import darl  # noqa
import gym
import tqdm
from diffusers.experimental import ValueGuidedRLPipeline


__UpperCamelCase	: Union[str, Any]    		=  {
    'n_samples': 64,
    'horizon': 32,
    'num_inference_steps': 20,
    'n_guide_steps': 2,  # can set to 0 for faster sampling, does not use value network
    'scale_grad_by_std': True,
    'scale': 0.1,
    'eta': 0.0,
    't_grad_cutoff': 2,
    'device': 'cpu',
}


if __name__ == "__main__":
					__UpperCamelCase	: List[Any]    		=  'hopper-medium-v2'
					__UpperCamelCase	: Optional[int]    		=  gym.make(env_name)

					__UpperCamelCase	: int    		=  ValueGuidedRLPipeline.from_pretrained(
					    'bglick13/hopper-medium-v2-value-function-hor32',
					    env=env,
					)

					env.seed(0)
					__UpperCamelCase	: str    		=  env.reset()
					__UpperCamelCase	: Optional[Any]    		=  0
					__UpperCamelCase	: Optional[int]    		=  0
					__UpperCamelCase	: Optional[Any]    		=  1000
					__UpperCamelCase	: Dict    		=  [obs.copy()]
					try:
										for t in tqdm.tqdm(range(T)):
															# call the policy
															__UpperCamelCase	: Optional[int]    		=  pipeline(obs, planning_horizon=32)

															# execute action in environment
															__UpperCamelCase		,    __UpperCamelCase		,    __UpperCamelCase		,    __UpperCamelCase	: Union[str, Any]    		=  env.step(denorm_actions)
															__UpperCamelCase	: Dict    		=  env.get_normalized_score(total_reward)

															# update return
															total_reward += reward
															total_score += score
															print(
															    f"""Step: {t}, Reward: {reward}, Total Reward: {total_reward}, Score: {score}, Total Score:"""
															    f""" {total_score}"""
															)

															# save observations for rendering
															rollout.append(next_observation.copy())

															__UpperCamelCase	: Optional[Any]    		=  next_observation
					except KeyboardInterrupt:
										pass

					print(f"""Total reward: {total_reward}""")






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

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...onnx.utils import compute_effective_axis_dimension
from ...utils import logging


if TYPE_CHECKING:
					from ...processing_utils import ProcessorMixin
					from ...utils import TensorType


__UpperCamelCase	: str    		=  logging.get_logger(__name__)

__UpperCamelCase	: str    		=  {
    'microsoft/layoutlmv3-base': 'https://huggingface.co/microsoft/layoutlmv3-base/resolve/main/config.json',
}





class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	"""layoutlmv3"""


						def __init__( self			:       Optional[int]					,	UpperCamelCase__			:       Union[str, Any]=5_0265					,	UpperCamelCase__			:       List[Any]=768					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Union[str, Any]=12					,	UpperCamelCase__			:       Tuple=3072					,	UpperCamelCase__			:       List[Any]="gelu"					,	UpperCamelCase__			:       List[Any]=0.1					,	UpperCamelCase__			:       List[str]=0.1					,	UpperCamelCase__			:       Any=512					,	UpperCamelCase__			:       List[Any]=2					,	UpperCamelCase__			:       List[Any]=0.02					,	UpperCamelCase__			:       Optional[Any]=1E-5					,	UpperCamelCase__			:       List[str]=1					,	UpperCamelCase__			:       int=0					,	UpperCamelCase__			:       str=2					,	UpperCamelCase__			:       List[str]=1024					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       str=128					,	UpperCamelCase__			:       Tuple=True					,	UpperCamelCase__			:       Optional[int]=32					,	UpperCamelCase__			:       Any=128					,	UpperCamelCase__			:       Optional[Any]=64					,	UpperCamelCase__			:       Dict=256					,	UpperCamelCase__			:       List[Any]=True					,	UpperCamelCase__			:       str=True					,	UpperCamelCase__			:       Dict=True					,	UpperCamelCase__			:       Dict=224					,	UpperCamelCase__			:       Optional[Any]=3					,	UpperCamelCase__			:       Any=16					,	UpperCamelCase__			:       Any=None					,	**UpperCamelCase__			:       Optional[Any]					,	):





												'''simple docstring'''





												super().__init__(
												    vocab_size=UpperCamelCase__					,	hidden_size=UpperCamelCase__					,	num_hidden_layers=UpperCamelCase__					,	num_attention_heads=UpperCamelCase__					,	intermediate_size=UpperCamelCase__					,	hidden_act=UpperCamelCase__					,	hidden_dropout_prob=UpperCamelCase__					,	attention_probs_dropout_prob=UpperCamelCase__					,	max_position_embeddings=UpperCamelCase__					,	type_vocab_size=UpperCamelCase__					,	initializer_range=UpperCamelCase__					,	layer_norm_eps=UpperCamelCase__					,	pad_token_id=UpperCamelCase__					,	bos_token_id=UpperCamelCase__					,	eos_token_id=UpperCamelCase__					,	**UpperCamelCase__					,	)
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_ad_position_embeddings
												SCREAMING_SNAKE_CASE							:					List[Any]           =							coordinate_size
												SCREAMING_SNAKE_CASE							:					List[str]           =							shape_size
												SCREAMING_SNAKE_CASE							:					Optional[int]           =							has_relative_attention_bias
												SCREAMING_SNAKE_CASE							:					List[Any]           =							rel_pos_bins
												SCREAMING_SNAKE_CASE							:					str           =							max_rel_pos
												SCREAMING_SNAKE_CASE							:					Any           =							has_spatial_attention_bias
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							rel_ad_pos_bins
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							max_rel_ad_pos
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							text_embed
												SCREAMING_SNAKE_CASE							:					List[str]           =							visual_embed
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							input_size
												SCREAMING_SNAKE_CASE							:					Optional[Any]           =							num_channels
												SCREAMING_SNAKE_CASE							:					List[Any]           =							patch_size
												SCREAMING_SNAKE_CASE							:					List[Any]           =							classifier_dropout






class       lowercase__		(     UpperCamelCase_):
						UpperCamelCase_  =	version.parse("""1.12""")


						@property
						def  __A ( self			:       str		):





												'''simple docstring'''





												if self.task in ["question-answering", "sequence-classification"]:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels''', 2: '''height''', 3: '''width'''}),
																		    ]		)
												else:
																		return OrderedDict(
																		    [
																		        ('''input_ids''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''bbox''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''attention_mask''', {0: '''batch''', 1: '''sequence'''}),
																		        ('''pixel_values''', {0: '''batch''', 1: '''num_channels'''}),
																		    ]		)


						@property
						def  __A ( self			:       int		):





												'''simple docstring'''





												return 1E-5


						@property
						def  __A ( self			:       Union[str, Any]		):





												'''simple docstring'''





												return 12



						def  __A ( self			:       Optional[Any]					,	UpperCamelCase__			:       "ProcessorMixin"					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       int = -1					,	UpperCamelCase__			:       bool = False					,	UpperCamelCase__			:       Optional["TensorType"] = None					,	UpperCamelCase__			:       int = 3					,	UpperCamelCase__			:       int = 40					,	UpperCamelCase__			:       int = 40					,	):





												'''simple docstring'''





												setattr(processor.image_processor					,	'''apply_ocr'''					,	UpperCamelCase__		)

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					Any           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_batch					,	num_token_to_add=0		)
												# If dynamic axis (-1) we forward with a fixed dimension of 8 tokens to avoid optimizations made by ONNX
												SCREAMING_SNAKE_CASE							:					List[Any]           =							processor.tokenizer.num_special_tokens_to_add(UpperCamelCase__		)
												SCREAMING_SNAKE_CASE							:					Tuple           =							compute_effective_axis_dimension(
												    UpperCamelCase__					,	fixed_dimension=OnnxConfig.default_fixed_sequence					,	num_token_to_add=UpperCamelCase__		)
												# Generate dummy inputs according to compute batch and sequence
												SCREAMING_SNAKE_CASE							:					Union[str, Any]           =							[[''' '''.join([processor.tokenizer.unk_token]		) * seq_length]] * batch_size

												# Generate dummy bounding boxes
												SCREAMING_SNAKE_CASE							:					Any           =							[[[48, 84, 73, 128]]] * batch_size

												# If dynamic axis (-1) we forward with a fixed dimension of 2 samples to avoid optimizations made by ONNX
												# batch_size = compute_effective_axis_dimension(batch_size, fixed_dimension=OnnxConfig.default_fixed_batch)
												SCREAMING_SNAKE_CASE							:					List[Any]           =							self._generate_dummy_images(UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__					,	UpperCamelCase__		)

												SCREAMING_SNAKE_CASE							:					Optional[int]           =							dict(
												    processor(
												        UpperCamelCase__					,	text=UpperCamelCase__					,	boxes=UpperCamelCase__					,	return_tensors=UpperCamelCase__					,	)		)

												return inputs





