


'''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 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int       ,  SCREAMING_SNAKE_CASE_   :		Optional[Any]	):





			"""simple docstring"""




			UpperCamelCase         =					XCLIPTextConfig()

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

			if "large" in model_name:
						UpperCamelCase         =					768
						UpperCamelCase         =					3_072
						UpperCamelCase         =					12

						UpperCamelCase         =					1_024
						UpperCamelCase         =					4_096
						UpperCamelCase         =					16
						UpperCamelCase         =					24
						UpperCamelCase         =					768
						UpperCamelCase         =					3_072

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

			UpperCamelCase         =					XCLIPConfig.from_text_vision_configs(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			if "large" in model_name:
						UpperCamelCase         =					768

			return config






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[int]	):





			"""simple docstring"""




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

			return name






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Union[str, Any]       ,  SCREAMING_SNAKE_CASE_   :		List[Any]	):





			"""simple docstring"""




			for key in orig_state_dict.copy().keys():
						UpperCamelCase         =					orig_state_dict.pop(SCREAMING_SNAKE_CASE_	)

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

			return orig_state_dict






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Dict	):





			"""simple docstring"""




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






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		Optional[Any]       ,  SCREAMING_SNAKE_CASE_   :		List[str]=None       ,  SCREAMING_SNAKE_CASE_   :		Tuple=False	):





			"""simple docstring"""




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

			UpperCamelCase         =					get_xclip_config(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					XCLIPModel(SCREAMING_SNAKE_CASE_	)
			model.eval()

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

			UpperCamelCase         =					convert_state_dict(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					XCLIPModel(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase  ,  UpperCamelCase         =					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()

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

			UpperCamelCase         =					prepare_video(SCREAMING_SNAKE_CASE_	)
			UpperCamelCase         =					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():
						UpperCamelCase         =					model(**SCREAMING_SNAKE_CASE_	)

			# Verify outputs
			UpperCamelCase         =					outputs.logits_per_video
			UpperCamelCase         =					logits_per_video.softmax(dim=1	)
			print("""Probs:"""       ,  SCREAMING_SNAKE_CASE_	)
			# kinetics-400
			if model_name == "xclip-base-patch32":
						UpperCamelCase         =					torch.tensor([[0.0019, 0.9951, 0.0030]]	)
			elif model_name == "xclip-base-patch32-16-frames":
						UpperCamelCase         =					torch.tensor([[7.0999e-04, 9.9883e-01, 4.5580e-04]]	)
			elif model_name == "xclip-base-patch16":
						UpperCamelCase         =					torch.tensor([[0.0083, 0.9681, 0.0236]]	)
			elif model_name == "xclip-base-patch16-16-frames":
						UpperCamelCase         =					torch.tensor([[7.6937e-04, 9.9728e-01, 1.9473e-03]]	)
			elif model_name == "xclip-large-patch14":
						UpperCamelCase         =					torch.tensor([[0.0062, 0.9864, 0.0075]]	)
			elif model_name == "xclip-large-patch14-16-frames":
						UpperCamelCase         =					torch.tensor([[3.3877e-04, 9.9937e-01, 2.8888e-04]]	)
			# kinetics-600
			elif model_name == "xclip-base-patch16-kinetics-600":
						UpperCamelCase         =					torch.tensor([[0.0555, 0.8914, 0.0531]]	)
			elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
						UpperCamelCase         =					torch.tensor([[3.8554e-04, 9.9929e-01, 3.2754e-04]]	)
			elif model_name == "xclip-large-patch14-kinetics-600":
						UpperCamelCase         =					torch.tensor([[0.0036, 0.9920, 0.0045]]	)
			# few shot
			elif model_name == "xclip-base-patch16-hmdb-2-shot":
						UpperCamelCase         =					torch.tensor([[7.1890e-06, 9.9994e-01, 5.6559e-05]]	)
			elif model_name == "xclip-base-patch16-hmdb-4-shot":
						UpperCamelCase         =					torch.tensor([[1.0320e-05, 9.9993e-01, 6.2435e-05]]	)
			elif model_name == "xclip-base-patch16-hmdb-8-shot":
						UpperCamelCase         =					torch.tensor([[4.1377e-06, 9.9990e-01, 9.8386e-05]]	)
			elif model_name == "xclip-base-patch16-hmdb-16-shot":
						UpperCamelCase         =					torch.tensor([[4.1347e-05, 9.9962e-01, 3.3411e-04]]	)
			elif model_name == "xclip-base-patch16-ucf-2-shot":
						UpperCamelCase         =					torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]	)
			elif model_name == "xclip-base-patch16-ucf-4-shot":
						UpperCamelCase         =					torch.tensor([[8.5857e-05, 9.9928e-01, 6.3291e-04]]	)
			elif model_name == "xclip-base-patch16-ucf-8-shot":
						UpperCamelCase         =					torch.tensor([[0.0027, 0.9904, 0.0070]]	)
			elif model_name == "xclip-base-patch16-ucf-16-shot":
						UpperCamelCase         =					torch.tensor([[9.8219e-04, 9.9593e-01, 3.0863e-03]]	)
			# zero shot
			elif model_name == "xclip-base-patch16-zero-shot":
						UpperCamelCase         =					torch.tensor([[3.5082e-04, 9.9785e-01, 1.7966e-03]]	)
			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__":
			__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."
			)

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








import os
import random
import sys

from . import cryptomath_module as cryptomath
from . import rabin_miller

__snake_case            =   3






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Generating primitive root of p"""	)
			while True:
						UpperCamelCase         =					random.randrange(3       ,  SCREAMING_SNAKE_CASE_	)
						if pow(SCREAMING_SNAKE_CASE_       ,  2       ,  SCREAMING_SNAKE_CASE_	) == 1:
									continue
						if pow(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	) == 1:
									continue
						return g






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			print("""Generating prime p..."""	)
			UpperCamelCase         =					rabin_miller.generate_large_prime(SCREAMING_SNAKE_CASE_	)  # select large prime number.
			UpperCamelCase         =					primitive_root(SCREAMING_SNAKE_CASE_	)  # one primitive root on modulo p.
			UpperCamelCase         =					random.randrange(3       ,  SCREAMING_SNAKE_CASE_	)  # private_key -> have to be greater than 2 for safety.
			UpperCamelCase         =					cryptomath.find_mod_inverse(pow(SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_       ,  SCREAMING_SNAKE_CASE_	)       ,  SCREAMING_SNAKE_CASE_	)

			UpperCamelCase         =					(key_size, e_a, e_a, p)
			UpperCamelCase         =					(key_size, d)

			return public_key, private_key






def 							_lowercase    (		SCREAMING_SNAKE_CASE_   :		str       ,  SCREAMING_SNAKE_CASE_   :		int	):





			"""simple docstring"""




			if os.path.exists(f'{name}_pubkey.txt'	) or os.path.exists(f'{name}_privkey.txt'	):
						print("""\nWARNING:"""	)
						print(
						    f'"{name}_pubkey.txt" or "{name}_privkey.txt" already exists. \n'
						    """Use a different name or delete these files and re-run this program."""	)
						sys.exit()

			UpperCamelCase  ,  UpperCamelCase         =					generate_key(SCREAMING_SNAKE_CASE_	)
			print(f'\nWriting public key to file {name}_pubkey.txt...'	)
			with open(f'{name}_pubkey.txt'       ,  """w"""	) as fo:
						fo.write(f'{public_key[0]},{public_key[1]},{public_key[2]},{public_key[3]}'	)

			print(f'Writing private key to file {name}_privkey.txt...'	)
			with open(f'{name}_privkey.txt'       ,  """w"""	) as fo:
						fo.write(f'{private_key[0]},{private_key[1]}'	)






def 							_lowercase    (		):





			"""simple docstring"""




			print("""Making key files..."""	)
			make_key_files("""elgamal"""       ,  2_048	)
			print("""Key files generation successful"""	)


if __name__ == "__main__":
			main()




