
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 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Optional[Any]:
       lowercase							:					List[str]	     =				XCLIPTextConfig()

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

       if "large" in model_name:
              lowercase							:					Optional[Any]	     =				768
              lowercase							:					Any	     =				3_072
              lowercase							:					Optional[int]	     =				12

              lowercase							:					Tuple	     =				1_024
              lowercase							:					Optional[int]	     =				4_096
              lowercase							:					Any	     =				16
              lowercase							:					Optional[Any]	     =				24
              lowercase							:					Union[str, Any]	     =				768
              lowercase							:					Any	     =				3_072

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

       lowercase							:					Tuple	     =				XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

       if "large" in model_name:
              lowercase							:					Tuple	     =				768

       return config







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

       return name







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )			->   Union[str, Any]:
       for key in orig_state_dict.copy().keys():
              lowercase							:					str	     =				orig_state_dict.pop(SCREAMING_SNAKE_CASE__    )

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

       return orig_state_dict







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   str:
       if num_frames == 8:
              lowercase							:					int	     =				"""eating_spaghetti_8_frames.npy"""
       elif num_frames == 16:
              lowercase							:					Optional[Any]	     =				"""eating_spaghetti.npy"""
       elif num_frames == 32:
              lowercase							:					Tuple	     =				"""eating_spaghetti_32_frames.npy"""
       lowercase							:					Optional[int]	     =				hf_hub_download(
           repo_id="""hf-internal-testing/spaghetti-video""" ,     filename=SCREAMING_SNAKE_CASE__ ,     repo_type="""dataset""" ,     )
       lowercase							:					Any	     =				np.load(SCREAMING_SNAKE_CASE__    )
       return list(SCREAMING_SNAKE_CASE__    )







def 						_snake_case(  SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__=None ,     SCREAMING_SNAKE_CASE__=False    )			->   Dict:
       lowercase							:					Dict	     =				{
           # 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""",
       }

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

       lowercase							:					List[str]	     =				get_xclip_config(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )
       lowercase							:					Optional[Any]	     =				XCLIPModel(SCREAMING_SNAKE_CASE__    )
       model.eval()

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

       lowercase							:					str	     =				convert_state_dict(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__    )

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

       lowercase							:					Any	     =				336 if model_name == """xclip-large-patch14-16-frames""" else 224
       lowercase							:					Dict	     =				VideoMAEImageProcessor(size=SCREAMING_SNAKE_CASE__    )
       lowercase							:					Dict	     =				CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""    )
       lowercase							:					Optional[Any]	     =				CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""    )
       lowercase							:					Optional[Any]	     =				XCLIPProcessor(image_processor=SCREAMING_SNAKE_CASE__ ,     tokenizer=SCREAMING_SNAKE_CASE__    )

       lowercase							:					Union[str, Any]	     =				prepare_video(SCREAMING_SNAKE_CASE__    )
       lowercase							:					int	     =				processor(
           text=["""playing sports""", """eating spaghetti""", """go shopping"""] ,     videos=SCREAMING_SNAKE_CASE__ ,     return_tensors="""pt""" ,     padding=SCREAMING_SNAKE_CASE__    )

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

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

       # Verify outputs
       lowercase							:					Dict	     =				outputs.logits_per_video
       lowercase							:					str	     =				logits_per_video.softmax(dim=1    )
       print("""Probs:""" ,     SCREAMING_SNAKE_CASE__    )
       # kinetics-400
       if model_name == "xclip-base-patch32":
              lowercase							:					str	     =				torch.tensor([[0.0019, 0.9951, 0.0030]]    )
       elif model_name == "xclip-base-patch32-16-frames":
              lowercase							:					List[Any]	     =				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":
              lowercase							:					Optional[int]	     =				torch.tensor([[0.0083, 0.9681, 0.0236]]    )
       elif model_name == "xclip-base-patch16-16-frames":
              lowercase							:					List[Any]	     =				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":
              lowercase							:					Optional[int]	     =				torch.tensor([[0.0062, 0.9864, 0.0075]]    )
       elif model_name == "xclip-large-patch14-16-frames":
              lowercase							:					Tuple	     =				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":
              lowercase							:					Union[str, Any]	     =				torch.tensor([[0.0555, 0.8914, 0.0531]]    )
       elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
              lowercase							:					Tuple	     =				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":
              lowercase							:					List[str]	     =				torch.tensor([[0.0036, 0.9920, 0.0045]]    )
       # few shot
       elif model_name == "xclip-base-patch16-hmdb-2-shot":
              lowercase							:					Union[str, Any]	     =				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":
              lowercase							:					Any	     =				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":
              lowercase							:					List[str]	     =				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":
              lowercase							:					Optional[int]	     =				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":
              lowercase							:					str	     =				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":
              lowercase							:					int	     =				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":
              lowercase							:					Any	     =				torch.tensor([[0.0027, 0.9904, 0.0070]]    )
       elif model_name == "xclip-base-patch16-ucf-16-shot":
              lowercase							:					Any	     =				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":
              lowercase							:					str	     =				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(SCREAMING_SNAKE_CASE__ ,     SCREAMING_SNAKE_CASE__ ,     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(SCREAMING_SNAKE_CASE__    )

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


if __name__ == "__main__":
  lowercase  : Optional[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."""
  )

  lowercase  : Dict						   =							parser.parse_args()
  convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)


import math
from datetime import datetime, timedelta







def 						_snake_case(  SCREAMING_SNAKE_CASE__    )			->   datetime:
       lowercase							:					Any	     =				year % 19
       lowercase							:					Optional[int]	     =				year % 4
       lowercase							:					Any	     =				year % 7
       lowercase							:					str	     =				math.floor(year / 100    )
       lowercase							:					List[str]	     =				math.floor((13 + 8 * leap_day_inhibits) / 25    )
       lowercase							:					Tuple	     =				leap_day_inhibits / 4
       lowercase							:					Any	     =				(
           15 - lunar_orbit_correction + leap_day_inhibits - leap_day_reinstall_number
       ) % 30
       lowercase							:					Union[str, Any]	     =				(4 + leap_day_inhibits - leap_day_reinstall_number) % 7

       # days to be added to March 21
       lowercase							:					str	     =				(19 * metonic_cycle + secular_moon_shift) % 30

       # PHM -> Paschal Full Moon
       lowercase							:					List[Any]	     =				(
           2 * julian_leap_year
           + 4 * non_leap_year
           + 6 * days_to_add
           + century_starting_point
       ) % 7

       if days_to_add == 29 and days_from_phm_to_sunday == 6:
              return datetime(SCREAMING_SNAKE_CASE__ ,     4 ,     19    )
       elif days_to_add == 28 and days_from_phm_to_sunday == 6:
              return datetime(SCREAMING_SNAKE_CASE__ ,     4 ,     18    )
       else:
              return datetime(SCREAMING_SNAKE_CASE__ ,     3 ,     22    ) + timedelta(
                  days=int(days_to_add + days_from_phm_to_sunday    )    )


if __name__ == "__main__":
  for year in (1994, 2000, 2010, 2021, 2023):
    lowercase  : List[str]						   =							"""will be""" if year > datetime.now().year else """was"""
    print(F'''Easter in {year} {tense} {gauss_easter(year)}''')
