

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_							:					Any		 =   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_							:					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 							__SCREAMING_SNAKE_CASE     (   a__    :   str       )					->				str:
	__A :			List[Any]		    =			EfficientNetConfig()
	__A :			Union[str, Any]		    =			CONFIG_MAP[model_name]["""hidden_dim"""]
	__A :			str		    =			CONFIG_MAP[model_name]["""width_coef"""]
	__A :			List[Any]		    =			CONFIG_MAP[model_name]["""depth_coef"""]
	__A :			Union[str, Any]		    =			CONFIG_MAP[model_name]["""image_size"""]
	__A :			Tuple		    =			CONFIG_MAP[model_name]["""dropout_rate"""]
	__A :			Optional[Any]		    =			CONFIG_MAP[model_name]["""dw_padding"""]

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

	__A :			Any		    =			idalabel
	__A :			Any		    =			{v: k for k, v in idalabel.items()}
	return config
def 							__SCREAMING_SNAKE_CASE     (   )					->				int:
	__A :			Optional[Any]		    =			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	__A :			int		    =			Image.open(requests.get(a__			,stream=a__       ).raw       )
	return im
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Any       )					->				Optional[int]:
	__A :			Any		    =			CONFIG_MAP[model_name]["""image_size"""]
	__A :			Any		    =			EfficientNetImageProcessor(
	    size={"""height""": size, """width""": size}			,image_mean=[0.485, 0.456, 0.406]			,image_std=[0.47_853_944, 0.4_732_864, 0.47_434_163]			,do_center_crop=a__			,)
	return preprocessor
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]       )					->				str:
	__A :			Optional[int]		    =			[v.split("""_"""       )[0].split("""block"""       )[1] for v in original_param_names if v.startswith("""block"""       )]
	__A :			Union[str, Any]		    =			sorted(set(a__       )       )
	__A :			Union[str, Any]		    =			len(a__       )
	__A :			Tuple		    =			{b: str(a__       ) for b, i in zip(a__			,range(a__       )       )}

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

	__A :			Dict		    =			"""classifier.weight"""
	__A :			List[str]		    =			"""classifier.bias"""
	return key_mapping
def 							__SCREAMING_SNAKE_CASE     (   a__    :   List[str]			,a__    :   str			,a__    :   Any       )					->				Optional[int]:
	for key, value in tf_params.items():
		if "normalization" in key:
			continue

		__A :			Any		    =			key_mapping[key]
		if "_conv" in key and "kernel" in key:
			__A :			Dict		    =			torch.from_numpy(a__       ).permute(3			,2			,0			,1       )
		elif "depthwise_kernel" in key:
			__A :			List[Any]		    =			torch.from_numpy(a__       ).permute(2			,3			,0			,1       )
		elif "kernel" in key:
			__A :			Tuple		    =			torch.from_numpy(np.transpose(a__       )       )
		else:
			__A :			List[Any]		    =			torch.from_numpy(a__       )

		# Replace HF parameters with original TF model parameters
		assert hf_params[hf_key].shape == new_hf_value.shape
		hf_params[hf_key].copy_(a__       )
@torch.no_grad()
def 							__SCREAMING_SNAKE_CASE     (   a__    :   Optional[Any]			,a__    :   Dict			,a__    :   Any			,a__    :   Dict       )					->				Tuple:
	__A :			Union[str, Any]		    =			model_classes[model_name](
	    include_top=a__			,weights="""imagenet"""			,input_tensor=a__			,input_shape=a__			,pooling=a__			,classes=1000			,classifier_activation="""softmax"""			,)

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

	# Load HuggingFace model
	__A :			str		    =			get_efficientnet_config(a__       )
	__A :			int		    =			EfficientNetForImageClassification(a__       ).eval()
	__A :			Optional[Any]		    =			hf_model.state_dict()

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

	# Initialize preprocessor and preprocess input image
	__A :			Tuple		    =			convert_image_processor(a__       )
	__A :			Union[str, Any]		    =			preprocessor(images=prepare_img()			,return_tensors="""pt"""       )

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

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

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

	if push_to_hub:
		# Push model and image processor to hub
		print(f"""Pushing converted {model_name} to the hub..."""       )
		__A :			List[str]		    =			f"""efficientnet-{model_name}"""
		preprocessor.push_to_hub(a__       )
		hf_model.push_to_hub(a__       )


if __name__ == "__main__":
			UpperCAmelCase_							:					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_							:					Optional[int]		 =   parser.parse_args()
			convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)








from __future__ import annotations

from collections.abc import Sequence
from typing import Literal
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str       )					->				str | Literal[False]:
	__A :			Tuple		    =			list(a__       )
	__A :			Optional[int]		    =			list(a__       )
	__A :			int		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count += 1
			__A :			int		    =			"""_"""
	if count > 1:
		return False
	else:
		return "".join(a__       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]       )					->				list[str]:
	__A :			Optional[Any]		    =			[]
	while True:
		__A :			Tuple		    =			["""$"""] * len(a__       )
		__A :			Union[str, Any]		    =			[]
		for i in range(len(a__       )       ):
			for j in range(i + 1			,len(a__       )       ):
				__A :			int		    =			compare_string(binary[i]			,binary[j]       )
				if k is False:
					__A :			List[str]		    =			"""*"""
					__A :			Any		    =			"""*"""
					temp.append("""X"""       )
		for i in range(len(a__       )       ):
			if checka[i] == "$":
				pi.append(binary[i]       )
		if len(a__       ) == 0:
			return pi
		__A :			Optional[Any]		    =			list(set(a__       )       )
def 							__SCREAMING_SNAKE_CASE     (   a__    :   int			,a__    :   Sequence[float]       )					->				list[str]:
	__A :			List[str]		    =			[]
	for minterm in minterms:
		__A :			List[Any]		    =			""""""
		for _ in range(a__       ):
			__A :			Union[str, Any]		    =			str(minterm % 2       ) + string
			minterm //= 2
		temp.append(a__       )
	return temp
def 							__SCREAMING_SNAKE_CASE     (   a__    :   str			,a__    :   str			,a__    :   int       )					->				bool:
	__A :			Optional[Any]		    =			list(a__       )
	__A :			Tuple		    =			list(a__       )
	__A :			Any		    =			0
	for i in range(len(a__       )       ):
		if lista[i] != lista[i]:
			count_n += 1
	return count_n == count
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[list[int]]			,a__    :   list[str]       )					->				list[str]:
	__A :			Optional[int]		    =			[]
	__A :			Tuple		    =			[0] * len(a__       )
	for i in range(len(chart[0]       )       ):
		__A :			str		    =			0
		__A :			Any		    =			-1
		for j in range(len(a__       )       ):
			if chart[j][i] == 1:
				count += 1
				__A :			Optional[Any]		    =			j
		if count == 1:
			__A :			int		    =			1
	for i in range(len(a__       )       ):
		if select[i] == 1:
			for j in range(len(chart[0]       )       ):
				if chart[i][j] == 1:
					for k in range(len(a__       )       ):
						__A :			List[str]		    =			0
			temp.append(prime_implicants[i]       )
	while True:
		__A :			Optional[Any]		    =			0
		__A :			Any		    =			-1
		__A :			int		    =			0
		for i in range(len(a__       )       ):
			__A :			List[Any]		    =			chart[i].count(1       )
			if count_n > max_n:
				__A :			Dict		    =			count_n
				__A :			Tuple		    =			i

		if max_n == 0:
			return temp

		temp.append(prime_implicants[rem]       )

		for i in range(len(chart[0]       )       ):
			if chart[rem][i] == 1:
				for j in range(len(a__       )       ):
					__A :			Union[str, Any]		    =			0
def 							__SCREAMING_SNAKE_CASE     (   a__    :   list[str]			,a__    :   list[str]       )					->				list[list[int]]:
	__A :			Any		    =			[[0 for x in range(len(a__       )       )] for x in range(len(a__       )       )]
	for i in range(len(a__       )       ):
		__A :			List[Any]		    =			prime_implicants[i].count("""_"""       )
		for j in range(len(a__       )       ):
			if is_for_table(prime_implicants[i]			,binary[j]			,a__       ):
				__A :			Union[str, Any]		    =			1

	return chart
def 							__SCREAMING_SNAKE_CASE     (   )					->				None:
	__A :			Any		    =			int(input("""Enter the no. of variables\n"""       )       )
	__A :			List[str]		    =			[
	    float(a__       )
	    for x in input(
	        """Enter the decimal representation of Minterms 'Spaces Separated'\n"""       ).split()
	]
	__A :			Dict		    =			decimal_to_binary(a__			,a__       )

	__A :			Union[str, Any]		    =			check(a__       )
	print("""Prime Implicants are:"""       )
	print(a__       )
	__A :			Optional[Any]		    =			prime_implicant_chart(a__			,a__       )

	__A :			Any		    =			selection(a__			,a__       )
	print("""Essential Prime Implicants are:"""       )
	print(a__       )


if __name__ == "__main__":
			import doctest

			doctest.testmod()
			main()





