






"""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()
_snake_case          =   logging.get_logger(__name__)

def 			lowerCAmelCase__     (     UpperCamelCase__				):



  '''simple docstring'''
  _a  :       str             =	MobileNetVaConfig(layer_norm_eps=0.001				)

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

  _a  :       str             =	re.match(R"""^mobilenet_v1_([^_]*)_([^_]*)$"""     ,   __snake_case				)
  if matches:
    _a  :       List[Any]             =	float(matches[1]				)
    _a  :       Tuple             =	int(matches[2]				)

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

  return config

def 			lowerCAmelCase__     (     ):



  '''simple docstring'''
  _a  :       int             =	"http://images.cocodataset.org/val2017/000000039769.jpg"
  _a  :       Optional[Any]             =	Image.open(requests.get(__snake_case     ,   stream=__snake_case				).raw				)
  return im

@torch.no_grad()
def 			lowerCAmelCase__     (     UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__     ,   UpperCamelCase__=False				):



  '''simple docstring'''
  _a  :       List[Any]             =	get_mobilenet_va_config(__snake_case				)

  # Load 🤗 model
  _a  :       Tuple             =	MobileNetVaForImageClassification(__snake_case				).eval()

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

  # Check outputs on an image, prepared by MobileNetV1ImageProcessor
  _a  :       Any             =	MobileNetVaImageProcessor(
      crop_size={"""width""": config.image_size, """height""": config.image_size}     ,   size={"""shortest_edge""": config.image_size + 3_2}     ,   )
  _a  :       Tuple             =	image_processor(images=prepare_img()     ,   return_tensors="""pt"""				)
  _a  :       Any             =	model(**__snake_case				)
  _a  :       List[Any]             =	outputs.logits

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

  if model_name == "mobilenet_v1_1.0_224":
    _a  :       Tuple             =	torch.tensor([-4.1_739, -1.1_233, 3.1_205]				)
  elif model_name == "mobilenet_v1_0.75_192":
    _a  :       Any             =	torch.tensor([-3.9_440, -2.3_141, -0.3_333]				)
  else:
    _a  :       Union[str, Any]             =	None

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

  Path(__snake_case				).mkdir(exist_ok=__snake_case				)
  print(F"""Saving model {model_name} to {pytorch_dump_folder_path}"""				)
  model.save_pretrained(__snake_case				)
  print(F"""Saving image processor to {pytorch_dump_folder_path}"""				)
  image_processor.save_pretrained(__snake_case				)

  if push_to_hub:
    print("""Pushing to the hub..."""				)
    _a  :       str             =	"google/" + model_name
    image_processor.push_to_hub(__snake_case				)
    model.push_to_hub(__snake_case				)


if __name__ == "__main__":
 _snake_case          =   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.'
 )

 _snake_case          =   parser.parse_args()
 convert_movilevit_checkpoint(
     args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
 )






"""simple docstring"""






from __future__ import annotations

import time

import numpy as np

__snake_case							:   Optional[Any]      =    [8, 5, 9, 7]
__snake_case							:   List[Any]      =    [
    [2, 0, 1, 1],
    [0, 1, 2, 1],
    [4, 0, 0, 3],
    [0, 2, 1, 0],
    [1, 0, 3, 0],
]
__snake_case							:   Optional[int]      =    [
    [3, 2, 1, 4],
    [0, 2, 5, 2],
    [5, 1, 0, 5],
    [1, 5, 3, 0],
    [3, 0, 3, 3],
]



class 			A__   :




	'''simple docstring'''





	def __init__(     self:		Any       ,			_SCREAMING_SNAKE_CASE:		list[int]       ,			_SCREAMING_SNAKE_CASE:		list[list[int]]       ,			_SCREAMING_SNAKE_CASE:		list[list[int]]       ,			)				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Any												=       claim_vector
				__lowerCAmelCase      :  Tuple												=       allocated_resources_table
				__lowerCAmelCase      :  Tuple												=       maximum_claim_table

	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       list[int]:

				"""simple docstring"""

				return [
				    sum(p_item[i] for p_item in self.__allocated_resources_table)
				    for i in range(len(self.__allocated_resources_table[0]))
				]

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       list[int]:

				"""simple docstring"""

				return np.array(self.__claim_vector) - np.array(
				    self.__processes_resource_summation())

	def 							_SCREAMING_SNAKE_CASE   (     self:		int)				->       list[list[int]]:

				"""simple docstring"""

				return [
				    list(np.array(self.__maximum_claim_table[i]) - np.array(_SCREAMING_SNAKE_CASE))
				    for i, allocated_resource in enumerate(self.__allocated_resources_table)
				]

	def 							_SCREAMING_SNAKE_CASE   (     self:		List[Any])				->       dict[int, list[int]]:

				"""simple docstring"""

				return {self.__need().index(_SCREAMING_SNAKE_CASE): i for i in self.__need()}

	def 							_SCREAMING_SNAKE_CASE   (     self:		Optional[Any]       ,			**_SCREAMING_SNAKE_CASE:		List[Any])				->       None:

				"""simple docstring"""

				__lowerCAmelCase      :  Optional[int]												=       self.__need()
				__lowerCAmelCase      :  int												=       self.__allocated_resources_table
				__lowerCAmelCase      :  Dict												=       self.__available_resources()
				__lowerCAmelCase      :  str												=       self.__need_index_manager()
				for kw, val in kwargs.items():
							if kw and val is True:
										self.__pretty_data()
				print("_" * 50 + "\n")
				while need_list:
							__lowerCAmelCase      :  int												=       False
							for each_need in need_list:
										__lowerCAmelCase      :  Dict												=       True
										for index, need in enumerate(_SCREAMING_SNAKE_CASE):
													if need > available_resources[index]:
																__lowerCAmelCase      :  Dict												=       False
																break
										if execution:
													__lowerCAmelCase      :  Any												=       True
													# get the original index of the process from ind_ctrl db
													for original_need_index, need_clone in need_index_manager.items():
																if each_need == need_clone:
																			__lowerCAmelCase      :  Union[str, Any]												=       original_need_index
													print(F"""Process {process_number + 1} is executing.""")
													# remove the process run from stack
													need_list.remove(_SCREAMING_SNAKE_CASE)
													# update available/freed resources stack
													__lowerCAmelCase      :  Dict												=       np.array(_SCREAMING_SNAKE_CASE) + np.array(
													    alloc_resources_table[process_number])
													print(
													    "Updated available resource stack for processes: "
													    + " ".join([str(_SCREAMING_SNAKE_CASE) for x in available_resources]))
													break
							if safe:
										print("The process is in a safe state.\n")
							else:
										print("System in unsafe state. Aborting...\n")
										break







	def 							_SCREAMING_SNAKE_CASE   (     self:		Union[str, Any])				->       List[Any]:

				"""simple docstring"""

				print(" " * 9 + "Allocated Resource Table")
				for item in self.__allocated_resources_table:
							print(
							    F"""P{self.__allocated_resources_table.index(_SCREAMING_SNAKE_CASE) + 1}"""
							    + " ".join(F"""{it:>8}""" for it in item)
							    + "\n")
				print(" " * 9 + "System Resource Table")
				for item in self.__maximum_claim_table:
							print(
							    F"""P{self.__maximum_claim_table.index(_SCREAMING_SNAKE_CASE) + 1}"""
							    + " ".join(F"""{it:>8}""" for it in item)
							    + "\n")
				print(
				    "Current Usage by Active Processes: "
				    + " ".join(str(_SCREAMING_SNAKE_CASE) for x in self.__claim_vector))
				print(
				    "Initial Available Resources:       "
				    + " ".join(str(_SCREAMING_SNAKE_CASE) for x in self.__available_resources()))
				time.sleep(1)


if __name__ == "__main__":
	import doctest

	doctest.testmod()