



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__     				=	logging.get_logger(__name__)


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   ConvNextVaConfig()

					if "atto" in checkpoint_url:
										_lowerCAmelCase      =   [2, 2, 6, 2]
										_lowerCAmelCase      =   [40, 80, 1_60, 3_20]
					if "femto" in checkpoint_url:
										_lowerCAmelCase      =   [2, 2, 6, 2]
										_lowerCAmelCase      =   [48, 96, 1_92, 3_84]
					if "pico" in checkpoint_url:
										_lowerCAmelCase      =   [2, 2, 6, 2]
										_lowerCAmelCase      =   [64, 1_28, 2_56, 5_12]
					if "nano" in checkpoint_url:
										_lowerCAmelCase      =   [2, 2, 8, 2]
										_lowerCAmelCase      =   [80, 1_60, 3_20, 6_40]
					if "tiny" in checkpoint_url:
										_lowerCAmelCase      =   [3, 3, 9, 3]
										_lowerCAmelCase      =   [96, 1_92, 3_84, 7_68]
					if "base" in checkpoint_url:
										_lowerCAmelCase      =   [3, 3, 27, 3]
										_lowerCAmelCase      =   [1_28, 2_56, 5_12, 10_24]
					if "large" in checkpoint_url:
										_lowerCAmelCase      =   [3, 3, 27, 3]
										_lowerCAmelCase      =   [1_92, 3_84, 7_68, 15_36]
					if "huge" in checkpoint_url:
										_lowerCAmelCase      =   [3, 3, 27, 3]
										_lowerCAmelCase      =   [3_52, 7_04, 14_08, 28_16]

					_lowerCAmelCase      =   10_00
					_lowerCAmelCase      =   """imagenet-1k-id2label.json"""
					_lowerCAmelCase      =   (1, 10_00)

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

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

					return config, expected_shape


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

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

					return name


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   """http://images.cocodataset.org/val2017/000000039769.jpg"""
					_lowerCAmelCase      =   Image.open(requests.get(snake_case     ,						stream=snake_case      ).raw      )
					return im


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					if "224" in checkpoint_url:
										_lowerCAmelCase      =   2_24
										_lowerCAmelCase      =   2_24 / 2_56
					elif "384" in checkpoint_url:
										_lowerCAmelCase      =   3_84
										_lowerCAmelCase      =   None
					else:
										_lowerCAmelCase      =   5_12
										_lowerCAmelCase      =   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 		_UpperCAmelCase    (				snake_case     ,						snake_case     ,						snake_case     ,						snake_case      ):





					"""simple docstring"""

					print("""Downloading original model from checkpoint..."""      )
					# define ConvNeXTV2 configuration based on URL
					_lowerCAmelCase     ,     _lowerCAmelCase      =   get_convnextva_config(snake_case      )
					# load original state_dict from URL
					_lowerCAmelCase      =   torch.hub.load_state_dict_from_url(snake_case      )["""model"""]

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

					# load HuggingFace model
					_lowerCAmelCase      =   ConvNextVaForImageClassification(snake_case      )
					model.load_state_dict(snake_case      )
					model.eval()

					# Check outputs on an image, prepared by ConvNextImageProcessor
					_lowerCAmelCase      =   convert_preprocessor(snake_case      )
					_lowerCAmelCase      =   preprocessor(images=prepare_img()     ,						return_tensors="""pt"""      )
					_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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":
										_lowerCAmelCase      =   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]     ,						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      )

					_lowerCAmelCase      =   """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__":
	A__     				=	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__     				=	parser.parse_args()
	convert_convnextva_checkpoint(
	    args.checkpoint_url, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub
	)








import json
import os
import subprocess
import sys
import warnings
from argparse import ArgumentParser
from contextlib import AbstractContextManager
from typing import Dict, List, Optional

import requests

from ..utils import logging
from . import BaseTransformersCLICommand


A__     				=	logging.get_logger(__name__)  # pylint: disable=invalid-name


A__     				=	"""lfs-multipart-upload"""





class  __lowerCAmelCase			(					lowerCamelCase__	):




				@staticmethod
				def 							snake_case       (							_snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   parser.add_parser(
									    """lfs-enable-largefiles"""						,    help=(
									        """Deprecated: use `huggingface-cli` instead. Configure your repository to enable upload of files > 5GB."""
									    )						,    )
									enable_parser.add_argument("""path"""						,    type=_snake_case						,    help="""Local path to repository you want to configure."""		)
									enable_parser.set_defaults(func=lambda _snake_case		: LfsEnableCommand(_snake_case		)		)

									_lowerCAmelCase      =   parser.add_parser(
									    _snake_case						,    help=(
									        """Deprecated: use `huggingface-cli` instead. """
									        """Command will get called by git-lfs, do not call it directly."""
									    )						,    )
									upload_parser.set_defaults(func=lambda _snake_case		: LfsUploadCommand(_snake_case		)		)





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   args




				def 							snake_case       (							self		):



									"""simple docstring"""





									warnings.warn(
									    """Managing repositories through transformers-cli is deprecated. Please use `huggingface-cli` instead."""		)
									_lowerCAmelCase      =   os.path.abspath(self.args.path		)
									if not os.path.isdir(_snake_case		):
														print("""This does not look like a valid git repo."""		)
														exit(1		)
									subprocess.run(
									    """git config lfs.customtransfer.multipart.path transformers-cli""".split()						,    check=_snake_case						,    cwd=_snake_case		)
									subprocess.run(
									    F'git config lfs.customtransfer.multipart.args {LFS_MULTIPART_UPLOAD_COMMAND}'.split()						,    check=_snake_case						,    cwd=_snake_case						,    )
									print("""Local repo set up for largefiles"""		)


def 		_UpperCAmelCase    (				snake_case      ):





					"""simple docstring"""

					_lowerCAmelCase      =   json.dumps(snake_case      ) + """\n"""
					sys.stdout.write(snake_case      )
					sys.stdout.flush()


def 		_UpperCAmelCase    (				):





					"""simple docstring"""

					_lowerCAmelCase      =   json.loads(sys.stdin.readline().strip()      )

					if "terminate" in (msg.get("""type"""      ), msg.get("""event"""      )):
										# terminate message received
										return None

					if msg.get("""event"""      ) not in ("download", "upload"):
										logger.critical("""Received unexpected message"""      )
										sys.exit(1      )

					return msg





class  __lowerCAmelCase			(					lowerCamelCase__	):




				def __init__(							self						,    _snake_case						,    _snake_case						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   filepath
									_lowerCAmelCase      =   seek_from
									_lowerCAmelCase      =   read_limit
									_lowerCAmelCase      =   0




				def __enter__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   open(self.filepath						,    """rb"""		)
									self.f.seek(self.seek_from		)
									return self




				def __len__(							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   os.fstat(self.f.fileno()		).st_size
									return min(self.read_limit						,    total_length - self.seek_from		)




				def 							snake_case       (							self						,    _snake_case=-1		):



									"""simple docstring"""





									if self.n_seen >= self.read_limit:
														return b""
									_lowerCAmelCase      =   self.read_limit - self.n_seen
									_lowerCAmelCase      =   self.f.read(remaining_amount if n < 0 else min(_snake_case						,    _snake_case		)		)
									self.n_seen += len(_snake_case		)
									return data




				def __iter__(							self		):



									"""simple docstring"""





									yield self.read(n=4 * 1024 * 1024		)




				def __exit__(							self						,    *_snake_case		):



									"""simple docstring"""





									self.f.close()





class  __lowerCAmelCase			:




				def __init__(							self						,    _snake_case		):



									"""simple docstring"""





									_lowerCAmelCase      =   args




				def 							snake_case       (							self		):



									"""simple docstring"""





									_lowerCAmelCase      =   json.loads(sys.stdin.readline().strip()		)
									if not (init_msg.get("""event"""		) == "init" and init_msg.get("""operation"""		) == "upload"):
														write_msg({"""error""": {"""code""": 32, """message""": """Wrong lfs init operation"""}}		)
														sys.exit(1		)

									# The transfer process should use the information it needs from the
									# initiation structure, and also perform any one-off setup tasks it
									# needs to do. It should then respond on stdout with a simple empty
									# confirmation structure, as follows:
									write_msg({}		)

									# After the initiation exchange, git-lfs will send any number of
									# transfer requests to the stdin of the transfer process, in a serial sequence.
									while True:
														_lowerCAmelCase      =   read_msg()
														if msg is None:
																			# When all transfers have been processed, git-lfs will send
																			# a terminate event to the stdin of the transfer process.
																			# On receiving this message the transfer process should
																			# clean up and terminate. No response is expected.
																			sys.exit(0		)

														_lowerCAmelCase      =   msg["""oid"""]
														_lowerCAmelCase      =   msg["""path"""]
														_lowerCAmelCase      =   msg["""action"""]["""href"""]
														_lowerCAmelCase      =   msg["""action"""]["""header"""]
														_lowerCAmelCase      =   int(header.pop("""chunk_size"""		)		)
														_lowerCAmelCase      =   list(header.values()		)

														_lowerCAmelCase      =   []
														for i, presigned_url in enumerate(_snake_case		):
																			with FileSlice(_snake_case						,    seek_from=i * chunk_size						,    read_limit=_snake_case		) as data:
																								_lowerCAmelCase      =   requests.put(_snake_case						,    data=_snake_case		)
																								r.raise_for_status()
																								parts.append(
																								    {
																								        """etag""": r.headers.get("""etag"""		),
																								        """partNumber""": i + 1,
																								    }		)
																								# In order to support progress reporting while data is uploading / downloading,
																								# the transfer process should post messages to stdout
																								write_msg(
																								    {
																								        """event""": """progress""",
																								        """oid""": oid,
																								        """bytesSoFar""": (i + 1) * chunk_size,
																								        """bytesSinceLast""": chunk_size,
																								    }		)
																								# Not precise but that's ok.

														_lowerCAmelCase      =   requests.post(
														    _snake_case						,    json={
														        """oid""": oid,
														        """parts""": parts,
														    }						,    )
														r.raise_for_status()

														write_msg({"""event""": """complete""", """oid""": oid}		)



