

import argparse
import gc
import json
import os
import shutil
import warnings

import torch

from transformers import LlamaConfig, LlamaForCausalLM, LlamaTokenizer


try:
						from transformers import LlamaTokenizerFast
except ImportError as e:
						warnings.warn(e)
						warnings.warn(
						    """The converted tokenizer will be the `slow` tokenizer. To use the fast, update your `tokenizers` library and re-run the tokenizer conversion"""
						)
						lowerCAmelCase							:  Union[str, Any]            =						None
lowerCAmelCase							:  str            =						{
    """7B""": 11008,
    """13B""": 13824,
    """30B""": 17920,
    """65B""": 22016,
    """70B""": 28672,
}
lowerCAmelCase							:  Union[str, Any]            =						{
    """7B""": 1,
    """7Bf""": 1,
    """13B""": 2,
    """13Bf""": 2,
    """30B""": 4,
    """65B""": 8,
    """70B""": 8,
    """70Bf""": 8,
}







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase=1    ,     _UpperCAmelCase=2_56   ):
						return multiple_of * ((int(ffn_dim_multiplier * int(8 * n / 3   )   ) + multiple_of - 1) // multiple_of)







def       A_				(   _UpperCAmelCase   ):
						with open(_UpperCAmelCase    ,     "r"   ) as f:
												return json.load(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						with open(_UpperCAmelCase    ,     "w"   ) as f:
												json.dump(_UpperCAmelCase    ,     _UpperCAmelCase   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase=True   ):
						os.makedirs(_UpperCAmelCase    ,     exist_ok=_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	os.path.join(_UpperCAmelCase    ,     "tmp"   )
						os.makedirs(_UpperCAmelCase    ,     exist_ok=_UpperCAmelCase   )

						SCREAMING_SNAKE_CASE_:			Tuple  		=	read_json(os.path.join(_UpperCAmelCase    ,     "params.json"   )   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	NUM_SHARDS[model_size]
						SCREAMING_SNAKE_CASE_:			int  		=	params["n_layers"]
						SCREAMING_SNAKE_CASE_:			Dict  		=	params["n_heads"]
						SCREAMING_SNAKE_CASE_:			Any  		=	n_heads // num_shards
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	params["dim"]
						SCREAMING_SNAKE_CASE_:			Tuple  		=	dim // n_heads
						SCREAMING_SNAKE_CASE_:			Dict  		=	1_0_0_0_0.0
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	1.0 / (base ** (torch.arange(0    ,     _UpperCAmelCase    ,     2   ).float() / dims_per_head))

						if "n_kv_heads" in params:
												SCREAMING_SNAKE_CASE_:			Dict  		=	params["n_kv_heads"]  # for GQA / MQA
												SCREAMING_SNAKE_CASE_:			Any  		=	n_heads_per_shard // num_key_value_heads
												SCREAMING_SNAKE_CASE_:			List[Any]  		=	dim // num_key_value_heads
						else:  # compatibility with other checkpoints
												SCREAMING_SNAKE_CASE_:			Tuple  		=	n_heads
												SCREAMING_SNAKE_CASE_:			Any  		=	n_heads_per_shard
												SCREAMING_SNAKE_CASE_:			Dict  		=	dim

						# permute for sliced rotary
						def permute(_UpperCAmelCase    ,     _UpperCAmelCase=n_heads    ,     _UpperCAmelCase=dim    ,     _UpperCAmelCase=dim   ):
												return w.view(_UpperCAmelCase    ,     dima // n_heads // 2    ,     2    ,     _UpperCAmelCase   ).transpose(1    ,     2   ).reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )

						print(f"Fetching all parameters from the checkpoint at {input_base_path}."   )
						# Load weights
						if model_size == "7B":
												# Not sharded
												# (The sharded implementation would also work, but this is simpler.)
												SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.load(os.path.join(_UpperCAmelCase    ,     "consolidated.00.pth"   )    ,     map_location="cpu"   )
						else:
												# Sharded
												SCREAMING_SNAKE_CASE_:			int  		=	[
												    torch.load(os.path.join(_UpperCAmelCase    ,     f"consolidated.{i:02d}.pth"   )    ,     map_location="cpu"   )
												    for i in range(_UpperCAmelCase   )
												]
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	0
						SCREAMING_SNAKE_CASE_:			int  		=	{"weight_map": {}}
						for layer_i in range(_UpperCAmelCase   ):
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	f"pytorch_model-{layer_i + 1}-of-{n_layers + 1}.bin"
												if model_size == "7B":
																		# Unsharded
																		SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{
																		    f"model.layers.{layer_i}.self_attn.q_proj.weight": permute(
																		        loaded[f"layers.{layer_i}.attention.wq.weight"]   ),
																		    f"model.layers.{layer_i}.self_attn.k_proj.weight": permute(
																		        loaded[f"layers.{layer_i}.attention.wk.weight"]   ),
																		    f"model.layers.{layer_i}.self_attn.v_proj.weight": loaded[f"layers.{layer_i}.attention.wv.weight"],
																		    f"model.layers.{layer_i}.self_attn.o_proj.weight": loaded[f"layers.{layer_i}.attention.wo.weight"],
																		    f"model.layers.{layer_i}.mlp.gate_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w1.weight"],
																		    f"model.layers.{layer_i}.mlp.down_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w2.weight"],
																		    f"model.layers.{layer_i}.mlp.up_proj.weight": loaded[f"layers.{layer_i}.feed_forward.w3.weight"],
																		    f"model.layers.{layer_i}.input_layernorm.weight": loaded[f"layers.{layer_i}.attention_norm.weight"],
																		    f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[f"layers.{layer_i}.ffn_norm.weight"],
																		}
												else:
																		# Sharded
																		# Note that attention.w{q,k,v,o}, feed_fordward.w[1,2,3], attention_norm.weight and ffn_norm.weight share
																		# the same storage object, saving attention_norm and ffn_norm will save other weights too, which is
																		# redundant as other weights will be stitched from multiple shards. To avoid that, they are cloned.

																		SCREAMING_SNAKE_CASE_:			List[str]  		=	{
																		    f"model.layers.{layer_i}.input_layernorm.weight": loaded[0][
																		        f"layers.{layer_i}.attention_norm.weight"
																		    ].clone(),
																		    f"model.layers.{layer_i}.post_attention_layernorm.weight": loaded[0][
																		        f"layers.{layer_i}.ffn_norm.weight"
																		    ].clone(),
																		}
																		SCREAMING_SNAKE_CASE_:			Any  		=	permute(
																		    torch.cat(
																		        [
																		            loaded[i][f"layers.{layer_i}.attention.wq.weight"].view(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
																		            for i in range(_UpperCAmelCase   )
																		        ]    ,     dim=0    ,     ).reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )   )
																		SCREAMING_SNAKE_CASE_:			int  		=	permute(
																		    torch.cat(
																		        [
																		            loaded[i][f"layers.{layer_i}.attention.wk.weight"].view(
																		                _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
																		            for i in range(_UpperCAmelCase   )
																		        ]    ,     dim=0    ,     ).reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase    ,     )
																		SCREAMING_SNAKE_CASE_:			int  		=	torch.cat(
																		    [
																		        loaded[i][f"layers.{layer_i}.attention.wv.weight"].view(
																		            _UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )
																		        for i in range(_UpperCAmelCase   )
																		    ]    ,     dim=0    ,     ).reshape(_UpperCAmelCase    ,     _UpperCAmelCase   )

																		SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.cat(
																		    [loaded[i][f"layers.{layer_i}.attention.wo.weight"] for i in range(_UpperCAmelCase   )]    ,     dim=1   )
																		SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	torch.cat(
																		    [loaded[i][f"layers.{layer_i}.feed_forward.w1.weight"] for i in range(_UpperCAmelCase   )]    ,     dim=0   )
																		SCREAMING_SNAKE_CASE_:			Optional[int]  		=	torch.cat(
																		    [loaded[i][f"layers.{layer_i}.feed_forward.w2.weight"] for i in range(_UpperCAmelCase   )]    ,     dim=1   )
																		SCREAMING_SNAKE_CASE_:			Dict  		=	torch.cat(
																		    [loaded[i][f"layers.{layer_i}.feed_forward.w3.weight"] for i in range(_UpperCAmelCase   )]    ,     dim=0   )

												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	inv_freq
												for k, v in state_dict.items():
																		SCREAMING_SNAKE_CASE_:			str  		=	filename
																		param_count += v.numel()
												torch.save(_UpperCAmelCase    ,     os.path.join(_UpperCAmelCase    ,     _UpperCAmelCase   )   )

						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	f"pytorch_model-{n_layers + 1}-of-{n_layers + 1}.bin"
						if model_size == "7B":
												# Unsharded
												SCREAMING_SNAKE_CASE_:			str  		=	{
												    "model.embed_tokens.weight": loaded["tok_embeddings.weight"],
												    "model.norm.weight": loaded["norm.weight"],
												    "lm_head.weight": loaded["output.weight"],
												}
						else:
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	{
												    "model.norm.weight": loaded[0]["norm.weight"],
												    "model.embed_tokens.weight": torch.cat(
												        [loaded[i]["tok_embeddings.weight"] for i in range(_UpperCAmelCase   )]    ,     dim=1   ),
												    "lm_head.weight": torch.cat([loaded[i]["output.weight"] for i in range(_UpperCAmelCase   )]    ,     dim=0   ),
												}

						for k, v in state_dict.items():
												SCREAMING_SNAKE_CASE_:			Optional[int]  		=	filename
												param_count += v.numel()
						torch.save(_UpperCAmelCase    ,     os.path.join(_UpperCAmelCase    ,     _UpperCAmelCase   )   )

						# Write configs
						SCREAMING_SNAKE_CASE_:			Any  		=	{"total_size": param_count * 2}
						write_json(_UpperCAmelCase    ,     os.path.join(_UpperCAmelCase    ,     "pytorch_model.bin.index.json"   )   )
						SCREAMING_SNAKE_CASE_:			Dict  		=	params["ffn_dim_multiplier"] if "ffn_dim_multiplier" in params else 1
						SCREAMING_SNAKE_CASE_:			List[Any]  		=	params["multiple_of"] if "multiple_of" in params else 2_56
						SCREAMING_SNAKE_CASE_:			int  		=	LlamaConfig(
						    hidden_size=_UpperCAmelCase    ,     intermediate_size=compute_intermediate_size(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )    ,     num_attention_heads=params["n_heads"]    ,     num_hidden_layers=params["n_layers"]    ,     rms_norm_eps=params["norm_eps"]    ,     num_key_value_heads=_UpperCAmelCase    ,     )
						config.save_pretrained(_UpperCAmelCase   )

						# Make space so we can load the model properly now.
						del state_dict
						del loaded
						gc.collect()

						print("Loading the checkpoint in a Llama model."   )
						SCREAMING_SNAKE_CASE_:			Union[str, Any]  		=	LlamaForCausalLM.from_pretrained(_UpperCAmelCase    ,     torch_dtype=torch.floataa    ,     low_cpu_mem_usage=_UpperCAmelCase   )
						# Avoid saving this as part of the config.
						del model.config._name_or_path

						print("Saving in the Transformers format."   )
						model.save_pretrained(_UpperCAmelCase    ,     safe_serialization=_UpperCAmelCase   )
						shutil.rmtree(_UpperCAmelCase   )







def       A_				(   _UpperCAmelCase    ,     _UpperCAmelCase   ):
						# Initialize the tokenizer based on the `spm` model
						SCREAMING_SNAKE_CASE_:			int  		=	LlamaTokenizer if LlamaTokenizerFast is None else LlamaTokenizerFast
						print(f"Saving a {tokenizer_class.__name__} to {tokenizer_path}."   )
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	tokenizer_class(_UpperCAmelCase   )
						tokenizer.save_pretrained(_UpperCAmelCase   )







def       A_				(   ):
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	argparse.ArgumentParser()
						parser.add_argument(
						    "--input_dir"    ,     help="Location of LLaMA weights, which contains tokenizer.model and model folders"    ,     )
						parser.add_argument(
						    "--model_size"    ,     choices=["7B", "7Bf", "13B", "13Bf", "30B", "65B", "70B", "70Bf", "tokenizer_only"]    ,     )
						parser.add_argument(
						    "--output_dir"    ,     help="Location to write HF model and tokenizer"    ,     )
						parser.add_argument("--safe_serialization"    ,     type=_UpperCAmelCase    ,     help="Whether or not to save using `safetensors`."   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	parser.parse_args()
						if args.model_size != "tokenizer_only":
												write_model(
												    model_path=args.output_dir    ,     input_base_path=os.path.join(args.input_dir    ,     args.model_size   )    ,     model_size=args.model_size    ,     safe_serialization=args.safe_serialization    ,     )
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	os.path.join(args.input_dir    ,     "tokenizer.model"   )
						write_tokenizer(args.output_dir    ,     _UpperCAmelCase   )


if __name__ == "__main__":
						main()









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							:  Tuple            =						logging.get_logger(__name__)

lowerCAmelCase							:  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,
}

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

						SCREAMING_SNAKE_CASE_:			str  		=	"huggingface/label-files"
						SCREAMING_SNAKE_CASE_:			str  		=	"imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE_:			int  		=	10_00
						SCREAMING_SNAKE_CASE_:			int  		=	json.load(open(hf_hub_download(_UpperCAmelCase    ,     _UpperCAmelCase    ,     repo_type="dataset"   )    ,     "r"   )   )
						SCREAMING_SNAKE_CASE_:			int  		=	{int(_UpperCAmelCase   ): v for k, v in idalabel.items()}

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







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







def       A_				(   _UpperCAmelCase   ):
						SCREAMING_SNAKE_CASE_:			int  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	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=_UpperCAmelCase    ,     )
						return preprocessor







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

						SCREAMING_SNAKE_CASE_:			List[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:
												SCREAMING_SNAKE_CASE_:			List[str]  		=	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")   )

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

						SCREAMING_SNAKE_CASE_:			List[str]  		=	"classifier.weight"
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	"classifier.bias"
						return key_mapping







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

												SCREAMING_SNAKE_CASE_:			List[str]  		=	key_mapping[key]
												if "_conv" in key and "kernel" in key:
																		SCREAMING_SNAKE_CASE_:			str  		=	torch.from_numpy(_UpperCAmelCase   ).permute(3    ,     2    ,     0    ,     1   )
												elif "depthwise_kernel" in key:
																		SCREAMING_SNAKE_CASE_:			int  		=	torch.from_numpy(_UpperCAmelCase   ).permute(2    ,     3    ,     0    ,     1   )
												elif "kernel" in key:
																		SCREAMING_SNAKE_CASE_:			Tuple  		=	torch.from_numpy(np.transpose(_UpperCAmelCase   )   )
												else:
																		SCREAMING_SNAKE_CASE_:			List[str]  		=	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   ):
						SCREAMING_SNAKE_CASE_:			Any  		=	model_classes[model_name](
						    include_top=_UpperCAmelCase    ,     weights="imagenet"    ,     input_tensor=_UpperCAmelCase    ,     input_shape=_UpperCAmelCase    ,     pooling=_UpperCAmelCase    ,     classes=10_00    ,     classifier_activation="softmax"    ,     )

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

						# Load HuggingFace model
						SCREAMING_SNAKE_CASE_:			Any  		=	get_efficientnet_config(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	EfficientNetForImageClassification(_UpperCAmelCase   ).eval()
						SCREAMING_SNAKE_CASE_:			str  		=	hf_model.state_dict()

						# Create src-to-dst parameter name mapping dictionary
						print("Converting parameters..."   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	rename_keys(_UpperCAmelCase   )
						replace_params(_UpperCAmelCase    ,     _UpperCAmelCase    ,     _UpperCAmelCase   )

						# Initialize preprocessor and preprocess input image
						SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	convert_image_processor(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			int  		=	preprocessor(images=prepare_img()    ,     return_tensors="pt"   )

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

						# Original model inference
						SCREAMING_SNAKE_CASE_:			Dict  		=	False
						SCREAMING_SNAKE_CASE_:			Optional[int]  		=	CONFIG_MAP[model_name]["image_size"]
						SCREAMING_SNAKE_CASE_:			int  		=	prepare_img().resize((image_size, image_size)    ,     resample=PIL.Image.NEAREST   )
						SCREAMING_SNAKE_CASE_:			Tuple  		=	image.img_to_array(_UpperCAmelCase   )
						SCREAMING_SNAKE_CASE_:			List[str]  		=	np.expand_dims(_UpperCAmelCase    ,     axis=0   )
						SCREAMING_SNAKE_CASE_:			str  		=	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..."   )
												SCREAMING_SNAKE_CASE_:			Optional[Any]  		=	f"efficientnet-{model_name}"
												preprocessor.push_to_hub(_UpperCAmelCase   )
												hf_model.push_to_hub(_UpperCAmelCase   )


if __name__ == "__main__":
						lowerCAmelCase							:  List[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""")

						lowerCAmelCase							:  Any            =						parser.parse_args()
						convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






