





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				(  _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Any      , _lowerCamelCase   :	Optional[Any]      , _lowerCamelCase   :	Union[str, Any]      , _lowerCamelCase   :	str      , _lowerCamelCase   :	List[str]  ):





						'''simple docstring'''


						SCREAMING_SNAKE_CASE__  :					Dict =       states.reshape(1      , -1      , model.config.state_dim  )
						SCREAMING_SNAKE_CASE__  :					Optional[int] =       actions.reshape(1      , -1      , model.config.act_dim  )
						SCREAMING_SNAKE_CASE__  :					Dict =       returns_to_go.reshape(1      , -1      , 1  )
						SCREAMING_SNAKE_CASE__  :					Optional[Any] =       timesteps.reshape(1      , -1  )

						if model.config.max_length is not None:
												SCREAMING_SNAKE_CASE__  :					List[str] =       states[:, -model.config.max_length :]
												SCREAMING_SNAKE_CASE__  :					List[Any] =       actions[:, -model.config.max_length :]
												SCREAMING_SNAKE_CASE__  :					Any =       returns_to_go[:, -model.config.max_length :]
												SCREAMING_SNAKE_CASE__  :					int =       timesteps[:, -model.config.max_length :]

												# pad all tokens to sequence length
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       torch.cat(
												    [torch.zeros(model.config.max_length - states.shape[1]  ), torch.ones(states.shape[1]  )]  )
												SCREAMING_SNAKE_CASE__  :					List[str] =       attention_mask.to(dtype=torch.long      , device=states.device  ).reshape(1      , -1  )
												SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       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__  :					Optional[int] =       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__  :					Optional[Any] =       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__  :					Any =       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__  :					Tuple =       None

						SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       model(
						    states=_lowerCamelCase      , actions=_lowerCamelCase      , rewards=_lowerCamelCase      , returns_to_go=_lowerCamelCase      , timesteps=_lowerCamelCase      , attention_mask=_lowerCamelCase      , return_dict=_lowerCamelCase      , )

						return action_preds[0, -1]


# build the environment

__lowercase							:int							=	gym.make("Hopper-v3")
__lowercase							:Union[str, Any]							=	env.observation_space.shape[0]
__lowercase							:int							=	env.action_space.shape[0]
__lowercase							:int							=	1_000
__lowercase							:str							=	"cuda"
__lowercase							:List[str]							=	1_0_0_0.0  # normalization for rewards/returns
__lowercase							:List[str]							=	3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
__lowercase							:List[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,
    ]
)
__lowercase							:Dict							=	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,
    ]
)
__lowercase							:Any							=	torch.from_numpy(state_mean).to(device=device)
__lowercase							:Union[str, Any]							=	torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
__lowercase							:Tuple							=	DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
__lowercase							:str							=	model.to(device)
model.eval()

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

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

								__lowercase							:Optional[int]							=	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),
								)
								__lowercase							:Tuple							=	action
								__lowercase							:Optional[Any]							=	action.detach().cpu().numpy()

								__lowercase							,  __lowercase							,  __lowercase							,  __lowercase							:Union[str, Any]							=	env.step(action)

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

								__lowercase							:Tuple							=	target_return[0, -1] - (reward / scale)
								__lowercase							:Optional[Any]							=	torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
								__lowercase							:Optional[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






import collections
import types

import numpy as np

from ..utils import (
    add_end_docstrings,
    is_tensorflow_probability_available,
    is_tf_available,
    is_torch_available,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ArgumentHandler, Dataset, Pipeline, PipelineException


if is_torch_available():
				import torch

				from ..models.auto.modeling_auto import (
				    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				)

if is_tf_available() and is_tensorflow_probability_available():
				import tensorflow as tf
				import tensorflow_probability as tfp

				from ..models.auto.modeling_tf_auto import (
				    TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING,
				    TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING,
				)



class  _a  (					lowercase__				):






							"""simple docstring"""

							def __call__(   self					:  List[Any]				,						a					:  str=None				,						a					:  Dict=None				,						**a					:  Tuple     )						->Dict:
													# Returns tqa_pipeline_inputs of shape:
													# [
													#   {"table": pd.DataFrame, "query": List[str]},
													#   ...,
													#   {"table": pd.DataFrame, "query" : List[str]}
													# ]
													requires_backends(self				,						"pandas"     )
													import pandas as pd

													if table is None:
																			raise ValueError("Keyword argument `table` cannot be None."     )
													elif query is None:
																			if isinstance(a				,						a     ) and table.get("query"     ) is not None and table.get("table"     ) is not None:
																									SCREAMING_SNAKE_CASE__  :					int =       [table]
																			elif isinstance(a				,						a     ) and len(a     ) > 0:
																									if not all(isinstance(a				,						a     ) for d in table     ):
																															raise ValueError(
																															    f"""Keyword argument `table` should be a list of dict, but is {(type(a     ) for d in table)}"""     )

																									if table[0].get("query"     ) is not None and table[0].get("table"     ) is not None:
																															SCREAMING_SNAKE_CASE__  :					List[str] =       table
																									else:
																															raise ValueError(
																															    "If keyword argument `table` is a list of dictionaries, each dictionary should have a `table`"
																															    f""" and `query` key, but only dictionary has keys {table[0].keys()} `table` and `query` keys."""     )
																			elif Dataset is not None and isinstance(a				,						a     ) or isinstance(a				,						types.GeneratorType     ):
																									return table
																			else:
																									raise ValueError(
																									    "Invalid input. Keyword argument `table` should be either of type `dict` or `list`, but "
																									    f"""is {type(a     )})"""     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       [{"table": table, "query": query}]

													for tqa_pipeline_input in tqa_pipeline_inputs:
																			if not isinstance(tqa_pipeline_input["table"]				,						pd.DataFrame     ):
																									if tqa_pipeline_input["table"] is None:
																															raise ValueError("Table cannot be None."     )

																									SCREAMING_SNAKE_CASE__  :					Dict =       pd.DataFrame(tqa_pipeline_input["table"]     )

													return tqa_pipeline_inputs







@add_end_docstrings(lowercase__				)
class  _a  (					lowercase__				):






							"""simple docstring"""

							snake_case_							=    "table,query"

							def __init__(   self					:  List[str]				,						a					:  Optional[int]=TableQuestionAnsweringArgumentHandler()				,						*a					:  List[str]				,						**a					:  Dict     )						->Any:
													super().__init__(*a				,						**a     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       args_parser

													self.check_model_type(
													    dict(
													        TF_MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items()
													        + TF_MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )
													    if self.framework == "tf"
													    else dict(
													        MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING.items() + MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING.items()     )     )

													SCREAMING_SNAKE_CASE__  :					str =       bool(getattr(self.model.config				,						"aggregation_labels"				,						a     )     ) and bool(
													    getattr(self.model.config				,						"num_aggregation_labels"				,						a     )     )
													SCREAMING_SNAKE_CASE__  :					int =       "tapas" if hasattr(self.model.config				,						"aggregation_labels"     ) else None

							def   A_	(   self					:  List[str]				,						**a					:  Any     )						->Optional[Any]:
													return self.model(**a     )

							def   A_	(   self					:  Any				,						**a					:  Any     )						->Dict:
													if self.framework == "pt":
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					int =       []
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

																			SCREAMING_SNAKE_CASE__  :					int =       inputs["input_ids"].to(self.device     )
																			SCREAMING_SNAKE_CASE__  :					int =       inputs["attention_mask"].to(self.device     )
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       inputs["token_type_ids"].to(self.device     )
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       None

																			for index in range(a     ):
																									# If sequences have already been processed, the token type IDs will be created according to the previous
																									# answer.
																									if prev_answers is not None:
																															SCREAMING_SNAKE_CASE__  :					Tuple =       token_type_ids_example[:, 3]  # shape (seq_len,)
																															SCREAMING_SNAKE_CASE__  :					Optional[Any] =       np.zeros_like(prev_labels_example.cpu().numpy()     )  # shape (seq_len,)

																															SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       token_type_ids[index]  # shape (seq_len, 7)
																															for i in range(model_labels.shape[0]     ):
																																					SCREAMING_SNAKE_CASE__  :					int =       token_type_ids_example[:, 0].tolist()[i]
																																					SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       token_type_ids_example[:, 1].tolist()[i] - 1
																																					SCREAMING_SNAKE_CASE__  :					Dict =       token_type_ids_example[:, 2].tolist()[i] - 1

																																					if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																											SCREAMING_SNAKE_CASE__  :					Dict =       int(prev_answers[(col_id, row_id)]     )

																															SCREAMING_SNAKE_CASE__  :					Dict =       torch.from_numpy(a     ).type(torch.long     ).to(self.device     )

																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       input_ids[index]
																									SCREAMING_SNAKE_CASE__  :					Any =       attention_mask[index]  # shape (seq_len,)
																									SCREAMING_SNAKE_CASE__  :					List[str] =       token_type_ids[index]  # shape (seq_len, 7)
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       self.model(
																									    input_ids=input_ids_example.unsqueeze(0     )				,						attention_mask=attention_mask_example.unsqueeze(0     )				,						token_type_ids=token_type_ids_example.unsqueeze(0     )				,						)
																									SCREAMING_SNAKE_CASE__  :					Any =       outputs.logits

																									if self.aggregate:
																															all_aggregations.append(outputs.logits_aggregation     )

																									all_logits.append(a     )

																									SCREAMING_SNAKE_CASE__  :					Any =       torch.distributions.Bernoulli(logits=a     )
																									SCREAMING_SNAKE_CASE__  :					Tuple =       dist_per_token.probs * attention_mask_example.type(torch.floataa     ).to(
																									    dist_per_token.probs.device     )

																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       collections.defaultdict(a     )
																									for i, p in enumerate(probabilities.squeeze().tolist()     ):
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       token_type_ids_example[:, 0].tolist()[i]
																															SCREAMING_SNAKE_CASE__  :					Dict =       token_type_ids_example[:, 1].tolist()[i] - 1
																															SCREAMING_SNAKE_CASE__  :					Optional[Any] =       token_type_ids_example[:, 2].tolist()[i] - 1
																															if col >= 0 and row >= 0 and segment_id == 1:
																																					coords_to_probs[(col, row)].append(a     )

																									SCREAMING_SNAKE_CASE__  :					Tuple =       {key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

																			SCREAMING_SNAKE_CASE__  :					List[str] =       torch.cat(tuple(a     )				,						0     )

																			return (logits_batch,) if not self.aggregate else (logits_batch, torch.cat(tuple(a     )				,						0     ))
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       []
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       None
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["input_ids"].shape[0]

																			SCREAMING_SNAKE_CASE__  :					Tuple =       inputs["input_ids"]
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       inputs["attention_mask"]
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       inputs["token_type_ids"].numpy()
																			SCREAMING_SNAKE_CASE__  :					Tuple =       None

																			for index in range(a     ):
																									# If sequences have already been processed, the token type IDs will be created according to the previous
																									# answer.
																									if prev_answers is not None:
																															SCREAMING_SNAKE_CASE__  :					Dict =       token_type_ids_example[:, 3]  # shape (seq_len,)
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       np.zeros_like(a				,						dtype=np.intaa     )  # shape (seq_len,)

																															SCREAMING_SNAKE_CASE__  :					str =       token_type_ids[index]  # shape (seq_len, 7)
																															for i in range(model_labels.shape[0]     ):
																																					SCREAMING_SNAKE_CASE__  :					str =       token_type_ids_example[:, 0].tolist()[i]
																																					SCREAMING_SNAKE_CASE__  :					Any =       token_type_ids_example[:, 1].tolist()[i] - 1
																																					SCREAMING_SNAKE_CASE__  :					Dict =       token_type_ids_example[:, 2].tolist()[i] - 1

																																					if row_id >= 0 and col_id >= 0 and segment_id == 1:
																																											SCREAMING_SNAKE_CASE__  :					int =       int(prev_answers[(col_id, row_id)]     )

																															SCREAMING_SNAKE_CASE__  :					Any =       model_labels

																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       input_ids[index]
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       attention_mask[index]  # shape (seq_len,)
																									SCREAMING_SNAKE_CASE__  :					Any =       token_type_ids[index]  # shape (seq_len, 7)
																									SCREAMING_SNAKE_CASE__  :					Any =       self.model(
																									    input_ids=np.expand_dims(a				,						axis=0     )				,						attention_mask=np.expand_dims(a				,						axis=0     )				,						token_type_ids=np.expand_dims(a				,						axis=0     )				,						)
																									SCREAMING_SNAKE_CASE__  :					str =       outputs.logits

																									if self.aggregate:
																															all_aggregations.append(outputs.logits_aggregation     )

																									all_logits.append(a     )

																									SCREAMING_SNAKE_CASE__  :					int =       tfp.distributions.Bernoulli(logits=a     )
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       dist_per_token.probs_parameter() * tf.cast(a				,						tf.floataa     )

																									SCREAMING_SNAKE_CASE__  :					Tuple =       collections.defaultdict(a     )
																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       token_type_ids_example
																									for i, p in enumerate(tf.squeeze(a     ).numpy().tolist()     ):
																															SCREAMING_SNAKE_CASE__  :					str =       token_type_ids_example[:, 0].tolist()[i]
																															SCREAMING_SNAKE_CASE__  :					List[Any] =       token_type_ids_example[:, 1].tolist()[i] - 1
																															SCREAMING_SNAKE_CASE__  :					Optional[Any] =       token_type_ids_example[:, 2].tolist()[i] - 1
																															if col >= 0 and row >= 0 and segment_id == 1:
																																					coords_to_probs[(col, row)].append(a     )

																									SCREAMING_SNAKE_CASE__  :					int =       {key: np.array(coords_to_probs[key]     ).mean() > 0.5 for key in coords_to_probs}

																			SCREAMING_SNAKE_CASE__  :					List[Any] =       tf.concat(tuple(a     )				,						0     )

																			return (logits_batch,) if not self.aggregate else (logits_batch, tf.concat(tuple(a     )				,						0     ))

							def __call__(   self					:  Optional[Any]				,						*a					:  Any				,						**a					:  Optional[int]     )						->Dict:
													SCREAMING_SNAKE_CASE__  :					Tuple =       self._args_parser(*a				,						**a     )

													SCREAMING_SNAKE_CASE__  :					Dict =       super().__call__(a				,						**a     )
													if len(a     ) == 1:
																			return results[0]
													return results

							def   A_	(   self					:  Any				,						a					:  Dict=None				,						a					:  Dict=None				,						a					:  Union[str, Any]=None				,						**a					:  int     )						->Any:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       {}
													if padding is not None:
																			SCREAMING_SNAKE_CASE__  :					Optional[int] =       padding
													if truncation is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       truncation

													SCREAMING_SNAKE_CASE__  :					Optional[int] =       {}
													if sequential is not None:
																			SCREAMING_SNAKE_CASE__  :					int =       sequential
													return preprocess_params, forward_params, {}

							def   A_	(   self					:  Any				,						a					:  Any				,						a					:  Union[str, Any]=None				,						a					:  Union[str, Any]=True				,						a					:  Dict=None     )						->int:
													if truncation is None:
																			if self.type == "tapas":
																									SCREAMING_SNAKE_CASE__  :					Any =       "drop_rows_to_fit"
																			else:
																									SCREAMING_SNAKE_CASE__  :					List[str] =       "do_not_truncate"

													SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[Any] =       pipeline_input["table"], pipeline_input["query"]
													if table.empty:
																			raise ValueError("table is empty"     )
													if query is None or query == "":
																			raise ValueError("query is empty"     )
													SCREAMING_SNAKE_CASE__  :					Tuple =       self.tokenizer(a				,						a				,						return_tensors=self.framework				,						truncation=a				,						padding=a     )
													SCREAMING_SNAKE_CASE__  :					str =       table
													return inputs

							def   A_	(   self					:  Any				,						a					:  Tuple				,						a					:  Union[str, Any]=False     )						->Union[str, Any]:
													SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       model_inputs.pop("table"     )

													if self.type == "tapas":
																			if sequential:
																									SCREAMING_SNAKE_CASE__  :					List[str] =       self.sequential_inference(**a     )
																			else:
																									SCREAMING_SNAKE_CASE__  :					List[Any] =       self.batch_inference(**a     )
													else:
																			SCREAMING_SNAKE_CASE__  :					Optional[Any] =       self.model.generate(**a     )
													SCREAMING_SNAKE_CASE__  :					str =       {"model_inputs": model_inputs, "table": table, "outputs": outputs}
													return model_outputs






							def   A_	(   self					:  str				,						a					:  Any     )						->Tuple:
													SCREAMING_SNAKE_CASE__  :					Dict =       model_outputs["model_inputs"]
													SCREAMING_SNAKE_CASE__  :					Optional[Any] =       model_outputs["table"]
													SCREAMING_SNAKE_CASE__  :					Dict =       model_outputs["outputs"]
													if self.type == "tapas":
																			if self.aggregate:
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					List[str] =       outputs[:2]
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer.convert_logits_to_predictions(a				,						a				,						a     )
																									SCREAMING_SNAKE_CASE__,				SCREAMING_SNAKE_CASE__  :					Optional[Any] =       predictions
																									SCREAMING_SNAKE_CASE__  :					Tuple =       {i: self.model.config.aggregation_labels[pred] for i, pred in enumerate(a     )}

																									SCREAMING_SNAKE_CASE__  :					Tuple =       self.model.config.no_aggregation_label_index
																									SCREAMING_SNAKE_CASE__  :					List[Any] =       {
																									    i: aggregators[i] + " > " for i, pred in enumerate(a     ) if pred != no_agg_label_index
																									}
																			else:
																									SCREAMING_SNAKE_CASE__  :					Optional[Any] =       outputs[0]
																									SCREAMING_SNAKE_CASE__  :					Optional[int] =       self.tokenizer.convert_logits_to_predictions(a				,						a     )
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       predictions[0]
																									SCREAMING_SNAKE_CASE__  :					Dict =       {}
																									SCREAMING_SNAKE_CASE__  :					Dict =       {}
																			SCREAMING_SNAKE_CASE__  :					List[str] =       []
																			for index, coordinates in enumerate(a     ):
																									SCREAMING_SNAKE_CASE__  :					int =       [table.iat[coordinate] for coordinate in coordinates]
																									SCREAMING_SNAKE_CASE__  :					int =       aggregators.get(a				,						""     )
																									SCREAMING_SNAKE_CASE__  :					Union[str, Any] =       aggregators_prefix.get(a				,						""     )
																									SCREAMING_SNAKE_CASE__  :					Any =       {
																									    "answer": aggregator_prefix + ", ".join(a     ),
																									    "coordinates": coordinates,
																									    "cells": [table.iat[coordinate] for coordinate in coordinates],
																									}
																									if aggregator:
																															SCREAMING_SNAKE_CASE__  :					Optional[Any] =       aggregator

																									answers.append(a     )
																			if len(a     ) == 0:
																									raise PipelineException("Empty answer"     )
													else:
																			SCREAMING_SNAKE_CASE__  :					List[Any] =       [{"answer": answer} for answer in self.tokenizer.batch_decode(a				,						skip_special_tokens=a     )]

													return answers if len(a     ) > 1 else answers[0]