





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


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


__UpperCamelCase      : Any					      =   'lfs-multipart-upload'

class 	__lowerCAmelCase			(			__magic_name__				):
							@staticmethod
							def    lowerCamelCase__							(		__magic_name__		:ArgumentParser       ):







								'''simple docstring'''







								a		       =							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=A_	,   help="""Local path to repository you want to configure."""       )
								enable_parser.set_defaults(func=lambda __magic_name__       : LfsEnableCommand(A_       )       )

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







class 	__lowerCAmelCase			:
							def __init__(		self		:List[Any]	,   __magic_name__		:str       ):







								'''simple docstring'''







								a		       =							args





							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







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







def 	__A	(			__lowerCamelCase					)   -> Optional[Any]:
	a		       =							json.dumps(lowercase__					) + """\n"""
	sys.stdout.write(lowercase__					)
	sys.stdout.flush()





def 	__A	(			)   -> str:
	a		       =							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			(			__magic_name__				):
							def __init__(		self		:Tuple	,   __magic_name__		:str	,   __magic_name__		:int	,   __magic_name__		:int       ):







								'''simple docstring'''







								a		       =							filepath
								a		       =							seek_from
								a		       =							read_limit
								a		       =							0
							def __enter__(		self		:Dict       ):







								'''simple docstring'''







								a		       =							open(self.filepath	,   """rb"""       )
								self.f.seek(self.seek_from       )
								return self
							def __len__(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							os.fstat(self.f.fileno()       ).st_size
								return min(self.read_limit	,   total_length - self.seek_from       )
							def    lowerCamelCase__							(		self		:Optional[int]	,   __magic_name__		:Optional[Any]=-1       ):







								'''simple docstring'''







								if self.n_seen >= self.read_limit:
									return b""
								a		       =							self.read_limit - self.n_seen
								a		       =							self.f.read(remaining_amount if n < 0 else min(A_	,   A_       )       )
								self.n_seen += len(A_       )
								return data
							def __iter__(		self		:Dict       ):







								'''simple docstring'''







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





							def __exit__(		self		:Optional[Any]	,   *__magic_name__		:str       ):







								'''simple docstring'''







								self.f.close()







class 	__lowerCAmelCase			:
							def __init__(		self		:Union[str, Any]	,   __magic_name__		:Optional[int]       ):







								'''simple docstring'''







								a		       =							args





							def    lowerCamelCase__							(		self		:Union[str, Any]       ):







								'''simple docstring'''







								a		       =							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:
									a		       =							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       )

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

									a		       =							[]
									for i, presigned_url in enumerate(A_       ):
										with FileSlice(A_	,   seek_from=i * chunk_size	,   read_limit=A_       ) as data:
											a		       =							requests.put(A_	,   data=A_       )
											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.

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

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







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)

