






import collections
import json
import os
import re
from typing import TYPE_CHECKING, List, Optional, Tuple

import numpy as np

from ...tokenization_utils_fast import PreTrainedTokenizer
from ...utils import logging


if TYPE_CHECKING:
						from transformers.pipelines.conversational import Conversation


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

__UpperCamelCase   :   Dict      =    {"""vocab_file""": """vocab.txt""", """emoji_file""": """emoji.json"""}

__UpperCamelCase   :   str      =    {
    """vocab_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/vocab.txt""",
    },
    """emoji_file""": {
        """abeja/gpt-neox-japanese-2.7b""": """https://huggingface.co/abeja/gpt-neox-japanese-2.7b/resolve/main/emoji.json""",
    },
}

__UpperCamelCase   :   Dict      =    {
    """abeja/gpt-neox-japanese-2.7b""": 2048,
}





def 					snake_case	(  lowerCamelCase					,	lowerCamelCase       ):




	'''simple docstring'''







	with open(lowerCamelCase					,	"""r"""					,	encoding="""utf-8"""       ) as f:
		__lowercase   							=	json.loads(f.read()       )

	__lowercase   							=	collections.OrderedDict()
	__lowercase   							=	collections.OrderedDict()
	__lowercase   							=	collections.OrderedDict()
	with open(lowerCamelCase					,	"""r"""					,	encoding="""utf-8"""       ) as f:
		__lowercase   							=	f.readlines()
	__lowercase   							=	[[t.rstrip("""\n"""       )] if (t == """,""" or """,""" not in t) else t.rstrip("""\n"""       ).split(""","""       ) for t in token]
	for idx, b in enumerate(lowerCamelCase       ):
		__lowercase   							=	b
		__lowercase   							=	idx
		for wd in b:
			__lowercase   							=	idx

	return vocab, raw_vocab, ids_to_tokens, emoji


class 							__UpperCamelCase     (  _lowerCAmelCase ):
	__snake_case						:int =							VOCAB_FILES_NAMES
	__snake_case						:Optional[Any] =							PRETRAINED_VOCAB_FILES_MAP
	__snake_case						:int =							PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
	__snake_case						:Any =							['input_ids', 'attention_mask']







	def __init__(	self   :				Dict							,					_lowerCAmelCase   :				Any							,					_lowerCAmelCase   :				List[str]							,					_lowerCAmelCase   :				Any="<|endoftext|>"							,					_lowerCAmelCase   :				int="<|endoftext|>"							,					_lowerCAmelCase   :				Optional[int]="<|startoftext|>"							,					_lowerCAmelCase   :				int="<|endoftext|>"							,					_lowerCAmelCase   :				Dict=False							,					**_lowerCAmelCase   :				Any							,					)		->			Optional[int]:
		"""simple docstring"""







		super().__init__(
		    unk_token=_lowerCAmelCase							,					pad_token=_lowerCAmelCase							,					bos_token=_lowerCAmelCase							,					eos_token=_lowerCAmelCase							,					do_clean_text=_lowerCAmelCase							,					**_lowerCAmelCase							,					)
		if not os.path.isfile(_lowerCAmelCase      ):
			raise ValueError(
			    F'Can\'t find a vocabulary file at path \'{vocab_file}\'. To load the vocabulary from a Google pretrained'
			    """ model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""      )
		if not os.path.isfile(_lowerCAmelCase      ):
			raise ValueError(
			    F'Can\'t find a emoji file at path \'{emoji_file}\'. To load the emoji information from a Google'
			    """ pretrained model use `tokenizer = GPTNeoXJapaneseokenizer.from_pretrained(PRETRAINED_MODEL_NAME)`"""      )
		__lowercase   							=	do_clean_text
		__lowercase							,    __lowercase							,    __lowercase							,    __lowercase   							=	load_vocab_and_emoji(_lowerCAmelCase							,					_lowerCAmelCase      )
		__lowercase   							=	SubWordJapaneseTokenizer(
		    vocab=self.vocab							,					ids_to_tokens=self.ids_to_tokens							,					emoji=self.emoji      )







	@property
	def 			_a (	self   :				List[str]      )		->			str:
		"""simple docstring"""







		return len(self.raw_vocab      )







	def 			_a (	self   :				Tuple      )		->			str:
		"""simple docstring"""







		return dict(self.raw_vocab							,					**self.added_tokens_encoder      )







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				Optional[Any]      )		->			List[str]:
		"""simple docstring"""







		return self.subword_tokenizer.tokenize(_lowerCAmelCase							,					clean=self.do_clean_text      )







	def 			_a (	self   :				Any							,					_lowerCAmelCase   :				Union[str, Any]      )		->			Union[str, Any]:
		"""simple docstring"""







		return self.vocab.get(_lowerCAmelCase							,					self.vocab.get(self.unk_token      )      )







	def 			_a (	self   :				List[Any]							,					_lowerCAmelCase   :				Tuple      )		->			int:
		"""simple docstring"""







		return self.subword_tokenizer.convert_id_to_token(_lowerCAmelCase      )







	def 			_a (	self   :				Union[str, Any]							,					_lowerCAmelCase   :				List[Any]      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	"""""".join(_lowerCAmelCase      ).strip()
		return out_string







	def 			_a (	self   :				str							,					_lowerCAmelCase   :				"Conversation"      )		->			List[int]:
		"""simple docstring"""







		__lowercase   							=	[]
		for is_user, text in conversation.iter_texts():
			input_ids.extend(self.encode(_lowerCAmelCase							,					add_special_tokens=_lowerCAmelCase      ) + [self.eos_token_id]      )

		if len(_lowerCAmelCase      ) > self.model_max_length:
			__lowercase   							=	input_ids[-self.model_max_length :]
		return input_ids







	def 			_a (	self   :				List[str]							,					_lowerCAmelCase   :				str							,					_lowerCAmelCase   :				Optional[str] = None      )		->			Tuple[str]:
		"""simple docstring"""







		__lowercase   							=	0
		if os.path.isdir(_lowerCAmelCase      ):
			__lowercase   							=	os.path.join(
			    _lowerCAmelCase							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""vocab_file"""]      )
			__lowercase   							=	os.path.join(
			    _lowerCAmelCase							,					(filename_prefix + """-""" if filename_prefix else """""") + VOCAB_FILES_NAMES["""emoji_file"""]      )
		else:
			__lowercase   							=	(
			    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""vocab_file"""]
			)
			__lowercase   							=	(
			    (filename_prefix + """-""" if filename_prefix else """""") + save_directory + VOCAB_FILES_NAMES["""emoji_file"""]
			)
		with open(_lowerCAmelCase							,					"""w"""							,					encoding="""utf-8"""      ) as writer:
			for token_index, token in self.ids_to_tokens.items():
				if index != token_index:
					logger.warning(
					    F'Saving vocabulary to {vocab_file}: vocabulary indices are not consecutive.'
					    """ Please check that the vocabulary is not corrupted!"""      )
					__lowercase   							=	token_index
				writer.write(""",""".join(_lowerCAmelCase      ) + """\n"""      )
				index += 1
		with open(_lowerCAmelCase							,					"""w"""							,					encoding="""utf-8"""      ) as writer:
			json.dump(self.emoji							,					_lowerCAmelCase      )
		return vocab_file, emoji_file


class 							__UpperCamelCase     (  _lowerCAmelCase ):







	def __init__(	self   :				List[Any]							,					_lowerCAmelCase   :				List[str]							,					_lowerCAmelCase   :				int							,					_lowerCAmelCase   :				Tuple      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	vocab  # same as swe
		__lowercase   							=	ids_to_tokens  # same as bpe
		__lowercase   							=	emoji
		__lowercase   							=	np.max([len(_lowerCAmelCase      ) for w in self.vocab.keys()]      )
		__lowercase   							=	re.compile(r"""(https?|ftp)(:\/\/[-_\.!~*\'()a-zA-Z0-9;\/?:\@&=\+$,%#]+)"""      )
		__lowercase   							=	re.compile(r"""[A-Za-z0-9\._+]*@[\-_0-9A-Za-z]+(\.[A-Za-z]+)*"""      )
		__lowercase   							=	re.compile(r"""[\(]{0,1}[0-9]{2,4}[\)\-\(]{0,1}[0-9]{2,4}[\)\-]{0,1}[0-9]{3,4}"""      )
		__lowercase   							=	re.compile(
		    r"""([12]\d{3}[/\-年])*(0?[1-9]|1[0-2])[/\-月]((0?[1-9]|[12][0-9]|3[01])日?)*(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""      )
		__lowercase   							=	re.compile(
		    r"""(明治|大正|昭和|平成|令和|㍾|㍽|㍼|㍻|\u32ff)\d{1,2}年(0?[1-9]|1[0-2])月(0?[1-9]|[12][0-9]|3[01])日(\d{1,2}|:|\d{1,2}時|\d{1,2}分|\(日\)|\(月\)|\(火\)|\(水\)|\(木\)|\(金\)|\(土\)|㈰|㈪|㈫|㈬|㈭|㈮|㈯)*"""      )
		__lowercase   							=	re.compile(
		    r"""((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*億)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*万)*((0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*千)*(0|[1-9]\d*|[1-9]\d{0,2}(,\d{3})+)*(千円|万円|千万円|円|千ドル|万ドル|千万ドル|ドル|千ユーロ|万ユーロ|千万ユーロ|ユーロ)+(\(税込\)|\(税抜\)|\+tax)*"""      )
		__lowercase   							=	"""─━│┃┄┅┆┇┈┉┊┋┌┍┎┏┐┑┒┓└┕┖┗┘┙┚┛├┝┞┟┠┡┢┣┤┥┦┧┨┩┪┫┬┭┮┯┰┱┲┳┴┵┶┷┸┹┺┻┼┽┾┿╀╁╂╃╄╅╆╇╈╉╊╋╌╍╎╏═║╒╓╔╕╖╗╘╙╚╛╜╝╞╟╠╡╢╣╤╥╦╧╨╩╪╫╬╭╮╯╰╱╲╳╴╵╶╷╸╹╺╻╼╽╾╿"""
		__lowercase   							=	"""▀▁▂▃▄▅▆▇█▉▊▋▌▍▎▏▐░▒▓▔▕▖▗▘▙▚▛▜▝▞▟"""
		__lowercase   							=	str.maketrans({k: """<BLOCK>""" for k in keisen + blocks}      )







	def __len__(	self   :				Optional[Any]      )		->			Tuple:
		"""simple docstring"""







		return len(self.ids_to_tokens      )







	def 			_a (	self   :				Dict							,					_lowerCAmelCase   :				str      )		->			Any:
		"""simple docstring"""







		__lowercase   							=	self.content_repattera.sub("""<URL>"""							,					_lowerCAmelCase      )
		__lowercase   							=	self.content_repattera.sub("""<EMAIL>"""							,					_lowerCAmelCase      )
		__lowercase   							=	self.content_repattera.sub("""<TEL>"""							,					_lowerCAmelCase      )
		__lowercase   							=	self.content_repattera.sub("""<DATE>"""							,					_lowerCAmelCase      )
		__lowercase   							=	self.content_repattera.sub("""<DATE>"""							,					_lowerCAmelCase      )
		__lowercase   							=	self.content_repattera.sub("""<PRICE>"""							,					_lowerCAmelCase      )
		__lowercase   							=	content.translate(self.content_transa      )
		while "<BLOCK><BLOCK>" in content:
			__lowercase   							=	content.replace("""<BLOCK><BLOCK>"""							,					"""<BLOCK>"""      )
		return content







	def 			_a (	self   :				int							,					_lowerCAmelCase   :				Optional[Any]							,					_lowerCAmelCase   :				List[str]=False      )		->			int:
		"""simple docstring"""







		__lowercase   							=	text.replace(""" """							,					"""<SP>"""      )
		__lowercase   							=	text.replace("""　"""							,					"""<SP>"""      )
		__lowercase   							=	text.replace("""\r\n"""							,					"""<BR>"""      )
		__lowercase   							=	text.replace("""\n"""							,					"""<BR>"""      )
		__lowercase   							=	text.replace("""\r"""							,					"""<BR>"""      )
		__lowercase   							=	text.replace("""\t"""							,					"""<TAB>"""      )
		__lowercase   							=	text.replace("""—"""							,					"""ー"""      )
		__lowercase   							=	text.replace("""−"""							,					"""ー"""      )
		for k, v in self.emoji["emoji"].items():
			if k in text:
				__lowercase   							=	text.replace(_lowerCAmelCase							,					_lowerCAmelCase      )
		if clean:
			__lowercase   							=	self.clean_text(_lowerCAmelCase      )

		def check_simbol(_lowerCAmelCase   :				List[Any]      ):
			__lowercase   							=	x.encode()
			if len(_lowerCAmelCase      ) == 1 and len(_lowerCAmelCase      ) == 2:
				__lowercase   							=	(int(e[0]      ) << 8) + int(e[1]      )
				if (
				    (c >= 0Xc2a1 and c <= 0Xc2bf)
				    or (c >= 0Xc780 and c <= 0Xc783)
				    or (c >= 0Xcab9 and c <= 0Xcbbf)
				    or (c >= 0Xcc80 and c <= 0Xcda2)
				):
					return True
			return False

		def checkuae(_lowerCAmelCase   :				int      ):
			__lowercase   							=	x.encode()
			if len(_lowerCAmelCase      ) == 1 and len(_lowerCAmelCase      ) == 3:
				__lowercase   							=	(int(e[0]      ) << 16) + (int(e[1]      ) << 8) + int(e[2]      )
				if c >= 0Xe2_8080 and c <= 0Xe2_b07f:
					return True
			return False

		__lowercase   							=	0
		__lowercase   							=	[]
		while pos < len(_lowerCAmelCase      ):
			__lowercase   							=	min(len(_lowerCAmelCase      )							,					pos + self.maxlen + 1      ) if text[pos] == """<""" else pos + 3
			__lowercase   							=	[]  # (token_id, token, pos)
			for e in range(_lowerCAmelCase							,					_lowerCAmelCase							,					-1      ):
				__lowercase   							=	text[pos:e]
				if wd in self.vocab:
					if wd[0] == "<" and len(_lowerCAmelCase      ) > 2:
						__lowercase   							=	[(self.vocab[wd], wd, e)]
						break
					else:
						candidates.append((self.vocab[wd], wd, e)      )
			if len(_lowerCAmelCase      ) > 0:
				# the smallest token_id is adopted
				__lowercase							,    __lowercase							,    __lowercase   							=	sorted(_lowerCAmelCase							,					key=lambda _lowerCAmelCase      : x[0]      )[0]
				result.append(_lowerCAmelCase      )
				__lowercase   							=	e
			else:
				__lowercase   							=	pos + 1
				__lowercase   							=	text[pos:end]
				if check_simbol(_lowerCAmelCase      ):
					result.append("""<KIGOU>"""      )
				elif checkuae(_lowerCAmelCase      ):
					result.append("""<U2000U2BFF>"""      )
				else:
					for i in wd.encode("""utf-8"""      ):
						result.append("""<|byte%d|>""" % i      )
				__lowercase   							=	end
		return result







	def 			_a (	self   :				Optional[Any]							,					_lowerCAmelCase   :				Any							,					_lowerCAmelCase   :				Optional[int]="\n"      )		->			List[Any]:
		"""simple docstring"""







		__lowercase   							=	[]
		__lowercase   							=	[]
		__lowercase   							=	self.ids_to_tokens[index][0]
		if word[:6] == "<|byte" and word[-2:] == "|>":
			byte_tokens.append(int(word[6:-2]      )      )
		else:
			if len(_lowerCAmelCase      ) > 0:
				words.append(bytearray(_lowerCAmelCase      ).decode("""utf-8"""							,					errors="""replace"""      )      )
				__lowercase   							=	[]
			if word[:7] == "<|emoji" and word[-2:] == "|>":
				words.append(self.emoji["""emoji_inv"""][word]      )
			elif word == "<SP>":
				words.append(""" """      )
			elif word == "<BR>":
				words.append(_lowerCAmelCase      )
			elif word == "<TAB>":
				words.append("""\t"""      )
			elif word == "<BLOCK>":
				words.append("""▀"""      )
			elif word == "<KIGOU>":
				words.append("""ǀ"""      )
			elif word == "<U2000U2BFF>":
				words.append("""‖"""      )
			else:
				words.append(_lowerCAmelCase      )
		if len(_lowerCAmelCase      ) > 0:
			words.append(bytearray(_lowerCAmelCase      ).decode("""utf-8"""							,					errors="""replace"""      )      )
		__lowercase   							=	"""""".join(_lowerCAmelCase      )
		return text









"""simple docstring"""







import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_lowerCAmelCase            =	logging.get_logger(__name__)

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

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


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

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

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


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


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


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

				lowercase    :      Union[str, Any]       = []
				rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""") )
				rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""") )
				rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""") )
				rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""") )
				rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""") )

				for b in block_names:
								lowercase    :      str       = block_name_mapping[b]
								rename_keys.append((F"""block{b}_expand_conv/kernel:0""", F"""encoder.blocks.{hf_b}.expansion.expand_conv.weight""") )
								rename_keys.append((F"""block{b}_expand_bn/gamma:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.weight""") )
								rename_keys.append((F"""block{b}_expand_bn/beta:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.bias""") )
								rename_keys.append(
								    (F"""block{b}_expand_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_mean""") )
								rename_keys.append(
								    (F"""block{b}_expand_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.expansion.expand_bn.running_var""") )
								rename_keys.append(
								    (F"""block{b}_dwconv/depthwise_kernel:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight""") )
								rename_keys.append((F"""block{b}_bn/gamma:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight""") )
								rename_keys.append((F"""block{b}_bn/beta:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias""") )
								rename_keys.append(
								    (F"""block{b}_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean""") )
								rename_keys.append(
								    (F"""block{b}_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var""") )

								rename_keys.append((F"""block{b}_se_reduce/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.weight""") )
								rename_keys.append((F"""block{b}_se_reduce/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.reduce.bias""") )
								rename_keys.append((F"""block{b}_se_expand/kernel:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.weight""") )
								rename_keys.append((F"""block{b}_se_expand/bias:0""", F"""encoder.blocks.{hf_b}.squeeze_excite.expand.bias""") )
								rename_keys.append(
								    (F"""block{b}_project_conv/kernel:0""", F"""encoder.blocks.{hf_b}.projection.project_conv.weight""") )
								rename_keys.append((F"""block{b}_project_bn/gamma:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.weight""") )
								rename_keys.append((F"""block{b}_project_bn/beta:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.bias""") )
								rename_keys.append(
								    (F"""block{b}_project_bn/moving_mean:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_mean""") )
								rename_keys.append(
								    (F"""block{b}_project_bn/moving_variance:0""", F"""encoder.blocks.{hf_b}.projection.project_bn.running_var""") )

				rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""") )
				rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""") )
				rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""") )
				rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""") )
				rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""") )

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

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


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

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

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


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

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

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

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

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

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

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

				# Check whether original and HF model outputs match  -> np.allclose
				assert np.allclose(_A  ,				_A  ,				atol=1e-3 ), "The predicted logits are not the same."
				print("""Model outputs match!""" )

				if save_model:
								# Create folder to save model
								if not os.path.isdir(_A ):
												os.mkdir(_A )
								# Save converted model and image processor
								hf_model.save_pretrained(_A )
								preprocessor.save_pretrained(_A )

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


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

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