
"""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 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		lowerCamelCase					:      List[Any]          =   XCLIPTextConfig()

		# derive patch size from model name
		lowerCamelCase					:      Tuple          =   model_name.find('patch'      )
		lowerCamelCase					:      Optional[Any]          =   int(model_name[start_idx + len('patch'      ) : start_idx + len('patch'      ) + 2]      )
		lowerCamelCase					:      Any          =   XCLIPVisionConfig(patch_size=a_,						num_frames=a_      )

		if "large" in model_name:
				lowerCamelCase					:      Optional[int]          =   768
				lowerCamelCase					:      int          =   3072
				lowerCamelCase					:      Any          =   12

				lowerCamelCase					:      Union[str, Any]          =   1024
				lowerCamelCase					:      Any          =   4096
				lowerCamelCase					:      List[Any]          =   16
				lowerCamelCase					:      List[str]          =   24
				lowerCamelCase					:      Union[str, Any]          =   768
				lowerCamelCase					:      str          =   3072

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

		lowerCamelCase					:      List[Any]          =   XCLIPConfig.from_text_vision_configs(a_,						a_      )

		if "large" in model_name:
				lowerCamelCase					:      Any          =   768

		return config
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		if name == "token_embedding.weight":
				lowerCamelCase					:      List[str]          =   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					:      Dict          =   name.replace('ln_1',						'layer_norm1'      )
		if "ln_2" in name:
				lowerCamelCase					:      str          =   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					:      str          =   name.replace('c_proj',						'fc2'      )
		if name.startswith('transformer.resblocks'      ):
				lowerCamelCase					:      int          =   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					:      Dict          =   name.replace('ln_final',						'text_model.final_layer_norm'      )
		# visual encoder
		if name == "visual.class_embedding":
				lowerCamelCase					:      Any          =   name.replace('visual.class_embedding',						'vision_model.embeddings.class_embedding'      )
		if name == "visual.positional_embedding":
				lowerCamelCase					:      List[Any]          =   name.replace('visual.positional_embedding',						'vision_model.embeddings.position_embedding.weight'      )
		if name.startswith('visual.transformer.resblocks'      ):
				lowerCamelCase					:      int          =   name.replace('visual.transformer.resblocks',						'vision_model.encoder.layers'      )
		if "visual.conv1" in name:
				lowerCamelCase					:      Union[str, Any]          =   name.replace('visual.conv1',						'vision_model.embeddings.patch_embedding'      )
		if "visual.ln_pre" in name:
				lowerCamelCase					:      int          =   name.replace('visual.ln_pre',						'vision_model.pre_layernorm'      )
		if "visual.ln_post" in name:
				lowerCamelCase					:      Optional[Any]          =   name.replace('visual.ln_post',						'vision_model.post_layernorm'      )
		if "visual.proj" in name:
				lowerCamelCase					:      Union[str, Any]          =   name.replace('visual.proj',						'visual_projection.weight'      )
		if "text_projection" in name:
				lowerCamelCase					:      int          =   name.replace('text_projection',						'text_projection.weight'      )
		# things on top
		if "prompts_visual_proj" in name:
				lowerCamelCase					:      List[str]          =   name.replace('prompts_visual_proj',						'prompts_visual_projection'      )
		if "prompts_visual_ln" in name:
				lowerCamelCase					:      Dict          =   name.replace('prompts_visual_ln',						'prompts_visual_layernorm'      )
		# mit
		if name == "mit.positional_embedding":
				lowerCamelCase					:      int          =   name.replace('positional',						'position'      )
		if name.startswith('mit.resblocks'      ):
				lowerCamelCase					:      Optional[int]          =   name.replace('mit.resblocks',						'mit.encoder.layers'      )
		# prompts generator
		if name.startswith('prompts_generator.norm'      ):
				lowerCamelCase					:      Tuple          =   name.replace('prompts_generator.norm',						'prompts_generator.layernorm'      )

		return name
def 		UpperCAmelCase (		a_,						a_      ):





		'''simple docstring'''


		for key in orig_state_dict.copy().keys():
				lowerCamelCase					:      str          =   orig_state_dict.pop(a_      )

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

		return orig_state_dict
def 		UpperCAmelCase (		a_      ):





		'''simple docstring'''


		if num_frames == 8:
				lowerCamelCase					:      Optional[int]          =   'eating_spaghetti_8_frames.npy'
		elif num_frames == 16:
				lowerCamelCase					:      Optional[int]          =   'eating_spaghetti.npy'
		elif num_frames == 32:
				lowerCamelCase					:      int          =   'eating_spaghetti_32_frames.npy'
		lowerCamelCase					:      int          =   hf_hub_download(
		    repo_id='hf-internal-testing/spaghetti-video',						filename=a_,						repo_type='dataset',						)
		lowerCamelCase					:      Tuple          =   np.load(a_      )
		return list(a_      )
def 		UpperCAmelCase (		a_,						a_=None,						a_=False      ):





		'''simple docstring'''


		lowerCamelCase					:      List[str]          =   {
		    # 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					:      Union[str, Any]          =   model_to_url[model_name]
		lowerCamelCase					:      Tuple          =   8
		if "16-frames" in model_name:
				lowerCamelCase					:      List[str]          =   16
		elif "shot" in model_name:
				lowerCamelCase					:      Any          =   32

		lowerCamelCase					:      int          =   get_xclip_config(a_,						a_      )
		lowerCamelCase					:      Optional[Any]          =   XCLIPModel(a_      )
		model.eval()

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

		lowerCamelCase					:      int          =   convert_state_dict(a_,						a_      )

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

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

		lowerCamelCase					:      Optional[Any]          =   prepare_video(a_      )
		lowerCamelCase					:      Dict          =   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					:      Tuple          =   model(**a_      )

		# Verify outputs
		lowerCamelCase					:      Optional[Any]          =   outputs.logits_per_video
		lowerCamelCase					:      Union[str, Any]          =   logits_per_video.softmax(dim=1      )
		print('Probs:',						a_      )
		# kinetics-400
		if model_name == "xclip-base-patch32":
				lowerCamelCase					:      List[str]          =   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":
				lowerCamelCase					:      str          =   torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]      )
		elif model_name == "xclip-base-patch16":
				lowerCamelCase					:      List[Any]          =   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":
				lowerCamelCase					:      int          =   torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]      )
		elif model_name == "xclip-large-patch14":
				lowerCamelCase					:      Union[str, Any]          =   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":
				lowerCamelCase					:      List[str]          =   torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]      )
		# kinetics-600
		elif model_name == "xclip-base-patch16-kinetics-600":
				lowerCamelCase					:      int          =   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":
				lowerCamelCase					:      Optional[Any]          =   torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]      )
		elif model_name == "xclip-large-patch14-kinetics-600":
				lowerCamelCase					:      Optional[int]          =   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":
				lowerCamelCase					:      Optional[Any]          =   torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]      )
		elif model_name == "xclip-base-patch16-hmdb-4-shot":
				lowerCamelCase					:      List[Any]          =   torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]      )
		elif model_name == "xclip-base-patch16-hmdb-8-shot":
				lowerCamelCase					:      int          =   torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]      )
		elif model_name == "xclip-base-patch16-hmdb-16-shot":
				lowerCamelCase					:      int          =   torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]      )
		elif model_name == "xclip-base-patch16-ucf-2-shot":
				lowerCamelCase					:      Any          =   torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]      )
		elif model_name == "xclip-base-patch16-ucf-4-shot":
				lowerCamelCase					:      Optional[Any]          =   torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]      )
		elif model_name == "xclip-base-patch16-ucf-8-shot":
				lowerCamelCase					:      str          =   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":
				lowerCamelCase					:      Optional[int]          =   torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]      )
		# zero shot
		elif model_name == "xclip-base-patch16-zero-shot":
				lowerCamelCase					:      str          =   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__":
					_A						=			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.'
					)

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








"""simple docstring"""

import copy

from ...configuration_utils import PretrainedConfig
from ...utils import logging


_A						=			logging.get_logger(__name__)






class 	_lowercase       (    __UpperCAmelCase		):
	lowercase_				=     'encoder-decoder'
	lowercase_				=     True




	def __init__(			self		,			**UpperCAmelCase_ )		->							str:
			super().__init__(**UpperCAmelCase_ )
			assert (
			    "encoder" in kwargs and "decoder" in kwargs
			), "Config has to be initialized with encoder and decoder config"
			lowerCamelCase					:      List[Any]          =   kwargs.pop('encoder' )
			lowerCamelCase					:      Optional[int]          =   encoder_config.pop('model_type' )
			lowerCamelCase					:      str          =   kwargs.pop('decoder' )
			lowerCamelCase					:      Dict          =   decoder_config.pop('model_type' )

			from ..auto.configuration_auto import AutoConfig

			lowerCamelCase					:      int          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   AutoConfig.for_model(UpperCAmelCase_		,			**UpperCAmelCase_ )
			lowerCamelCase					:      List[str]          =   True




	@classmethod
	def       _UpperCamelCase   (			cls		,			UpperCAmelCase_		,			UpperCAmelCase_		,			**UpperCAmelCase_ )		->							PretrainedConfig:
			logger.info('Set `config.is_decoder=True` and `config.add_cross_attention=True` for decoder_config' )
			lowerCamelCase					:      str          =   True
			lowerCamelCase					:      Optional[Any]          =   True

			return cls(encoder=encoder_config.to_dict()		,			decoder=decoder_config.to_dict()		,			**UpperCAmelCase_ )




	def       _UpperCamelCase   (			self )		->							Union[str, Any]:
			lowerCamelCase					:      Union[str, Any]          =   copy.deepcopy(self.__dict__ )
			lowerCamelCase					:      Union[str, Any]          =   self.encoder.to_dict()
			lowerCamelCase					:      List[Any]          =   self.decoder.to_dict()
			lowerCamelCase					:      Tuple          =   self.__class__.model_type
			return output






