




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  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						str	,					__UpperCamelCase      :						Any				)  ->							List[str]:
    UpperCAmelCase_										=					XCLIPTextConfig()

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

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

        UpperCAmelCase_										=					1024
        UpperCAmelCase_										=					4096
        UpperCAmelCase_										=					16
        UpperCAmelCase_										=					24
        UpperCAmelCase_										=					768
        UpperCAmelCase_										=					3072

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

    UpperCAmelCase_										=					XCLIPConfig.from_text_vision_configs(__UpperCamelCase	,					__UpperCamelCase				)

    if "large" in model_name:
        UpperCAmelCase_										=					768

    return config




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

    return name




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						List[Any]	,					__UpperCamelCase      :						Optional[Any]				)  ->							List[str]:
    for key in orig_state_dict.copy().keys():
        UpperCAmelCase_										=					orig_state_dict.pop(__UpperCamelCase				)

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

    return orig_state_dict




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




def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Union[str, Any]	,					__UpperCamelCase      :						str=None	,					__UpperCamelCase      :						str=False				)  ->							str:
    UpperCAmelCase_										=					{
        # 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_										=					model_to_url[model_name]
    UpperCAmelCase_										=					8
    if "16-frames" in model_name:
        UpperCAmelCase_										=					16
    elif "shot" in model_name:
        UpperCAmelCase_										=					32

    UpperCAmelCase_										=					get_xclip_config(__UpperCamelCase	,					__UpperCamelCase				)
    UpperCAmelCase_										=					XCLIPModel(__UpperCamelCase				)
    model.eval()

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

    UpperCAmelCase_										=					convert_state_dict(__UpperCamelCase	,					__UpperCamelCase				)

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

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

    UpperCAmelCase_										=					prepare_video(__UpperCamelCase				)
    UpperCAmelCase_										=					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_										=					model(**__UpperCamelCase				)

    # Verify outputs
    UpperCAmelCase_										=					outputs.logits_per_video
    UpperCAmelCase_										=					logits_per_video.softmax(dim=1				)
    print('''Probs:'''	,					__UpperCamelCase				)
    # kinetics-400
    if model_name == "xclip-base-patch32":
        UpperCAmelCase_										=					torch.tensor([[0.0_019, 0.9_951, 0.0_030]]				)
    elif model_name == "xclip-base-patch32-16-frames":
        UpperCAmelCase_										=					torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]]				)
    elif model_name == "xclip-base-patch16":
        UpperCAmelCase_										=					torch.tensor([[0.0_083, 0.9_681, 0.0_236]]				)
    elif model_name == "xclip-base-patch16-16-frames":
        UpperCAmelCase_										=					torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]]				)
    elif model_name == "xclip-large-patch14":
        UpperCAmelCase_										=					torch.tensor([[0.0_062, 0.9_864, 0.0_075]]				)
    elif model_name == "xclip-large-patch14-16-frames":
        UpperCAmelCase_										=					torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]]				)
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        UpperCAmelCase_										=					torch.tensor([[0.0_555, 0.8_914, 0.0_531]]				)
    elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
        UpperCAmelCase_										=					torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]]				)
    elif model_name == "xclip-large-patch14-kinetics-600":
        UpperCAmelCase_										=					torch.tensor([[0.0_036, 0.9_920, 0.0_045]]				)
    # few shot
    elif model_name == "xclip-base-patch16-hmdb-2-shot":
        UpperCAmelCase_										=					torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]]				)
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        UpperCAmelCase_										=					torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]]				)
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        UpperCAmelCase_										=					torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]]				)
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        UpperCAmelCase_										=					torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]]				)
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        UpperCAmelCase_										=					torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]				)
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        UpperCAmelCase_										=					torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]]				)
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        UpperCAmelCase_										=					torch.tensor([[0.0_027, 0.9_904, 0.0_070]]				)
    elif model_name == "xclip-base-patch16-ucf-16-shot":
        UpperCAmelCase_										=					torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]]				)
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        UpperCAmelCase_										=					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(__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__":
 _lowerCamelCase      			=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     '--model_name',
     default='xclip-base-patch32',
     type=str,
     help='Name of the model.',
 )
 parser.add_argument(
     '--pytorch_dump_folder_path', default=None, type=str, help='Path to the output PyTorch model directory.'
 )
 parser.add_argument(
     '--push_to_hub', action='store_true', help='Whether or not to push the converted model to the 🤗 hub.'
 )

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











def  SCREAMING_SNAKE_CASE     (	__UpperCamelCase      :						Dict=2_8123				)  ->							str:
    UpperCAmelCase_										=					[1] * (limit + 1)

    for i in range(2	,					int(limit**0.5				) + 1				):
        sum_divs[i * i] += i
        for k in range(i + 1	,					limit // i + 1				):
            sum_divs[k * i] += k + i

    UpperCAmelCase_										=					set()
    UpperCAmelCase_										=					0

    for n in range(1	,					limit + 1				):
        if sum_divs[n] > n:
            abundants.add(__UpperCamelCase				)

        if not any((n - a in abundants) for a in abundants				):
            res += n

    return res


if __name__ == "__main__":
 print(solution())





