

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


lowerCamelCase									= logging.get_logger(__name__)

lowerCamelCase									= {
    '''facebook/convnext-tiny-224''': '''https://huggingface.co/facebook/convnext-tiny-224/resolve/main/config.json''',
    # See all ConvNeXT models at https://huggingface.co/models?filter=convnext
}







class 						__magic_name__      (							lowerCamelCase__		,	lowerCamelCase__							):
   lowerCamelCase__			:     Optional[Any]    =       'convnext'



   def __init__(    self,   lowercase_=3,   lowercase_=4,   lowercase_=4,   lowercase_=None,   lowercase_=None,   lowercase_="gelu",   lowercase_=0.02,   lowercase_=1E-12,   lowercase_=1E-6,   lowercase_=0.0,   lowercase_=224,   lowercase_=None,   lowercase_=None,   **lowercase_,   )							->   str:






     """simple docstring"""






     super().__init__(**lowercase_	)

     a__  =num_channels
     a__  =patch_size
     a__  =num_stages
     a__  =[96, 192, 384, 768] if hidden_sizes is None else hidden_sizes
     a__  =[3, 3, 9, 3] if depths is None else depths
     a__  =hidden_act
     a__  =initializer_range
     a__  =layer_norm_eps
     a__  =layer_scale_init_value
     a__  =drop_path_rate
     a__  =image_size
     a__  =['''stem'''] + [F"""stage{idx}""" for idx in range(1,   len(self.depths	) + 1	)]
     a__,				a__  =get_aligned_output_features_output_indices(
         out_features=lowercase_,   out_indices=lowercase_,   stage_names=self.stage_names	)







class 						__magic_name__      (							lowerCamelCase__							):
   lowerCamelCase__			:     int    =       version.parse('1.11'							)



   @property
   def 			_UpperCAmelCase			(    self	)							->   Mapping[str, Mapping[int, str]]:






     """simple docstring"""






     return OrderedDict(
         [
             ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
         ]	)



   @property
   def 			_UpperCAmelCase			(    self	)							->   float:






     """simple docstring"""






     return 1E-5



import os
import random
from pathlib import Path
from typing import List

import numpy as np
import torch
from torch.cuda.amp import GradScaler

from .utils import (
    MODEL_NAME,
    OPTIMIZER_NAME,
    RNG_STATE_NAME,
    SCALER_NAME,
    SCHEDULER_NAME,
    get_pretty_name,
    is_tpu_available,
    is_xpu_available,
    save,
)


if is_tpu_available(check_device=False):
       import torch_xla.core.xla_model as xm

from .logging import get_logger
from .state import PartialState


lowerCamelCase									= get_logger(__name__)




def       UpperCAmelCase__    (      _A				:			str     ,				_A				:			List[dict]     ,				_A				:			list     ,				_A				:			list     ,				_A				:			int     ,				_A				:			GradScaler = None     ,				):





  '''simple docstring'''







  for i, state in enumerate(_A							):
    a__  =F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Model weights saved in {output_model_file}"""							)
  # Optimizer states
  for i, opt in enumerate(_A							):
    a__  =opt.state_dict()
    a__  =F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Optimizer state saved in {output_optimizer_file}"""							)
  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__  =scheduler.state_dict()
    a__  =F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    save(_A     ,				_A							)
    logger.info(F"""Scheduler state saved in {output_scheduler_file}"""							)
  # GradScaler state
  if scaler is not None:
    a__  =scaler.state_dict()
    a__  =os.path.join(_A     ,				_A							)
    torch.save(_A     ,				_A							)
    logger.info(F"""Gradient scaler state saved in {output_scaler_file}"""							)
  # Random number generator states
  a__  ={}
  a__  =F"""{RNG_STATE_NAME}_{process_index}.pkl"""
  a__  =random.getstate()
  a__  =np.random.get_state()
  a__  =torch.get_rng_state()
  if is_xpu_available():
    a__  =torch.xpu.get_rng_state_all()
  else:
    a__  =torch.cuda.get_rng_state_all()
  if is_tpu_available():
    a__  =xm.get_rng_state()
  a__  =os.path.join(_A     ,				_A							)
  torch.save(_A     ,				_A							)
  logger.info(F"""Random states saved in {output_states_file}"""							)
  return output_dir




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			int     ,				_A				:			Tuple     ,				_A				:			List[Any]     ,				_A				:			List[str]=None     ,				_A				:			Dict=None     ,				**_A				:			Tuple     ,				):





  '''simple docstring'''







  if map_location not in [None, "cpu", "on_device"]:
    raise TypeError(
        '''Unsupported optimizer map location passed, please choose one of `None`, `\'cpu\'`, or `\'on_device\'`'''							)
  if map_location is None:
    a__  ='''cpu'''
  elif map_location == "on_device":
    a__  =PartialState().device
  # Model states
  for i, model in enumerate(_A							):
    a__  =F"""{MODEL_NAME}.bin""" if i == 0 else F"""{MODEL_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    models[i].load_state_dict(torch.load(_A     ,				map_location=_A							)     ,				**_A							)
  logger.info('''All model weights loaded successfully'''							)

  # Optimizer states
  for i, opt in enumerate(_A							):
    a__  =F"""{OPTIMIZER_NAME}.bin""" if i == 0 else F"""{OPTIMIZER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    a__  =torch.load(_A     ,				map_location=_A							)
    optimizers[i].load_state_dict(_A							)
  logger.info('''All optimizer states loaded successfully'''							)

  # Scheduler states
  for i, scheduler in enumerate(_A							):
    a__  =F"""{SCHEDULER_NAME}.bin""" if i == 0 else F"""{SCHEDULER_NAME}_{i}.bin"""
    a__  =os.path.join(_A     ,				_A							)
    scheduler.load_state_dict(torch.load(_A							)							)
  logger.info('''All scheduler states loaded successfully'''							)

  # GradScaler state
  if scaler is not None:
    a__  =os.path.join(_A     ,				_A							)
    scaler.load_state_dict(torch.load(_A							)							)
    logger.info('''GradScaler state loaded successfully'''							)

  # Random states
  try:
    a__  =torch.load(os.path.join(_A     ,				F"""{RNG_STATE_NAME}_{process_index}.pkl"""							)							)
    random.setstate(states['''random_state''']							)
    np.random.set_state(states['''numpy_random_seed''']							)
    torch.set_rng_state(states['''torch_manual_seed''']							)
    if is_xpu_available():
      torch.xpu.set_rng_state_all(states['''torch_xpu_manual_seed''']							)
    else:
      torch.cuda.set_rng_state_all(states['''torch_cuda_manual_seed''']							)
    if is_tpu_available():
      xm.set_rng_state(states['''xm_seed''']							)
    logger.info('''All random states loaded successfully'''							)
  except Exception:
    logger.info('''Could not load random states'''							)




def       UpperCAmelCase__    (      _A				:			Tuple     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =Path(_A							) / F"""custom_checkpoint_{index}.pkl"""
  logger.info(F"""Saving the state of {get_pretty_name(_A							)} to {save_location}"""							)
  torch.save(obj.state_dict()     ,				_A							)




def       UpperCAmelCase__    (      _A				:			int     ,				_A				:			Tuple     ,				_A				:			int = 0							):





  '''simple docstring'''







  a__  =F"""{path}/custom_checkpoint_{index}.pkl"""
  logger.info(F"""Loading the state of {get_pretty_name(_A							)} from {load_location}"""							)
  obj.load_state_dict(torch.load(_A     ,				map_location='''cpu'''							)							)
