




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 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				__magic_name__						:						List[Any]										=XCLIPTextConfig()

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

				if "large" in model_name:
								__magic_name__						:						int										=768
								__magic_name__						:						Tuple										=3072
								__magic_name__						:						str										=12

								__magic_name__						:						Optional[Any]										=1024
								__magic_name__						:						List[str]										=4096
								__magic_name__						:						Union[str, Any]										=16
								__magic_name__						:						Union[str, Any]										=24
								__magic_name__						:						Tuple										=768
								__magic_name__						:						Union[str, Any]										=3072

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

				__magic_name__						:						Any										=XCLIPConfig.from_text_vision_configs(lowerCamelCase						,  lowerCamelCase )

				if "large" in model_name:
								__magic_name__						:						int										=768

				return config




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

				return name




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase ):
				for key in orig_state_dict.copy().keys():
								__magic_name__						:						Dict										=orig_state_dict.pop(lowerCamelCase )

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

				return orig_state_dict




def 		lowerCAmelCase_ (						lowerCamelCase ):
				if num_frames == 8:
								__magic_name__						:						Union[str, Any]										="""eating_spaghetti_8_frames.npy"""
				elif num_frames == 16:
								__magic_name__						:						Union[str, Any]										="""eating_spaghetti.npy"""
				elif num_frames == 32:
								__magic_name__						:						Union[str, Any]										="""eating_spaghetti_32_frames.npy"""
				__magic_name__						:						str										=hf_hub_download(
				    repo_id="""hf-internal-testing/spaghetti-video"""						,  filename=lowerCamelCase						,  repo_type="""dataset"""						,  )
				__magic_name__						:						Union[str, Any]										=np.load(lowerCamelCase )
				return list(lowerCamelCase )




def 		lowerCAmelCase_ (						lowerCamelCase						,  lowerCamelCase=None						,  lowerCamelCase=False ):
				__magic_name__						:						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""",
				}

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

				__magic_name__						:						Union[str, Any]										=get_xclip_config(lowerCamelCase						,  lowerCamelCase )
				__magic_name__						:						Any										=XCLIPModel(lowerCamelCase )
				model.eval()

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

				__magic_name__						:						Optional[Any]										=convert_state_dict(lowerCamelCase						,  lowerCamelCase )

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

				__magic_name__						:						str										=336 if model_name == """xclip-large-patch14-16-frames""" else 224
				__magic_name__						:						Union[str, Any]										=VideoMAEImageProcessor(size=lowerCamelCase )
				__magic_name__						:						Any										=CLIPTokenizer.from_pretrained("""openai/clip-vit-base-patch32""" )
				__magic_name__						:						List[str]										=CLIPTokenizerFast.from_pretrained("""openai/clip-vit-base-patch32""" )
				__magic_name__						:						Tuple										=XCLIPProcessor(image_processor=lowerCamelCase						,  tokenizer=lowerCamelCase )

				__magic_name__						:						str										=prepare_video(lowerCamelCase )
				__magic_name__						:						List[Any]										=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():
								__magic_name__						:						Any										=model(**lowerCamelCase )

				# Verify outputs
				__magic_name__						:						Dict										=outputs.logits_per_video
				__magic_name__						:						Optional[Any]										=logits_per_video.softmax(dim=1 )
				print("""Probs:"""						,  lowerCamelCase )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								__magic_name__						:						Union[str, Any]										=torch.tensor([[0.0_0_1_9, 0.9_9_5_1, 0.0_0_3_0]] )
				elif model_name == "xclip-base-patch32-16-frames":
								__magic_name__						:						Tuple										=torch.tensor([[7.0_999E-04, 9.9_883E-01, 4.5_580E-04]] )
				elif model_name == "xclip-base-patch16":
								__magic_name__						:						str										=torch.tensor([[0.0_0_8_3, 0.9_6_8_1, 0.0_2_3_6]] )
				elif model_name == "xclip-base-patch16-16-frames":
								__magic_name__						:						List[Any]										=torch.tensor([[7.6_937E-04, 9.9_728E-01, 1.9_473E-03]] )
				elif model_name == "xclip-large-patch14":
								__magic_name__						:						Optional[int]										=torch.tensor([[0.0_0_6_2, 0.9_8_6_4, 0.0_0_7_5]] )
				elif model_name == "xclip-large-patch14-16-frames":
								__magic_name__						:						Union[str, Any]										=torch.tensor([[3.3_877E-04, 9.9_937E-01, 2.8_888E-04]] )
				# kinetics-600
				elif model_name == "xclip-base-patch16-kinetics-600":
								__magic_name__						:						Any										=torch.tensor([[0.0_5_5_5, 0.8_9_1_4, 0.0_5_3_1]] )
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								__magic_name__						:						str										=torch.tensor([[3.8_554E-04, 9.9_929E-01, 3.2_754E-04]] )
				elif model_name == "xclip-large-patch14-kinetics-600":
								__magic_name__						:						Dict										=torch.tensor([[0.0_0_3_6, 0.9_9_2_0, 0.0_0_4_5]] )
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								__magic_name__						:						str										=torch.tensor([[7.1_890E-06, 9.9_994E-01, 5.6_559E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-4-shot":
								__magic_name__						:						Tuple										=torch.tensor([[1.0_320E-05, 9.9_993E-01, 6.2_435E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-8-shot":
								__magic_name__						:						List[str]										=torch.tensor([[4.1_377E-06, 9.9_990E-01, 9.8_386E-05]] )
				elif model_name == "xclip-base-patch16-hmdb-16-shot":
								__magic_name__						:						List[str]										=torch.tensor([[4.1_347E-05, 9.9_962E-01, 3.3_411E-04]] )
				elif model_name == "xclip-base-patch16-ucf-2-shot":
								__magic_name__						:						Optional[Any]										=torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]] )
				elif model_name == "xclip-base-patch16-ucf-4-shot":
								__magic_name__						:						int										=torch.tensor([[8.5_857E-05, 9.9_928E-01, 6.3_291E-04]] )
				elif model_name == "xclip-base-patch16-ucf-8-shot":
								__magic_name__						:						Optional[Any]										=torch.tensor([[0.0_0_2_7, 0.9_9_0_4, 0.0_0_7_0]] )
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								__magic_name__						:						Any										=torch.tensor([[9.8_219E-04, 9.9_593E-01, 3.0_863E-03]] )
				# zero shot
				elif model_name == "xclip-base-patch16-zero-shot":
								__magic_name__						:						int										=torch.tensor([[3.5_082E-04, 9.9_785E-01, 1.7_966E-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__":
							UpperCAmelCase_		:					str          =  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_		:					Union[str, Any]          =  parser.parse_args()
							convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)










from dataclasses import dataclass, field
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import pyarrow as pa


if TYPE_CHECKING:
							from .features import FeatureType




@dataclass
class 		__A   :
						UpperCamelCase      =       42
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""Translation"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def __call__(		self :Union[str, Any]			):
										'''simple docstring'''

										return pa.struct({lang: pa.string() for lang in sorted(self.languages			)}			)


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Value

										return {k: Value("""string"""			) for k in sorted(self.languages			)}




@dataclass
class 		__A   :
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						UpperCamelCase      =       None
						# Automatically constructed
						UpperCamelCase      =       "dict"
						UpperCamelCase      =       None
						UpperCamelCase      =       field(default="""TranslationVariableLanguages"""       ,    init=UpperCamelCase__       ,    repr=UpperCamelCase__      )

						def      A__    (		self :Optional[Any]			):
										'''simple docstring'''

										__magic_name__						:						List[Any]										=sorted(set(self.languages			)			) if self.languages else None
										__magic_name__						:						Optional[int]										=len(self.languages			) if self.languages else None

						def __call__(		self :List[str]			):
										'''simple docstring'''

										return pa.struct({"""language""": pa.list_(pa.string()			), """translation""": pa.list_(pa.string()			)}			)

						def      A__    (		self :str     ,			__snake_case :str			):
										'''simple docstring'''

										__magic_name__						:						Optional[int]										=set(self.languages			)
										if self.languages and set(__snake_case			) - lang_set:
														raise ValueError(
														    f"Some languages in example ({', '.join(sorted(set(__snake_case			) - lang_set			)			)}) are not in valid set ({', '.join(__snake_case			)})."			)

										# Convert dictionary into tuples, splitting out cases where there are
										# multiple translations for a single language.
										__magic_name__						:						Any										=[]
										for lang, text in translation_dict.items():
														if isinstance(__snake_case     ,			__snake_case			):
																		translation_tuples.append((lang, text)			)
														else:
																		translation_tuples.extend([(lang, el) for el in text]			)

        # Ensure translations are in ascending order by language code.
										__magic_name__					,			__magic_name__						:						List[str]										=zip(*sorted(__snake_case			)			)

										return {"language": languages, "translation": translations}


						def      A__    (		self :List[Any]			):
										'''simple docstring'''

										from .features import Sequence, Value

										return {
										    "language": Sequence(Value("""string"""			)			),
										    "translation": Sequence(Value("""string"""			)			),
										}




