
'''simple docstring'''







import itertools
import json
import os
import unittest

from transformers import AddedToken, RobertaTokenizer, RobertaTokenizerFast
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES
from transformers.testing_utils import require_tokenizers, slow

from ...test_tokenization_common import TokenizerTesterMixin






@require_tokenizers
class  lowerCAmelCase__   (  lowerCamelCase_    ,						unittest.TestCase     ):
					lowerCAmelCase_        =     RobertaTokenizer
					lowerCAmelCase_        =     RobertaTokenizerFast
					lowerCAmelCase_        =     True
					lowerCAmelCase_        =     {'''cls_token''': '''<s>'''}






					def 	_snake_case    (   self		):


											"""simple docstring"""





											super().setUp()

											# Adapted from Sennrich et al. 2015 and https://github.com/rsennrich/subword-nmt
											lowercase_	:     Any													=       [
											    '''l''',
											    '''o''',
											    '''w''',
											    '''e''',
											    '''r''',
											    '''s''',
											    '''t''',
											    '''i''',
											    '''d''',
											    '''n''',
											    '''\u0120''',
											    '''\u0120l''',
											    '''\u0120n''',
											    '''\u0120lo''',
											    '''\u0120low''',
											    '''er''',
											    '''\u0120lowest''',
											    '''\u0120newer''',
											    '''\u0120wider''',
											    '''<unk>''',
											]
											lowercase_	:     List[str]													=       dict(zip(__SCREAMING_SNAKE_CASE				,							range(len(__SCREAMING_SNAKE_CASE		)		)		)		)
											lowercase_	:     List[Any]													=       ['''#version: 0.2''', '''\u0120 l''', '''\u0120l o''', '''\u0120lo w''', '''e r''', '''''']
											lowercase_	:     Union[str, Any]													=       {'''unk_token''': '''<unk>'''}

											lowercase_	:     Optional[int]													=       os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['''vocab_file''']		)
											lowercase_	:     Optional[Any]													=       os.path.join(self.tmpdirname				,							VOCAB_FILES_NAMES['''merges_file''']		)
											with open(self.vocab_file				,							'''w'''				,							encoding='''utf-8'''		) as fp:
																	fp.write(json.dumps(__SCREAMING_SNAKE_CASE		) + '''\n'''		)
											with open(self.merges_file				,							'''w'''				,							encoding='''utf-8'''		) as fp:
																	fp.write('''\n'''.join(__SCREAMING_SNAKE_CASE		)		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											kwargs.update(self.special_tokens_map		)
											return self.tokenizer_class.from_pretrained(self.tmpdirname				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							**__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											kwargs.update(self.special_tokens_map		)
											return RobertaTokenizerFast.from_pretrained(self.tmpdirname				,							**__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self				,							__SCREAMING_SNAKE_CASE		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       '''lower newer'''
											lowercase_	:     Dict													=       '''lower newer'''
											return input_text, output_text






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Optional[int]													=       self.tokenizer_class(self.vocab_file				,							self.merges_file				,							**self.special_tokens_map		)
											lowercase_	:     List[Any]													=       '''lower newer'''
											lowercase_	:     Union[str, Any]													=       ['''l''', '''o''', '''w''', '''er''', '''\u0120''', '''n''', '''e''', '''w''', '''er''']
											lowercase_	:     Union[str, Any]													=       tokenizer.tokenize(__SCREAMING_SNAKE_CASE		)  # , add_prefix_space=True)
											self.assertListEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       tokens + [tokenizer.unk_token]
											lowercase_	:     Tuple													=       [0, 1, 2, 15, 10, 9, 3, 2, 15, 19]
											self.assertListEqual(tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[str]													=       self.get_tokenizer()

											self.assertListEqual(tokenizer.encode('''Hello world!'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)				,							[0, 3_14_14, 2_32, 3_28, 2]		)
											self.assertListEqual(
											    tokenizer.encode('''Hello world! cécé herlolip 418'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)				,							[0, 3_14_14, 2_32, 3_28, 7_40, 11_40, 1_26_95, 69, 4_60_78, 15_88, 2]				,							)






					@slow
					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     List[Any]													=       self.tokenizer_class.from_pretrained('''roberta-base'''		)

											lowercase_	:     Optional[Any]													=       tokenizer.encode('''sequence builders'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       tokenizer.encode('''multi-sequence build'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       tokenizer.encode(
											    '''sequence builders'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       tokenizer.encode(
											    '''sequence builders'''				,							'''multi-sequence build'''				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)

											lowercase_	:     str													=       tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       tokenizer.build_inputs_with_special_tokens(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											assert encoded_sentence == encoded_text_from_decode
											assert encoded_pair == encoded_pair_from_decode






					def 	_snake_case    (   self		):


											"""simple docstring"""





											lowercase_	:     Tuple													=       self.get_tokenizer()

											lowercase_	:     Optional[Any]													=       '''Encode this sequence.'''
											lowercase_	:     Optional[Any]													=       tokenizer.byte_encoder[''' '''.encode('''utf-8'''		)[0]]

											# Testing encoder arguments
											lowercase_	:     int													=       tokenizer.encode(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Tuple													=       tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
											self.assertNotEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     Optional[int]													=       tokenizer.encode(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     List[Any]													=       tokenizer.convert_ids_to_tokens(encoded[0]		)[0]
											self.assertEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											tokenizer.add_special_tokens({'''bos_token''': '''<s>'''}		)
											lowercase_	:     List[str]													=       tokenizer.encode(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Any													=       tokenizer.convert_ids_to_tokens(encoded[1]		)[0]
											self.assertNotEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											# Testing spaces after special tokens
											lowercase_	:     Any													=       '''<mask>'''
											tokenizer.add_special_tokens(
											    {'''mask_token''': AddedToken(__SCREAMING_SNAKE_CASE				,							lstrip=__SCREAMING_SNAKE_CASE				,							rstrip=__SCREAMING_SNAKE_CASE		)}		)  # mask token has a left space
											lowercase_	:     List[Any]													=       tokenizer.convert_tokens_to_ids(__SCREAMING_SNAKE_CASE		)

											lowercase_	:     int													=       '''Encode <mask> sequence'''
											lowercase_	:     List[Any]													=       '''Encode <mask>sequence'''

											lowercase_	:     Tuple													=       tokenizer.encode(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Dict													=       encoded.index(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[int]													=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
											self.assertEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)

											lowercase_	:     List[str]													=       tokenizer.encode(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     Optional[Any]													=       encoded.index(__SCREAMING_SNAKE_CASE		)
											lowercase_	:     str													=       tokenizer.convert_ids_to_tokens(encoded[mask_loc + 1]		)[0]
											self.assertNotEqual(__SCREAMING_SNAKE_CASE				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											pass






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																							lowercase_	:     Optional[Any]													=       self.rust_tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Union[str, Any]													=       self.tokenizer_class.from_pretrained(__SCREAMING_SNAKE_CASE				,							**__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     str													=       '''A, <mask> AllenNLP sentence.'''
																							lowercase_	:     Optional[int]													=       tokenizer_r.encode_plus(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							return_token_type_ids=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Optional[int]													=       tokenizer_p.encode_plus(__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE				,							return_token_type_ids=__SCREAMING_SNAKE_CASE		)

																							# token_type_ids should put 0 everywhere
																							self.assertEqual(sum(tokens_r['''token_type_ids''']		)				,							sum(tokens_p['''token_type_ids''']		)		)

																							# attention_mask should put 1 everywhere, so sum over length should be 1
																							self.assertEqual(
																							    sum(tokens_r['''attention_mask''']		) / len(tokens_r['''attention_mask''']		)				,							sum(tokens_p['''attention_mask''']		) / len(tokens_p['''attention_mask''']		)				,							)

																							lowercase_	:     Union[str, Any]													=       tokenizer_r.convert_ids_to_tokens(tokens_r['''input_ids''']		)
																							lowercase_	:     List[Any]													=       tokenizer_p.convert_ids_to_tokens(tokens_p['''input_ids''']		)

																							# Rust correctly handles the space before the mask while python doesnt
																							self.assertSequenceEqual(tokens_p['''input_ids''']				,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)
																							self.assertSequenceEqual(tokens_r['''input_ids''']				,							[0, 2_50, 6, 5_02_64, 38_23, 4_87, 2_19_92, 36_45, 4, 2]		)

																							self.assertSequenceEqual(
																							    __SCREAMING_SNAKE_CASE				,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)
																							self.assertSequenceEqual(
																							    __SCREAMING_SNAKE_CASE				,							['''<s>''', '''A''', ''',''', '''<mask>''', '''ĠAllen''', '''N''', '''LP''', '''Ġsentence''', '''.''', '''</s>''']		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for trim_offsets, add_prefix_space in itertools.product([True, False]				,							repeat=2		):
																	lowercase_	:     List[str]													=       self.rust_tokenizer_class.from_pretrained(
																	    self.tmpdirname				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)

																	lowercase_	:     List[str]													=       json.loads(tokenizer_r.backend_tokenizer.pre_tokenizer.__getstate__()		)
																	lowercase_	:     List[Any]													=       json.loads(tokenizer_r.backend_tokenizer.post_processor.__getstate__()		)

																	self.assertEqual(pre_tokenizer_state['''add_prefix_space''']				,							__SCREAMING_SNAKE_CASE		)

																	self.assertEqual(post_processor_state['''add_prefix_space''']				,							__SCREAMING_SNAKE_CASE		)
																	self.assertEqual(post_processor_state['''trim_offsets''']				,							__SCREAMING_SNAKE_CASE		)






					def 	_snake_case    (   self		):


											"""simple docstring"""





											for tokenizer, pretrained_name, kwargs in self.tokenizers_list:
																	with self.subTest(F'''{tokenizer.__class__.__name__} ({pretrained_name})'''		):
																							lowercase_	:     Optional[Any]													=       '''hello'''  # `hello` is a token in the vocabulary of `pretrained_name`
																							lowercase_	:     int													=       F'''{text_of_1_token} {text_of_1_token}'''

																							lowercase_	:     Dict													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Tuple													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		) + 1, len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Optional[int]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     List[str]													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		) + 1, len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Optional[Any]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Dict													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		), len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Union[str, Any]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Dict													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(len(__SCREAMING_SNAKE_CASE		), len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     List[str]													=       F''' {text}'''

																							# tokenizer_r = self.rust_tokenizer_class.from_pretrained(
																							#     pretrained_name, use_fast=True, add_prefix_space=True, trim_offsets=True
																							# )
																							# encoding = tokenizer_r(text, return_offsets_mapping=True, add_special_tokens=False)
																							# self.assertEqual(encoding.offset_mapping[0], (1, 1 + len(text_of_1_token)))
																							# self.assertEqual(
																							#     encoding.offset_mapping[1],
																							#     (1 + len(text_of_1_token) + 1, 1 + len(text_of_1_token) + 1 + len(text_of_1_token)),
																							# )

																							lowercase_	:     str													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Optional[Any]													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(1, 1 + len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(1 + len(__SCREAMING_SNAKE_CASE		) + 1, 1 + len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     Any													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     str													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(1 + len(__SCREAMING_SNAKE_CASE		), 1 + len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)

																							lowercase_	:     List[str]													=       self.rust_tokenizer_class.from_pretrained(
																							    __SCREAMING_SNAKE_CASE				,							use_fast=__SCREAMING_SNAKE_CASE				,							add_prefix_space=__SCREAMING_SNAKE_CASE				,							trim_offsets=__SCREAMING_SNAKE_CASE		)
																							lowercase_	:     Dict													=       tokenizer_r(__SCREAMING_SNAKE_CASE				,							return_offsets_mapping=__SCREAMING_SNAKE_CASE				,							add_special_tokens=__SCREAMING_SNAKE_CASE		)
																							self.assertEqual(encoding.offset_mapping[0]				,							(0, 1 + len(__SCREAMING_SNAKE_CASE		))		)
																							self.assertEqual(
																							    encoding.offset_mapping[1]				,							(1 + len(__SCREAMING_SNAKE_CASE		), 1 + len(__SCREAMING_SNAKE_CASE		) + 1 + len(__SCREAMING_SNAKE_CASE		))				,							)









'''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()
A     =logging.get_logger(__name__)

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

A     ={
    '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 						snake_case_							(_a	:							List[str]     ):
    UpperCAmelCase			       =		EfficientNetConfig()
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''hidden_dim''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''width_coef''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''depth_coef''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''image_size''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''dropout_rate''']
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''dw_padding''']

    UpperCAmelCase			       =		'''huggingface/label-files'''
    UpperCAmelCase			       =		'''imagenet-1k-id2label.json'''
    UpperCAmelCase			       =		1_0_0_0
    UpperCAmelCase			       =		json.load(open(hf_hub_download(_a    ,   _a    ,   repo_type='''dataset'''     )    ,   '''r'''     )     )
    UpperCAmelCase			       =		{int(_a     ): v for k, v in idalabel.items()}

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





def 						snake_case_							():
    UpperCAmelCase			       =		'''http://images.cocodataset.org/val2017/000000039769.jpg'''
    UpperCAmelCase			       =		Image.open(requests.get(_a    ,   stream=_a     ).raw     )
    return im





def 						snake_case_							(_a	:							str     ):
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''image_size''']
    UpperCAmelCase			       =		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 						snake_case_							(_a	:							Optional[Any]     ):
    UpperCAmelCase			       =		[v.split('''_'''     )[0].split('''block'''     )[1] for v in original_param_names if v.startswith('''block'''     )]
    UpperCAmelCase			       =		sorted(set(_a     )     )
    UpperCAmelCase			       =		len(_a     )
    UpperCAmelCase			       =		{b: str(_a     ) for b, i in zip(_a    ,   range(_a     )     )}

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

    UpperCAmelCase			       =		'''classifier.weight'''
    UpperCAmelCase			       =		'''classifier.bias'''
    return key_mapping





def 						snake_case_							(_a	:							Dict    ,   _a	:							List[str]    ,   _a	:							Dict     ):
    for key, value in tf_params.items():
        if "normalization" in key:
            continue

        UpperCAmelCase			       =		key_mapping[key]
        if "_conv" in key and "kernel" in key:
            UpperCAmelCase			       =		torch.from_numpy(_a     ).permute(3    ,   2    ,   0    ,   1     )
        elif "depthwise_kernel" in key:
            UpperCAmelCase			       =		torch.from_numpy(_a     ).permute(2    ,   3    ,   0    ,   1     )
        elif "kernel" in key:
            UpperCAmelCase			       =		torch.from_numpy(np.transpose(_a     )     )
        else:
            UpperCAmelCase			       =		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 						snake_case_							(_a	:							Optional[Any]    ,   _a	:							List[str]    ,   _a	:							Optional[int]    ,   _a	:							Dict     ):
    UpperCAmelCase			       =		model_classes[model_name](
        include_top=_a    ,   weights='''imagenet'''    ,   input_tensor=_a    ,   input_shape=_a    ,   pooling=_a    ,   classes=1_0_0_0    ,   classifier_activation='''softmax'''    ,   )

    UpperCAmelCase			       =		original_model.trainable_variables
    UpperCAmelCase			       =		original_model.non_trainable_variables
    UpperCAmelCase			       =		{param.name: param.numpy() for param in tf_params}
    for param in tf_non_train_params:
        UpperCAmelCase			       =		param.numpy()
    UpperCAmelCase			       =		list(tf_params.keys()     )

    # Load HuggingFace model
    UpperCAmelCase			       =		get_efficientnet_config(_a     )
    UpperCAmelCase			       =		EfficientNetForImageClassification(_a     ).eval()
    UpperCAmelCase			       =		hf_model.state_dict()

    # Create src-to-dst parameter name mapping dictionary
    print('''Converting parameters...'''     )
    UpperCAmelCase			       =		rename_keys(_a     )
    replace_params(_a    ,   _a    ,   _a     )

    # Initialize preprocessor and preprocess input image
    UpperCAmelCase			       =		convert_image_processor(_a     )
    UpperCAmelCase			       =		preprocessor(images=prepare_img()    ,   return_tensors='''pt'''     )

    # HF model inference
    hf_model.eval()
    with torch.no_grad():
        UpperCAmelCase			       =		hf_model(**_a     )
    UpperCAmelCase			       =		outputs.logits.detach().numpy()

    # Original model inference
    UpperCAmelCase			       =		False
    UpperCAmelCase			       =		CONFIG_MAP[model_name]['''image_size''']
    UpperCAmelCase			       =		prepare_img().resize((image_size, image_size)    ,   resample=PIL.Image.NEAREST     )
    UpperCAmelCase			       =		image.img_to_array(_a     )
    UpperCAmelCase			       =		np.expand_dims(_a    ,   axis=0     )
    UpperCAmelCase			       =		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			       =		F"efficientnet-{model_name}"
        preprocessor.push_to_hub(_a     )
        hf_model.push_to_hub(_a     )


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

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




