
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()
__a        = logging.get_logger(__name__)




def     a						(							snake_case__: Tuple ):


				'''simple docstring'''
				lowercase_					  = ConvNextConfig()

				if "tiny" in checkpoint_url:
								lowercase_					  = [3, 3, 9, 3]
								lowercase_					  = [96, 192, 384, 768]
				if "small" in checkpoint_url:
								lowercase_					  = [3, 3, 27, 3]
								lowercase_					  = [96, 192, 384, 768]
				if "base" in checkpoint_url:
								lowercase_					  = [3, 3, 27, 3]
								lowercase_					  = [128, 256, 512, 1_024]
				if "large" in checkpoint_url:
								lowercase_					  = [3, 3, 27, 3]
								lowercase_					  = [192, 384, 768, 1_536]
				if "xlarge" in checkpoint_url:
								lowercase_					  = [3, 3, 27, 3]
								lowercase_					  = [256, 512, 1_024, 2_048]

				if "1k" in checkpoint_url:
								lowercase_					  = 1_000
								lowercase_					  = "imagenet-1k-id2label.json"
								lowercase_					  = (1, 1_000)
				else:
								lowercase_					  = 21_841
								lowercase_					  = "imagenet-22k-id2label.json"
								lowercase_					  = (1, 21_841)

				lowercase_					  = "huggingface/label-files"
				lowercase_					  = num_labels
				lowercase_					  = json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE_						,       SCREAMING_SNAKE_CASE_						,       repo_type='''dataset''' )						,       '''r''' ) )
				lowercase_					  = {int(SCREAMING_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[9_205]
								del idalabel[15_027]
				lowercase_					  = idalabel
				lowercase_					  = {v: k for k, v in idalabel.items()}
				lowercase_					  = hidden_sizes
				lowercase_					  = depths

				return config, expected_shape




def     a						(							snake_case__: Dict ):


				'''simple docstring'''
				if "downsample_layers.0.0" in name:
								lowercase_					  = name.replace('''downsample_layers.0.0'''						,       '''embeddings.patch_embeddings''' )
				if "downsample_layers.0.1" in name:
								lowercase_					  = name.replace('''downsample_layers.0.1'''						,       '''embeddings.norm''' )  # we rename to layernorm later on
				if "downsample_layers.1.0" in name:
								lowercase_					  = name.replace('''downsample_layers.1.0'''						,       '''stages.1.downsampling_layer.0''' )
				if "downsample_layers.1.1" in name:
								lowercase_					  = name.replace('''downsample_layers.1.1'''						,       '''stages.1.downsampling_layer.1''' )
				if "downsample_layers.2.0" in name:
								lowercase_					  = name.replace('''downsample_layers.2.0'''						,       '''stages.2.downsampling_layer.0''' )
				if "downsample_layers.2.1" in name:
								lowercase_					  = name.replace('''downsample_layers.2.1'''						,       '''stages.2.downsampling_layer.1''' )
				if "downsample_layers.3.0" in name:
								lowercase_					  = name.replace('''downsample_layers.3.0'''						,       '''stages.3.downsampling_layer.0''' )
				if "downsample_layers.3.1" in name:
								lowercase_					  = 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.
								lowercase_					  = name[: len('''stages.0''' )] + ".layers" + name[len('''stages.0''' ) :]
				if "stages" in name:
								lowercase_					  = name.replace('''stages'''						,       '''encoder.stages''' )
				if "norm" in name:
								lowercase_					  = name.replace('''norm'''						,       '''layernorm''' )
				if "gamma" in name:
								lowercase_					  = name.replace('''gamma'''						,       '''layer_scale_parameter''' )
				if "head" in name:
								lowercase_					  = name.replace('''head'''						,       '''classifier''' )

				return name




def     a						(							):


				'''simple docstring'''
				lowercase_					  = "http://images.cocodataset.org/val2017/000000039769.jpg"
				lowercase_					  = Image.open(requests.get(SCREAMING_SNAKE_CASE_						,       stream=SCREAMING_SNAKE_CASE_ ).raw )
				return im




@torch.no_grad()
def     a						(							snake_case__: Any						,       snake_case__: List[Any] ):


				'''simple docstring'''
				lowercase_					  = get_convnext_config(SCREAMING_SNAKE_CASE_ )
				# load original state_dict from URL
				lowercase_					  = torch.hub.load_state_dict_from_url(SCREAMING_SNAKE_CASE_ )["model"]
				# rename keys
				for key in state_dict.copy().keys():
								lowercase_					  = state_dict.pop(SCREAMING_SNAKE_CASE_ )
								lowercase_					  = val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								lowercase_					  = state_dict.pop(SCREAMING_SNAKE_CASE_ )
								if not key.startswith('''classifier''' ):
												lowercase_					  = "convnext." + key
								lowercase_					  = val

				# load HuggingFace model
				lowercase_					  = ConvNextForImageClassification(SCREAMING_SNAKE_CASE_ )
				model.load_state_dict(SCREAMING_SNAKE_CASE_ )
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				lowercase_					  = 224 if "224" in checkpoint_url else 384
				lowercase_					  = ConvNextImageProcessor(size=SCREAMING_SNAKE_CASE_ )
				lowercase_					  = image_processor(images=prepare_img()						,       return_tensors='''pt''' ).pixel_values

				lowercase_					  = model(SCREAMING_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":
								lowercase_					  = torch.tensor([-0.1_2_1_0, -0.6_6_0_5, 0.1_9_1_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
								lowercase_					  = torch.tensor([-0.4_4_7_3, -0.1_8_4_7, -0.6_3_6_5] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
								lowercase_					  = torch.tensor([0.4_5_2_5, 0.7_5_3_9, 0.0_3_0_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
								lowercase_					  = torch.tensor([0.3_5_6_1, 0.6_3_5_0, -0.0_3_8_4] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
								lowercase_					  = torch.tensor([0.4_1_7_4, -0.0_9_8_9, 0.1_4_8_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
								lowercase_					  = torch.tensor([0.2_5_1_3, -0.1_3_4_9, -0.1_6_1_3] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
								lowercase_					  = torch.tensor([1.2_9_8_0, 0.3_6_3_1, -0.1_1_9_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
								lowercase_					  = torch.tensor([1.2_9_6_3, 0.1_2_2_7, 0.1_7_2_3] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
								lowercase_					  = torch.tensor([1.7_9_5_6, 0.8_3_9_0, 0.2_8_2_0] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
								lowercase_					  = torch.tensor([-0.2_8_2_2, -0.0_5_0_2, -0.0_8_7_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
								lowercase_					  = torch.tensor([-0.5_6_7_2, -0.0_7_3_0, -0.4_3_4_8] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
								lowercase_					  = torch.tensor([0.2_6_8_1, 0.2_3_6_5, 0.6_2_4_6] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
								lowercase_					  = torch.tensor([-0.2_6_4_2, 0.3_9_3_1, 0.5_1_1_6] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
								lowercase_					  = torch.tensor([-0.6_6_7_7, -0.1_8_7_3, -0.8_3_7_9] )
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
								lowercase_					  = torch.tensor([-0.7_7_4_9, -0.2_9_6_7, -0.6_4_4_4] )
				else:
								raise ValueError(F'''Unknown URL: {checkpoint_url}''' )

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

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

				print('''Pushing model to the hub...''' )
				lowercase_					  = "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(SCREAMING_SNAKE_CASE_						,       SCREAMING_SNAKE_CASE_ )						,       organization='''nielsr'''						,       commit_message='''Add model'''						,       )


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

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






from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


__a        = {
    'configuration_nystromformer': ['NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'NystromformerConfig'],
}

try:
						if not is_torch_available():
												raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
						pass
else:
						__a        = [
						    'NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
						    'NystromformerForMaskedLM',
						    'NystromformerForMultipleChoice',
						    'NystromformerForQuestionAnswering',
						    'NystromformerForSequenceClassification',
						    'NystromformerForTokenClassification',
						    'NystromformerLayer',
						    'NystromformerModel',
						    'NystromformerPreTrainedModel',
						]


if TYPE_CHECKING:
						from .configuration_nystromformer import NYSTROMFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, NystromformerConfig

						try:
												if not is_torch_available():
																		raise OptionalDependencyNotAvailable()
						except OptionalDependencyNotAvailable:
												pass
						else:
												from .modeling_nystromformer import (
												    NYSTROMFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
												    NystromformerForMaskedLM,
												    NystromformerForMultipleChoice,
												    NystromformerForQuestionAnswering,
												    NystromformerForSequenceClassification,
												    NystromformerForTokenClassification,
												    NystromformerLayer,
												    NystromformerModel,
												    NystromformerPreTrainedModel,
												)


else:
						import sys

						__a        = _LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




