"""simple docstring"""




import argparse

import requests
import torch
from PIL import Image

from transformers import (
    CLIPSegConfig,
    CLIPSegForImageSegmentation,
    CLIPSegProcessor,
    CLIPSegTextConfig,
    CLIPSegVisionConfig,
    CLIPTokenizer,
    ViTImageProcessor,
)



def 				__a       (			_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Optional[int]  				=					CLIPSegTextConfig()
					lowerCamelCase__		:   Union[str, Any]  				=					CLIPSegVisionConfig(patch_size=16						)

					lowerCamelCase__		:   Tuple  				=					True if '''refined''' in model_name else False
					lowerCamelCase__		:   Any  				=					16 if '''rd16''' in model_name else 64

					lowerCamelCase__		:   int  				=					CLIPSegConfig.from_text_vision_configs(
					    _lowercase							, _lowercase							, use_complex_transposed_convolution=_lowercase							, reduce_dim=_lowercase							, )
					return config



def 				__a       (			_lowercase						):







					"""simple docstring"""





					if "clip_model" in name:
										lowerCamelCase__		:   Tuple  				=					name.replace('''clip_model'''							, '''clip'''						)
					if "transformer" in name:
										if "visual" in name:
															lowerCamelCase__		:   List[Any]  				=					name.replace('''visual.transformer'''							, '''vision_model'''						)
										else:
															lowerCamelCase__		:   int  				=					name.replace('''transformer'''							, '''text_model'''						)
					if "resblocks" in name:
										lowerCamelCase__		:   List[Any]  				=					name.replace('''resblocks'''							, '''encoder.layers'''						)
					if "ln_1" in name:
										lowerCamelCase__		:   Optional[int]  				=					name.replace('''ln_1'''							, '''layer_norm1'''						)
					if "ln_2" in name:
										lowerCamelCase__		:   Optional[int]  				=					name.replace('''ln_2'''							, '''layer_norm2'''						)
					if "c_fc" in name:
										lowerCamelCase__		:   Optional[Any]  				=					name.replace('''c_fc'''							, '''fc1'''						)
					if "c_proj" in name:
										lowerCamelCase__		:   Any  				=					name.replace('''c_proj'''							, '''fc2'''						)
					if "attn" in name and "self" not in name:
										lowerCamelCase__		:   Optional[int]  				=					name.replace('''attn'''							, '''self_attn'''						)
					# text encoder
					if "token_embedding" in name:
										lowerCamelCase__		:   Any  				=					name.replace('''token_embedding'''							, '''text_model.embeddings.token_embedding'''						)
					if "positional_embedding" in name and "visual" not in name:
										lowerCamelCase__		:   Any  				=					name.replace('''positional_embedding'''							, '''text_model.embeddings.position_embedding.weight'''						)
					if "ln_final" in name:
										lowerCamelCase__		:   Tuple  				=					name.replace('''ln_final'''							, '''text_model.final_layer_norm'''						)
					# vision encoder
					if "visual.class_embedding" in name:
										lowerCamelCase__		:   Union[str, Any]  				=					name.replace('''visual.class_embedding'''							, '''vision_model.embeddings.class_embedding'''						)
					if "visual.conv1" in name:
										lowerCamelCase__		:   Dict  				=					name.replace('''visual.conv1'''							, '''vision_model.embeddings.patch_embedding'''						)
					if "visual.positional_embedding" in name:
										lowerCamelCase__		:   List[str]  				=					name.replace('''visual.positional_embedding'''							, '''vision_model.embeddings.position_embedding.weight'''						)
					if "visual.ln_pre" in name:
										lowerCamelCase__		:   Optional[Any]  				=					name.replace('''visual.ln_pre'''							, '''vision_model.pre_layrnorm'''						)
					if "visual.ln_post" in name:
										lowerCamelCase__		:   str  				=					name.replace('''visual.ln_post'''							, '''vision_model.post_layernorm'''						)
					# projection layers
					if "visual.proj" in name:
										lowerCamelCase__		:   Optional[Any]  				=					name.replace('''visual.proj'''							, '''visual_projection.weight'''						)
					if "text_projection" in name:
										lowerCamelCase__		:   str  				=					name.replace('''text_projection'''							, '''text_projection.weight'''						)
					# decoder
					if "trans_conv" in name:
										lowerCamelCase__		:   List[Any]  				=					name.replace('''trans_conv'''							, '''transposed_convolution'''						)
					if "film_mul" in name or "film_add" in name or "reduce" in name or "transposed_convolution" in name:
										lowerCamelCase__		:   Dict  				=					'''decoder.''' + name
					if "blocks" in name:
										lowerCamelCase__		:   Any  				=					name.replace('''blocks'''							, '''decoder.layers'''						)
					if "linear1" in name:
										lowerCamelCase__		:   List[Any]  				=					name.replace('''linear1'''							, '''mlp.fc1'''						)
					if "linear2" in name:
										lowerCamelCase__		:   Optional[Any]  				=					name.replace('''linear2'''							, '''mlp.fc2'''						)
					if "norm1" in name and "layer_" not in name:
										lowerCamelCase__		:   List[Any]  				=					name.replace('''norm1'''							, '''layer_norm1'''						)
					if "norm2" in name and "layer_" not in name:
										lowerCamelCase__		:   Tuple  				=					name.replace('''norm2'''							, '''layer_norm2'''						)

					return name



def 				__a       (			_lowercase							, _lowercase						):







					"""simple docstring"""





					for key in orig_state_dict.copy().keys():
										lowerCamelCase__		:   Dict  				=					orig_state_dict.pop(_lowercase						)

										if key.startswith('''clip_model'''						) and "attn.in_proj" in key:
															lowerCamelCase__		:   List[Any]  				=					key.split('''.'''						)
															if "visual" in key:
																				lowerCamelCase__		:   Any  				=					int(key_split[4]						)
																				lowerCamelCase__		:   Optional[Any]  				=					config.vision_config.hidden_size
																				lowerCamelCase__		:   Any  				=					'''vision_model'''
															else:
																				lowerCamelCase__		:   Optional[Any]  				=					int(key_split[3]						)
																				lowerCamelCase__		:   Optional[int]  				=					config.text_config.hidden_size
																				lowerCamelCase__		:   int  				=					'''text_model'''

															if "weight" in key:
																				lowerCamelCase__		:   Tuple  				=					val[:dim, :]
																				lowerCamelCase__		:   Tuple  				=					val[
																				    dim : dim * 2, :
																				]
																				lowerCamelCase__		:   Dict  				=					val[-dim:, :]
															else:
																				lowerCamelCase__		:   Optional[Any]  				=					val[:dim]
																				lowerCamelCase__		:   Dict  				=					val[dim : dim * 2]
																				lowerCamelCase__		:   Dict  				=					val[-dim:]
										elif "self_attn" in key and "out_proj" not in key:
															lowerCamelCase__		:   List[str]  				=					key.split('''.'''						)
															lowerCamelCase__		:   int  				=					int(key_split[1]						)
															lowerCamelCase__		:   List[str]  				=					config.reduce_dim
															if "weight" in key:
																				lowerCamelCase__		:   Tuple  				=					val[:dim, :]
																				lowerCamelCase__		:   Optional[int]  				=					val[dim : dim * 2, :]
																				lowerCamelCase__		:   Dict  				=					val[-dim:, :]
															else:
																				lowerCamelCase__		:   Optional[Any]  				=					val[:dim]
																				lowerCamelCase__		:   Union[str, Any]  				=					val[dim : dim * 2]
																				lowerCamelCase__		:   Tuple  				=					val[-dim:]
										else:
															lowerCamelCase__		:   Optional[int]  				=					rename_key(_lowercase						)
															if "visual_projection" in new_name or "text_projection" in new_name:
																				lowerCamelCase__		:   Dict  				=					val.T
															lowerCamelCase__		:   Union[str, Any]  				=					val

					return orig_state_dict



def 				__a       (			):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					'''http://images.cocodataset.org/val2017/000000039769.jpg'''
					lowerCamelCase__		:   Optional[int]  				=					Image.open(requests.get(_lowercase							, stream=_lowercase						).raw						)
					return image



def 				__a       (			_lowercase							, _lowercase							, _lowercase							, _lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Union[str, Any]  				=					get_clipseg_config(_lowercase						)
					lowerCamelCase__		:   Any  				=					CLIPSegForImageSegmentation(_lowercase						)
					model.eval()

					lowerCamelCase__		:   Optional[int]  				=					torch.load(_lowercase							, map_location='''cpu'''						)

					# remove some keys
					for key in state_dict.copy().keys():
										if key.startswith('''model'''						):
															state_dict.pop(_lowercase							, _lowercase						)

    # rename some keys
					lowerCamelCase__		:   int  				=					convert_state_dict(_lowercase							, _lowercase						)
					lowerCamelCase__				,						lowerCamelCase__		:   List[str]  				=					model.load_state_dict(_lowercase							, strict=_lowercase						)

					if missing_keys != ["clip.text_model.embeddings.position_ids", "clip.vision_model.embeddings.position_ids"]:
										raise ValueError('''Missing keys that are not expected: {}'''.format(_lowercase						)						)
					if unexpected_keys != ["decoder.reduce.weight", "decoder.reduce.bias"]:
										raise ValueError(f"""Unexpected keys: {unexpected_keys}"""						)

					lowerCamelCase__		:   List[str]  				=					ViTImageProcessor(size=352						)
					lowerCamelCase__		:   Any  				=					CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''						)
					lowerCamelCase__		:   Optional[Any]  				=					CLIPSegProcessor(image_processor=_lowercase							, tokenizer=_lowercase						)

					lowerCamelCase__		:   str  				=					prepare_img()
					lowerCamelCase__		:   str  				=					['''a glass''', '''something to fill''', '''wood''', '''a jar''']

					lowerCamelCase__		:   Union[str, Any]  				=					processor(text=_lowercase							, images=[image] * len(_lowercase						)							, padding='''max_length'''							, return_tensors='''pt'''						)

					with torch.no_grad():
										lowerCamelCase__		:   Dict  				=					model(**_lowercase						)

					# verify values
					lowerCamelCase__		:   List[str]  				=					torch.tensor([0.1110, -0.1882, 0.1645]						)
					lowerCamelCase__		:   List[Any]  				=					torch.tensor([0.2692, -0.7197, -0.1328]						)
					if model_name == "clipseg-rd64-refined":
										lowerCamelCase__		:   int  				=					torch.tensor(
										    [[-10.0407, -9.9431, -10.2646], [-9.9751, -9.7064, -9.9586], [-9.6891, -9.5645, -9.9618]]						)
					elif model_name == "clipseg-rd64":
										lowerCamelCase__		:   Any  				=					torch.tensor(
										    [[-7.2877, -7.2711, -7.2463], [-7.2652, -7.2780, -7.2520], [-7.2239, -7.2204, -7.2001]]						)
					elif model_name == "clipseg-rd16":
										lowerCamelCase__		:   List[Any]  				=					torch.tensor(
										    [[-6.3955, -6.4055, -6.4151], [-6.3911, -6.4033, -6.4100], [-6.3474, -6.3702, -6.3762]]						)
					else:
										raise ValueError(f"""Model name {model_name} not supported."""						)

					assert torch.allclose(outputs.logits[0, :3, :3]							, _lowercase							, atol=1e-3						)
					assert torch.allclose(outputs.conditional_embeddings[0, :3]							, _lowercase							, atol=1e-3						)
					assert torch.allclose(outputs.pooled_output[0, :3]							, _lowercase							, atol=1e-3						)
					print('''Looks ok!'''						)

					if pytorch_dump_folder_path is not None:
										print(f"""Saving model and processor to {pytorch_dump_folder_path}"""						)
										model.save_pretrained(_lowercase						)
										processor.save_pretrained(_lowercase						)

					if push_to_hub:
										print(f"""Pushing model and processor for {model_name} to the hub"""						)
										model.push_to_hub(f"""CIDAS/{model_name}"""						)
										processor.push_to_hub(f"""CIDAS/{model_name}"""						)


if __name__ == "__main__":
					UpperCAmelCase       :    Optional[Any]									=			argparse.ArgumentParser()
					# Required parameters
					parser.add_argument(
					    "--model_name",
					    default="clipseg-rd64",
					    type=str,
					    choices=["clipseg-rd16", "clipseg-rd64", "clipseg-rd64-refined"],
					    help=(
					        "Name of the model. Supported models are: clipseg-rd64, clipseg-rd16 and clipseg-rd64-refined (rd meaning"
					        " reduce dimension)"
					    ),
					)
					parser.add_argument(
					    "--checkpoint_path",
					    default="/Users/nielsrogge/Documents/CLIPSeg/clip_plus_rd64-uni.pth",
					    type=str,
					    help=(
					        "Path to the original checkpoint. Note that the script assumes that the checkpoint includes both CLIP and"
					        " the decoder weights."
					    ),
					)
					parser.add_argument(
					    "--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model directory."
					)
					parser.add_argument(
					    "--push_to_hub", action="store_true", help="Whether or not to push the converted model to the 🤗 hub."
					)

					UpperCAmelCase       :    Dict									=			parser.parse_args()
					convert_clipseg_checkpoint(args.model_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub)







"""simple docstring"""




import math
from collections.abc import Generator



def 				__a       (			_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Generator  				=					(i for i in range(1							, (max_n + 1)						))
					for i in (n for n in numbers if n > 1):
										for j in range(2							, _lowercase						):
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Generator  				=					(i for i in range(1							, (max_n + 1)						))
					for i in (n for n in numbers if n > 1):
										# only need to check for factors up to sqrt(i)
										lowerCamelCase__		:   Tuple  				=					int(math.sqrt(_lowercase						)						) + 1
										for j in range(2							, _lowercase						):
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			_lowercase						):







					"""simple docstring"""





					lowerCamelCase__		:   Generator  				=					(i for i in range(1							, (max_n + 1)							, 2						))
					# It's useless to test even numbers as they will not be prime
					if max_n > 2:
										yield 2  # Because 2 will not be tested, it's necessary to yield it now
					for i in (n for n in numbers if n > 1):
										lowerCamelCase__		:   Union[str, Any]  				=					int(math.sqrt(_lowercase						)						) + 1
										for j in range(3							, _lowercase							, 2						):
															# As we removed the even numbers, we don't need them now
															if (i % j) == 0:
																				break
										else:
															yield i



def 				__a       (			):







					"""simple docstring"""





					from timeit import timeit

					lowerCamelCase__		:   Any  				=					'''from __main__ import slow_primes, primes, fast_primes'''
					print(timeit('''slow_primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)
					print(timeit('''primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)
					print(timeit('''fast_primes(1_000_000_000_000)'''							, setup=_lowercase							, number=1000000						)						)


if __name__ == "__main__":
					UpperCAmelCase       :    Tuple									=			int(input("Calculate primes up to:\n>> ").strip())
					for ret in primes(number):
										print(ret)
					benchmark()






