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

lowercase__	: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,
}

lowercase__	:Optional[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  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		EfficientNetConfig()
					lowercase					=		CONFIG_MAP[model_name]["hidden_dim"]
					lowercase					=		CONFIG_MAP[model_name]["width_coef"]
					lowercase					=		CONFIG_MAP[model_name]["depth_coef"]
					lowercase					=		CONFIG_MAP[model_name]["image_size"]
					lowercase					=		CONFIG_MAP[model_name]["dropout_rate"]
					lowercase					=		CONFIG_MAP[model_name]["dw_padding"]

					lowercase					=		"huggingface/label-files"
					lowercase					=		"imagenet-1k-id2label.json"
					lowercase					=		1000
					lowercase					=		json.load(open(hf_hub_download(_lowerCamelCase				,    _lowerCamelCase				,    repo_type='''dataset'''	)				,    '''r'''	)	)
					lowercase					=		{int(_lowerCamelCase	): v for k, v in idalabel.items()}

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



def  UpperCamelCase					(							):
					'''simple docstring'''
					lowercase					=		"http://images.cocodataset.org/val2017/000000039769.jpg"
					lowercase					=		Image.open(requests.get(_lowerCamelCase				,    stream=_lowerCamelCase	).raw	)
					return im



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		CONFIG_MAP[model_name]["image_size"]
					lowercase					=		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=_lowerCamelCase				,    )
					return preprocessor



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[v.split('''_'''	)[0].split('''block'''	)[1] for v in original_param_names if v.startswith('''block'''	)]
					lowercase					=		sorted(set(_lowerCamelCase	)	)
					lowercase					=		len(_lowerCamelCase	)
					lowercase					=		{b: str(_lowerCamelCase	) for b, i in zip(_lowerCamelCase				,    range(_lowerCamelCase	)	)}

					lowercase					=		[]
					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:
										lowercase					=		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''')	)

					lowercase					=		{}
					for item in rename_keys:
										if item[0] in original_param_names:
															lowercase					=		"efficientnet." + item[1]

					lowercase					=		"classifier.weight"
					lowercase					=		"classifier.bias"
					return key_mapping



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					for key, value in tf_params.items():
										if "normalization" in key:
															continue

										lowercase					=		key_mapping[key]
										if "_conv" in key and "kernel" in key:
															lowercase					=		torch.from_numpy(_lowerCamelCase	).permute(3				,    2				,    0				,    1	)
										elif "depthwise_kernel" in key:
															lowercase					=		torch.from_numpy(_lowerCamelCase	).permute(2				,    3				,    0				,    1	)
										elif "kernel" in key:
															lowercase					=		torch.from_numpy(np.transpose(_lowerCamelCase	)	)
										else:
															lowercase					=		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  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		model_classes[model_name](
					    include_top=_lowerCamelCase				,    weights='''imagenet'''				,    input_tensor=_lowerCamelCase				,    input_shape=_lowerCamelCase				,    pooling=_lowerCamelCase				,    classes=1000				,    classifier_activation='''softmax'''				,    )

					lowercase					=		original_model.trainable_variables
					lowercase					=		original_model.non_trainable_variables
					lowercase					=		{param.name: param.numpy() for param in tf_params}
					for param in tf_non_train_params:
										lowercase					=		param.numpy()
					lowercase					=		list(tf_params.keys()	)

					# Load HuggingFace model
					lowercase					=		get_efficientnet_config(_lowerCamelCase	)
					lowercase					=		EfficientNetForImageClassification(_lowerCamelCase	).eval()
					lowercase					=		hf_model.state_dict()

					# Create src-to-dst parameter name mapping dictionary
					print('''Converting parameters...'''	)
					lowercase					=		rename_keys(_lowerCamelCase	)
					replace_params(_lowerCamelCase				,    _lowerCamelCase				,    _lowerCamelCase	)

					# Initialize preprocessor and preprocess input image
					lowercase					=		convert_image_processor(_lowerCamelCase	)
					lowercase					=		preprocessor(images=prepare_img()				,    return_tensors='''pt'''	)

					# HF model inference
					hf_model.eval()
					with torch.no_grad():
										lowercase					=		hf_model(**_lowerCamelCase	)
					lowercase					=		outputs.logits.detach().numpy()

					# Original model inference
					lowercase					=		False
					lowercase					=		CONFIG_MAP[model_name]["image_size"]
					lowercase					=		prepare_img().resize((image_size, image_size)				,    resample=PIL.Image.NEAREST	)
					lowercase					=		image.img_to_array(_lowerCamelCase	)
					lowercase					=		np.expand_dims(_lowerCamelCase				,    axis=0	)
					lowercase					=		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...'	)
										lowercase					=		f'efficientnet-{model_name}'
										preprocessor.push_to_hub(_lowerCamelCase	)
										hf_model.push_to_hub(_lowerCamelCase	)


if __name__ == "__main__":
							lowercase__	: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")

							lowercase__	:int										=					parser.parse_args()
							convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)






import json
import multiprocessing as mp
import re
from collections import defaultdict
from functools import partial
from typing import Dict, List, Optional, Set, Tuple, Type

from datasets import Dataset
from datasketch import MinHash, MinHashLSH
from dpu_utils.utils.iterators import ThreadedIterator
from tqdm import tqdm


lowercase__	:Optional[Any]										=					re.compile("[^A-Za-z_0-9]")
# parameters used in DuplicationIndex
lowercase__	:List[str]										=					10
lowercase__	:Tuple										=					256



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					if len(lowerCAmelCase__	) < MIN_NUM_TOKENS:
										return None
					lowercase					=		MinHash(num_perm=lowerCAmelCase__	)
					for token in set(lowerCAmelCase__	):
										min_hash.update(token.encode()	)
					return min_hash



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					return {t for t in NON_ALPHA.split(lowerCAmelCase__	) if len(t.strip()	) > 0}







class        lowercase	:




							def __init__(       self     ,*,
							    A__ = 0.85     ,):
												lowercase					=		duplication_jaccard_threshold
												lowercase					=		NUM_PERM
												lowercase					=		MinHashLSH(threshold=self._duplication_jaccard_threshold     ,num_perm=self._num_perm)

												lowercase					=		defaultdict(A__)




							def 				A__   (       self     ,A__     ,A__):
												lowercase					=		self._index.query(A__)
												if code_key in self._index.keys:
																	print(f'Duplicate key {code_key}')
																	return

												self._index.insert(A__     ,A__)
												if len(A__) > 0:
																	for base_duplicate in close_duplicates:
																						if base_duplicate in self._duplicate_clusters:
																											self._duplicate_clusters[base_duplicate].add(A__)
																											break
																	else:
																						self._duplicate_clusters[close_duplicates[0]].add(A__)




							def 				A__   (       self):
												lowercase					=		[]
												for base, duplicates in self._duplicate_clusters.items():
																	lowercase					=		[base] + list(A__)
																	# reformat the cluster to be a list of dict
																	lowercase					=		[{'''base_index''': el[0], '''repo_name''': el[1], '''path''': el[2]} for el in cluster]
																	duplicate_clusters.append(A__)
												return duplicate_clusters





							def 				A__   (       self     ,A__):
												lowercase					=		self.get_duplicate_clusters()
												with open(A__     ,'''w''') as f:
																	json.dump(A__     ,A__)



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					lowercase			,		lowercase					=		element
					lowercase					=		get_min_hash([t for t in NON_ALPHA.split(data['''content''']	) if len(t.strip()	) > 0]	)
					if min_hash is not None:
										return (index, data["repo_name"], data["path"]), min_hash



def  UpperCamelCase					(							lowerCAmelCase__	):
					'''simple docstring'''
					with mp.Pool() as pool:
										for data in pool.imap_unordered(
										    _compute_min_hash				,    ThreadedIterator(lowerCAmelCase__				,    max_queue_size=1_0000	)				,    chunksize=100				,    ):
															if data is not None:
																				yield data



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		DuplicationIndex(duplication_jaccard_threshold=lowerCAmelCase__	)

					for filename, min_hash in tqdm(ThreadedIterator(minhash_iter(enumerate(lowerCAmelCase__	)	)				,    max_queue_size=100	)	):
										di.add(lowerCAmelCase__				,    lowerCAmelCase__	)

					# Returns a List[Cluster] where Cluster is List[str] with the filenames.
					return di.get_duplicate_clusters()



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		get_tokens(lowerCAmelCase__	)
					lowercase					=		get_tokens(lowerCAmelCase__	)
					return len(tokensa & tokensa	) / len(tokensa | tokensa	)


lowercase__	:List[Any]										=					None



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					lowercase					=		[]
					for elementa in cluster:
										lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
										for elementa in extremes:
															lowercase					=		_shared_dataset[elementa['''base_index''']]['''content''']
															if jaccard_similarity(lowerCAmelCase__				,    lowerCAmelCase__	) >= jaccard_threshold:
																				elementa["copies"] += 1
																				break
										else:
															lowercase					=		1
															extremes.append(lowerCAmelCase__	)
					return extremes



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	):
					'''simple docstring'''
					global _shared_dataset
					lowercase					=		dataset
					lowercase					=		[]
					lowercase					=		partial(_find_cluster_extremes_shared				,    jaccard_threshold=lowerCAmelCase__	)
					with mp.Pool() as pool:
										for extremes in tqdm(
										    pool.imap_unordered(
										        lowerCAmelCase__				,    lowerCAmelCase__				,    )				,    total=len(lowerCAmelCase__	)				,    ):
															extremes_list.append(lowerCAmelCase__	)
					return extremes_list



def  UpperCamelCase					(							lowerCAmelCase__				,    lowerCAmelCase__ = 0.85	):
					'''simple docstring'''
					lowercase					=		make_duplicate_clusters(lowerCAmelCase__				,    lowerCAmelCase__	)
					lowercase					=		{x['''base_index'''] for cluster in duplicate_clusters for x in cluster}
					lowercase					=		{}
					lowercase					=		find_extremes(lowerCAmelCase__				,    lowerCAmelCase__				,    lowerCAmelCase__	)
					for extremes in extremes_clusters:
										for element in extremes:
															lowercase					=		element
					lowercase					=		duplicate_indices - set(extreme_dict.keys()	)
					lowercase					=		dataset.filter(lambda lowerCAmelCase__				,    lowerCAmelCase__	: idx not in remove_indices				,    with_indices=lowerCAmelCase__	)

					# update duplicate_clusters
					for cluster in duplicate_clusters:
										for element in cluster:
															lowercase					=		element['''base_index'''] in extreme_dict
															if element["is_extreme"]:
																				lowercase					=		extreme_dict[element['''base_index''']]['''copies''']

					print(f'Original dataset size: {len(lowerCAmelCase__	)}'	)
					print(f'Number of duplicate clusters: {len(lowerCAmelCase__	)}'	)
					print(f'Files in duplicate cluster: {len(lowerCAmelCase__	)}'	)
					print(f'Unique files in duplicate cluster: {len(lowerCAmelCase__	)}'	)
					print(f'Filtered dataset size: {len(lowerCAmelCase__	)}'	)

					return ds_filter, duplicate_clusters




