





"""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()
_snake_case     :		str       						= logging.get_logger(__name__)

_snake_case     :		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,
}

_snake_case     :		Union[str, 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 					A__				(			UpperCamelCase       ):
							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		=  1_000
							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 					A__				(			):
							A		=  "http://images.cocodataset.org/val2017/000000039769.jpg"
							A		=  Image.open(requests.get(UpperCamelCase					,  stream=UpperCamelCase       ).raw       )
							return im






def 					A__				(			UpperCamelCase       ):
							A		=  CONFIG_MAP[model_name]["image_size"]
							A		=  EfficientNetImageProcessor(
							    size={"height": size, "width": size}					,  image_mean=[0.4_85, 0.4_56, 0.4_06]					,  image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]					,  do_center_crop=UpperCamelCase					,  )
							return preprocessor






def 					A__				(			UpperCamelCase       ):
							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 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							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 					A__				(			UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase					,  UpperCamelCase       ):
							A		=  model_classes[model_name](
							    include_top=UpperCamelCase					,  weights="imagenet"					,  input_tensor=UpperCamelCase					,  input_shape=UpperCamelCase					,  pooling=UpperCamelCase					,  classes=1_000					,  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__":
		_snake_case     :		Optional[Any]       						= 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')

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









"""simple docstring"""
from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case     :		Optional[int]       						= logging.get_logger(__name__)

_snake_case     :		Optional[int]       						= {
    'google/vivit-b-16x2-kinetics400': (
        'https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/main/config.json'
    ),
    # See all Vivit models at https://huggingface.co/models?filter=vivit
}






class 					_UpperCAmelCase   (	lowercase_ ):
					UpperCamelCase      =							'''vivit'''


					def __init__(					self			:Optional[Any]     ,       __UpperCamelCase			:Dict=2_24     ,       __UpperCamelCase			:int=32     ,       __UpperCamelCase			:Union[str, Any]=[2, 16, 16]     ,       __UpperCamelCase			:Optional[Any]=3     ,       __UpperCamelCase			:Optional[Any]=7_68     ,       __UpperCamelCase			:Any=12     ,       __UpperCamelCase			:List[str]=12     ,       __UpperCamelCase			:List[str]=30_72     ,       __UpperCamelCase			:Any="gelu_fast"     ,       __UpperCamelCase			:List[Any]=0.0     ,       __UpperCamelCase			:str=0.0     ,       __UpperCamelCase			:Dict=0.02     ,       __UpperCamelCase			:Optional[Any]=1e-06     ,       __UpperCamelCase			:Dict=True     ,       **__UpperCamelCase			:Tuple     ,       ):
												A		=  hidden_size
												A		=  num_hidden_layers
												A		=  num_attention_heads
												A		=  intermediate_size
												A		=  hidden_act
												A		=  hidden_dropout_prob
												A		=  attention_probs_dropout_prob
												A		=  initializer_range
												A		=  layer_norm_eps

												A		=  image_size
												A		=  num_frames
												A		=  tubelet_size
												A		=  num_channels
												A		=  qkv_bias

												super().__init__(**__UpperCamelCase		)


