

import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

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


A_						:	Dict   =      logging.get_logger(__name__)


A_						:	str   =      {'vocab_file': 'vocab.json', 'merges_file': 'merges.txt'}

# See all BART models at https://huggingface.co/models?filter=bart
A_						:	Optional[int]   =      {
    'vocab_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/vocab.json',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/vocab.json',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json',
    },
    'merges_file': {
        'facebook/bart-base': 'https://huggingface.co/facebook/bart-base/resolve/main/merges.txt',
        'facebook/bart-large': 'https://huggingface.co/facebook/bart-large/resolve/main/merges.txt',
        'facebook/bart-large-mnli': 'https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt',
        'facebook/bart-large-cnn': 'https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt',
        'facebook/bart-large-xsum': 'https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt',
        'yjernite/bart_eli5': 'https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt',
    },
}

A_						:	List[Any]   =      {
    'facebook/bart-base': 1024,
    'facebook/bart-large': 1024,
    'facebook/bart-large-mnli': 1024,
    'facebook/bart-large-cnn': 1024,
    'facebook/bart-large-xsum': 1024,
    'yjernite/bart_eli5': 1024,
}


@lru_cache()
def 					__a				(      )					->   Union[str, Any]:






    '''simple docstring'''
    __UpperCAmelCase     =	(
        list(range(ord('''!'''					)				,      ord('''~'''					) + 1					)					) + list(range(ord('''¡'''					)				,      ord('''¬'''					) + 1					)					) + list(range(ord('''®'''					)				,      ord('''ÿ'''					) + 1					)					)
    )
    __UpperCAmelCase     =	bs[:]
    __UpperCAmelCase     =	0
    for b in range(2**8					):
        if b not in bs:
            bs.append(_SCREAMING_SNAKE_CASE					)
            cs.append(2**8 + n					)
            n += 1
    __UpperCAmelCase     =	[chr(_SCREAMING_SNAKE_CASE					) for n in cs]
    return dict(zip(_SCREAMING_SNAKE_CASE				,      _SCREAMING_SNAKE_CASE					)					)


def 					__a				(      SCREAMING_SNAKE_CASE					)					->   List[str]:






    '''simple docstring'''
    __UpperCAmelCase     =	set()
    __UpperCAmelCase     =	word[0]
    for char in word[1:]:
        pairs.add((prev_char, char)					)
        __UpperCAmelCase     =	char
    return pairs
class     A_      (					lowerCAmelCase__  ):


    '''simple docstring'''






    a__   							=				VOCAB_FILES_NAMES
    a__   							=				PRETRAINED_VOCAB_FILES_MAP
    a__   							=				PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    a__   							=				["""input_ids""", """attention_mask"""]


    def __init__(self   ,					lowercase__   ,					lowercase__   ,					lowercase__="replace"   ,					lowercase__="<s>"   ,					lowercase__="</s>"   ,					lowercase__="</s>"   ,					lowercase__="<s>"   ,					lowercase__="<unk>"   ,					lowercase__="<pad>"   ,					lowercase__="<mask>"   ,					lowercase__=False   ,					**lowercase__   ,					)   ->					List[str]:
        __UpperCAmelCase     =	AddedToken(UpperCamelCase__   ,					lstrip=UpperCamelCase__   ,					rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__   ,					UpperCamelCase__     ) else bos_token
        __UpperCAmelCase     =	AddedToken(UpperCamelCase__   ,					lstrip=UpperCamelCase__   ,					rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__   ,					UpperCamelCase__     ) else eos_token
        __UpperCAmelCase     =	AddedToken(UpperCamelCase__   ,					lstrip=UpperCamelCase__   ,					rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__   ,					UpperCamelCase__     ) else sep_token
        __UpperCAmelCase     =	AddedToken(UpperCamelCase__   ,					lstrip=UpperCamelCase__   ,					rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__   ,					UpperCamelCase__     ) else cls_token
        __UpperCAmelCase     =	AddedToken(UpperCamelCase__   ,					lstrip=UpperCamelCase__   ,					rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__   ,					UpperCamelCase__     ) else unk_token
        __UpperCAmelCase     =	AddedToken(UpperCamelCase__   ,					lstrip=UpperCamelCase__   ,					rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__   ,					UpperCamelCase__     ) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __UpperCAmelCase     =	AddedToken(UpperCamelCase__   ,					lstrip=UpperCamelCase__   ,					rstrip=UpperCamelCase__     ) if isinstance(UpperCamelCase__   ,					UpperCamelCase__     ) else mask_token

        super().__init__(
            errors=UpperCamelCase__   ,					bos_token=UpperCamelCase__   ,					eos_token=UpperCamelCase__   ,					unk_token=UpperCamelCase__   ,					sep_token=UpperCamelCase__   ,					cls_token=UpperCamelCase__   ,					pad_token=UpperCamelCase__   ,					mask_token=UpperCamelCase__   ,					add_prefix_space=UpperCamelCase__   ,					**UpperCamelCase__   ,					)

        with open(UpperCamelCase__   ,					encoding='''utf-8'''     ) as vocab_handle:
            __UpperCAmelCase     =	json.load(UpperCamelCase__     )
        __UpperCAmelCase     =	{v: k for k, v in self.encoder.items()}
        __UpperCAmelCase     =	errors  # how to handle errors in decoding
        __UpperCAmelCase     =	bytes_to_unicode()
        __UpperCAmelCase     =	{v: k for k, v in self.byte_encoder.items()}
        with open(UpperCamelCase__   ,					encoding='''utf-8'''     ) as merges_handle:
            __UpperCAmelCase     =	merges_handle.read().split('''\n'''     )[1:-1]
        __UpperCAmelCase     =	[tuple(merge.split()     ) for merge in bpe_merges]
        __UpperCAmelCase     =	dict(zip(UpperCamelCase__   ,					range(len(UpperCamelCase__     )     )     )     )
        __UpperCAmelCase     =	{}
        __UpperCAmelCase     =	add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __UpperCAmelCase     =	re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''     )


    @property
    def    lowerCAmelCase_  (self     )   ->					List[str]:
        return len(self.encoder     )


    def    lowerCAmelCase_  (self     )   ->					Dict:
        return dict(self.encoder   ,					**self.added_tokens_encoder     )


    def    lowerCAmelCase_  (self   ,					lowercase__     )   ->					str:
        if token in self.cache:
            return self.cache[token]
        __UpperCAmelCase     =	tuple(UpperCamelCase__     )
        __UpperCAmelCase     =	get_pairs(UpperCamelCase__     )

        if not pairs:
            return token

        while True:
            __UpperCAmelCase     =	min(UpperCamelCase__   ,					key=lambda lowercase__     : self.bpe_ranks.get(UpperCamelCase__   ,					float('''inf'''     )     )     )
            if bigram not in self.bpe_ranks:
                break
            __UpperCAmelCase     =	bigram
            __UpperCAmelCase     =	[]
            __UpperCAmelCase     =	0
            while i < len(UpperCamelCase__     ):
                try:
                    __UpperCAmelCase     =	word.index(UpperCamelCase__   ,					UpperCamelCase__     )
                except ValueError:
                    new_word.extend(word[i:]     )
                    break
                else:
                    new_word.extend(word[i:j]     )
                    __UpperCAmelCase     =	j

                if word[i] == first and i < len(UpperCamelCase__     ) - 1 and word[i + 1] == second:
                    new_word.append(first + second     )
                    i += 2
                else:
                    new_word.append(word[i]     )
                    i += 1
            __UpperCAmelCase     =	tuple(UpperCamelCase__     )
            __UpperCAmelCase     =	new_word
            if len(UpperCamelCase__     ) == 1:
                break
            else:
                __UpperCAmelCase     =	get_pairs(UpperCamelCase__     )
        __UpperCAmelCase     =	" ".join(UpperCamelCase__     )
        __UpperCAmelCase     =	word
        return word


    def    lowerCAmelCase_  (self   ,					lowercase__     )   ->					str:
        __UpperCAmelCase     =	[]
        for token in re.findall(self.pat   ,					UpperCamelCase__     ):
            __UpperCAmelCase     =	"".join(
                self.byte_encoder[b] for b in token.encode('''utf-8'''     )     )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
            bpe_tokens.extend(bpe_token for bpe_token in self.bpe(UpperCamelCase__     ).split(''' '''     )     )
        return bpe_tokens


    def    lowerCAmelCase_  (self   ,					lowercase__     )   ->					Union[str, Any]:
        return self.encoder.get(UpperCamelCase__   ,					self.encoder.get(self.unk_token     )     )


    def    lowerCAmelCase_  (self   ,					lowercase__     )   ->					Tuple:
        return self.decoder.get(UpperCamelCase__     )


    def    lowerCAmelCase_  (self   ,					lowercase__     )   ->					Optional[int]:
        __UpperCAmelCase     =	"".join(UpperCamelCase__     )
        __UpperCAmelCase     =	bytearray([self.byte_decoder[c] for c in text]     ).decode('''utf-8'''   ,					errors=self.errors     )
        return text


    def    lowerCAmelCase_  (self   ,					lowercase__   ,					lowercase__ = None     )   ->					Tuple[str]:
        if not os.path.isdir(UpperCamelCase__     ):
            logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''     )
            return
        __UpperCAmelCase     =	os.path.join(
            UpperCamelCase__   ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']     )
        __UpperCAmelCase     =	os.path.join(
            UpperCamelCase__   ,					(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']     )

        with open(UpperCamelCase__   ,					'''w'''   ,					encoding='''utf-8'''     ) as f:
            f.write(json.dumps(self.encoder   ,					indent=2   ,					sort_keys=UpperCamelCase__   ,					ensure_ascii=UpperCamelCase__     ) + '''\n'''     )

        __UpperCAmelCase     =	0
        with open(UpperCamelCase__   ,					'''w'''   ,					encoding='''utf-8'''     ) as writer:
            writer.write('''#version: 0.2\n'''     )
            for bpe_tokens, token_index in sorted(self.bpe_ranks.items()   ,					key=lambda lowercase__     : kv[1]     ):
                if index != token_index:
                    logger.warning(
                        F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                        ''' Please check that the tokenizer is not corrupted!'''     )
                    __UpperCAmelCase     =	token_index
                writer.write(''' '''.join(UpperCamelCase__     ) + '''\n'''     )
                index += 1

        return vocab_file, merge_file


    def    lowerCAmelCase_  (self   ,					lowercase__   ,					lowercase__ = None     )   ->					List[int]:
        if token_ids_a is None:
            return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __UpperCAmelCase     =	[self.cls_token_id]
        __UpperCAmelCase     =	[self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


    def    lowerCAmelCase_  (self   ,					lowercase__   ,					lowercase__ = None   ,					lowercase__ = False     )   ->					List[int]:
        if already_has_special_tokens:
            return super().get_special_tokens_mask(
                token_ids_a=UpperCamelCase__   ,					token_ids_a=UpperCamelCase__   ,					already_has_special_tokens=UpperCamelCase__     )

        if token_ids_a is None:
            return [1] + ([0] * len(UpperCamelCase__     )) + [1]
        return [1] + ([0] * len(UpperCamelCase__     )) + [1, 1] + ([0] * len(UpperCamelCase__     )) + [1]


    def    lowerCAmelCase_  (self   ,					lowercase__   ,					lowercase__ = None     )   ->					List[int]:
        __UpperCAmelCase     =	[self.sep_token_id]
        __UpperCAmelCase     =	[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   ,					lowercase__   ,					lowercase__=False   ,					**lowercase__     )   ->					Optional[int]:
        __UpperCAmelCase     =	kwargs.pop('''add_prefix_space'''   ,					self.add_prefix_space     )
        if (is_split_into_words or add_prefix_space) and (len(UpperCamelCase__     ) > 0 and not text[0].isspace()):
            __UpperCAmelCase     =	" " + text
        return (text, kwargs)










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()
SCREAMING_SNAKE_CASE__       :      List[str]  =   logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__       :      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,
}

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

def      A      (      _SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      int			 =					EfficientNetConfig()
       lowerCamelCase   :      List[str]			 =					CONFIG_MAP[model_name]["hidden_dim"]
       lowerCamelCase   :      List[str]			 =					CONFIG_MAP[model_name]["width_coef"]
       lowerCamelCase   :      Any			 =					CONFIG_MAP[model_name]["depth_coef"]
       lowerCamelCase   :      Union[str, Any]			 =					CONFIG_MAP[model_name]["image_size"]
       lowerCamelCase   :      Optional[int]			 =					CONFIG_MAP[model_name]["dropout_rate"]
       lowerCamelCase   :      str			 =					CONFIG_MAP[model_name]["dw_padding"]

       lowerCamelCase   :      Tuple			 =					"huggingface/label-files"
       lowerCamelCase   :      List[str]			 =					"imagenet-1k-id2label.json"
       lowerCamelCase   :      Any			 =					1000
       lowerCamelCase   :      Any			 =					json.load(open(hf_hub_download(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,repo_type="dataset"    ) ,"r"    )    )
       lowerCamelCase   :      List[str]			 =					{int(_SCREAMING_SNAKE_CASE    ): v for k, v in idalabel.items()}

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

def      A      (      )	-> int:
       lowerCamelCase   :      str			 =					"http://images.cocodataset.org/val2017/000000039769.jpg"
       lowerCamelCase   :      Tuple			 =					Image.open(requests.get(_SCREAMING_SNAKE_CASE ,stream=_SCREAMING_SNAKE_CASE    ).raw    )
       return im

def      A      (      _SCREAMING_SNAKE_CASE    )	-> str:
       lowerCamelCase   :      List[Any]			 =					CONFIG_MAP[model_name]["image_size"]
       lowerCamelCase   :      str			 =					EfficientNetImageProcessor(
           size={"height": size, "width": size} ,image_mean=[0.485, 0.456, 0.406] ,image_std=[0.47853944, 0.4732864, 0.47434163] ,do_center_crop=_SCREAMING_SNAKE_CASE ,)
       return preprocessor

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

       lowerCamelCase   :      List[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:
              lowerCamelCase   :      Dict			 =					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")    )

       lowerCamelCase   :      Optional[int]			 =					{}
       for item in rename_keys:
              if item[0] in original_param_names:
                     lowerCamelCase   :      List[str]			 =					"efficientnet." + item[1]

       lowerCamelCase   :      int			 =					"classifier.weight"
       lowerCamelCase   :      Union[str, Any]			 =					"classifier.bias"
       return key_mapping

def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Dict:
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

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

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

@torch.no_grad()
def      A      (      _SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )	-> Optional[int]:
       lowerCamelCase   :      Optional[int]			 =					model_classes[model_name](
           include_top=_SCREAMING_SNAKE_CASE ,weights="imagenet" ,input_tensor=_SCREAMING_SNAKE_CASE ,input_shape=_SCREAMING_SNAKE_CASE ,pooling=_SCREAMING_SNAKE_CASE ,classes=1000 ,classifier_activation="softmax" ,)

       lowerCamelCase   :      List[Any]			 =					original_model.trainable_variables
       lowerCamelCase   :      Tuple			 =					original_model.non_trainable_variables
       lowerCamelCase   :      Union[str, Any]			 =					{param.name: param.numpy() for param in tf_params}
       for param in tf_non_train_params:
              lowerCamelCase   :      List[str]			 =					param.numpy()
       lowerCamelCase   :      int			 =					list(tf_params.keys()    )

       # Load HuggingFace model
       lowerCamelCase   :      Union[str, Any]			 =					get_efficientnet_config(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      Optional[int]			 =					EfficientNetForImageClassification(_SCREAMING_SNAKE_CASE    ).eval()
       lowerCamelCase   :      Tuple			 =					hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print("Converting parameters..."    )
       lowerCamelCase   :      Union[str, Any]			 =					rename_keys(_SCREAMING_SNAKE_CASE    )
       replace_params(_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE ,_SCREAMING_SNAKE_CASE    )

       # Initialize preprocessor and preprocess input image
       lowerCamelCase   :      int			 =					convert_image_processor(_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      int			 =					preprocessor(images=prepare_img() ,return_tensors="pt"    )

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              lowerCamelCase   :      Optional[Any]			 =					hf_model(**_SCREAMING_SNAKE_CASE    )
       lowerCamelCase   :      str			 =					outputs.logits.detach().numpy()

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

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

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


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

       SCREAMING_SNAKE_CASE__       :      Tuple  =   parser.parse_args()
       convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






