'''simple docstring'''

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

import regex as re

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


if TYPE_CHECKING:
		from transformers.pipelines.conversational import Conversation

UpperCamelCase__     :	Any				=			logging.get_logger(__name__)


UpperCamelCase__     :	List[str]				=			{
    'vocab_file': 'vocab.json',
    'merges_file': 'merges.txt',
    'tokenizer_config_file': 'tokenizer_config.json',
}

UpperCamelCase__     :	Optional[Any]				=			{
    'vocab_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/vocab.json'},
    'merges_file': {'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/merges.txt'},
    'tokenizer_config_file': {
        'facebook/blenderbot-3B': 'https://huggingface.co/facebook/blenderbot-3B/resolve/main/tokenizer_config.json'
    },
}

UpperCamelCase__     :	Optional[int]				=			{'facebook/blenderbot-3B': 128}







@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def __UpperCamelCase(   ):
			'''simple docstring'''
			UpperCAmelCase__							:				Any   				=  (
			    list(range(ord('''!'''    )  ,			ord('''~'''    ) + 1    )    ) + list(range(ord('''¡'''    )  ,			ord('''¬'''    ) + 1    )    ) + list(range(ord('''®'''    )  ,			ord('''ÿ'''    ) + 1    )    )
			)
			UpperCAmelCase__							:				Any   				=  bs[:]
			UpperCAmelCase__							:				str   				=  0
			for b in range(2**8    ):
						if b not in bs:
									bs.append(_A    )
									cs.append(2**8 + n    )
									n += 1
			UpperCAmelCase__							:				Union[str, Any]   				=  [chr(_A    ) for n in cs]
			return dict(zip(_A  ,			_A    )    )







def __UpperCamelCase(   _A					:				List[str]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				Optional[Any]   				=  set()
			UpperCAmelCase__							:				Optional[int]   				=  word[0]
			for char in word[1:]:
						pairs.add((prev_char, char)    )
						UpperCAmelCase__							:				Optional[int]   				=  char
			return pairs


class 				_lowercase	(      lowerCAmelCase   ):
						'''simple docstring'''


						UpperCAmelCase_      :      Union[str, Any]      							=   VOCAB_FILES_NAMES
						UpperCAmelCase_      :      List[str]      							=   PRETRAINED_VOCAB_FILES_MAP
						UpperCAmelCase_      :      str      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
						UpperCAmelCase_      :      Optional[int]      							=   ['''input_ids''', '''attention_mask''']




						def __init__(				self					,lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_="replace"					,lowerCamelCase_="<s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="</s>"					,lowerCamelCase_="<s>"					,lowerCamelCase_="<unk>"					,lowerCamelCase_="<pad>"					,lowerCamelCase_="<mask>"					,lowerCamelCase_=False					,**lowerCamelCase_					,)			->			str:



									'''simple docstring'''



									UpperCAmelCase__							:				List[str]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else bos_token
									UpperCAmelCase__							:				Union[str, Any]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else eos_token
									UpperCAmelCase__							:				Any   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else sep_token
									UpperCAmelCase__							:				List[Any]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else cls_token
									UpperCAmelCase__							:				Union[str, Any]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else unk_token
									UpperCAmelCase__							:				List[str]   				=  AddedToken(lowerCamelCase_					,lstrip=lowerCamelCase_					,rstrip=lowerCamelCase_							) if isinstance(lowerCamelCase_					,lowerCamelCase_							) else pad_token

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

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

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

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




						@property
						# Copied from transformers.models.roberta.tokenization_roberta.RobertaTokenizer.vocab_size with Roberta->Blenderbot, RoBERTa->Blenderbot
						def 		lowerCAmelCase__					(				self							)			->			Optional[Any]:



									'''simple docstring'''



									return len(self.encoder							)




						def 		lowerCAmelCase__					(				self							)			->			int:



									'''simple docstring'''



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




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[Any]:



									'''simple docstring'''



									if token in self.cache:
												return self.cache[token]
									UpperCAmelCase__							:				Any   				=  tuple(lowerCamelCase_							)
									UpperCAmelCase__							:				List[str]   				=  get_pairs(lowerCamelCase_							)

									if not pairs:
												return token

									while True:
												UpperCAmelCase__							:				List[Any]   				=  min(lowerCamelCase_					,key=lambda lowerCamelCase_							: self.bpe_ranks.get(lowerCamelCase_					,float('''inf'''							)							)							)
												if bigram not in self.bpe_ranks:
															break
												UpperCAmelCase__							:				List[Any]   				=  bigram
												UpperCAmelCase__							:				Union[str, Any]   				=  []
												UpperCAmelCase__							:				Optional[Any]   				=  0
												while i < len(lowerCamelCase_							):
															try:
																		UpperCAmelCase__							:				str   				=  word.index(lowerCamelCase_					,lowerCamelCase_							)
															except ValueError:
																		new_word.extend(word[i:]							)
																		break
															else:
																		new_word.extend(word[i:j]							)
																		UpperCAmelCase__							:				Tuple   				=  j

															if word[i] == first and i < len(lowerCamelCase_							) - 1 and word[i + 1] == second:
																		new_word.append(first + second							)
																		i += 2
															else:
																		new_word.append(word[i]							)
																		i += 1
												UpperCAmelCase__							:				List[Any]   				=  tuple(lowerCamelCase_							)
												UpperCAmelCase__							:				Optional[Any]   				=  new_word
												if len(lowerCamelCase_							) == 1:
															break
												else:
															UpperCAmelCase__							:				Optional[Any]   				=  get_pairs(lowerCamelCase_							)
									UpperCAmelCase__							:				Dict   				=  ''' '''.join(lowerCamelCase_							)
									UpperCAmelCase__							:				Tuple   				=  word
									return word




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Optional[Any]:



									'''simple docstring'''



									UpperCAmelCase__							:				List[Any]   				=  []
									for token in re.findall(self.pat					,lowerCamelCase_							):
												UpperCAmelCase__							:				Optional[Any]   				=  ''''''.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(lowerCamelCase_							).split(''' '''							)							)
									return bpe_tokens




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Union[str, Any]:



									'''simple docstring'''



									return self.encoder.get(lowerCamelCase_					,self.encoder.get(self.unk_token							)							)




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			Any:



									'''simple docstring'''



									return self.decoder.get(lowerCamelCase_							)




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[str]:



									'''simple docstring'''



									UpperCAmelCase__							:				Optional[int]   				=  ''''''.join(lowerCamelCase_							)
									UpperCAmelCase__							:				Tuple   				=  bytearray([self.byte_decoder[c] for c in text]							).decode('''utf-8'''					,errors=self.errors							)
									return text




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Tuple[str]:



									'''simple docstring'''



									if not os.path.isdir(lowerCamelCase_							):
												logger.error(f'''Vocabulary path ({save_directory}) should be a directory'''							)
												return
									UpperCAmelCase__							:				List[str]   				=  os.path.join(
									    lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']							)
									UpperCAmelCase__							:				Dict   				=  os.path.join(
									    lowerCamelCase_					,(filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']							)

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

									UpperCAmelCase__							:				Any   				=  0
									with open(lowerCamelCase_					,'''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 lowerCamelCase_							: 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__							:				List[str]   				=  token_index
															writer.write(''' '''.join(lowerCamelCase_							) + '''\n'''							)
															index += 1

									return vocab_file, merge_file




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None					,lowerCamelCase_ = False							)			->			List[int]:



									'''simple docstring'''



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

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




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			List[int]:



									'''simple docstring'''



									UpperCAmelCase__							:				Dict   				=  [self.sep_token_id]
									UpperCAmelCase__							:				Union[str, Any]   				=  [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					,lowerCamelCase_					,lowerCamelCase_=False					,**lowerCamelCase_							)			->			Dict:



									'''simple docstring'''



									UpperCAmelCase__							:				Union[str, Any]   				=  kwargs.pop('''add_prefix_space'''					,self.add_prefix_space							)
									if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase_							) > 0 and not text[0].isspace()):
												UpperCAmelCase__							:				List[str]   				=  ''' ''' + text
									return (text, kwargs)




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_ = None							)			->			Union[str, Any]:



									'''simple docstring'''



									return token_ids_a + [self.eos_token_id]




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_							)			->			List[int]:



									'''simple docstring'''



									UpperCAmelCase__							:				List[str]   				=  []
									for is_user, text in conversation.iter_texts():
												if is_user:
															# We need to space prefix as it's being done within blenderbot
															inputs.append(''' ''' + text							)
												else:
															# Generated responses should contain them already.
															inputs.append(lowerCamelCase_							)

									UpperCAmelCase__							:				Dict   				=  '''  '''.join(lowerCamelCase_							)
									UpperCAmelCase__							:				List[str]   				=  self.encode(lowerCamelCase_							)
									if len(lowerCamelCase_							) > self.model_max_length:
												UpperCAmelCase__							:				int   				=  input_ids[-self.model_max_length :]
												logger.warning(f'''Trimmed input from conversation as it was longer than {self.model_max_length} tokens.'''							)
									return input_ids





'''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__     :	Optional[Any]				=			logging.get_logger(__name__)

UpperCamelCase__     :	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,
}

UpperCamelCase__     :	int				=			{
    '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 __UpperCamelCase(   _A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  EfficientNetConfig()
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''hidden_dim''']
   UpperCAmelCase__							:				Optional[Any]   				=  CONFIG_MAP[model_name]['''width_coef''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''depth_coef''']
   UpperCAmelCase__							:				Optional[int]   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''dropout_rate''']
   UpperCAmelCase__							:				Any   				=  CONFIG_MAP[model_name]['''dw_padding''']

   UpperCAmelCase__							:				Tuple   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				Dict   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				List[Any]   				=  10_00
   UpperCAmelCase__							:				Union[str, Any]   				=  json.load(open(hf_hub_download(_A  ,			_A  ,			repo_type='''dataset'''    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Union[str, Any]   				=  {int(_A    ): v for k, v in idalabel.items()}

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







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__							:				List[str]   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    )
   return im







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				Tuple   				=  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=_A  ,			)
   return preprocessor







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  [v.split('''_'''    )[0].split('''block'''    )[1] for v in original_param_names if v.startswith('''block'''    )]
   UpperCAmelCase__							:				List[str]   				=  sorted(set(_A    )    )
   UpperCAmelCase__							:				Optional[Any]   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  {b: str(_A    ) for b, i in zip(_A  ,			range(_A    )    )}

   UpperCAmelCase__							:				int   				=  []
   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:
      UpperCAmelCase__							:				Tuple   				=  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''')    )

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

   UpperCAmelCase__							:				List[str]   				=  '''classifier.weight'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''classifier.bias'''
   return key_mapping







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

      UpperCAmelCase__							:				Tuple   				=  key_mapping[key]
      if "_conv" in key and "kernel" in key:
         UpperCAmelCase__							:				List[str]   				=  torch.from_numpy(_A    ).permute(3  ,			2  ,			0  ,			1    )
      elif "depthwise_kernel" in key:
         UpperCAmelCase__							:				Dict   				=  torch.from_numpy(_A    ).permute(2  ,			3  ,			0  ,			1    )
      elif "kernel" in key:
         UpperCAmelCase__							:				Optional[int]   				=  torch.from_numpy(np.transpose(_A    )    )
      else:
         UpperCAmelCase__							:				str   				=  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					:				Tuple  ,			_A					:				str  ,			_A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  model_classes[model_name](
       include_top=_A  ,			weights='''imagenet'''  ,			input_tensor=_A  ,			input_shape=_A  ,			pooling=_A  ,			classes=10_00  ,			classifier_activation='''softmax'''  ,			)

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

   # Load HuggingFace model
   UpperCAmelCase__							:				Union[str, Any]   				=  get_efficientnet_config(_A    )
   UpperCAmelCase__							:				Any   				=  EfficientNetForImageClassification(_A    ).eval()
   UpperCAmelCase__							:				Tuple   				=  hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('''Converting parameters...'''    )
   UpperCAmelCase__							:				Tuple   				=  rename_keys(_A    )
   replace_params(_A  ,			_A  ,			_A    )

   # Initialize preprocessor and preprocess input image
   UpperCAmelCase__							:				List[Any]   				=  convert_image_processor(_A    )
   UpperCAmelCase__							:				List[Any]   				=  preprocessor(images=prepare_img()  ,			return_tensors='''pt'''    )

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

   # Original model inference
   UpperCAmelCase__							:				str   				=  False
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				List[str]   				=  prepare_img().resize((image_size, image_size)  ,			resample=PIL.Image.NEAREST    )
   UpperCAmelCase__							:				List[Any]   				=  image.img_to_array(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.expand_dims(_A  ,			axis=0    )
   UpperCAmelCase__							:				List[Any]   				=  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...'''    )
      UpperCAmelCase__							:				Any   				=  F'''efficientnet-{model_name}'''
      preprocessor.push_to_hub(_A    )
      hf_model.push_to_hub(_A    )


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

  UpperCamelCase__     :	Union[str, Any]				=			parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




