




'''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()
lowercase      :   Tuple      =       logging.get_logger(__name__)

lowercase      :   Any      =       {
    """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""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.0,
        """image_size""": 224,
        """dropout_rate""": 0.2,
        """dw_padding""": [],
    },
    """b1""": {
        """hidden_dim""": 1280,
        """width_coef""": 1.0,
        """depth_coef""": 1.1,
        """image_size""": 240,
        """dropout_rate""": 0.2,
        """dw_padding""": [16],
    },
    """b2""": {
        """hidden_dim""": 1408,
        """width_coef""": 1.1,
        """depth_coef""": 1.2,
        """image_size""": 260,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 8, 16],
    },
    """b3""": {
        """hidden_dim""": 1536,
        """width_coef""": 1.2,
        """depth_coef""": 1.4,
        """image_size""": 300,
        """dropout_rate""": 0.3,
        """dw_padding""": [5, 18],
    },
    """b4""": {
        """hidden_dim""": 1792,
        """width_coef""": 1.4,
        """depth_coef""": 1.8,
        """image_size""": 380,
        """dropout_rate""": 0.4,
        """dw_padding""": [6],
    },
    """b5""": {
        """hidden_dim""": 2048,
        """width_coef""": 1.6,
        """depth_coef""": 2.2,
        """image_size""": 456,
        """dropout_rate""": 0.4,
        """dw_padding""": [13, 27],
    },
    """b6""": {
        """hidden_dim""": 2304,
        """width_coef""": 1.8,
        """depth_coef""": 2.6,
        """image_size""": 528,
        """dropout_rate""": 0.5,
        """dw_padding""": [31],
    },
    """b7""": {
        """hidden_dim""": 2560,
        """width_coef""": 2.0,
        """depth_coef""": 3.1,
        """image_size""": 600,
        """dropout_rate""": 0.5,
        """dw_padding""": [18],
    },
}



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      Dict        =			EfficientNetConfig()
     snake_case			:      Optional[Any]        =			CONFIG_MAP[model_name]["""hidden_dim"""]
     snake_case			:      Optional[int]        =			CONFIG_MAP[model_name]["""width_coef"""]
     snake_case			:      List[Any]        =			CONFIG_MAP[model_name]["""depth_coef"""]
     snake_case			:      Optional[int]        =			CONFIG_MAP[model_name]["""image_size"""]
     snake_case			:      List[str]        =			CONFIG_MAP[model_name]["""dropout_rate"""]
     snake_case			:      List[str]        =			CONFIG_MAP[model_name]["""dw_padding"""]

     snake_case			:      str        =			"""huggingface/label-files"""
     snake_case			:      Any        =			"""imagenet-1k-id2label.json"""
     snake_case			:      Dict        =			1_000
     snake_case			:      str        =			json.load(open(hf_hub_download(lowerCAmelCase__     ,   lowerCAmelCase__     ,   repo_type="""dataset"""				)     ,   """r"""				)				)
     snake_case			:      Any        =			{int(lowerCAmelCase__				): v for k, v in idalabel.items()}

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



def        lowerCamelCase__				(      ):
     snake_case			:      Tuple        =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
     snake_case			:      int        =			Image.open(requests.get(lowerCAmelCase__     ,   stream=lowerCAmelCase__				).raw				)
     return im



def        lowerCamelCase__				(      __lowercase				):
     snake_case			:      Any        =			CONFIG_MAP[model_name]["""image_size"""]
     snake_case			:      int        =			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=lowerCAmelCase__     ,   )
     return preprocessor



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

     snake_case			:      Optional[int]        =			[]
     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:
          snake_case			:      Union[str, 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""")				)

     snake_case			:      int        =			{}
     for item in rename_keys:
          if item[0] in original_param_names:
               snake_case			:      Optional[Any]        =			"""efficientnet.""" + item[1]

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



def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase				):
     for key, value in tf_params.items():
          if "normalization" in key:
               continue

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

          # Replace HF parameters with original TF model parameters
          assert hf_params[hf_key].shape == new_hf_value.shape
          hf_params[hf_key].copy_(lowerCAmelCase__				)



@torch.no_grad()
def        lowerCamelCase__				(      __lowercase     ,   __lowercase     ,   __lowercase     ,   __lowercase				):
     snake_case			:      Optional[Any]        =			model_classes[model_name](
         include_top=lowerCAmelCase__     ,   weights="""imagenet"""     ,   input_tensor=lowerCAmelCase__     ,   input_shape=lowerCAmelCase__     ,   pooling=lowerCAmelCase__     ,   classes=1_000     ,   classifier_activation="""softmax"""     ,   )

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

     # Load HuggingFace model
     snake_case			:      Dict        =			get_efficientnet_config(lowerCAmelCase__				)
     snake_case			:      str        =			EfficientNetForImageClassification(lowerCAmelCase__				).eval()
     snake_case			:      List[str]        =			hf_model.state_dict()

     # Create src-to-dst parameter name mapping dictionary
     print("""Converting parameters..."""				)
     snake_case			:      str        =			rename_keys(lowerCAmelCase__				)
     replace_params(lowerCAmelCase__     ,   lowerCAmelCase__     ,   lowerCAmelCase__				)

     # Initialize preprocessor and preprocess input image
     snake_case			:      List[str]        =			convert_image_processor(lowerCAmelCase__				)
     snake_case			:      List[str]        =			preprocessor(images=prepare_img()     ,   return_tensors="""pt"""				)

     # HF model inference
     hf_model.eval()
     with torch.no_grad():
          snake_case			:      int        =			hf_model(**lowerCAmelCase__				)
     snake_case			:      Union[str, Any]        =			outputs.logits.detach().numpy()

     # Original model inference
     snake_case			:      Dict        =			False
     snake_case			:      int        =			CONFIG_MAP[model_name]["""image_size"""]
     snake_case			:      Optional[int]        =			prepare_img().resize((image_size, image_size)     ,   resample=PIL.Image.NEAREST				)
     snake_case			:      Optional[Any]        =			image.img_to_array(lowerCAmelCase__				)
     snake_case			:      Optional[Any]        =			np.expand_dims(lowerCAmelCase__     ,   axis=0				)
     snake_case			:      Optional[int]        =			original_model.predict(lowerCAmelCase__				)

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

     if push_to_hub:
          # Push model and image processor to hub
          print(F'''Pushing converted {model_name} to the hub...'''				)
          snake_case			:      List[str]        =			F'''efficientnet-{model_name}'''
          preprocessor.push_to_hub(lowerCAmelCase__				)
          hf_model.push_to_hub(lowerCAmelCase__				)


if __name__ == "__main__":
      lowercase      :   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""")

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







import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


SCREAMING_SNAKE_CASE							    =							{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}


class     __UpperCAmelCase							(       nn.Module							):


     """simple docstring"""


     def __init__(	self  ,       __A    ):
        super().__init__()
        __a					=  torchvision.models.resnetaaa(pretrained=__A    )
        __a					=  list(model.children()    )[:-2]
        __a					=  nn.Sequential(*__A    )
        __a					=  nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]    )


     def 	snake_case_  (	self  ,       __A    ):
        # Bx3x224x224 -> Bx2048x7x7 -> Bx2048xN -> BxNx2048
        __a					=  self.pool(self.model(__A    )    )
        __a					=  torch.flatten(__A  ,       start_dim=2    )
        __a					=  out.transpose(1  ,       2    ).contiguous()
        return out  # BxNx2048







class     __UpperCAmelCase							(       __A							):


     """simple docstring"""


     def __init__(	self  ,       __A  ,       __A  ,       __A  ,       __A  ,       __A    ):
        __a					=  [json.loads(__A    ) for l in open(__A    )]
        __a					=  os.path.dirname(__A    )
        __a					=  tokenizer
        __a					=  labels
        __a					=  len(__A    )
        __a					=  max_seq_length

        __a					=  transforms


     def __len__(	self    ):
        return len(self.data    )


     def __getitem__(	self  ,       __A    ):
        __a					=  torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""]  ,       add_special_tokens=__A    )    )
        __a	,    __a	,    __a					=  sentence[0], sentence[1:-1], sentence[-1]
        __a					=  sentence[: self.max_seq_length]

        __a					=  torch.zeros(self.n_classes    )
        __a					=  1

        __a					=  Image.open(os.path.join(self.data_dir  ,       self.data[index]["""img"""]    )    ).convert("""RGB"""    )
        __a					=  self.transforms(__A    )

        return {
            "image_start_token": start_token,
            "image_end_token": end_token,
            "sentence": sentence,
            "image": image,
            "label": label,
        }


     def 	snake_case_  (	self    ):
        __a					=  Counter()
        for row in self.data:
           label_freqs.update(row["""label"""]    )
        return label_freqs







def  a   (lowerCAmelCase__   ):
   __a					=  [len(row["""sentence"""]   ) for row in batch]
   __a	,    __a					=  len(lowerCAmelCase__   ), max(lowerCAmelCase__   )

   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )
   __a					=  torch.zeros(lowerCAmelCase__		,				lowerCAmelCase__		,				dtype=torch.long   )

   for i_batch, (input_row, length) in enumerate(zip(lowerCAmelCase__		,				lowerCAmelCase__   )   ):
      __a					=  input_row["""sentence"""]
      __a					=  1

   __a					=  torch.stack([row["""image"""] for row in batch]   )
   __a					=  torch.stack([row["""label"""] for row in batch]   )
   __a					=  torch.stack([row["""image_start_token"""] for row in batch]   )
   __a					=  torch.stack([row["""image_end_token"""] for row in batch]   )

   return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor




def  a   ():
   return [
       "Crime",
       "Drama",
       "Thriller",
       "Action",
       "Comedy",
       "Romance",
       "Documentary",
       "Short",
       "Mystery",
       "History",
       "Family",
       "Adventure",
       "Fantasy",
       "Sci-Fi",
       "Western",
       "Horror",
       "Sport",
       "War",
       "Music",
       "Musical",
       "Animation",
       "Biography",
       "Film-Noir",
   ]




def  a   ():
   return transforms.Compose(
       [
           transforms.Resize(256   ),
           transforms.CenterCrop(224   ),
           transforms.ToTensor(),
           transforms.Normalize(
               mean=[0.4_6_7_7_7_0_4_4, 0.4_4_5_3_1_4_2_9, 0.4_0_6_6_1_0_1_7]		,				std=[0.1_2_2_2_1_9_9_4, 0.1_2_1_4_5_8_3_5, 0.1_4_3_8_0_4_6_9]		,				),
       ]   )



