

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    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE   )   ->  int:






    '''simple docstring'''
    __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=lowercase__							, num_frames=lowercase__   )

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

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

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

    __UpperCAmelCase					       =      XCLIPConfig.from_text_vision_configs(lowercase__							, lowercase__   )

    if "large" in model_name:
        __UpperCAmelCase					       =      7_6_8

    return config


def    __a							( SCREAMING_SNAKE_CASE   )   ->  Dict:






    '''simple docstring'''
    # 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    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE   )   ->  List[str]:






    '''simple docstring'''
    for key in orig_state_dict.copy().keys():
        __UpperCAmelCase					       =      orig_state_dict.pop(lowercase__   )

        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(lowercase__   )
            if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                __UpperCAmelCase					       =      val.T
            __UpperCAmelCase					       =      val

    return orig_state_dict


def    __a							( SCREAMING_SNAKE_CASE   )   ->  Tuple:






    '''simple docstring'''
    if num_frames == 8:
        __UpperCAmelCase					       =      '''eating_spaghetti_8_frames.npy'''
    elif num_frames == 1_6:
        __UpperCAmelCase					       =      '''eating_spaghetti.npy'''
    elif num_frames == 3_2:
        __UpperCAmelCase					       =      '''eating_spaghetti_32_frames.npy'''
    __UpperCAmelCase					       =      hf_hub_download(
        repo_id='''hf-internal-testing/spaghetti-video'''							, filename=lowercase__							, repo_type='''dataset'''							, )
    __UpperCAmelCase					       =      np.load(lowercase__   )
    return list(lowercase__   )


def    __a							( SCREAMING_SNAKE_CASE							, SCREAMING_SNAKE_CASE=None							, SCREAMING_SNAKE_CASE=False   )   ->  int:






    '''simple docstring'''
    __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					       =      1_6
    elif "shot" in model_name:
        __UpperCAmelCase					       =      3_2

    __UpperCAmelCase					       =      get_xclip_config(lowercase__							, lowercase__   )
    __UpperCAmelCase					       =      XCLIPModel(lowercase__   )
    model.eval()

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

    __UpperCAmelCase					       =      convert_state_dict(lowercase__							, lowercase__   )

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

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

    __UpperCAmelCase					       =      prepare_video(lowercase__   )
    __UpperCAmelCase					       =      processor(
        text=['''playing sports''', '''eating spaghetti''', '''go shopping''']							, videos=lowercase__							, return_tensors='''pt'''							, padding=lowercase__   )

    print('''Shape of pixel values:'''							, inputs.pixel_values.shape   )

    with torch.no_grad():
        __UpperCAmelCase					       =      model(**lowercase__   )

    # Verify outputs
    __UpperCAmelCase					       =      outputs.logits_per_video
    __UpperCAmelCase					       =      logits_per_video.softmax(dim=1   )
    print('''Probs:'''							, lowercase__   )
    # kinetics-400
    if model_name == "xclip-base-patch32":
        __UpperCAmelCase					       =      torch.tensor([[0.0019, 0.9951, 0.0030]]   )
    elif model_name == "xclip-base-patch32-16-frames":
        __UpperCAmelCase					       =      torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]   )
    elif model_name == "xclip-base-patch16":
        __UpperCAmelCase					       =      torch.tensor([[0.0083, 0.9681, 0.0236]]   )
    elif model_name == "xclip-base-patch16-16-frames":
        __UpperCAmelCase					       =      torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]   )
    elif model_name == "xclip-large-patch14":
        __UpperCAmelCase					       =      torch.tensor([[0.0062, 0.9864, 0.0075]]   )
    elif model_name == "xclip-large-patch14-16-frames":
        __UpperCAmelCase					       =      torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]   )
    # kinetics-600
    elif model_name == "xclip-base-patch16-kinetics-600":
        __UpperCAmelCase					       =      torch.tensor([[0.0555, 0.8914, 0.0531]]   )
    elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
        __UpperCAmelCase					       =      torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]   )
    elif model_name == "xclip-large-patch14-kinetics-600":
        __UpperCAmelCase					       =      torch.tensor([[0.0036, 0.9920, 0.0045]]   )
    # few shot
    elif model_name == "xclip-base-patch16-hmdb-2-shot":
        __UpperCAmelCase					       =      torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]   )
    elif model_name == "xclip-base-patch16-hmdb-4-shot":
        __UpperCAmelCase					       =      torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]   )
    elif model_name == "xclip-base-patch16-hmdb-8-shot":
        __UpperCAmelCase					       =      torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]   )
    elif model_name == "xclip-base-patch16-hmdb-16-shot":
        __UpperCAmelCase					       =      torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]   )
    elif model_name == "xclip-base-patch16-ucf-2-shot":
        __UpperCAmelCase					       =      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]   )
    elif model_name == "xclip-base-patch16-ucf-4-shot":
        __UpperCAmelCase					       =      torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]   )
    elif model_name == "xclip-base-patch16-ucf-8-shot":
        __UpperCAmelCase					       =      torch.tensor([[0.0027, 0.9904, 0.0070]]   )
    elif model_name == "xclip-base-patch16-ucf-16-shot":
        __UpperCAmelCase					       =      torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]   )
    # zero shot
    elif model_name == "xclip-base-patch16-zero-shot":
        __UpperCAmelCase					       =      torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]   )
    else:
        raise ValueError(f'''Model name {model_name} not supported'''   )
    assert torch.allclose(lowercase__							, lowercase__							, 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(lowercase__   )

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


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

       A_		:      int					    = parser.parse_args()
       convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)








"""simple docstring"""




from ...configuration_utils import PretrainedConfig
from ...utils import logging


__magic_name__						: str    =				logging.get_logger(__name__)

__magic_name__						: List[Any]    =				{
    'uclanlp/visualbert-vqa': 'https://huggingface.co/uclanlp/visualbert-vqa/resolve/main/config.json',
    'uclanlp/visualbert-vqa-pre': 'https://huggingface.co/uclanlp/visualbert-vqa-pre/resolve/main/config.json',
    'uclanlp/visualbert-vqa-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vqa-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-vcr': 'https://huggingface.co/uclanlp/visualbert-vcr/resolve/main/config.json',
    'uclanlp/visualbert-vcr-pre': 'https://huggingface.co/uclanlp/visualbert-vcr-pre/resolve/main/config.json',
    'uclanlp/visualbert-vcr-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-vcr-coco-pre/resolve/main/config.json'
    ),
    'uclanlp/visualbert-nlvr2': 'https://huggingface.co/uclanlp/visualbert-nlvr2/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-pre': 'https://huggingface.co/uclanlp/visualbert-nlvr2-pre/resolve/main/config.json',
    'uclanlp/visualbert-nlvr2-coco-pre': (
        'https://huggingface.co/uclanlp/visualbert-nlvr2-coco-pre/resolve/main/config.json'
    )
    # See all VisualBERT models at https://huggingface.co/models?filter=visual_bert
}




class 			__snake_case   (lowerCamelCase  ):
							__a					 =       '''visual_bert'''




							def __init__(     self:    Any     ,							A_:    List[str]=3_05_22     ,							A_:    Tuple=7_68     ,							A_:    Dict=5_12     ,							A_:    List[Any]=12     ,							A_:    str=12     ,							A_:    Dict=30_72     ,							A_:    str="gelu"     ,							A_:    Union[str, Any]=0.1     ,							A_:    Any=0.1     ,							A_:    List[Any]=5_12     ,							A_:    int=2     ,							A_:    int=0.02     ,							A_:    Any=1E-12     ,							A_:    List[str]=False     ,							A_:    str=True     ,							A_:    Union[str, Any]=1     ,							A_:    Optional[int]=0     ,							A_:    Dict=2     ,							**A_:    Optional[int]     ,							):
										super().__init__(pad_token_id=A_     ,							bos_token_id=A_     ,							eos_token_id=A_     ,							**A_   )

										__lowerCamelCase          =      vocab_size
										__lowerCamelCase          =      max_position_embeddings
										__lowerCamelCase          =      hidden_size
										__lowerCamelCase          =      visual_embedding_dim
										__lowerCamelCase          =      num_hidden_layers
										__lowerCamelCase          =      num_attention_heads
										__lowerCamelCase          =      intermediate_size
										__lowerCamelCase          =      hidden_act
										__lowerCamelCase          =      hidden_dropout_prob
										__lowerCamelCase          =      attention_probs_dropout_prob
										__lowerCamelCase          =      initializer_range
										__lowerCamelCase          =      type_vocab_size
										__lowerCamelCase          =      layer_norm_eps
										__lowerCamelCase          =      bypass_transformer
										__lowerCamelCase          =      special_visual_initialize




