'''simple docstring'''

import json
from typing import List, Optional, Tuple

from tokenizers import normalizers
from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_roformer import RoFormerTokenizer
from .tokenization_utils import JiebaPreTokenizer


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

UpperCamelCase__     :	List[str]				=			{'vocab_file': 'vocab.txt', 'tokenizer_file': 'tokenizer.json'}

UpperCamelCase__     :	Tuple				=			{
    'vocab_file': {
        'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/vocab.txt',
        'junnyu/roformer_chinese_char_small': (
            'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_chinese_char_base': (
            'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_discriminator': (
            'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/vocab.txt'
        ),
        'junnyu/roformer_small_generator': (
            'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/vocab.txt'
        ),
    }
}

UpperCamelCase__     :	Optional[Any]				=			{
    'junnyu/roformer_chinese_small': 1_536,
    'junnyu/roformer_chinese_base': 1_536,
    'junnyu/roformer_chinese_char_small': 512,
    'junnyu/roformer_chinese_char_base': 512,
    'junnyu/roformer_small_discriminator': 128,
    'junnyu/roformer_small_generator': 128,
}


UpperCamelCase__     :	int				=			{
    'junnyu/roformer_chinese_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_base': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_small': {'do_lower_case': True},
    'junnyu/roformer_chinese_char_base': {'do_lower_case': True},
    'junnyu/roformer_small_discriminator': {'do_lower_case': True},
    'junnyu/roformer_small_generator': {'do_lower_case': True},
}


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


      UpperCAmelCase_      :      Optional[int]      							=   VOCAB_FILES_NAMES
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_VOCAB_FILES_MAP
      UpperCAmelCase_      :      Tuple      							=   PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      UpperCAmelCase_      :      Dict      							=   PRETRAINED_INIT_CONFIGURATION
      UpperCAmelCase_      :      str      							=   RoFormerTokenizer




      def __init__(				self					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=True					,lowerCamelCase_="[UNK]"					,lowerCamelCase_="[SEP]"					,lowerCamelCase_="[PAD]"					,lowerCamelCase_="[CLS]"					,lowerCamelCase_="[MASK]"					,lowerCamelCase_=True					,lowerCamelCase_=None					,**lowerCamelCase_					,)			->			str:



         '''simple docstring'''



         super().__init__(
             lowerCamelCase_					,tokenizer_file=lowerCamelCase_					,do_lower_case=lowerCamelCase_					,unk_token=lowerCamelCase_					,sep_token=lowerCamelCase_					,pad_token=lowerCamelCase_					,cls_token=lowerCamelCase_					,mask_token=lowerCamelCase_					,tokenize_chinese_chars=lowerCamelCase_					,strip_accents=lowerCamelCase_					,**lowerCamelCase_					,)

         UpperCAmelCase__							:				Any   				=  json.loads(self.backend_tokenizer.normalizer.__getstate__()							)
         if (
             pre_tok_state.get('''lowercase'''					,lowerCamelCase_							) != do_lower_case
             or pre_tok_state.get('''strip_accents'''					,lowerCamelCase_							) != strip_accents
         ):
            UpperCAmelCase__							:				Optional[int]   				=  getattr(lowerCamelCase_					,pre_tok_state.pop('''type'''							)							)
            UpperCAmelCase__							:				List[str]   				=  do_lower_case
            UpperCAmelCase__							:				Any   				=  strip_accents
            UpperCAmelCase__							:				Union[str, Any]   				=  pre_tok_class(**lowerCamelCase_							)

         UpperCAmelCase__							:				Optional[int]   				=  do_lower_case




      def __getstate__(				self							)			->			Dict:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[Any]   				=  self.__dict__.copy()
         UpperCAmelCase__							:				Dict   				=  BertPreTokenizer()
         return state




      def __setstate__(				self					,lowerCamelCase_							)			->			List[Any]:



         '''simple docstring'''



         UpperCAmelCase__							:				Optional[int]   				=  d
         UpperCAmelCase__							:				Optional[int]   				=  self.__dict__['''_tokenizer'''].get_vocab()
         UpperCAmelCase__							:				List[Any]   				=  PreTokenizer.custom(JiebaPreTokenizer(lowerCamelCase_							)							)




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



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  [self.cls_token_id] + token_ids_a + [self.sep_token_id]

         if token_ids_a:
            output += token_ids_a + [self.sep_token_id]

         return output




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



         '''simple docstring'''



         UpperCAmelCase__							:				Dict   				=  [self.sep_token_id]
         UpperCAmelCase__							:				List[str]   				=  [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							) * [0] + len(token_ids_a + sep							) * [1]




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



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  self._tokenizer.model.save(lowerCamelCase_					,name=lowerCamelCase_							)
         return tuple(lowerCamelCase_							)




      def 		lowerCAmelCase__					(				self					,lowerCamelCase_					,lowerCamelCase_=None					,lowerCamelCase_=None					,lowerCamelCase_=False					,**lowerCamelCase_					,)			->			int:



         '''simple docstring'''



         UpperCAmelCase__							:				Tuple   				=  BertPreTokenizer()
         return super().save_pretrained(lowerCamelCase_					,lowerCamelCase_					,lowerCamelCase_					,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)




