'''simple docstring'''

import json
import os
import re
import shutil
import tempfile
import unittest
from typing import Tuple

from transformers import AddedToken, BatchEncoding, PerceiverTokenizer
from transformers.utils import cached_property, is_tf_available, is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


if is_torch_available():
		UpperCamelCase__     :	Tuple				=			'pt'
elif is_tf_available():
		UpperCamelCase__     :	int				=			'tf'
else:
		UpperCamelCase__     :	List[Any]				=			'jax'


class 				_lowercase	(      lowerCAmelCase		,unittest.TestCase   ):
						'''simple docstring'''


						UpperCAmelCase_      :      Union[str, Any]      							=   PerceiverTokenizer
						UpperCAmelCase_      :      Tuple      							=   False




						def 		lowerCAmelCase__					(				self							)			->			str:



									'''simple docstring'''



									super().setUp()
									UpperCAmelCase__							:				Tuple   				=  PerceiverTokenizer()
									tokenizer.save_pretrained(self.tmpdirname							)




						@cached_property
						def 		lowerCAmelCase__					(				self							)			->			Dict:



									'''simple docstring'''



									return PerceiverTokenizer.from_pretrained('''deepmind/language-perceiver'''							)




						def 		lowerCAmelCase__					(				self					,**lowerCamelCase_							)			->			PerceiverTokenizer:



									'''simple docstring'''



									return self.tokenizer_class.from_pretrained(self.tmpdirname					,**lowerCamelCase_							)




						def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=False					,lowerCamelCase_=20					,lowerCamelCase_=5							)			->			Tuple[str, list]:



									'''simple docstring'''



									UpperCAmelCase__							:				Dict   				=  []
									for i in range(len(lowerCamelCase_							)							):
												try:
															UpperCAmelCase__							:				List[Any]   				=  tokenizer.decode([i]					,clean_up_tokenization_spaces=lowerCamelCase_							)
												except UnicodeDecodeError:
															pass
												toks.append((i, tok)							)

									UpperCAmelCase__							:				Dict   				=  list(filter(lambda lowerCamelCase_							: re.match(r'''^[ a-zA-Z]+$'''					,t[1]							)					,lowerCamelCase_							)							)
									UpperCAmelCase__							:				List[Any]   				=  list(filter(lambda lowerCamelCase_							: [t[0]] == tokenizer.encode(t[1]					,add_special_tokens=lowerCamelCase_							)					,lowerCamelCase_							)							)
									if max_length is not None and len(lowerCamelCase_							) > max_length:
												UpperCAmelCase__							:				str   				=  toks[:max_length]
									if min_length is not None and len(lowerCamelCase_							) < min_length and len(lowerCamelCase_							) > 0:
												while len(lowerCamelCase_							) < min_length:
															UpperCAmelCase__							:				int   				=  toks + toks
        # toks_str = [t[1] for t in toks]
									UpperCAmelCase__							:				Any   				=  [t[0] for t in toks]

									# Ensure consistency
									UpperCAmelCase__							:				Optional[int]   				=  tokenizer.decode(lowerCamelCase_					,clean_up_tokenization_spaces=lowerCamelCase_							)
									if " " not in output_txt and len(lowerCamelCase_							) > 1:
												UpperCAmelCase__							:				Optional[int]   				=  (
												    tokenizer.decode([toks_ids[0]]					,clean_up_tokenization_spaces=lowerCamelCase_							)
												    + ''' '''
												    + tokenizer.decode(toks_ids[1:]					,clean_up_tokenization_spaces=lowerCamelCase_							)
												)
									if with_prefix_space:
												UpperCAmelCase__							:				List[str]   				=  ''' ''' + output_txt
									UpperCAmelCase__							:				str   				=  tokenizer.encode(lowerCamelCase_					,add_special_tokens=lowerCamelCase_							)
									return output_txt, output_ids




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



									'''simple docstring'''



									UpperCAmelCase__							:				Union[str, Any]   				=  self.perceiver_tokenizer
									UpperCAmelCase__							:				Any   				=  '''Unicode €.'''
									UpperCAmelCase__							:				Union[str, Any]   				=  tokenizer(lowerCamelCase_							)
									UpperCAmelCase__							:				str   				=  [4, 91, 116, 111, 105, 117, 106, 107, 38, 232, 136, 178, 52, 5]
									self.assertEqual(encoded['''input_ids''']					,lowerCamelCase_							)

									# decoding
									UpperCAmelCase__							:				List[Any]   				=  tokenizer.decode(lowerCamelCase_							)
									self.assertEqual(lowerCamelCase_					,'''[CLS]Unicode €.[SEP]'''							)

									UpperCAmelCase__							:				List[Any]   				=  tokenizer('''e è é ê ë'''							)
									UpperCAmelCase__							:				Any   				=  [4, 107, 38, 201, 174, 38, 201, 175, 38, 201, 176, 38, 201, 177, 5]
									self.assertEqual(encoded['''input_ids''']					,lowerCamelCase_							)
									# decoding
									UpperCAmelCase__							:				Optional[Any]   				=  tokenizer.decode(lowerCamelCase_							)
									self.assertEqual(lowerCamelCase_					,'''[CLS]e è é ê ë[SEP]'''							)

									# encode/decode, but with `encode` instead of `__call__`
									self.assertEqual(tokenizer.decode(tokenizer.encode('''e è é ê ë'''							)							)					,'''[CLS]e è é ê ë[SEP]'''							)




						def 		lowerCAmelCase__					(				self							)			->			Tuple:



									'''simple docstring'''



									UpperCAmelCase__							:				Optional[Any]   				=  self.perceiver_tokenizer
									UpperCAmelCase__							:				Optional[int]   				=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									# fmt: off
									UpperCAmelCase__							:				Optional[int]   				=  [4, 71, 38, 114, 117, 116, 109, 38, 118, 103, 120, 103, 109, 120, 103, 118, 110, 38, 108, 117, 120, 38, 121, 123, 115, 115, 103, 120, 111, 128, 103, 122, 111, 117, 116, 52, 5, 0]
									# fmt: on
									UpperCAmelCase__							:				Dict   				=  tokenizer(lowerCamelCase_					,padding=lowerCamelCase_					,return_tensors=lowerCamelCase_							)
									self.assertIsInstance(lowerCamelCase_					,lowerCamelCase_							)

									if FRAMEWORK != "jax":
												UpperCAmelCase__							:				Dict   				=  list(batch.input_ids.numpy()[0]							)
									else:
												UpperCAmelCase__							:				Any   				=  list(batch.input_ids.tolist()[0]							)

									self.assertListEqual(lowerCamelCase_					,lowerCamelCase_							)

									self.assertEqual((2, 38)					,batch.input_ids.shape							)
									self.assertEqual((2, 38)					,batch.attention_mask.shape							)




						def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



									'''simple docstring'''



									UpperCAmelCase__							:				List[str]   				=  self.perceiver_tokenizer
									UpperCAmelCase__							:				Dict   				=  ['''A long paragraph for summarization.''', '''Another paragraph for summarization.''']
									UpperCAmelCase__							:				int   				=  tokenizer(lowerCamelCase_					,padding=lowerCamelCase_					,return_tensors=lowerCamelCase_							)
									# check if input_ids are returned and no decoder_input_ids
									self.assertIn('''input_ids'''					,lowerCamelCase_							)
									self.assertIn('''attention_mask'''					,lowerCamelCase_							)
									self.assertNotIn('''decoder_input_ids'''					,lowerCamelCase_							)
									self.assertNotIn('''decoder_attention_mask'''					,lowerCamelCase_							)




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



									'''simple docstring'''



									UpperCAmelCase__							:				List[Any]   				=  self.perceiver_tokenizer
									UpperCAmelCase__							:				Dict   				=  [
									    '''Summary of the text.''',
									    '''Another summary.''',
									]
									UpperCAmelCase__							:				Any   				=  tokenizer(
									    text_target=lowerCamelCase_					,max_length=32					,padding='''max_length'''					,truncation=lowerCamelCase_					,return_tensors=lowerCamelCase_							)
									self.assertEqual(32					,targets['''input_ids'''].shape[1]							)




						def 		lowerCAmelCase__					(				self							)			->			Any:



									'''simple docstring'''



									UpperCAmelCase__							:				Tuple   				=  self.get_tokenizers()
									for tokenizer in tokenizers:
												with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
															self.assertNotEqual(tokenizer.model_max_length					,42							)

        # Now let's start the test
									UpperCAmelCase__							:				Optional[int]   				=  self.get_tokenizers()
									for tokenizer in tokenizers:
												with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
															# Isolate this from the other tests because we save additional tokens/etc
															UpperCAmelCase__							:				List[Any]   				=  tempfile.mkdtemp()

															UpperCAmelCase__							:				Tuple   				=  ''' He is very happy, UNwant\u00E9d,running'''
															UpperCAmelCase__							:				Tuple   				=  tokenizer.encode(lowerCamelCase_					,add_special_tokens=lowerCamelCase_							)
															tokenizer.save_pretrained(lowerCamelCase_							)

															UpperCAmelCase__							:				List[str]   				=  tokenizer.__class__.from_pretrained(lowerCamelCase_							)
															UpperCAmelCase__							:				Dict   				=  after_tokenizer.encode(lowerCamelCase_					,add_special_tokens=lowerCamelCase_							)
															self.assertListEqual(lowerCamelCase_					,lowerCamelCase_							)

															shutil.rmtree(lowerCamelCase_							)

									UpperCAmelCase__							:				int   				=  self.get_tokenizers(model_max_length=42							)
									for tokenizer in tokenizers:
												with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
															# Isolate this from the other tests because we save additional tokens/etc
															UpperCAmelCase__							:				Optional[Any]   				=  tempfile.mkdtemp()

															UpperCAmelCase__							:				List[str]   				=  ''' He is very happy, UNwant\u00E9d,running'''
															tokenizer.add_tokens(['''bim''', '''bambam''']							)
															UpperCAmelCase__							:				List[str]   				=  tokenizer.additional_special_tokens
															additional_special_tokens.append('''new_additional_special_token'''							)
															tokenizer.add_special_tokens({'''additional_special_tokens''': additional_special_tokens}							)
															UpperCAmelCase__							:				int   				=  tokenizer.encode(lowerCamelCase_					,add_special_tokens=lowerCamelCase_							)
															tokenizer.save_pretrained(lowerCamelCase_							)

															UpperCAmelCase__							:				Dict   				=  tokenizer.__class__.from_pretrained(lowerCamelCase_							)
															UpperCAmelCase__							:				str   				=  after_tokenizer.encode(lowerCamelCase_					,add_special_tokens=lowerCamelCase_							)
															self.assertListEqual(lowerCamelCase_					,lowerCamelCase_							)
															self.assertIn('''new_additional_special_token'''					,after_tokenizer.additional_special_tokens							)
															self.assertEqual(after_tokenizer.model_max_length					,42							)

															UpperCAmelCase__							:				Optional[Any]   				=  tokenizer.__class__.from_pretrained(lowerCamelCase_					,model_max_length=43							)
															self.assertEqual(tokenizer.model_max_length					,43							)

															shutil.rmtree(lowerCamelCase_							)




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



									'''simple docstring'''



									UpperCAmelCase__							:				Optional[int]   				=  []
									if self.test_slow_tokenizer:
												tokenizer_list.append((self.tokenizer_class, self.get_tokenizer())							)

									if self.test_rust_tokenizer:
												tokenizer_list.append((self.rust_tokenizer_class, self.get_rust_tokenizer())							)

									for tokenizer_class, tokenizer_utils in tokenizer_list:
												with tempfile.TemporaryDirectory() as tmp_dir:
															tokenizer_utils.save_pretrained(lowerCamelCase_							)

															with open(os.path.join(lowerCamelCase_					,'''special_tokens_map.json'''							)					,encoding='''utf-8'''							) as json_file:
																		UpperCAmelCase__							:				Dict   				=  json.load(lowerCamelCase_							)

															with open(os.path.join(lowerCamelCase_					,'''tokenizer_config.json'''							)					,encoding='''utf-8'''							) as json_file:
																		UpperCAmelCase__							:				Dict   				=  json.load(lowerCamelCase_							)

															UpperCAmelCase__							:				int   				=  [f'''<extra_id_{i}>''' for i in range(125							)]

															UpperCAmelCase__							:				Union[str, Any]   				=  added_tokens_extra_ids + [
															    '''an_additional_special_token'''
															]
															UpperCAmelCase__							:				Dict   				=  added_tokens_extra_ids + [
															    '''an_additional_special_token'''
															]

															with open(os.path.join(lowerCamelCase_					,'''special_tokens_map.json'''							)					,'''w'''					,encoding='''utf-8'''							) as outfile:
																		json.dump(lowerCamelCase_					,lowerCamelCase_							)
															with open(os.path.join(lowerCamelCase_					,'''tokenizer_config.json'''							)					,'''w'''					,encoding='''utf-8'''							) as outfile:
																		json.dump(lowerCamelCase_					,lowerCamelCase_							)

															# the following checks allow us to verify that our test works as expected, i.e. that the tokenizer takes
															# into account the new value of additional_special_tokens given in the "tokenizer_config.json" and
															# "special_tokens_map.json" files
															UpperCAmelCase__							:				Union[str, Any]   				=  tokenizer_class.from_pretrained(
															    lowerCamelCase_					,)
															self.assertIn(
															    '''an_additional_special_token'''					,tokenizer_without_change_in_init.additional_special_tokens							)
															self.assertEqual(
															    ['''an_additional_special_token''']					,tokenizer_without_change_in_init.convert_ids_to_tokens(
															        tokenizer_without_change_in_init.convert_tokens_to_ids(['''an_additional_special_token''']							)							)					,)

															# Now we test that we can change the value of additional_special_tokens in the from_pretrained
															UpperCAmelCase__							:				List[str]   				=  added_tokens_extra_ids + [AddedToken('''a_new_additional_special_token'''					,lstrip=lowerCamelCase_							)]
															UpperCAmelCase__							:				Dict   				=  tokenizer_class.from_pretrained(
															    lowerCamelCase_					,additional_special_tokens=lowerCamelCase_					,)

															self.assertIn('''a_new_additional_special_token'''					,tokenizer.additional_special_tokens							)
															self.assertEqual(
															    ['''a_new_additional_special_token''']					,tokenizer.convert_ids_to_tokens(
															        tokenizer.convert_tokens_to_ids(['''a_new_additional_special_token''']							)							)					,)




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



									'''simple docstring'''



									UpperCAmelCase__							:				int   				=  self.perceiver_tokenizer
									self.assertEqual(tokenizer.decode([178]							)					,'''�'''							)




						def 		lowerCAmelCase__					(				self							)			->			Any:



									'''simple docstring'''



									pass




						def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



									'''simple docstring'''



									pass




						def 		lowerCAmelCase__					(				self							)			->			Optional[int]:



									'''simple docstring'''



									pass




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



									'''simple docstring'''



									pass




						def 		lowerCAmelCase__					(				self							)			->			int:



									'''simple docstring'''



									UpperCAmelCase__							:				Any   				=  self.get_tokenizers(fast=lowerCamelCase_					,do_lower_case=lowerCamelCase_							)
									for tokenizer in tokenizers:
												with self.subTest(f'''{tokenizer.__class__.__name__}'''							):
															UpperCAmelCase__							:				Dict   				=  ['''[CLS]''', '''t''', '''h''', '''i''', '''s''', ''' ''', '''i''', '''s''', ''' ''', '''a''', ''' ''', '''t''', '''e''', '''s''', '''t''', '''[SEP]''']
															UpperCAmelCase__							:				Tuple   				=  tokenizer.convert_tokens_to_string(lowerCamelCase_							)

															self.assertIsInstance(lowerCamelCase_					,lowerCamelCase_							)





'''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)




