
from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    get_resize_output_image_size,
    normalize,
    pad,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    IMAGENET_STANDARD_MEAN,
    IMAGENET_STANDARD_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    get_image_size,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, logging
from ...utils.import_utils import is_vision_available


A  :			Any     				=				logging.get_logger(__name__)


if is_vision_available():
					import PIL







class     __A(				a    ):
				snake_case_		     =					['''pixel_values''']




				def __init__(  self	,  _snake_case = True	,  _snake_case = None	,  _snake_case = PILImageResampling.BILINEAR	,  _snake_case = True	,  _snake_case = False	,  _snake_case = True	,  _snake_case = True	,  _snake_case = 1 / 255	,  _snake_case = True	,  _snake_case = None	,  _snake_case = None	,  **_snake_case	,  )					->   None:


								'''simple docstring'''




								super().__init__(**_snake_case       )

								__a    							=					size if size is not None else {'''height''': 2_560, '''width''': 1_920}
								if isinstance(_snake_case	,  (tuple, list)       ):
												# The previous feature extractor size parameter was in (width, height) format
												__a    							=					size[::-1]
								__a    							=					get_size_dict(_snake_case       )

								__a    							=					do_resize
								__a    							=					size
								__a    							=					resample
								__a    							=					do_thumbnail
								__a    							=					do_align_long_axis
								__a    							=					do_pad
								__a    							=					do_rescale
								__a    							=					rescale_factor
								__a    							=					do_normalize
								__a    							=					image_mean if image_mean is not None else IMAGENET_STANDARD_MEAN
								__a    							=					image_std if image_std is not None else IMAGENET_STANDARD_STD




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case = None       )					->   np.ndarray:


								'''simple docstring'''




								__a							,  __a    							=					get_image_size(_snake_case       )
								__a							,  __a    							=					size['''height'''], size['''width''']

								if (output_width < output_height and input_width > input_height) or (
								    output_width > output_height and input_width < input_height
								):
												__a    							=					np.rotaa(_snake_case	,  3       )

								if data_format is not None:
												__a    							=					to_channel_dimension_format(_snake_case	,  _snake_case       )

								return image




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case = False	,  _snake_case = None	,  )					->   np.ndarray:


								'''simple docstring'''




								__a							,  __a    							=					size['''height'''], size['''width''']
								__a							,  __a    							=					get_image_size(_snake_case       )

								__a    							=					output_width - input_width
								__a    							=					output_height - input_height

								if random_padding:
												__a    							=					np.random.randint(low=0	,  high=delta_height + 1       )
												__a    							=					np.random.randint(low=0	,  high=delta_width + 1       )
								else:
												__a    							=					delta_height // 2
												__a    							=					delta_width // 2

								__a    							=					delta_height - pad_top
								__a    							=					delta_width - pad_left

								__a    							=					((pad_top, pad_bottom), (pad_left, pad_right))
								return pad(_snake_case	,  _snake_case	,  data_format=_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  *_snake_case	,  **_snake_case       )					->   List[str]:


								'''simple docstring'''




								logger.info('''pad is deprecated and will be removed in version 4.27. Please use pad_image instead.'''       )
								return self.pad_image(*_snake_case	,  **_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case = PILImageResampling.BICUBIC	,  _snake_case = None	,  **_snake_case	,  )					->   np.ndarray:


								'''simple docstring'''




								__a							,  __a    							=					get_image_size(_snake_case       )
								__a							,  __a    							=					size['''height'''], size['''width''']

								# We always resize to the smallest of either the input or output size.
								__a    							=					min(_snake_case	,  _snake_case       )
								__a    							=					min(_snake_case	,  _snake_case       )

								if height == input_height and width == input_width:
												return image

								if input_height > input_width:
												__a    							=					int(input_width * height / input_height       )
								elif input_width > input_height:
												__a    							=					int(input_height * width / input_width       )

								return resize(
								    _snake_case	,  size=(height, width)	,  resample=_snake_case	,  reducing_gap=2.0	,  data_format=_snake_case	,  **_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case = PILImageResampling.BICUBIC	,  _snake_case = None	,  **_snake_case	,  )					->   np.ndarray:


								'''simple docstring'''




								__a    							=					get_size_dict(_snake_case       )
								__a    							=					min(size['''height''']	,  size['''width''']       )
								__a    							=					get_resize_output_image_size(_snake_case	,  size=_snake_case	,  default_to_square=_snake_case       )
								__a    							=					resize(_snake_case	,  size=_snake_case	,  resample=_snake_case	,  data_format=_snake_case	,  **_snake_case       )
								return resized_image




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case = None	,  **_snake_case	,  )					->   Optional[int]:


								'''simple docstring'''




								return rescale(_snake_case	,  scale=_snake_case	,  data_format=_snake_case	,  **_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case	,  _snake_case	,  _snake_case = None	,  **_snake_case	,  )					->   np.ndarray:


								'''simple docstring'''




								return normalize(_snake_case	,  mean=_snake_case	,  std=_snake_case	,  data_format=_snake_case	,  **_snake_case       )




				def        SCREAMING_SNAKE_CASE_	(  self	,  _snake_case	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = False	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = None	,  _snake_case = ChannelDimension.FIRST	,  **_snake_case	,  )					->   PIL.Image.Image:


								'''simple docstring'''




								__a    							=					do_resize if do_resize is not None else self.do_resize
								__a    							=					size if size is not None else self.size
								if isinstance(_snake_case	,  (tuple, list)       ):
												# Previous feature extractor had size in (width, height) format
												__a    							=					size[::-1]
								__a    							=					get_size_dict(_snake_case       )
								__a    							=					resample if resample is not None else self.resample
								__a    							=					do_thumbnail if do_thumbnail is not None else self.do_thumbnail
								__a    							=					do_align_long_axis if do_align_long_axis is not None else self.do_align_long_axis
								__a    							=					do_pad if do_pad is not None else self.do_pad
								__a    							=					do_rescale if do_rescale is not None else self.do_rescale
								__a    							=					rescale_factor if rescale_factor is not None else self.rescale_factor
								__a    							=					do_normalize if do_normalize is not None else self.do_normalize
								__a    							=					image_mean if image_mean is not None else self.image_mean
								__a    							=					image_std if image_std is not None else self.image_std

								__a    							=					make_list_of_images(_snake_case       )

								if not valid_images(_snake_case       ):
												raise ValueError(
												    '''Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, '''
												    '''torch.Tensor, tf.Tensor or jax.ndarray.'''       )

								if do_resize and size is None:
												raise ValueError('''Size must be specified if do_resize is True.'''       )

								if do_rescale and rescale_factor is None:
												raise ValueError('''Rescale factor must be specified if do_rescale is True.'''       )

								if do_pad and size is None:
												raise ValueError('''Size must be specified if do_pad is True.'''       )

								if do_normalize and (image_mean is None or image_std is None):
												raise ValueError('''Image mean and std must be specified if do_normalize is True.'''       )

								# All transformations expect numpy arrays.
								__a    							=					[to_numpy_array(_snake_case       ) for image in images]

								if do_align_long_axis:
												__a    							=					[self.align_long_axis(_snake_case	,  size=_snake_case       ) for image in images]

								if do_resize:
												__a    							=					[self.resize(image=_snake_case	,  size=_snake_case	,  resample=_snake_case       ) for image in images]

								if do_thumbnail:
												__a    							=					[self.thumbnail(image=_snake_case	,  size=_snake_case       ) for image in images]

								if do_pad:
												__a    							=					[self.pad_image(image=_snake_case	,  size=_snake_case	,  random_padding=_snake_case       ) for image in images]

								if do_rescale:
												__a    							=					[self.rescale(image=_snake_case	,  scale=_snake_case       ) for image in images]

								if do_normalize:
												__a    							=					[self.normalize(image=_snake_case	,  mean=_snake_case	,  std=_snake_case       ) for image in images]

								__a    							=					[to_channel_dimension_format(_snake_case	,  _snake_case       ) for image in images]

								__a    							=					{'''pixel_values''': images}
								return BatchFeature(data=_snake_case	,  tensor_type=_snake_case       )

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()
A  :			Tuple     				=				logging.get_logger(__name__)


def 					__lowerCAmelCase    (    a__						) ->						Optional[int]:
				__a    							=					ConvNextVaConfig()

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

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

				__a    							=					'''huggingface/label-files'''
				__a    							=					num_labels
				__a    							=					json.load(open(hf_hub_download(a__		,					a__		,					repo_type='''dataset'''						)		,					'''r'''						)						)
				__a    							=					{int(a__						): 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    (    a__						) ->						str:
				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    (    ) ->						Tuple:
				__a    							=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__a    							=					Image.open(requests.get(a__		,					stream=a__						).raw						)
				return im


def 					__lowerCAmelCase    (    a__						) ->						List[str]:
				if "224" in checkpoint_url:
								__a    							=					224
								__a    							=					224 / 256
				elif "384" in checkpoint_url:
								__a    							=					384
								__a    							=					None
				else:
								__a    							=					512
								__a    							=					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 					__lowerCAmelCase    (    a__		,					a__		,					a__		,					a__						) ->						List[Any]:
				print('''Downloading original model from checkpoint...'''						)
				# define ConvNeXTV2 configuration based on URL
				__a							,  __a    							=					get_convnextva_config(a__						)
				# load original state_dict from URL
				__a    							=					torch.hub.load_state_dict_from_url(a__						)['''model''']

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

				# load HuggingFace model
				__a    							=					ConvNextVaForImageClassification(a__						)
				model.load_state_dict(a__						)
				model.eval()

				# Check outputs on an image, prepared by ConvNextImageProcessor
				__a    							=					convert_preprocessor(a__						)
				__a    							=					preprocessor(images=prepare_img()		,					return_tensors='''pt'''						)
				__a    							=					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":
								__a    							=					torch.tensor([-0.3_930, 0.1_747, -0.5_246, 0.4_177, 0.4_295]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_femto_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.1_727, -0.5_341, -0.7_818, -0.4_745, -0.6_566]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_pico_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_333, 0.1_563, -0.9_137, 0.1_054, 0.0_381]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_nano_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.1_744, -0.1_555, -0.0_713, 0.0_950, -0.1_431]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_tiny_1k_224_ema.pt":
								__a    							=					torch.tensor([0.9_996, 0.1_966, -0.4_386, -0.3_472, 0.6_661]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_base_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.2_553, -0.6_708, -0.1_359, 0.2_518, -0.2_488]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_large_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_673, -0.5_627, -0.3_753, -0.2_722, 0.0_178]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im1k/convnextv2_huge_1k_224_ema.pt":
								__a    							=					torch.tensor([-0.6_377, -0.7_458, -0.2_150, 0.1_184, -0.0_597]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_224_ema.pt":
								__a    							=					torch.tensor([1.0_799, 0.2_322, -0.8_860, 1.0_219, 0.6_231]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_nano_22k_384_ema.pt":
								__a    							=					torch.tensor([0.3_766, 0.4_917, -1.1_426, 0.9_942, 0.6_024]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_224_ema.pt":
								__a    							=					torch.tensor([0.4_220, -0.6_919, -0.4_317, -0.2_881, -0.6_609]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_tiny_22k_384_ema.pt":
								__a    							=					torch.tensor([0.1_082, -0.8_286, -0.5_095, 0.4_681, -0.8_085]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_224_ema.pt":
								__a    							=					torch.tensor([-0.2_419, -0.6_221, 0.2_176, -0.0_980, -0.7_527]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_base_22k_384_ema.pt":
								__a    							=					torch.tensor([0.0_391, -0.4_371, 0.3_786, 0.1_251, -0.2_784]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_224_ema.pt":
								__a    							=					torch.tensor([-0.0_504, 0.5_636, -0.1_729, -0.6_507, -0.3_949]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_large_22k_384_ema.pt":
								__a    							=					torch.tensor([0.3_560, 0.9_486, 0.3_149, -0.2_667, -0.5_138]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_384_ema.pt":
								__a    							=					torch.tensor([-0.2_469, -0.4_550, -0.5_853, -0.0_810, 0.0_309]						)
				elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnextv2/im22k/convnextv2_huge_22k_512_ema.pt":
								__a    							=					torch.tensor([-0.3_090, 0.0_802, -0.0_682, -0.1_979, -0.2_826]						)
				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__						)

				__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(a__						)
								preprocessor.push_to_hub(a__						)


if __name__ == "__main__":
					A  :			Optional[int]     				=				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')

					A  :			int     				=				parser.parse_args()
					convert_convnextva_checkpoint(
					    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
					)