





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						,	_UpperCAmelCase )			->     Tuple:
    lowerCamelCase__    :      Optional[Any]					 =		XCLIPTextConfig()

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

    if "large" in model_name:
        lowerCamelCase__    :      int					 =		768
        lowerCamelCase__    :      Dict					 =		3072
        lowerCamelCase__    :      Optional[Any]					 =		12

        lowerCamelCase__    :      List[str]					 =		1024
        lowerCamelCase__    :      str					 =		4096
        lowerCamelCase__    :      Dict					 =		16
        lowerCamelCase__    :      int					 =		24
        lowerCamelCase__    :      List[Any]					 =		768
        lowerCamelCase__    :      int					 =		3072

    if model_name == "xclip-large-patch14-16-frames":
        lowerCamelCase__    :      Optional[Any]					 =		336

    lowerCamelCase__    :      List[Any]					 =		XCLIPConfig.from_text_vision_configs(_UpperCAmelCase						,	_UpperCAmelCase )

    if "large" in model_name:
        lowerCamelCase__    :      List[str]					 =		768

    return config


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

    return name


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase )			->     Optional[Any]:
    for key in orig_state_dict.copy().keys():
        lowerCamelCase__    :      Tuple					 =		orig_state_dict.pop(_UpperCAmelCase )

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

    return orig_state_dict


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


def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase						,	_UpperCAmelCase=None						,	_UpperCAmelCase=False )			->     Union[str, Any]:
    lowerCamelCase__    :      Any					 =		{
        # 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',
    }

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

    lowerCamelCase__    :      Tuple					 =		get_xclip_config(_UpperCAmelCase						,	_UpperCAmelCase )
    lowerCamelCase__    :      List[Any]					 =		XCLIPModel(_UpperCAmelCase )
    model.eval()

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

    lowerCamelCase__    :      Any					 =		convert_state_dict(_UpperCAmelCase						,	_UpperCAmelCase )

    lowerCamelCase__    :      List[Any]					 =		XCLIPModel(_UpperCAmelCase )
    lowerCamelCase__ ,							lowerCamelCase__    :      List[Any]					 =		model.load_state_dict(_UpperCAmelCase						,	strict=_UpperCAmelCase )
    assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
    model.eval()

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

    lowerCamelCase__    :      int					 =		prepare_video(_UpperCAmelCase )
    lowerCamelCase__    :      Tuple					 =		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():
        lowerCamelCase__    :      List[Any]					 =		model(**_UpperCAmelCase )

    # Verify outputs
    lowerCamelCase__    :      str					 =		outputs.logits_per_video
    lowerCamelCase__    :      Union[str, Any]					 =		logits_per_video.softmax(dim=1 )
    print('Probs:'						,	_UpperCAmelCase )
    # kinetics-400
    if model_name == "xclip-base-patch32":
        lowerCamelCase__    :      Optional[Any]					 =		torch.tensor([[0.0_019, 0.9_951, 0.0_030]] )
    elif model_name == "xclip-base-patch32-16-frames":
        lowerCamelCase__    :      Union[str, Any]					 =		torch.tensor([[7.0_999e-04, 9.9_883e-01, 4.5_580e-04]] )
    elif model_name == "xclip-base-patch16":
        lowerCamelCase__    :      int					 =		torch.tensor([[0.0_083, 0.9_681, 0.0_236]] )
    elif model_name == "xclip-base-patch16-16-frames":
        lowerCamelCase__    :      Any					 =		torch.tensor([[7.6_937e-04, 9.9_728e-01, 1.9_473e-03]] )
    elif model_name == "xclip-large-patch14":
        lowerCamelCase__    :      Tuple					 =		torch.tensor([[0.0_062, 0.9_864, 0.0_075]] )
    elif model_name == "xclip-large-patch14-16-frames":
        lowerCamelCase__    :      Optional[int]					 =		torch.tensor([[3.3_877e-04, 9.9_937e-01, 2.8_888e-04]] )
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        lowerCamelCase__    :      Tuple					 =		torch.tensor([[0.0_555, 0.8_914, 0.0_531]] )
    elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
        lowerCamelCase__    :      Tuple					 =		torch.tensor([[3.8_554e-04, 9.9_929e-01, 3.2_754e-04]] )
    elif model_name == "xclip-large-patch14-kinetics-600":
        lowerCamelCase__    :      List[Any]					 =		torch.tensor([[0.0_036, 0.9_920, 0.0_045]] )
    # few shot
    elif model_name == "xclip-base-patch16-hmdb-2-shot":
        lowerCamelCase__    :      Tuple					 =		torch.tensor([[7.1_890e-06, 9.9_994e-01, 5.6_559e-05]] )
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        lowerCamelCase__    :      Any					 =		torch.tensor([[1.0_320e-05, 9.9_993e-01, 6.2_435e-05]] )
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        lowerCamelCase__    :      Optional[Any]					 =		torch.tensor([[4.1_377e-06, 9.9_990e-01, 9.8_386e-05]] )
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        lowerCamelCase__    :      Optional[int]					 =		torch.tensor([[4.1_347e-05, 9.9_962e-01, 3.3_411e-04]] )
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        lowerCamelCase__    :      int					 =		torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]] )
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        lowerCamelCase__    :      Dict					 =		torch.tensor([[8.5_857e-05, 9.9_928e-01, 6.3_291e-04]] )
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        lowerCamelCase__    :      List[Any]					 =		torch.tensor([[0.0_027, 0.9_904, 0.0_070]] )
    elif model_name == "xclip-base-patch16-ucf-16-shot":
        lowerCamelCase__    :      Union[str, Any]					 =		torch.tensor([[9.8_219e-04, 9.9_593e-01, 3.0_863e-03]] )
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        lowerCamelCase__    :      int					 =		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__":
    _UpperCAmelCase				:       List[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."""
    )

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












def        SCREAMING_SNAKE_CASE      (       _UpperCAmelCase = 100_0000 )			->     int:
    lowerCamelCase__    :      int					 =		limit + 1
    lowerCamelCase__    :      Optional[Any]					 =		[0] * limit
    for first_term in range(1						,	_UpperCAmelCase ):
        for n in range(_UpperCAmelCase						,	_UpperCAmelCase						,	_UpperCAmelCase ):
            lowerCamelCase__    :      Optional[Any]					 =		first_term + n / first_term
            if common_difference % 4:  # d must be divisble by 4
                continue
            else:
                common_difference /= 4
                if (
                    first_term > common_difference
                    and first_term < 4 * common_difference
                ):  # since x,y,z are positive integers
                    frequency[n] += 1  # so z>0 and a>d ,also 4d<a

    lowerCamelCase__    :      List[str]					 =		sum(1 for x in frequency[1:limit] if x == 10 )

    return count


if __name__ == "__main__":
    print(F"""{solution() = }""")





