


"""simple docstring"""






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      lowerCamelCase__   :







					"""simple docstring"""


					def __init__(       self    : int ,       UpperCamelCase    : Optional[Any] ,       UpperCamelCase    : Tuple=13 ,       UpperCamelCase    : str=7 ,       UpperCamelCase    : Tuple=6 ,       UpperCamelCase    : Any=17 ,       UpperCamelCase    : Dict=23 ,       UpperCamelCase    : int=11 ,       UpperCamelCase    : Dict=True ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					parent
												__UpperCAmelCase   :						Tuple      							=					batch_size
												__UpperCAmelCase   :						int      							=					seq_length
												__UpperCAmelCase   :						List[str]      							=					act_dim
												__UpperCAmelCase   :						List[Any]      							=					state_dim
												__UpperCAmelCase   :						List[str]      							=					hidden_size
												__UpperCAmelCase   :						List[Any]      							=					max_length
												__UpperCAmelCase   :						Optional[int]      							=					is_training






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						str      							=					floats_tensor((self.batch_size, self.seq_length, self.state_dim)  )
												__UpperCAmelCase   :						Dict      							=					floats_tensor((self.batch_size, self.seq_length, self.act_dim)  )
												__UpperCAmelCase   :						Tuple      							=					floats_tensor((self.batch_size, self.seq_length, 1)  )
												__UpperCAmelCase   :						List[str]      							=					floats_tensor((self.batch_size, self.seq_length, 1)  )
												__UpperCAmelCase   :						Optional[int]      							=					ids_tensor((self.batch_size, self.seq_length) ,       vocab_size=1_000  )
												__UpperCAmelCase   :						Union[str, Any]      							=					random_attention_mask((self.batch_size, self.seq_length)  )

												__UpperCAmelCase   :						List[str]      							=					self.get_config()

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






					def lowerCamelCase__							(       self    : Tuple  ):
												'''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    : Union[str, Any] ,       UpperCamelCase    : Union[str, Any] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Union[str, Any] ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : List[Any] ,       ):
												'''simple docstring'''






												__UpperCAmelCase   :						List[str]      							=					DecisionTransformerModel(config=UpperCamelCase  )
												model.to(UpperCamelCase  )
												model.eval()
												__UpperCAmelCase   :						str      							=					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    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Any      							=					self.prepare_config_and_inputs()
												(
												    (
												    __UpperCAmelCase
												)							,(
												    __UpperCAmelCase
												)							,(
												    __UpperCAmelCase
												)							,(
												    __UpperCAmelCase
												)							,(
												    __UpperCAmelCase
												)							,(
												    __UpperCAmelCase
												)							,(
												    __UpperCAmelCase
												)							,
												)   :						Tuple      							=					config_and_inputs
												__UpperCAmelCase   :						int      							=					{
												    """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      lowerCamelCase__   (     A     ,    A     ,    A     ,    unittest.TestCase				):







					"""simple docstring"""


					__a	       =							(DecisionTransformerModel,) if is_torch_available() else ()
					__a	       =							()
					__a	       =							{"""feature-extraction""": DecisionTransformerModel} if is_torch_available() else {}

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

					# Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
					__a	       =							False
					__a	       =							False
					__a	       =							False
					__a	       =							False
					__a	       =							False
					__a	       =							False
					__a	       =							False
					__a	       =							False
					__a	       =							False






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Tuple      							=					DecisionTransformerModelTester(self  )
												__UpperCAmelCase   :						List[Any]      							=					ConfigTester(self ,       config_class=UpperCamelCase ,       hidden_size=37  )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												self.config_tester.run_common_tests()






					def lowerCamelCase__							(       self    : Optional[Any]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Dict      							=					self.model_tester.prepare_config_and_inputs()
												self.model_tester.create_and_check_model(*UpperCamelCase  )






					@slow
					def lowerCamelCase__							(       self    : Optional[int]  ):
												'''simple docstring'''






												for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			__UpperCAmelCase   :						int      							=					DecisionTransformerModel.from_pretrained(UpperCamelCase  )
																			self.assertIsNotNone(UpperCamelCase  )






					def lowerCamelCase__							(       self    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase							,__UpperCAmelCase   :						int      							=					self.model_tester.prepare_config_and_inputs_for_common()

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

																			__UpperCAmelCase   :						List[Any]      							=					[
																			    """states""",
																			    """actions""",
																			    """rewards""",
																			    """returns_to_go""",
																			    """timesteps""",
																			    """attention_mask""",
																			]

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






@require_torch
class      lowerCamelCase__   (     unittest.TestCase				):







					"""simple docstring"""


					@slow
					def lowerCamelCase__							(       self    : int  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					2  # number of steps of autoregressive prediction we will perform
												__UpperCAmelCase   :						Any      							=					10  # defined by the RL environment, may be normalized
												__UpperCAmelCase   :						Optional[int]      							=					DecisionTransformerModel.from_pretrained("""edbeeching/decision-transformer-gym-hopper-expert"""  )
												__UpperCAmelCase   :						List[str]      							=					model.to(UpperCamelCase  )
												__UpperCAmelCase   :						int      							=					model.config
												torch.manual_seed(0  )
												__UpperCAmelCase   :						Optional[int]      							=					torch.randn(1 ,       1 ,       config.state_dim  ).to(device=UpperCamelCase ,       dtype=torch.floataa  )  # env.reset()

												__UpperCAmelCase   :						List[str]      							=					torch.tensor(
												    [[0.242793, -0.28693074, 0.8742613], [0.67815274, -0.08101085, -0.12952147]] ,       device=UpperCamelCase  )

												__UpperCAmelCase   :						Optional[Any]      							=					torch.tensor(UpperCamelCase ,       device=UpperCamelCase ,       dtype=torch.floataa  ).reshape(1 ,       1 ,       1  )
												__UpperCAmelCase   :						Tuple      							=					state
												__UpperCAmelCase   :						Tuple      							=					torch.zeros(1 ,       0 ,       config.act_dim ,       device=UpperCamelCase ,       dtype=torch.floataa  )
												__UpperCAmelCase   :						int      							=					torch.zeros(1 ,       0 ,       device=UpperCamelCase ,       dtype=torch.floataa  )
												__UpperCAmelCase   :						List[str]      							=					torch.tensor(0 ,       device=UpperCamelCase ,       dtype=torch.long  ).reshape(1 ,       1  )

												for step in range(UpperCamelCase  ):
																			__UpperCAmelCase   :						Optional[int]      							=					torch.cat([actions, torch.zeros(1 ,       1 ,       config.act_dim ,       device=UpperCamelCase  )] ,       dim=1  )
																			__UpperCAmelCase   :						Tuple      							=					torch.cat([rewards, torch.zeros(1 ,       1 ,       device=UpperCamelCase  )] ,       dim=1  )

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

																			with torch.no_grad():
																										__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Any      							=					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  )  )
																			__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					(  # env.step(action)
																			    torch.randn(1 ,       1 ,       config.state_dim  ).to(device=UpperCamelCase ,       dtype=torch.floataa  ),
																			    1.0,
																			    False,
																			    {},
																			)

																			__UpperCAmelCase   :						List[Any]      							=					action_pred[0, -1]
																			__UpperCAmelCase   :						Dict      							=					torch.cat([states, state] ,       dim=1  )
																			__UpperCAmelCase   :						Tuple      							=					returns_to_go[0, -1] - reward
																			__UpperCAmelCase   :						Any      							=					torch.cat([returns_to_go, pred_return.reshape(1 ,       1 ,       1  )] ,       dim=1  )
																			__UpperCAmelCase   :						List[str]      							=					torch.cat(
																			    [timesteps, torch.ones((1, 1) ,       device=UpperCamelCase ,       dtype=torch.long  ) * (step + 1)] ,       dim=1  )




"""simple docstring"""






import json
import os
from collections import Counter

import torch
import torchvision
import torchvision.transforms as transforms
from PIL import Image
from torch import nn
from torch.utils.data import Dataset


UpperCAmelCase    :     str   						=		{1: (1, 1), 2: (2, 1), 3: (3, 1), 4: (2, 2), 5: (5, 1), 6: (3, 2), 7: (7, 1), 8: (4, 2), 9: (3, 3)}





class      lowerCamelCase__   (     nn.Module				):







					"""simple docstring"""


					def __init__(       self    : Union[str, Any] ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												super().__init__()
												__UpperCAmelCase   :						Union[str, Any]      							=					torchvision.models.resnetaaa(pretrained=UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					list(model.children()  )[:-2]
												__UpperCAmelCase   :						Tuple      							=					nn.Sequential(*UpperCamelCase  )
												__UpperCAmelCase   :						Tuple      							=					nn.AdaptiveAvgPoolad(POOLING_BREAKDOWN[args.num_image_embeds]  )






					def lowerCamelCase__							(       self    : List[Any] ,       UpperCamelCase    : List[str]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					self.pool(self.model(UpperCamelCase  )  )
												__UpperCAmelCase   :						str      							=					torch.flatten(UpperCamelCase ,       start_dim=2  )
												__UpperCAmelCase   :						Union[str, Any]      							=					out.transpose(1 ,       2  ).contiguous()
												return out  # BxNx2048






class      lowerCamelCase__   (     A				):







					"""simple docstring"""


					def __init__(       self    : str ,       UpperCamelCase    : str ,       UpperCamelCase    : Tuple ,       UpperCamelCase    : str ,       UpperCamelCase    : Any ,       UpperCamelCase    : Optional[int]  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Optional[Any]      							=					[json.loads(UpperCamelCase  ) for l in open(UpperCamelCase  )]
												__UpperCAmelCase   :						Optional[Any]      							=					os.path.dirname(UpperCamelCase  )
												__UpperCAmelCase   :						List[Any]      							=					tokenizer
												__UpperCAmelCase   :						List[str]      							=					labels
												__UpperCAmelCase   :						Optional[int]      							=					len(UpperCamelCase  )
												__UpperCAmelCase   :						Union[str, Any]      							=					max_seq_length

												__UpperCAmelCase   :						Tuple      							=					transforms






					def __len__(       self    : Tuple  ):
												'''simple docstring'''






												return len(self.data  )






					def __getitem__(       self    : int ,       UpperCamelCase    : Any  ):
												'''simple docstring'''






												__UpperCAmelCase   :						Union[str, Any]      							=					torch.LongTensor(self.tokenizer.encode(self.data[index]["""text"""] ,       add_special_tokens=UpperCamelCase  )  )
												__UpperCAmelCase							,__UpperCAmelCase							,__UpperCAmelCase   :						Dict      							=					sentence[0], sentence[1:-1], sentence[-1]
												__UpperCAmelCase   :						Union[str, Any]      							=					sentence[: self.max_seq_length]

												__UpperCAmelCase   :						Tuple      							=					torch.zeros(self.n_classes  )
												__UpperCAmelCase   :						str      							=					1

												__UpperCAmelCase   :						List[str]      							=					Image.open(os.path.join(self.data_dir ,       self.data[index]["""img"""]  )  ).convert("""RGB"""  )
												__UpperCAmelCase   :						Any      							=					self.transforms(UpperCamelCase  )

												return {
												    "image_start_token": start_token,
												    "image_end_token": end_token,
												    "sentence": sentence,
												    "image": image,
												    "label": label,
												}






					def lowerCamelCase__							(       self    : Tuple  ):
												'''simple docstring'''






												__UpperCAmelCase   :						int      							=					Counter()
												for row in self.data:
																			label_freqs.update(row["""label"""]  )
												return label_freqs






def      lowerCamelCase       (     _UpperCamelCase		:      Union[str, Any]							)		->       Any:

							'''simple docstring'''



							__UpperCAmelCase   :						Union[str, Any]      							=					[len(row["""sentence"""]							) for row in batch]
							__UpperCAmelCase							,__UpperCAmelCase   :						Union[str, Any]      							=					len(_UpperCamelCase							), max(_UpperCamelCase							)

							__UpperCAmelCase   :						int      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)
							__UpperCAmelCase   :						Any      							=					torch.zeros(_UpperCamelCase       ,			_UpperCamelCase       ,			dtype=torch.long							)

							for i_batch, (input_row, length) in enumerate(zip(_UpperCamelCase       ,			_UpperCamelCase							)							):
														__UpperCAmelCase   :						List[str]      							=					input_row["""sentence"""]
														__UpperCAmelCase   :						Optional[Any]      							=					1

							__UpperCAmelCase   :						int      							=					torch.stack([row["""image"""] for row in batch]							)
							__UpperCAmelCase   :						Optional[int]      							=					torch.stack([row["""label"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_start_token"""] for row in batch]							)
							__UpperCAmelCase   :						int      							=					torch.stack([row["""image_end_token"""] for row in batch]							)

							return text_tensor, mask_tensor, img_tensor, img_start_token, img_end_token, tgt_tensor
def      lowerCamelCase       (     )		->       List[str]:

							'''simple docstring'''



							return [
							    "Crime",
							    "Drama",
							    "Thriller",
							    "Action",
							    "Comedy",
							    "Romance",
							    "Documentary",
							    "Short",
							    "Mystery",
							    "History",
							    "Family",
							    "Adventure",
							    "Fantasy",
							    "Sci-Fi",
							    "Western",
							    "Horror",
							    "Sport",
							    "War",
							    "Music",
							    "Musical",
							    "Animation",
							    "Biography",
							    "Film-Noir",
							]
def      lowerCamelCase       (     )		->       Dict:

							'''simple docstring'''



							return transforms.Compose(
							    [
							        transforms.Resize(2_5_6							),
							        transforms.CenterCrop(2_2_4							),
							        transforms.ToTensor(),
							        transforms.Normalize(
							            mean=[0.46_777_044, 0.44_531_429, 0.40_661_017]       ,			std=[0.12_221_994, 0.12_145_835, 0.14_380_469]       ,			),
							    ]							)
