



from collections import defaultdict
from typing import Optional

from ..image_utils import load_image
from ..utils import (
    add_end_docstrings,
    is_torch_available,
    logging,
    requires_backends,
)
from .base import PIPELINE_INIT_ARGS, ChunkPipeline


if is_torch_available():
 import torch

 from ..models.auto.modeling_auto import MODEL_FOR_MASK_GENERATION_MAPPING

__snake_case						:List[Any]      		=     logging.get_logger(__name__)

@add_end_docstrings(__UpperCAmelCase    )
class     _A       (	__UpperCAmelCase    ):



       def __init__(		self							:     Dict     ,		**__SCREAMING_SNAKE_CASE							:     Tuple):


           '''simple docstring'''

           super().__init__(**__SCREAMING_SNAKE_CASE)
           requires_backends(self     ,		'''vision''')
           requires_backends(self     ,		'''torch''')

           if self.framework != "pt":
               raise ValueError(F'The {self.__class__} is only available in PyTorch.')

           self.check_model_type(__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Any     ,		**__SCREAMING_SNAKE_CASE							:     Any):


           '''simple docstring'''

           __a						       =				{}
           __a						       =				{}
           __a						       =				{}
           # preprocess args
           if "points_per_batch" in kwargs:
               __a						       =				kwargs['''points_per_batch''']
           if "points_per_crop" in kwargs:
               __a						       =				kwargs['''points_per_crop''']
           if "crops_n_layers" in kwargs:
               __a						       =				kwargs['''crops_n_layers''']
           if "crop_overlap_ratio" in kwargs:
               __a						       =				kwargs['''crop_overlap_ratio''']
           if "crop_n_points_downscale_factor" in kwargs:
               __a						       =				kwargs['''crop_n_points_downscale_factor''']
           # postprocess args
           if "pred_iou_thresh" in kwargs:
               __a						       =				kwargs['''pred_iou_thresh''']
           if "stability_score_offset" in kwargs:
               __a						       =				kwargs['''stability_score_offset''']
           if "mask_threshold" in kwargs:
               __a						       =				kwargs['''mask_threshold''']
           if "stability_score_thresh" in kwargs:
               __a						       =				kwargs['''stability_score_thresh''']
           if "crops_nms_thresh" in kwargs:
               __a						       =				kwargs['''crops_nms_thresh''']
           if "output_rle_mask" in kwargs:
               __a						       =				kwargs['''output_rle_mask''']
           if "output_bboxes_mask" in kwargs:
               __a						       =				kwargs['''output_bboxes_mask''']
           return preprocess_kwargs, forward_params, postprocess_kwargs



       def __call__(		self							:     Union[str, Any]     ,		__SCREAMING_SNAKE_CASE							:     int     ,		*__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     Tuple=None     ,		__SCREAMING_SNAKE_CASE							:     Any=None     ,		**__SCREAMING_SNAKE_CASE							:     str):


           '''simple docstring'''

           return super().__call__(__SCREAMING_SNAKE_CASE     ,		*__SCREAMING_SNAKE_CASE     ,		num_workers=__SCREAMING_SNAKE_CASE     ,		batch_size=__SCREAMING_SNAKE_CASE     ,		**__SCREAMING_SNAKE_CASE)



       def 		_lowerCamelCase			(		self							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     str     ,		__SCREAMING_SNAKE_CASE							:     Any=64     ,		__SCREAMING_SNAKE_CASE							:     int = 0     ,		__SCREAMING_SNAKE_CASE							:     float = 512 / 1_500     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 32     ,		__SCREAMING_SNAKE_CASE							:     Optional[int] = 1     ,		):


           '''simple docstring'''

           __a						       =				load_image(__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor.size['''longest_edge''']
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.generate_crop_boxes(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)
           __a						       =				self.image_processor(images=__SCREAMING_SNAKE_CASE     ,		return_tensors='''pt''')

           with self.device_placement():
               if self.framework == "pt":
                   __a						       =				self.get_inference_context()
                   with inference_context():
                       __a						       =				self._ensure_tensor_on_device(__SCREAMING_SNAKE_CASE     ,		device=self.device)
                       __a						       =				self.model.get_image_embeddings(model_inputs.pop('''pixel_values'''))
                       __a						       =				image_embeddings

           __a						       =				grid_points.shape[1]
           __a						       =				points_per_batch if points_per_batch is not None else n_points

           if points_per_batch <= 0:
               raise ValueError(
                   '''Cannot have points_per_batch<=0. Must be >=1 to returned batched outputs. '''
                   '''To return all points at once, set points_per_batch to None''')

           for i in range(0     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE):
               __a						       =				grid_points[:, i : i + points_per_batch, :, :]
               __a						       =				input_labels[:, i : i + points_per_batch]
               __a						       =				i == n_points - points_per_batch
               yield {
                   "input_points": batched_points,
                   "input_labels": labels,
                   "input_boxes": crop_boxes,
                   "is_last": is_last,
                   **model_inputs,
               }



       def 		_lowerCamelCase			(		self							:     Any     ,		__SCREAMING_SNAKE_CASE							:     Optional[Any]     ,		__SCREAMING_SNAKE_CASE							:     int=0.88     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0.95     ,		__SCREAMING_SNAKE_CASE							:     List[Any]=0     ,		__SCREAMING_SNAKE_CASE							:     int=1     ,		):


           '''simple docstring'''

           __a						       =				model_inputs.pop('''input_boxes''')
           __a						       =				model_inputs.pop('''is_last''')
           __a						       =				model_inputs.pop('''original_sizes''').tolist()
           __a						       =				model_inputs.pop('''reshaped_input_sizes''').tolist()

           __a						       =				self.model(**__SCREAMING_SNAKE_CASE)

           # post processing happens here in order to avoid CPU GPU copies of ALL the masks
           __a						       =				model_outputs['''pred_masks''']
           __a						       =				self.image_processor.post_process_masks(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		binarize=__SCREAMING_SNAKE_CASE)
           __a						       =				model_outputs['''iou_scores''']
           __a       ,   __a       ,   __a						       =				self.image_processor.filter_masks(
               masks[0]     ,		iou_scores[0]     ,		original_sizes[0]     ,		input_boxes[0]     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		)
           return {
               "masks": masks,
               "is_last": is_last,
               "boxes": boxes,
               "iou_scores": iou_scores,
           }





       def 		_lowerCamelCase			(		self							:     int     ,		__SCREAMING_SNAKE_CASE							:     Dict     ,		__SCREAMING_SNAKE_CASE							:     Dict=False     ,		__SCREAMING_SNAKE_CASE							:     Tuple=False     ,		__SCREAMING_SNAKE_CASE							:     int=0.7     ,		):


           '''simple docstring'''

           __a						       =				[]
           __a						       =				[]
           __a						       =				[]
           for model_output in model_outputs:
               all_scores.append(model_output.pop('''iou_scores'''))
               all_masks.extend(model_output.pop('''masks'''))
               all_boxes.append(model_output.pop('''boxes'''))

           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a						       =				torch.cat(__SCREAMING_SNAKE_CASE)
           __a       ,   __a       ,   __a       ,   __a						       =				self.image_processor.post_process_for_mask_generation(
               __SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE     ,		__SCREAMING_SNAKE_CASE)

           __a						       =				defaultdict(__SCREAMING_SNAKE_CASE)
           for output in model_outputs:
               for k, v in output.items():
                   extra[k].append(__SCREAMING_SNAKE_CASE)

           __a						       =				{}
           if output_rle_mask:
               __a						       =				rle_mask

           if output_bboxes_mask:
               __a						       =				bounding_boxes

           return {"masks": output_masks, "scores": iou_scores, **optional, **extra}









import inspect
import unittest

from transformers import DecisionTransformerConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
			import torch

			from transformers import DecisionTransformerModel
			from transformers.models.decision_transformer.modeling_decision_transformer import (
			    DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
			)


class        _a							:

							def __init__(							self:	Tuple    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	Any=13    ,						UpperCamelCase_:	Optional[Any]=7    ,						UpperCamelCase_:	Optional[Any]=6    ,						UpperCamelCase_:	Any=17    ,						UpperCamelCase_:	str=23    ,						UpperCamelCase_:	List[Any]=11    ,						UpperCamelCase_:	Optional[int]=True    ,						)			->   List[Any]:





									"""simple docstring"""







									lowercase__            =      parent
									lowercase__            =      batch_size
									lowercase__            =      seq_length
									lowercase__            =      act_dim
									lowercase__            =      state_dim
									lowercase__            =      hidden_size
									lowercase__            =      max_length
									lowercase__            =      is_training

							def       lowerCamelCase_ (							self:	Optional[Any]							)			->   Tuple:





									"""simple docstring"""







									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, self.state_dim)							)
									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, self.act_dim)							)
									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, 1)							)
									lowercase__            =      floats_tensor((self.batch_size, self.seq_length, 1)							)
									lowercase__            =      ids_tensor((self.batch_size, self.seq_length)    ,						vocab_size=1_000							)
									lowercase__            =      random_attention_mask((self.batch_size, self.seq_length)							)

									lowercase__            =      self.get_config()

									return (
									    config,
									    states,
									    actions,
									    rewards,
									    returns_to_go,
									    timesteps,
									    attention_mask,
									)

							def       lowerCamelCase_ (							self:	Dict							)			->   List[Any]:





									"""simple docstring"""







									return DecisionTransformerConfig(
									    batch_size=self.batch_size    ,						seq_length=self.seq_length    ,						act_dim=self.act_dim    ,						state_dim=self.state_dim    ,						hidden_size=self.hidden_size    ,						max_length=self.max_length    ,						)

							def       lowerCamelCase_ (							self:	List[str]    ,						UpperCamelCase_:	int    ,						UpperCamelCase_:	List[str]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	Any    ,						UpperCamelCase_:	List[Any]    ,						UpperCamelCase_:	int    ,						)			->   Dict:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModel(config=UpperCamelCase_							)
									model.to(UpperCamelCase_							)
									model.eval()
									lowercase__            =      model(UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_    ,						UpperCamelCase_							)

									self.parent.assertEqual(result.state_preds.shape    ,						states.shape							)
									self.parent.assertEqual(result.action_preds.shape    ,						actions.shape							)
									self.parent.assertEqual(result.return_preds.shape    ,						returns_to_go.shape							)
									self.parent.assertEqual(
									    result.last_hidden_state.shape    ,						(self.batch_size, self.seq_length * 3, self.hidden_size)							)  # seq length *3 as there are 3 modelities: states, returns and actions


							def       lowerCamelCase_ (							self:	List[Any]							)			->   str:





									"""simple docstring"""







									lowercase__            =      self.prepare_config_and_inputs()
									(
									    (
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		(
									    lowercase__
									)     ,		
									)            =      config_and_inputs
									lowercase__            =      {
									    '''states''': states,
									    '''actions''': actions,
									    '''rewards''': rewards,
									    '''returns_to_go''': returns_to_go,
									    '''timesteps''': timesteps,
									    '''attention_mask''': attention_mask,
									}
									return config, inputs_dict


@require_torch
class        _a							(			UpperCamelCase__     ,	UpperCamelCase__     ,	UpperCamelCase__     ,	unittest.TestCase			):
							_lowercase	:			str        = (DecisionTransformerModel,) if is_torch_available() else ()
							_lowercase	:			List[str]        = ()
							_lowercase	:			List[Any]        = {'''feature-extraction''': DecisionTransformerModel} if is_torch_available() else {}

							# Ignoring of a failing test from GenerationTesterMixin, as the model does not use inputs_ids
							_lowercase	:			Any        = False

							# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
							_lowercase	:			Tuple        = False
							_lowercase	:			str        = False
							_lowercase	:			Tuple        = False
							_lowercase	:			Optional[Any]        = False
							_lowercase	:			Tuple        = False
							_lowercase	:			Dict        = False
							_lowercase	:			Tuple        = False
							_lowercase	:			Optional[Any]        = False
							_lowercase	:			Optional[int]        = False

							def       lowerCamelCase_ (							self:	Union[str, Any]							)			->   Optional[Any]:





									"""simple docstring"""







									lowercase__            =      DecisionTransformerModelTester(self							)
									lowercase__            =      ConfigTester(self    ,						config_class=UpperCamelCase_    ,						hidden_size=37							)

							def       lowerCamelCase_ (							self:	Any							)			->   Optional[Any]:





									"""simple docstring"""







									self.config_tester.run_common_tests()

							def       lowerCamelCase_ (							self:	int							)			->   Optional[int]:





									"""simple docstring"""







									lowercase__            =      self.model_tester.prepare_config_and_inputs()
									self.model_tester.create_and_check_model(*UpperCamelCase_							)

							@slow
							def       lowerCamelCase_ (							self:	Dict							)			->   Optional[Any]:





									"""simple docstring"""







									for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
											lowercase__            =      DecisionTransformerModel.from_pretrained(UpperCamelCase_							)
											self.assertIsNotNone(UpperCamelCase_							)


							def       lowerCamelCase_ (							self:	List[str]							)			->   Dict:





									"""simple docstring"""







									lowercase__     ,		lowercase__            =      self.model_tester.prepare_config_and_inputs_for_common()

									for model_class in self.all_model_classes:
											lowercase__            =      model_class(UpperCamelCase_							)
											lowercase__            =      inspect.signature(model.forward							)
											# signature.parameters is an OrderedDict => so arg_names order is deterministic
											lowercase__            =      [*signature.parameters.keys()]

											lowercase__            =      [
											    '''states''',
											    '''actions''',
											    '''rewards''',
											    '''returns_to_go''',
											    '''timesteps''',
											    '''attention_mask''',
											]

											self.assertListEqual(arg_names[: len(UpperCamelCase_							)]    ,						UpperCamelCase_							)


@require_torch
class        _a							(			unittest.TestCase			):

							@slow
							def       lowerCamelCase_ (							self:	Tuple							)			->   Any:





									"""simple docstring"""







									lowercase__            =      2  # number of steps of autoregressive prediction we will perform
									lowercase__            =      10  # defined by the RL environment, may be normalized
									lowercase__            =      DecisionTransformerModel.from_pretrained('''edbeeching/decision-transformer-gym-hopper-expert'''							)
									lowercase__            =      model.to(UpperCamelCase_							)
									lowercase__            =      model.config
									torch.manual_seed(0							)
									lowercase__            =      torch.randn(1    ,						1    ,						config.state_dim							).to(device=UpperCamelCase_    ,						dtype=torch.floataa							)  # env.reset()

									lowercase__            =      torch.tensor(
									    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]]    ,						device=UpperCamelCase_							)

									lowercase__            =      torch.tensor(UpperCamelCase_    ,						device=UpperCamelCase_    ,						dtype=torch.floataa							).reshape(1    ,						1    ,						1							)
									lowercase__            =      state
									lowercase__            =      torch.zeros(1    ,						0    ,						config.act_dim    ,						device=UpperCamelCase_    ,						dtype=torch.floataa							)
									lowercase__            =      torch.zeros(1    ,						0    ,						device=UpperCamelCase_    ,						dtype=torch.floataa							)
									lowercase__            =      torch.tensor(0    ,						device=UpperCamelCase_    ,						dtype=torch.long							).reshape(1    ,						1							)

									for step in range(UpperCamelCase_							):
											lowercase__            =      torch.cat([actions, torch.zeros(1    ,						1    ,						config.act_dim    ,						device=UpperCamelCase_							)]    ,						dim=1							)
											lowercase__            =      torch.cat([rewards, torch.zeros(1    ,						1    ,						device=UpperCamelCase_							)]    ,						dim=1							)

											lowercase__            =      torch.ones(1    ,						states.shape[1]							).to(dtype=torch.long    ,						device=states.device							)

											with torch.no_grad():
													lowercase__     ,		lowercase__     ,		lowercase__            =      model(
													    states=UpperCamelCase_    ,						actions=UpperCamelCase_    ,						rewards=UpperCamelCase_    ,						returns_to_go=UpperCamelCase_    ,						timesteps=UpperCamelCase_    ,						attention_mask=UpperCamelCase_    ,						return_dict=UpperCamelCase_    ,						)

											self.assertEqual(action_pred.shape    ,						actions.shape							)
											self.assertTrue(torch.allclose(action_pred[0, -1]    ,						expected_outputs[step]    ,						atol=1E-4							)							)
											lowercase__     ,		lowercase__     ,		lowercase__     ,		lowercase__            =      (  # env.step(action)
											    torch.randn(1    ,						1    ,						config.state_dim							).to(device=UpperCamelCase_    ,						dtype=torch.floataa							),
											    1.0,
											    False,
											    {},
											)

											lowercase__            =      action_pred[0, -1]
											lowercase__            =      torch.cat([states, state]    ,						dim=1							)
											lowercase__            =      returns_to_go[0, -1] - reward
											lowercase__            =      torch.cat([returns_to_go, pred_return.reshape(1    ,						1    ,						1							)]    ,						dim=1							)
											lowercase__            =      torch.cat(
											    [timesteps, torch.ones((1, 1)    ,						device=UpperCamelCase_    ,						dtype=torch.long							) * (step + 1)]    ,						dim=1							)


