



'''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__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						_UpperCamelCase					:		Dict 				=   XCLIPTextConfig()

						# derive patch size from model name
						_UpperCamelCase					:		Optional[int] 				=   model_name.find('patch'							)
						_UpperCamelCase					:		int 				=   int(model_name[start_idx + len('patch'							) : start_idx + len('patch'							) + 2]							)
						_UpperCamelCase					:		Tuple 				=   XCLIPVisionConfig(patch_size=UpperCAmelCase_  ,  num_frames=UpperCAmelCase_							)

						if "large" in model_name:
												_UpperCamelCase					:		Dict 				=   7_6_8
												_UpperCamelCase					:		Any 				=   3_0_7_2
												_UpperCamelCase					:		List[Any] 				=   1_2

												_UpperCamelCase					:		List[str] 				=   1_0_2_4
												_UpperCamelCase					:		List[Any] 				=   4_0_9_6
												_UpperCamelCase					:		Any 				=   1_6
												_UpperCamelCase					:		Any 				=   2_4
												_UpperCamelCase					:		Tuple 				=   7_6_8
												_UpperCamelCase					:		Union[str, Any] 				=   3_0_7_2

						if model_name == "xclip-large-patch14-16-frames":
												_UpperCamelCase					:		Optional[int] 				=   3_3_6

						_UpperCamelCase					:		Union[str, Any] 				=   XCLIPConfig.from_text_vision_configs(UpperCAmelCase_  ,  UpperCAmelCase_							)

						if "large" in model_name:
												_UpperCamelCase					:		List[str] 				=   7_6_8

						return config






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

						return name






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_							):
						for key in orig_state_dict.copy().keys():
												_UpperCamelCase					:		str 				=   orig_state_dict.pop(UpperCAmelCase_							)

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

						return orig_state_dict






def A__    (     UpperCAmelCase_							):
						if num_frames == 8:
												_UpperCamelCase					:		Optional[Any] 				=   'eating_spaghetti_8_frames.npy'
						elif num_frames == 1_6:
												_UpperCamelCase					:		Optional[int] 				=   'eating_spaghetti.npy'
						elif num_frames == 3_2:
												_UpperCamelCase					:		str 				=   'eating_spaghetti_32_frames.npy'
						_UpperCamelCase					:		str 				=   hf_hub_download(
						    repo_id='hf-internal-testing/spaghetti-video'  ,  filename=UpperCAmelCase_  ,  repo_type='dataset'  ,  )
						_UpperCamelCase					:		str 				=   np.load(UpperCAmelCase_							)
						return list(UpperCAmelCase_							)






def A__    (     UpperCAmelCase_  ,  UpperCAmelCase_=None  ,  UpperCAmelCase_=False							):
						_UpperCamelCase					:		str 				=   {
						    # 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					:		Dict 				=   model_to_url[model_name]
						_UpperCamelCase					:		Union[str, Any] 				=   8
						if "16-frames" in model_name:
												_UpperCamelCase					:		Optional[Any] 				=   1_6
						elif "shot" in model_name:
												_UpperCamelCase					:		Tuple 				=   3_2

						_UpperCamelCase					:		List[Any] 				=   get_xclip_config(UpperCAmelCase_  ,  UpperCAmelCase_							)
						_UpperCamelCase					:		Any 				=   XCLIPModel(UpperCAmelCase_							)
						model.eval()

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

						_UpperCamelCase					:		Any 				=   convert_state_dict(UpperCAmelCase_  ,  UpperCAmelCase_							)

						_UpperCamelCase					:		Optional[Any] 				=   XCLIPModel(UpperCAmelCase_							)
						_UpperCamelCase				,			_UpperCamelCase					:		Optional[Any] 				=   model.load_state_dict(UpperCAmelCase_  ,  strict=UpperCAmelCase_							)
						assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
						model.eval()

						_UpperCamelCase					:		List[str] 				=   3_3_6 if model_name == 'xclip-large-patch14-16-frames' else 2_2_4
						_UpperCamelCase					:		Any 				=   VideoMAEImageProcessor(size=UpperCAmelCase_							)
						_UpperCamelCase					:		Tuple 				=   CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'							)
						_UpperCamelCase					:		int 				=   CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'							)
						_UpperCamelCase					:		List[str] 				=   XCLIPProcessor(image_processor=UpperCAmelCase_  ,  tokenizer=UpperCAmelCase_							)

						_UpperCamelCase					:		Any 				=   prepare_video(UpperCAmelCase_							)
						_UpperCamelCase					:		List[str] 				=   processor(
						    text=['playing sports', 'eating spaghetti', 'go shopping']  ,  videos=UpperCAmelCase_  ,  return_tensors='pt'  ,  padding=UpperCAmelCase_							)

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

						with torch.no_grad():
												_UpperCamelCase					:		int 				=   model(**UpperCAmelCase_							)

						# Verify outputs
						_UpperCamelCase					:		Optional[Any] 				=   outputs.logits_per_video
						_UpperCamelCase					:		str 				=   logits_per_video.softmax(dim=1							)
						print('Probs:'  ,  UpperCAmelCase_							)
						# kinetics-400
						if model_name == "xclip-base-patch32":
												_UpperCamelCase					:		Optional[int] 				=   torch.tensor([[0.0_019, 0.9_951, 0.0_030]]							)
						elif model_name == "xclip-base-patch32-16-frames":
												_UpperCamelCase					:		Tuple 				=   torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]							)
						elif model_name == "xclip-base-patch16":
												_UpperCamelCase					:		Any 				=   torch.tensor([[0.0_083, 0.9_681, 0.0_236]]							)
						elif model_name == "xclip-base-patch16-16-frames":
												_UpperCamelCase					:		Dict 				=   torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]							)
						elif model_name == "xclip-large-patch14":
												_UpperCamelCase					:		int 				=   torch.tensor([[0.0_062, 0.9_864, 0.0_075]]							)
						elif model_name == "xclip-large-patch14-16-frames":
												_UpperCamelCase					:		str 				=   torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]							)
						# kinetics-600
						elif model_name == "xclip-base-patch16-kinetics-600":
												_UpperCamelCase					:		List[str] 				=   torch.tensor([[0.0_555, 0.8_914, 0.0_531]]							)
						elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
												_UpperCamelCase					:		Optional[Any] 				=   torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]							)
						elif model_name == "xclip-large-patch14-kinetics-600":
												_UpperCamelCase					:		List[str] 				=   torch.tensor([[0.0_036, 0.9_920, 0.0_045]]							)
						# few shot
						elif model_name == "xclip-base-patch16-hmdb-2-shot":
												_UpperCamelCase					:		List[str] 				=   torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-4-shot":
												_UpperCamelCase					:		List[Any] 				=   torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-8-shot":
												_UpperCamelCase					:		int 				=   torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]							)
						elif model_name == "xclip-base-patch16-hmdb-16-shot":
												_UpperCamelCase					:		int 				=   torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]							)
						elif model_name == "xclip-base-patch16-ucf-2-shot":
												_UpperCamelCase					:		Dict 				=   torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
						elif model_name == "xclip-base-patch16-ucf-4-shot":
												_UpperCamelCase					:		List[Any] 				=   torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]							)
						elif model_name == "xclip-base-patch16-ucf-8-shot":
												_UpperCamelCase					:		List[str] 				=   torch.tensor([[0.0_027, 0.9_904, 0.0_070]]							)
						elif model_name == "xclip-base-patch16-ucf-16-shot":
												_UpperCamelCase					:		List[Any] 				=   torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]							)
						# zero shot
						elif model_name == "xclip-base-patch16-zero-shot":
												_UpperCamelCase					:		Dict 				=   torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]							)
						else:
												raise ValueError(f'Model name {model_name} not supported'							)
						assert torch.allclose(UpperCAmelCase_  ,  UpperCAmelCase_  ,  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(UpperCAmelCase_							)

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


if __name__ == "__main__":
					snake_case_ :	str         =  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.'
					)

					snake_case_ :	List[str]         =  parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
class       _UpperCAmelCase					(       A__	):

				"""simple docstring"""




				lowercase__											=       ["""image_processor""", """tokenizer"""]
				lowercase__											=       """BlipImageProcessor"""
				lowercase__											=       ("""BertTokenizer""", """BertTokenizerFast""")




				def __init__(     self						:    Dict,  lowerCamelCase						:    Dict,  lowerCamelCase						:    str  ):

								'''simple docstring'''
								lowercase__    =			False
								super().__init__(lowerCamelCase,  lowerCamelCase  )
								lowercase__    =			self.image_processor




				def __call__(     self						:    int,  lowerCamelCase						:    ImageInput = None,  lowerCamelCase						:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Union[bool, str, PaddingStrategy] = False,  lowerCamelCase						:    Union[bool, str, TruncationStrategy] = None,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    int = 0,  lowerCamelCase						:    Optional[int] = None,  lowerCamelCase						:    Optional[bool] = None,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = False,  lowerCamelCase						:    bool = True,  lowerCamelCase						:    Optional[Union[str, TensorType]] = None,  **lowerCamelCase						:    Any,  ):

								'''simple docstring'''
								if images is None and text is None:
												raise ValueError('''You have to specify either images or text.'''  )

								# Get only text
								if images is None:
												lowercase__    =			self.tokenizer
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
												return text_encoding

								# add pixel_values
								lowercase__    =			self.image_processor(lowerCamelCase,  return_tensors=lowerCamelCase  )

								if text is not None:
												lowercase__    =			self.tokenizer(
												    text=lowerCamelCase,  add_special_tokens=lowerCamelCase,  padding=lowerCamelCase,  truncation=lowerCamelCase,  max_length=lowerCamelCase,  stride=lowerCamelCase,  pad_to_multiple_of=lowerCamelCase,  return_attention_mask=lowerCamelCase,  return_overflowing_tokens=lowerCamelCase,  return_special_tokens_mask=lowerCamelCase,  return_offsets_mapping=lowerCamelCase,  return_token_type_ids=lowerCamelCase,  return_length=lowerCamelCase,  verbose=lowerCamelCase,  return_tensors=lowerCamelCase,  **lowerCamelCase,  )
								else:
												lowercase__    =			None

								if text_encoding is not None:
												encoding_image_processor.update(lowerCamelCase  )

								return encoding_image_processor




				def 				lowercase__   (     self						:    Tuple,  *lowerCamelCase						:    Union[str, Any],  **lowerCamelCase						:    Optional[int]  ):

								'''simple docstring'''
								return self.tokenizer.batch_decode(*lowerCamelCase,  **lowerCamelCase  )




				def 				lowercase__   (     self						:    List[str],  *lowerCamelCase						:    int,  **lowerCamelCase						:    List[str]  ):

								'''simple docstring'''
								return self.tokenizer.decode(*lowerCamelCase,  **lowerCamelCase  )




				@property
				def 				lowercase__   (     self						:    List[str]  ):

								'''simple docstring'''
								lowercase__    =			self.tokenizer.model_input_names
								lowercase__    =			self.image_processor.model_input_names
								return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )




