

import timeit
from typing import Callable, Optional

from ..configuration_utils import PretrainedConfig
from ..models.auto.modeling_auto import MODEL_MAPPING, MODEL_WITH_LM_HEAD_MAPPING
from ..utils import is_pyanvml_available, is_torch_available, logging
from .benchmark_utils import (
    Benchmark,
    Memory,
    MemorySummary,
    measure_peak_memory_cpu,
    start_memory_tracing,
    stop_memory_tracing,
)


if is_torch_available():
						import torch

						from .benchmark_args import PyTorchBenchmarkArguments


if is_pyanvml_available():
						import pyanvml.pyanvml as nvml


lowerCAmelCase							:						Tuple     			=		logging.get_logger(__name__)



class 						__lowercase       (				_UpperCAmelCase			):




			"""simple docstring"""







			_UpperCAmelCase     :							Any    							=  42
			_UpperCAmelCase     :							Tuple    							=  42
			_UpperCAmelCase     :							Union[str, Any]    							=  '''PyTorch'''



			@property
			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple):
									return torch.__version__



			def  _SCREAMING_SNAKE_CASE    (				self						:  Tuple     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  int):
									SCREAMING_SNAKE_CASE_:				Optional[Any]							= self._prepare_inference_func(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)
									return self._measure_speed(_inference)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  Any):
									SCREAMING_SNAKE_CASE_:				str							= self._prepare_inference_func(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)
									return self._measure_memory(_inference)



			def  _SCREAMING_SNAKE_CASE    (				self						:  List[str]     ,		lowerCAmelCase__						:  str     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  List[str]):
									SCREAMING_SNAKE_CASE_:				str							= self._prepare_train_func(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)
									return self._measure_speed(_train)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Dict     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  List[str]     ,		lowerCAmelCase__						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				Dict							= self._prepare_train_func(lowerCAmelCase__     ,		lowerCAmelCase__     ,		lowerCAmelCase__)
									return self._measure_memory(_train)



			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[int]     ,		lowerCAmelCase__						:  Optional[int]     ,		lowerCAmelCase__						:  Optional[Any]     ,		lowerCAmelCase__						:  List[Any]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.config_dict[model_name]

									if self.args.torchscript:
															SCREAMING_SNAKE_CASE_:				List[Any]							= True

									SCREAMING_SNAKE_CASE_:				Tuple							= (
									    hasattr(lowerCAmelCase__     ,		"architectures")
									    and isinstance(config.architectures     ,		lowerCAmelCase__)
									    and len(config.architectures) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					SCREAMING_SNAKE_CASE_:				List[str]							= config.architectures[0]
																					SCREAMING_SNAKE_CASE_:				Tuple							= __import__("transformers"     ,		fromlist=[model_class])
																					SCREAMING_SNAKE_CASE_:				Tuple							= getattr(lowerCAmelCase__     ,		lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				Optional[int]							= model_cls(lowerCAmelCase__)
															except ImportError:
																					raise ImportError(
																					    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																					    " set `--only_pretrain_model` or `args.only_pretrain_model=True`.")
									else:
															SCREAMING_SNAKE_CASE_:				Tuple							= MODEL_MAPPING[config.__class__](lowerCAmelCase__)

									model.eval()
									model.to(self.args.device)

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE_:				Union[str, Any]							= config.vocab_size if hasattr(lowerCAmelCase__     ,		"vocab_size") else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE_:				List[str]							= torch.randint(lowerCAmelCase__     ,		(batch_size, sequence_length)     ,		dtype=torch.long     ,		device=self.args.device)

									if self.args.fpaa:
															logger.info("Running training in Mixed Precision...")
															if not self.args.is_gpu:
																					raise ValueError("Mixed precision is possible only for GPU.")
															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									if self.args.torchscript:
															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_:				Dict							= torch.jit.trace(lowerCAmelCase__     ,		lowerCAmelCase__)
									else:
															SCREAMING_SNAKE_CASE_:				Tuple							= model

									def encoder_decoder_forward():
															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_:				int							= inference_model(lowerCAmelCase__     ,		decoder_input_ids=lowerCAmelCase__)
															return outputs

									def encoder_forward():
															with torch.no_grad():
																					SCREAMING_SNAKE_CASE_:				List[Any]							= inference_model(lowerCAmelCase__)
															return outputs

									SCREAMING_SNAKE_CASE_:				List[Any]							= encoder_decoder_forward if config.is_encoder_decoder else encoder_forward
									return _forward



			def  _SCREAMING_SNAKE_CASE    (				self						:  int     ,		lowerCAmelCase__						:  Tuple     ,		lowerCAmelCase__						:  int     ,		lowerCAmelCase__						:  Optional[int]):
									SCREAMING_SNAKE_CASE_:				Optional[int]							= self.config_dict[model_name]

									SCREAMING_SNAKE_CASE_:				int							= (
									    hasattr(lowerCAmelCase__     ,		"architectures")
									    and isinstance(config.architectures     ,		lowerCAmelCase__)
									    and len(config.architectures) > 0
									)
									if not self.args.only_pretrain_model and has_model_class_in_config:
															try:
																					SCREAMING_SNAKE_CASE_:				Optional[int]							= config.architectures[0]
																					SCREAMING_SNAKE_CASE_:				Union[str, Any]							= __import__("transformers"     ,		fromlist=[model_class])
																					SCREAMING_SNAKE_CASE_:				Tuple							= getattr(lowerCAmelCase__     ,		lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				Any							= model_cls(lowerCAmelCase__)
															except ImportError:
																					raise ImportError(
																					    F"{model_class} does not exist. If you just want to test the pretrained model, you might want to"
																					    " set `--only_pretrain_model` or `args.only_pretrain_model=True`.")
									else:
															SCREAMING_SNAKE_CASE_:				Any							= MODEL_WITH_LM_HEAD_MAPPING[config.__class__](lowerCAmelCase__)

									if self.args.torchscript:
															raise NotImplementedError("Training for torchscript is currently not implemented")
									else:
															SCREAMING_SNAKE_CASE_:				Optional[Any]							= model

									model.train()
									model.to(self.args.device)

									# encoder-decoder has vocab size saved differently
									SCREAMING_SNAKE_CASE_:				List[Any]							= config.vocab_size if hasattr(lowerCAmelCase__     ,		"vocab_size") else config.encoder.vocab_size
									SCREAMING_SNAKE_CASE_:				Tuple							= torch.randint(lowerCAmelCase__     ,		(batch_size, sequence_length)     ,		dtype=torch.long     ,		device=self.args.device)

									if self.args.fpaa:
															logger.info("Running training in Mixed Precision...")
															if not self.args.is_gpu:
																					raise ValueError("Mixed precision is possible only for GPU.")

															# amp seems to have memory leaks so that memory usage
															# is measured using .half() for now https://github.com/NVIDIA/apex/issues/439
															model.half()

									def compute_loss_and_backprob_encoder():
															SCREAMING_SNAKE_CASE_:				List[Any]							= train_model(lowerCAmelCase__     ,		labels=lowerCAmelCase__)[0]
															loss.backward()
															return loss

									def compute_loss_and_backprob_encoder_decoder():
															SCREAMING_SNAKE_CASE_:				Dict							= train_model(lowerCAmelCase__     ,		decoder_input_ids=lowerCAmelCase__     ,		labels=lowerCAmelCase__)[0]
															loss.backward()
															return loss

									SCREAMING_SNAKE_CASE_:				int							= (
									    compute_loss_and_backprob_encoder_decoder
									    if config.is_encoder_decoder
									    else compute_loss_and_backprob_encoder
									)
									return _train



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






									try:
															if self.args.is_tpu or self.args.torchscript:
																					# run additional 10 times to stabilize compilation for tpu and torchscript
																					logger.info("Do inference on TPU or torchscript. Running model 5 times to stabilize compilation")
																					timeit.repeat(
																					    lowerCAmelCase__     ,		repeat=1     ,		number=5     ,		)

															# as written in https://docs.python.org/2/library/timeit.html#timeit.Timer.repeat, min should be taken rather than the average
															SCREAMING_SNAKE_CASE_:				List[Any]							= timeit.repeat(
															    lowerCAmelCase__     ,		repeat=self.args.repeat     ,		number=10     ,		)

															if self.args.is_tpu and self.args.torch_xla_tpu_print_metrics:
																					import torch_xla.debug.metrics as met

																					self.print_fn(met.metrics_report())

															return min(lowerCAmelCase__) / 10.0
									except RuntimeError as e:
															self.print_fn(F"Doesn\'t fit on GPU. {e}")
															return "N/A"




			def  _SCREAMING_SNAKE_CASE    (				self						:  Optional[Any]     ,		lowerCAmelCase__						:  List[Any]):






									try:
															if self.args.trace_memory_line_by_line:
																					SCREAMING_SNAKE_CASE_:				Dict							= start_memory_tracing("transformers")

															if self.args.is_tpu:
																					# tpu
																					raise NotImplementedError(
																					    "Memory Benchmarking is currently not implemented for TPU. Please disable memory benchmarking with"
																					    " `--no-memory` or `args.memory=False`")
															elif self.args.is_gpu:
																					if not is_pyanvml_available():
																											logger.warning(
																											    "py3nvml not installed, we won't log GPU memory usage. "
																											    "Install py3nvml (pip install py3nvml) to log information about GPU.")
																											SCREAMING_SNAKE_CASE_:				Optional[int]							= "N/A"
																					else:
																											logger.info(
																											    "Measuring total GPU usage on GPU device. Make sure to not have additional processes running"
																											    " on the same GPU.")
																											# init nvml
																											nvml.nvmlInit()
																											func()
																											SCREAMING_SNAKE_CASE_:				Dict							= nvml.nvmlDeviceGetHandleByIndex(self.args.device_idx)
																											SCREAMING_SNAKE_CASE_:				Union[str, Any]							= nvml.nvmlDeviceGetMemoryInfo(lowerCAmelCase__)
																											SCREAMING_SNAKE_CASE_:				Optional[int]							= meminfo.used
																											SCREAMING_SNAKE_CASE_:				Union[str, Any]							= Memory(lowerCAmelCase__)
																											# shutdown nvml
																											nvml.nvmlShutdown()
															else:
																					# cpu
																					SCREAMING_SNAKE_CASE_:				int							= measure_peak_memory_cpu(lowerCAmelCase__)
																					SCREAMING_SNAKE_CASE_:				List[str]							= Memory(lowerCAmelCase__) if isinstance(lowerCAmelCase__     ,		lowerCAmelCase__) else memory_bytes

															if self.args.trace_memory_line_by_line:
																					SCREAMING_SNAKE_CASE_:				List[str]							= stop_memory_tracing(lowerCAmelCase__)
															else:
																					SCREAMING_SNAKE_CASE_:				Optional[int]							= None

															return memory, summary
									except RuntimeError as e:
															self.print_fn(F"Doesn\'t fit on GPU. {e}")
															return "N/A", None









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							:						Optional[Any]     			=		logging.get_logger(__name__)







def     A_   (					_UpperCAmelCase					):
						SCREAMING_SNAKE_CASE_:				Tuple							= ConvNextVaConfig()

						if "atto" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				str							= [2, 2, 6, 2]
												SCREAMING_SNAKE_CASE_:				str							= [40, 80, 1_60, 3_20]
						if "femto" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				List[str]							= [2, 2, 6, 2]
												SCREAMING_SNAKE_CASE_:				Dict							= [48, 96, 1_92, 3_84]
						if "pico" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				Any							= [2, 2, 6, 2]
												SCREAMING_SNAKE_CASE_:				Dict							= [64, 1_28, 2_56, 5_12]
						if "nano" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				Tuple							= [2, 2, 8, 2]
												SCREAMING_SNAKE_CASE_:				str							= [80, 1_60, 3_20, 6_40]
						if "tiny" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				List[str]							= [3, 3, 9, 3]
												SCREAMING_SNAKE_CASE_:				Dict							= [96, 1_92, 3_84, 7_68]
						if "base" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				List[Any]							= [3, 3, 27, 3]
												SCREAMING_SNAKE_CASE_:				Tuple							= [1_28, 2_56, 5_12, 10_24]
						if "large" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				str							= [3, 3, 27, 3]
												SCREAMING_SNAKE_CASE_:				str							= [1_92, 3_84, 7_68, 15_36]
						if "huge" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				List[str]							= [3, 3, 27, 3]
												SCREAMING_SNAKE_CASE_:				Tuple							= [3_52, 7_04, 14_08, 28_16]

						SCREAMING_SNAKE_CASE_:				Optional[Any]							= 10_00
						SCREAMING_SNAKE_CASE_:				List[Any]							= "imagenet-1k-id2label.json"
						SCREAMING_SNAKE_CASE_:				str							= (1, 10_00)

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

						SCREAMING_SNAKE_CASE_:				List[Any]							= idalabel
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= {v: k for k, v in idalabel.items()}
						SCREAMING_SNAKE_CASE_:				Any							= hidden_sizes
						SCREAMING_SNAKE_CASE_:				Tuple							= depths

						return config, expected_shape







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

						return name







def     A_   (					):
						SCREAMING_SNAKE_CASE_:				Optional[int]							= "http://images.cocodataset.org/val2017/000000039769.jpg"
						SCREAMING_SNAKE_CASE_:				Optional[int]							= Image.open(requests.get(_UpperCAmelCase       ,			stream=_UpperCAmelCase					).raw					)
						return im







def     A_   (					_UpperCAmelCase					):
						if "224" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				Tuple							= 2_24
												SCREAMING_SNAKE_CASE_:				Optional[int]							= 2_24 / 2_56
						elif "384" in checkpoint_url:
												SCREAMING_SNAKE_CASE_:				Any							= 3_84
												SCREAMING_SNAKE_CASE_:				Tuple							= None
						else:
												SCREAMING_SNAKE_CASE_:				Tuple							= 5_12
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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					):
						print("Downloading original model from checkpoint..."					)
						# define ConvNeXTV2 configuration based on URL
						SCREAMING_SNAKE_CASE_	,					SCREAMING_SNAKE_CASE_:				Optional[int]							= get_convnextva_config(_UpperCAmelCase					)
						# load original state_dict from URL
						SCREAMING_SNAKE_CASE_:				List[str]							= torch.hub.load_state_dict_from_url(_UpperCAmelCase					)["model"]

						print("Converting model parameters..."					)
						# rename keys
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE_:				List[str]							= state_dict.pop(_UpperCAmelCase					)
												SCREAMING_SNAKE_CASE_:				str							= val
						# add prefix to all keys expect classifier head
						for key in state_dict.copy().keys():
												SCREAMING_SNAKE_CASE_:				str							= state_dict.pop(_UpperCAmelCase					)
												if not key.startswith("classifier"					):
																		SCREAMING_SNAKE_CASE_:				Union[str, Any]							= "convnextv2." + key
												SCREAMING_SNAKE_CASE_:				Dict							= val

						# load HuggingFace model
						SCREAMING_SNAKE_CASE_:				Tuple							= ConvNextVaForImageClassification(_UpperCAmelCase					)
						model.load_state_dict(_UpperCAmelCase					)
						model.eval()

						# Check outputs on an image, prepared by ConvNextImageProcessor
						SCREAMING_SNAKE_CASE_:				Union[str, Any]							= convert_preprocessor(_UpperCAmelCase					)
						SCREAMING_SNAKE_CASE_:				int							= preprocessor(images=prepare_img()       ,			return_tensors="pt"					)
						SCREAMING_SNAKE_CASE_:				Optional[int]							= 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":
												SCREAMING_SNAKE_CASE_:				int							= 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":
												SCREAMING_SNAKE_CASE_:				List[str]							= 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":
												SCREAMING_SNAKE_CASE_:				List[Any]							= 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":
												SCREAMING_SNAKE_CASE_:				List[str]							= 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":
												SCREAMING_SNAKE_CASE_:				Optional[int]							= 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":
												SCREAMING_SNAKE_CASE_:				Any							= 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":
												SCREAMING_SNAKE_CASE_:				Union[str, Any]							= 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":
												SCREAMING_SNAKE_CASE_:				Optional[Any]							= 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":
												SCREAMING_SNAKE_CASE_:				int							= 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":
												SCREAMING_SNAKE_CASE_:				Any							= 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":
												SCREAMING_SNAKE_CASE_:				int							= 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":
												SCREAMING_SNAKE_CASE_:				Any							= 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":
												SCREAMING_SNAKE_CASE_:				Optional[int]							= 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":
												SCREAMING_SNAKE_CASE_:				str							= 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":
												SCREAMING_SNAKE_CASE_:				Any							= 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":
												SCREAMING_SNAKE_CASE_:				Dict							= 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":
												SCREAMING_SNAKE_CASE_:				Tuple							= 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":
												SCREAMING_SNAKE_CASE_:				str							= 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					)

						SCREAMING_SNAKE_CASE_:				Optional[int]							= "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							:						Tuple     			=		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							:						Union[str, Any]     			=		parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)






