




import os
import shutil
import unittest
from unittest.mock import patch

from transformers.testing_utils import CaptureStd, is_pt_tf_cross_test, require_torch

class 	__lowerCAmelCase			(			unittest.TestCase				):
							@patch("""sys.argv"""	,   ["""fakeprogrampath""", """env"""]       )
							def    lowerCamelCase__							(		self		:int       ):







								'''simple docstring'''







								import transformers.commands.transformers_cli

								with CaptureStd() as cs:
									transformers.commands.transformers_cli.main()
								self.assertIn("""Python version"""	,   cs.out       )
								self.assertIn("""Platform"""	,   cs.out       )
								self.assertIn("""Using distributed or parallel set-up in script?"""	,   cs.out       )
							@is_pt_tf_cross_test
							@patch(
							    """sys.argv"""	,   ["""fakeprogrampath""", """pt-to-tf""", """--model-name""", """hf-internal-testing/tiny-random-gptj""", """--no-pr"""]       )
							def    lowerCamelCase__							(		self		:Dict       ):







								'''simple docstring'''







								import transformers.commands.transformers_cli

								shutil.rmtree("""/tmp/hf-internal-testing/tiny-random-gptj"""	,   ignore_errors=__magic_name__       )  # cleans potential past runs
								transformers.commands.transformers_cli.main()

								# The original repo has no TF weights -- if they exist, they were created by the CLI
								self.assertTrue(os.path.exists("""/tmp/hf-internal-testing/tiny-random-gptj/tf_model.h5"""       )       )
							@require_torch
							@patch("""sys.argv"""	,   ["""fakeprogrampath""", """download""", """hf-internal-testing/tiny-random-gptj""", """--cache-dir""", """/tmp"""]       )
							def    lowerCamelCase__							(		self		:Optional[Any]       ):







								'''simple docstring'''







								import transformers.commands.transformers_cli

								# # remove any previously downloaded model to start clean
								shutil.rmtree("""/tmp/models--hf-internal-testing--tiny-random-gptj"""	,   ignore_errors=__magic_name__       )

								# run the command
								transformers.commands.transformers_cli.main()

								# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--tiny-random-gptj
								self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/blobs"""       )       )
								self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/refs"""       )       )
								self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--tiny-random-gptj/snapshots"""       )       )





							@require_torch
							@patch(
							    """sys.argv"""	,   [
							        """fakeprogrampath""",
							        """download""",
							        """hf-internal-testing/test_dynamic_model_with_tokenizer""",
							        """--trust-remote-code""",
							        """--cache-dir""",
							        """/tmp""",
							    ]	,   )
							def    lowerCamelCase__							(		self		:Any       ):







								'''simple docstring'''







								import transformers.commands.transformers_cli

								# # remove any previously downloaded model to start clean
								shutil.rmtree("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer"""	,   ignore_errors=__magic_name__       )

								# run the command
								transformers.commands.transformers_cli.main()

								# check if the model files are downloaded correctly on /tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer
								self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/blobs"""       )       )
								self.assertTrue(os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/refs"""       )       )
								self.assertTrue(
								    os.path.exists("""/tmp/models--hf-internal-testing--test_dynamic_model_with_tokenizer/snapshots"""       )       )







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)

