






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()
lowerCamelCase__							=     logging.get_logger(__name__)

lowerCamelCase__							=     {
    """b0""": efficientnet.EfficientNetBa,
    """b1""": efficientnet.EfficientNetBa,
    """b2""": efficientnet.EfficientNetBa,
    """b3""": efficientnet.EfficientNetBa,
    """b4""": efficientnet.EfficientNetBa,
    """b5""": efficientnet.EfficientNetBa,
    """b6""": efficientnet.EfficientNetBa,
    """b7""": efficientnet.EfficientNetBa,
}

lowerCamelCase__							=     {
    """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   UpperCamelCase  (       snake_case__     :   Union[str, Any]  ):




					'''simple docstring'''






					__snake_case			:Optional[Any]          =						EfficientNetConfig()
					__snake_case			:Dict          =						CONFIG_MAP[model_name]["""hidden_dim"""]
					__snake_case			:List[Any]          =						CONFIG_MAP[model_name]["""width_coef"""]
					__snake_case			:Dict          =						CONFIG_MAP[model_name]["""depth_coef"""]
					__snake_case			:Union[str, Any]          =						CONFIG_MAP[model_name]["""image_size"""]
					__snake_case			:Tuple          =						CONFIG_MAP[model_name]["""dropout_rate"""]
					__snake_case			:Optional[int]          =						CONFIG_MAP[model_name]["""dw_padding"""]

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

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






def   UpperCamelCase  (       ):




					'''simple docstring'''






					__snake_case			:Any          =						"""http://images.cocodataset.org/val2017/000000039769.jpg"""
					__snake_case			:Optional[int]          =						Image.open(requests.get(snake_case__	,stream=snake_case__  ).raw  )
					return im






def   UpperCamelCase  (       snake_case__     :   Optional[Any]  ):




					'''simple docstring'''






					__snake_case			:Dict          =						CONFIG_MAP[model_name]["""image_size"""]
					__snake_case			:Optional[int]          =						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=snake_case__	,)
					return preprocessor






def   UpperCamelCase  (       snake_case__     :   List[str]  ):




					'''simple docstring'''






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

					__snake_case			: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:
										__snake_case			:int          =						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			:List[Any]          =						{}
					for item in rename_keys:
										if item[0] in original_param_names:
															__snake_case			:Union[str, Any]          =						"""efficientnet.""" + item[1]

					__snake_case			:Optional[int]          =						"""classifier.weight"""
					__snake_case			:List[str]          =						"""classifier.bias"""
					return key_mapping






def   UpperCamelCase  (       snake_case__     :   int	,snake_case__     :   List[str]	,snake_case__     :   Union[str, Any]  ):




					'''simple docstring'''






					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(snake_case__  ).permute(3	,2	,0	,1  )
										elif "depthwise_kernel" in key:
															__snake_case			:Optional[Any]          =						torch.from_numpy(snake_case__  ).permute(2	,3	,0	,1  )
										elif "kernel" in key:
															__snake_case			:str          =						torch.from_numpy(np.transpose(snake_case__  )  )
										else:
															__snake_case			:Optional[int]          =						torch.from_numpy(snake_case__  )

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






@torch.no_grad()
def   UpperCamelCase  (       snake_case__     :   Tuple	,snake_case__     :   Union[str, Any]	,snake_case__     :   str	,snake_case__     :   str  ):




					'''simple docstring'''






					__snake_case			:Any          =						model_classes[model_name](
					    include_top=snake_case__	,weights="""imagenet"""	,input_tensor=snake_case__	,input_shape=snake_case__	,pooling=snake_case__	,classes=1000	,classifier_activation="""softmax"""	,)

					__snake_case			:Any          =						original_model.trainable_variables
					__snake_case			:Optional[int]          =						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			:Optional[Any]          =						param.numpy()
					__snake_case			:Any          =						list(tf_params.keys()  )

					# Load HuggingFace model
					__snake_case			:Optional[Any]          =						get_efficientnet_config(snake_case__  )
					__snake_case			:str          =						EfficientNetForImageClassification(snake_case__  ).eval()
					__snake_case			:List[Any]          =						hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print("""Converting parameters..."""  )
					__snake_case			:Dict          =						rename_keys(snake_case__  )
					replace_params(snake_case__	,snake_case__	,snake_case__  )

					# Initialize preprocessor and preprocess input image
					__snake_case			:Dict          =						convert_image_processor(snake_case__  )
					__snake_case			:List[Any]          =						preprocessor(images=prepare_img()	,return_tensors="""pt"""  )

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										__snake_case			:Optional[int]          =						hf_model(**snake_case__  )
					__snake_case			:Optional[int]          =						outputs.logits.detach().numpy()

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

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

					if push_to_hub:
										# Push model and image processor to hub
										print(f'''Pushing converted {model_name} to the hub...'''  )
										__snake_case			:int          =						f'''efficientnet-{model_name}'''
										preprocessor.push_to_hub(snake_case__  )
										hf_model.push_to_hub(snake_case__  )


if __name__ == "__main__":
					lowerCamelCase__							=     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""")

					lowerCamelCase__							=     parser.parse_args()
					convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)










import numpy as np
import torch
from torch.utils.data import DataLoader

from accelerate.utils.dataclasses import DistributedType




class 			snake_case__   :

       '''simple docstring'''



       def __init__(							self			,						a__=2			,						a__=3			,						a__=64			,						a__=None    ) ->      int:






            '''simple docstring'''





            __snake_case			:Any          =						np.random.default_rng(a__    )
            __snake_case			:List[str]          =						length
            __snake_case			:Optional[Any]          =						rng.normal(size=(length,)    ).astype(np.floataa    )
            __snake_case			:Optional[int]          =						a * self.x + b + rng.normal(scale=0.1			,						size=(length,)    ).astype(np.floataa    )



       def __len__(							self    ) ->      str:






            '''simple docstring'''





            return self.length



       def __getitem__(							self			,						a__    ) ->      int:






            '''simple docstring'''





            return {"x": self.x[i], "y": self.y[i]}




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Any          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:int          =						torch.nn.Parameter(torch.tensor([2, 3]    ).float()    )
            __snake_case			:Dict          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      Optional[Any]:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:Tuple          =						False
            return x * self.a[0] + self.b[0]




class 			snake_case__   (							torch.nn.Module):

       '''simple docstring'''



       def __init__(							self			,						a__=0			,						a__=0			,						a__=False    ) ->      List[str]:






            '''simple docstring'''





            super().__init__()
            __snake_case			:Optional[int]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:List[str]          =						torch.nn.Parameter(torch.tensor(a__    ).float()    )
            __snake_case			:str          =						True



       def 					__lowercase						(							self			,						a__=None    ) ->      str:






            '''simple docstring'''





            if self.first_batch:
                 print(F'''Model dtype: {self.a.dtype}, {self.b.dtype}. Input dtype: {x.dtype}'''    )
                 __snake_case			:List[Any]          =						False
            return x * self.a + self.b






def   UpperCamelCase  (       snake_case__     :   Optional[int]	,snake_case__     :   int = 16  ):




     '''simple docstring'''






     from datasets import load_dataset
     from transformers import AutoTokenizer

     __snake_case			:Union[str, Any]          =						AutoTokenizer.from_pretrained("""bert-base-cased"""  )
     __snake_case			:Dict          =						{"""train""": """tests/test_samples/MRPC/train.csv""", """validation""": """tests/test_samples/MRPC/dev.csv"""}
     __snake_case			:Any          =						load_dataset("""csv"""	,data_files=snake_case__  )
     __snake_case			:Dict          =						datasets["""train"""].unique("""label"""  )

     __snake_case			:List[Any]          =						{v: i for i, v in enumerate(snake_case__  )}

     def tokenize_function(snake_case__     :   List[Any]  ):
          # max_length=None => use the model max length (it's actually the default)
          __snake_case			:Optional[Any]          =						tokenizer(
              examples["""sentence1"""]	,examples["""sentence2"""]	,truncation=snake_case__	,max_length=snake_case__	,padding="""max_length"""  )
          if "label" in examples:
               __snake_case			:Dict          =						[label_to_id[l] for l in examples["""label"""]]
          return outputs

     # Apply the method we just defined to all the examples in all the splits of the dataset
     __snake_case			:List[str]          =						datasets.map(
         snake_case__	,batched=snake_case__	,remove_columns=["""sentence1""", """sentence2""", """label"""]	,)

     def collate_fn(snake_case__     :   Optional[int]  ):
          # On TPU it's best to pad everything to the same length or training will be very slow.
          if accelerator.distributed_type == DistributedType.TPU:
               return tokenizer.pad(snake_case__	,padding="""max_length"""	,max_length=128	,return_tensors="""pt"""  )
          return tokenizer.pad(snake_case__	,padding="""longest"""	,return_tensors="""pt"""  )

     # Instantiate dataloaders.
     __snake_case			:List[Any]          =						DataLoader(tokenized_datasets["""train"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=2  )
     __snake_case			:str          =						DataLoader(tokenized_datasets["""validation"""]	,shuffle=snake_case__	,collate_fn=snake_case__	,batch_size=1  )

     return train_dataloader, eval_dataloader


