"""simple docstring"""



import argparse
import json
from pathlib import Path

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

from transformers import ConvNextConfig, ConvNextForImageClassification, ConvNextImageProcessor
from transformers.utils import logging


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

def    _A(		lowerCAmelCase       ):
							A__					:    Tuple														= ConvNextConfig()

							if "tiny" in checkpoint_url:
														A__					:    Any														= [3, 3, 9, 3]
														A__					:    Optional[Any]														= [96, 192, 384, 768]
							if "small" in checkpoint_url:
														A__					:    List[str]														= [3, 3, 27, 3]
														A__					:    Union[str, Any]														= [96, 192, 384, 768]
							if "base" in checkpoint_url:
														A__					:    int														= [3, 3, 27, 3]
														A__					:    int														= [128, 256, 512, 1024]
							if "large" in checkpoint_url:
														A__					:    int														= [3, 3, 27, 3]
														A__					:    Optional[Any]														= [192, 384, 768, 1536]
							if "xlarge" in checkpoint_url:
														A__					:    Optional[int]														= [3, 3, 27, 3]
														A__					:    Any														= [256, 512, 1024, 2048]

							if "1k" in checkpoint_url:
														A__					:    Optional[int]														= 1000
														A__					:    Union[str, Any]														= """imagenet-1k-id2label.json"""
														A__					:    Tuple														= (1, 1000)
							else:
														A__					:    List[Any]														= 2_1841
														A__					:    List[Any]														= """imagenet-22k-id2label.json"""
														A__					:    int														= (1, 2_1841)

							A__					:    Union[str, Any]														= """huggingface/label-files"""
							A__					:    str														= num_labels
							A__					:    Any														= json.load(open(hf_hub_download(__snake_case ,     __snake_case ,     repo_type="""dataset"""       ) ,     """r"""       )       )
							A__					:    Dict														= {int(__snake_case       ): v for k, v in idalabel.items()}
							if "1k" not in checkpoint_url:
														# this dataset contains 21843 labels but the model only has 21841
														# we delete the classes as mentioned in https://github.com/google-research/big_transfer/issues/18
														del idalabel[9205]
														del idalabel[1_5027]
							A__					:    List[str]														= idalabel
							A__					:    Any														= {v: k for k, v in idalabel.items()}
							A__					:    Union[str, Any]														= hidden_sizes
							A__					:    int														= depths

							return config, expected_shape

def    _A(		lowerCAmelCase       ):
							if "downsample_layers.0.0" in name:
														A__					:    Optional[Any]														= name.replace("""downsample_layers.0.0""" ,     """embeddings.patch_embeddings"""       )
							if "downsample_layers.0.1" in name:
														A__					:    Optional[Any]														= name.replace("""downsample_layers.0.1""" ,     """embeddings.norm"""       )  # we rename to layernorm later on
							if "downsample_layers.1.0" in name:
														A__					:    int														= name.replace("""downsample_layers.1.0""" ,     """stages.1.downsampling_layer.0"""       )
							if "downsample_layers.1.1" in name:
														A__					:    Tuple														= name.replace("""downsample_layers.1.1""" ,     """stages.1.downsampling_layer.1"""       )
							if "downsample_layers.2.0" in name:
														A__					:    int														= name.replace("""downsample_layers.2.0""" ,     """stages.2.downsampling_layer.0"""       )
							if "downsample_layers.2.1" in name:
														A__					:    Dict														= name.replace("""downsample_layers.2.1""" ,     """stages.2.downsampling_layer.1"""       )
							if "downsample_layers.3.0" in name:
														A__					:    int														= name.replace("""downsample_layers.3.0""" ,     """stages.3.downsampling_layer.0"""       )
							if "downsample_layers.3.1" in name:
														A__					:    str														= 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__					:    Optional[int]														= name[: len("""stages.0"""       )] + """.layers""" + name[len("""stages.0"""       ) :]
							if "stages" in name:
														A__					:    List[Any]														= name.replace("""stages""" ,     """encoder.stages"""       )
							if "norm" in name:
														A__					:    Dict														= name.replace("""norm""" ,     """layernorm"""       )
							if "gamma" in name:
														A__					:    Union[str, Any]														= name.replace("""gamma""" ,     """layer_scale_parameter"""       )
							if "head" in name:
														A__					:    int														= name.replace("""head""" ,     """classifier"""       )

							return name

def    _A(		):
							A__					:    List[str]														= """http://images.cocodataset.org/val2017/000000039769.jpg"""
							A__					:    str														= Image.open(requests.get(__snake_case ,     stream=__snake_case       ).raw       )
							return im

@torch.no_grad()
def    _A(		lowerCAmelCase ,     lowerCAmelCase       ):
							A__							,       A__					:    Tuple														= get_convnext_config(__snake_case       )
							# load original state_dict from URL
							A__					:    Union[str, Any]														= torch.hub.load_state_dict_from_url(__snake_case       )["""model"""]
							# rename keys
							for key in state_dict.copy().keys():
														A__					:    Union[str, Any]														= state_dict.pop(__snake_case       )
														A__					:    Dict														= val
							# add prefix to all keys expect classifier head
							for key in state_dict.copy().keys():
														A__					:    Optional[int]														= state_dict.pop(__snake_case       )
														if not key.startswith("""classifier"""       ):
																					A__					:    List[Any]														= """convnext.""" + key
														A__					:    Optional[int]														= val

							# load HuggingFace model
							A__					:    str														= ConvNextForImageClassification(__snake_case       )
							model.load_state_dict(__snake_case       )
							model.eval()

							# Check outputs on an image, prepared by ConvNextImageProcessor
							A__					:    Any														= 224 if """224""" in checkpoint_url else 384
							A__					:    int														= ConvNextImageProcessor(size=__snake_case       )
							A__					:    List[Any]														= image_processor(images=prepare_img() ,     return_tensors="""pt"""       ).pixel_values

							A__					:    Optional[Any]														= model(__snake_case       ).logits

							# note: the logits below were obtained without center cropping
							if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
														A__					:    Dict														= torch.tensor([-0.1210, -0.6605, 0.1918]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
														A__					:    Optional[int]														= torch.tensor([-0.4473, -0.1847, -0.6365]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
														A__					:    Optional[Any]														= torch.tensor([0.4525, 0.7539, 0.0308]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
														A__					:    Dict														= torch.tensor([0.3561, 0.6350, -0.0384]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
														A__					:    Optional[Any]														= torch.tensor([0.4174, -0.0989, 0.1489]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
														A__					:    str														= torch.tensor([0.2513, -0.1349, -0.1613]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
														A__					:    List[Any]														= torch.tensor([1.2980, 0.3631, -0.1198]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
														A__					:    List[Any]														= torch.tensor([1.2963, 0.1227, 0.1723]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
														A__					:    Tuple														= torch.tensor([1.7956, 0.8390, 0.2820]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
														A__					:    Optional[int]														= torch.tensor([-0.2822, -0.0502, -0.0878]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
														A__					:    Optional[Any]														= torch.tensor([-0.5672, -0.0730, -0.4348]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
														A__					:    List[Any]														= torch.tensor([0.2681, 0.2365, 0.6246]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
														A__					:    Dict														= torch.tensor([-0.2642, 0.3931, 0.5116]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
														A__					:    Optional[int]														= torch.tensor([-0.6677, -0.1873, -0.8379]       )
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
														A__					:    Union[str, Any]														= torch.tensor([-0.7749, -0.2967, -0.6444]       )
							else:
														raise ValueError(F'''Unknown URL: {checkpoint_url}'''       )

							assert torch.allclose(logits[0, :3] ,     __snake_case ,     atol=1E-3       )
							assert logits.shape == expected_shape

							Path(__snake_case       ).mkdir(exist_ok=__snake_case       )
							print(F'''Saving model 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       )

							print("""Pushing model to the hub..."""       )
							A__					:    List[Any]														= """convnext"""
							if "tiny" in checkpoint_url:
														model_name += "-tiny"
							elif "small" in checkpoint_url:
														model_name += "-small"
							elif "base" in checkpoint_url:
														model_name += "-base"
							elif "xlarge" in checkpoint_url:
														model_name += "-xlarge"
							elif "large" in checkpoint_url:
														model_name += "-large"
							if "224" in checkpoint_url:
														model_name += "-224"
							elif "384" in checkpoint_url:
														model_name += "-384"
							if "22k" in checkpoint_url and "1k" not in checkpoint_url:
														model_name += "-22k"
							if "22k" in checkpoint_url and "1k" in checkpoint_url:
														model_name += "-22k-1k"

							model.push_to_hub(
							    repo_path_or_name=Path(__snake_case ,     __snake_case       ) ,     organization="""nielsr""" ,     commit_message="""Add model""" ,     )


if __name__ == "__main__":
				_UpperCamelCase         =		argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    "--checkpoint_url",
				    default="https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth",
				    type=str,
				    help="URL of the original ConvNeXT checkpoint you'd like to convert.",
				)
				parser.add_argument(
				    "--pytorch_dump_folder_path",
				    default=None,
				    type=str,
				    required=True,
				    help="Path to the output PyTorch model directory.",
				)

				_UpperCamelCase         =		parser.parse_args()
				convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






"""simple docstring"""



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


_UpperCamelCase         =		get_logger(__name__)

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

def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase=None ,     lowerCAmelCase=None ,     **lowerCAmelCase ,     ):
							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__					:    Union[str, Any]														= """cpu"""
							elif map_location == "on_device":
														A__					:    str														= PartialState().device
							# Model states
							for i, model in enumerate(lowerCAmelCase       ):
														A__					:    List[str]														= F'''{MODEL_NAME}.bin''' if i == 0 else F'''{MODEL_NAME}_{i}.bin'''
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														models[i].load_state_dict(torch.load(lowerCAmelCase ,     map_location=lowerCAmelCase       ) ,     **lowerCAmelCase       )
							logger.info("""All model weights loaded successfully"""       )

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

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

							# GradScaler state
							if scaler is not None:
														A__					:    Optional[Any]														= os.path.join(lowerCAmelCase ,     lowerCAmelCase       )
														scaler.load_state_dict(torch.load(lowerCAmelCase       )       )
														logger.info("""GradScaler state loaded successfully"""       )

							# Random states
							try:
														A__					:    Union[str, Any]														= torch.load(os.path.join(lowerCAmelCase ,     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    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    int														= Path(lowerCAmelCase       ) / F'''custom_checkpoint_{index}.pkl'''
							logger.info(F'''Saving the state of {get_pretty_name(lowerCAmelCase       )} to {save_location}'''       )
							torch.save(obj.state_dict() ,     lowerCAmelCase       )



def    _A(		lowerCAmelCase ,     lowerCAmelCase ,     lowerCAmelCase = 0       ):
							A__					:    List[str]														= F'''{path}/custom_checkpoint_{index}.pkl'''
							logger.info(F'''Loading the state of {get_pretty_name(lowerCAmelCase       )} from {load_location}'''       )
							obj.load_state_dict(torch.load(lowerCAmelCase ,     map_location="""cpu"""       )       )





