

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_ (__A	:		Tuple   ,  __A	:		Union[str, Any]	)     ->      Dict:
   __lowerCAmelCase    :     List[Any]          =		XCLIPTextConfig()

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

   if "large" in model_name:
      __lowerCAmelCase    :     int          =		7_6_8
      __lowerCAmelCase    :     Tuple          =		3_0_7_2
      __lowerCAmelCase    :     str          =		1_2

      __lowerCAmelCase    :     Optional[Any]          =		1_0_2_4
      __lowerCAmelCase    :     List[str]          =		4_0_9_6
      __lowerCAmelCase    :     Union[str, Any]          =		1_6
      __lowerCAmelCase    :     Union[str, Any]          =		2_4
      __lowerCAmelCase    :     Tuple          =		7_6_8
      __lowerCAmelCase    :     Union[str, Any]          =		3_0_7_2

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

   __lowerCAmelCase    :     Any          =		XCLIPConfig.from_text_vision_configs(__A   ,  __A	)

   if "large" in model_name:
      __lowerCAmelCase    :     int          =		7_6_8

   return config





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

   return name





def       snake_case_ (__A	:		Union[str, Any]   ,  __A	:		Optional[Any]	)     ->      Tuple:
   for key in orig_state_dict.copy().keys():
      __lowerCAmelCase    :     Dict          =		orig_state_dict.pop(__A	)

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

   return orig_state_dict





def       snake_case_ (__A	:		Optional[Any]	)     ->      Optional[int]:
   if num_frames == 8:
      __lowerCAmelCase    :     Union[str, Any]          =		"""eating_spaghetti_8_frames.npy"""
   elif num_frames == 1_6:
      __lowerCAmelCase    :     Union[str, Any]          =		"""eating_spaghetti.npy"""
   elif num_frames == 3_2:
      __lowerCAmelCase    :     Union[str, Any]          =		"""eating_spaghetti_32_frames.npy"""
   __lowerCAmelCase    :     str          =		hf_hub_download(
       repo_id="""hf-internal-testing/spaghetti-video"""   ,  filename=__A   ,  repo_type="""dataset"""   ,  )
   __lowerCAmelCase    :     Union[str, Any]          =		np.load(__A	)
   return list(__A	)





def       snake_case_ (__A	:		Optional[int]   ,  __A	:		Any=None   ,  __A	:		str=False	)     ->      Tuple:
   __lowerCAmelCase    :     Tuple          =		{
       # 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    :     List[str]          =		model_to_url[model_name]
   __lowerCAmelCase    :     List[Any]          =		8
   if "16-frames" in model_name:
      __lowerCAmelCase    :     Any          =		1_6
   elif "shot" in model_name:
      __lowerCAmelCase    :     Optional[int]          =		3_2

   __lowerCAmelCase    :     Union[str, Any]          =		get_xclip_config(__A   ,  __A	)
   __lowerCAmelCase    :     Any          =		XCLIPModel(__A	)
   model.eval()

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

   __lowerCAmelCase    :     Optional[Any]          =		convert_state_dict(__A   ,  __A	)

   __lowerCAmelCase    :     Any          =		XCLIPModel(__A	)
   __lowerCAmelCase    :     Dict          =		model.load_state_dict(__A   ,  strict=__A	)
   assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
   model.eval()

   __lowerCAmelCase    :     str          =		3_3_6 if model_name == """xclip-large-patch14-16-frames""" else 2_2_4
   __lowerCAmelCase    :     Union[str, Any]          =		VideoMAEImageProcessor(size=__A	)
   __lowerCAmelCase    :     Any          =		CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32"""	)
   __lowerCAmelCase    :     List[str]          =		CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32"""	)
   __lowerCAmelCase    :     Tuple          =		XCLIPProcessor(image_processor=__A   ,  tokenizer=__A	)

   __lowerCAmelCase    :     str          =		prepare_video(__A	)
   __lowerCAmelCase    :     List[Any]          =		processor(
       text=["""playing sports""", """eating spaghetti""", """go shopping"""]   ,  videos=__A   ,  return_tensors="""pt"""   ,  padding=__A	)

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

   with torch.no_grad():
      __lowerCAmelCase    :     Any          =		model(**__A	)

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

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








from pathlib import Path

import fire
from tqdm import tqdm




def 		lowerCAmelCase_ (						lowerCamelCase="ro"						,  lowerCamelCase="en"						,  lowerCamelCase="wmt16"						,  lowerCamelCase=None ):
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets""" )
				__magic_name__						:						Dict										=F"{src_lang}-{tgt_lang}"
				print(F"Converting {dataset}-{pair}" )
				__magic_name__						:						Dict										=datasets.load_dataset(lowerCamelCase						,  lowerCamelCase )
				if save_dir is None:
								__magic_name__						:						Optional[int]										=F"{dataset}-{pair}"
				__magic_name__						:						int										=Path(lowerCamelCase )
				save_dir.mkdir(exist_ok=lowerCamelCase )

				for split in ds.keys():
								print(F"Splitting {split} with {ds[split].num_rows} records" )

								# to save to val.source, val.target like summary datasets
								__magic_name__						:						Dict										="""val""" if split == """validation""" else split
								__magic_name__						:						List[Any]										=save_dir.joinpath(F"{fn}.source" )
								__magic_name__						:						Optional[int]										=save_dir.joinpath(F"{fn}.target" )
								__magic_name__						:						Optional[Any]										=src_path.open("""w+""" )
								__magic_name__						:						List[Any]										=tgt_path.open("""w+""" )

								# reader is the bottleneck so writing one record at a time doesn't slow things down
								for x in tqdm(ds[split] ):
												__magic_name__						:						str										=x["""translation"""]
												src_fp.write(ex[src_lang] + """\n""" )
												tgt_fp.write(ex[tgt_lang] + """\n""" )

				print(F"Saved {dataset} dataset to {save_dir}" )


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)




