





'''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    _lowerCAmelCase						(				lowerCamelCase_			:  Dict		,       lowerCamelCase_			:  Optional[int]				):
			__lowercase										=							XCLIPTextConfig()

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

			if "large" in model_name:
						__lowercase										=							7_6_8
						__lowercase										=							3_0_7_2
						__lowercase										=							1_2

						__lowercase										=							1_0_2_4
						__lowercase										=							4_0_9_6
						__lowercase										=							1_6
						__lowercase										=							2_4
						__lowercase										=							7_6_8
						__lowercase										=							3_0_7_2

			if model_name == "xclip-large-patch14-16-frames":
						__lowercase										=							3_3_6

			__lowercase										=							XCLIPConfig.from_text_vision_configs(_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				)

			if "large" in model_name:
						__lowercase										=							7_6_8

			return config




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

			return name




def    _lowerCAmelCase						(				lowerCamelCase_			:  List[str]		,       lowerCamelCase_			:  List[str]				):
			for key in orig_state_dict.copy().keys():
						__lowercase										=							orig_state_dict.pop(_SCREAMING_SNAKE_CASE				)

						if "attn.in_proj" in key:
									__lowercase										=							key.split('''.'''				)
									if key.startswith('''visual'''				):
												__lowercase										=							key_split[3]
												__lowercase										=							config.vision_config.hidden_size
												if "message_attn" in key:
															if "weight" in key:
																		__lowercase										=							val[
																		    :dim, :
																		]
																		__lowercase										=							val[
																		    dim : dim * 2, :
																		]
																		__lowercase										=							val[
																		    -dim:, :
																		]
															else:
																		__lowercase										=							val[
																		    :dim
																		]
																		__lowercase										=							val[
																		    dim : dim * 2
																		]
																		__lowercase										=							val[
																		    -dim:
																		]
												else:
															if "weight" in key:
																		__lowercase										=							val[
																		    :dim, :
																		]
																		__lowercase										=							val[
																		    dim : dim * 2, :
																		]
																		__lowercase										=							val[
																		    -dim:, :
																		]
															else:
																		__lowercase										=							val[:dim]
																		__lowercase										=							val[
																		    dim : dim * 2
																		]
																		__lowercase										=							val[-dim:]
									elif key.startswith('''mit'''				):
												__lowercase										=							key_split[2]
												__lowercase										=							config.vision_config.mit_hidden_size
												if "weight" in key:
															__lowercase										=							val[:dim, :]
															__lowercase										=							val[dim : dim * 2, :]
															__lowercase										=							val[-dim:, :]
												else:
															__lowercase										=							val[:dim]
															__lowercase										=							val[dim : dim * 2]
															__lowercase										=							val[-dim:]
									else:
												__lowercase										=							key_split[2]
												__lowercase										=							config.text_config.hidden_size
												if "weight" in key:
															__lowercase										=							val[:dim, :]
															__lowercase										=							val[
															    dim : dim * 2, :
															]
															__lowercase										=							val[-dim:, :]
												else:
															__lowercase										=							val[:dim]
															__lowercase										=							val[
															    dim : dim * 2
															]
															__lowercase										=							val[-dim:]
						else:
									__lowercase										=							rename_key(_SCREAMING_SNAKE_CASE				)
									if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
												__lowercase										=							val.T
									__lowercase										=							val

			return orig_state_dict




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




def    _lowerCAmelCase						(				lowerCamelCase_			:  Optional[int]		,       lowerCamelCase_			:  Optional[Any]=None		,       lowerCamelCase_			:  Optional[Any]=False				):
			__lowercase										=							{
			    # 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										=							model_to_url[model_name]
			__lowercase										=							8
			if "16-frames" in model_name:
						__lowercase										=							1_6
			elif "shot" in model_name:
						__lowercase										=							3_2

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

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

			__lowercase										=							convert_state_dict(_SCREAMING_SNAKE_CASE		,       _SCREAMING_SNAKE_CASE				)

			__lowercase										=							XCLIPModel(_SCREAMING_SNAKE_CASE				)
			__lowercase						,		__lowercase										=							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()

			__lowercase										=							3_3_6 if model_name == '''xclip-large-patch14-16-frames''' else 2_2_4
			__lowercase										=							VideoMAEImageProcessor(size=_SCREAMING_SNAKE_CASE				)
			__lowercase										=							CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32'''				)
			__lowercase										=							CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32'''				)
			__lowercase										=							XCLIPProcessor(image_processor=_SCREAMING_SNAKE_CASE		,       tokenizer=_SCREAMING_SNAKE_CASE				)

			__lowercase										=							prepare_video(_SCREAMING_SNAKE_CASE				)
			__lowercase										=							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():
						__lowercase										=							model(**_SCREAMING_SNAKE_CASE				)

			# Verify outputs
			__lowercase										=							outputs.logits_per_video
			__lowercase										=							logits_per_video.softmax(dim=1				)
			print('''Probs:'''		,       _SCREAMING_SNAKE_CASE				)
			# kinetics-400
			if model_name == "xclip-base-patch32":
						__lowercase										=							torch.tensor([[0.00_19, 0.99_51, 0.00_30]]				)
			elif model_name == "xclip-base-patch32-16-frames":
						__lowercase										=							torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]]				)
			elif model_name == "xclip-base-patch16":
						__lowercase										=							torch.tensor([[0.00_83, 0.96_81, 0.02_36]]				)
			elif model_name == "xclip-base-patch16-16-frames":
						__lowercase										=							torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]]				)
			elif model_name == "xclip-large-patch14":
						__lowercase										=							torch.tensor([[0.00_62, 0.98_64, 0.00_75]]				)
			elif model_name == "xclip-large-patch14-16-frames":
						__lowercase										=							torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]]				)
			# kinetics-600
			elif model_name == "xclip-base-patch16-kinetics-600":
						__lowercase										=							torch.tensor([[0.05_55, 0.89_14, 0.05_31]]				)
			elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
						__lowercase										=							torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]]				)
			elif model_name == "xclip-large-patch14-kinetics-600":
						__lowercase										=							torch.tensor([[0.00_36, 0.99_20, 0.00_45]]				)
			# few shot
			elif model_name == "xclip-base-patch16-hmdb-2-shot":
						__lowercase										=							torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]]				)
			elif model_name == "xclip-base-patch16-hmdb-4-shot":
						__lowercase										=							torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]]				)
			elif model_name == "xclip-base-patch16-hmdb-8-shot":
						__lowercase										=							torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]]				)
			elif model_name == "xclip-base-patch16-hmdb-16-shot":
						__lowercase										=							torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]]				)
			elif model_name == "xclip-base-patch16-ucf-2-shot":
						__lowercase										=							torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]				)
			elif model_name == "xclip-base-patch16-ucf-4-shot":
						__lowercase										=							torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]]				)
			elif model_name == "xclip-base-patch16-ucf-8-shot":
						__lowercase										=							torch.tensor([[0.00_27, 0.99_04, 0.00_70]]				)
			elif model_name == "xclip-base-patch16-ucf-16-shot":
						__lowercase										=							torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]]				)
			# zero shot
			elif model_name == "xclip-base-patch16-zero-shot":
						__lowercase										=							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(_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__":
	_SCREAMING_SNAKE_CASE					=    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.'''
	)

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






'''simple docstring'''



import json
from typing import Iterator, List, Union

from tokenizers import AddedToken, Regex, Tokenizer, decoders, normalizers, pre_tokenizers, trainers
from tokenizers.implementations.base_tokenizer import BaseTokenizer
from tokenizers.models import Unigram
from tokenizers.processors import TemplateProcessing






class 							__lowercase       (      lowerCAmelCase__						):







	'''simple docstring'''

	def __init__(self     ,_lowerCamelCase = "▁"     ,_lowerCamelCase = True     ,_lowerCamelCase = "<unk>"     ,_lowerCamelCase = "</s>"     ,_lowerCamelCase = "<pad>"     ,)							->							List[Any]:







				'''simple docstring'''





				__lowercase										=							{
				    '''pad''': {'''id''': 0, '''token''': pad_token},
				    '''eos''': {'''id''': 1, '''token''': eos_token},
				    '''unk''': {'''id''': 2, '''token''': unk_token},
				}

				__lowercase										=							[None] * len(self.special_tokens )
				for token_dict in self.special_tokens.values():
							__lowercase										=							token_dict['''token''']

				__lowercase										=							Tokenizer(Unigram() )

				__lowercase										=							normalizers.Sequence(
				    [
				        normalizers.Nmt(),
				        normalizers.NFKC(),
				        normalizers.Replace(Regex(''' {2,}''' )     ,''' ''' ),
				        normalizers.Lowercase(),
				    ] )
				__lowercase										=							pre_tokenizers.Sequence(
				    [
				        pre_tokenizers.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase ),
				        pre_tokenizers.Digits(individual_digits=_lowerCamelCase ),
				        pre_tokenizers.Punctuation(),
				    ] )
				__lowercase										=							decoders.Metaspace(replacement=_lowerCamelCase     ,add_prefix_space=_lowerCamelCase )

				__lowercase										=							TemplateProcessing(
				    single=f"$A {self.special_tokens['eos']['token']}"     ,special_tokens=[(self.special_tokens['''eos''']['''token'''], self.special_tokens['''eos''']['''id'''])]     ,)

				__lowercase										=							{
				    '''model''': '''SentencePieceUnigram''',
				    '''replacement''': replacement,
				    '''add_prefix_space''': add_prefix_space,
				}

				super().__init__(_lowerCamelCase     ,_lowerCamelCase )


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							Union[str, Any]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				if isinstance(_lowerCamelCase     ,_lowerCamelCase ):
							__lowercase										=							[files]
				self._tokenizer.train(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self     ,_lowerCamelCase     ,_lowerCamelCase = 8000     ,_lowerCamelCase = True     ,)							->							List[str]:







				'''simple docstring'''





				__lowercase										=							trainers.UnigramTrainer(
				    vocab_size=_lowerCamelCase     ,special_tokens=self.special_tokens_list     ,show_progress=_lowerCamelCase     ,)

				self._tokenizer.train_from_iterator(_lowerCamelCase     ,trainer=_lowerCamelCase )

				self.add_unk_id()


	def 						_UpperCAmelCase     (self )							->							Tuple:







				'''simple docstring'''





				__lowercase										=							json.loads(self._tokenizer.to_str() )

				__lowercase										=							self.special_tokens['''unk''']['''id''']

				__lowercase										=							Tokenizer.from_str(json.dumps(_lowerCamelCase ) )





