
"""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__				:   str   							=			logging.get_logger(__name__)

def lowercase_  (       _snake_case						):
							SCREAMING_SNAKE_CASE__						:	Dict									=       ConvNextVaConfig()

							if "atto" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	Dict									=       [2, 2, 6, 2]
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [40, 80, 160, 320]
							if "femto" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	Tuple									=       [2, 2, 6, 2]
														SCREAMING_SNAKE_CASE__						:	Tuple									=       [48, 96, 192, 384]
							if "pico" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	List[str]									=       [2, 2, 6, 2]
														SCREAMING_SNAKE_CASE__						:	Any									=       [64, 128, 256, 512]
							if "nano" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	Tuple									=       [2, 2, 8, 2]
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [80, 160, 320, 640]
							if "tiny" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [3, 3, 9, 3]
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       [96, 192, 384, 768]
							if "base" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       [3, 3, 27, 3]
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       [128, 256, 512, 1_024]
							if "large" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	Any									=       [3, 3, 27, 3]
														SCREAMING_SNAKE_CASE__						:	Any									=       [192, 384, 768, 1_536]
							if "huge" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	List[str]									=       [3, 3, 27, 3]
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       [352, 704, 1_408, 2_816]

							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       1_000
							SCREAMING_SNAKE_CASE__						:	str									=       """imagenet-1k-id2label.json"""
							SCREAMING_SNAKE_CASE__						:	int									=       (1, 1_000)

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

							SCREAMING_SNAKE_CASE__						:	List[Any]									=       idalabel
							SCREAMING_SNAKE_CASE__						:	Dict									=       {v: k for k, v in idalabel.items()}
							SCREAMING_SNAKE_CASE__						:	Any									=       hidden_sizes
							SCREAMING_SNAKE_CASE__						:	Any									=       depths

							return config, expected_shape

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

							return name

def lowercase_  (       ):
							SCREAMING_SNAKE_CASE__						:	Optional[Any]									=       """http://images.cocodataset.org/val2017/000000039769.jpg"""
							SCREAMING_SNAKE_CASE__						:	str									=       Image.open(requests.get(_snake_case       ,stream=_snake_case						).raw						)
							return im

def lowercase_  (       _snake_case						):
							if "224" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       224
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       224 / 256
							elif "384" in checkpoint_url:
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       384
														SCREAMING_SNAKE_CASE__						:	Any									=       None
							else:
														SCREAMING_SNAKE_CASE__						:	int									=       512
														SCREAMING_SNAKE_CASE__						:	Tuple									=       None

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

@torch.no_grad()
def lowercase_  (       _snake_case       ,_snake_case       ,_snake_case       ,_snake_case						):
							print("""Downloading original model from checkpoint..."""						)
							# define ConvNeXTV2 configuration based on URL
							SCREAMING_SNAKE_CASE__				,				SCREAMING_SNAKE_CASE__						:	Dict									=       get_convnextva_config(_snake_case						)
							# load original state_dict from URL
							SCREAMING_SNAKE_CASE__						:	List[Any]									=       torch.hub.load_state_dict_from_url(_snake_case						)["""model"""]

							print("""Converting model parameters..."""						)
							# rename keys
							for key in state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       state_dict.pop(_snake_case						)
														SCREAMING_SNAKE_CASE__						:	Any									=       val
							# add prefix to all keys expect classifier head
							for key in state_dict.copy().keys():
														SCREAMING_SNAKE_CASE__						:	Any									=       state_dict.pop(_snake_case						)
														if not key.startswith("""classifier"""						):
																					SCREAMING_SNAKE_CASE__						:	Optional[int]									=       """convnextv2.""" + key
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       val

							# load HuggingFace model
							SCREAMING_SNAKE_CASE__						:	str									=       ConvNextVaForImageClassification(_snake_case						)
							model.load_state_dict(_snake_case						)
							model.eval()

							# Check outputs on an image, prepared by ConvNextImageProcessor
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       convert_preprocessor(_snake_case						)
							SCREAMING_SNAKE_CASE__						:	str									=       preprocessor(images=prepare_img()       ,return_tensors="""pt"""						)
							SCREAMING_SNAKE_CASE__						:	Optional[int]									=       model(**_snake_case						).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__						:	List[Any]									=       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":
														SCREAMING_SNAKE_CASE__						:	int									=       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":
														SCREAMING_SNAKE_CASE__						:	List[str]									=       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":
														SCREAMING_SNAKE_CASE__						:	int									=       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":
														SCREAMING_SNAKE_CASE__						:	str									=       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":
														SCREAMING_SNAKE_CASE__						:	List[str]									=       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":
														SCREAMING_SNAKE_CASE__						:	int									=       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":
														SCREAMING_SNAKE_CASE__						:	str									=       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":
														SCREAMING_SNAKE_CASE__						:	Dict									=       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":
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       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":
														SCREAMING_SNAKE_CASE__						:	List[Any]									=       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":
														SCREAMING_SNAKE_CASE__						:	List[str]									=       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":
														SCREAMING_SNAKE_CASE__						:	Dict									=       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":
														SCREAMING_SNAKE_CASE__						:	Optional[int]									=       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":
														SCREAMING_SNAKE_CASE__						:	Union[str, Any]									=       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":
														SCREAMING_SNAKE_CASE__						:	Any									=       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":
														SCREAMING_SNAKE_CASE__						:	Any									=       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":
														SCREAMING_SNAKE_CASE__						:	int									=       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]       ,_snake_case       ,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(_snake_case						):
																					os.mkdir(_snake_case						)

														model.save_pretrained(_snake_case						)
														preprocessor.save_pretrained(_snake_case						)

							SCREAMING_SNAKE_CASE__						:	List[str]									=       """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(_snake_case						)
														preprocessor.push_to_hub(_snake_case						)


if __name__ == "__main__":
					UpperCAmelCase__				:   List[str]   							=			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__				:   str   							=			parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)






"""simple docstring"""




from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available


UpperCAmelCase__				:   Optional[Any]   							=			{'configuration_umt5': ['UMT5Config', 'UMT5OnnxConfig']}


try:
					if not is_torch_available():
										raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
					pass
else:
					UpperCAmelCase__				:   Tuple   							=			[
					    'UMT5EncoderModel',
					    'UMT5ForConditionalGeneration',
					    'UMT5ForQuestionAnswering',
					    'UMT5Model',
					    'UMT5PreTrainedModel',
					]

if TYPE_CHECKING:
					from .configuration_umta import UMTaConfig, UMTaOnnxConfig

					try:
										if not is_torch_available():
															raise OptionalDependencyNotAvailable()
					except OptionalDependencyNotAvailable:
										pass
					else:
										from .modeling_umta import (
										    UMTaEncoderModel,
										    UMTaForConditionalGeneration,
										    UMTaForQuestionAnswering,
										    UMTaModel,
										    UMTaPreTrainedModel,
										)
else:
					import sys

					UpperCAmelCase__				:   Optional[Any]   							=			_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)




