

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()
SCREAMING_SNAKE_CASE_						=					logging.get_logger(__name__)

def 							__SCREAMING_SNAKE_CASE							( lowerCAmelCase:     Optional[Any]       )      ->				List[str]:
				_UpperCAmelCase				:						Union[str, Any]       						=			ConvNextConfig()

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

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

				_UpperCAmelCase				:						Dict       						=			"huggingface/label-files"
				_UpperCAmelCase				:						Tuple       						=			num_labels
				_UpperCAmelCase				:						List[Any]       						=			json.load(open(hf_hub_download(lowerCAmelCase     ,					lowerCAmelCase     ,					repo_type="dataset"       )     ,					"r"       )       )
				_UpperCAmelCase				:						Optional[Any]       						=			{int(lowerCAmelCase       ): 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]
				_UpperCAmelCase				:						Tuple       						=			idalabel
				_UpperCAmelCase				:						int       						=			{v: k for k, v in idalabel.items()}
				_UpperCAmelCase				:						str       						=			hidden_sizes
				_UpperCAmelCase				:						int       						=			depths

				return config, expected_shape

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

				return name

def 							__SCREAMING_SNAKE_CASE							( )      ->				Union[str, Any]:
				_UpperCAmelCase				:						Union[str, Any]       						=			"http://images.cocodataset.org/val2017/000000039769.jpg"
				_UpperCAmelCase				:						int       						=			Image.open(requests.get(lowerCAmelCase     ,					stream=lowerCAmelCase       ).raw       )
				return im

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

				# load HuggingFace model
				_UpperCAmelCase				:						List[str]       						=			ConvNextForImageClassification(lowerCAmelCase       )
				model.load_state_dict(lowerCAmelCase       )
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				_UpperCAmelCase				:						Tuple       						=			224 if "224" in checkpoint_url else 384
				_UpperCAmelCase				:						Dict       						=			ConvNextImageProcessor(size=lowerCAmelCase       )
				_UpperCAmelCase				:						Optional[Any]       						=			image_processor(images=prepare_img()     ,					return_tensors="pt"       ).pixel_values

				_UpperCAmelCase				:						List[str]       						=			model(lowerCAmelCase       ).logits

				# note: the logits below were obtained without center cropping
				if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
								_UpperCAmelCase				:						int       						=			torch.tensor([-0.1_210, -0.6_605, 0.1_918]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
								_UpperCAmelCase				:						Dict       						=			torch.tensor([-0.4_473, -0.1_847, -0.6_365]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
								_UpperCAmelCase				:						str       						=			torch.tensor([0.4_525, 0.7_539, 0.0_308]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
								_UpperCAmelCase				:						str       						=			torch.tensor([0.3_561, 0.6_350, -0.0_384]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
								_UpperCAmelCase				:						Optional[int]       						=			torch.tensor([0.4_174, -0.0_989, 0.1_489]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
								_UpperCAmelCase				:						Tuple       						=			torch.tensor([0.2_513, -0.1_349, -0.1_613]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
								_UpperCAmelCase				:						Tuple       						=			torch.tensor([1.2_980, 0.3_631, -0.1_198]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
								_UpperCAmelCase				:						int       						=			torch.tensor([1.2_963, 0.1_227, 0.1_723]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
								_UpperCAmelCase				:						int       						=			torch.tensor([1.7_956, 0.8_390, 0.2_820]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
								_UpperCAmelCase				:						Optional[Any]       						=			torch.tensor([-0.2_822, -0.0_502, -0.0_878]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
								_UpperCAmelCase				:						Optional[Any]       						=			torch.tensor([-0.5_672, -0.0_730, -0.4_348]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
								_UpperCAmelCase				:						Tuple       						=			torch.tensor([0.2_681, 0.2_365, 0.6_246]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
								_UpperCAmelCase				:						List[Any]       						=			torch.tensor([-0.2_642, 0.3_931, 0.5_116]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
								_UpperCAmelCase				:						List[str]       						=			torch.tensor([-0.6_677, -0.1_873, -0.8_379]       )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
								_UpperCAmelCase				:						Dict       						=			torch.tensor([-0.7_749, -0.2_967, -0.6_444]       )
				else:
								raise ValueError(F'Unknown URL: {checkpoint_url}'       )

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

				Path(lowerCAmelCase       ).mkdir(exist_ok=lowerCAmelCase       )
				print(F'Saving model to {pytorch_dump_folder_path}'       )
				model.save_pretrained(lowerCAmelCase       )
				print(F'Saving image processor to {pytorch_dump_folder_path}'       )
				image_processor.save_pretrained(lowerCAmelCase       )

				print("Pushing model to the hub..."       )
				_UpperCAmelCase				:						Optional[int]       						=			"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(lowerCAmelCase     ,					lowerCAmelCase       )     ,					organization="nielsr"     ,					commit_message="Add model"     ,					)


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE_						=					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.',
					)

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




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







def 						A_(			A		:						Optional[RNGType] = None						,			A		:						Optional[torch.Generator] = None):
							# Get the proper rng state
							if rng_type == RNGType.TORCH:
														UpperCamelCase					       =			torch.get_rng_state()
							elif rng_type == RNGType.CUDA:
														UpperCamelCase					       =			torch.cuda.get_rng_state()
							elif rng_type == RNGType.XLA:
														assert is_tpu_available(), "Can't synchronize XLA seeds on an environment without TPUs."
														UpperCamelCase					       =			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."
														UpperCamelCase					       =			torch.npu.get_rng_state()
							elif rng_type == RNGType.XPU:
														assert is_xpu_available(), "Can't synchronize XPU seeds on an environment without XPUs."
														UpperCamelCase					       =			torch.xpu.get_rng_state()
							elif rng_type == RNGType.GENERATOR:
														assert generator is not None, "Need a generator to synchronize its seed."
														UpperCamelCase					       =			generator.get_state()

							# Broadcast the rng state from device 0 to other devices
							UpperCamelCase					       =			AcceleratorState()
							if state.distributed_type == DistributedType.TPU:
														UpperCamelCase					       =			rng_state.to(xm.xla_device())
														xm.collective_broadcast([rng_state])
														xm.mark_step()
														UpperCamelCase					       =			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
							):
														UpperCamelCase					       =			rng_state.to(state.device)
														torch.distributed.broadcast(A						,			0)
														UpperCamelCase					       =			rng_state.cpu()
							elif state.distributed_type == DistributedType.MULTI_CPU:
														torch.distributed.broadcast(A						,			0)

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







def 						A_(			A		:						List[Union[str, RNGType]]						,			A		:						Optional[torch.Generator] = None):
							for rng_type in rng_types:
														synchronize_rng_state(RNGType(A)						,			generator=A)




