"""simple docstring"""






import argparse
import json
import re
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileNetVaConfig,
    MobileNetVaForImageClassification,
    MobileNetVaImageProcessor,
    load_tf_weights_in_mobilenet_va,
)
from transformers.utils import logging


logging.set_verbosity_info()
lowerCAmelCase__						:						str        =			logging.get_logger(__name__)



def 						a_		(      lowerCamelCase ):
				UpperCAmelCase__    =							MobileNetVaConfig(layer_norm_eps=0.001 )

				if "_quant" in model_name:
								raise ValueError('Quantized models are not supported.' )

				UpperCAmelCase__    =							re.match(r'^mobilenet_v1_([^_]*)_([^_]*)$'      ,    A_ )
				if matches:
								UpperCAmelCase__    =							float(matches[1] )
								UpperCAmelCase__    =							int(matches[2] )

				# The TensorFlow version of MobileNetV1 predicts 1001 classes instead of
				# the usual 1000. The first class (index 0) is "background".
				UpperCAmelCase__    =							1_0_0_1
				UpperCAmelCase__    =							'imagenet-1k-id2label.json'
				UpperCAmelCase__    =							'huggingface/label-files'
				UpperCAmelCase__    =							json.load(open(hf_hub_download(A_      ,    A_      ,    repo_type='dataset' )      ,    'r' ) )
				UpperCAmelCase__    =							{int(A_ ) + 1: v for k, v in idalabel.items()}
				UpperCAmelCase__    =							'background'
				UpperCAmelCase__    =							idalabel
				UpperCAmelCase__    =							{v: k for k, v in idalabel.items()}

				return config



def 						a_		(      ):
				UpperCAmelCase__    =							'http://images.cocodataset.org/val2017/000000039769.jpg'
				UpperCAmelCase__    =							Image.open(requests.get(A_      ,    stream=A_ ).raw )
				return im



@torch.no_grad()
def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase=False ):
				UpperCAmelCase__    =							get_mobilenet_va_config(A_ )

				# Load 🤗 model
				UpperCAmelCase__    =							MobileNetVaForImageClassification(A_ ).eval()

				# Load weights from TensorFlow checkpoint
				load_tf_weights_in_mobilenet_va(A_      ,    A_      ,    A_ )

				# Check outputs on an image, prepared by MobileNetV1ImageProcessor
				UpperCAmelCase__    =							MobileNetVaImageProcessor(
				    crop_size={'width': config.image_size, 'height': config.image_size}      ,    size={'shortest_edge': config.image_size + 3_2}      ,    )
				UpperCAmelCase__    =							image_processor(images=prepare_img()      ,    return_tensors='pt' )
				UpperCAmelCase__    =							model(**A_ )
				UpperCAmelCase__    =							outputs.logits

				assert logits.shape == (1, 1_0_0_1)

				if model_name == "mobilenet_v1_1.0_224":
								UpperCAmelCase__    =							torch.tensor([-4.1739, -1.1233, 3.1205] )
				elif model_name == "mobilenet_v1_0.75_192":
								UpperCAmelCase__    =							torch.tensor([-3.9440, -2.3141, -0.3333] )
				else:
								UpperCAmelCase__    =							None

				if expected_logits is not None:
								assert torch.allclose(logits[0, :3]      ,    A_      ,    atol=1e-4 )

				Path(A_ ).mkdir(exist_ok=A_ )
				print(f'''Saving model {model_name} to {pytorch_dump_folder_path}''' )
				model.save_pretrained(A_ )
				print(f'''Saving image processor to {pytorch_dump_folder_path}''' )
				image_processor.save_pretrained(A_ )

				if push_to_hub:
								print('Pushing to the hub...' )
								UpperCAmelCase__    =							'google/' + model_name
								image_processor.push_to_hub(A_ )
								model.push_to_hub(A_ )


if __name__ == "__main__":
						lowerCAmelCase__						:						str        =			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '--model_name',
						    default='mobilenet_v1_1.0_224',
						    type=str,
						    help='Name of the MobileNetV1 model you\'d like to convert. Should in the form \'mobilenet_v1_<depth>_<size>\'.',
						)
						parser.add_argument(
						    '--checkpoint_path', required=True, type=str, help='Path to the original TensorFlow checkpoint (.ckpt file).'
						)
						parser.add_argument(
						    '--pytorch_dump_folder_path', required=True, type=str, help='Path to the output PyTorch model directory.'
						)
						parser.add_argument(
						    '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
						)

						lowerCAmelCase__						:						str        =			parser.parse_args()
						convert_movilevit_checkpoint(
						    args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
						)




"""simple docstring"""






lowerCAmelCase__						:						Tuple		  =			range(2, 20 + 1)
lowerCAmelCase__						:						Optional[Any]		  =			[10**k for k in range(ks[-1] + 1)]
lowerCAmelCase__						:						dict[int, dict[int, list[list[int]]]]		  =			{}



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				UpperCAmelCase__    =							sum(a_i[j] for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ) )
				UpperCAmelCase__    =							sum(a_i[j] * base[j] for j in range(min(len(lowerCamelCase )      ,    lowerCamelCase ) ) )

				UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0
				UpperCAmelCase__    =							n - i

				UpperCAmelCase__    =							memo.get(lowerCamelCase )

				if sub_memo is not None:
								UpperCAmelCase__    =							sub_memo.get(lowerCamelCase )

								if jumps is not None and len(lowerCamelCase ) > 0:
												# find and make the largest jump without going over
												UpperCAmelCase__    =							-1
												for _k in range(len(lowerCamelCase ) - 1      ,    -1      ,    -1 ):
																if jumps[_k][2] <= k and jumps[_k][1] <= max_dn:
																				UpperCAmelCase__    =							_k
																				break

												if max_jump >= 0:
																UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							jumps[max_jump]
																# since the difference between jumps is cached, add c
																UpperCAmelCase__    =							diff + c
																for j in range(min(lowerCamelCase      ,    len(lowerCamelCase ) ) ):
																				UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
																if new_c > 0:
																				add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )

								else:
												UpperCAmelCase__    =							[]
				else:
								UpperCAmelCase__    =							{c: []}
								UpperCAmelCase__    =							sub_memo

				if dn >= max_dn or c + diff >= base[k]:
								return diff, dn

				if k > ks[0]:
								while True:
												# keep doing smaller jumps
												UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    k - 1      ,    i + dn      ,    lowerCamelCase )
												diff += _diff
												dn += terms_jumped

												if dn >= max_dn or c + diff >= base[k]:
																break
				else:
								# would be too small a jump, just compute sequential terms instead
								UpperCAmelCase__   ,			UpperCAmelCase__    =							compute(lowerCamelCase      ,    lowerCamelCase      ,    i + dn      ,    lowerCamelCase )
								diff += _diff
								dn += terms_jumped

				UpperCAmelCase__    =							sub_memo[c]

				# keep jumps sorted by # of terms skipped
				UpperCAmelCase__    =							0
				while j < len(lowerCamelCase ):
								if jumps[j][1] > dn:
												break
								j += 1

				# cache the jump for this value digitsum(b) and c
				sub_memo[c].insert(lowerCamelCase      ,    (diff, dn, k) )
				return (diff, dn)



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				if i >= n:
								return 0, i
				if k > len(lowerCamelCase ):
								a_i.extend([0 for _ in range(k - len(lowerCamelCase ) )] )

				# note: a_i -> b * 10^k + c
				# ds_b -> digitsum(b)
				# ds_c -> digitsum(c)
				UpperCAmelCase__    =							i
				UpperCAmelCase__   ,			UpperCAmelCase__   ,			UpperCAmelCase__    =							0, 0, 0
				for j in range(len(lowerCamelCase ) ):
								if j >= k:
												ds_b += a_i[j]
								else:
												ds_c += a_i[j]

				while i < n:
								i += 1
								UpperCAmelCase__    =							ds_c + ds_b
								diff += addend
								UpperCAmelCase__    =							0
								for j in range(lowerCamelCase ):
												UpperCAmelCase__    =							a_i[j] + addend
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )

												ds_c += a_i[j]

								if addend > 0:
												break

				if addend > 0:
								add(lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase )
				return diff, i - start_i



def 						a_		(      lowerCamelCase      ,    lowerCamelCase      ,    lowerCamelCase ):
				for j in range(lowerCamelCase      ,    len(lowerCamelCase ) ):
								UpperCAmelCase__    =							digits[j] + addend
								if s >= 1_0:
												UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
												UpperCAmelCase__    =							addend // 1_0 + quotient
								else:
												UpperCAmelCase__    =							s
												UpperCAmelCase__    =							addend // 1_0

								if addend == 0:
												break

				while addend > 0:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							divmod(lowerCamelCase      ,    1_0 )
								digits.append(lowerCamelCase )



def 						a_		(      lowerCamelCase = 1_0**1_5 ):
				UpperCAmelCase__    =							[1]
				UpperCAmelCase__    =							1
				UpperCAmelCase__    =							0
				while True:
								UpperCAmelCase__   ,			UpperCAmelCase__    =							next_term(lowerCamelCase      ,    2_0      ,    i + dn      ,    lowerCamelCase )
								dn += terms_jumped
								if dn == n - i:
												break

				UpperCAmelCase__    =							0
				for j in range(len(lowerCamelCase ) ):
								a_n += digits[j] * 1_0**j
				return a_n


if __name__ == "__main__":
						print(F"""{solution() = }""")



