
'''simple docstring'''
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()
lowerCAmelCase :  Dict      =      logging.get_logger(__name__)







def 						A_(			A		:						Union[str, Any]):
							UpperCamelCase					       =			ConvNextConfig()

							if "tiny" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 9, 3]
														UpperCamelCase					       =			[96, 192, 384, 768]
							if "small" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 27, 3]
														UpperCamelCase					       =			[96, 192, 384, 768]
							if "base" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 27, 3]
														UpperCamelCase					       =			[128, 256, 512, 1024]
							if "large" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 27, 3]
														UpperCamelCase					       =			[192, 384, 768, 1536]
							if "xlarge" in checkpoint_url:
														UpperCamelCase					       =			[3, 3, 27, 3]
														UpperCamelCase					       =			[256, 512, 1024, 2048]

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

							UpperCamelCase					       =			'huggingface/label-files'
							UpperCamelCase					       =			num_labels
							UpperCamelCase					       =			json.load(open(hf_hub_download(A						,			A						,			repo_type='dataset')						,			'r'))
							UpperCamelCase					       =			{int(A): 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]
							UpperCamelCase					       =			idalabel
							UpperCamelCase					       =			{v: k for k, v in idalabel.items()}
							UpperCamelCase					       =			hidden_sizes
							UpperCamelCase					       =			depths

							return config, expected_shape







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

							return name







def 						A_(			):
							UpperCamelCase					       =			'http://images.cocodataset.org/val2017/000000039769.jpg'
							UpperCamelCase					       =			Image.open(requests.get(A						,			stream=A).raw)
							return im







@torch.no_grad()
def 						A_(			A		:						Optional[Any]						,			A		:						Union[str, Any]):
							UpperCamelCase       ,     UpperCamelCase					       =			get_convnext_config(A)
							# load original state_dict from URL
							UpperCamelCase					       =			torch.hub.load_state_dict_from_url(A)['model']
							# rename keys
							for key in state_dict.copy().keys():
														UpperCamelCase					       =			state_dict.pop(A)
														UpperCamelCase					       =			val
							# add prefix to all keys expect classifier head
							for key in state_dict.copy().keys():
														UpperCamelCase					       =			state_dict.pop(A)
														if not key.startswith('classifier'):
																					UpperCamelCase					       =			'convnext.' + key
														UpperCamelCase					       =			val

							# load HuggingFace model
							UpperCamelCase					       =			ConvNextForImageClassification(A)
							model.load_state_dict(A)
							model.eval()

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

							UpperCamelCase					       =			model(A).logits

							# note: the logits below were obtained without center cropping
							if checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_tiny_1k_224_ema.pth":
														UpperCamelCase					       =			torch.tensor([-0.1_210, -0.6_605, 0.1_918])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_small_1k_224_ema.pth":
														UpperCamelCase					       =			torch.tensor([-0.4_473, -0.1_847, -0.6_365])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_224_ema.pth":
														UpperCamelCase					       =			torch.tensor([0.4_525, 0.7_539, 0.0_308])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_1k_384.pth":
														UpperCamelCase					       =			torch.tensor([0.3_561, 0.6_350, -0.0_384])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_224_ema.pth":
														UpperCamelCase					       =			torch.tensor([0.4_174, -0.0_989, 0.1_489])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_1k_384.pth":
														UpperCamelCase					       =			torch.tensor([0.2_513, -0.1_349, -0.1_613])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_224.pth":
														UpperCamelCase					       =			torch.tensor([1.2_980, 0.3_631, -0.1_198])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_224.pth":
														UpperCamelCase					       =			torch.tensor([1.2_963, 0.1_227, 0.1_723])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_224.pth":
														UpperCamelCase					       =			torch.tensor([1.7_956, 0.8_390, 0.2_820])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_224.pth":
														UpperCamelCase					       =			torch.tensor([-0.2_822, -0.0_502, -0.0_878])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_base_22k_1k_384.pth":
														UpperCamelCase					       =			torch.tensor([-0.5_672, -0.0_730, -0.4_348])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_224.pth":
														UpperCamelCase					       =			torch.tensor([0.2_681, 0.2_365, 0.6_246])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_large_22k_1k_384.pth":
														UpperCamelCase					       =			torch.tensor([-0.2_642, 0.3_931, 0.5_116])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_224_ema.pth":
														UpperCamelCase					       =			torch.tensor([-0.6_677, -0.1_873, -0.8_379])
							elif checkpoint_url == "https://dl.fbaipublicfiles.com/convnext/convnext_xlarge_22k_1k_384_ema.pth":
														UpperCamelCase					       =			torch.tensor([-0.7_749, -0.2_967, -0.6_444])
							else:
														raise ValueError(f'''Unknown URL: {checkpoint_url}''')

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

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

							print('Pushing model to the hub...')
							UpperCamelCase					       =			'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(A						,			A)						,			organization='nielsr'						,			commit_message='Add model'						,			)


if __name__ == "__main__":
						lowerCAmelCase :  Union[str, Any]      =      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.',
						)

						lowerCAmelCase :  Any      =      parser.parse_args()
						convert_convnext_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path)






'''simple docstring'''
def 						A_(			A		:						list[list[int]]):
							if len(A) == 0 or not all(len(A) == 2 for pair in coordinates):
														raise ValueError('The program cannot work out a fitting polynomial.')

							if len({tuple(A) for pair in coordinates}) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			{x for x, _ in coordinates}
							if len(A) == 1:
														return f'''x={coordinates[0][0]}'''

							if len(A) != len(A):
														raise ValueError('The program cannot work out a fitting polynomial.')

							UpperCamelCase					       =			len(A)

							UpperCamelCase					       =			0
							UpperCamelCase					       =			[]
							# put the x and x to the power values in a matrix
							while count_of_line < x:
														UpperCamelCase					       =			0
														UpperCamelCase					       =			coordinates[count_of_line][0]
														UpperCamelCase					       =			[]
														while count_in_line < x:
																					count_line.append(a ** (x - (count_in_line + 1)))
																					count_in_line += 1
														matrix.append(A)
														count_of_line += 1

							UpperCamelCase					       =			0
							# put the y values into a vector
							UpperCamelCase					       =			[]
							while count_of_line < x:
														vector.append(coordinates[count_of_line][1])
														count_of_line += 1

							UpperCamelCase					       =			0

							while count < x:
														UpperCamelCase					       =			0
														while zahlen < x:
																					if count == zahlen:
																												zahlen += 1
																					if zahlen == x:
																												break
																					UpperCamelCase					       =			matrix[zahlen][count] / matrix[count][count]
																					for counting_columns, item in enumerate(matrix[count]):
																												# manipulating all the values in the matrix
																												matrix[zahlen][counting_columns] -= item * bruch
																					# manipulating the values in the vector
																					vector[zahlen] -= vector[count] * bruch
																					zahlen += 1
														count += 1

							UpperCamelCase					       =			0
							# make solutions
							UpperCamelCase					       =			[]
							while count < x:
														solution.append(str(vector[count] / matrix[count][count]))
														count += 1

							UpperCamelCase					       =			0
							UpperCamelCase					       =			'f(x)='

							while count < x:
														UpperCamelCase					       =			solution[count].split('E')
														if len(A) > 1:
																					UpperCamelCase					       =			f'''{remove_e[0]}*10^{remove_e[1]}'''
														solved += f'''x^{x - (count + 1)}*{solution[count]}'''
														if count + 1 != x:
																					solved += "+"
														count += 1

							return solved


if __name__ == "__main__":
						print(points_to_polynomial([]))
						print(points_to_polynomial([[]]))
						print(points_to_polynomial([[1, 0], [2, 0], [3, 0]]))
						print(points_to_polynomial([[1, 1], [2, 1], [3, 1]]))
						print(points_to_polynomial([[1, 3], [2, 3], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 2], [3, 3]]))
						print(points_to_polynomial([[1, 1], [2, 4], [3, 9]]))
						print(points_to_polynomial([[1, 3], [2, 6], [3, 11]]))
						print(points_to_polynomial([[1, -3], [2, -6], [3, -11]]))
						print(points_to_polynomial([[1, 5], [2, 2], [3, 9]]))




