




import json
import os
import tempfile

from transformers.testing_utils import check_json_file_has_correct_format, require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available


if is_torch_available():
		import numpy as np
		import torch

if is_vision_available():
		from PIL import Image





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=False							,    __lowerCamelCase=False							,    __lowerCamelCase=False					)   -> List[str]:
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	a		       =							[]
	for i in range(image_processor_tester.batch_size					):
		if equal_resolution:
			a		       =							a		       =							image_processor_tester.max_resolution
		else:
			# To avoid getting image width/height 0
			a		       =							image_processor_tester.min_resolution
			if getattr(__lowerCamelCase							,    """size_divisor"""							,    __lowerCamelCase					):
				# If `size_divisor` is defined, the image needs to have width/size >= `size_divisor`
				a		       =							max(image_processor_tester.size_divisor							,    __lowerCamelCase					)
			a       ,				a		       =							np.random.choice(np.arange(__lowerCamelCase							,    image_processor_tester.max_resolution					)							,    2					)
		image_inputs.append(
		    np.random.randint(255							,    size=(image_processor_tester.num_channels, width, height)							,    dtype=np.uinta					)					)

	if not numpify and not torchify:
		# PIL expects the channel dimension as last dimension
		a		       =							[Image.fromarray(np.moveaxis(__lowerCamelCase							,    0							,    -1					)					) for image in image_inputs]

	if torchify:
		a		       =							[torch.from_numpy(__lowerCamelCase					) for image in image_inputs]

	return image_inputs





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=10							,    __lowerCamelCase=10							,    __lowerCamelCase=False							,    __lowerCamelCase=False					)   -> Union[str, Any]:
	a		       =							[]
	for i in range(image_processor_tester.num_frames					):
		video.append(np.random.randint(255							,    size=(image_processor_tester.num_channels, width, height)							,    dtype=np.uinta					)					)

	if not numpify and not torchify:
		# PIL expects the channel dimension as last dimension
		a		       =							[Image.fromarray(np.moveaxis(__lowerCamelCase							,    0							,    -1					)					) for frame in video]

	if torchify:
		a		       =							[torch.from_numpy(__lowerCamelCase					) for frame in video]

	return video





def 	__A	(			__lowerCamelCase							,    __lowerCamelCase=False							,    __lowerCamelCase=False							,    __lowerCamelCase=False					)   -> Tuple:
	assert not (numpify and torchify), "You cannot specify both numpy and PyTorch tensors at the same time"

	a		       =							[]
	for i in range(image_processor_tester.batch_size					):
		if equal_resolution:
			a		       =							a		       =							image_processor_tester.max_resolution
		else:
			a       ,				a		       =							np.random.choice(
			    np.arange(image_processor_tester.min_resolution							,    image_processor_tester.max_resolution					)							,    2					)
			a		       =							prepare_video(
			    image_processor_tester=__lowerCamelCase							,    width=__lowerCamelCase							,    height=__lowerCamelCase							,    numpify=__lowerCamelCase							,    torchify=__lowerCamelCase							,    )
		video_inputs.append(__lowerCamelCase					)

	return video_inputs



class 	__lowerCAmelCase			:
							UpperCamelCase__        =						None
							def    lowerCamelCase__							(		self		:List[Any]       ):







								'''simple docstring'''







								a		       =							self.image_processing_class(**self.image_processor_dict       )
								a		       =							json.loads(image_processor.to_json_string()       )
								for key, value in self.image_processor_dict.items():
									self.assertEqual(obj[key]	,   __magic_name__       )
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								a		       =							self.image_processing_class(**self.image_processor_dict       )

								with tempfile.TemporaryDirectory() as tmpdirname:
									a		       =							os.path.join(__magic_name__	,   """image_processor.json"""       )
									image_processor_first.to_json_file(__magic_name__       )
									a		       =							self.image_processing_class.from_json_file(__magic_name__       )

								self.assertEqual(image_processor_second.to_dict()	,   image_processor_first.to_dict()       )
							def    lowerCamelCase__							(		self		:Optional[int]       ):







								'''simple docstring'''







								a		       =							self.image_processing_class(**self.image_processor_dict       )

								with tempfile.TemporaryDirectory() as tmpdirname:
									a		       =							image_processor_first.save_pretrained(__magic_name__       )[0]
									check_json_file_has_correct_format(__magic_name__       )
									a		       =							self.image_processing_class.from_pretrained(__magic_name__       )

								self.assertEqual(image_processor_second.to_dict()	,   image_processor_first.to_dict()       )
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								a		       =							self.image_processing_class()
								self.assertIsNotNone(__magic_name__       )





							@require_torch
							@require_vision
							def    lowerCamelCase__							(		self		:str       ):







								'''simple docstring'''







								if self.test_cast_dtype is not None:
									# Initialize image_processor
									a		       =							self.image_processing_class(**self.image_processor_dict       )

									# create random PyTorch tensors
									a		       =							prepare_image_inputs(self.image_processor_tester	,   equal_resolution=__magic_name__	,   torchify=__magic_name__       )

									a		       =							image_processor(__magic_name__	,   return_tensors="""pt"""       )
									# for layoutLM compatiblity
									self.assertEqual(encoding.pixel_values.device	,   torch.device("""cpu"""       )       )
									self.assertEqual(encoding.pixel_values.dtype	,   torch.floataa       )

									a		       =							image_processor(__magic_name__	,   return_tensors="""pt"""       ).to(torch.floataa       )
									self.assertEqual(encoding.pixel_values.device	,   torch.device("""cpu"""       )       )
									self.assertEqual(encoding.pixel_values.dtype	,   torch.floataa       )

									a		       =							image_processor(__magic_name__	,   return_tensors="""pt"""       ).to("""cpu"""	,   torch.bfloataa       )
									self.assertEqual(encoding.pixel_values.device	,   torch.device("""cpu"""       )       )
									self.assertEqual(encoding.pixel_values.dtype	,   torch.bfloataa       )

									with self.assertRaises(__magic_name__       ):
										a		       =							image_processor(__magic_name__	,   return_tensors="""pt"""       ).to(torch.bfloataa	,   """cpu"""       )

									# Try with text + image feature
									a		       =							image_processor(__magic_name__	,   return_tensors="""pt"""       )
									encoding.update({"""input_ids""": torch.LongTensor([[1, 2, 3], [4, 5, 6]]       )}       )
									a		       =							encoding.to(torch.floataa       )

									self.assertEqual(encoding.pixel_values.device	,   torch.device("""cpu"""       )       )
									self.assertEqual(encoding.pixel_values.dtype	,   torch.floataa       )
									self.assertEqual(encoding.input_ids.dtype	,   torch.long       )







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()
__UpperCamelCase      : List[Any]					      =   logging.get_logger(__name__)





def 	__A	(			__lowerCamelCase					)   -> Optional[int]:
	a		       =							ConvNextConfig()

	if "tiny" in checkpoint_url:
		a		       =							[3, 3, 9, 3]
		a		       =							[96, 192, 384, 768]
	if "small" in checkpoint_url:
		a		       =							[3, 3, 27, 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 "xlarge" in checkpoint_url:
		a		       =							[3, 3, 27, 3]
		a		       =							[256, 512, 1024, 2048]

	if "1k" in checkpoint_url:
		a		       =							1000
		a		       =							"""imagenet-1k-id2label.json"""
		a		       =							(1, 1000)
	else:
		a		       =							2_1841
		a		       =							"""imagenet-22k-id2label.json"""
		a		       =							(1, 2_1841)

	a		       =							"""huggingface/label-files"""
	a		       =							num_labels
	a		       =							json.load(open(hf_hub_download(__lowerCamelCase							,    __lowerCamelCase							,    repo_type="""dataset"""					)							,    """r"""					)					)
	a		       =							{int(__lowerCamelCase					): 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[9205]
		del idalabel[1_5027]
	a		       =							idalabel
	a		       =							{v: k for k, v in idalabel.items()}
	a		       =							hidden_sizes
	a		       =							depths

	return config, expected_shape





def 	__A	(			__lowerCamelCase					)   -> int:
	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 "stages" in name:
		a		       =							name.replace("""stages"""							,    """encoder.stages"""					)
	if "norm" in name:
		a		       =							name.replace("""norm"""							,    """layernorm"""					)
	if "gamma" in name:
		a		       =							name.replace("""gamma"""							,    """layer_scale_parameter"""					)
	if "head" in name:
		a		       =							name.replace("""head"""							,    """classifier"""					)

	return name





def 	__A	(			)   -> Optional[Any]:
	a		       =							"""http://images.cocodataset.org/val2017/000000039769.jpg"""
	a		       =							Image.open(requests.get(__lowerCamelCase							,    stream=__lowerCamelCase					).raw					)
	return im





@torch.no_grad()
def 	__A	(			__lowerCamelCase							,    __lowerCamelCase					)   -> List[Any]:
	a       ,				a		       =							get_convnext_config(__lowerCamelCase					)
	# load original state_dict from URL
	a		       =							torch.hub.load_state_dict_from_url(__lowerCamelCase					)["""model"""]
	# rename keys
	for key in state_dict.copy().keys():
		a		       =							state_dict.pop(__lowerCamelCase					)
		a		       =							val
	# add prefix to all keys expect classifier head
	for key in state_dict.copy().keys():
		a		       =							state_dict.pop(__lowerCamelCase					)
		if not key.startswith("""classifier"""					):
			a		       =							"""convnext.""" + key
		a		       =							val

	# load HuggingFace model
	a		       =							ConvNextForImageClassification(__lowerCamelCase					)
	model.load_state_dict(__lowerCamelCase					)
	model.eval()

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

	a		       =							model(__lowerCamelCase					).logits

	# note: the logits below were obtained without center cropping
	if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
		a		       =							torch.tensor([-0.1210, -0.6605, 0.1918]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
		a		       =							torch.tensor([-0.4473, -0.1847, -0.6365]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
		a		       =							torch.tensor([0.4525, 0.7539, 0.0308]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
		a		       =							torch.tensor([0.3561, 0.6350, -0.0384]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
		a		       =							torch.tensor([0.4174, -0.0989, 0.1489]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
		a		       =							torch.tensor([0.2513, -0.1349, -0.1613]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
		a		       =							torch.tensor([1.2980, 0.3631, -0.1198]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
		a		       =							torch.tensor([1.2963, 0.1227, 0.1723]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
		a		       =							torch.tensor([1.7956, 0.8390, 0.2820]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
		a		       =							torch.tensor([-0.2822, -0.0502, -0.0878]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
		a		       =							torch.tensor([-0.5672, -0.0730, -0.4348]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
		a		       =							torch.tensor([0.2681, 0.2365, 0.6246]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
		a		       =							torch.tensor([-0.2642, 0.3931, 0.5116]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
		a		       =							torch.tensor([-0.6677, -0.1873, -0.8379]					)
	elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
		a		       =							torch.tensor([-0.7749, -0.2967, -0.6444]					)
	else:
		raise ValueError(f'Unknown URL: {checkpoint_url}'					)

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

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

	print("""Pushing model to the hub..."""					)
	a		       =							"""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(__lowerCamelCase							,    __lowerCamelCase					)							,    organization="""nielsr"""							,    commit_message="""Add model"""							,    )


if __name__ == "__main__":
		__UpperCamelCase      : int					      =   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.",
		)

		__UpperCamelCase      : List[Any]					      =   parser.parse_args()
		convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)

