




import gym
import numpy as np
import torch
from mujoco_py import GlfwContext

from transformers import DecisionTransformerModel


GlfwContext(offscreen=True)  # Create a window to init GLFW.




def      __magic_name__							(					A						:   Tuple,    A						:   Tuple,    A						:   Optional[Any],    A						:   str,    A						:   int,    A						:   List[str]  ):






			'''simple docstring'''







			a       =     states.reshape(1,    -1,    model.config.state_dim  )
			a       =     actions.reshape(1,    -1,    model.config.act_dim  )
			a       =     returns_to_go.reshape(1,    -1,    1  )
			a       =     timesteps.reshape(1,    -1  )

			if model.config.max_length is not None:
						a       =     states[:, -model.config.max_length :]
						a       =     actions[:, -model.config.max_length :]
						a       =     returns_to_go[:, -model.config.max_length :]
						a       =     timesteps[:, -model.config.max_length :]

						# pad all tokens to sequence length
						a       =     torch.cat(
						    [torch.zeros(model.config.max_length - states.shape[1]  ), torch.ones(states.shape[1]  )]  )
						a       =     attention_mask.to(dtype=torch.long,    device=states.device  ).reshape(1,    -1  )
						a       =     torch.cat(
						    [
						        torch.zeros(
						            (states.shape[0], model.config.max_length - states.shape[1], model.config.state_dim),    device=states.device,    ),
						        states,
						    ],    dim=1,    ).to(dtype=torch.floataa  )
						a       =     torch.cat(
						    [
						        torch.zeros(
						            (actions.shape[0], model.config.max_length - actions.shape[1], model.config.act_dim),    device=actions.device,    ),
						        actions,
						    ],    dim=1,    ).to(dtype=torch.floataa  )
						a       =     torch.cat(
						    [
						        torch.zeros(
						            (returns_to_go.shape[0], model.config.max_length - returns_to_go.shape[1], 1),    device=returns_to_go.device,    ),
						        returns_to_go,
						    ],    dim=1,    ).to(dtype=torch.floataa  )
						a       =     torch.cat(
						    [
						        torch.zeros(
						            (timesteps.shape[0], model.config.max_length - timesteps.shape[1]),    device=timesteps.device  ),
						        timesteps,
						    ],    dim=1,    ).to(dtype=torch.long  )
			else:
						a       =     None

			a       =     model(
			    states=_snake_case,    actions=_snake_case,    rewards=_snake_case,    returns_to_go=_snake_case,    timesteps=_snake_case,    attention_mask=_snake_case,    return_dict=_snake_case,    )

			return action_preds[0, -1]


# build the environment

__lowerCAmelCase						:							Optional[Any]			     =						gym.make('Hopper-v3')
__lowerCAmelCase						:							List[str]			     =						env.observation_space.shape[0]
__lowerCAmelCase						:							Union[str, Any]			     =						env.action_space.shape[0]
__lowerCAmelCase						:							Optional[int]			     =						1000
__lowerCAmelCase						:							Tuple			     =						"""cuda"""
__lowerCAmelCase						:							List[Any]			     =						1000.0  # normalization for rewards/returns
__lowerCAmelCase						:							Union[str, Any]			     =						3600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__lowerCAmelCase						:							Union[str, Any]			     =						np.array(
    [
        1.3_1_1_2_7_9,
        -0.0_8_4_6_9_5_2_1,
        -0.5_3_8_2_7_1_9,
        -0.0_7_2_0_1_5_7_6,
        0.0_4_9_3_2_3_6_6,
        2.1_0_6_6_8_5_6,
        -0.1_5_0_1_7_3_5_4,
        0.0_0_8_7_8_3_4_5,
        -0.2_8_4_8_1_8_6,
        -0.1_8_5_4_0_0_9_6,
        -0.2_8_4_6_1_2_8_6,
    ]
)
__lowerCAmelCase						:							List[Any]			     =						np.array(
    [
        0.1_7_7_9_0_7_5_1,
        0.0_5_4_4_4_6_2_1,
        0.2_1_2_9_7_1_3_9,
        0.1_4_5_3_0_4_1_9,
        0.6_1_2_4_4_4_4,
        0.8_5_1_7_4_4_6_5,
        1.4_5_1_5_2_5_2,
        0.6_7_5_1_6_9_6,
        1.5_3_6_2_3_9,
        1.6_1_6_0_7_4_6,
        5.6_0_7_2_5_3_6,
    ]
)
__lowerCAmelCase						:							List[str]			     =						torch.from_numpy(state_mean).to(device=device)
__lowerCAmelCase						:							Union[str, Any]			     =						torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__lowerCAmelCase						:							List[Any]			     =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
__lowerCAmelCase						:							Optional[int]			     =						model.to(device)
model.eval()

for ep in range(10):
		__lowerCAmelCase						:							str			     =						0, 0
		__lowerCAmelCase						:							int			     =						env.reset()
		__lowerCAmelCase						:							Optional[int]			     =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
		__lowerCAmelCase						:							str			     =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
		__lowerCAmelCase						:							Tuple			     =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
		__lowerCAmelCase						:							Optional[int]			     =						torch.zeros(0, device=device, dtype=torch.floataa)

		__lowerCAmelCase						:							int			     =						torch.tensor(0, device=device, dtype=torch.long).reshape(1, 1)
		for t in range(max_ep_len):
				env.render()
				# add padding
				__lowerCAmelCase						:							str			     =						torch.cat([actions, torch.zeros((1, act_dim), device=device)], dim=0)
				__lowerCAmelCase						:							List[Any]			     =						torch.cat([rewards, torch.zeros(1, device=device)])

				__lowerCAmelCase						:							Dict			     =						get_action(
				    model,
				    (states.to(dtype=torch.floataa) - state_mean) / state_std,
				    actions.to(dtype=torch.floataa),
				    rewards.to(dtype=torch.floataa),
				    target_return.to(dtype=torch.floataa),
				    timesteps.to(dtype=torch.long),
				)
				__lowerCAmelCase						:							Optional[int]			     =						action
				__lowerCAmelCase						:							List[Any]			     =						action.detach().cpu().numpy()

				__lowerCAmelCase						:							List[str]			     =						env.step(action)

				__lowerCAmelCase						:							Dict			     =						torch.from_numpy(state).to(device=device).reshape(1, state_dim)
				__lowerCAmelCase						:							int			     =						torch.cat([states, cur_state], dim=0)
				__lowerCAmelCase						:							List[str]			     =						reward

				__lowerCAmelCase						:							Union[str, Any]			     =						target_return[0, -1] - (reward / scale)
				__lowerCAmelCase						:							Dict			     =						torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
				__lowerCAmelCase						:							List[Any]			     =						torch.cat([timesteps, torch.ones((1, 1), device=device, dtype=torch.long) * (t + 1)], dim=1)

				episode_return += reward
				episode_length += 1

				if done:
						break










"""simple docstring"""
import argparse
import json
import os.path
from collections import OrderedDict

import numpy as np
import requests
import torch
from flax.training.checkpoints import restore_checkpoint
from huggingface_hub import hf_hub_download

from transformers import VivitConfig, VivitForVideoClassification, VivitImageProcessor
from transformers.image_utils import PILImageResampling






def 						lowercase				(	_snake_case		: Optional[Any]	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		str         =							'''https://storage.googleapis.com/scenic-bucket/vivit/kinetics_400/vivit_base_16x2_unfactorized/checkpoint'''

	with open(_snake_case	,  '''wb'''	) as f:
		with requests.get(_snake_case	,  stream=_snake_case	) as req:
			for chunk in req.iter_content(chunk_size=2_048	):
				f.write(_snake_case	)






def 						lowercase				(	)	->VivitConfig:
	"""simple docstring"""





	__snake_case      :		int         =							VivitConfig()

	__snake_case      :		Tuple         =							400
	__snake_case      :		Tuple         =							'''huggingface/label-files'''
	__snake_case      :		List[str]         =							'''kinetics400-id2label.json'''

	__snake_case      :		Dict         =							json.load(open(hf_hub_download(_snake_case	,  _snake_case	,  repo_type='''dataset'''	)	,  '''r'''	)	)
	__snake_case      :		List[str]         =							{int(_snake_case	): v for k, v in idalabel.items()}
	__snake_case      :		Tuple         =							idalabel
	__snake_case      :		Optional[int]         =							{v: k for k, v in idalabel.items()}
	return config






def 						lowercase				(	)	->Optional[int]:
	"""simple docstring"""





	__snake_case      :		List[Any]         =							hf_hub_download(
	    repo_id='''hf-internal-testing/spaghetti-video'''	,  filename='''eating_spaghetti_32_frames.npy'''	,  repo_type='''dataset'''	)
	__snake_case      :		str         =							np.load(_snake_case	)
	return list(_snake_case	)






def 						lowercase				(	_snake_case		: np.ndarray	)	->str:
	"""simple docstring"""





	if np.ndim(_snake_case	) == 2:
		return transform_attention_bias(_snake_case	)

	elif np.ndim(_snake_case	) == 3:
		return transform_attention_kernel(_snake_case	)

	else:
		raise Exception(f"""Invalid number of dimesions: {np.ndim(_snake_case	)}"""	)






def 						lowercase				(	_snake_case		: np.ndarray	)	->Tuple:
	"""simple docstring"""





	return current.flatten()






def 						lowercase				(	_snake_case		: np.ndarray	)	->List[str]:
	"""simple docstring"""





	return np.reshape(_snake_case	,  (current.shape[0], current.shape[1] * current.shape[2])	).T






def 						lowercase				(	_snake_case		: np.ndarray	)	->Optional[Any]:
	"""simple docstring"""





	return np.reshape(_snake_case	,  (current.shape[0] * current.shape[1], current.shape[2])	).T






def 						lowercase				(	_snake_case		: Dict	,  _snake_case		: Tuple	)	->Tuple:
	"""simple docstring"""





	__snake_case      :		int         =							state_dict['''optimizer''']['''target''']['''Transformer'''][f"""encoderblock_{i}"""]

	__snake_case      :		Optional[int]         =							f"""encoder.layer.{i}."""
	__snake_case      :		Dict         =							{
	    prefix + '''intermediate.dense.bias''': state['''MlpBlock_0''']['''Dense_0''']['''bias'''],
	    prefix + '''intermediate.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_0''']['''kernel''']	),
	    prefix + '''output.dense.bias''': state['''MlpBlock_0''']['''Dense_1''']['''bias'''],
	    prefix + '''output.dense.weight''': np.transpose(state['''MlpBlock_0''']['''Dense_1''']['''kernel''']	),
	    prefix + '''layernorm_before.bias''': state['''LayerNorm_0''']['''bias'''],
	    prefix + '''layernorm_before.weight''': state['''LayerNorm_0''']['''scale'''],
	    prefix + '''layernorm_after.bias''': state['''LayerNorm_1''']['''bias'''],
	    prefix + '''layernorm_after.weight''': state['''LayerNorm_1''']['''scale'''],
	    prefix
	    + '''attention.attention.query.bias''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''query''']['''bias''']	),
	    prefix
	    + '''attention.attention.query.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''query''']['''kernel''']	),
	    prefix
	    + '''attention.attention.key.bias''': transform_attention(state['''MultiHeadDotProductAttention_0''']['''key''']['''bias''']	),
	    prefix
	    + '''attention.attention.key.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''key''']['''kernel''']	),
	    prefix
	    + '''attention.attention.value.bias''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''value''']['''bias''']	),
	    prefix
	    + '''attention.attention.value.weight''': transform_attention(
	        state['''MultiHeadDotProductAttention_0''']['''value''']['''kernel''']	),
	    prefix + '''attention.output.dense.bias''': state['''MultiHeadDotProductAttention_0''']['''out''']['''bias'''],
	    prefix
	    + '''attention.output.dense.weight''': transform_attention_output_weight(
	        state['''MultiHeadDotProductAttention_0''']['''out''']['''kernel''']	),
	}

	return new_state






def 						lowercase				(	_snake_case		: int	)	->List[str]:
	"""simple docstring"""





	return sum([1 if '''encoderblock_''' in k else 0 for k in state_dict['''optimizer''']['''target''']['''Transformer'''].keys()]	)






def 						lowercase				(	_snake_case		: Optional[Any]	,  _snake_case		: Optional[int]=False	)	->Union[str, Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							get_n_layers(_snake_case	)

	__snake_case      :		str         =							OrderedDict()

	__snake_case      :		List[Any]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''bias''']
	__snake_case      :		List[str]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''encoder_norm''']['''scale''']

	__snake_case      :		Optional[Any]         =							np.transpose(
	    state_dict['''optimizer''']['''target''']['''embedding''']['''kernel''']	,  (4, 3, 0, 1, 2)	)
	__snake_case      :		Optional[Any]         =							state_dict['''optimizer''']['''target''']['''embedding''']['''bias''']

	__snake_case      :		Union[str, Any]         =							state_dict['''optimizer''']['''target''']['''cls''']
	__snake_case      :		List[str]         =							state_dict['''optimizer''']['''target''']['''Transformer''']['''posembed_input'''][
	    '''pos_embedding'''
	]

	for i in range(_snake_case	):
		new_state.update(transform_state_encoder_block(_snake_case	,  _snake_case	)	)

	if classification_head:
		__snake_case      :		List[Any]         =							{'''vivit.''' + k: v for k, v in new_state.items()}
		__snake_case      :		str         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''kernel''']	)
		__snake_case      :		int         =							np.transpose(state_dict['''optimizer''']['''target''']['''output_projection''']['''bias''']	)

	return {k: torch.tensor(_snake_case	) for k, v in new_state.items()}






def 						lowercase				(	)	->VivitImageProcessor:
	"""simple docstring"""





	__snake_case      :		List[str]         =							VivitImageProcessor()

	assert extractor.do_resize is True
	assert extractor.size == {"shortest_edge": 256}
	assert extractor.do_center_crop is True
	assert extractor.crop_size == {"width": 224, "height": 224}
	assert extractor.resample == PILImageResampling.BILINEAR

	# here: https://github.com/deepmind/dmvr/blob/master/dmvr/modalities.py
	# one can seen that add_image has default values for normalization_mean and normalization_std set to 0 and 1
	# which effectively means no normalization (and ViViT does not overwrite those when calling this func)
	assert extractor.do_normalize is False
	assert extractor.do_rescale is True
	assert extractor.rescale_factor == 1 / 255

	# zero-centering = True in original implementation
	assert extractor.do_zero_centering is True

	return extractor






def 						lowercase				(	_snake_case		: str	)	->Optional[Any]:
	"""simple docstring"""





	__snake_case      :		Tuple         =							'''checkpoint'''

	if not os.path.exists(_snake_case	):
		download_checkpoint(_snake_case	)

	__snake_case      :		List[Any]         =							restore_checkpoint(_snake_case	,  _snake_case	)
	__snake_case      :		Union[str, Any]         =							transform_state(_snake_case	,  classification_head=_snake_case	)

	__snake_case      :		Optional[Any]         =							get_vivit_config()

	assert config.image_size == 224
	assert config.num_frames == 32

	__snake_case      :		List[str]         =							VivitForVideoClassification(_snake_case	)
	model.load_state_dict(_snake_case	)
	model.eval()

	__snake_case      :		Tuple         =							get_processor()

	__snake_case      :		Any         =							prepare_video()
	__snake_case      :		List[str]         =							extractor(_snake_case	,  return_tensors='''pt'''	)

	__snake_case      :		Union[str, Any]         =							model(**_snake_case	)

	__snake_case      :		List[str]         =							torch.Size([1, 400]	)
	__snake_case      :		Optional[int]         =							torch.tensor([-1.0543, 2.0764, -0.2104, 0.4439, -0.9658]	)

	assert outputs.logits.shape == expected_shape
	assert torch.allclose(outputs.logits[0, :5]	,  _snake_case	,  atol=1e-4	), outputs.logits[0, :5]

	model.save_pretrained(_snake_case	)
	extractor.save_pretrained(_snake_case	)


if __name__ == "__main__":
			SCREAMING_SNAKE_CASE					:       Optional[Any]							   =							argparse.ArgumentParser()

			parser.add_argument("""--output_model_name""", """-o""", type=str, help="""Output path for the converted HuggingFace model""")

			SCREAMING_SNAKE_CASE					:       List[str]							   =							parser.parse_args()
			convert(args.output_model_name)





