.eos_token_id]










"""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()
UpperCamelCase							       =    logging.get_logger(__name__)

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

UpperCamelCase							       =    {
    """b0""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1_280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1_408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1_536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1_792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2_048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2_304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2_560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}






def        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]					)       ->							List[Any]:







  '''simple docstring'''







  _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        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
  _a							=	Image.open(requests.get(UpperCamelCase_     , stream=UpperCamelCase_					).raw					)
  return im






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							List[str]:







  '''simple docstring'''







  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	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=UpperCamelCase_     , )
  return preprocessor






def        lowerCAmelCase (      UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _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        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any					)       ->							int:







  '''simple docstring'''







  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        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							str     , UpperCamelCase_:							str					)       ->							List[Any]:







  '''simple docstring'''







  _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__":
     UpperCamelCase							       =    argparse.ArgumentParser()
     # Required parameters
     parser.add_argument(
         """--model_name""",
         default="""b0""",
         type=str,
         help="""Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].""",
     )
     parser.add_argument(
         """--pytorch_dump_folder_path""",
         default="""hf_model""",
         type=str,
         help="""Path to the output PyTorch model directory.""",
     )
     parser.add_argument("""--save_model""", action="""store_true""", help="""Save model to local""")
     parser.add_argument("""--push_to_hub""", action="""store_true""", help="""Push model and image processor to the hub""")

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


