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()
_lowercase				:   int   				=logging.get_logger(__name__)

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

_lowercase				:   Any   				={
    '''b0''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 2_2_4,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1_2_8_0,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 2_4_0,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [1_6],
    },
    '''b2''': {
        '''hidden_dim''': 1_4_0_8,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 2_6_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 1_6],
    },
    '''b3''': {
        '''hidden_dim''': 1_5_3_6,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 3_0_0,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 1_8],
    },
    '''b4''': {
        '''hidden_dim''': 1_7_9_2,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 3_8_0,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2_0_4_8,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 4_5_6,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [1_3, 2_7],
    },
    '''b6''': {
        '''hidden_dim''': 2_3_0_4,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 5_2_8,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [3_1],
    },
    '''b7''': {
        '''hidden_dim''': 2_5_6_0,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 6_0_0,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [1_8],
    },
}



def A__	(      lowercase:  Union[str, Any]       )					->		List[Any]:
       A    :  str								=EfficientNetConfig()
       A    :  Tuple								=CONFIG_MAP[model_name]['hidden_dim']
       A    :  Any								=CONFIG_MAP[model_name]['width_coef']
       A    :  List[Any]								=CONFIG_MAP[model_name]['depth_coef']
       A    :  Tuple								=CONFIG_MAP[model_name]['image_size']
       A    :  List[str]								=CONFIG_MAP[model_name]['dropout_rate']
       A    :  Optional[int]								=CONFIG_MAP[model_name]['dw_padding']

       A    :  Optional[int]								='huggingface/label-files'
       A    :  Any								='imagenet-1k-id2label.json'
       A    :  List[str]								=1_000
       A    :  Dict								=json.load(open(hf_hub_download(__A,		__A,		repo_type='dataset'       ),		'r'       )       )
       A    :  Any								={int(__A       ): v for k, v in idalabel.items()}

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



def A__	(      )					->		Dict:
       A    :  Optional[int]								='http://images.cocodataset.org/val2017/000000039769.jpg'
       A    :  int								=Image.open(requests.get(__A,		stream=__A       ).raw       )
       return im



def A__	(      lowercase:  int       )					->		Optional[Any]:
       A    :  int								=CONFIG_MAP[model_name]['image_size']
       A    :  int								=EfficientNetImageProcessor(
           size={'height': size, 'width': size},		image_mean=[0.4_85, 0.4_56, 0.4_06],		image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63],		do_center_crop=__A,		)
       return preprocessor



def A__	(      lowercase:  Tuple       )					->		Optional[int]:
       A    :  str								=[v.split('_'       )[0].split('block'       )[1] for v in original_param_names if v.startswith('block'       )]
       A    :  str								=sorted(set(__A       )       )
       A    :  Optional[int]								=len(__A       )
       A    :  int								={b: str(__A       ) for b, i in zip(__A,		range(__A       )       )}

       A    :  List[Any]								=[]
       rename_keys.append(('stem_conv/kernel:0', 'embeddings.convolution.weight')       )
       rename_keys.append(('stem_bn/gamma:0', 'embeddings.batchnorm.weight')       )
       rename_keys.append(('stem_bn/beta:0', 'embeddings.batchnorm.bias')       )
       rename_keys.append(('stem_bn/moving_mean:0', 'embeddings.batchnorm.running_mean')       )
       rename_keys.append(('stem_bn/moving_variance:0', 'embeddings.batchnorm.running_var')       )

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

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

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

       A    :  Optional[Any]								={}
       for item in rename_keys:
              if item[0] in original_param_names:
                     A    :  int								='efficientnet.' + item[1]

       A    :  int								='classifier.weight'
       A    :  List[str]								='classifier.bias'
       return key_mapping



def A__	(      lowercase:  List[Any],		lowercase:  List[Any],		lowercase:  int       )					->		Dict:
       for key, value in tf_params.items():
              if "normalization" in key:
                     continue

              A    :  Tuple								=key_mapping[key]
              if "_conv" in key and "kernel" in key:
                     A    :  List[Any]								=torch.from_numpy(__A       ).permute(3,		2,		0,		1       )
              elif "depthwise_kernel" in key:
                     A    :  Dict								=torch.from_numpy(__A       ).permute(2,		3,		0,		1       )
              elif "kernel" in key:
                     A    :  Any								=torch.from_numpy(np.transpose(__A       )       )
              else:
                     A    :  Any								=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 A__	(      lowercase:  int,		lowercase:  Dict,		lowercase:  List[str],		lowercase:  List[str]       )					->		List[Any]:
       A    :  int								=model_classes[model_name](
           include_top=__A,		weights='imagenet',		input_tensor=__A,		input_shape=__A,		pooling=__A,		classes=1_000,		classifier_activation='softmax',		)

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

       # Load HuggingFace model
       A    :  Optional[Any]								=get_efficientnet_config(__A       )
       A    :  Dict								=EfficientNetForImageClassification(__A       ).eval()
       A    :  Tuple								=hf_model.state_dict()

       # Create src-to-dst parameter name mapping dictionary
       print('Converting parameters...'       )
       A    :  Any								=rename_keys(__A       )
       replace_params(__A,		__A,		__A       )

       # Initialize preprocessor and preprocess input image
       A    :  int								=convert_image_processor(__A       )
       A    :  Union[str, Any]								=preprocessor(images=prepare_img(),		return_tensors='pt'       )

       # HF model inference
       hf_model.eval()
       with torch.no_grad():
              A    :  List[str]								=hf_model(**__A       )
       A    :  Dict								=outputs.logits.detach().numpy()

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


if __name__ == "__main__":
    _lowercase				:   Tuple   				=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''')

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









from typing import Dict, List, Optional

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


_lowerCamelCase      :					Any					    =      logging.get_logger(__name__)


_lowerCamelCase      :					Tuple					    =      {
    '''nielsr/canine-s''': 2048,
}

# Unicode defines 1,114,112 total “codepoints”
_lowerCamelCase      :					List[str]					    =      111_4112

# Below: Constants defining canonical codepoints for special, pseudo-characters.
# Copied from https://github.com/google-research/language/blob/master/language/canine/special_codepoints.py
_lowerCamelCase      :					List[str]					    =      0

_lowerCamelCase      :					Any					    =      0xe_0_0_0
_lowerCamelCase      :					Union[str, Any]					    =      0xe_0_0_1
_lowerCamelCase      :					Any					    =      0xe_0_0_2
_lowerCamelCase      :					List[str]					    =      0xe_0_0_3
_lowerCamelCase      :					Any					    =      0xe_0_0_4

# Maps special codepoints to human-readable names.
_lowerCamelCase      :					Dict[int, str]					    =      {
    # Special symbols are represented using codepoints values that are valid,
    # but designated as "Private Use", meaning that they will never be assigned
    # characters by the Unicode Consortium, and are thus safe for use here.
    #
    # NOTE: Do *NOT* add any sort of [UNK_CHAR] here. They are explicitly
    # excluded and should fail with a hard error.
    CLS: "[CLS]",
    SEP: "[SEP]",
    BOS: "[BOS]",
    MASK: "[MASK]",
    PAD: "[PAD]",
    RESERVED: "[RESERVED]",
}

# Maps special codepoint human-readable names to their codepoint values.
_lowerCamelCase      :					Dict[str, int]					    =      {name: codepoint for codepoint, name in SPECIAL_CODEPOINTS.items()}





class        lowerCAmelCase__  (					__magic_name__			):



       '''simple docstring'''







       lowercase_						=    PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES

       def __init__(							self ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=chr(lowercase__     ) ,   lowercase__=False ,   lowercase__=2_0_4_8 ,   **lowercase__ ,   ):







             '''simple docstring'''



             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else bos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else eos_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else sep_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else cls_token
             __A    =AddedToken(lowercase__ ,   lstrip=lowercase__ ,   rstrip=lowercase__     ) if isinstance(lowercase__ ,   lowercase__     ) else pad_token

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

             super().__init__(
                 bos_token=lowercase__ ,   eos_token=lowercase__ ,   sep_token=lowercase__ ,   cls_token=lowercase__ ,   pad_token=lowercase__ ,   mask_token=lowercase__ ,   add_prefix_space=lowercase__ ,   model_max_length=lowercase__ ,   **lowercase__ ,   )

             # Creates a mapping for looking up the IDs of special symbols.
             __A    ={}
             for codepoint, name in SPECIAL_CODEPOINTS.items():
                   __A    =codepoint

             # Creates a mapping for looking up the string forms of special symbol IDs.
             __A    ={
                 codepoint: name for name, codepoint in self._special_codepoints.items()
             }

             __A    =UNICODE_VOCAB_SIZE
             __A    =len(self._special_codepoints     )

       @property
       def     __UpperCamelCase     (							self     ):







             '''simple docstring'''



             return self._unicode_vocab_size

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return list(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   return ord(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid token: \'{token}\''''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             try:
                   if index in SPECIAL_CODEPOINTS:
                         return SPECIAL_CODEPOINTS[index]
                   return chr(lowercase__     )
             except TypeError:
                   raise ValueError(f'''invalid id: {index}'''     )

       def     __UpperCamelCase     (							self ,   lowercase__     ):







             '''simple docstring'''



             return "".join(lowercase__     )

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =cls + token_ids_a + sep
             if token_ids_a is not None:
                   result += token_ids_a + sep
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None ,   lowercase__ = False     ):







             '''simple docstring'''



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

             __A    =[1] + ([0] * len(lowercase__     )) + [1]
             if token_ids_a is not None:
                   result += ([0] * len(lowercase__     )) + [1]
             return result

       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             __A    =[self.sep_token_id]
             __A    =[self.cls_token_id]

             __A    =len(cls + token_ids_a + sep     ) * [0]
             if token_ids_a is not None:
                   result += len(token_ids_a + sep     ) * [1]
             return result


       def     __UpperCamelCase     (							self ,   lowercase__ ,   lowercase__ = None     ):







             '''simple docstring'''



             return ()


