




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   :			int          = logging.get_logger(__name__)

__UpperCamelCase   :			str          = {
    "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   :			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 						__A						(      __lowerCamelCase       ) ->			Union[str, Any]:
	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									=				1000
	a									=				json.load(open(hf_hub_download(__lowerCamelCase				,     __lowerCamelCase				,     repo_type="""dataset"""       )				,     """r"""       )       )
	a									=				{int(__lowerCamelCase       ): v for k, v in idalabel.items()}

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





def 						__A						(      ) ->			Dict:
	a									=				"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a									=				Image.open(requests.get(__lowerCamelCase				,     stream=__lowerCamelCase       ).raw       )
	return im





def 						__A						(      __lowerCamelCase       ) ->			str:
	a									=				CONFIG_MAP[model_name]["""image_size"""]
	a									=				EfficientNetImageProcessor(
	    size={"""height""": size, """width""": size}				,     image_mean=[0.485, 0.456, 0.406]				,     image_std=[0.47853944, 0.4732864, 0.47434163]				,     do_center_crop=__lowerCamelCase				,     )
	return preprocessor





def 						__A						(      __lowerCamelCase       ) ->			List[str]:
	a									=				[v.split("""_"""       )[0].split("""block"""       )[1] for v in original_param_names if v.startswith("""block"""       )]
	a									=				sorted(set(__lowerCamelCase       )       )
	a									=				len(__lowerCamelCase       )
	a									=				{b: str(__lowerCamelCase       ) for b, i in zip(__lowerCamelCase				,     range(__lowerCamelCase       )       )}

	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						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Dict:
	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(__lowerCamelCase       ).permute(3				,     2				,     0				,     1       )
		elif "depthwise_kernel" in key:
			a									=				torch.from_numpy(__lowerCamelCase       ).permute(2				,     3				,     0				,     1       )
		elif "kernel" in key:
			a									=				torch.from_numpy(np.transpose(__lowerCamelCase       )       )
		else:
			a									=				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 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			int:
	a									=				model_classes[model_name](
	    include_top=__lowerCamelCase				,     weights="""imagenet"""				,     input_tensor=__lowerCamelCase				,     input_shape=__lowerCamelCase				,     pooling=__lowerCamelCase				,     classes=1000				,     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(__lowerCamelCase       )
	a									=				EfficientNetForImageClassification(__lowerCamelCase       ).eval()
	a									=				hf_model.state_dict()

	# Create src-to-dst parameter name mapping dictionary
	print("""Converting parameters..."""       )
	a									=				rename_keys(__lowerCamelCase       )
	replace_params(__lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       )

	# Initialize preprocessor and preprocess input image
	a									=				convert_image_processor(__lowerCamelCase       )
	a									=				preprocessor(images=prepare_img()				,     return_tensors="""pt"""       )

	# HF model inference
	hf_model.eval()
	with torch.no_grad():
		a									=				hf_model(**__lowerCamelCase       )
	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(__lowerCamelCase       )
	a									=				np.expand_dims(__lowerCamelCase				,     axis=0       )
	a									=				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...'       )
		a									=				f'efficientnet-{model_name}'
		preprocessor.push_to_hub(__lowerCamelCase       )
		hf_model.push_to_hub(__lowerCamelCase       )


if __name__ == "__main__":
		__UpperCamelCase   :			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")

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







import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
__UpperCamelCase   :			Tuple          = "."

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
__UpperCamelCase   :			List[str]          = [
    "Assert",
    "AssignVariableOp",
    "EmptyTensorList",
    "MergeV2Checkpoints",
    "ReadVariableOp",
    "ResourceGather",
    "RestoreV2",
    "SaveV2",
    "ShardedFilename",
    "StatefulPartitionedCall",
    "StaticRegexFullMatch",
    "VarHandleOp",
]





def 						__A						(      __lowerCamelCase				,     __lowerCamelCase				,     __lowerCamelCase       ) ->			Union[str, Any]:
	a									=				SavedModel()
	a									=				[]

	with open(os.path.join(__lowerCamelCase				,     """utils"""				,     """tf_ops"""				,     """onnx.json"""       )       ) as f:
		a									=				json.load(__lowerCamelCase       )["""opsets"""]

	for i in range(1				,     opset + 1       ):
		onnx_ops.extend(onnx_opsets[str(__lowerCamelCase       )]       )

	with open(__lowerCamelCase				,     """rb"""       ) as f:
		saved_model.ParseFromString(f.read()       )

	a									=				set()

	# Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
	for meta_graph in saved_model.meta_graphs:
		# Add operations in the graph definition
		model_op_names.update(node.op for node in meta_graph.graph_def.node       )

		# Go through the functions in the graph definition
		for func in meta_graph.graph_def.library.function:
			# Add operations in each function
			model_op_names.update(node.op for node in func.node_def       )

    # Convert to list, sorted if you want
	a									=				sorted(__lowerCamelCase       )
	a									=				[]

	for op in model_op_names:
		if op not in onnx_ops and op not in INTERNAL_OPS:
			incompatible_ops.append(__lowerCamelCase       )

	if strict and len(__lowerCamelCase       ) > 0:
		raise Exception(f'Found the following incompatible ops for the opset {opset}:\n' + incompatible_ops       )
	elif len(__lowerCamelCase       ) > 0:
		print(f'Found the following incompatible ops for the opset {opset}:'       )
		print(*__lowerCamelCase				,     sep="""\n"""       )
	else:
		print(f'The saved model {saved_model_path} can properly be converted with ONNX.'       )


if __name__ == "__main__":
		__UpperCamelCase   :			Optional[Any]          = argparse.ArgumentParser()
		parser.add_argument("--saved_model_path", help="Path of the saved model to check (the .pb file).")
		parser.add_argument(
		    "--opset", default=12, type=int, help="The ONNX opset against which the model has to be tested."
		)
		parser.add_argument(
		    "--framework", choices=["onnx"], default="onnx", help="Frameworks against which to test the saved model."
		)
		parser.add_argument(
		    "--strict", action="store_true", help="Whether make the checking strict (raise errors) or not (raise warnings)"
		)
		__UpperCamelCase   :			str          = parser.parse_args()

		if args.framework == "onnx":
				onnx_compliancy(args.saved_model_path, args.strict, args.opset)

