






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					( snake_case,  snake_case ):







				"""simple docstring"""






				__magic_name__		:Dict        =      XCLIPTextConfig()

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

				if "large" in model_name:
								__magic_name__		:Tuple        =      7_6_8
								__magic_name__		:int        =      3_0_7_2
								__magic_name__		:Dict        =      1_2

								__magic_name__		:Optional[int]        =      1_0_2_4
								__magic_name__		:Optional[int]        =      4_0_9_6
								__magic_name__		:List[Any]        =      1_6
								__magic_name__		:Optional[Any]        =      2_4
								__magic_name__		:Optional[int]        =      7_6_8
								__magic_name__		:Tuple        =      3_0_7_2

				if model_name == "xclip-large-patch14-16-frames":
								__magic_name__		:Union[str, Any]        =      3_3_6

				__magic_name__		:Union[str, Any]        =      XCLIPConfig.from_text_vision_configs(snake_case,  snake_case )

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

				return config

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if name == "token_embedding.weight":
								__magic_name__		:str        =      name.replace('''token_embedding.weight''',  '''text_model.embeddings.token_embedding.weight''' )
				if name == "positional_embedding":
								__magic_name__		:Tuple        =      name.replace('''positional_embedding''',  '''text_model.embeddings.position_embedding.weight''' )
				if "ln_1" in name:
								__magic_name__		:int        =      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[int]        =      name.replace('''c_fc''',  '''fc1''' )
				if "c_proj" in name:
								__magic_name__		:Optional[Any]        =      name.replace('''c_proj''',  '''fc2''' )
				if name.startswith('''transformer.resblocks''' ):
								__magic_name__		:str        =      name.replace('''transformer.resblocks''',  '''text_model.encoder.layers''' )
				if "attn.out_proj" in name and "message" not in name:
								__magic_name__		:Any        =      name.replace('''attn.out_proj''',  '''self_attn.out_proj''' )
				if "ln_final" in name:
								__magic_name__		:Tuple        =      name.replace('''ln_final''',  '''text_model.final_layer_norm''' )
				# visual encoder
				if name == "visual.class_embedding":
								__magic_name__		:List[str]        =      name.replace('''visual.class_embedding''',  '''vision_model.embeddings.class_embedding''' )
				if name == "visual.positional_embedding":
								__magic_name__		:Any        =      name.replace('''visual.positional_embedding''',  '''vision_model.embeddings.position_embedding.weight''' )
				if name.startswith('''visual.transformer.resblocks''' ):
								__magic_name__		:Dict        =      name.replace('''visual.transformer.resblocks''',  '''vision_model.encoder.layers''' )
				if "visual.conv1" in name:
								__magic_name__		:Optional[int]        =      name.replace('''visual.conv1''',  '''vision_model.embeddings.patch_embedding''' )
				if "visual.ln_pre" in name:
								__magic_name__		:List[str]        =      name.replace('''visual.ln_pre''',  '''vision_model.pre_layernorm''' )
				if "visual.ln_post" in name:
								__magic_name__		:List[str]        =      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__		:List[Any]        =      name.replace('''text_projection''',  '''text_projection.weight''' )
				# things on top
				if "prompts_visual_proj" in name:
								__magic_name__		:Dict        =      name.replace('''prompts_visual_proj''',  '''prompts_visual_projection''' )
				if "prompts_visual_ln" in name:
								__magic_name__		:List[str]        =      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__		:Optional[int]        =      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      __lowercase					( snake_case,  snake_case ):







				"""simple docstring"""






				for key in orig_state_dict.copy().keys():
								__magic_name__		:Tuple        =      orig_state_dict.pop(snake_case )

								if "attn.in_proj" in key:
												__magic_name__		:Union[str, Any]        =      key.split('''.''' )
												if key.startswith('''visual''' ):
																__magic_name__		:Any        =      key_split[3]
																__magic_name__		:Optional[Any]        =      config.vision_config.hidden_size
																if "message_attn" in key:
																				if "weight" in key:
																								__magic_name__		:Union[str, Any]        =      val[
																								    :dim, :
																								]
																								__magic_name__		:Optional[int]        =      val[
																								    dim : dim * 2, :
																								]
																								__magic_name__		:Any        =      val[
																								    -dim:, :
																								]
																				else:
																								__magic_name__		:Any        =      val[
																								    :dim
																								]
																								__magic_name__		:Optional[int]        =      val[
																								    dim : dim * 2
																								]
																								__magic_name__		:Dict        =      val[
																								    -dim:
																								]
																else:
																				if "weight" in key:
																								__magic_name__		:Optional[int]        =      val[
																								    :dim, :
																								]
																								__magic_name__		:Dict        =      val[
																								    dim : dim * 2, :
																								]
																								__magic_name__		:List[str]        =      val[
																								    -dim:, :
																								]
																				else:
																								__magic_name__		:Tuple        =      val[:dim]
																								__magic_name__		:Optional[Any]        =      val[
																								    dim : dim * 2
																								]
																								__magic_name__		:List[Any]        =      val[-dim:]
												elif key.startswith('''mit''' ):
																__magic_name__		:int        =      key_split[2]
																__magic_name__		:List[str]        =      config.vision_config.mit_hidden_size
																if "weight" in key:
																				__magic_name__		:str        =      val[:dim, :]
																				__magic_name__		:List[Any]        =      val[dim : dim * 2, :]
																				__magic_name__		:Union[str, Any]        =      val[-dim:, :]
																else:
																				__magic_name__		:Tuple        =      val[:dim]
																				__magic_name__		:List[Any]        =      val[dim : dim * 2]
																				__magic_name__		:Union[str, Any]        =      val[-dim:]
												else:
																__magic_name__		:str        =      key_split[2]
																__magic_name__		:Optional[Any]        =      config.text_config.hidden_size
																if "weight" in key:
																				__magic_name__		:Any        =      val[:dim, :]
																				__magic_name__		:Dict        =      val[
																				    dim : dim * 2, :
																				]
																				__magic_name__		:Union[str, Any]        =      val[-dim:, :]
																else:
																				__magic_name__		:List[str]        =      val[:dim]
																				__magic_name__		:Tuple        =      val[
																				    dim : dim * 2
																				]
																				__magic_name__		:Optional[int]        =      val[-dim:]
								else:
												__magic_name__		:str        =      rename_key(snake_case )
												if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
																__magic_name__		:Any        =      val.T
												__magic_name__		:List[str]        =      val

				return orig_state_dict

def      __lowercase					( snake_case ):







				"""simple docstring"""






				if num_frames == 8:
								__magic_name__		:Tuple        =      '''eating_spaghetti_8_frames.npy'''
				elif num_frames == 1_6:
								__magic_name__		:Any        =      '''eating_spaghetti.npy'''
				elif num_frames == 3_2:
								__magic_name__		:List[str]        =      '''eating_spaghetti_32_frames.npy'''
				__magic_name__		:Tuple        =      hf_hub_download(
				    repo_id='''hf-internal-testing/spaghetti-video''',  filename=snake_case,  repo_type='''dataset''',  )
				__magic_name__		:str        =      np.load(snake_case )
				return list(snake_case )

def      __lowercase					( snake_case,  snake_case=None,  snake_case=False ):







				"""simple docstring"""






				__magic_name__		:Optional[int]        =      {
				    # 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__		:int        =      model_to_url[model_name]
				__magic_name__		:List[Any]        =      8
				if "16-frames" in model_name:
								__magic_name__		:List[Any]        =      1_6
				elif "shot" in model_name:
								__magic_name__		:List[Any]        =      3_2

				__magic_name__		:str        =      get_xclip_config(snake_case,  snake_case )
				__magic_name__		:Optional[Any]        =      XCLIPModel(snake_case )
				model.eval()

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

				__magic_name__		:Optional[int]        =      convert_state_dict(snake_case,  snake_case )

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

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

				__magic_name__		:Union[str, Any]        =      prepare_video(snake_case )
				__magic_name__		:Any        =      processor(
				    text=['''playing sports''', '''eating spaghetti''', '''go shopping'''],  videos=snake_case,  return_tensors='''pt''',  padding=snake_case )

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

				with torch.no_grad():
								__magic_name__		:Any        =      model(**snake_case )

				# Verify outputs
				__magic_name__		:List[Any]        =      outputs.logits_per_video
				__magic_name__		:Union[str, Any]        =      logits_per_video.softmax(dim=1 )
				print('''Probs:''',  snake_case )
				# kinetics-400
				if model_name == "xclip-base-patch32":
								__magic_name__		:List[str]        =      torch.tensor([[0.0019, 0.9951, 0.0030]] )
				elif model_name == "xclip-base-patch32-16-frames":
								__magic_name__		:int        =      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":
								__magic_name__		:Dict        =      torch.tensor([[0.0083, 0.9681, 0.0236]] )
				elif model_name == "xclip-base-patch16-16-frames":
								__magic_name__		:Optional[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":
								__magic_name__		:str        =      torch.tensor([[0.0062, 0.9864, 0.0075]] )
				elif model_name == "xclip-large-patch14-16-frames":
								__magic_name__		: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":
								__magic_name__		:Tuple        =      torch.tensor([[0.0555, 0.8914, 0.0531]] )
				elif model_name == "xclip-base-patch16-kinetics-600-16-frames":
								__magic_name__		:str        =      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":
								__magic_name__		:List[str]        =      torch.tensor([[0.0036, 0.9920, 0.0045]] )
				# few shot
				elif model_name == "xclip-base-patch16-hmdb-2-shot":
								__magic_name__		:List[str]        =      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":
								__magic_name__		:Tuple        =      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":
								__magic_name__		:int        =      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":
								__magic_name__		:Optional[Any]        =      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":
								__magic_name__		: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-4-shot":
								__magic_name__		:List[Any]        =      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":
								__magic_name__		:Optional[Any]        =      torch.tensor([[0.0027, 0.9904, 0.0070]] )
				elif model_name == "xclip-base-patch16-ucf-16-shot":
								__magic_name__		:List[str]        =      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":
								__magic_name__		:List[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(snake_case,  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(snake_case )

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


if __name__ == "__main__":
					SCREAMING_SNAKE_CASE__      :			Union[str, 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."""
					)

					SCREAMING_SNAKE_CASE__      :			str							=		parser.parse_args()
					convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)














from ...processing_utils import ProcessorMixin
class 						lowerCamelCase_				(      lowerCamelCase ):
				a__					       =  '''WhisperFeatureExtractor'''
				a__					       =  '''WhisperTokenizer'''







				def __init__(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase     ):
								"""simple docstring"""
								super().__init__(__lowerCAmelCase       ,					__lowerCAmelCase     )
								__magic_name__		:int        =      self.feature_extractor
								__magic_name__		:int        =      False







				def 			A	(						self       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=None       ,					__lowerCAmelCase=True     ):
								"""simple docstring"""
								return self.tokenizer.get_decoder_prompt_ids(task=__lowerCAmelCase       ,					language=__lowerCAmelCase       ,					no_timestamps=__lowerCAmelCase     )







				def __call__(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								# For backward compatibility
								if self._in_target_context_manager:
												return self.current_processor(*__lowerCAmelCase       ,					**__lowerCAmelCase     )

								__magic_name__		:List[Any]        =      kwargs.pop('''audio'''       ,					__lowerCAmelCase     )
								__magic_name__		:Any        =      kwargs.pop('''sampling_rate'''       ,					__lowerCAmelCase     )
								__magic_name__		:str        =      kwargs.pop('''text'''       ,					__lowerCAmelCase     )
								if len(__lowerCAmelCase     ) > 0:
												__magic_name__		:Any        =      args[0]
												__magic_name__		:Union[str, Any]        =      args[1:]

								if audio is None and text is None:
												raise ValueError('''You need to specify either an `audio` or `text` input to process.'''     )

								if audio is not None:
												__magic_name__		:Optional[int]        =      self.feature_extractor(__lowerCAmelCase       ,					*__lowerCAmelCase       ,					sampling_rate=__lowerCAmelCase       ,					**__lowerCAmelCase     )
								if text is not None:
												__magic_name__		:Optional[int]        =      self.tokenizer(__lowerCAmelCase       ,					**__lowerCAmelCase     )

								if text is None:
												return inputs

								elif audio is None:
												return encodings
								else:
												__magic_name__		:str        =      encodings['''input_ids''']
												return inputs







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return self.tokenizer.batch_decode(*__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					*__lowerCAmelCase       ,					**__lowerCAmelCase     ):
								"""simple docstring"""
								return self.tokenizer.decode(*__lowerCAmelCase       ,					**__lowerCAmelCase     )







				def 			A	(						self       ,					__lowerCAmelCase       ,					__lowerCAmelCase="np"     ):
								"""simple docstring"""
								return self.tokenizer.get_prompt_ids(__lowerCAmelCase       ,					return_tensors=__lowerCAmelCase     )






