


'''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 			__UpperCamelCase    (    _A    :				Any					,   _A    :				List[str]				)    ->      List[Any]:




	"""simple docstring"""





	lowerCAmelCase					:    Optional[Any]      							= XCLIPTextConfig()

	# derive patch size from model name
	lowerCAmelCase					:    str      							= model_name.find('patch'				)
	lowerCAmelCase					:    Tuple      							= int(model_name[start_idx + len('patch'				) : start_idx + len('patch'				) + 2]				)
	lowerCAmelCase					:    List[Any]      							= XCLIPVisionConfig(patch_size=_A					,   num_frames=_A				)

	if "large" in model_name:
		lowerCAmelCase					:    List[Any]      							= 7_68
		lowerCAmelCase					:    List[str]      							= 30_72
		lowerCAmelCase					:    Dict      							= 12

		lowerCAmelCase					:    Tuple      							= 10_24
		lowerCAmelCase					:    Any      							= 40_96
		lowerCAmelCase					:    str      							= 16
		lowerCAmelCase					:    Union[str, Any]      							= 24
		lowerCAmelCase					:    Union[str, Any]      							= 7_68
		lowerCAmelCase					:    Optional[Any]      							= 30_72

	if model_name == "xclip-large-patch14-16-frames":
		lowerCAmelCase					:    Optional[Any]      							= 3_36

	lowerCAmelCase					:    Tuple      							= XCLIPConfig.from_text_vision_configs(_A					,   _A				)

	if "large" in model_name:
		lowerCAmelCase					:    int      							= 7_68

	return config

def 			__UpperCamelCase    (    _A    :				Union[str, Any]				)    ->      Optional[int]:




	"""simple docstring"""





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

	return name

def 			__UpperCamelCase    (    _A    :				Optional[int]					,   _A    :				Any				)    ->      str:




	"""simple docstring"""





	for key in orig_state_dict.copy().keys():
		lowerCAmelCase					:    List[str]      							= orig_state_dict.pop(_A				)

		if "attn.in_proj" in key:
			lowerCAmelCase					:    Optional[int]      							= key.split('.'				)
			if key.startswith('visual'				):
				lowerCAmelCase					:    str      							= key_split[3]
				lowerCAmelCase					:    int      							= config.vision_config.hidden_size
				if "message_attn" in key:
					if "weight" in key:
						lowerCAmelCase					:    Optional[Any]      							= val[
						    :dim, :
						]
						lowerCAmelCase					:    Dict      							= val[
						    dim : dim * 2, :
						]
						lowerCAmelCase					:    int      							= val[
						    -dim:, :
						]
					else:
						lowerCAmelCase					:    int      							= val[
						    :dim
						]
						lowerCAmelCase					:    Union[str, Any]      							= val[
						    dim : dim * 2
						]
						lowerCAmelCase					:    Optional[int]      							= val[
						    -dim:
						]
				else:
					if "weight" in key:
						lowerCAmelCase					:    Any      							= val[
						    :dim, :
						]
						lowerCAmelCase					:    List[str]      							= val[
						    dim : dim * 2, :
						]
						lowerCAmelCase					:    str      							= val[
						    -dim:, :
						]
					else:
						lowerCAmelCase					:    int      							= val[:dim]
						lowerCAmelCase					:    List[Any]      							= val[
						    dim : dim * 2
						]
						lowerCAmelCase					:    str      							= val[-dim:]
			elif key.startswith('mit'				):
				lowerCAmelCase					:    List[Any]      							= key_split[2]
				lowerCAmelCase					:    Union[str, Any]      							= config.vision_config.mit_hidden_size
				if "weight" in key:
					lowerCAmelCase					:    List[str]      							= val[:dim, :]
					lowerCAmelCase					:    str      							= val[dim : dim * 2, :]
					lowerCAmelCase					:    Tuple      							= val[-dim:, :]
				else:
					lowerCAmelCase					:    Optional[int]      							= val[:dim]
					lowerCAmelCase					:    str      							= val[dim : dim * 2]
					lowerCAmelCase					:    int      							= val[-dim:]
			else:
				lowerCAmelCase					:    str      							= key_split[2]
				lowerCAmelCase					:    str      							= config.text_config.hidden_size
				if "weight" in key:
					lowerCAmelCase					:    Union[str, Any]      							= val[:dim, :]
					lowerCAmelCase					:    Union[str, Any]      							= val[
					    dim : dim * 2, :
					]
					lowerCAmelCase					:    Tuple      							= val[-dim:, :]
				else:
					lowerCAmelCase					:    Union[str, Any]      							= val[:dim]
					lowerCAmelCase					:    Dict      							= val[
					    dim : dim * 2
					]
					lowerCAmelCase					:    Optional[int]      							= val[-dim:]
		else:
			lowerCAmelCase					:    int      							= rename_key(_A				)
			if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
				lowerCAmelCase					:    Dict      							= val.T
			lowerCAmelCase					:    Optional[Any]      							= val

	return orig_state_dict

def 			__UpperCamelCase    (    _A    :				Any				)    ->      Optional[Any]:




	"""simple docstring"""





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

def 			__UpperCamelCase    (    _A    :				Optional[Any]					,   _A    :				Union[str, Any]=None					,   _A    :				Dict=False				)    ->      List[str]:




	"""simple docstring"""





	lowerCAmelCase					:    int      							= {
	    # 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',
	}

	lowerCAmelCase					:    Any      							= model_to_url[model_name]
	lowerCAmelCase					:    Optional[Any]      							= 8
	if "16-frames" in model_name:
		lowerCAmelCase					:    Union[str, Any]      							= 16
	elif "shot" in model_name:
		lowerCAmelCase					:    Optional[Any]      							= 32

	lowerCAmelCase					:    Dict      							= get_xclip_config(_A					,   _A				)
	lowerCAmelCase					:    Tuple      							= XCLIPModel(_A				)
	model.eval()

	if "drive" in checkpoint_url:
		lowerCAmelCase					:    Union[str, Any]      							= 'pytorch_model.bin'
		gdown.cached_download(_A					,   _A					,   quiet=_A				)
		lowerCAmelCase					:    Optional[Any]      							= torch.load(_A					,   map_location='cpu'				)['model']
	else:
		lowerCAmelCase					:    List[str]      							= torch.hub.load_state_dict_from_url(_A				)['model']

	lowerCAmelCase					:    Optional[int]      							= convert_state_dict(_A					,   _A				)

	lowerCAmelCase					:    int      							= XCLIPModel(_A				)
	lowerCAmelCase					:    Union[str, Any]      							= model.load_state_dict(_A					,   strict=_A				)
	assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
	model.eval()

	lowerCAmelCase					:    int      							= 3_36 if model_name == 'xclip-large-patch14-16-frames' else 2_24
	lowerCAmelCase					:    Dict      							= VideoMAEImageProcessor(size=_A				)
	lowerCAmelCase					:    Optional[int]      							= CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'				)
	lowerCAmelCase					:    Union[str, Any]      							= CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'				)
	lowerCAmelCase					:    Optional[int]      							= XCLIPProcessor(image_processor=_A					,   tokenizer=_A				)

	lowerCAmelCase					:    Dict      							= prepare_video(_A				)
	lowerCAmelCase					:    List[str]      							= processor(
	    text=['playing sports', 'eating spaghetti', 'go shopping']					,   videos=_A					,   return_tensors='pt'					,   padding=_A				)

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

	with torch.no_grad():
		lowerCAmelCase					:    Any      							= model(**_A				)

	# Verify outputs
	lowerCAmelCase					:    str      							= outputs.logits_per_video
	lowerCAmelCase					:    List[str]      							= logits_per_video.softmax(dim=1				)
	print('Probs:'					,   _A				)
	# kinetics-400
	if model_name == "xclip-base-patch32":
		lowerCAmelCase					:    Any      							= torch.tensor([[0.00_19, 0.99_51, 0.00_30]]				)
	elif model_name == "xclip-base-patch32-16-frames":
		lowerCAmelCase					:    str      							= torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]				)
	elif model_name == "xclip-base-patch16":
		lowerCAmelCase					:    Union[str, Any]      							= torch.tensor([[0.00_83, 0.96_81, 0.02_36]]				)
	elif model_name == "xclip-base-patch16-16-frames":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]				)
	elif model_name == "xclip-large-patch14":
		lowerCAmelCase					:    List[str]      							= torch.tensor([[0.00_62, 0.98_64, 0.00_75]]				)
	elif model_name == "xclip-large-patch14-16-frames":
		lowerCAmelCase					:    str      							= torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]				)
	# kinetics-600
	elif model_name == "xclip-base-patch16-kinetics-600":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[0.05_55, 0.89_14, 0.05_31]]				)
	elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
		lowerCAmelCase					:    str      							= torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]				)
	elif model_name == "xclip-large-patch14-kinetics-600":
		lowerCAmelCase					:    str      							= torch.tensor([[0.00_36, 0.99_20, 0.00_45]]				)
	# few shot
	elif model_name == "xclip-base-patch16-hmdb-2-shot":
		lowerCAmelCase					:    Tuple      							= torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]				)
	elif model_name == "xclip-base-patch16-hmdb-4-shot":
		lowerCAmelCase					:    List[str]      							= torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]				)
	elif model_name == "xclip-base-patch16-hmdb-8-shot":
		lowerCAmelCase					:    Optional[int]      							= torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]				)
	elif model_name == "xclip-base-patch16-hmdb-16-shot":
		lowerCAmelCase					:    List[Any]      							= torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]				)
	elif model_name == "xclip-base-patch16-ucf-2-shot":
		lowerCAmelCase					:    Union[str, Any]      							= torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]				)
	elif model_name == "xclip-base-patch16-ucf-4-shot":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]				)
	elif model_name == "xclip-base-patch16-ucf-8-shot":
		lowerCAmelCase					:    Optional[Any]      							= torch.tensor([[0.00_27, 0.99_04, 0.00_70]]				)
	elif model_name == "xclip-base-patch16-ucf-16-shot":
		lowerCAmelCase					:    Any      							= torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]				)
	# zero shot
	elif model_name == "xclip-base-patch16-zero-shot":
		lowerCAmelCase					:    List[str]      							= torch.tensor([[3.5_082e-04, 9.9_785e-01, 1.7_966e-03]]				)
	else:
		raise ValueError(F"Model name {model_name} not supported"				)
	assert torch.allclose(_A					,   _A					,   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(_A				)

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


if __name__ == "__main__":
							_lowerCAmelCase  :		Optional[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.'
							)

							_lowerCAmelCase  :		Dict		     =	parser.parse_args()
							convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)




'''simple docstring'''




from ...configuration_utils import PretrainedConfig
from ...utils import logging


_lowerCAmelCase  :		str		     =	logging.get_logger(__name__)

_lowerCAmelCase  :		Tuple		     =	{
    's-JoL/Open-Llama-V1': 'https://huggingface.co/s-JoL/Open-Llama-V1/blob/main/config.json',
}



class  lowerCAmelCase				(				a							):
	_lowerCamelCase   :					Union[str, Any]			      = """open-llama"""



	def __init__(					self ,					snake_case__=10_0000 ,					snake_case__=4096 ,					snake_case__=1_1008 ,					snake_case__=32 ,					snake_case__=32 ,					snake_case__="silu" ,					snake_case__=2048 ,					snake_case__=0.0_2 ,					snake_case__=1e-6 ,					snake_case__=True ,					snake_case__=0 ,					snake_case__=1 ,					snake_case__=2 ,					snake_case__=False ,					snake_case__=True ,					snake_case__=0.1 ,					snake_case__=0.1 ,					snake_case__=True ,					snake_case__=True ,					snake_case__=None ,					**snake_case__ ,					):
		lowerCAmelCase					:    Tuple      							= vocab_size
		lowerCAmelCase					:    Optional[Any]      							= max_position_embeddings
		lowerCAmelCase					:    List[Any]      							= hidden_size
		lowerCAmelCase					:    List[Any]      							= intermediate_size
		lowerCAmelCase					:    Tuple      							= num_hidden_layers
		lowerCAmelCase					:    List[Any]      							= num_attention_heads
		lowerCAmelCase					:    List[Any]      							= hidden_act
		lowerCAmelCase					:    Union[str, Any]      							= initializer_range
		lowerCAmelCase					:    str      							= rms_norm_eps
		lowerCAmelCase					:    Optional[int]      							= use_cache
		lowerCAmelCase					:    Dict      							= kwargs.pop(
		    'use_memorry_efficient_attention' ,					snake_case__    )
		lowerCAmelCase					:    Optional[int]      							= hidden_dropout_prob
		lowerCAmelCase					:    Optional[Any]      							= attention_dropout_prob
		lowerCAmelCase					:    Union[str, Any]      							= use_stable_embedding
		lowerCAmelCase					:    Tuple      							= shared_input_output_embedding
		lowerCAmelCase					:    Tuple      							= rope_scaling
		self._rope_scaling_validation()

		super().__init__(
		    pad_token_id=snake_case__ ,					bos_token_id=snake_case__ ,					eos_token_id=snake_case__ ,					tie_word_embeddings=snake_case__ ,					**snake_case__ ,					)



	def 				lowercase     (					self    ):
		if self.rope_scaling is None:
			return

		if not isinstance(self.rope_scaling ,					snake_case__    ) or len(self.rope_scaling    ) != 2:
			raise ValueError(
			    '`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, '
			    f"got {self.rope_scaling}"    )
		lowerCAmelCase					:    List[Any]      							= self.rope_scaling.get('type' ,					snake_case__    )
		lowerCAmelCase					:    List[str]      							= self.rope_scaling.get('factor' ,					snake_case__    )
		if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
			raise ValueError(
			    f"`rope_scaling`'s name field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"    )
		if rope_scaling_factor is None or not isinstance(snake_case__ ,					snake_case__    ) or rope_scaling_factor <= 1.0:
			raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}"    )
