
import argparse
import json

import gdown
import numpy as np
import torch
from huggingface_hub import hf_hub_download

from transformers import (
    VideoMAEConfig,
    VideoMAEForPreTraining,
    VideoMAEForVideoClassification,
    VideoMAEImageProcessor,
)






def 							_lowercase							(     UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= VideoMAEConfig()

					set_architecture_configs(UpperCamelCase_	,	UpperCamelCase_		)

					if "finetuned" not in model_name:
										SCREAMING_SNAKE_CASE__													= False

					if "finetuned" in model_name:
										SCREAMING_SNAKE_CASE__													= 'huggingface/label-files'
										if "kinetics" in model_name:
															SCREAMING_SNAKE_CASE__													= 400
															SCREAMING_SNAKE_CASE__													= 'kinetics400-id2label.json'
										elif "ssv2" in model_name:
															SCREAMING_SNAKE_CASE__													= 174
															SCREAMING_SNAKE_CASE__													= 'something-something-v2-id2label.json'
										else:
															raise ValueError('Model name should either contain \'kinetics\' or \'ssv2\' in case it\'s fine-tuned.'		)
										SCREAMING_SNAKE_CASE__													= json.load(open(hf_hub_download(UpperCamelCase_	,	UpperCamelCase_	,	repo_type='dataset'		)	,	'r'		)		)
										SCREAMING_SNAKE_CASE__													= {int(UpperCamelCase_		): v for k, v in idalabel.items()}
										SCREAMING_SNAKE_CASE__													= idalabel
										SCREAMING_SNAKE_CASE__													= {v: k for k, v in idalabel.items()}

					return config






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		Union[str, Any]:
					'''simple docstring'''


					if "small" in model_name:
										SCREAMING_SNAKE_CASE__													= 384
										SCREAMING_SNAKE_CASE__													= 1536
										SCREAMING_SNAKE_CASE__													= 12
										SCREAMING_SNAKE_CASE__													= 16
										SCREAMING_SNAKE_CASE__													= 12
										SCREAMING_SNAKE_CASE__													= 3
										SCREAMING_SNAKE_CASE__													= 192
										SCREAMING_SNAKE_CASE__													= 768
					elif "large" in model_name:
										SCREAMING_SNAKE_CASE__													= 1024
										SCREAMING_SNAKE_CASE__													= 4096
										SCREAMING_SNAKE_CASE__													= 24
										SCREAMING_SNAKE_CASE__													= 16
										SCREAMING_SNAKE_CASE__													= 12
										SCREAMING_SNAKE_CASE__													= 8
										SCREAMING_SNAKE_CASE__													= 512
										SCREAMING_SNAKE_CASE__													= 2048
					elif "huge" in model_name:
										SCREAMING_SNAKE_CASE__													= 1280
										SCREAMING_SNAKE_CASE__													= 5120
										SCREAMING_SNAKE_CASE__													= 32
										SCREAMING_SNAKE_CASE__													= 16
										SCREAMING_SNAKE_CASE__													= 12
										SCREAMING_SNAKE_CASE__													= 8
										SCREAMING_SNAKE_CASE__													= 640
										SCREAMING_SNAKE_CASE__													= 2560
					elif "base" not in model_name:
										raise ValueError('Model name should include either "small", "base", "large", or "huge"'		)






def 							_lowercase							(     UpperCamelCase_		)		->		Optional[int]:
					'''simple docstring'''


					if "encoder." in name:
										SCREAMING_SNAKE_CASE__													= name.replace('encoder.'	,	''		)
					if "cls_token" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('cls_token'	,	'videomae.embeddings.cls_token'		)
					if "decoder_pos_embed" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('decoder_pos_embed'	,	'decoder.decoder_pos_embed'		)
					if "pos_embed" in name and "decoder" not in name:
										SCREAMING_SNAKE_CASE__													= name.replace('pos_embed'	,	'videomae.embeddings.position_embeddings'		)
					if "patch_embed.proj" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('patch_embed.proj'	,	'videomae.embeddings.patch_embeddings.projection'		)
					if "patch_embed.norm" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('patch_embed.norm'	,	'videomae.embeddings.norm'		)
					if "decoder.blocks" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('decoder.blocks'	,	'decoder.decoder_layers'		)
					if "blocks" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('blocks'	,	'videomae.encoder.layer'		)
					if "attn.proj" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('attn.proj'	,	'attention.output.dense'		)
					if "attn" in name and "bias" not in name:
										SCREAMING_SNAKE_CASE__													= name.replace('attn'	,	'attention.self'		)
					if "attn" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('attn'	,	'attention.attention'		)
					if "norm1" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('norm1'	,	'layernorm_before'		)
					if "norm2" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('norm2'	,	'layernorm_after'		)
					if "mlp.fc1" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('mlp.fc1'	,	'intermediate.dense'		)
					if "mlp.fc2" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('mlp.fc2'	,	'output.dense'		)
					if "decoder_embed" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('decoder_embed'	,	'decoder.decoder_embed'		)
					if "decoder_norm" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('decoder_norm'	,	'decoder.decoder_norm'		)
					if "decoder_pred" in name:
										SCREAMING_SNAKE_CASE__													= name.replace('decoder_pred'	,	'decoder.decoder_pred'		)
					if "norm.weight" in name and "decoder" not in name and "fc" not in name:
										SCREAMING_SNAKE_CASE__													= name.replace('norm.weight'	,	'videomae.layernorm.weight'		)
					if "norm.bias" in name and "decoder" not in name and "fc" not in name:
										SCREAMING_SNAKE_CASE__													= name.replace('norm.bias'	,	'videomae.layernorm.bias'		)
					if "head" in name and "decoder" not in name:
										SCREAMING_SNAKE_CASE__													= name.replace('head'	,	'classifier'		)

					return name






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_		)		->		List[Any]:
					'''simple docstring'''


					for key in orig_state_dict.copy().keys():
										SCREAMING_SNAKE_CASE__													= orig_state_dict.pop(UpperCamelCase_		)

										if key.startswith('encoder.'		):
															SCREAMING_SNAKE_CASE__													= key.replace('encoder.'	,	''		)

										if "qkv" in key:
															SCREAMING_SNAKE_CASE__													= key.split('.'		)
															if key.startswith('decoder.blocks'		):
																				SCREAMING_SNAKE_CASE__													= config.decoder_hidden_size
																				SCREAMING_SNAKE_CASE__													= int(key_split[2]		)
																				SCREAMING_SNAKE_CASE__													= 'decoder.decoder_layers.'
																				if "weight" in key:
																									SCREAMING_SNAKE_CASE__													= val[:dim, :]
																									SCREAMING_SNAKE_CASE__													= val[dim : dim * 2, :]
																									SCREAMING_SNAKE_CASE__													= val[-dim:, :]
															else:
																				SCREAMING_SNAKE_CASE__													= config.hidden_size
																				SCREAMING_SNAKE_CASE__													= int(key_split[1]		)
																				SCREAMING_SNAKE_CASE__													= 'videomae.encoder.layer.'
																				if "weight" in key:
																									SCREAMING_SNAKE_CASE__													= val[:dim, :]
																									SCREAMING_SNAKE_CASE__													= val[dim : dim * 2, :]
																									SCREAMING_SNAKE_CASE__													= val[-dim:, :]
										else:
															SCREAMING_SNAKE_CASE__													= val

					return orig_state_dict






def 							_lowercase							(     )		->		str:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= hf_hub_download(
					    repo_id='hf-internal-testing/spaghetti-video'	,	filename='eating_spaghetti.npy'	,	repo_type='dataset'		)
					SCREAMING_SNAKE_CASE__													= np.load(UpperCamelCase_		)
					return list(UpperCamelCase_		)






def 							_lowercase							(     UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_	,	UpperCamelCase_		)		->		int:
					'''simple docstring'''


					SCREAMING_SNAKE_CASE__													= get_videomae_config(UpperCamelCase_		)

					if "finetuned" in model_name:
										SCREAMING_SNAKE_CASE__													= VideoMAEForVideoClassification(UpperCamelCase_		)
					else:
										SCREAMING_SNAKE_CASE__													= VideoMAEForPreTraining(UpperCamelCase_		)

					# download original checkpoint, hosted on Google Drive
					SCREAMING_SNAKE_CASE__													= 'pytorch_model.bin'
					gdown.cached_download(UpperCamelCase_	,	UpperCamelCase_	,	quiet=UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= torch.load(UpperCamelCase_	,	map_location='cpu'		)
					if "model" in files:
										SCREAMING_SNAKE_CASE__													= files['model']
					else:
										SCREAMING_SNAKE_CASE__													= files['module']
					SCREAMING_SNAKE_CASE__													= convert_state_dict(UpperCamelCase_	,	UpperCamelCase_		)

					model.load_state_dict(UpperCamelCase_		)
					model.eval()

					# verify model on basic input
					SCREAMING_SNAKE_CASE__													= VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5]	,	image_std=[0.5, 0.5, 0.5]		)
					SCREAMING_SNAKE_CASE__													= prepare_video()
					SCREAMING_SNAKE_CASE__													= image_processor(UpperCamelCase_	,	return_tensors='pt'		)

					if "finetuned" not in model_name:
										SCREAMING_SNAKE_CASE__													= hf_hub_download(repo_id='hf-internal-testing/bool-masked-pos'	,	filename='bool_masked_pos.pt'		)
										SCREAMING_SNAKE_CASE__													= torch.load(UpperCamelCase_		)

					SCREAMING_SNAKE_CASE__													= model(**UpperCamelCase_		)
					SCREAMING_SNAKE_CASE__													= outputs.logits

					SCREAMING_SNAKE_CASE__													= [
					    'videomae-small-finetuned-kinetics',
					    'videomae-small-finetuned-ssv2',
					    # Kinetics-400 checkpoints (short = pretrained only for 800 epochs instead of 1600)
					    'videomae-base-short',
					    'videomae-base-short-finetuned-kinetics',
					    'videomae-base',
					    'videomae-base-finetuned-kinetics',
					    'videomae-large',
					    'videomae-large-finetuned-kinetics',
					    'videomae-huge-finetuned-kinetics',
					    # Something-Something-v2 checkpoints (short = pretrained only for 800 epochs instead of 2400)
					    'videomae-base-short-ssv2',
					    'videomae-base-short-finetuned-ssv2',
					    'videomae-base-ssv2',
					    'videomae-base-finetuned-ssv2',
					]

					# NOTE: logits were tested with image_mean and image_std equal to [0.5, 0.5, 0.5] and [0.5, 0.5, 0.5]
					if model_name == "videomae-small-finetuned-kinetics":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 400]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([-0.9_291, -0.4_061, -0.9_307]		)
					elif model_name == "videomae-small-finetuned-ssv2":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 174]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([0.2_671, -0.4_689, -0.8_235]		)
					elif model_name == "videomae-base":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 1408, 1536]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([[0.7_739, 0.7_968, 0.7_089], [0.6_701, 0.7_487, 0.6_209], [0.4_287, 0.5_158, 0.4_773]]		)
					elif model_name == "videomae-base-short":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 1408, 1536]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]]		)
										# we verified the loss both for normalized and unnormalized targets for this one
										SCREAMING_SNAKE_CASE__													= torch.tensor([0.5_142]		) if config.norm_pix_loss else torch.tensor([0.6_469]		)
					elif model_name == "videomae-large":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 1408, 1536]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([[0.7_149, 0.7_997, 0.6_966], [0.6_768, 0.7_869, 0.6_948], [0.5_139, 0.6_221, 0.5_605]]		)
					elif model_name == "videomae-large-finetuned-kinetics":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 400]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([0.0_771, 0.0_011, -0.3_625]		)
					elif model_name == "videomae-huge-finetuned-kinetics":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 400]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([0.2_433, 0.1_632, -0.4_894]		)
					elif model_name == "videomae-base-short-finetuned-kinetics":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 400]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([0.6_588, 0.0_990, -0.2_493]		)
					elif model_name == "videomae-base-finetuned-kinetics":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 400]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([0.3_669, -0.0_688, -0.2_421]		)
					elif model_name == "videomae-base-short-ssv2":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 1408, 1536]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([[0.4_712, 0.5_296, 0.5_786], [0.2_278, 0.2_729, 0.4_026], [0.0_352, 0.0_730, 0.2_506]]		)
					elif model_name == "videomae-base-short-finetuned-ssv2":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 174]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([-0.0_537, -0.1_539, -0.3_266]		)
					elif model_name == "videomae-base-ssv2":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 1408, 1536]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([[0.8_131, 0.8_727, 0.8_546], [0.7_366, 0.9_377, 0.8_870], [0.5_935, 0.8_874, 0.8_564]]		)
					elif model_name == "videomae-base-finetuned-ssv2":
										SCREAMING_SNAKE_CASE__													= torch.Size([1, 174]		)
										SCREAMING_SNAKE_CASE__													= torch.tensor([0.1_961, -0.8_337, -0.6_389]		)
					else:
										raise ValueError(F'Model name not supported. Should be one of {model_names}'		)

					# verify logits
					assert logits.shape == expected_shape
					if "finetuned" in model_name:
										assert torch.allclose(logits[0, :3]	,	UpperCamelCase_	,	atol=1e-4		)
					else:
										print('Logits:'	,	logits[0, :3, :3]		)
										assert torch.allclose(logits[0, :3, :3]	,	UpperCamelCase_	,	atol=1e-4		)
					print('Logits ok!'		)

					# verify loss, if applicable
					if model_name == "videomae-base-short":
										SCREAMING_SNAKE_CASE__													= outputs.loss
										assert torch.allclose(UpperCamelCase_	,	UpperCamelCase_	,	atol=1e-4		)
										print('Loss ok!'		)

					if pytorch_dump_folder_path is not None:
										print(F'Saving model and image processor to {pytorch_dump_folder_path}'		)
										image_processor.save_pretrained(UpperCamelCase_		)
										model.save_pretrained(UpperCamelCase_		)

					if push_to_hub:
										print('Pushing to the hub...'		)
										model.push_to_hub(UpperCamelCase_	,	organization='nielsr'		)


if __name__ == "__main__":
				__snake_case								=  argparse.ArgumentParser()
				# Required parameters
				parser.add_argument(
				    """--checkpoint_url""",
				    default="""https://drive.google.com/u/1/uc?id=1tEhLyskjb755TJ65ptsrafUG2llSwQE1&amp;export=download&amp;confirm=t&amp;uuid=aa3276eb-fb7e-482a-adec-dc7171df14c4""",
				    type=str,
				    help=(
				        """URL of the original PyTorch checkpoint (on Google Drive) you'd like to convert. Should be a direct"""
				        """ download link."""
				    ),
				)
				parser.add_argument(
				    """--pytorch_dump_folder_path""",
				    default="""/Users/nielsrogge/Documents/VideoMAE/Test""",
				    type=str,
				    help="""Path to the output PyTorch model directory.""",
				)
				parser.add_argument("""--model_name""", default="""videomae-base""", type=str, help="""Name of the model.""")
				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_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)







import argparse

from diffusers.pipelines.stable_diffusion.convert_from_ckpt import download_controlnet_from_original_ckpt


if __name__ == "__main__":
				__snake_case								=  argparse.ArgumentParser()

				parser.add_argument(
				    """--checkpoint_path""", default=None, type=str, required=True, help="""Path to the checkpoint to convert."""
				)
				parser.add_argument(
				    """--original_config_file""",
				    type=str,
				    required=True,
				    help="""The YAML config file corresponding to the original architecture.""",
				)
				parser.add_argument(
				    """--num_in_channels""",
				    default=None,
				    type=int,
				    help="""The number of input channels. If `None` number of input channels will be automatically inferred.""",
				)
				parser.add_argument(
				    """--image_size""",
				    default=5_12,
				    type=int,
				    help=(
				        """The image size that the model was trained on. Use 512 for Stable Diffusion v1.X and Stable Siffusion v2"""
				        """ Base. Use 768 for Stable Diffusion v2."""
				    ),
				)
				parser.add_argument(
				    """--extract_ema""",
				    action="""store_true""",
				    help=(
				        """Only relevant for checkpoints that have both EMA and non-EMA weights. Whether to extract the EMA weights"""
				        """ or not. Defaults to `False`. Add `--extract_ema` to extract the EMA weights. EMA weights usually yield"""
				        """ higher quality images for inference. Non-EMA weights are usually better to continue fine-tuning."""
				    ),
				)
				parser.add_argument(
				    """--upcast_attention""",
				    action="""store_true""",
				    help=(
				        """Whether the attention computation should always be upcasted. This is necessary when running stable"""
				        """ diffusion 2.1."""
				    ),
				)
				parser.add_argument(
				    """--from_safetensors""",
				    action="""store_true""",
				    help="""If `--checkpoint_path` is in `safetensors` format, load checkpoint with safetensors instead of PyTorch.""",
				)
				parser.add_argument(
				    """--to_safetensors""",
				    action="""store_true""",
				    help="""Whether to store pipeline in safetensors format or not.""",
				)
				parser.add_argument("""--dump_path""", default=None, type=str, required=True, help="""Path to the output model.""")
				parser.add_argument("""--device""", type=str, help="""Device to use (e.g. cpu, cuda:0, cuda:1, etc.)""")






				def 							_lowercase							(     UpperCamelCase_		)		->		Dict:
									'''simple docstring'''


									if string == "True":
														return True
									elif string == "False":
														return False
									else:
														raise ValueError(F'could not parse string as bool {string}'		)

				parser.add_argument(
				    """--use_linear_projection""", help="""Override for use linear projection""", required=False, type=parse_bool
				)

				parser.add_argument("""--cross_attention_dim""", help="""Override for cross attention_dim""", required=False, type=int)

				__snake_case								=  parser.parse_args()

				__snake_case								=  download_controlnet_from_original_ckpt(
				    checkpoint_path=args.checkpoint_path,
				    original_config_file=args.original_config_file,
				    image_size=args.image_size,
				    extract_ema=args.extract_ema,
				    num_in_channels=args.num_in_channels,
				    upcast_attention=args.upcast_attention,
				    from_safetensors=args.from_safetensors,
				    device=args.device,
				    use_linear_projection=args.use_linear_projection,
				    cross_attention_dim=args.cross_attention_dim,
				)



				controlnet.save_pretrained(args.dump_path, safe_serialization=args.to_safetensors)





