




"""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     snake_case__							(       _lowerCamelCase,	_lowerCamelCase					)      ->str:






		"""simple docstring"""


		__lowercase					:		Any        =					XCLIPTextConfig()

		# derive patch size from model name
		__lowercase					:		List[Any]        =					model_name.find("patch"					)
		__lowercase					:		List[str]        =					int(model_name[start_idx + len("patch"					) : start_idx + len("patch"					) + 2]					)
		__lowercase					:		Dict        =					XCLIPVisionConfig(patch_size=_lowerCamelCase,	num_frames=_lowerCamelCase					)

		if "large" in model_name:
				__lowercase					:		Tuple        =					7_68
				__lowercase					:		str        =					30_72
				__lowercase					:		int        =					12

				__lowercase					:		Optional[int]        =					10_24
				__lowercase					:		Any        =					40_96
				__lowercase					:		Union[str, Any]        =					16
				__lowercase					:		str        =					24
				__lowercase					:		List[str]        =					7_68
				__lowercase					:		Optional[int]        =					30_72

		if model_name == "xclip-large-patch14-16-frames":
				__lowercase					:		Any        =					3_36

		__lowercase					:		Any        =					XCLIPConfig.from_text_vision_configs(_lowerCamelCase,	_lowerCamelCase					)

		if "large" in model_name:
				__lowercase					:		Tuple        =					7_68

		return config

def     snake_case__							(       _lowerCamelCase					)      ->str:






		"""simple docstring"""


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

		return name

def     snake_case__							(       _lowerCamelCase,	_lowerCamelCase					)      ->Optional[Any]:






		"""simple docstring"""


		for key in orig_state_dict.copy().keys():
				__lowercase					:		Tuple        =					orig_state_dict.pop(_lowerCamelCase					)

				if "attn.in_proj" in key:
						__lowercase					:		int        =					key.split("."					)
						if key.startswith("visual"					):
								__lowercase					:		Optional[Any]        =					key_split[3]
								__lowercase					:		List[Any]        =					config.vision_config.hidden_size
								if "message_attn" in key:
										if "weight" in key:
												__lowercase					:		Dict        =					val[
												    :dim, :
												]
												__lowercase					:		List[Any]        =					val[
												    dim : dim * 2, :
												]
												__lowercase					:		Any        =					val[
												    -dim:, :
												]
										else:
												__lowercase					:		Tuple        =					val[
												    :dim
												]
												__lowercase					:		Tuple        =					val[
												    dim : dim * 2
												]
												__lowercase					:		Optional[int]        =					val[
												    -dim:
												]
								else:
										if "weight" in key:
												__lowercase					:		Optional[Any]        =					val[
												    :dim, :
												]
												__lowercase					:		Optional[Any]        =					val[
												    dim : dim * 2, :
												]
												__lowercase					:		int        =					val[
												    -dim:, :
												]
										else:
												__lowercase					:		Optional[int]        =					val[:dim]
												__lowercase					:		Dict        =					val[
												    dim : dim * 2
												]
												__lowercase					:		Optional[Any]        =					val[-dim:]
						elif key.startswith("mit"					):
								__lowercase					:		str        =					key_split[2]
								__lowercase					:		int        =					config.vision_config.mit_hidden_size
								if "weight" in key:
										__lowercase					:		Optional[int]        =					val[:dim, :]
										__lowercase					:		List[str]        =					val[dim : dim * 2, :]
										__lowercase					:		Dict        =					val[-dim:, :]
								else:
										__lowercase					:		List[str]        =					val[:dim]
										__lowercase					:		int        =					val[dim : dim * 2]
										__lowercase					:		Dict        =					val[-dim:]
						else:
								__lowercase					:		Tuple        =					key_split[2]
								__lowercase					:		str        =					config.text_config.hidden_size
								if "weight" in key:
										__lowercase					:		int        =					val[:dim, :]
										__lowercase					:		List[str]        =					val[
										    dim : dim * 2, :
										]
										__lowercase					:		Optional[int]        =					val[-dim:, :]
								else:
										__lowercase					:		Union[str, Any]        =					val[:dim]
										__lowercase					:		List[str]        =					val[
										    dim : dim * 2
										]
										__lowercase					:		Optional[Any]        =					val[-dim:]
				else:
						__lowercase					:		Optional[Any]        =					rename_key(_lowerCamelCase					)
						if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
								__lowercase					:		Dict        =					val.T
						__lowercase					:		Optional[int]        =					val

		return orig_state_dict

def     snake_case__							(       _lowerCamelCase					)      ->str:






		"""simple docstring"""


		if num_frames == 8:
				__lowercase					:		Union[str, Any]        =					"eating_spaghetti_8_frames.npy"
		elif num_frames == 16:
				__lowercase					:		str        =					"eating_spaghetti.npy"
		elif num_frames == 32:
				__lowercase					:		Optional[int]        =					"eating_spaghetti_32_frames.npy"
		__lowercase					:		int        =					hf_hub_download(
		    repo_id="hf-internal-testing/spaghetti-video",	filename=_lowerCamelCase,	repo_type="dataset",	)
		__lowercase					:		int        =					np.load(_lowerCamelCase					)
		return list(_lowerCamelCase					)

def     snake_case__							(       _lowerCamelCase,	_lowerCamelCase=None,	_lowerCamelCase=False					)      ->Dict:






		"""simple docstring"""


		__lowercase					:		Tuple        =					{
		    # 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",
		}

		__lowercase					:		List[Any]        =					model_to_url[model_name]
		__lowercase					:		str        =					8
		if "16-frames" in model_name:
				__lowercase					:		int        =					16
		elif "shot" in model_name:
				__lowercase					:		int        =					32

		__lowercase					:		Optional[Any]        =					get_xclip_config(_lowerCamelCase,	_lowerCamelCase					)
		__lowercase					:		str        =					XCLIPModel(_lowerCamelCase					)
		model.eval()

		if "drive" in checkpoint_url:
				__lowercase					:		Tuple        =					"pytorch_model.bin"
				gdown.cached_download(_lowerCamelCase,	_lowerCamelCase,	quiet=_lowerCamelCase					)
				__lowercase					:		int        =					torch.load(_lowerCamelCase,	map_location="cpu"					)["model"]
		else:
				__lowercase					:		Union[str, Any]        =					torch.hub.load_state_dict_from_url(_lowerCamelCase					)["model"]

		__lowercase					:		Union[str, Any]        =					convert_state_dict(_lowerCamelCase,	_lowerCamelCase					)

		__lowercase					:		Dict        =					XCLIPModel(_lowerCamelCase					)
		__lowercase    ,__lowercase					:		Union[str, Any]        =					model.load_state_dict(_lowerCamelCase,	strict=_lowerCamelCase					)
		assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
		model.eval()

		__lowercase					:		List[Any]        =					3_36 if model_name == "xclip-large-patch14-16-frames" else 2_24
		__lowercase					:		Optional[Any]        =					VideoMAEImageProcessor(size=_lowerCamelCase					)
		__lowercase					:		Optional[Any]        =					CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"					)
		__lowercase					:		Optional[Any]        =					CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"					)
		__lowercase					:		str        =					XCLIPProcessor(image_processor=_lowerCamelCase,	tokenizer=_lowerCamelCase					)

		__lowercase					:		str        =					prepare_video(_lowerCamelCase					)
		__lowercase					:		Union[str, Any]        =					processor(
		    text=["playing sports", "eating spaghetti", "go shopping"],	videos=_lowerCamelCase,	return_tensors="pt",	padding=_lowerCamelCase					)

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

		with torch.no_grad():
				__lowercase					:		Optional[Any]        =					model(**_lowerCamelCase					)

		# Verify outputs
		__lowercase					:		Optional[int]        =					outputs.logits_per_video
		__lowercase					:		Any        =					logits_per_video.softmax(dim=1					)
		print("Probs:",	_lowerCamelCase					)
		# kinetics-400
		if model_name == "xclip-base-patch32":
				__lowercase					:		Optional[int]        =					torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]]					)
		elif model_name == "xclip-base-patch32-16-frames":
				__lowercase					:		str        =					torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]					)
		elif model_name == "xclip-base-patch16":
				__lowercase					:		Union[str, Any]        =					torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]]					)
		elif model_name == "xclip-base-patch16-16-frames":
				__lowercase					:		List[str]        =					torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]					)
		elif model_name == "xclip-large-patch14":
				__lowercase					:		Any        =					torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]]					)
		elif model_name == "xclip-large-patch14-16-frames":
				__lowercase					:		Optional[Any]        =					torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]					)
		# kinetics-600
		elif model_name == "xclip-base-patch16-kinetics-600":
				__lowercase					:		int        =					torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]]					)
		elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
				__lowercase					:		Optional[int]        =					torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]					)
		elif model_name == "xclip-large-patch14-kinetics-600":
				__lowercase					:		Optional[int]        =					torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]]					)
		# few shot
		elif model_name == "xclip-base-patch16-hmdb-2-shot":
				__lowercase					:		int        =					torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]					)
		elif model_name == "xclip-base-patch16-hmdb-4-shot":
				__lowercase					:		List[Any]        =					torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]					)
		elif model_name == "xclip-base-patch16-hmdb-8-shot":
				__lowercase					:		Any        =					torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]					)
		elif model_name == "xclip-base-patch16-hmdb-16-shot":
				__lowercase					:		Dict        =					torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]					)
		elif model_name == "xclip-base-patch16-ucf-2-shot":
				__lowercase					:		str        =					torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]					)
		elif model_name == "xclip-base-patch16-ucf-4-shot":
				__lowercase					:		Optional[int]        =					torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]					)
		elif model_name == "xclip-base-patch16-ucf-8-shot":
				__lowercase					:		Any        =					torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]]					)
		elif model_name == "xclip-base-patch16-ucf-16-shot":
				__lowercase					:		Optional[Any]        =					torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]					)
		# zero shot
		elif model_name == "xclip-base-patch16-zero-shot":
				__lowercase					:		Tuple        =					torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]					)
		else:
				raise ValueError(F'Model name {model_name} not supported'					)
		assert torch.allclose(_lowerCamelCase,	_lowerCamelCase,	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(_lowerCamelCase					)

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


if __name__ == "__main__":
						__A :	int       =   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.'
						)

						__A :	int       =   parser.parse_args()
						convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







from typing import Union

from ..utils import add_end_docstrings, is_torch_available, is_vision_available, logging
from .base import PIPELINE_INIT_ARGS, Pipeline


if is_vision_available():
		from PIL import Image

		from ..image_utils import load_image

if is_torch_available():
		from ..models.auto.modeling_auto import MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING

SCREAMING_SNAKE_CASE_   :      Union[str, Any]			=   logging.get_logger(__name__)

@add_end_docstrings(UpperCAmelCase_						)
class 		snake_case_   (							UpperCAmelCase_						):







	'''simple docstring'''




	def __init__(						self							:     Optional[int]					,      *__lowerCamelCase							:     Optional[int]					,      **__lowerCamelCase							:     List[Any]   )       -> Optional[Any]:

			'''simple docstring'''




			super().__init__(*__lowerCamelCase					,      **__lowerCamelCase   )
			self.check_model_type(__lowerCamelCase   )




	def    UpperCAmelCase			(						self							:     Optional[int]					,      __lowerCamelCase							:     int=None					,      __lowerCamelCase							:     List[Any]=None					,      __lowerCamelCase							:     Any=None					,      **__lowerCamelCase							:     Optional[int]   )       -> Any:

			'''simple docstring'''




			__lowercase						,      __lowercase											=     {}, {}
			if padding is not None:
					__lowercase											=     padding
			if truncation is not None:
					__lowercase											=     truncation
			if top_k is not None:
					__lowercase											=     top_k
			return preprocess_params, {}, postprocess_params




	def __call__(						self							:     str					,      __lowerCamelCase							:     Union["Image.Image", str]					,      __lowerCamelCase							:     str = None					,      **__lowerCamelCase							:     Dict   )       -> Tuple:

			'''simple docstring'''




			if isinstance(__lowerCamelCase					,      (Image.Image, str)   ) and isinstance(__lowerCamelCase					,      __lowerCamelCase   ):
					__lowercase											=     {'image': image, 'question': question}
			else:
					__lowercase											=     image
			__lowercase											=     super().__call__(__lowerCamelCase					,      **__lowerCamelCase   )
			return results




	def    UpperCAmelCase			(						self							:     List[Any]					,      __lowerCamelCase							:     List[str]					,      __lowerCamelCase							:     Optional[Any]=False					,      __lowerCamelCase							:     Optional[Any]=False   )       -> Optional[int]:

			'''simple docstring'''




			__lowercase											=     load_image(inputs['image']   )
			__lowercase											=     self.tokenizer(
			    inputs['question']					,      return_tensors=self.framework					,      padding=__lowerCamelCase					,      truncation=__lowerCamelCase   )
			__lowercase											=     self.image_processor(images=__lowerCamelCase					,      return_tensors=self.framework   )
			model_inputs.update(__lowerCamelCase   )
			return model_inputs




	def    UpperCAmelCase			(						self							:     str					,      __lowerCamelCase							:     Tuple   )       -> Any:

			'''simple docstring'''




			__lowercase											=     self.model(**__lowerCamelCase   )
			return model_outputs






	def    UpperCAmelCase			(						self							:     int					,      __lowerCamelCase							:     Any					,      __lowerCamelCase							:     int=5   )       -> Tuple:

			'''simple docstring'''




			if top_k > self.model.config.num_labels:
					__lowercase											=     self.model.config.num_labels

			if self.framework == "pt":
					__lowercase											=     model_outputs.logits.sigmoid()[0]
					__lowercase						,      __lowercase											=     probs.topk(__lowerCamelCase   )
			else:
					raise ValueError(F"Unsupported framework: {self.framework}"   )

			__lowercase											=     scores.tolist()
			__lowercase											=     ids.tolist()
			return [{"score": score, "answer": self.model.config.idalabel[_id]} for score, _id in zip(__lowerCamelCase					,      __lowerCamelCase   )]




