


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

UpperCAmelCase_			:  Dict										=   {
    '''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_			:  str										=   {
    '''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       _lowercase							(					UpperCamelCase__							:       Any      ):
 __A	:		Optional[Any]	  =      EfficientNetConfig()
 __A	:		List[Any]	  =      CONFIG_MAP[model_name]['hidden_dim']
 __A	:		Union[str, Any]	  =      CONFIG_MAP[model_name]['width_coef']
 __A	:		Dict	  =      CONFIG_MAP[model_name]['depth_coef']
 __A	:		Union[str, Any]	  =      CONFIG_MAP[model_name]['image_size']
 __A	:		List[Any]	  =      CONFIG_MAP[model_name]['dropout_rate']
 __A	:		Union[str, Any]	  =      CONFIG_MAP[model_name]['dw_padding']

 __A	:		Any	  =      'huggingface/label-files'
 __A	:		Any	  =      'imagenet-1k-id2label.json'
 __A	:		int	  =      1000
 __A	:		Any	  =      json.load(open(hf_hub_download(__snake_case,					__snake_case,					repo_type='dataset'      ),					'r'      )      )
 __A	:		str	  =      {int(__snake_case      ): v for k, v in idalabel.items()}

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


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


def       _lowercase							(					UpperCamelCase__							:       Optional[int]      ):
 __A	:		Tuple	  =      CONFIG_MAP[model_name]['image_size']
 __A	:		Optional[Any]	  =      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=__snake_case,					)
 return preprocessor


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

 __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	:		Any	  =      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	:		Union[str, Any]	  =      {}
 for item in rename_keys:
  if item[0] in original_param_names:
   __A	:		int	  =      'efficientnet.' + item[1]

 __A	:		Union[str, Any]	  =      'classifier.weight'
 __A	:		Dict	  =      'classifier.bias'
 return key_mapping


def       _lowercase							(					UpperCamelCase__							:       int,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       str      ):
 for key, value in tf_params.items():
  if "normalization" in key:
   continue

  __A	:		Any	  =      key_mapping[key]
  if "_conv" in key and "kernel" in key:
   __A	:		Tuple	  =      torch.from_numpy(__snake_case      ).permute(3,					2,					0,					1      )
  elif "depthwise_kernel" in key:
   __A	:		Tuple	  =      torch.from_numpy(__snake_case      ).permute(2,					3,					0,					1      )
  elif "kernel" in key:
   __A	:		Optional[int]	  =      torch.from_numpy(np.transpose(__snake_case      )      )
  else:
   __A	:		int	  =      torch.from_numpy(__snake_case      )

  # Replace HF parameters with original TF model parameters
  assert hf_params[hf_key].shape == new_hf_value.shape
  hf_params[hf_key].copy_(__snake_case      )


@torch.no_grad()
def       _lowercase							(					UpperCamelCase__							:       str,					UpperCamelCase__							:       Dict,					UpperCamelCase__							:       List[Any],					UpperCamelCase__							:       Optional[int]      ):
 __A	:		Dict	  =      model_classes[model_name](
     include_top=__snake_case,					weights='imagenet',					input_tensor=__snake_case,					input_shape=__snake_case,					pooling=__snake_case,					classes=1000,					classifier_activation='softmax',					)

 __A	:		Dict	  =      original_model.trainable_variables
 __A	:		int	  =      original_model.non_trainable_variables
 __A	:		Tuple	  =      {param.name: param.numpy() for param in tf_params}
 for param in tf_non_train_params:
  __A	:		List[Any]	  =      param.numpy()
 __A	:		Dict	  =      list(tf_params.keys()      )

 # Load HuggingFace model
 __A	:		Dict	  =      get_efficientnet_config(__snake_case      )
 __A	:		int	  =      EfficientNetForImageClassification(__snake_case      ).eval()
 __A	:		Any	  =      hf_model.state_dict()

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

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

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

 # Original model inference
 __A	:		List[Any]	  =      False
 __A	:		List[str]	  =      CONFIG_MAP[model_name]['image_size']
 __A	:		Any	  =      prepare_img().resize((image_size, image_size),					resample=PIL.Image.NEAREST      )
 __A	:		int	  =      image.img_to_array(__snake_case      )
 __A	:		Optional[Any]	  =      np.expand_dims(__snake_case,					axis=0      )
 __A	:		List[str]	  =      original_model.predict(__snake_case      )

 # Check whether original and HF model outputs match  -> np.allclose
 assert np.allclose(__snake_case,					__snake_case,					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(__snake_case      ):
   os.mkdir(__snake_case      )
  # Save converted model and image processor
  hf_model.save_pretrained(__snake_case      )
  preprocessor.save_pretrained(__snake_case      )

 if push_to_hub:
  # Push model and image processor to hub
  print(f"""Pushing converted {model_name} to the hub..."""      )
  __A	:		Union[str, Any]	  =      f"""efficientnet-{model_name}"""
  preprocessor.push_to_hub(__snake_case      )
  hf_model.push_to_hub(__snake_case      )


if __name__ == "__main__":
 UpperCAmelCase_			:  List[Any]										=   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_			:  Optional[Any]										=   parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







'''simple docstring'''






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


_UpperCAmelCase    :     Any         =       {
    '''configuration_maskformer''': ['''MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''MaskFormerConfig'''],
    '''configuration_maskformer_swin''': ['''MaskFormerSwinConfig'''],
}

try:
   if not is_vision_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     str         =       ['''MaskFormerFeatureExtractor''']
   _UpperCAmelCase    :     str         =       ['''MaskFormerImageProcessor''']


try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   _UpperCAmelCase    :     Tuple         =       [
       '''MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''MaskFormerForInstanceSegmentation''',
       '''MaskFormerModel''',
       '''MaskFormerPreTrainedModel''',
   ]
   _UpperCAmelCase    :     int         =       [
       '''MaskFormerSwinBackbone''',
       '''MaskFormerSwinModel''',
       '''MaskFormerSwinPreTrainedModel''',
   ]

if TYPE_CHECKING:
   from .configuration_maskformer import MASKFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, MaskFormerConfig
   from .configuration_maskformer_swin import MaskFormerSwinConfig

   try:
      if not is_vision_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .feature_extraction_maskformer import MaskFormerFeatureExtractor
      from .image_processing_maskformer import MaskFormerImageProcessor
   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_maskformer import (
          MASKFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          MaskFormerForInstanceSegmentation,
          MaskFormerModel,
          MaskFormerPreTrainedModel,
      )
      from .modeling_maskformer_swin import (
          MaskFormerSwinBackbone,
          MaskFormerSwinModel,
          MaskFormerSwinPreTrainedModel,
      )


else:
   import sys

   _UpperCAmelCase    :     str         =       _LazyModule(__name__, globals()['''__file__'''], _import_structure)



