

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	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							snake_case_							=    XCLIPTextConfig()

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

							if "large" in model_name:
														snake_case_							=    768
														snake_case_							=    3072
														snake_case_							=    12

														snake_case_							=    1024
														snake_case_							=    4096
														snake_case_							=    16
														snake_case_							=    24
														snake_case_							=    768
														snake_case_							=    3072

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

							snake_case_							=    XCLIPConfig.from_text_vision_configs(UpperCamelCase__   ,							UpperCamelCase__      )

							if "large" in model_name:
														snake_case_							=    768

							return config





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






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

							return name





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__      ):







							'''simple docstring'''






							for key in orig_state_dict.copy().keys():
														snake_case_							=    orig_state_dict.pop(UpperCamelCase__      )

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

							return orig_state_dict





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if num_frames == 8:
														snake_case_							=    'eating_spaghetti_8_frames.npy'
							elif num_frames == 16:
														snake_case_							=    'eating_spaghetti.npy'
							elif num_frames == 32:
														snake_case_							=    'eating_spaghetti_32_frames.npy'
							snake_case_							=    hf_hub_download(
							    repo_id='hf-internal-testing/spaghetti-video'   ,							filename=UpperCamelCase__   ,							repo_type='dataset'   ,							)
							snake_case_							=    np.load(UpperCamelCase__      )
							return list(UpperCamelCase__      )





def 					__lowerCamelCase	( UpperCamelCase__   ,							UpperCamelCase__=None   ,							UpperCamelCase__=False      ):







							'''simple docstring'''






							snake_case_							=    {
							    # 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',
							}

							snake_case_							=    model_to_url[model_name]
							snake_case_							=    8
							if "16-frames" in model_name:
														snake_case_							=    16
							elif "shot" in model_name:
														snake_case_							=    32

							snake_case_							=    get_xclip_config(UpperCamelCase__   ,							UpperCamelCase__      )
							snake_case_							=    XCLIPModel(UpperCamelCase__      )
							model.eval()

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

							snake_case_							=    convert_state_dict(UpperCamelCase__   ,							UpperCamelCase__      )

							snake_case_							=    XCLIPModel(UpperCamelCase__      )
							snake_case_				,    snake_case_							=    model.load_state_dict(UpperCamelCase__   ,							strict=UpperCamelCase__      )
							assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
							model.eval()

							snake_case_							=    336 if model_name == 'xclip-large-patch14-16-frames' else 224
							snake_case_							=    VideoMAEImageProcessor(size=UpperCamelCase__      )
							snake_case_							=    CLIPTokenizer.from_pretrained('openai/clip-vit-base-patch32'      )
							snake_case_							=    CLIPTokenizerFast.from_pretrained('openai/clip-vit-base-patch32'      )
							snake_case_							=    XCLIPProcessor(image_processor=UpperCamelCase__   ,							tokenizer=UpperCamelCase__      )

							snake_case_							=    prepare_video(UpperCamelCase__      )
							snake_case_							=    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():
														snake_case_							=    model(**UpperCamelCase__      )

							# Verify outputs
							snake_case_							=    outputs.logits_per_video
							snake_case_							=    logits_per_video.softmax(dim=1      )
							print('Probs:'   ,							UpperCamelCase__      )
							# kinetics-400
							if model_name == "xclip-base-patch32":
														snake_case_							=    torch.tensor([[0.00_19, 0.99_51, 0.00_30]]      )
							elif model_name == "xclip-base-patch32-16-frames":
														snake_case_							=    torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]      )
							elif model_name == "xclip-base-patch16":
														snake_case_							=    torch.tensor([[0.00_83, 0.96_81, 0.02_36]]      )
							elif model_name == "xclip-base-patch16-16-frames":
														snake_case_							=    torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]      )
							elif model_name == "xclip-large-patch14":
														snake_case_							=    torch.tensor([[0.00_62, 0.98_64, 0.00_75]]      )
							elif model_name == "xclip-large-patch14-16-frames":
														snake_case_							=    torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]      )
							# kinetics-600
							elif model_name == "xclip-base-patch16-kinetics-600":
														snake_case_							=    torch.tensor([[0.05_55, 0.89_14, 0.05_31]]      )
							elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
														snake_case_							=    torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]      )
							elif model_name == "xclip-large-patch14-kinetics-600":
														snake_case_							=    torch.tensor([[0.00_36, 0.99_20, 0.00_45]]      )
							# few shot
							elif model_name == "xclip-base-patch16-hmdb-2-shot":
														snake_case_							=    torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]      )
							elif model_name == "xclip-base-patch16-hmdb-4-shot":
														snake_case_							=    torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]      )
							elif model_name == "xclip-base-patch16-hmdb-8-shot":
														snake_case_							=    torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]      )
							elif model_name == "xclip-base-patch16-hmdb-16-shot":
														snake_case_							=    torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]      )
							elif model_name == "xclip-base-patch16-ucf-2-shot":
														snake_case_							=    torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]      )
							elif model_name == "xclip-base-patch16-ucf-4-shot":
														snake_case_							=    torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]      )
							elif model_name == "xclip-base-patch16-ucf-8-shot":
														snake_case_							=    torch.tensor([[0.00_27, 0.99_04, 0.00_70]]      )
							elif model_name == "xclip-base-patch16-ucf-16-shot":
														snake_case_							=    torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]      )
							# zero shot
							elif model_name == "xclip-base-patch16-zero-shot":
														snake_case_							=    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__":
					_UpperCAmelCase		:   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."""
					)

					_UpperCAmelCase		:   Optional[int]										=       parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)







def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return 3 * ((25 + 10 * (5 ** (1 / 2))) ** (1 / 2)) * (edge**2)





def 					__lowerCamelCase	( UpperCamelCase__      ):







							'''simple docstring'''






							if edge <= 0 or not isinstance(UpperCamelCase__   ,							UpperCamelCase__      ):
														raise ValueError('Length must be a positive.'      )
							return ((15 + (7 * (5 ** (1 / 2)))) / 4) * (edge**3)


if __name__ == "__main__":
					import doctest

					doctest.testmod()




