'''simple docstring'''

import argparse
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import (
    RobertaTokenizer,
    TrOCRConfig,
    TrOCRForCausalLM,
    TrOCRProcessor,
    VisionEncoderDecoderModel,
    ViTConfig,
    ViTImageProcessor,
    ViTModel,
)
from transformers.utils import logging


logging.set_verbosity_info()
UpperCamelCase__     :	str				=			logging.get_logger(__name__)







def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Dict    ):
			'''simple docstring'''
			UpperCAmelCase__							:				str   				=  []
			for i in range(encoder_config.num_hidden_layers    ):
						# encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
						rename_keys.append(
						    (F'''encoder.deit.blocks.{i}.norm1.weight''', F'''encoder.encoder.layer.{i}.layernorm_before.weight''')    )
						rename_keys.append((F'''encoder.deit.blocks.{i}.norm1.bias''', F'''encoder.encoder.layer.{i}.layernorm_before.bias''')    )
						rename_keys.append(
						    (F'''encoder.deit.blocks.{i}.attn.proj.weight''', F'''encoder.encoder.layer.{i}.attention.output.dense.weight''')    )
						rename_keys.append(
						    (F'''encoder.deit.blocks.{i}.attn.proj.bias''', F'''encoder.encoder.layer.{i}.attention.output.dense.bias''')    )
						rename_keys.append(
						    (F'''encoder.deit.blocks.{i}.norm2.weight''', F'''encoder.encoder.layer.{i}.layernorm_after.weight''')    )
						rename_keys.append((F'''encoder.deit.blocks.{i}.norm2.bias''', F'''encoder.encoder.layer.{i}.layernorm_after.bias''')    )
						rename_keys.append(
						    (F'''encoder.deit.blocks.{i}.mlp.fc1.weight''', F'''encoder.encoder.layer.{i}.intermediate.dense.weight''')    )
						rename_keys.append(
						    (F'''encoder.deit.blocks.{i}.mlp.fc1.bias''', F'''encoder.encoder.layer.{i}.intermediate.dense.bias''')    )
						rename_keys.append(
						    (F'''encoder.deit.blocks.{i}.mlp.fc2.weight''', F'''encoder.encoder.layer.{i}.output.dense.weight''')    )
						rename_keys.append((F'''encoder.deit.blocks.{i}.mlp.fc2.bias''', F'''encoder.encoder.layer.{i}.output.dense.bias''')    )

			# cls token, position embeddings and patch embeddings of encoder
			rename_keys.extend(
			    [
			        ('''encoder.deit.cls_token''', '''encoder.embeddings.cls_token'''),
			        ('''encoder.deit.pos_embed''', '''encoder.embeddings.position_embeddings'''),
			        ('''encoder.deit.patch_embed.proj.weight''', '''encoder.embeddings.patch_embeddings.projection.weight'''),
			        ('''encoder.deit.patch_embed.proj.bias''', '''encoder.embeddings.patch_embeddings.projection.bias'''),
			        ('''encoder.deit.norm.weight''', '''encoder.layernorm.weight'''),
			        ('''encoder.deit.norm.bias''', '''encoder.layernorm.bias'''),
			    ]    )

			return rename_keys







def __UpperCamelCase(   _A					:				int  ,			_A					:				Optional[int]    ):
			'''simple docstring'''
			for i in range(encoder_config.num_hidden_layers    ):
						# queries, keys and values (only weights, no biases)
						UpperCAmelCase__							:				str   				=  state_dict.pop(F'''encoder.deit.blocks.{i}.attn.qkv.weight'''    )

						UpperCAmelCase__							:				Optional[int]   				=  in_proj_weight[
						    : encoder_config.hidden_size, :
						]
						UpperCAmelCase__							:				Dict   				=  in_proj_weight[
						    encoder_config.hidden_size : encoder_config.hidden_size * 2, :
						]
						UpperCAmelCase__							:				Optional[Any]   				=  in_proj_weight[
						    -encoder_config.hidden_size :, :
						]







def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				List[str]  ,			_A					:				List[Any]    ):
			'''simple docstring'''
			UpperCAmelCase__							:				str   				=  dct.pop(_A    )
			UpperCAmelCase__							:				Union[str, Any]   				=  val







def __UpperCamelCase(   _A					:				int    ):
			'''simple docstring'''
			if "handwritten" in checkpoint_url:
						UpperCAmelCase__							:				Optional[int]   				=  '''https://fki.tic.heia-fr.ch/static/img/a01-122-02-00.jpg'''  # industry
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-12.jpg" # have
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02-10.jpg" # let
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122-02.jpg"  #
						# url = "https://fki.tic.heia-fr.ch/static/img/a01-122.jpg"
			elif "printed" in checkpoint_url or "stage1" in checkpoint_url:
						UpperCAmelCase__							:				int   				=  '''https://www.researchgate.net/profile/Dinh-Sang/publication/338099565/figure/fig8/AS:840413229350922@1577381536857/An-receipt-example-in-the-SROIE-2019-dataset_Q640.jpg'''
			UpperCAmelCase__							:				str   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    ).convert('''RGB'''    )
			return im







@torch.no_grad()
def __UpperCamelCase(   _A					:				Dict  ,			_A					:				Dict    ):
			'''simple docstring'''
			UpperCAmelCase__							:				List[str]   				=  ViTConfig(image_size=3_84  ,			qkv_bias=_A    )
			UpperCAmelCase__							:				List[Any]   				=  TrOCRConfig()

			# size of the architecture
			if "base" in checkpoint_url:
						UpperCAmelCase__							:				Dict   				=  7_68
			elif "large" in checkpoint_url:
						# use ViT-large encoder
						UpperCAmelCase__							:				Optional[int]   				=  10_24
						UpperCAmelCase__							:				Optional[int]   				=  40_96
						UpperCAmelCase__							:				Optional[int]   				=  24
						UpperCAmelCase__							:				List[str]   				=  16
						UpperCAmelCase__							:				List[Any]   				=  10_24
			else:
						raise ValueError('''Should either find \'base\' or \'large\' in checkpoint URL'''    )

			# the large-printed + stage1 checkpoints uses sinusoidal position embeddings, no layernorm afterwards
			if "large-printed" in checkpoint_url or "stage1" in checkpoint_url:
						UpperCAmelCase__							:				Optional[int]   				=  False
						UpperCAmelCase__							:				Tuple   				=  '''relu'''
						UpperCAmelCase__							:				List[Any]   				=  10_24
						UpperCAmelCase__							:				str   				=  True
						UpperCAmelCase__							:				int   				=  False
						UpperCAmelCase__							:				Optional[int]   				=  False

			# load HuggingFace model
			UpperCAmelCase__							:				List[Any]   				=  ViTModel(_A  ,			add_pooling_layer=_A    )
			UpperCAmelCase__							:				int   				=  TrOCRForCausalLM(_A    )
			UpperCAmelCase__							:				Union[str, Any]   				=  VisionEncoderDecoderModel(encoder=_A  ,			decoder=_A    )
			model.eval()

			# load state_dict of original model, rename some keys
			UpperCAmelCase__							:				Dict   				=  torch.hub.load_state_dict_from_url(_A  ,			map_location='''cpu'''  ,			check_hash=_A    )['''model''']

			UpperCAmelCase__							:				Optional[int]   				=  create_rename_keys(_A  ,			_A    )
			for src, dest in rename_keys:
						rename_key(_A  ,			_A  ,			_A    )
			read_in_q_k_v(_A  ,			_A    )

			# remove parameters we don't need
			del state_dict["encoder.deit.head.weight"]
			del state_dict["encoder.deit.head.bias"]
			del state_dict["decoder.version"]

			# add prefix to decoder keys
			for key, val in state_dict.copy().items():
						UpperCAmelCase__							:				Dict   				=  state_dict.pop(_A    )
						if key.startswith('''decoder'''    ) and "output_projection" not in key:
									UpperCAmelCase__							:				int   				=  val
						else:
									UpperCAmelCase__							:				List[Any]   				=  val

    # load state dict
			model.load_state_dict(_A    )

			# Check outputs on an image
			UpperCAmelCase__							:				Any   				=  ViTImageProcessor(size=encoder_config.image_size    )
			UpperCAmelCase__							:				Optional[Any]   				=  RobertaTokenizer.from_pretrained('''roberta-large'''    )
			UpperCAmelCase__							:				Optional[int]   				=  TrOCRProcessor(_A  ,			_A    )

			UpperCAmelCase__							:				Tuple   				=  processor(images=prepare_img(_A    )  ,			return_tensors='''pt'''    ).pixel_values

			# verify logits
			UpperCAmelCase__							:				Optional[Any]   				=  torch.tensor([[model.config.decoder.decoder_start_token_id]]    )
			UpperCAmelCase__							:				int   				=  model(pixel_values=_A  ,			decoder_input_ids=_A    )
			UpperCAmelCase__							:				List[Any]   				=  outputs.logits

			UpperCAmelCase__							:				int   				=  torch.Size([1, 1, 5_02_65]    )
			if "trocr-base-handwritten" in checkpoint_url:
						UpperCAmelCase__							:				int   				=  torch.tensor(
						    [-1.4_5_0_2, -4.6_6_8_3, -0.5_3_4_7, -2.9_2_9_1, 9.1_4_3_5, -3.0_5_7_1, 8.9_7_6_4, 1.7_5_6_0, 8.7_3_5_8, -1.5_3_1_1]    )
			elif "trocr-large-handwritten" in checkpoint_url:
						UpperCAmelCase__							:				Optional[int]   				=  torch.tensor(
						    [-2.6_4_3_7, -1.3_1_2_9, -2.2_5_9_6, -5.3_4_5_5, 6.3_5_3_9, 1.7_6_0_4, 5.4_9_9_1, 1.4_7_0_2, 5.6_1_1_3, 2.0_1_7_0]    )
			elif "trocr-base-printed" in checkpoint_url:
						UpperCAmelCase__							:				Dict   				=  torch.tensor(
						    [-5.6_8_1_6, -5.8_3_8_8, 1.1_3_9_8, -6.9_0_3_4, 6.8_5_0_5, -2.4_3_9_3, 1.2_2_8_4, -1.0_2_3_2, -1.9_6_6_1, -3.9_2_1_0]    )
			elif "trocr-large-printed" in checkpoint_url:
						UpperCAmelCase__							:				Dict   				=  torch.tensor(
						    [-6.0_1_6_2, -7.0_9_5_9, 4.4_1_5_5, -5.1_0_6_3, 7.0_4_6_8, -3.1_6_3_1, 2.6_4_6_6, -0.3_0_8_1, -0.8_1_0_6, -1.7_5_3_5]    )

			if "stage1" not in checkpoint_url:
						assert logits.shape == expected_shape, "Shape of logits not as expected"
						assert torch.allclose(logits[0, 0, :10]  ,			_A  ,			atol=1e-3    ), "First elements of logits not as expected"

			Path(_A    ).mkdir(exist_ok=_A    )
			print(F'''Saving model to {pytorch_dump_folder_path}'''    )
			model.save_pretrained(_A    )
			print(F'''Saving processor to {pytorch_dump_folder_path}'''    )
			processor.save_pretrained(_A    )


if __name__ == "__main__":
		UpperCamelCase__     :	Any				=			argparse.ArgumentParser()

		parser.add_argument(
		    '--checkpoint_url',
		    default='https://layoutlm.blob.core.windows.net/trocr/model_zoo/fairseq/trocr-base-handwritten.pt',
		    type=str,
		    help='URL to the original PyTorch checkpoint (.pth file).',
		)
		parser.add_argument(
		    '--pytorch_dump_folder_path', default=None, type=str, help='Path to the folder to output PyTorch model.'
		)
		UpperCamelCase__     :	Union[str, Any]				=			parser.parse_args()
		convert_tr_ocr_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)





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

UpperCamelCase__     :	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,
}

UpperCamelCase__     :	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 __UpperCamelCase(   _A					:				List[Any]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Dict   				=  EfficientNetConfig()
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''hidden_dim''']
   UpperCAmelCase__							:				Optional[Any]   				=  CONFIG_MAP[model_name]['''width_coef''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''depth_coef''']
   UpperCAmelCase__							:				Optional[int]   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''dropout_rate''']
   UpperCAmelCase__							:				Any   				=  CONFIG_MAP[model_name]['''dw_padding''']

   UpperCAmelCase__							:				Tuple   				=  '''huggingface/label-files'''
   UpperCAmelCase__							:				Dict   				=  '''imagenet-1k-id2label.json'''
   UpperCAmelCase__							:				List[Any]   				=  10_00
   UpperCAmelCase__							:				Union[str, Any]   				=  json.load(open(hf_hub_download(_A  ,			_A  ,			repo_type='''dataset'''    )  ,			'''r'''    )    )
   UpperCAmelCase__							:				Union[str, Any]   				=  {int(_A    ): v for k, v in idalabel.items()}

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







def __UpperCamelCase(   ):
   '''simple docstring'''
   UpperCAmelCase__							:				Union[str, Any]   				=  '''http://images.cocodataset.org/val2017/000000039769.jpg'''
   UpperCAmelCase__							:				List[str]   				=  Image.open(requests.get(_A  ,			stream=_A    ).raw    )
   return im







def __UpperCamelCase(   _A					:				Tuple    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Tuple   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				Tuple   				=  EfficientNetImageProcessor(
       size={'''height''': size, '''width''': size}  ,			image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]  ,			image_std=[0.4_7_8_5_3_9_4_4, 0.4_7_3_2_8_6_4, 0.4_7_4_3_4_1_6_3]  ,			do_center_crop=_A  ,			)
   return preprocessor







def __UpperCamelCase(   _A					:				str    ):
   '''simple docstring'''
   UpperCAmelCase__							:				Optional[int]   				=  [v.split('''_'''    )[0].split('''block'''    )[1] for v in original_param_names if v.startswith('''block'''    )]
   UpperCAmelCase__							:				List[str]   				=  sorted(set(_A    )    )
   UpperCAmelCase__							:				Optional[Any]   				=  len(_A    )
   UpperCAmelCase__							:				int   				=  {b: str(_A    ) for b, i in zip(_A  ,			range(_A    )    )}

   UpperCAmelCase__							:				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:
      UpperCAmelCase__							:				Tuple   				=  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__							:				Union[str, Any]   				=  '''classifier.bias'''
   return key_mapping







def __UpperCamelCase(   _A					:				Optional[Any]  ,			_A					:				List[Any]  ,			_A					:				Optional[Any]    ):
   '''simple docstring'''
   for key, value in tf_params.items():
      if "normalization" in key:
         continue

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

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







@torch.no_grad()
def __UpperCamelCase(   _A					:				Tuple  ,			_A					:				str  ,			_A					:				Union[str, Any]  ,			_A					:				List[str]    ):
   '''simple docstring'''
   UpperCAmelCase__							:				int   				=  model_classes[model_name](
       include_top=_A  ,			weights='''imagenet'''  ,			input_tensor=_A  ,			input_shape=_A  ,			pooling=_A  ,			classes=10_00  ,			classifier_activation='''softmax'''  ,			)

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

   # Load HuggingFace model
   UpperCAmelCase__							:				Union[str, Any]   				=  get_efficientnet_config(_A    )
   UpperCAmelCase__							:				Any   				=  EfficientNetForImageClassification(_A    ).eval()
   UpperCAmelCase__							:				Tuple   				=  hf_model.state_dict()

   # Create src-to-dst parameter name mapping dictionary
   print('''Converting parameters...'''    )
   UpperCAmelCase__							:				Tuple   				=  rename_keys(_A    )
   replace_params(_A  ,			_A  ,			_A    )

   # Initialize preprocessor and preprocess input image
   UpperCAmelCase__							:				List[Any]   				=  convert_image_processor(_A    )
   UpperCAmelCase__							:				List[Any]   				=  preprocessor(images=prepare_img()  ,			return_tensors='''pt'''    )

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

   # Original model inference
   UpperCAmelCase__							:				str   				=  False
   UpperCAmelCase__							:				int   				=  CONFIG_MAP[model_name]['''image_size''']
   UpperCAmelCase__							:				List[str]   				=  prepare_img().resize((image_size, image_size)  ,			resample=PIL.Image.NEAREST    )
   UpperCAmelCase__							:				List[Any]   				=  image.img_to_array(_A    )
   UpperCAmelCase__							:				List[Any]   				=  np.expand_dims(_A  ,			axis=0    )
   UpperCAmelCase__							:				List[Any]   				=  original_model.predict(_A    )

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

   if push_to_hub:
      # Push model and image processor to hub
      print(F'''Pushing converted {model_name} to the hub...'''    )
      UpperCAmelCase__							:				Any   				=  F'''efficientnet-{model_name}'''
      preprocessor.push_to_hub(_A    )
      hf_model.push_to_hub(_A    )


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

  UpperCamelCase__     :	Union[str, Any]				=			parser.parse_args()
  convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)




