



import argparse

import requests
import torch
from PIL import Image

from transformers import CLIPProcessor, GroupViTConfig, GroupViTModel






def  lowerCamelCase_	(      lowerCAmelCase__	:	List[str]				)       ->							Tuple:

							'''simple docstring'''






							if "img_encoder.pos_embed" in name:
														A      						=    name.replace('img_encoder.pos_embed'			,	'vision_model.embeddings.position_embeddings'				)
							if "img_encoder.patch_embed.proj" in name:
														A      						=    name.replace('img_encoder.patch_embed.proj'			,	'vision_model.embeddings.patch_embeddings.projection'				)
							if "img_encoder.patch_embed.norm" in name:
														A      						=    name.replace('img_encoder.patch_embed.norm'			,	'vision_model.embeddings.layernorm'				)
							if "img_encoder.layers" in name:
														A      						=    name.replace('img_encoder.layers'			,	'vision_model.encoder.stages'				)
							if "blocks" in name and "res" not in name:
														A      						=    name.replace('blocks'			,	'layers'				)
							if "attn" in name and "pre_assign" not in name:
														A      						=    name.replace('attn'			,	'self_attn'				)
							if "proj" in name and "self_attn" in name and "text" not in name:
														A      						=    name.replace('proj'			,	'out_proj'				)
							if "pre_assign_attn.attn.proj" in name:
														A      						=    name.replace('pre_assign_attn.attn.proj'			,	'pre_assign_attn.attn.out_proj'				)
							if "norm1" in name:
														A      						=    name.replace('norm1'			,	'layer_norm1'				)
							if "norm2" in name and "pre_assign" not in name:
														A      						=    name.replace('norm2'			,	'layer_norm2'				)
							if "img_encoder.norm" in name:
														A      						=    name.replace('img_encoder.norm'			,	'vision_model.layernorm'				)
							# text encoder
							if "text_encoder.token_embedding" in name:
														A      						=    name.replace('text_encoder.token_embedding'			,	'text_model.embeddings.token_embedding'				)
							if "text_encoder.positional_embedding" in name:
														A      						=    name.replace('text_encoder.positional_embedding'			,	'text_model.embeddings.position_embedding.weight'				)
							if "text_encoder.transformer.resblocks." in name:
														A      						=    name.replace('text_encoder.transformer.resblocks.'			,	'text_model.encoder.layers.'				)
							if "ln_1" in name:
														A      						=    name.replace('ln_1'			,	'layer_norm1'				)
							if "ln_2" in name:
														A      						=    name.replace('ln_2'			,	'layer_norm2'				)
							if "c_fc" in name:
														A      						=    name.replace('c_fc'			,	'fc1'				)
							if "c_proj" in name:
														A      						=    name.replace('c_proj'			,	'fc2'				)
							if "text_encoder" in name:
														A      						=    name.replace('text_encoder'			,	'text_model'				)
							if "ln_final" in name:
														A      						=    name.replace('ln_final'			,	'final_layer_norm'				)
							# projection layers
							if "img_projector.linear_hidden." in name:
														A      						=    name.replace('img_projector.linear_hidden.'			,	'visual_projection.'				)
							if "img_projector.linear_out." in name:
														A      						=    name.replace('img_projector.linear_out.'			,	'visual_projection.3.'				)
							if "text_projector.linear_hidden" in name:
														A      						=    name.replace('text_projector.linear_hidden'			,	'text_projection'				)
							if "text_projector.linear_out" in name:
														A      						=    name.replace('text_projector.linear_out'			,	'text_projection.3'				)

							return name






def  lowerCamelCase_	(      lowerCAmelCase__	:	Any			,	lowerCAmelCase__	:	Optional[Any]				)       ->							int:

							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														A      						=    orig_state_dict.pop(lowerCAmelCase__				)

														if "qkv" in key:
																					# weights and biases of the key, value and query projections of vision encoder's attention layers require special treatment:
																					# we need to split them up into separate matrices/vectors
																					A      						=    key.split('.'				)
																					A						,   A      						=    int(key_split[2]				), int(key_split[4]				)
																					A      						=    config.vision_config.hidden_size
																					if "weight" in key:
																												A      						=    val[:dim, :]
																												A      						=    val[dim : dim * 2, :]
																												A      						=    val[-dim:, :]
																					else:
																												A      						=    val[:dim]
																												A      						=    val[dim : dim * 2]
																												A      						=    val[-dim:]
														elif "in_proj" in key:
																					# weights and biases of the key, value and query projections of text encoder's attention layers require special treatment:
																					# we need to split them up into separate matrices/vectors
																					A      						=    key.split('.'				)
																					A      						=    int(key_split[3]				)
																					A      						=    config.text_config.hidden_size
																					if "weight" in key:
																												A      						=    val[:dim, :]
																												A      						=    val[
																												    dim : dim * 2, :
																												]
																												A      						=    val[-dim:, :]
																					else:
																												A      						=    val[:dim]
																												A      						=    val[dim : dim * 2]
																												A      						=    val[-dim:]
														else:
																					A      						=    rename_key(lowerCAmelCase__				)
																					# squeeze if necessary
																					if (
																					    "text_projection.0" in new_name
																					    or "text_projection.3" in new_name
																					    or "visual_projection.0" in new_name
																					    or "visual_projection.3" in new_name
																					):
																												A      						=    val.squeeze_()
																					else:
																												A      						=    val

							return orig_state_dict






def  lowerCamelCase_	(      )       ->							List[Any]:

							'''simple docstring'''






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






@torch.no_grad()
def  lowerCamelCase_	(      lowerCAmelCase__	:	Union[str, Any]			,	lowerCAmelCase__	:	Tuple			,	lowerCAmelCase__	:	Optional[int]="groupvit-gcc-yfcc"			,	lowerCAmelCase__	:	str=False				)       ->							Any:

							'''simple docstring'''






							A      						=    GroupViTConfig()
							A      						=    GroupViTModel(lowerCAmelCase__				).eval()

							A      						=    torch.load(lowerCAmelCase__			,	map_location='cpu'				)['model']
							A      						=    convert_state_dict(lowerCAmelCase__			,	lowerCAmelCase__				)
							A						,   A      						=    model.load_state_dict(lowerCAmelCase__			,	strict=lowerCAmelCase__				)
							assert missing_keys == ["text_model.embeddings.position_ids"]
							assert (unexpected_keys == ["multi_label_logit_scale"]) or (len(lowerCAmelCase__				) == 0)

							# verify result
							A      						=    CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32'				)
							A      						=    prepare_img()
							A      						=    processor(text=['a photo of a cat', 'a photo of a dog']			,	images=lowerCAmelCase__			,	padding=lowerCAmelCase__			,	return_tensors='pt'				)

							with torch.no_grad():
														A      						=    model(**lowerCAmelCase__				)

							if model_name == "groupvit-gcc-yfcc":
														A      						=    torch.tensor([[13.3523, 6.3629]]				)
							elif model_name == "groupvit-gcc-redcaps":
														A      						=    torch.tensor([[16.1873, 8.6230]]				)
							else:
														raise ValueError(F'''Model name {model_name} not supported.'''				)
							assert torch.allclose(outputs.logits_per_image			,	lowerCAmelCase__			,	atol=1E-3				)

							processor.save_pretrained(lowerCAmelCase__				)
							model.save_pretrained(lowerCAmelCase__				)
							print('Successfully saved processor and model to'			,	lowerCAmelCase__				)

							if push_to_hub:
														print('Pushing to the hub...'				)
														processor.push_to_hub(lowerCAmelCase__			,	organization='nielsr'				)
														model.push_to_hub(lowerCAmelCase__			,	organization='nielsr'				)


if __name__ == "__main__":
				__snake_case     :List[Any]			       =argparse.ArgumentParser()
				parser.add_argument(
				    '--pytorch_dump_folder_path', default=None, type=str, help='Path to dump the processor and PyTorch model.'
				)
				parser.add_argument('--checkpoint_path', default=None, type=str, help='Path to GroupViT checkpoint')
				parser.add_argument(
				    '--model_name',
				    default='groupvit-gccy-fcc',
				    type=str,
				    help='Name of the model. Expecting either \'groupvit-gcc-yfcc\' or \'groupvit-gcc-redcaps\'',
				)
				parser.add_argument(
				    '--push_to_hub',
				    action='store_true',
				    help='Whether or not to push the converted model and processor to the 🤗 hub using the provided `model_name`.',
				)
				__snake_case     :Any			       =parser.parse_args()

				convert_groupvit_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)

"""simple docstring"""





from __future__ import annotations

from functools import lru_cache
from math import ceil

_a								=						100

_a								=						set(range(3, NUM_PRIMES, 2))
primes.add(2)
_a								=						42

for prime in range(3, ceil(NUM_PRIMES**0.5), 2):
				if prime not in primes:
								continue
				primes.difference_update(set(range(prime * prime, NUM_PRIMES, prime)))



@lru_cache(maxsize=1_00      )
def  lowerCamelCase__						(     __snake_case      )				->						set[int]:


					"""simple docstring"""





					if number_to_partition < 0:
										return set()
					elif number_to_partition == 0:
										return {1}

					_UpperCamelCase									=    set()
					_UpperCamelCase									=    42
					_UpperCamelCase									=    42

					for prime in primes:
										if prime > number_to_partition:
															continue
										for sub in partition(number_to_partition - prime      ):
															ret.add(sub * prime      )

					return ret



def  lowerCamelCase__						(     __snake_case = 50_00      )				->						int | None:


					"""simple docstring"""





					for number_to_partition in range(1,	__snake_case      ):
										if len(partition(__snake_case      )      ) > number_unique_partitions:
															return number_to_partition
					return None


if __name__ == "__main__":
				print(F"""{solution() = }""")






