




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_					: Optional[int]       ,    lowerCAmelCase_					: Dict      ):
     """simple docstring"""

     lowerCAmelCase__          =			XCLIPTextConfig()

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

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

          lowerCAmelCase__          =			1024
          lowerCAmelCase__          =			4096
          lowerCAmelCase__          =			16
          lowerCAmelCase__          =			24
          lowerCAmelCase__          =			768
          lowerCAmelCase__          =			3072

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

     lowerCAmelCase__          =			XCLIPConfig.from_text_vision_configs(lowerCAmelCase_       ,    lowerCAmelCase_      )

     if "large" in model_name:
          lowerCAmelCase__          =			768

     return config



def 							_A       (						lowerCAmelCase_					: Union[str, Any]      ):
     """simple docstring"""

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

     return name



def 							_A       (						lowerCAmelCase_					: int       ,    lowerCAmelCase_					: Optional[Any]      ):
     """simple docstring"""

     for key in orig_state_dict.copy().keys():
          lowerCAmelCase__          =			orig_state_dict.pop(lowerCAmelCase_      )

          if "attn.in_proj" in key:
               lowerCAmelCase__          =			key.split("."      )
               if key.startswith("visual"      ):
                    lowerCAmelCase__          =			key_split[3]
                    lowerCAmelCase__          =			config.vision_config.hidden_size
                    if "message_attn" in key:
                         if "weight" in key:
                              lowerCAmelCase__          =			val[
                                  :dim, :
                              ]
                              lowerCAmelCase__          =			val[
                                  dim : dim * 2, :
                              ]
                              lowerCAmelCase__          =			val[
                                  -dim:, :
                              ]
                         else:
                              lowerCAmelCase__          =			val[
                                  :dim
                              ]
                              lowerCAmelCase__          =			val[
                                  dim : dim * 2
                              ]
                              lowerCAmelCase__          =			val[
                                  -dim:
                              ]
                    else:
                         if "weight" in key:
                              lowerCAmelCase__          =			val[
                                  :dim, :
                              ]
                              lowerCAmelCase__          =			val[
                                  dim : dim * 2, :
                              ]
                              lowerCAmelCase__          =			val[
                                  -dim:, :
                              ]
                         else:
                              lowerCAmelCase__          =			val[:dim]
                              lowerCAmelCase__          =			val[
                                  dim : dim * 2
                              ]
                              lowerCAmelCase__          =			val[-dim:]
               elif key.startswith("mit"      ):
                    lowerCAmelCase__          =			key_split[2]
                    lowerCAmelCase__          =			config.vision_config.mit_hidden_size
                    if "weight" in key:
                         lowerCAmelCase__          =			val[:dim, :]
                         lowerCAmelCase__          =			val[dim : dim * 2, :]
                         lowerCAmelCase__          =			val[-dim:, :]
                    else:
                         lowerCAmelCase__          =			val[:dim]
                         lowerCAmelCase__          =			val[dim : dim * 2]
                         lowerCAmelCase__          =			val[-dim:]
               else:
                    lowerCAmelCase__          =			key_split[2]
                    lowerCAmelCase__          =			config.text_config.hidden_size
                    if "weight" in key:
                         lowerCAmelCase__          =			val[:dim, :]
                         lowerCAmelCase__          =			val[
                             dim : dim * 2, :
                         ]
                         lowerCAmelCase__          =			val[-dim:, :]
                    else:
                         lowerCAmelCase__          =			val[:dim]
                         lowerCAmelCase__          =			val[
                             dim : dim * 2
                         ]
                         lowerCAmelCase__          =			val[-dim:]
          else:
               lowerCAmelCase__          =			rename_key(lowerCAmelCase_      )
               if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
                    lowerCAmelCase__          =			val.T
               lowerCAmelCase__          =			val

     return orig_state_dict



def 							_A       (						lowerCAmelCase_					: Dict      ):
     """simple docstring"""

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



def 							_A       (						lowerCAmelCase_					: List[Any]       ,    lowerCAmelCase_					: Any=None       ,    lowerCAmelCase_					: int=False      ):
     """simple docstring"""

     lowerCAmelCase__          =			{
         # 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__          =			model_to_url[model_name]
     lowerCAmelCase__          =			8
     if "16-frames" in model_name:
          lowerCAmelCase__          =			16
     elif "shot" in model_name:
          lowerCAmelCase__          =			32

     lowerCAmelCase__          =			get_xclip_config(lowerCAmelCase_       ,    lowerCAmelCase_      )
     lowerCAmelCase__          =			XCLIPModel(lowerCAmelCase_      )
     model.eval()

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

     lowerCAmelCase__          =			convert_state_dict(lowerCAmelCase_       ,    lowerCAmelCase_      )

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

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

     lowerCAmelCase__          =			prepare_video(lowerCAmelCase_      )
     lowerCAmelCase__          =			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():
          lowerCAmelCase__          =			model(**lowerCAmelCase_      )

     # Verify outputs
     lowerCAmelCase__          =			outputs.logits_per_video
     lowerCAmelCase__          =			logits_per_video.softmax(dim=1      )
     print("Probs:"       ,    lowerCAmelCase_      )
     # kinetics-400
     if model_name == "xclip-base-patch32":
          lowerCAmelCase__          =			torch.tensor([[0.0019, 0.9951, 0.0030]]      )
     elif model_name == "xclip-base-patch32-16-frames":
          lowerCAmelCase__          =			torch.tensor([[7.0_9_9_9E-0_4, 9.9_8_8_3E-0_1, 4.5_5_8_0E-0_4]]      )
     elif model_name == "xclip-base-patch16":
          lowerCAmelCase__          =			torch.tensor([[0.0083, 0.9681, 0.0236]]      )
     elif model_name == "xclip-base-patch16-16-frames":
          lowerCAmelCase__          =			torch.tensor([[7.6_9_3_7E-0_4, 9.9_7_2_8E-0_1, 1.9_4_7_3E-0_3]]      )
     elif model_name == "xclip-large-patch14":
          lowerCAmelCase__          =			torch.tensor([[0.0062, 0.9864, 0.0075]]      )
     elif model_name == "xclip-large-patch14-16-frames":
          lowerCAmelCase__          =			torch.tensor([[3.3_8_7_7E-0_4, 9.9_9_3_7E-0_1, 2.8_8_8_8E-0_4]]      )
     # kinetics-600
     elif model_name == "xclip-base-patch16-kinetics-600":
          lowerCAmelCase__          =			torch.tensor([[0.0555, 0.8914, 0.0531]]      )
     elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
          lowerCAmelCase__          =			torch.tensor([[3.8_5_5_4E-0_4, 9.9_9_2_9E-0_1, 3.2_7_5_4E-0_4]]      )
     elif model_name == "xclip-large-patch14-kinetics-600":
          lowerCAmelCase__          =			torch.tensor([[0.0036, 0.9920, 0.0045]]      )
     # few shot
     elif model_name == "xclip-base-patch16-hmdb-2-shot":
          lowerCAmelCase__          =			torch.tensor([[7.1_8_9_0E-0_6, 9.9_9_9_4E-0_1, 5.6_5_5_9E-0_5]]      )
     elif model_name == "xclip-base-patch16-hmdb-4-shot":
          lowerCAmelCase__          =			torch.tensor([[1.0_3_2_0E-0_5, 9.9_9_9_3E-0_1, 6.2_4_3_5E-0_5]]      )
     elif model_name == "xclip-base-patch16-hmdb-8-shot":
          lowerCAmelCase__          =			torch.tensor([[4.1_3_7_7E-0_6, 9.9_9_9_0E-0_1, 9.8_3_8_6E-0_5]]      )
     elif model_name == "xclip-base-patch16-hmdb-16-shot":
          lowerCAmelCase__          =			torch.tensor([[4.1_3_4_7E-0_5, 9.9_9_6_2E-0_1, 3.3_4_1_1E-0_4]]      )
     elif model_name == "xclip-base-patch16-ucf-2-shot":
          lowerCAmelCase__          =			torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]      )
     elif model_name == "xclip-base-patch16-ucf-4-shot":
          lowerCAmelCase__          =			torch.tensor([[8.5_8_5_7E-0_5, 9.9_9_2_8E-0_1, 6.3_2_9_1E-0_4]]      )
     elif model_name == "xclip-base-patch16-ucf-8-shot":
          lowerCAmelCase__          =			torch.tensor([[0.0027, 0.9904, 0.0070]]      )
     elif model_name == "xclip-base-patch16-ucf-16-shot":
          lowerCAmelCase__          =			torch.tensor([[9.8_2_1_9E-0_4, 9.9_5_9_3E-0_1, 3.0_8_6_3E-0_3]]      )
     # zero shot
     elif model_name == "xclip-base-patch16-zero-shot":
          lowerCAmelCase__          =			torch.tensor([[3.5_0_8_2E-0_4, 9.9_7_8_5E-0_1, 1.7_9_6_6E-0_3]]      )
     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__":
     UpperCamelCase								=					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								=					parser.parse_args()
     convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)






import warnings
from contextlib import contextmanager

from ...processing_utils import ProcessorMixin





class 			__lowerCamelCase	(   UpperCamelCase__   ):




    """simple docstring"""

    snake_case__ =				"Speech2TextFeatureExtractor"
    snake_case__ =				"Speech2TextTokenizer"


    def __init__(	self					:					int       ,     SCREAMING_SNAKE_CASE__					:					Dict       ,     SCREAMING_SNAKE_CASE__					:					Tuple       )				->	Optional[Any]:
         super().__init__(SCREAMING_SNAKE_CASE__       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			self.feature_extractor
         lowerCAmelCase__          =			False


    def __call__(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					int       ,     **SCREAMING_SNAKE_CASE__					:					int       )				->	Optional[Any]:
         # For backward compatibility
         if self._in_target_context_manager:
              return self.current_processor(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if "raw_speech" in kwargs:
              warnings.warn("Using `raw_speech` as a keyword argument is deprecated. Use `audio` instead."       )
              lowerCAmelCase__          =			kwargs.pop("raw_speech"       )
         else:
              lowerCAmelCase__          =			kwargs.pop("audio"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			kwargs.pop("sampling_rate"       ,     SCREAMING_SNAKE_CASE__       )
         lowerCAmelCase__          =			kwargs.pop("text"       ,     SCREAMING_SNAKE_CASE__       )
         if len(SCREAMING_SNAKE_CASE__       ) > 0:
              lowerCAmelCase__          =			args[0]
              lowerCAmelCase__          =			args[1:]

         if audio is None and text is None:
              raise ValueError("You need to specify either an `audio` or `text` input to process."       )

         if audio is not None:
              lowerCAmelCase__          =			self.feature_extractor(SCREAMING_SNAKE_CASE__       ,     *SCREAMING_SNAKE_CASE__       ,     sampling_rate=SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )
         if text is not None:
              lowerCAmelCase__          =			self.tokenizer(SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )

         if text is None:
              return inputs
         elif audio is None:
              return encodings
         else:
              lowerCAmelCase__          =			encodings["input_ids"]
              return inputs


    def        a				(	self					:					str       ,     *SCREAMING_SNAKE_CASE__					:					Dict       ,     **SCREAMING_SNAKE_CASE__					:					int       )				->	Union[str, Any]:
         return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    def        a				(	self					:					Dict       ,     *SCREAMING_SNAKE_CASE__					:					Optional[int]       ,     **SCREAMING_SNAKE_CASE__					:					Optional[int]       )				->	Optional[Any]:
         return self.tokenizer.decode(*SCREAMING_SNAKE_CASE__       ,     **SCREAMING_SNAKE_CASE__       )


    @contextmanager
    def        a				(	self					:					Union[str, Any]       )				->	Any:
         warnings.warn(
             "`as_target_processor` is deprecated and will be removed in v5 of Transformers. You can process your "
             "labels by using the argument `text` of the regular `__call__` method (either in the same call as "
             "your audio inputs, or in a separate call."       )
         lowerCAmelCase__          =			True
         lowerCAmelCase__          =			self.tokenizer
         yield
         lowerCAmelCase__          =			self.feature_extractor
         lowerCAmelCase__          =			False
