
"""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 							a__ (				lowerCAmelCase	:       Dict       ,    lowerCAmelCase	:       Union[str, Any]		):






     '''simple docstring'''
     UpperCAmelCase__							: Any 							=						XCLIPTextConfig()

     # derive patch size from model name
     UpperCAmelCase__							: List[Any] 							=						model_name.find("patch"		)
     UpperCAmelCase__							: Optional[int] 							=						int(model_name[start_idx + len("patch"		) : start_idx + len("patch"		) + 2]		)
     UpperCAmelCase__							: Optional[Any] 							=						XCLIPVisionConfig(patch_size=lowerCAmelCase       ,    num_frames=lowerCAmelCase		)

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

          UpperCAmelCase__							: Optional[int] 							=						1024
          UpperCAmelCase__							: Optional[Any] 							=						4096
          UpperCAmelCase__							: Optional[Any] 							=						16
          UpperCAmelCase__							: Tuple 							=						24
          UpperCAmelCase__							: List[Any] 							=						768
          UpperCAmelCase__							: int 							=						3072

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

     UpperCAmelCase__							: int 							=						XCLIPConfig.from_text_vision_configs(lowerCAmelCase       ,    lowerCAmelCase		)

     if "large" in model_name:
          UpperCAmelCase__							: Optional[Any] 							=						768

     return config



def 							a__ (				lowerCAmelCase	:       List[Any]		):






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

     return name



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Tuple		):






     '''simple docstring'''
     for key in orig_state_dict.copy().keys():
          UpperCAmelCase__							: Union[str, Any] 							=						orig_state_dict.pop(lowerCAmelCase		)

          if "attn.in_proj" in key:
               UpperCAmelCase__							: Optional[Any] 							=						key.split("."		)
               if key.startswith("visual"		):
                    UpperCAmelCase__							: Union[str, Any] 							=						key_split[3]
                    UpperCAmelCase__							: int 							=						config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              UpperCAmelCase__							: Dict 							=						val[
                                  :dim, :
                              ]
                              UpperCAmelCase__							: Optional[int] 							=						val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase__							: Any 							=						val[
                                  -dim:, :
                              ]
                         else:
                              UpperCAmelCase__							: Optional[int] 							=						val[
                                  :dim
                              ]
                              UpperCAmelCase__							: List[Any] 							=						val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase__							: int 							=						val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              UpperCAmelCase__							: int 							=						val[
                                  :dim, :
                              ]
                              UpperCAmelCase__							: Optional[Any] 							=						val[
                                  dim : dim * 2, :
                              ]
                              UpperCAmelCase__							: Union[str, Any] 							=						val[
                                  -dim:, :
                              ]
                         else:
                              UpperCAmelCase__							: List[Any] 							=						val[:dim]
                              UpperCAmelCase__							: List[Any] 							=						val[
                                  dim : dim * 2
                              ]
                              UpperCAmelCase__							: Tuple 							=						val[-dim:]
               elif key.startswith("mit"		):
                    UpperCAmelCase__							: Optional[Any] 							=						key_split[2]
                    UpperCAmelCase__							: str 							=						config.vision_config.mit_hidden_size
                    if "weight" in key:
                         UpperCAmelCase__							: Optional[Any] 							=						val[:dim, :]
                         UpperCAmelCase__							: List[Any] 							=						val[dim : dim * 2, :]
                         UpperCAmelCase__							: Tuple 							=						val[-dim:, :]
                    else:
                         UpperCAmelCase__							: Optional[int] 							=						val[:dim]
                         UpperCAmelCase__							: Any 							=						val[dim : dim * 2]
                         UpperCAmelCase__							: Tuple 							=						val[-dim:]
               else:
                    UpperCAmelCase__							: Any 							=						key_split[2]
                    UpperCAmelCase__							: List[str] 							=						config.text_config.hidden_size
                    if "weight" in key:
                         UpperCAmelCase__							: List[Any] 							=						val[:dim, :]
                         UpperCAmelCase__							: List[str] 							=						val[
                             dim : dim * 2, :
                         ]
                         UpperCAmelCase__							: List[Any] 							=						val[-dim:, :]
                    else:
                         UpperCAmelCase__							: List[str] 							=						val[:dim]
                         UpperCAmelCase__							: List[str] 							=						val[
                             dim : dim * 2
                         ]
                         UpperCAmelCase__							: List[Any] 							=						val[-dim:]
          else:
               UpperCAmelCase__							: List[str] 							=						rename_key(lowerCAmelCase		)
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    UpperCAmelCase__							: Optional[int] 							=						val.T
               UpperCAmelCase__							: List[str] 							=						val

     return orig_state_dict



def 							a__ (				lowerCAmelCase	:       List[str]		):






     '''simple docstring'''
     if num_frames == 8:
          UpperCAmelCase__							: int 							=						"eating_spaghetti_8_frames.npy"
     elif num_frames == 16:
          UpperCAmelCase__							: Optional[int] 							=						"eating_spaghetti.npy"
     elif num_frames == 32:
          UpperCAmelCase__							: Optional[int] 							=						"eating_spaghetti_32_frames.npy"
     UpperCAmelCase__							: Any 							=						hf_hub_download(
         repo_id="hf-internal-testing/spaghetti-video"       ,    filename=lowerCAmelCase       ,    repo_type="dataset"       ,    )
     UpperCAmelCase__							: Union[str, Any] 							=						np.load(lowerCAmelCase		)
     return list(lowerCAmelCase		)



def 							a__ (				lowerCAmelCase	:       str       ,    lowerCAmelCase	:       str=None       ,    lowerCAmelCase	:       Any=False		):






     '''simple docstring'''
     UpperCAmelCase__							: Optional[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",
     }

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

     UpperCAmelCase__							: str 							=						get_xclip_config(lowerCAmelCase       ,    lowerCAmelCase		)
     UpperCAmelCase__							: List[str] 							=						XCLIPModel(lowerCAmelCase		)
     model.eval()

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

     UpperCAmelCase__							: Any 							=						convert_state_dict(lowerCAmelCase       ,    lowerCAmelCase		)

     UpperCAmelCase__							: Tuple 							=						XCLIPModel(lowerCAmelCase		)
     UpperCAmelCase__							,	UpperCAmelCase__							: Union[str, Any] 							=						model.load_state_dict(lowerCAmelCase       ,    strict=lowerCAmelCase		)
     assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
     model.eval()

     UpperCAmelCase__							: Dict 							=						336 if model_name == "xclip-large-patch14-16-frames" else 224
     UpperCAmelCase__							: str 							=						VideoMAEImageProcessor(size=lowerCAmelCase		)
     UpperCAmelCase__							: int 							=						CLIPTokenizer.from_pretrained("openai/clip-vit-base-patch32"		)
     UpperCAmelCase__							: int 							=						CLIPTokenizerFast.from_pretrained("openai/clip-vit-base-patch32"		)
     UpperCAmelCase__							: Dict 							=						XCLIPProcessor(image_processor=lowerCAmelCase       ,    tokenizer=lowerCAmelCase		)

     UpperCAmelCase__							: Optional[int] 							=						prepare_video(lowerCAmelCase		)
     UpperCAmelCase__							: Dict 							=						processor(
         text=["playing sports", "eating spaghetti", "go shopping"]       ,    videos=lowerCAmelCase       ,    return_tensors="pt"       ,    padding=lowerCAmelCase		)

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

     with torch.no_grad():
          UpperCAmelCase__							: Union[str, Any] 							=						model(**lowerCAmelCase		)

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

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


if __name__ == "__main__":
    A__      :	Union[str, Any]          =    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__      :	Optional[int]          =    parser.parse_args()
    convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)





"""simple docstring"""




import argparse

import torch

from transformers import GPTaConfig, GPTaModel, load_tf_weights_in_gpta
from transformers.utils import CONFIG_NAME, WEIGHTS_NAME, logging


logging.set_verbosity_info()



def 							a__ (				lowerCAmelCase	:       List[Any]       ,    lowerCAmelCase	:       Any       ,    lowerCAmelCase	:       List[Any]		):






     '''simple docstring'''
     # Construct model
     if gpta_config_file == "":
          UpperCAmelCase__							: Optional[int] 							=						GPTaConfig()
     else:
          UpperCAmelCase__							: Dict 							=						GPTaConfig.from_json_file(lowerCAmelCase		)
     UpperCAmelCase__							: Optional[Any] 							=						GPTaModel(lowerCAmelCase		)

     # Load weights from numpy
     load_tf_weights_in_gpta(lowerCAmelCase       ,    lowerCAmelCase       ,    lowerCAmelCase		)

     # Save pytorch-model
     UpperCAmelCase__							: Optional[int] 							=						pytorch_dump_folder_path + "/" + WEIGHTS_NAME
     UpperCAmelCase__							: Any 							=						pytorch_dump_folder_path + "/" + CONFIG_NAME
     print(F"Save PyTorch model to {pytorch_weights_dump_path}"		)
     torch.save(model.state_dict()       ,    lowerCAmelCase		)
     print(F"Save configuration file to {pytorch_config_dump_path}"		)
     with open(lowerCAmelCase       ,    "w"       ,    encoding="utf-8"		) as f:
          f.write(config.to_json_string()		)


if __name__ == "__main__":
    A__      :	Optional[int]          =    argparse.ArgumentParser()
    # Required parameters
    parser.add_argument(
        """--gpt2_checkpoint_path""", default=None, type=str, required=True, help="""Path to the TensorFlow checkpoint path."""
    )
    parser.add_argument(
        """--pytorch_dump_folder_path""", default=None, type=str, required=True, help="""Path to the output PyTorch model."""
    )
    parser.add_argument(
        """--gpt2_config_file""",
        default="""""",
        type=str,
        help=(
            """An optional config json file corresponding to the pre-trained OpenAI model. \n"""
            """This specifies the model architecture."""
        ),
    )
    A__      :	Optional[Any]          =    parser.parse_args()
    convert_gpta_checkpoint_to_pytorch(args.gpta_checkpoint_path, args.gpta_config_file, args.pytorch_dump_folder_path)



