





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        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						XCLIPTextConfig()

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

      if "large" in model_name:
            SCREAMING_SNAKE_CASE		=						7_68
            SCREAMING_SNAKE_CASE		=						30_72
            SCREAMING_SNAKE_CASE		=						12

            SCREAMING_SNAKE_CASE		=						10_24
            SCREAMING_SNAKE_CASE		=						40_96
            SCREAMING_SNAKE_CASE		=						16
            SCREAMING_SNAKE_CASE		=						24
            SCREAMING_SNAKE_CASE		=						7_68
            SCREAMING_SNAKE_CASE		=						30_72

      if model_name == "xclip-large-patch14-16-frames":
            SCREAMING_SNAKE_CASE		=						3_36

      SCREAMING_SNAKE_CASE		=						XCLIPConfig.from_text_vision_configs(lowerCAmelCase__			,						lowerCAmelCase__     )

      if "large" in model_name:
            SCREAMING_SNAKE_CASE		=						7_68

      return config





def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       Tuple:


      '''simple docstring'''


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

      return name





def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Dict:


      '''simple docstring'''


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

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

      return orig_state_dict





def        __lowercase       (				_SCREAMING_SNAKE_CASE     )			->       str:


      '''simple docstring'''


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





def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE=None			,						_SCREAMING_SNAKE_CASE=False     )			->       Dict:


      '''simple docstring'''


      SCREAMING_SNAKE_CASE		=						{
          # 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""",
      }

      SCREAMING_SNAKE_CASE		=						model_to_url[model_name]
      SCREAMING_SNAKE_CASE		=						8
      if "16-frames" in model_name:
            SCREAMING_SNAKE_CASE		=						16
      elif "shot" in model_name:
            SCREAMING_SNAKE_CASE		=						32

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

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

      SCREAMING_SNAKE_CASE		=						convert_state_dict(lowerCAmelCase__			,						lowerCAmelCase__     )

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

      SCREAMING_SNAKE_CASE		=						3_36 if model_name == """xclip-large-patch14-16-frames""" else 2_24
      SCREAMING_SNAKE_CASE		=						VideoMAEImageProcessor(size=lowerCAmelCase__     )
      SCREAMING_SNAKE_CASE		=						CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""     )
      SCREAMING_SNAKE_CASE		=						CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""     )
      SCREAMING_SNAKE_CASE		=						XCLIPProcessor(image_processor=lowerCAmelCase__			,						tokenizer=lowerCAmelCase__     )

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

      # Verify outputs
      SCREAMING_SNAKE_CASE		=						outputs.logits_per_video
      SCREAMING_SNAKE_CASE		=						logits_per_video.softmax(dim=1     )
      print("""Probs:"""			,						lowerCAmelCase__     )
      # kinetics-400
      if model_name == "xclip-base-patch32":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[0.0_019, 0.9_951, 0.0_030]]     )
      elif model_name == "xclip-base-patch32-16-frames":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[7.09_99E-04, 9.98_83E-01, 4.55_80E-04]]     )
      elif model_name == "xclip-base-patch16":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[0.0_083, 0.9_681, 0.0_236]]     )
      elif model_name == "xclip-base-patch16-16-frames":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[7.69_37E-04, 9.97_28E-01, 1.94_73E-03]]     )
      elif model_name == "xclip-large-patch14":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[0.0_062, 0.9_864, 0.0_075]]     )
      elif model_name == "xclip-large-patch14-16-frames":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[3.38_77E-04, 9.99_37E-01, 2.88_88E-04]]     )
      # kinetics-600
      elif model_name == "xclip-base-patch16-kinetics-600":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[0.0_555, 0.8_914, 0.0_531]]     )
      elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[3.85_54E-04, 9.99_29E-01, 3.27_54E-04]]     )
      elif model_name == "xclip-large-patch14-kinetics-600":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[0.0_036, 0.9_920, 0.0_045]]     )
      # few shot
      elif model_name == "xclip-base-patch16-hmdb-2-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[7.18_90E-06, 9.99_94E-01, 5.65_59E-05]]     )
      elif model_name == "xclip-base-patch16-hmdb-4-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[1.03_20E-05, 9.99_93E-01, 6.24_35E-05]]     )
      elif model_name == "xclip-base-patch16-hmdb-8-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[4.13_77E-06, 9.99_90E-01, 9.83_86E-05]]     )
      elif model_name == "xclip-base-patch16-hmdb-16-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[4.13_47E-05, 9.99_62E-01, 3.34_11E-04]]     )
      elif model_name == "xclip-base-patch16-ucf-2-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]     )
      elif model_name == "xclip-base-patch16-ucf-4-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[8.58_57E-05, 9.99_28E-01, 6.32_91E-04]]     )
      elif model_name == "xclip-base-patch16-ucf-8-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[0.0_027, 0.9_904, 0.0_070]]     )
      elif model_name == "xclip-base-patch16-ucf-16-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[9.82_19E-04, 9.95_93E-01, 3.08_63E-03]]     )
      # zero shot
      elif model_name == "xclip-base-patch16-zero-shot":
            SCREAMING_SNAKE_CASE		=						torch.tensor([[3.50_82E-04, 9.97_85E-01, 1.79_66E-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__":
      SCREAMING_SNAKE_CASE_						       =	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."""
      )

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









from PIL import Image



def        __lowercase       (				_SCREAMING_SNAKE_CASE			,						_SCREAMING_SNAKE_CASE     )			->       Image:


      '''simple docstring'''


      def brightness(_SCREAMING_SNAKE_CASE     ) -> float:
            return 1_28 + level + (c - 1_28)

      if not -255.0 <= level <= 255.0:
            raise ValueError("""level must be between -255.0 (black) and 255.0 (white)"""     )
      return img.point(_SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
      # Load image
      with Image.open("""image_data/lena.jpg""") as img:
            # Change brightness to 100
            SCREAMING_SNAKE_CASE_						       =	change_brightness(img, 1_0_0)
            brigt_img.save("""image_data/lena_brightness.png""", format="""png""")


