

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

_lowerCamelCase      :					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,
}

_lowerCamelCase      :					Any					    =      {
    '''b0''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.0,
        '''image_size''': 224,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [],
    },
    '''b1''': {
        '''hidden_dim''': 1280,
        '''width_coef''': 1.0,
        '''depth_coef''': 1.1,
        '''image_size''': 240,
        '''dropout_rate''': 0.2,
        '''dw_padding''': [16],
    },
    '''b2''': {
        '''hidden_dim''': 1408,
        '''width_coef''': 1.1,
        '''depth_coef''': 1.2,
        '''image_size''': 260,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 8, 16],
    },
    '''b3''': {
        '''hidden_dim''': 1536,
        '''width_coef''': 1.2,
        '''depth_coef''': 1.4,
        '''image_size''': 300,
        '''dropout_rate''': 0.3,
        '''dw_padding''': [5, 18],
    },
    '''b4''': {
        '''hidden_dim''': 1792,
        '''width_coef''': 1.4,
        '''depth_coef''': 1.8,
        '''image_size''': 380,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [6],
    },
    '''b5''': {
        '''hidden_dim''': 2048,
        '''width_coef''': 1.6,
        '''depth_coef''': 2.2,
        '''image_size''': 456,
        '''dropout_rate''': 0.4,
        '''dw_padding''': [13, 27],
    },
    '''b6''': {
        '''hidden_dim''': 2304,
        '''width_coef''': 1.8,
        '''depth_coef''': 2.6,
        '''image_size''': 528,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [31],
    },
    '''b7''': {
        '''hidden_dim''': 2560,
        '''width_coef''': 2.0,
        '''depth_coef''': 3.1,
        '''image_size''': 600,
        '''dropout_rate''': 0.5,
        '''dw_padding''': [18],
    },
}





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

      __A    ='''huggingface/label-files'''
      __A    ='''imagenet-1k-id2label.json'''
      __A    =10_00
      __A    =json.load(open(hf_hub_download(__A     ,				__A     ,				repo_type='''dataset'''			)     ,				'''r'''			)			)
      __A    ={int(__A			): v for k, v in idalabel.items()}

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





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





def      A__					(			__A  :						int			)					->Optional[Any]:
      __A    =CONFIG_MAP[model_name]['''image_size''']
      __A    =EfficientNetImageProcessor(
          size={'''height''': size, '''width''': size}     ,				image_mean=[0.485, 0.456, 0.406]     ,				image_std=[0.47853944, 0.4732864, 0.47434163]     ,				do_center_crop=__A     ,				)
      return preprocessor





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

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

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





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

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

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

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

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

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

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

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


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

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





import argparse
from pathlib import Path

from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration





def      A__					(			__A  :						Any     ,				__A  :						str     ,				__A  :						str     ,				__A  :						Path     ,				__A  :						str = None     ,				__A  :						str = None     ,				__A  :						str = None     ,				)					->Optional[Any]:
      if config_name_or_path is None:
            __A    ='''facebook/rag-token-base''' if model_type == '''rag_token''' else '''facebook/rag-sequence-base'''

      if generator_tokenizer_name_or_path is None:
            __A    =generator_name_or_path

      if question_encoder_tokenizer_name_or_path is None:
            __A    =question_encoder_name_or_path

      __A    =RagTokenForGeneration if model_type == '''rag_token''' else RagSequenceForGeneration

      # Save model.
      __A    =RagConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)
      __A    =AutoConfig.from_pretrained(__A			)

      __A    =gen_config
      __A    =question_encoder_config

      __A    =model_class.from_pretrained_question_encoder_generator(
          __A     ,				__A     ,				config=__A			)
      rag_model.save_pretrained(__A			)

      # Sanity check.
      model_class.from_pretrained(__A			)

      # Save tokenizers.
      __A    =AutoTokenizer.from_pretrained(__A			)
      gen_tokenizer.save_pretrained(dest_dir / '''generator_tokenizer/'''			)
      __A    =AutoTokenizer.from_pretrained(__A			)
      question_encoder_tokenizer.save_pretrained(dest_dir / '''question_encoder_tokenizer/'''			)


if __name__ == "__main__":
   _lowerCamelCase      :					List[str]					    =      argparse.ArgumentParser()
   parser.add_argument(
       '''--model_type''',
       choices=['''rag_sequence''', '''rag_token'''],
       required=True,
       type=str,
       help='''RAG model type: rag_sequence, rag_token''',
   )
   parser.add_argument('''--dest''', type=str, required=True, help='''Path to the output checkpoint directory.''')
   parser.add_argument('''--generator_name_or_path''', type=str, required=True, help='''Generator model identifier''')
   parser.add_argument(
       '''--question_encoder_name_or_path''', type=str, required=True, help='''Question encoder model identifier'''
   )

   parser.add_argument(
       '''--generator_tokenizer_name_or_path''',
       type=str,
       help='''Generator tokenizer identifier, if not specified, resolves to ``generator_name_or_path``''',
   )
   parser.add_argument(
       '''--question_encoder_tokenizer_name_or_path''',
       type=str,
       help='''Question encoder tokenizer identifier, if not specified, resolves to ``question_encoder_name_or_path``''',
   )
   parser.add_argument(
       '''--config_name_or_path''',
       type=str,
       help=(
           '''Identifier of the model config to use, if not provided, resolves to a base config for a given'''
           ''' ``model_type``'''
       ),
   )

   _lowerCamelCase      :					str					    =      parser.parse_args()

   _lowerCamelCase      :					Optional[int]					    =      Path(args.dest)
   dest_dir.mkdir(exist_ok=True)

   consolidate(
       args.model_type,
       args.generator_name_or_path,
       args.question_encoder_name_or_path,
       dest_dir,
       args.config_name_or_path,
       args.generator_tokenizer_name_or_path,
       args.question_encoder_tokenizer_name_or_path,
   )


