





"""simple docstring"""





from __future__ import annotations


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	[]
		UpperCamelCase   =	[]
		UpperCamelCase   =	0
		UpperCamelCase   =	sum(_SCREAMING_SNAKE_CASE    )
		create_state_space_tree(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		return result


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	):




		"""simple docstring"""


		if sum(_SCREAMING_SNAKE_CASE    ) > max_sum or (remaining_nums_sum + sum(_SCREAMING_SNAKE_CASE    )) < max_sum:
				return
		if sum(_SCREAMING_SNAKE_CASE    ) == max_sum:
				result.append(_SCREAMING_SNAKE_CASE    )
				return
		for index in range(_SCREAMING_SNAKE_CASE  ,	len(_SCREAMING_SNAKE_CASE    )    ):
				create_state_space_tree(
				    _SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	index + 1  ,	[*path, nums[index]]  ,	_SCREAMING_SNAKE_CASE  ,	remaining_nums_sum - nums[index]  ,	)
lowerCAmelCase__     			=			[3, 34, 4, 12, 5, 2]
lowerCAmelCase__     			=			9
lowerCAmelCase__     			=			generate_sum_of_subsets_soln(nums, max_sum)






print(*result)













"""simple docstring"""





import argparse

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    CLIPTokenizer,
    CLIPTokenizerFast,
    VideoMAEImageProcessor,
    XCLIPConfig,
    XCLIPModel,
    XCLIPProcessor,
    XCLIPTextConfig,
    XCLIPVisionConfig,
)


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		UpperCamelCase   =	XCLIPTextConfig()

		# derive patch size from model name
		UpperCamelCase   =	model_name.find("patch"    )
		UpperCamelCase   =	int(model_name[start_idx + len("patch"    ) : start_idx + len("patch"    ) + 2]    )
		UpperCamelCase   =	XCLIPVisionConfig(patch_size=_SCREAMING_SNAKE_CASE  ,	num_frames=_SCREAMING_SNAKE_CASE    )

		if "large" in model_name:
				UpperCamelCase   =	768
				UpperCamelCase   =	3_072
				UpperCamelCase   =	12

				UpperCamelCase   =	1_024
				UpperCamelCase   =	4_096
				UpperCamelCase   =	16
				UpperCamelCase   =	24
				UpperCamelCase   =	768
				UpperCamelCase   =	3_072

		if model_name == "xclip-large-patch14-16-frames":
				UpperCamelCase   =	336

		UpperCamelCase   =	XCLIPConfig.from_text_vision_configs(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		if "large" in model_name:
				UpperCamelCase   =	768

		return config


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if name == "token_embedding.weight":
				UpperCamelCase   =	name.replace("token_embedding.weight"  ,	"text_model.embeddings.token_embedding.weight"    )
		if name == "positional_embedding":
				UpperCamelCase   =	name.replace("positional_embedding"  ,	"text_model.embeddings.position_embedding.weight"    )
		if "ln_1" in name:
				UpperCamelCase   =	name.replace("ln_1"  ,	"layer_norm1"    )
		if "ln_2" in name:
				UpperCamelCase   =	name.replace("ln_2"  ,	"layer_norm2"    )
		if "c_fc" in name:
				UpperCamelCase   =	name.replace("c_fc"  ,	"fc1"    )
		if "c_proj" in name:
				UpperCamelCase   =	name.replace("c_proj"  ,	"fc2"    )
		if name.startswith("transformer.resblocks"    ):
				UpperCamelCase   =	name.replace("transformer.resblocks"  ,	"text_model.encoder.layers"    )
		if "attn.out_proj" in name and "message" not in name:
				UpperCamelCase   =	name.replace("attn.out_proj"  ,	"self_attn.out_proj"    )
		if "ln_final" in name:
				UpperCamelCase   =	name.replace("ln_final"  ,	"text_model.final_layer_norm"    )
		# visual encoder
		if name == "visual.class_embedding":
				UpperCamelCase   =	name.replace("visual.class_embedding"  ,	"vision_model.embeddings.class_embedding"    )
		if name == "visual.positional_embedding":
				UpperCamelCase   =	name.replace("visual.positional_embedding"  ,	"vision_model.embeddings.position_embedding.weight"    )
		if name.startswith("visual.transformer.resblocks"    ):
				UpperCamelCase   =	name.replace("visual.transformer.resblocks"  ,	"vision_model.encoder.layers"    )
		if "visual.conv1" in name:
				UpperCamelCase   =	name.replace("visual.conv1"  ,	"vision_model.embeddings.patch_embedding"    )
		if "visual.ln_pre" in name:
				UpperCamelCase   =	name.replace("visual.ln_pre"  ,	"vision_model.pre_layernorm"    )
		if "visual.ln_post" in name:
				UpperCamelCase   =	name.replace("visual.ln_post"  ,	"vision_model.post_layernorm"    )
		if "visual.proj" in name:
				UpperCamelCase   =	name.replace("visual.proj"  ,	"visual_projection.weight"    )
		if "text_projection" in name:
				UpperCamelCase   =	name.replace("text_projection"  ,	"text_projection.weight"    )
		# things on top
		if "prompts_visual_proj" in name:
				UpperCamelCase   =	name.replace("prompts_visual_proj"  ,	"prompts_visual_projection"    )
		if "prompts_visual_ln" in name:
				UpperCamelCase   =	name.replace("prompts_visual_ln"  ,	"prompts_visual_layernorm"    )
		# mit
		if name == "mit.positional_embedding":
				UpperCamelCase   =	name.replace("positional"  ,	"position"    )
		if name.startswith("mit.resblocks"    ):
				UpperCamelCase   =	name.replace("mit.resblocks"  ,	"mit.encoder.layers"    )
		# prompts generator
		if name.startswith("prompts_generator.norm"    ):
				UpperCamelCase   =	name.replace("prompts_generator.norm"  ,	"prompts_generator.layernorm"    )

		return name


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		for key in orig_state_dict.copy().keys():
				UpperCamelCase   =	orig_state_dict.pop(_SCREAMING_SNAKE_CASE    )

				if "attn.in_proj" in key:
						UpperCamelCase   =	key.split("."    )
						if key.startswith("visual"    ):
								UpperCamelCase   =	key_split[3]
								UpperCamelCase   =	config.vision_config.hidden_size
								if "message_attn" in key:
										if "weight" in key:
												UpperCamelCase   =	val[
												    :dim, :
												]
												UpperCamelCase   =	val[
												    dim : dim * 2, :
												]
												UpperCamelCase   =	val[
												    -dim:, :
												]
										else:
												UpperCamelCase   =	val[
												    :dim
												]
												UpperCamelCase   =	val[
												    dim : dim * 2
												]
												UpperCamelCase   =	val[
												    -dim:
												]
								else:
										if "weight" in key:
												UpperCamelCase   =	val[
												    :dim, :
												]
												UpperCamelCase   =	val[
												    dim : dim * 2, :
												]
												UpperCamelCase   =	val[
												    -dim:, :
												]
										else:
												UpperCamelCase   =	val[:dim]
												UpperCamelCase   =	val[
												    dim : dim * 2
												]
												UpperCamelCase   =	val[-dim:]
						elif key.startswith("mit"    ):
								UpperCamelCase   =	key_split[2]
								UpperCamelCase   =	config.vision_config.mit_hidden_size
								if "weight" in key:
										UpperCamelCase   =	val[:dim, :]
										UpperCamelCase   =	val[dim : dim * 2, :]
										UpperCamelCase   =	val[-dim:, :]
								else:
										UpperCamelCase   =	val[:dim]
										UpperCamelCase   =	val[dim : dim * 2]
										UpperCamelCase   =	val[-dim:]
						else:
								UpperCamelCase   =	key_split[2]
								UpperCamelCase   =	config.text_config.hidden_size
								if "weight" in key:
										UpperCamelCase   =	val[:dim, :]
										UpperCamelCase   =	val[
										    dim : dim * 2, :
										]
										UpperCamelCase   =	val[-dim:, :]
								else:
										UpperCamelCase   =	val[:dim]
										UpperCamelCase   =	val[
										    dim : dim * 2
										]
										UpperCamelCase   =	val[-dim:]
				else:
						UpperCamelCase   =	rename_key(_SCREAMING_SNAKE_CASE    )
						if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
								UpperCamelCase   =	val.T
						UpperCamelCase   =	val

		return orig_state_dict


def       a__					(					_SCREAMING_SNAKE_CASE    ):




		"""simple docstring"""


		if num_frames == 8:
				UpperCamelCase   =	"eating_spaghetti_8_frames.npy"
		elif num_frames == 16:
				UpperCamelCase   =	"eating_spaghetti.npy"
		elif num_frames == 32:
				UpperCamelCase   =	"eating_spaghetti_32_frames.npy"
		UpperCamelCase   =	hf_hub_download(
		    repo_id="hf-internal-testing/spaghetti-video"  ,	filename=_SCREAMING_SNAKE_CASE  ,	repo_type="dataset"  ,	)
		UpperCamelCase   =	np.load(_SCREAMING_SNAKE_CASE    )
		return list(_SCREAMING_SNAKE_CASE    )


def       a__					(					_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE=None  ,	_SCREAMING_SNAKE_CASE=False    ):




		"""simple docstring"""


		UpperCamelCase   =	{
		    # fully supervised kinetics-400 checkpoints
		    "xclip-base-patch32": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_8.pth",
		    "xclip-base-patch32-16-frames": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_32_16.pth"
		    ),
		    "xclip-base-patch16": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_8.pth",
		    "xclip-base-patch16-16-frames": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k400_16_16.pth"
		    ),
		    "xclip-large-patch14": "https://drive.google.com/u/0/uc?id=1NUOImq0o5DlQTST17iIP3vG7DgmHQuCx&amp;export=download&amp;confirm=t&amp;uuid=b26caedc-88e2-473e-830a-9d158b653cdb",
		    "xclip-large-patch14-16-frames": "https://drive.google.com/u/0/uc?id=1FOYgnJc097OJ4lGwtRCCydQyVPJEOH7d&amp;export=download&amp;confirm=t&amp;uuid=538fa810-e671-4050-b385-9a623f89804f",
		    # fully supervised kinetics-600 checkpoints
		    "xclip-base-patch16-kinetics-600": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_8.pth"
		    ),
		    "xclip-base-patch16-kinetics-600-16-frames": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/k600_16_16.pth"
		    ),
		    "xclip-large-patch14-kinetics-600": "https://drive.google.com/u/0/uc?id=1FV8C1INuM91sLAN4ImjzePLIlpMSihwV&amp;export=download&amp;confirm=t&amp;uuid=141d4977-4a65-44ae-864f-4b0c19f838be",
		    # few shot
		    "xclip-base-patch16-hmdb-2-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_2.pth"
		    ),
		    "xclip-base-patch16-hmdb-4-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_4.pth"
		    ),
		    "xclip-base-patch16-hmdb-8-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_8.pth"
		    ),
		    "xclip-base-patch16-hmdb-16-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_hmdb_16.pth"
		    ),
		    "xclip-base-patch16-ucf-2-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_2.pth"
		    ),
		    "xclip-base-patch16-ucf-4-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_4.pth"
		    ),
		    "xclip-base-patch16-ucf-8-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_8.pth"
		    ),
		    "xclip-base-patch16-ucf-16-shot": (
		        "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/few_ucf_16.pth"
		    ),
		    # zero shot
		    "xclip-base-patch16-zero-shot": "https://github.com/nbl97/X-CLIP_Model_Zoo/releases/download/v1.0/zero.pth",
		}

		UpperCamelCase   =	model_to_url[model_name]
		UpperCamelCase   =	8
		if "16-frames" in model_name:
				UpperCamelCase   =	16
		elif "shot" in model_name:
				UpperCamelCase   =	32

		UpperCamelCase   =	get_xclip_config(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	XCLIPModel(_SCREAMING_SNAKE_CASE    )
		model.eval()

		if "drive" in checkpoint_url:
				UpperCamelCase   =	"pytorch_model.bin"
				gdown.cached_download(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	quiet=_SCREAMING_SNAKE_CASE    )
				UpperCamelCase   =	torch.load(_SCREAMING_SNAKE_CASE  ,	map_location="cpu"    )["model"]
		else:
				UpperCamelCase   =	torch.hub.load_state_dict_from_url(_SCREAMING_SNAKE_CASE    )["model"]

		UpperCamelCase   =	convert_state_dict(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	XCLIPModel(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   ,				UpperCamelCase   =	model.load_state_dict(_SCREAMING_SNAKE_CASE  ,	strict=_SCREAMING_SNAKE_CASE    )
		assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
		model.eval()

		UpperCamelCase   =	336 if model_name == "xclip-large-patch14-16-frames" else 224
		UpperCamelCase   =	VideoMAEImageProcessor(size=_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"    )
		UpperCamelCase   =	CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"    )
		UpperCamelCase   =	XCLIPProcessor(image_processor=_SCREAMING_SNAKE_CASE  ,	tokenizer=_SCREAMING_SNAKE_CASE    )

		UpperCamelCase   =	prepare_video(_SCREAMING_SNAKE_CASE    )
		UpperCamelCase   =	processor(
		    text=["playing sports", "eating spaghetti", "go shopping"]  ,	videos=_SCREAMING_SNAKE_CASE  ,	return_tensors="pt"  ,	padding=_SCREAMING_SNAKE_CASE    )

		print("Shape of pixel values:"  ,	inputs.pixel_values.shape    )

		with torch.no_grad():
				UpperCamelCase   =	model(**_SCREAMING_SNAKE_CASE    )

		# Verify outputs
		UpperCamelCase   =	outputs.logits_per_video
		UpperCamelCase   =	logits_per_video.softmax(dim=1    )
		print("Probs:"  ,	_SCREAMING_SNAKE_CASE    )
		# kinetics-400
		if model_name == "xclip-base-patch32":
				UpperCamelCase   =	torch.tensor([[0.00_19, 0.99_51, 0.00_30]]    )
		elif model_name == "xclip-base-patch32-16-frames":
				UpperCamelCase   =	torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]    )
		elif model_name == "xclip-base-patch16":
				UpperCamelCase   =	torch.tensor([[0.00_83, 0.96_81, 0.02_36]]    )
		elif model_name == "xclip-base-patch16-16-frames":
				UpperCamelCase   =	torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]    )
		elif model_name == "xclip-large-patch14":
				UpperCamelCase   =	torch.tensor([[0.00_62, 0.98_64, 0.00_75]]    )
		elif model_name == "xclip-large-patch14-16-frames":
				UpperCamelCase   =	torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]    )
		# kinetics-600
		elif model_name == "xclip-base-patch16-kinetics-600":
				UpperCamelCase   =	torch.tensor([[0.05_55, 0.89_14, 0.05_31]]    )
		elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
				UpperCamelCase   =	torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]    )
		elif model_name == "xclip-large-patch14-kinetics-600":
				UpperCamelCase   =	torch.tensor([[0.00_36, 0.99_20, 0.00_45]]    )
		# few shot
		elif model_name == "xclip-base-patch16-hmdb-2-shot":
				UpperCamelCase   =	torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]    )
		elif model_name == "xclip-base-patch16-hmdb-4-shot":
				UpperCamelCase   =	torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]    )
		elif model_name == "xclip-base-patch16-hmdb-8-shot":
				UpperCamelCase   =	torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]    )
		elif model_name == "xclip-base-patch16-hmdb-16-shot":
				UpperCamelCase   =	torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]    )
		elif model_name == "xclip-base-patch16-ucf-2-shot":
				UpperCamelCase   =	torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
		elif model_name == "xclip-base-patch16-ucf-4-shot":
				UpperCamelCase   =	torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]    )
		elif model_name == "xclip-base-patch16-ucf-8-shot":
				UpperCamelCase   =	torch.tensor([[0.00_27, 0.99_04, 0.00_70]]    )
		elif model_name == "xclip-base-patch16-ucf-16-shot":
				UpperCamelCase   =	torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]    )
		# zero shot
		elif model_name == "xclip-base-patch16-zero-shot":
				UpperCamelCase   =	torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]    )
		else:
				raise ValueError(F"Model name {model_name} not supported"    )
		assert torch.allclose(_SCREAMING_SNAKE_CASE  ,	_SCREAMING_SNAKE_CASE  ,	atol=1e-3    )
		print("Looks ok!"    )

		if pytorch_dump_folder_path is not None:
				print(F"Saving model {model_name} to {pytorch_dump_folder_path}"    )
				model.save_pretrained(_SCREAMING_SNAKE_CASE    )

		if push_to_hub:
				print("Pushing model, processor and slow tokenizer files to the hub..."    )
				model.push_to_hub(_SCREAMING_SNAKE_CASE  ,	organization="nielsr"    )
				processor.push_to_hub(_SCREAMING_SNAKE_CASE  ,	organization="nielsr"    )
				slow_tokenizer.push_to_hub(_SCREAMING_SNAKE_CASE  ,	organization="nielsr"    )


if __name__ == "__main__":
						lowerCAmelCase__     			=			argparse.ArgumentParser()
						# Required parameters
						parser.add_argument(
						    '''--model_name''',
						    default='''xclip-base-patch32''',
						    type=str,
						    help='''Name of the model.''',
						)
						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.'''
						)

						lowerCAmelCase__     			=			parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






