


"""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					(a_						:Dict				,				a_						:Optional[Any])     ->							Dict:
							lowercase		:str       							=				XCLIPTextConfig()

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

							if "large" in model_name:
														lowercase		:int       							=				768
														lowercase		:int       							=				3072
														lowercase		:Dict       							=				12

														lowercase		:Any       							=				1024
														lowercase		:Dict       							=				4096
														lowercase		:Tuple       							=				16
														lowercase		:int       							=				24
														lowercase		:Tuple       							=				768
														lowercase		:Optional[int]       							=				3072

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

							lowercase		:List[str]       							=				XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_)

							if "large" in model_name:
														lowercase		:List[Any]       							=				768

							return config






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

							return name






def       lowerCamelCase					(a_						:Optional[Any]				,				a_						:str)     ->							Optional[int]:
							for key in orig_state_dict.copy().keys():
														lowercase		:Optional[Any]       							=				orig_state_dict.pop(SCREAMING_SNAKE_CASE_)

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

							return orig_state_dict






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






def       lowerCamelCase					(a_						:List[Any]				,				a_						:int=None				,				a_						:Optional[int]=False)     ->							Optional[int]:
							lowercase		: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''',
							}

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

							lowercase		:str       							=				get_xclip_config(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_)
							lowercase		:int       							=				XCLIPModel(SCREAMING_SNAKE_CASE_)
							model.eval()

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

							lowercase		:Any       							=				convert_state_dict(SCREAMING_SNAKE_CASE_				,				SCREAMING_SNAKE_CASE_)

							lowercase		:Any       							=				XCLIPModel(SCREAMING_SNAKE_CASE_)
							lowercase							,  lowercase		:List[Any]       							=				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		:Dict       							=				336 if model_name == '''xclip-large-patch14-16-frames''' else 224
							lowercase		:Optional[int]       							=				VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE_)
							lowercase		:List[Any]       							=				CLIPTokenizer.from_pretrained('''openai/clip-vit-base-patch32''')
							lowercase		:Optional[Any]       							=				CLIPTokenizerFast.from_pretrained('''openai/clip-vit-base-patch32''')
							lowercase		:int       							=				XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE_				,				tokenizer=SCREAMING_SNAKE_CASE_)

							lowercase		:Optional[int]       							=				prepare_video(SCREAMING_SNAKE_CASE_)
							lowercase		:Dict       							=				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		:Dict       							=				model(**SCREAMING_SNAKE_CASE_)

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

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

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


if __name__ == "__main__":
	UpperCAmelCase      		=			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.'''
	)

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





"""simple docstring"""



import unittest

import torch

from diffusers import VQModel
from diffusers.utils import floats_tensor, torch_device
from diffusers.utils.testing_utils import enable_full_determinism

from .test_modeling_common import ModelTesterMixin, UNetTesterMixin


enable_full_determinism()
class 					__magic_name__		(			__UpperCAmelCase			,						__UpperCAmelCase			,						unittest.TestCase     ):
       __A			:      List[str]        =   VQModel
       __A			:      Any        =   "sample"






       @property
       def  __snake_case						(   self  :    int  ,     snake_case__  :    int=(3_2, 3_2)				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				4
              lowercase		:Tuple       							=				3

              lowercase		:List[str]       							=				floats_tensor((batch_size, num_channels) + sizes				).to(snake_case__				)

              return {"sample": image}






       @property
       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              return (3, 3_2, 3_2)






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

              '''simple docstring'''






              return (3, 3_2, 3_2)






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				{
                  '''block_out_channels''': [3_2, 6_4],
                  '''in_channels''': 3,
                  '''out_channels''': 3,
                  '''down_block_types''': ['''DownEncoderBlock2D''', '''DownEncoderBlock2D'''],
                  '''up_block_types''': ['''UpDecoderBlock2D''', '''UpDecoderBlock2D'''],
                  '''latent_channels''': 3,
              }
              lowercase		:Dict       							=				self.dummy_input
              return init_dict, inputs_dict






       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Union[str, Any]				):

              '''simple docstring'''






              pass






       def  __snake_case						(   self  :    Dict				):

              '''simple docstring'''






              lowercase							,  lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''  ,     output_loading_info=snake_case__				)
              self.assertIsNotNone(snake_case__				)
              self.assertEqual(len(loading_info['''missing_keys''']				)  ,     0				)

              model.to(snake_case__				)
              lowercase		:List[str]       							=				model(**self.dummy_input				)

              assert image is not None, "Make sure output is not None"







       def  __snake_case						(   self  :    Optional[int]				):

              '''simple docstring'''






              lowercase		:Optional[int]       							=				VQModel.from_pretrained('''fusing/vqgan-dummy'''				)
              model.to(snake_case__				).eval()

              torch.manual_seed(0				)
              if torch.cuda.is_available():
                     torch.cuda.manual_seed_all(0				)

              lowercase		:List[Any]       							=				torch.randn(1  ,     model.config.in_channels  ,     model.config.sample_size  ,     model.config.sample_size				)
              lowercase		:int       							=				image.to(snake_case__				)
              with torch.no_grad():
                     lowercase		:str       							=				model(snake_case__				).sample

              lowercase		:List[Any]       							=				output[0, -1, -3:, -3:].flatten().cpu()
              # fmt: off
              lowercase		:Tuple       							=				torch.tensor([-0.01_53, -0.40_44, -0.18_80, -0.51_61, -0.24_18, -0.40_72, -0.16_12, -0.06_33, -0.01_43]				)
              # fmt: on
              self.assertTrue(torch.allclose(snake_case__  ,     snake_case__  ,     atol=1e-3				)				)

