



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 							A     (					_UpperCAmelCase    :       Tuple   )    ->			List[Any]:







			'''simple docstring'''
			_UpperCAmelCase			=       VideoMAEConfig()

			set_architecture_configs(_UpperCAmelCase							,       _UpperCAmelCase   )

			if "finetuned" not in model_name:
						_UpperCAmelCase			=       False

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

			return config





def 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       Optional[int]   )    ->			Any:







			'''simple docstring'''
			if "small" in model_name:
						_UpperCAmelCase			=       384
						_UpperCAmelCase			=       1_536
						_UpperCAmelCase			=       12
						_UpperCAmelCase			=       16
						_UpperCAmelCase			=       12
						_UpperCAmelCase			=       3
						_UpperCAmelCase			=       192
						_UpperCAmelCase			=       768
			elif "large" in model_name:
						_UpperCAmelCase			=       1_024
						_UpperCAmelCase			=       4_096
						_UpperCAmelCase			=       24
						_UpperCAmelCase			=       16
						_UpperCAmelCase			=       12
						_UpperCAmelCase			=       8
						_UpperCAmelCase			=       512
						_UpperCAmelCase			=       2_048
			elif "huge" in model_name:
						_UpperCAmelCase			=       1_280
						_UpperCAmelCase			=       5_120
						_UpperCAmelCase			=       32
						_UpperCAmelCase			=       16
						_UpperCAmelCase			=       12
						_UpperCAmelCase			=       8
						_UpperCAmelCase			=       640
						_UpperCAmelCase			=       2_560
			elif "base" not in model_name:
						raise ValueError('Model name should include either "small", "base", "large", or "huge"'   )





def 							A     (					_UpperCAmelCase    :       Any   )    ->			Optional[Any]:







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

			return name





def 							A     (					_UpperCAmelCase    :       Optional[int]							,       _UpperCAmelCase    :       List[str]   )    ->			Any:







			'''simple docstring'''
			for key in orig_state_dict.copy().keys():
						_UpperCAmelCase			=       orig_state_dict.pop(_UpperCAmelCase   )

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

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

			return orig_state_dict





def 							A     (					)    ->			Optional[Any]:







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





def 							A     (					_UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       Any							,       _UpperCAmelCase    :       List[Any]							,       _UpperCAmelCase    :       List[Any]   )    ->			Tuple:







			'''simple docstring'''
			_UpperCAmelCase			=       get_videomae_config(_UpperCAmelCase   )

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

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

			model.load_state_dict(_UpperCAmelCase   )
			model.eval()

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

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

			_UpperCAmelCase			=       model(**_UpperCAmelCase   )
			_UpperCAmelCase			=       outputs.logits

			_UpperCAmelCase			=       [
			    '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":
						_UpperCAmelCase			=       torch.Size([1, 400]   )
						_UpperCAmelCase			=       torch.tensor([-0.9291, -0.4061, -0.9307]   )
			elif model_name == "videomae-small-finetuned-ssv2":
						_UpperCAmelCase			=       torch.Size([1, 174]   )
						_UpperCAmelCase			=       torch.tensor([0.2671, -0.4689, -0.8235]   )
			elif model_name == "videomae-base":
						_UpperCAmelCase			=       torch.Size([1, 1_408, 1_536]   )
						_UpperCAmelCase			=       torch.tensor([[0.7739, 0.7968, 0.7089], [0.6701, 0.7487, 0.6209], [0.4287, 0.5158, 0.4773]]   )
			elif model_name == "videomae-base-short":
						_UpperCAmelCase			=       torch.Size([1, 1_408, 1_536]   )
						_UpperCAmelCase			=       torch.tensor([[0.7994, 0.9612, 0.8508], [0.7401, 0.8958, 0.8302], [0.5862, 0.7468, 0.7325]]   )
						# we verified the loss both for normalized and unnormalized targets for this one
						_UpperCAmelCase			=       torch.tensor([0.5142]   ) if config.norm_pix_loss else torch.tensor([0.6469]   )
			elif model_name == "videomae-large":
						_UpperCAmelCase			=       torch.Size([1, 1_408, 1_536]   )
						_UpperCAmelCase			=       torch.tensor([[0.7149, 0.7997, 0.6966], [0.6768, 0.7869, 0.6948], [0.5139, 0.6221, 0.5605]]   )
			elif model_name == "videomae-large-finetuned-kinetics":
						_UpperCAmelCase			=       torch.Size([1, 400]   )
						_UpperCAmelCase			=       torch.tensor([0.0771, 0.0011, -0.3625]   )
			elif model_name == "videomae-huge-finetuned-kinetics":
						_UpperCAmelCase			=       torch.Size([1, 400]   )
						_UpperCAmelCase			=       torch.tensor([0.2433, 0.1632, -0.4894]   )
			elif model_name == "videomae-base-short-finetuned-kinetics":
						_UpperCAmelCase			=       torch.Size([1, 400]   )
						_UpperCAmelCase			=       torch.tensor([0.6588, 0.0990, -0.2493]   )
			elif model_name == "videomae-base-finetuned-kinetics":
						_UpperCAmelCase			=       torch.Size([1, 400]   )
						_UpperCAmelCase			=       torch.tensor([0.3669, -0.0688, -0.2421]   )
			elif model_name == "videomae-base-short-ssv2":
						_UpperCAmelCase			=       torch.Size([1, 1_408, 1_536]   )
						_UpperCAmelCase			=       torch.tensor([[0.4712, 0.5296, 0.5786], [0.2278, 0.2729, 0.4026], [0.0352, 0.0730, 0.2506]]   )
			elif model_name == "videomae-base-short-finetuned-ssv2":
						_UpperCAmelCase			=       torch.Size([1, 174]   )
						_UpperCAmelCase			=       torch.tensor([-0.0537, -0.1539, -0.3266]   )
			elif model_name == "videomae-base-ssv2":
						_UpperCAmelCase			=       torch.Size([1, 1_408, 1_536]   )
						_UpperCAmelCase			=       torch.tensor([[0.8131, 0.8727, 0.8546], [0.7366, 0.9377, 0.8870], [0.5935, 0.8874, 0.8564]]   )
			elif model_name == "videomae-base-finetuned-ssv2":
						_UpperCAmelCase			=       torch.Size([1, 174]   )
						_UpperCAmelCase			=       torch.tensor([0.1961, -0.8337, -0.6389]   )
			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":
						_UpperCAmelCase			=       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__":
							UpperCAmelCase__       =	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."
							)

							UpperCAmelCase__       =	parser.parse_args()
							convert_videomae_checkpoint(args.checkpoint_url, args.pytorch_dump_folder_path, args.model_name, args.push_to_hub)





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


UpperCAmelCase__       =	logging.get_logger(__name__)

UpperCAmelCase__       =	{
    "edbeeching/decision-transformer-gym-hopper-medium": (
        "https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json"
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}







class       __lowerCAmelCase   (      A						):
			UpperCamelCase        =					'''decision_transformer'''
			UpperCamelCase        =					['''past_key_values''']
			UpperCamelCase        =					{
			    '''max_position_embeddings''': '''n_positions''',
			    '''num_attention_heads''': '''n_head''',
			    '''num_hidden_layers''': '''n_layer''',
			}



			def __init__(						self       :  Any			,		A       :  Optional[int]=17			,		A       :  List[str]=4			,		A       :  int=1_28			,		A       :  Union[str, Any]=40_96			,		A       :  Any=True			,		A       :  Any=1			,		A       :  List[Any]=10_24			,		A       :  List[Any]=3			,		A       :  Tuple=1			,		A       :  Any=None			,		A       :  Optional[int]="relu"			,		A       :  Union[str, Any]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=0.1			,		A       :  Optional[int]=1E-5			,		A       :  List[Any]=0.0_2			,		A       :  Tuple=True			,		A       :  Union[str, Any]=True			,		A       :  str=5_02_56			,		A       :  Union[str, Any]=5_02_56			,		A       :  List[Any]=False			,		A       :  Optional[int]=False			,		**A       :  int			,		)       ->   Union[str, Any]:



						"""simple docstring"""






						_UpperCAmelCase			=       state_dim
						_UpperCAmelCase			=       act_dim
						_UpperCAmelCase			=       hidden_size
						_UpperCAmelCase			=       max_ep_len
						_UpperCAmelCase			=       action_tanh
						_UpperCAmelCase			=       vocab_size
						_UpperCAmelCase			=       n_positions
						_UpperCAmelCase			=       n_layer
						_UpperCAmelCase			=       n_head
						_UpperCAmelCase			=       n_inner
						_UpperCAmelCase			=       activation_function
						_UpperCAmelCase			=       resid_pdrop
						_UpperCAmelCase			=       embd_pdrop
						_UpperCAmelCase			=       attn_pdrop
						_UpperCAmelCase			=       layer_norm_epsilon
						_UpperCAmelCase			=       initializer_range
						_UpperCAmelCase			=       scale_attn_weights
						_UpperCAmelCase			=       use_cache
						_UpperCAmelCase			=       scale_attn_by_inverse_layer_idx
						_UpperCAmelCase			=       reorder_and_upcast_attn

						_UpperCAmelCase			=       bos_token_id
						_UpperCAmelCase			=       eos_token_id

						super().__init__(bos_token_id=A			,		eos_token_id=A			,		**A)
