
"""simple docstring"""




from collections import OrderedDict
from typing import Any, List, Mapping, Optional

from ... import PretrainedConfig, PreTrainedTokenizer
from ...onnx import OnnxConfig, PatchingSpec
from ...utils import TensorType, is_torch_available, logging


__lowerCAmelCase							:  Union[str, Any] 				=		logging.get_logger(__name__)

__lowerCAmelCase							:  Union[str, Any] 				=		{
    '''microsoft/layoutlm-base-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-base-uncased/resolve/main/config.json'''
    ),
    '''microsoft/layoutlm-large-uncased''': (
        '''https://huggingface.co/microsoft/layoutlm-large-uncased/resolve/main/config.json'''
    ),
}
class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	snake_case__							:		str         =		"layoutlm"


	def __init__(  self :Union[str, Any]       ,    __magic_name__ :int=30522       ,    __magic_name__ :Tuple=768       ,    __magic_name__ :Tuple=12       ,    __magic_name__ :Optional[Any]=12       ,    __magic_name__ :int=3072       ,    __magic_name__ :Dict="gelu"       ,    __magic_name__ :List[str]=0.1       ,    __magic_name__ :Optional[int]=0.1       ,    __magic_name__ :Optional[int]=512       ,    __magic_name__ :List[Any]=2       ,    __magic_name__ :List[str]=0.02       ,    __magic_name__ :Optional[Any]=1e-12       ,    __magic_name__ :List[str]=0       ,    __magic_name__ :int="absolute"       ,    __magic_name__ :Tuple=True       ,    __magic_name__ :List[Any]=1024       ,    **__magic_name__ :Any       ,    )			->   Union[str, Any]:

			'''simple docstring'''






			super().__init__(pad_token_id=lowercase__       ,    **lowercase__			)
			a__   		=     vocab_size
			a__   		=     hidden_size
			a__   		=     num_hidden_layers
			a__   		=     num_attention_heads
			a__   		=     hidden_act
			a__   		=     intermediate_size
			a__   		=     hidden_dropout_prob
			a__   		=     attention_probs_dropout_prob
			a__   		=     max_position_embeddings
			a__   		=     type_vocab_size
			a__   		=     initializer_range
			a__   		=     layer_norm_eps
			a__   		=     position_embedding_type
			a__   		=     use_cache
			a__   		=     max_ad_position_embeddings

class SCREAMING_SNAKE_CASE       (      lowerCAmelCase					):






	'''simple docstring'''







	def __init__(  self :List[Any]       ,    __magic_name__ :Any       ,    __magic_name__ :List[str] = "default"       ,    __magic_name__ :Dict = None       ,    )			->   Optional[int]:

			'''simple docstring'''






			super().__init__(lowercase__       ,    task=lowercase__       ,    patching_specs=lowercase__			)
			a__   		=     config.max_ad_position_embeddings - 1


	@property
	def    _UpperCamelCase							(  self :str			)			->   Mapping[str, Mapping[int, str]]:

			'''simple docstring'''






			return OrderedDict(
			    [
			        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
			        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
			        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
			        ('''token_type_ids''', {0: '''batch''', 1: '''sequence'''}),
			    ]			)






	def    _UpperCamelCase							(  self :str       ,    __magic_name__ :Tuple       ,    __magic_name__ :Optional[int] = -1       ,    __magic_name__ :int = -1       ,    __magic_name__ :str = False       ,    __magic_name__ :Tuple = None       ,    )			->   Mapping[str, Any]:

			'''simple docstring'''






			a__   		=     super().generate_dummy_inputs(
			    lowercase__       ,    batch_size=lowercase__       ,    seq_length=lowercase__       ,    is_pair=lowercase__       ,    framework=lowercase__			)

			# Generate a dummy bbox
			a__   		=     [48, 84, 73, 128]

			if not framework == TensorType.PYTORCH:
					raise NotImplementedError('''Exporting LayoutLM to ONNX is currently only supported for PyTorch.'''			)

			if not is_torch_available():
					raise ValueError('''Cannot generate dummy inputs without PyTorch installed.'''			)
			import torch

			a__		,	a__   		=     input_dict['''input_ids'''].shape
			a__   		=     torch.tensor([*[box] * seq_length]			).tile(lowercase__       ,    1       ,    1			)
			return input_dict






"""simple docstring"""




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     UpperCamelCase_    (						SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_,	SCREAMING_SNAKE_CASE_     ):
					# we don't care about the past rewards in this model

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

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

										# pad all tokens to sequence length
										SCREAMING_SNAKE_CASE           =      torch.cat(
										    [torch.zeros(model.config.max_length - states.shape[1]     ), torch.ones(states.shape[1]     )]     )
										SCREAMING_SNAKE_CASE           =      attention_mask.to(dtype=torch.long,	device=states.device     ).reshape(1,	-1     )
										SCREAMING_SNAKE_CASE           =      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     )
										SCREAMING_SNAKE_CASE           =      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     )
										SCREAMING_SNAKE_CASE           =      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     )
										SCREAMING_SNAKE_CASE           =      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:
										SCREAMING_SNAKE_CASE           =      None

					SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE      ,       SCREAMING_SNAKE_CASE           =      model(
					    states=SCREAMING_SNAKE_CASE_,	actions=SCREAMING_SNAKE_CASE_,	rewards=SCREAMING_SNAKE_CASE_,	returns_to_go=SCREAMING_SNAKE_CASE_,	timesteps=SCREAMING_SNAKE_CASE_,	attention_mask=SCREAMING_SNAKE_CASE_,	return_dict=SCREAMING_SNAKE_CASE_,	)

					return action_preds[0, -1]


# build the environment

snake_case					  =						gym.make('Hopper-v3')
snake_case					  =						env.observation_space.shape[0]
snake_case					  =						env.action_space.shape[0]
snake_case					  =						1_0_0_0
snake_case					  =						'cuda'
snake_case					  =						1_0_0_0.0  # normalization for rewards/returns
snake_case					  =						3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
snake_case					  =						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,
    ]
)
snake_case					  =						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,
    ]
)
snake_case					  =						torch.from_numpy(state_mean).to(device=device)
snake_case					  =						torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
snake_case					  =						DecisionTransformerModel.from_pretrained('edbeeching/decision-transformer-gym-hopper-medium')
snake_case					  =						model.to(device)
model.eval()

for ep in range(1_0):
	snake_case  ,     snake_case					  =						0, 0
	snake_case					  =						env.reset()
	snake_case					  =						torch.tensor(TARGET_RETURN, device=device, dtype=torch.floataa).reshape(1, 1)
	snake_case					  =						torch.from_numpy(state).reshape(1, state_dim).to(device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros((0, act_dim), device=device, dtype=torch.floataa)
	snake_case					  =						torch.zeros(0, device=device, dtype=torch.floataa)

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

		snake_case					  =						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),
		)
		snake_case					  =						action
		snake_case					  =						action.detach().cpu().numpy()

		snake_case  ,     snake_case  ,     snake_case  ,     snake_case					  =						env.step(action)

		snake_case					  =						torch.from_numpy(state).to(device=device).reshape(1, state_dim)
		snake_case					  =						torch.cat([states, cur_state], dim=0)
		snake_case					  =						reward

		snake_case					  =						target_return[0, -1] - (reward / scale)
		snake_case					  =						torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
		snake_case					  =						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
