




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

lowercase__				:      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,
}

lowercase__				:      List[Any]	   = {
    "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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Optional[int]:
     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     =				10_00
     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  SCREAMING_SNAKE_CASE							(       )      ->					List[Any]:
     a     =				"http://images.cocodataset.org/val2017/000000039769.jpg"
     a     =				Image.open(requests.get(__UpperCamelCase		, stream=__UpperCamelCase).raw)
     return im

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					Union[str, Any]:
     a     =				CONFIG_MAP[model_name]["image_size"]
     a     =				EfficientNetImageProcessor(
         size={"height": size, "width": size}		, image_mean=[0.485, 0.456, 0.406]		, image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]		, do_center_crop=__UpperCamelCase		, )
     return preprocessor

def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase)      ->					int:
     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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					Optional[Any]:
     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  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[str]:
     a     =				model_classes[model_name](
         include_top=__UpperCamelCase		, weights="imagenet"		, input_tensor=__UpperCamelCase		, input_shape=__UpperCamelCase		, pooling=__UpperCamelCase		, classes=10_00		, 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__":
   lowercase__				:      Optional[int]	   = 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")

   lowercase__				:      str	   = parser.parse_args()
   convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)







import argparse

import pytorch_lightning as pl
import torch
from torch import nn

from transformers import LongformerForQuestionAnswering, LongformerModel




class       a__	( pl.LightningModule				):







     def __init__(     self  ,		A			)  ->   Optional[Any]:



          '''simple docstring'''



          super().__init__()
          a     =				model
          a     =				2
          a     =				nn.Linear(self.model.config.hidden_size  ,		self.num_labels			)







     def       lowerCAmelCase_    (     self			)  ->   List[Any]:



          '''simple docstring'''



          pass







def  SCREAMING_SNAKE_CASE							(       __UpperCamelCase		, __UpperCamelCase		, __UpperCamelCase)      ->					List[Any]:
     # load longformer model from model identifier
     a     =				LongformerModel.from_pretrained(__UpperCamelCase)
     a     =				LightningModel(__UpperCamelCase)

     a     =				torch.load(__UpperCamelCase		, map_location=torch.device("cpu"))
     lightning_model.load_state_dict(ckpt["state_dict"])

     # init longformer question answering model
     a     =				LongformerForQuestionAnswering.from_pretrained(__UpperCamelCase)

     # transfer weights
     longformer_for_qa.longformer.load_state_dict(lightning_model.model.state_dict())
     longformer_for_qa.qa_outputs.load_state_dict(lightning_model.qa_outputs.state_dict())
     longformer_for_qa.eval()

     # save model
     longformer_for_qa.save_pretrained(__UpperCamelCase)

     print(f'''Conversion successful. Model saved under {pytorch_dump_folder_path}''')


if __name__ == "__main__":
   lowercase__				:      Optional[Any]	   = argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       "--longformer_model",
       default=None,
       type=str,
       required=True,
       help="model identifier of longformer. Should be either `longformer-base-4096` or `longformer-large-4096`.",
   )
   parser.add_argument(
       "--longformer_question_answering_ckpt_path",
       default=None,
       type=str,
       required=True,
       help="Path the official PyTorch Lightning Checkpoint.",
   )
   parser.add_argument(
       "--pytorch_dump_folder_path", default=None, type=str, required=True, help="Path to the output PyTorch model."
   )
   lowercase__				:      Union[str, Any]	   = parser.parse_args()
   convert_longformer_qa_checkpoint_to_pytorch(
       args.longformer_model, args.longformer_question_answering_ckpt_path, args.pytorch_dump_folder_path
   )

