



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

A:     str										=	{
    "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:     Tuple										=	{
    "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    _snake_case		(		UpperCamelCase		:	Optional[int]		):
 UpperCAmelCase			:      int			=			EfficientNetConfig()
 UpperCAmelCase			:      List[Any]			=			CONFIG_MAP[model_name]["""hidden_dim"""]
 UpperCAmelCase			:      Tuple			=			CONFIG_MAP[model_name]["""width_coef"""]
 UpperCAmelCase			:      Optional[int]			=			CONFIG_MAP[model_name]["""depth_coef"""]
 UpperCAmelCase			:      Any			=			CONFIG_MAP[model_name]["""image_size"""]
 UpperCAmelCase			:      Optional[Any]			=			CONFIG_MAP[model_name]["""dropout_rate"""]
 UpperCAmelCase			:      str			=			CONFIG_MAP[model_name]["""dw_padding"""]

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

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

def    _snake_case		(		):
 UpperCAmelCase			:      str			=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
 UpperCAmelCase			:      Union[str, Any]			=			Image.open(requests.get(UpperCamelCase    ,	stream=UpperCamelCase		).raw		)
 return im

def    _snake_case		(		UpperCamelCase		:	Optional[int]		):
 UpperCAmelCase			:      Dict			=			CONFIG_MAP[model_name]["""image_size"""]
 UpperCAmelCase			:      Dict			=			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=UpperCamelCase    ,	)
 return preprocessor

def    _snake_case		(		UpperCamelCase		:	Dict		):
 UpperCAmelCase			:      Dict			=			[v.split("""_"""		)[0].split("""block"""		)[1] for v in original_param_names if v.startswith("""block"""		)]
 UpperCAmelCase			:      int			=			sorted(set(UpperCamelCase		)		)
 UpperCAmelCase			:      Union[str, Any]			=			len(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			{b: str(UpperCamelCase		) for b, i in zip(UpperCamelCase    ,	range(UpperCamelCase		)		)}

 UpperCAmelCase			:      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:
  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			:      List[str]			=			{}
 for item in rename_keys:
  if item[0] in original_param_names:
   UpperCAmelCase			:      str			=			"""efficientnet.""" + item[1]

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

def    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	List[str]    ,	UpperCamelCase		:	Dict		):
 for key, value in tf_params.items():
  if "normalization" in key:
   continue

  UpperCAmelCase			:      Optional[int]			=			key_mapping[key]
  if "_conv" in key and "kernel" in key:
   UpperCAmelCase			:      Dict			=			torch.from_numpy(UpperCamelCase		).permute(3    ,	2    ,	0    ,	1		)
  elif "depthwise_kernel" in key:
   UpperCAmelCase			:      Optional[Any]			=			torch.from_numpy(UpperCamelCase		).permute(2    ,	3    ,	0    ,	1		)
  elif "kernel" in key:
   UpperCAmelCase			:      int			=			torch.from_numpy(np.transpose(UpperCamelCase		)		)
  else:
   UpperCAmelCase			:      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    _snake_case		(		UpperCamelCase		:	Tuple    ,	UpperCamelCase		:	str    ,	UpperCamelCase		:	Any    ,	UpperCamelCase		:	Union[str, Any]		):
 UpperCAmelCase			:      Optional[int]			=			model_classes[model_name](
     include_top=UpperCamelCase    ,	weights="""imagenet"""    ,	input_tensor=UpperCamelCase    ,	input_shape=UpperCamelCase    ,	pooling=UpperCamelCase    ,	classes=1000    ,	classifier_activation="""softmax"""    ,	)

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

 # Load HuggingFace model
 UpperCAmelCase			:      int			=			get_efficientnet_config(UpperCamelCase		)
 UpperCAmelCase			:      int			=			EfficientNetForImageClassification(UpperCamelCase		).eval()
 UpperCAmelCase			:      int			=			hf_model.state_dict()

 # Create src-to-dst parameter name mapping dictionary
 print("""Converting parameters..."""		)
 UpperCAmelCase			:      int			=			rename_keys(UpperCamelCase		)
 replace_params(UpperCamelCase    ,	UpperCamelCase    ,	UpperCamelCase		)

 # Initialize preprocessor and preprocess input image
 UpperCAmelCase			:      Tuple			=			convert_image_processor(UpperCamelCase		)
 UpperCAmelCase			:      Optional[Any]			=			preprocessor(images=prepare_img()    ,	return_tensors="""pt"""		)

 # HF model inference
 hf_model.eval()
 with torch.no_grad():
  UpperCAmelCase			:      Dict			=			hf_model(**UpperCamelCase		)
 UpperCAmelCase			:      Tuple			=			outputs.logits.detach().numpy()

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


if __name__ == "__main__":
      A:     str										=	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:     Any										=	parser.parse_args()
      convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)





"""simple docstring"""
import os

import pytest
import yaml

from datasets.features.features import Features, Value
from datasets.info import DatasetInfo, DatasetInfosDict

@pytest.mark.parametrize(
    """files"""    ,	[
        ["""full:README.md""", """dataset_infos.json"""],
        ["""empty:README.md""", """dataset_infos.json"""],
        ["""dataset_infos.json"""],
        ["""full:README.md"""],
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	Optional[Any]		):
 UpperCAmelCase			:      List[Any]			=			tmp_path_factory.mktemp("""dset_infos_dir"""		)
 if "full:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write("""---\ndataset_info:\n  dataset_size: 42\n---"""		)
 if "empty:README.md" in files:
  with open(dataset_infos_dir / """README.md"""    ,	"""w"""		) as f:
   f.write(""""""		)
    # we want to support dataset_infos.json for backward compatibility
 if "dataset_infos.json" in files:
  with open(dataset_infos_dir / """dataset_infos.json"""    ,	"""w"""		) as f:
   f.write("""{\"default\": {\"dataset_size\": 42}}"""		)
 UpperCAmelCase			:      str			=			DatasetInfosDict.from_directory(UpperCamelCase		)
 assert dataset_infos
 assert dataset_infos["default"].dataset_size == 42

@pytest.mark.parametrize(
    """dataset_info"""    ,	[
        DatasetInfo(),
        DatasetInfo(
            description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	str    ,	UpperCamelCase		:	DatasetInfo		):
 UpperCAmelCase			:      List[Any]			=			str(UpperCamelCase		)
 dataset_info.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			DatasetInfo.from_directory(UpperCamelCase		)
 assert dataset_info == reloaded
 assert os.path.exists(os.path.join(UpperCamelCase    ,	"""dataset_info.json"""		)		)

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo(
     description="""foo"""    ,	citation="""bar"""    ,	homepage="""https://foo.bar"""    ,	license="""CC0"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	post_processed={}    ,	supervised_keys=()    ,	task_templates=[]    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train""", """num_examples""": 42}]    ,	download_checksums={}    ,	download_size=1337    ,	post_processing_size=442    ,	dataset_size=1234    ,	size_in_bytes=1337 + 442 + 1234    ,	)
 UpperCAmelCase			:      str			=			dataset_info._to_yaml_dict()
 assert sorted(UpperCamelCase		) == sorted(DatasetInfo._INCLUDED_INFO_IN_YAML		)
 for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
  assert key in dataset_info_yaml_dict
  assert isinstance(dataset_info_yaml_dict[key]    ,	(list, dict, int, str)		)
 UpperCAmelCase			:      Union[str, Any]			=			yaml.safe_dump(UpperCamelCase		)
 UpperCAmelCase			:      List[str]			=			yaml.safe_load(UpperCamelCase		)
 assert dataset_info_yaml_dict == reloaded

def    _snake_case		(		):
 UpperCAmelCase			:      List[str]			=			DatasetInfo()
 UpperCAmelCase			:      Union[str, Any]			=			dataset_info._to_yaml_dict()
 assert dataset_info_yaml_dict == {}

@pytest.mark.parametrize(
    """dataset_infos_dict"""    ,	[
        DatasetInfosDict(),
        DatasetInfosDict({"""default""": DatasetInfo()}		),
        DatasetInfosDict({"""my_config_name""": DatasetInfo()}		),
        DatasetInfosDict(
            {
                """default""": DatasetInfo(
                    description="""foo"""    ,	features=Features({"""a""": Value("""int32"""		)}		)    ,	builder_name="""builder"""    ,	config_name="""config"""    ,	version="""1.0.0"""    ,	splits=[{"""name""": """train"""}]    ,	download_size=42    ,	)
            }		),
        DatasetInfosDict(
            {
                """v1""": DatasetInfo(dataset_size=42		),
                """v2""": DatasetInfo(dataset_size=1337		),
            }		),
    ]    ,	)
def    _snake_case		(		UpperCamelCase		:	Optional[int]    ,	UpperCamelCase		:	DatasetInfosDict		):
 UpperCAmelCase			:      List[str]			=			str(UpperCamelCase		)
 dataset_infos_dict.write_to_directory(UpperCamelCase		)
 UpperCAmelCase			:      List[Any]			=			DatasetInfosDict.from_directory(UpperCamelCase		)

 # the config_name of the dataset_infos_dict take over the attribute
 for config_name, dataset_info in dataset_infos_dict.items():
  UpperCAmelCase			:      Union[str, Any]			=			config_name
  # the yaml representation doesn't include fields like description or citation
  # so we just test that we can recover what we can from the yaml
  UpperCAmelCase			:      Optional[Any]			=			DatasetInfo._from_yaml_dict(dataset_info._to_yaml_dict()		)
 assert dataset_infos_dict == reloaded

 if dataset_infos_dict:
  assert os.path.exists(os.path.join(UpperCamelCase    ,	"""README.md"""		)		)
