


import numpy as np
import skfuzzy as fuzz

if __name__ == "__main__":
		# Create universe of discourse in Python using linspace ()
		snake_case__      			=    np.linspace(start=0, stop=75, num=75, endpoint=True, retstep=False)

		# Create two fuzzy sets by defining any membership function
		# (trapmf(), gbellmf(), gaussmf(), etc).
		snake_case__      			=    [0, 25, 50]
		snake_case__      			=    [25, 50, 75]
		snake_case__      			=    fuzz.membership.trimf(X, abca)
		snake_case__      			=    fuzz.membership.trimf(X, abca)

		# Compute the different operations using inbuilt functions.
		snake_case__      			=    np.ones(75)
		snake_case__      			=    np.zeros((75,))
		# 1. Union = max(µA(x), µB(x))
		snake_case__      			=    fuzz.fuzzy_or(X, young, X, middle_aged)[1]
		# 2. Intersection = min(µA(x), µB(x))
		snake_case__      			=    fuzz.fuzzy_and(X, young, X, middle_aged)[1]
		# 3. Complement (A) = (1- min(µA(x))
		snake_case__      			=    fuzz.fuzzy_not(young)
		# 4. Difference (A/B) = min(µA(x),(1- µB(x)))
		snake_case__      			=    fuzz.fuzzy_and(X, young, X, fuzz.fuzzy_not(middle_aged)[1])[1]
		# 5. Algebraic Sum = [µA(x) + µB(x) – (µA(x) * µB(x))]
		snake_case__      			=    young + middle_aged - (young * middle_aged)
		# 6. Algebraic Product = (µA(x) * µB(x))
		snake_case__      			=    young * middle_aged
		# 7. Bounded Sum = min[1,(µA(x), µB(x))]
		snake_case__      			=    fuzz.fuzzy_and(X, one, X, young + middle_aged)[1]
		# 8. Bounded difference = min[0,(µA(x), µB(x))]
		snake_case__      			=    fuzz.fuzzy_or(X, zero, X, young - middle_aged)[1]

		# max-min composition
		# max-product composition

		# Plot each set A, set B and each operation result using plot() and subplot().
		from matplotlib import pyplot as plt

		plt.figure()

		plt.subplot(4, 3, 1)
		plt.plot(X, young)
		plt.title('''Young''')
		plt.grid(True)

		plt.subplot(4, 3, 2)
		plt.plot(X, middle_aged)
		plt.title('''Middle aged''')
		plt.grid(True)

		plt.subplot(4, 3, 3)
		plt.plot(X, union)
		plt.title('''union''')
		plt.grid(True)

		plt.subplot(4, 3, 4)
		plt.plot(X, intersection)
		plt.title('''intersection''')
		plt.grid(True)

		plt.subplot(4, 3, 5)
		plt.plot(X, complement_a)
		plt.title('''complement_a''')
		plt.grid(True)

		plt.subplot(4, 3, 6)
		plt.plot(X, difference)
		plt.title('''difference a/b''')
		plt.grid(True)

		plt.subplot(4, 3, 7)
		plt.plot(X, alg_sum)
		plt.title('''alg_sum''')
		plt.grid(True)

		plt.subplot(4, 3, 8)
		plt.plot(X, alg_product)
		plt.title('''alg_product''')
		plt.grid(True)

		plt.subplot(4, 3, 9)
		plt.plot(X, bdd_sum)
		plt.title('''bdd_sum''')
		plt.grid(True)

		plt.subplot(4, 3, 10)
		plt.plot(X, bdd_difference)
		plt.title('''bdd_difference''')
		plt.grid(True)

		plt.subplots_adjust(hspace=0.5)
		plt.show()






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()
SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__					:     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,
}

SCREAMING_SNAKE_CASE__					:     Optional[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    a__    (		snake_case__					:							Optional[Any]     ):
  _UpperCAmelCase		:							Optional[int]							=     EfficientNetConfig()
  _UpperCAmelCase		:							Tuple							=     CONFIG_MAP[model_name]["""hidden_dim"""]
  _UpperCAmelCase		:							Any							=     CONFIG_MAP[model_name]["""width_coef"""]
  _UpperCAmelCase		:							Optional[Any]							=     CONFIG_MAP[model_name]["""depth_coef"""]
  _UpperCAmelCase		:							int							=     CONFIG_MAP[model_name]["""image_size"""]
  _UpperCAmelCase		:							List[str]							=     CONFIG_MAP[model_name]["""dropout_rate"""]
  _UpperCAmelCase		:							Optional[int]							=     CONFIG_MAP[model_name]["""dw_padding"""]

  _UpperCAmelCase		:							int							=     """huggingface/label-files"""
  _UpperCAmelCase		:							Dict							=     """imagenet-1k-id2label.json"""
  _UpperCAmelCase		:							Optional[Any]							=     1000
  _UpperCAmelCase		:							List[str]							=     json.load(open(hf_hub_download(snake_case__						,   snake_case__						,   repo_type="""dataset"""     )						,   """r"""     )     )
  _UpperCAmelCase		:							Union[str, Any]							=     {int(snake_case__     ): v for k, v in idalabel.items()}

  _UpperCAmelCase		:							Any							=     idalabel
  _UpperCAmelCase		:							List[str]							=     {v: k for k, v in idalabel.items()}
  return config




def    a__    (		):
  _UpperCAmelCase		:							Optional[int]							=     """http://images.cocodataset.org/val2017/000000039769.jpg"""
  _UpperCAmelCase		:							str							=     Image.open(requests.get(snake_case__						,   stream=snake_case__     ).raw     )
  return im




def    a__    (		snake_case__					:							Tuple     ):
  _UpperCAmelCase		:							List[str]							=     CONFIG_MAP[model_name]["""image_size"""]
  _UpperCAmelCase		:							str							=     EfficientNetImageProcessor(
      size={"""height""": size, """width""": size}						,   image_mean=[0.485, 0.456, 0.406]						,   image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]						,   do_center_crop=snake_case__						,   )
  return preprocessor




def    a__    (		snake_case__					:							Optional[Any]     ):
  _UpperCAmelCase		:							Optional[int]							=     [v.split("""_"""     )[0].split("""block"""     )[1] for v in original_param_names if v.startswith("""block"""     )]
  _UpperCAmelCase		:							Dict							=     sorted(set(snake_case__     )     )
  _UpperCAmelCase		:							int							=     len(snake_case__     )
  _UpperCAmelCase		:							Optional[Any]							=     {b: str(snake_case__     ) for b, i in zip(snake_case__						,   range(snake_case__     )     )}

  _UpperCAmelCase		:							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:
    _UpperCAmelCase		:							List[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""")     )

  _UpperCAmelCase		:							Union[str, Any]							=     {}
  for item in rename_keys:
    if item[0] in original_param_names:
      _UpperCAmelCase		:							Tuple							=     """efficientnet.""" + item[1]

  _UpperCAmelCase		:							List[Any]							=     """classifier.weight"""
  _UpperCAmelCase		:							List[Any]							=     """classifier.bias"""
  return key_mapping




def    a__    (		snake_case__					:							Optional[int]						,   snake_case__					:							int						,   snake_case__					:							List[str]     ):
  for key, value in tf_params.items():
    if "normalization" in key:
      continue

    _UpperCAmelCase		:							Optional[Any]							=     key_mapping[key]
    if "_conv" in key and "kernel" in key:
      _UpperCAmelCase		:							Optional[Any]							=     torch.from_numpy(snake_case__     ).permute(3						,   2						,   0						,   1     )
    elif "depthwise_kernel" in key:
      _UpperCAmelCase		:							Dict							=     torch.from_numpy(snake_case__     ).permute(2						,   3						,   0						,   1     )
    elif "kernel" in key:
      _UpperCAmelCase		:							Any							=     torch.from_numpy(np.transpose(snake_case__     )     )
    else:
      _UpperCAmelCase		:							Optional[Any]							=     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    a__    (		snake_case__					:							str						,   snake_case__					:							Dict						,   snake_case__					:							int						,   snake_case__					:							str     ):
  _UpperCAmelCase		:							List[str]							=     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"""						,   )

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

  # Load HuggingFace model
  _UpperCAmelCase		:							Any							=     get_efficientnet_config(snake_case__     )
  _UpperCAmelCase		:							List[str]							=     EfficientNetForImageClassification(snake_case__     ).eval()
  _UpperCAmelCase		:							Any							=     hf_model.state_dict()

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

  # Initialize preprocessor and preprocess input image
  _UpperCAmelCase		:							Dict							=     convert_image_processor(snake_case__     )
  _UpperCAmelCase		:							List[Any]							=     preprocessor(images=prepare_img()						,   return_tensors="""pt"""     )

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    _UpperCAmelCase		:							List[str]							=     hf_model(**snake_case__     )
  _UpperCAmelCase		:							Any							=     outputs.logits.detach().numpy()

  # Original model inference
  _UpperCAmelCase		:							Dict							=     False
  _UpperCAmelCase		:							List[Any]							=     CONFIG_MAP[model_name]["""image_size"""]
  _UpperCAmelCase		:							Any							=     prepare_img().resize((image_size, image_size)						,   resample=PIL.Image.NEAREST     )
  _UpperCAmelCase		:							Optional[Any]							=     image.img_to_array(snake_case__     )
  _UpperCAmelCase		:							Any							=     np.expand_dims(snake_case__						,   axis=0     )
  _UpperCAmelCase		:							List[Any]							=     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...'''     )
    _UpperCAmelCase		:							int							=     f'''efficientnet-{model_name}'''
    preprocessor.push_to_hub(snake_case__     )
    hf_model.push_to_hub(snake_case__     )


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

 SCREAMING_SNAKE_CASE__					:     Optional[Any]       =     parser.parse_args()
 convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





