
from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_flax_available,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE   :		Dict					 =       {
    "configuration_mpnet": ["MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP", "MPNetConfig"],
    "tokenization_mpnet": ["MPNetTokenizer"],
}

try:
						if not is_tokenizers_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE   :		Any					 =       ["MPNetTokenizerFast"]

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE   :		List[str]					 =       [
						    "MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "MPNetForMaskedLM",
						    "MPNetForMultipleChoice",
						    "MPNetForQuestionAnswering",
						    "MPNetForSequenceClassification",
						    "MPNetForTokenClassification",
						    "MPNetLayer",
						    "MPNetModel",
						    "MPNetPreTrainedModel",
						]

try:
						if not is_tf_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						SCREAMING_SNAKE_CASE   :		Union[str, Any]					 =       [
						    "TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST",
						    "TFMPNetEmbeddings",
						    "TFMPNetForMaskedLM",
						    "TFMPNetForMultipleChoice",
						    "TFMPNetForQuestionAnswering",
						    "TFMPNetForSequenceClassification",
						    "TFMPNetForTokenClassification",
						    "TFMPNetMainLayer",
						    "TFMPNetModel",
						    "TFMPNetPreTrainedModel",
						]


if TYPE_CHECKING:
						from .configuration_mpnet import MPNET_PRETRAINED_CONFIG_ARCHIVE_MAP, MPNetConfig
						from .tokenization_mpnet import MPNetTokenizer

						try:
												if not is_tokenizers_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .tokenization_mpnet_fast import MPNetTokenizerFast

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_mpnet import (
												    MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    MPNetForMaskedLM,
												    MPNetForMultipleChoice,
												    MPNetForQuestionAnswering,
												    MPNetForSequenceClassification,
												    MPNetForTokenClassification,
												    MPNetLayer,
												    MPNetModel,
												    MPNetPreTrainedModel,
												)

						try:
												if not is_tf_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_tf_mpnet import (
												    TF_MPNET_PRETRAINED_MODEL_ARCHIVE_LIST,
												    TFMPNetEmbeddings,
												    TFMPNetForMaskedLM,
												    TFMPNetForMultipleChoice,
												    TFMPNetForQuestionAnswering,
												    TFMPNetForSequenceClassification,
												    TFMPNetForTokenClassification,
												    TFMPNetMainLayer,
												    TFMPNetModel,
												    TFMPNetPreTrainedModel,
												)

else:
						import sys

						SCREAMING_SNAKE_CASE   :		int					 =       _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)

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()
SCREAMING_SNAKE_CASE   :		List[str]					 =       logging.get_logger(__name__)



def        lowerCamelCase_  (   __UpperCamelCase     ):
				A_       	=  ConvNextVaConfig()

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

				A_       	=  10_00
				A_       	=  '''imagenet-1k-id2label.json'''
				A_       	=  (1, 10_00)

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



def        lowerCamelCase_  (   __UpperCamelCase     ):
				if "224" in checkpoint_url:
								A_       	=  2_24
								A_       	=  2_24 / 2_56
				elif "384" in checkpoint_url:
								A_       	=  3_84
								A_       	=  None
				else:
								A_       	=  5_12
								A_       	=  None

				return ConvNextImageProcessor(
				    size=__UpperCamelCase ,						crop_pct=__UpperCamelCase ,						image_mean=[0.485, 0.456, 0.406] ,						image_std=[0.229, 0.224, 0.225] ,						resample=PILImageResampling.BICUBIC ,						)



@torch.no_grad()
def        lowerCamelCase_  (   __UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase ,						__UpperCamelCase     ):
				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([-0.3930, 0.1747, -0.5246, 0.4177, 0.4295]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.1727, -0.5341, -0.7818, -0.4745, -0.6566]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.0333, 0.1563, -0.9137, 0.1054, 0.0381]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.1744, -0.1555, -0.0713, 0.0950, -0.1431]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								A_       	=  torch.tensor([0.9996, 0.1966, -0.4386, -0.3472, 0.6661]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.2553, -0.6708, -0.1359, 0.2518, -0.2488]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.0673, -0.5627, -0.3753, -0.2722, 0.0178]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								A_       	=  torch.tensor([-0.6377, -0.7458, -0.2150, 0.1184, -0.0597]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								A_       	=  torch.tensor([1.0799, 0.2322, -0.8860, 1.0219, 0.6231]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								A_       	=  torch.tensor([0.3766, 0.4917, -1.1426, 0.9942, 0.6024]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								A_       	=  torch.tensor([0.4220, -0.6919, -0.4317, -0.2881, -0.6609]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								A_       	=  torch.tensor([0.1082, -0.8286, -0.5095, 0.4681, -0.8085]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								A_       	=  torch.tensor([-0.2419, -0.6221, 0.2176, -0.0980, -0.7527]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								A_       	=  torch.tensor([0.0391, -0.4371, 0.3786, 0.1251, -0.2784]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								A_       	=  torch.tensor([-0.0504, 0.5636, -0.1729, -0.6507, -0.3949]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								A_       	=  torch.tensor([0.3560, 0.9486, 0.3149, -0.2667, -0.5138]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								A_       	=  torch.tensor([-0.2469, -0.4550, -0.5853, -0.0810, 0.0309]     )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								A_       	=  torch.tensor([-0.3090, 0.0802, -0.0682, -0.1979, -0.2826]     )
				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__":
						SCREAMING_SNAKE_CASE   :		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")

						SCREAMING_SNAKE_CASE   :		Dict					 =       parser.parse_args()
						convert_convnextva_checkpoint(
						    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
						)