






from typing import TYPE_CHECKING

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


UpperCamelCase							       =    {
    """configuration_biogpt""": ["""BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """BioGptConfig"""],
    """tokenization_biogpt""": ["""BioGptTokenizer"""],
}

try:
     if not is_torch_available():
          raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
     pass
else:
     UpperCamelCase							       =    [
         """BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
         """BioGptForCausalLM""",
         """BioGptForTokenClassification""",
         """BioGptForSequenceClassification""",
         """BioGptModel""",
         """BioGptPreTrainedModel""",
     ]


if TYPE_CHECKING:
     from .configuration_biogpt import BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP, BioGptConfig
     from .tokenization_biogpt import BioGptTokenizer

     try:
          if not is_torch_available():
               raise OptionalDependencyNotAvailable()
     except OptionalDependencyNotAvailable:
          pass
     else:
          from .modeling_biogpt import (
              BIOGPT_PRETRAINED_MODEL_ARCHIVE_LIST,
              BioGptForCausalLM,
              BioGptForSequenceClassification,
              BioGptForTokenClassification,
              BioGptModel,
              BioGptPreTrainedModel,
          )


else:
     import sys

     UpperCamelCase							       =    _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)










"""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							       =    logging.get_logger(__name__)

UpperCamelCase							       =    {
    """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							       =    {
    """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        lowerCAmelCase (      UpperCamelCase_:							Union[str, Any]					)       ->							List[Any]:







  '''simple docstring'''







  _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							=	1000
  _a							=	json.load(open(hf_hub_download(UpperCamelCase_     , UpperCamelCase_     , repo_type="dataset"					)     , "r"					)					)
  _a							=	{int(UpperCamelCase_					): v for k, v in idalabel.items()}

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






def        lowerCAmelCase (      )       ->							List[str]:







  '''simple docstring'''







  _a							=	"http://images.cocodataset.org/val2017/000000039769.jpg"
  _a							=	Image.open(requests.get(UpperCamelCase_     , stream=UpperCamelCase_					).raw					)
  return im






def        lowerCAmelCase (      UpperCamelCase_:							int					)       ->							List[str]:







  '''simple docstring'''







  _a							=	CONFIG_MAP[model_name]["image_size"]
  _a							=	EfficientNetImageProcessor(
      size={"height": size, "width": size}     , image_mean=[0.485, 0.456, 0.406]     , image_std=[0.4785_3944, 0.473_2864, 0.4743_4163]     , do_center_crop=UpperCamelCase_     , )
  return preprocessor






def        lowerCAmelCase (      UpperCamelCase_:							Dict					)       ->							Dict:







  '''simple docstring'''







  _a							=	[v.split("_"					)[0].split("block"					)[1] for v in original_param_names if v.startswith("block"					)]
  _a							=	sorted(set(UpperCamelCase_					)					)
  _a							=	len(UpperCamelCase_					)
  _a							=	{b: str(UpperCamelCase_					) for b, i in zip(UpperCamelCase_     , range(UpperCamelCase_					)					)}

  _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        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							Any					)       ->							int:







  '''simple docstring'''







  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(UpperCamelCase_					).permute(3     , 2     , 0     , 1					)
    elif "depthwise_kernel" in key:
      _a							=	torch.from_numpy(UpperCamelCase_					).permute(2     , 3     , 0     , 1					)
    elif "kernel" in key:
      _a							=	torch.from_numpy(np.transpose(UpperCamelCase_					)					)
    else:
      _a							=	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        lowerCAmelCase (      UpperCamelCase_:							int     , UpperCamelCase_:							Tuple     , UpperCamelCase_:							str     , UpperCamelCase_:							str					)       ->							List[Any]:







  '''simple docstring'''







  _a							=	model_classes[model_name](
      include_top=UpperCamelCase_     , weights="imagenet"     , input_tensor=UpperCamelCase_     , input_shape=UpperCamelCase_     , pooling=UpperCamelCase_     , classes=1000     , 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(UpperCamelCase_					)
  _a							=	EfficientNetForImageClassification(UpperCamelCase_					).eval()
  _a							=	hf_model.state_dict()

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

  # Initialize preprocessor and preprocess input image
  _a							=	convert_image_processor(UpperCamelCase_					)
  _a							=	preprocessor(images=prepare_img()     , return_tensors="pt"					)

  # HF model inference
  hf_model.eval()
  with torch.no_grad():
    _a							=	hf_model(**UpperCamelCase_					)
  _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(UpperCamelCase_					)
  _a							=	np.expand_dims(UpperCamelCase_     , axis=0					)
  _a							=	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...'''					)
    _a							=	f'''efficientnet-{model_name}'''
    preprocessor.push_to_hub(UpperCamelCase_					)
    hf_model.push_to_hub(UpperCamelCase_					)


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


