






"""simple docstring"""




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()
__UpperCamelCase   :						Optional[Any]							=  logging.get_logger(__name__)
def        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				snake_case__													=       ConvNextVaConfig()

				if "atto" in checkpoint_url:
								snake_case__													=       [2, 2, 6, 2]
								snake_case__													=       [40, 80, 160, 320]
				if "femto" in checkpoint_url:
								snake_case__													=       [2, 2, 6, 2]
								snake_case__													=       [48, 96, 192, 384]
				if "pico" in checkpoint_url:
								snake_case__													=       [2, 2, 6, 2]
								snake_case__													=       [64, 128, 256, 512]
				if "nano" in checkpoint_url:
								snake_case__													=       [2, 2, 8, 2]
								snake_case__													=       [80, 160, 320, 640]
				if "tiny" in checkpoint_url:
								snake_case__													=       [3, 3, 9, 3]
								snake_case__													=       [96, 192, 384, 768]
				if "base" in checkpoint_url:
								snake_case__													=       [3, 3, 27, 3]
								snake_case__													=       [128, 256, 512, 1024]
				if "large" in checkpoint_url:
								snake_case__													=       [3, 3, 27, 3]
								snake_case__													=       [192, 384, 768, 1536]
				if "huge" in checkpoint_url:
								snake_case__													=       [3, 3, 27, 3]
								snake_case__													=       [352, 704, 1408, 2816]

				snake_case__													=       1000
				snake_case__													=       'imagenet-1k-id2label.json'
				snake_case__													=       (1, 1000)

				snake_case__													=       'huggingface/label-files'
				snake_case__													=       num_labels
				snake_case__													=       json.load(open(hf_hub_download(_A ,				_A ,				repo_type='dataset'					) ,				'r'					)					)
				snake_case__													=       {int(_A					): v for k, v in idalabel.items()}

				snake_case__													=       idalabel
				snake_case__													=       {v: k for k, v in idalabel.items()}
				snake_case__													=       hidden_sizes
				snake_case__													=       depths

				return config, expected_shape
def        a_	( _A					)     ->     Union[str, Any]:







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

				return name
def        a_	( )     ->     str:







				"""simple docstring"""
				snake_case__													=       'http://images.cocodataset.org/val2017/000000039769.jpg'
				snake_case__													=       Image.open(requests.get(_A ,				stream=_A					).raw					)
				return im
def        a_	( _A					)     ->     Union[str, Any]:







				"""simple docstring"""
				if "224" in checkpoint_url:
								snake_case__													=       224
								snake_case__													=       224 / 256
				elif "384" in checkpoint_url:
								snake_case__													=       384
								snake_case__													=       None
				else:
								snake_case__													=       512
								snake_case__													=       None

				return ConvNextImageProcessor(
				    size=_A ,				crop_pct=_A ,				image_mean=[0.485, 0.456, 0.406] ,				image_std=[0.229, 0.224, 0.225] ,				resample=PILImageResampling.BICUBIC ,				)
@torch.no_grad()
def        a_	( _A ,				_A ,				_A ,				_A					)     ->     Dict:







				"""simple docstring"""
				print('Downloading original model from checkpoint...'					)
				# define ConvNeXTV2 configuration based on URL
				snake_case__							,  snake_case__													=       get_convnextva_config(_A					)
				# load original state_dict from URL
				snake_case__													=       torch.hub.load_state_dict_from_url(_A					)['model']

				print('Converting model parameters...'					)
				# rename keys
				for key in state_dict.copy().keys():
								snake_case__													=       state_dict.pop(_A					)
								snake_case__													=       val
				# add prefix to all keys expect classifier head
				for key in state_dict.copy().keys():
								snake_case__													=       state_dict.pop(_A					)
								if not key.startswith('classifier'					):
												snake_case__													=       'convnextv2.' + key
								snake_case__													=       val

				# load HuggingFace model
				snake_case__													=       ConvNextVaForImageClassification(_A					)
				model.load_state_dict(_A					)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				snake_case__													=       convert_preprocessor(_A					)
				snake_case__													=       preprocessor(images=prepare_img() ,				return_tensors='pt'					)
				snake_case__													=       model(**_A					).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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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":
								snake_case__													=       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] ,				_A ,				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(_A					):
												os.mkdir(_A					)

								model.save_pretrained(_A					)
								preprocessor.save_pretrained(_A					)

				snake_case__													=       '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(_A					)
								preprocessor.push_to_hub(_A					)


if __name__ == "__main__":
							__UpperCamelCase   :						Any							=  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""")

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












from typing import TYPE_CHECKING

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


__UpperCamelCase   :						int							=  {
    """configuration_openai""": ["""OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP""", """OpenAIGPTConfig"""],
    """tokenization_openai""": ["""OpenAIGPTTokenizer"""],
}

try:
							if not is_tokenizers_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Optional[Any]							=  ["""OpenAIGPTTokenizerFast"""]

try:
							if not is_torch_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						List[str]							=  [
							    """OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """OpenAIGPTDoubleHeadsModel""",
							    """OpenAIGPTForSequenceClassification""",
							    """OpenAIGPTLMHeadModel""",
							    """OpenAIGPTModel""",
							    """OpenAIGPTPreTrainedModel""",
							    """load_tf_weights_in_openai_gpt""",
							]

try:
							if not is_tf_available():
														raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
							pass
else:
							__UpperCamelCase   :						Dict							=  [
							    """TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST""",
							    """TFOpenAIGPTDoubleHeadsModel""",
							    """TFOpenAIGPTForSequenceClassification""",
							    """TFOpenAIGPTLMHeadModel""",
							    """TFOpenAIGPTMainLayer""",
							    """TFOpenAIGPTModel""",
							    """TFOpenAIGPTPreTrainedModel""",
							]


if TYPE_CHECKING:
							from .configuration_openai import OPENAI_GPT_PRETRAINED_CONFIG_ARCHIVE_MAP, OpenAIGPTConfig
							from .tokenization_openai import OpenAIGPTTokenizer

							try:
														if not is_tokenizers_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .tokenization_openai_fast import OpenAIGPTTokenizerFast

							try:
														if not is_torch_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_openai import (
														    OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    OpenAIGPTDoubleHeadsModel,
														    OpenAIGPTForSequenceClassification,
														    OpenAIGPTLMHeadModel,
														    OpenAIGPTModel,
														    OpenAIGPTPreTrainedModel,
														    load_tf_weights_in_openai_gpt,
														)

							try:
														if not is_tf_available():
																					raise OptionalDependencyNotAvailable()
							except OptionalDependencyNotAvailable:
														pass
							else:
														from .modeling_tf_openai import (
														    TF_OPENAI_GPT_PRETRAINED_MODEL_ARCHIVE_LIST,
														    TFOpenAIGPTDoubleHeadsModel,
														    TFOpenAIGPTForSequenceClassification,
														    TFOpenAIGPTLMHeadModel,
														    TFOpenAIGPTMainLayer,
														    TFOpenAIGPTModel,
														    TFOpenAIGPTPreTrainedModel,
														)

else:
							import sys

							__UpperCamelCase   :						List[Any]							=  _LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)




