






'''simple docstring'''
import argparse
import json
import os

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

from transformers import ConvNextImageProcessor, ConvNextVaConfig, ConvNextVaForImageClassification
from transformers.image_utils import PILImageResampling
from transformers.utils import logging


logging.set_verbosity_info()
__lowerCAmelCase							=logging.get_logger(__name__)

def 							a	(   _UpperCAmelCase )				->				List[str]:






					"""simple docstring"""







					a_									=   ConvNextVaConfig()

					if "atto" in checkpoint_url:
										a_									=   [2, 2, 6, 2]
										a_									=   [4_0, 8_0, 1_6_0, 3_2_0]
					if "femto" in checkpoint_url:
										a_									=   [2, 2, 6, 2]
										a_									=   [4_8, 9_6, 1_9_2, 3_8_4]
					if "pico" in checkpoint_url:
										a_									=   [2, 2, 6, 2]
										a_									=   [6_4, 1_2_8, 2_5_6, 5_1_2]
					if "nano" in checkpoint_url:
										a_									=   [2, 2, 8, 2]
										a_									=   [8_0, 1_6_0, 3_2_0, 6_4_0]
					if "tiny" in checkpoint_url:
										a_									=   [3, 3, 9, 3]
										a_									=   [9_6, 1_9_2, 3_8_4, 7_6_8]
					if "base" in checkpoint_url:
										a_									=   [3, 3, 2_7, 3]
										a_									=   [1_2_8, 2_5_6, 5_1_2, 1_0_2_4]
					if "large" in checkpoint_url:
										a_									=   [3, 3, 2_7, 3]
										a_									=   [1_9_2, 3_8_4, 7_6_8, 1_5_3_6]
					if "huge" in checkpoint_url:
										a_									=   [3, 3, 2_7, 3]
										a_									=   [3_5_2, 7_0_4, 1_4_0_8, 2_8_1_6]

					a_									=   1_0_0_0
					a_									=   'imagenet-1k-id2label.json'
					a_									=   (1, 1_0_0_0)

					a_									=   'huggingface/label-files'
					a_									=   num_labels
					a_									=   json.load(open(hf_hub_download(_UpperCAmelCase					,  _UpperCAmelCase					,  repo_type='dataset' )					,  'r' ) )
					a_									=   {int(_UpperCAmelCase ): v for k, v in idalabel.items()}

					a_									=   idalabel
					a_									=   {v: k for k, v in idalabel.items()}
					a_									=   hidden_sizes
					a_									=   depths

					return config, expected_shape

def 							a	(   _UpperCAmelCase )				->				Optional[int]:






					"""simple docstring"""







					if "downsample_layers.0.0" in name:
										a_									=   name.replace('downsample_layers.0.0'					,  'embeddings.patch_embeddings' )
					if "downsample_layers.0.1" in name:
										a_									=   name.replace('downsample_layers.0.1'					,  'embeddings.norm' )  # we rename to layernorm later on
					if "downsample_layers.1.0" in name:
										a_									=   name.replace('downsample_layers.1.0'					,  'stages.1.downsampling_layer.0' )
					if "downsample_layers.1.1" in name:
										a_									=   name.replace('downsample_layers.1.1'					,  'stages.1.downsampling_layer.1' )
					if "downsample_layers.2.0" in name:
										a_									=   name.replace('downsample_layers.2.0'					,  'stages.2.downsampling_layer.0' )
					if "downsample_layers.2.1" in name:
										a_									=   name.replace('downsample_layers.2.1'					,  'stages.2.downsampling_layer.1' )
					if "downsample_layers.3.0" in name:
										a_									=   name.replace('downsample_layers.3.0'					,  'stages.3.downsampling_layer.0' )
					if "downsample_layers.3.1" in name:
										a_									=   name.replace('downsample_layers.3.1'					,  'stages.3.downsampling_layer.1' )
					if "stages" in name and "downsampling_layer" not in name:
										# stages.0.0. for instance should be renamed to stages.0.layers.0.
										a_									=   name[: len('stages.0' )] + '.layers' + name[len('stages.0' ) :]
					if "gamma" in name:
										a_									=   name.replace('gamma'					,  'weight' )
					if "beta" in name:
										a_									=   name.replace('beta'					,  'bias' )
					if "stages" in name:
										a_									=   name.replace('stages'					,  'encoder.stages' )
					if "norm" in name:
										a_									=   name.replace('norm'					,  'layernorm' )
					if "head" in name:
										a_									=   name.replace('head'					,  'classifier' )

					return name

def 							a	(   )				->				str:






					"""simple docstring"""







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

def 							a	(   _UpperCAmelCase )				->				Tuple:






					"""simple docstring"""







					if "224" in checkpoint_url:
										a_									=   2_2_4
										a_									=   2_2_4 / 2_5_6
					elif "384" in checkpoint_url:
										a_									=   3_8_4
										a_									=   None
					else:
										a_									=   5_1_2
										a_									=   None

					return ConvNextImageProcessor(
					    size=_UpperCAmelCase					,  crop_pct=_UpperCAmelCase					,  image_mean=[0.4_8_5, 0.4_5_6, 0.4_0_6]					,  image_std=[0.2_2_9, 0.2_2_4, 0.2_2_5]					,  resample=PILImageResampling.BICUBIC					,  )

@torch.no_grad()
def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase					,  _UpperCAmelCase )				->				Dict:






					"""simple docstring"""







					print('Downloading original model from checkpoint...' )
					# define ConvNeXTV2 configuration based on URL
					a_							,					a_									=   get_convnextva_config(_UpperCAmelCase )
					# load original state_dict from URL
					a_									=   torch.hub.load_state_dict_from_url(_UpperCAmelCase )['model']

					print('Converting model parameters...' )
					# rename keys
					for key in state_dict.copy().keys():
										a_									=   state_dict.pop(_UpperCAmelCase )
										a_									=   val
					# add prefix to all keys expect classifier head
					for key in state_dict.copy().keys():
										a_									=   state_dict.pop(_UpperCAmelCase )
										if not key.startswith('classifier' ):
															a_									=   'convnextv2.' + key
										a_									=   val

					# load HuggingFace model
					a_									=   ConvNextVaForImageClassification(_UpperCAmelCase )
					model.load_state_dict(_UpperCAmelCase )
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					a_									=   convert_preprocessor(_UpperCAmelCase )
					a_									=   preprocessor(images=prepare_img()					,  return_tensors='pt' )
					a_									=   model(**_UpperCAmelCase ).logits

					# note: the logits below were obtained without center cropping
					if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt":
										a_									=   torch.tensor([-0.3_9_3_0, 0.1_7_4_7, -0.5_2_4_6, 0.4_1_7_7, 0.4_2_9_5] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
										a_									=   torch.tensor([-0.1_7_2_7, -0.5_3_4_1, -0.7_8_1_8, -0.4_7_4_5, -0.6_5_6_6] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
										a_									=   torch.tensor([-0.0_3_3_3, 0.1_5_6_3, -0.9_1_3_7, 0.1_0_5_4, 0.0_3_8_1] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
										a_									=   torch.tensor([-0.1_7_4_4, -0.1_5_5_5, -0.0_7_1_3, 0.0_9_5_0, -0.1_4_3_1] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
										a_									=   torch.tensor([0.9_9_9_6, 0.1_9_6_6, -0.4_3_8_6, -0.3_4_7_2, 0.6_6_6_1] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
										a_									=   torch.tensor([-0.2_5_5_3, -0.6_7_0_8, -0.1_3_5_9, 0.2_5_1_8, -0.2_4_8_8] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
										a_									=   torch.tensor([-0.0_6_7_3, -0.5_6_2_7, -0.3_7_5_3, -0.2_7_2_2, 0.0_1_7_8] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
										a_									=   torch.tensor([-0.6_3_7_7, -0.7_4_5_8, -0.2_1_5_0, 0.1_1_8_4, -0.0_5_9_7] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
										a_									=   torch.tensor([1.0_7_9_9, 0.2_3_2_2, -0.8_8_6_0, 1.0_2_1_9, 0.6_2_3_1] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
										a_									=   torch.tensor([0.3_7_6_6, 0.4_9_1_7, -1.1_4_2_6, 0.9_9_4_2, 0.6_0_2_4] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
										a_									=   torch.tensor([0.4_2_2_0, -0.6_9_1_9, -0.4_3_1_7, -0.2_8_8_1, -0.6_6_0_9] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
										a_									=   torch.tensor([0.1_0_8_2, -0.8_2_8_6, -0.5_0_9_5, 0.4_6_8_1, -0.8_0_8_5] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
										a_									=   torch.tensor([-0.2_4_1_9, -0.6_2_2_1, 0.2_1_7_6, -0.0_9_8_0, -0.7_5_2_7] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
										a_									=   torch.tensor([0.0_3_9_1, -0.4_3_7_1, 0.3_7_8_6, 0.1_2_5_1, -0.2_7_8_4] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
										a_									=   torch.tensor([-0.0_5_0_4, 0.5_6_3_6, -0.1_7_2_9, -0.6_5_0_7, -0.3_9_4_9] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
										a_									=   torch.tensor([0.3_5_6_0, 0.9_4_8_6, 0.3_1_4_9, -0.2_6_6_7, -0.5_1_3_8] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
										a_									=   torch.tensor([-0.2_4_6_9, -0.4_5_5_0, -0.5_8_5_3, -0.0_8_1_0, 0.0_3_0_9] )
					elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
										a_									=   torch.tensor([-0.3_0_9_0, 0.0_8_0_2, -0.0_6_8_2, -0.1_9_7_9, -0.2_8_2_6] )
					else:
										raise ValueError(F'''Unknown URL: {checkpoint_url}''' )

					assert torch.allclose(logits[0, :5]					,  _UpperCAmelCase					,  atol=1E-3 )
					assert logits.shape == expected_shape
					print('Model outputs match the original results!' )

					if save_model:
										print('Saving model to local...' )
										# Create folder to save model
										if not os.path.isdir(_UpperCAmelCase ):
															os.mkdir(_UpperCAmelCase )

										model.save_pretrained(_UpperCAmelCase )
										preprocessor.save_pretrained(_UpperCAmelCase )

					a_									=   'convnextv2'
					if "atto" in checkpoint_url:
										model_name += "-atto"
					if "femto" in checkpoint_url:
										model_name += "-femto"
					if "pico" in checkpoint_url:
										model_name += "-pico"
					if "nano" in checkpoint_url:
										model_name += "-nano"
					elif "tiny" in checkpoint_url:
										model_name += "-tiny"
					elif "base" in checkpoint_url:
										model_name += "-base"
					elif "large" in checkpoint_url:
										model_name += "-large"
					elif "huge" in checkpoint_url:
										model_name += "-huge"
					if "22k" in checkpoint_url and "1k" not in checkpoint_url:
										model_name += "-22k"
					elif "22k" in checkpoint_url and "1k" in checkpoint_url:
										model_name += "-22k-1k"
					elif "1k" in checkpoint_url:
										model_name += "-1k"
					if "224" in checkpoint_url:
										model_name += "-224"
					elif "384" in checkpoint_url:
										model_name += "-384"
					elif "512" in checkpoint_url:
										model_name += "-512"

					if push_to_hub:
										print(F'''Pushing {model_name} to the hub...''' )
										model.push_to_hub(_UpperCAmelCase )
										preprocessor.push_to_hub(_UpperCAmelCase )


if __name__ == "__main__":
						__lowerCAmelCase							=argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    "--checkpoint_url",
						    default="https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_atto_1k_224_ema.pt",
						    type=str,
						    help="URL of the original ConvNeXTV2 checkpoint you'd like to convert.",
						)
						parser.add_argument(
						    "--pytorch_dump_folder_path",
						    default="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 preprocessor to the hub")

						__lowerCAmelCase							=parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)









'''simple docstring'''
import random
from typing import List, Optional, Union

import numpy as np
import torch

from ..state import AcceleratorState
from .constants import CUDA_DISTRIBUTED_TYPES
from .dataclasses import DistributedType, RNGType
from .imports import is_npu_available, is_tpu_available, is_xpu_available


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

def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = False )				->				List[str]:






					"""simple docstring"""







					if device_specific:
										seed += AcceleratorState().process_index
					random.seed(_UpperCAmelCase )
					np.random.seed(_UpperCAmelCase )
					torch.manual_seed(_UpperCAmelCase )
					if is_xpu_available():
										torch.xpu.manual_seed_all(_UpperCAmelCase )
					elif is_npu_available():
										torch.npu.manual_seed_all(_UpperCAmelCase )
					else:
										torch.cuda.manual_seed_all(_UpperCAmelCase )
					# ^^ safe to call this function even if cuda is not available
					if is_tpu_available():
										xm.set_rng_state(_UpperCAmelCase )

def 							a	(   _UpperCAmelCase = None					,  _UpperCAmelCase = None )				->				Tuple:






					"""simple docstring"""







					if rng_type == RNGType.TORCH:
										a_									=   torch.get_rng_state()
					elif rng_type == RNGType.CUDA:
										a_									=   torch.cuda.get_rng_state()
					elif rng_type == RNGType.XLA:
										assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
										a_									=   torch.tensor(xm.get_rng_state() )
					elif rng_type == RNGType.NPU:
										assert is_npu_available(), "Can't synchronize NPU seeds on an environment without NPUs."
										a_									=   torch.npu.get_rng_state()
					elif rng_type == RNGType.XPU:
										assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
										a_									=   torch.xpu.get_rng_state()
					elif rng_type == RNGType.GENERATOR:
										assert generator is not None, "Need a generator to synchronize its seed."
										a_									=   generator.get_state()

					# Broadcast the rng state from device 0 to other devices
					a_									=   AcceleratorState()
					if state.distributed_type == DistributedType.TPU:
										a_									=   rng_state.to(xm.xla_device() )
										xm.collective_broadcast([rng_state] )
										xm.mark_step()
										a_									=   rng_state.cpu()
					elif (
					    state.distributed_type in CUDA_DISTRIBUTED_TYPES
					    or state.distributed_type == DistributedType.MULTI_NPU
					    or state.distributed_type == DistributedType.MULTI_XPU
					):
										a_									=   rng_state.to(state.device )
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )
										a_									=   rng_state.cpu()
					elif state.distributed_type == DistributedType.MULTI_CPU:
										torch.distributed.broadcast(_UpperCAmelCase					,  0 )

					# Set the broadcast rng state
					if rng_type == RNGType.TORCH:
										torch.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.CUDA:
										torch.cuda.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.NPU:
										torch.npu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XPU:
										torch.xpu.set_rng_state(_UpperCAmelCase )
					elif rng_type == RNGType.XLA:
										xm.set_rng_state(rng_state.item() )
					elif rng_type == RNGType.GENERATOR:
										generator.set_state(_UpperCAmelCase )





def 							a	(   _UpperCAmelCase					,  _UpperCAmelCase = None )				->				Any:






					"""simple docstring"""







					for rng_type in rng_types:
										synchronize_rng_state(RNGType(_UpperCAmelCase )					,  generator=_UpperCAmelCase )

