'''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:			str	       =       logging.get_logger(__name__)

__a:			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,
}

__a:			Optional[int]	       =       {
    """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       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   List[str]						= EfficientNetConfig()
 lowercase__    :   Union[str, Any]						= CONFIG_MAP[model_name]['''hidden_dim''']
 lowercase__    :   Any						= CONFIG_MAP[model_name]['''width_coef''']
 lowercase__    :   Union[str, Any]						= CONFIG_MAP[model_name]['''depth_coef''']
 lowercase__    :   Any						= CONFIG_MAP[model_name]['''image_size''']
 lowercase__    :   List[Any]						= CONFIG_MAP[model_name]['''dropout_rate''']
 lowercase__    :   Union[str, Any]						= CONFIG_MAP[model_name]['''dw_padding''']

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

 lowercase__    :   Any						= idalabel
 lowercase__    :   Any						= {v: k for k, v in idalabel.items()}
 return config
def       __UpperCamelCase	(       ):
 lowercase__    :   List[Any]						= '''http://images.cocodataset.org/val2017/000000039769.jpg'''
 lowercase__    :   str						= Image.open(requests.get(UpperCAmelCase		,					stream=UpperCAmelCase					).raw					)
 return im
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Dict						= CONFIG_MAP[model_name]['''image_size''']
 lowercase__    :   Optional[int]						= 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=UpperCAmelCase		,					)
 return preprocessor
def       __UpperCamelCase	(       UpperCAmelCase					):
 lowercase__    :   Any						= [v.split('''_'''					)[0].split('''block'''					)[1] for v in original_param_names if v.startswith('''block'''					)]
 lowercase__    :   str						= sorted(set(UpperCAmelCase					)					)
 lowercase__    :   Optional[int]						= len(UpperCAmelCase					)
 lowercase__    :   Dict						= {b: str(UpperCAmelCase					) for b, i in zip(UpperCAmelCase		,					range(UpperCAmelCase					)					)}

 lowercase__    :   Union[str, 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:
  lowercase__    :   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''')					)

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

 lowercase__    :   Any						= '''classifier.weight'''
 lowercase__    :   Optional[int]						= '''classifier.bias'''
 return key_mapping
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 for key, value in tf_params.items():
  if "normalization" in key:
   continue

  lowercase__    :   Union[str, Any]						= key_mapping[key]
  if "_conv" in key and "kernel" in key:
   lowercase__    :   Dict						= torch.from_numpy(UpperCAmelCase					).permute(3		,					2		,					0		,					1					)
  elif "depthwise_kernel" in key:
   lowercase__    :   Union[str, Any]						= torch.from_numpy(UpperCAmelCase					).permute(2		,					3		,					0		,					1					)
  elif "kernel" in key:
   lowercase__    :   Any						= torch.from_numpy(np.transpose(UpperCAmelCase					)					)
  else:
   lowercase__    :   str						= torch.from_numpy(UpperCAmelCase					)

  # Replace HF parameters with original TF model parameters
  assert hf_params[hf_key].shape == new_hf_value.shape
  hf_params[hf_key].copy_(UpperCAmelCase					)
@torch.no_grad()
def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   List[str]						= model_classes[model_name](
     include_top=UpperCAmelCase		,					weights='''imagenet'''		,					input_tensor=UpperCAmelCase		,					input_shape=UpperCAmelCase		,					pooling=UpperCAmelCase		,					classes=1000		,					classifier_activation='''softmax'''		,					)

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

 # Load HuggingFace model
 lowercase__    :   Optional[int]						= get_efficientnet_config(UpperCAmelCase					)
 lowercase__    :   List[Any]						= EfficientNetForImageClassification(UpperCAmelCase					).eval()
 lowercase__    :   Tuple						= hf_model.state_dict()

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

 # Initialize preprocessor and preprocess input image
 lowercase__    :   List[str]						= convert_image_processor(UpperCAmelCase					)
 lowercase__    :   int						= preprocessor(images=prepare_img()		,					return_tensors='''pt'''					)

 # HF model inference
 hf_model.eval()
 with torch.no_grad():
  lowercase__    :   Tuple						= hf_model(**UpperCAmelCase					)
 lowercase__    :   Optional[int]						= outputs.logits.detach().numpy()

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

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

 if push_to_hub:
  # Push model and image processor to hub
  print(F"""Pushing converted {model_name} to the hub..."""					)
  lowercase__    :   Any						= F"""efficientnet-{model_name}"""
  preprocessor.push_to_hub(UpperCAmelCase					)
  hf_model.push_to_hub(UpperCAmelCase					)


if __name__ == "__main__":
  __a:			Optional[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""")

  __a:			int	       =       parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)


'''simple docstring'''

def       __UpperCamelCase	(       UpperCAmelCase		,					UpperCAmelCase					):
 lowercase__    :   Dict						= len(UpperCAmelCase					)
 print('''The following activities are selected:'''					)

 # The first activity is always selected
 lowercase__    :   str						= 0
 print(UpperCAmelCase		,					end=''','''					)

 # Consider rest of the activities
 for j in range(UpperCAmelCase					):
  # If this activity has start time greater than
  # or equal to the finish time of previously
  # selected activity, then select it
  if start[j] >= finish[i]:
   print(UpperCAmelCase		,					end=''','''					)
   lowercase__    :   str						= j


if __name__ == "__main__":
  import doctest

  doctest.testmod()

  __a:			str	       =       [1, 3, 0, 5, 8, 5]
  __a:			Optional[Any]	       =       [2, 4, 6, 7, 9, 9]
  print_max_activities(start, finish)

