


"""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  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Tuple:







		"""simple docstring"""





		A					       = XCLIPTextConfig()

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

		if "large" in model_name:
				A					       = 768
				A					       = 3072
				A					       = 12

				A					       = 1024
				A					       = 4096
				A					       = 16
				A					       = 24
				A					       = 768
				A					       = 3072

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

		A					       = XCLIPConfig.from_text_vision_configs(UpperCamelCase__ ,			UpperCamelCase__	)

		if "large" in model_name:
				A					       = 768

		return config







def  __snake_case       (   UpperCamelCase__	)      ->		Optional[int]:







		"""simple docstring"""





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

		return name







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__	)      ->		Union[str, Any]:







		"""simple docstring"""





		for key in orig_state_dict.copy().keys():
				A					       = orig_state_dict.pop(UpperCamelCase__	)

				if "attn.in_proj" in key:
						A					       = key.split('.'	)
						if key.startswith('visual'	):
								A					       = key_split[3]
								A					       = config.vision_config.hidden_size
								if "message_attn" in key:
										if "weight" in key:
												A					       = val[
												    :dim, :
												]
												A					       = val[
												    dim : dim * 2, :
												]
												A					       = val[
												    -dim:, :
												]
										else:
												A					       = val[
												    :dim
												]
												A					       = val[
												    dim : dim * 2
												]
												A					       = val[
												    -dim:
												]
								else:
										if "weight" in key:
												A					       = val[
												    :dim, :
												]
												A					       = val[
												    dim : dim * 2, :
												]
												A					       = val[
												    -dim:, :
												]
										else:
												A					       = val[:dim]
												A					       = val[
												    dim : dim * 2
												]
												A					       = val[-dim:]
						elif key.startswith('mit'	):
								A					       = key_split[2]
								A					       = config.vision_config.mit_hidden_size
								if "weight" in key:
										A					       = val[:dim, :]
										A					       = val[dim : dim * 2, :]
										A					       = val[-dim:, :]
								else:
										A					       = val[:dim]
										A					       = val[dim : dim * 2]
										A					       = val[-dim:]
						else:
								A					       = key_split[2]
								A					       = config.text_config.hidden_size
								if "weight" in key:
										A					       = val[:dim, :]
										A					       = val[
										    dim : dim * 2, :
										]
										A					       = val[-dim:, :]
								else:
										A					       = val[:dim]
										A					       = val[
										    dim : dim * 2
										]
										A					       = val[-dim:]
				else:
						A					       = rename_key(UpperCamelCase__	)
						if new_key_name in ["visual_projection.weight", "text_projection.weight"]:
								A					       = val.T
						A					       = val

		return orig_state_dict







def  __snake_case       (   UpperCamelCase__	)      ->		Optional[int]:







		"""simple docstring"""





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







def  __snake_case       (   UpperCamelCase__ ,			UpperCamelCase__=None ,			UpperCamelCase__=False	)      ->		Optional[Any]:







		"""simple docstring"""





		A					       = {
		    # 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',
		}

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

		A					       = get_xclip_config(UpperCamelCase__ ,			UpperCamelCase__	)
		A					       = XCLIPModel(UpperCamelCase__	)
		model.eval()

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

		A					       = convert_state_dict(UpperCamelCase__ ,			UpperCamelCase__	)

		A					       = XCLIPModel(UpperCamelCase__	)
		A       , A					       = model.load_state_dict(UpperCamelCase__ ,			strict=UpperCamelCase__	)
		assert missing_keys == ["text_model.embeddings.position_ids", "vision_model.embeddings.position_ids"]
		model.eval()

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

		A					       = prepare_video(UpperCamelCase__	)
		A					       = processor(
		    text=['playing sports', 'eating spaghetti', 'go shopping'] ,			videos=UpperCamelCase__ ,			return_tensors='pt' ,			padding=UpperCamelCase__	)

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

		with torch.no_grad():
				A					       = model(**UpperCamelCase__	)

		# Verify outputs
		A					       = outputs.logits_per_video
		A					       = logits_per_video.softmax(dim=1	)
		print('Probs:' ,			UpperCamelCase__	)
		# kinetics-400
		if model_name == "xclip-base-patch32":
				A					       = 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":
				A					       = torch.tensor([[7.0999E-04, 9.9883E-01, 4.5580E-04]]	)
		elif model_name == "xclip-base-patch16":
				A					       = 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":
				A					       = torch.tensor([[7.6937E-04, 9.9728E-01, 1.9473E-03]]	)
		elif model_name == "xclip-large-patch14":
				A					       = 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":
				A					       = torch.tensor([[3.3877E-04, 9.9937E-01, 2.8888E-04]]	)
		# kinetics-600
		elif model_name == "xclip-base-patch16-kinetics-600":
				A					       = 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":
				A					       = torch.tensor([[3.8554E-04, 9.9929E-01, 3.2754E-04]]	)
		elif model_name == "xclip-large-patch14-kinetics-600":
				A					       = 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":
				A					       = torch.tensor([[7.1890E-06, 9.9994E-01, 5.6559E-05]]	)
		elif model_name == "xclip-base-patch16-hmdb-4-shot":
				A					       = torch.tensor([[1.0320E-05, 9.9993E-01, 6.2435E-05]]	)
		elif model_name == "xclip-base-patch16-hmdb-8-shot":
				A					       = torch.tensor([[4.1377E-06, 9.9990E-01, 9.8386E-05]]	)
		elif model_name == "xclip-base-patch16-hmdb-16-shot":
				A					       = torch.tensor([[4.1347E-05, 9.9962E-01, 3.3411E-04]]	)
		elif model_name == "xclip-base-patch16-ucf-2-shot":
				A					       = torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]	)
		elif model_name == "xclip-base-patch16-ucf-4-shot":
				A					       = torch.tensor([[8.5857E-05, 9.9928E-01, 6.3291E-04]]	)
		elif model_name == "xclip-base-patch16-ucf-8-shot":
				A					       = 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":
				A					       = torch.tensor([[9.8219E-04, 9.9593E-01, 3.0863E-03]]	)
		# zero shot
		elif model_name == "xclip-base-patch16-zero-shot":
				A					       = torch.tensor([[3.5082E-04, 9.9785E-01, 1.7966E-03]]	)
		else:
				raise ValueError(f'Model name {model_name} not supported'	)
		assert torch.allclose(UpperCamelCase__ ,			UpperCamelCase__ ,			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(UpperCamelCase__	)

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


if __name__ == "__main__":
	UpperCamelCase						:				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."
	)

	UpperCamelCase						:				int             =      parser.parse_args()
	convert_xclip_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.push_to_hub)









"""simple docstring"""


from ...configuration_utils import PretrainedConfig
from ...utils import logging
from ...utils.backbone_utils import BackboneConfigMixin, get_aligned_output_features_output_indices


UpperCamelCase						:				int             =      logging.get_logger(__name__)

UpperCamelCase						:				Optional[int]             =      {
    "google/bit-50": "https://huggingface.co/google/bit-50/resolve/main/config.json",
}



class 			lowerCamelCase__						(   UpperCAmelCase_							,    UpperCAmelCase_			):
						lowerCAmelCase       =     """bit"""
						lowerCAmelCase       =     ["""preactivation""", """bottleneck"""]
						lowerCAmelCase       =     ["""SAME""", """VALID"""]







						def __init__(							self   :    List[str]			,			_lowercase   :    int=3			,			_lowercase   :    List[str]=64			,			_lowercase   :    Optional[int]=[256, 512, 1_024, 2_048]			,			_lowercase   :    Optional[int]=[3, 4, 6, 3]			,			_lowercase   :    int="preactivation"			,			_lowercase   :    Union[str, Any]="relu"			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Tuple=32			,			_lowercase   :    Optional[int]=0.0			,			_lowercase   :    Union[str, Any]=False			,			_lowercase   :    Dict=32			,			_lowercase   :    Union[str, Any]=1			,			_lowercase   :    List[Any]=None			,			_lowercase   :    Any=None			,			**_lowercase   :    str			,			):
								super().__init__(**_lowercase		)
								if layer_type not in self.layer_types:
										raise ValueError(f'layer_type={layer_type} is not one of {",".join(self.layer_types		)}'		)
								if global_padding is not None:
										if global_padding.upper() in self.supported_padding:
												A					       = global_padding.upper()
										else:
												raise ValueError(f'Padding strategy {global_padding} not supported'		)
								A					       = num_channels
								A					       = embedding_size
								A					       = hidden_sizes
								A					       = depths
								A					       = layer_type
								A					       = hidden_act
								A					       = global_padding
								A					       = num_groups
								A					       = drop_path_rate
								A					       = embedding_dynamic_padding
								A					       = output_stride
								A					       = width_factor

								A					       = ['stem'] + [f'stage{idx}' for idx in range(1			,			len(_lowercase		) + 1		)]
								A       , A					       = get_aligned_output_features_output_indices(
								    out_features=_lowercase			,			out_indices=_lowercase			,			stage_names=self.stage_names		)





