


import inspect
import unittest

from transformers import DPTConfig
from transformers.file_utils import is_torch_available, is_vision_available
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
						import torch
						from torch import nn

						from transformers import MODEL_MAPPING, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTModel
						from transformers.models.dpt.modeling_dpt import DPT_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
						from PIL import Image

						from transformers import DPTImageProcessor







class 					snake_case__:







						'''simple docstring'''




						def __init__(					self ,    __lowercase ,    __lowercase=2 ,    __lowercase=3_2 ,    __lowercase=1_6 ,    __lowercase=3 ,    __lowercase=True ,    __lowercase=True ,    __lowercase=3_2 ,    __lowercase=4 ,    __lowercase=[0, 1, 2, 3] ,    __lowercase=4 ,    __lowercase=3_7 ,    __lowercase="gelu" ,    __lowercase=0.1 ,    __lowercase=0.1 ,    __lowercase=0.02 ,    __lowercase=3 ,    __lowercase=False ,    __lowercase=None ,    )       ->     Tuple:
										lowerCAmelCase_			:   Any     					=					parent
										lowerCAmelCase_			:   Optional[Any]     					=					batch_size
										lowerCAmelCase_			:   Dict     					=					image_size
										lowerCAmelCase_			:   Any     					=					patch_size
										lowerCAmelCase_			:   Union[str, Any]     					=					num_channels
										lowerCAmelCase_			:   List[Any]     					=					is_training
										lowerCAmelCase_			:   Dict     					=					use_labels
										lowerCAmelCase_			:   Union[str, Any]     					=					hidden_size
										lowerCAmelCase_			:   int     					=					num_hidden_layers
										lowerCAmelCase_			:   Optional[Any]     					=					backbone_out_indices
										lowerCAmelCase_			:   Optional[Any]     					=					num_attention_heads
										lowerCAmelCase_			:   Optional[Any]     					=					intermediate_size
										lowerCAmelCase_			:   List[Any]     					=					hidden_act
										lowerCAmelCase_			:   Tuple     					=					hidden_dropout_prob
										lowerCAmelCase_			:   Optional[int]     					=					attention_probs_dropout_prob
										lowerCAmelCase_			:   Dict     					=					initializer_range
										lowerCAmelCase_			:   Optional[Any]     					=					num_labels
										lowerCAmelCase_			:   Union[str, Any]     					=					scope
										lowerCAmelCase_			:   List[Any]     					=					is_hybrid
										# sequence length of DPT = num_patches + 1 (we add 1 for the [CLS] token)
										lowerCAmelCase_			:   Dict     					=					(image_size // patch_size) ** 2
										lowerCAmelCase_			:   Any     					=					num_patches + 1




						def     lowercase_			(					self				)       ->     str:
										lowerCAmelCase_			:   List[Any]     					=					floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]				)

										lowerCAmelCase_			:   List[Any]     					=					None
										if self.use_labels:
														lowerCAmelCase_			:   Dict     					=					ids_tensor([self.batch_size, self.image_size, self.image_size] ,    self.num_labels				)

										lowerCAmelCase_			:   List[Any]     					=					self.get_config()

										return config, pixel_values, labels




						def     lowercase_			(					self				)       ->     Any:
										return DPTConfig(
										    image_size=self.image_size ,    patch_size=self.patch_size ,    num_channels=self.num_channels ,    hidden_size=self.hidden_size ,    num_hidden_layers=self.num_hidden_layers ,    backbone_out_indices=self.backbone_out_indices ,    num_attention_heads=self.num_attention_heads ,    intermediate_size=self.intermediate_size ,    hidden_act=self.hidden_act ,    hidden_dropout_prob=self.hidden_dropout_prob ,    attention_probs_dropout_prob=self.attention_probs_dropout_prob ,    is_decoder=__lowercase ,    initializer_range=self.initializer_range ,    is_hybrid=self.is_hybrid ,    )




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     List[Any]:
										lowerCAmelCase_			:   Tuple     					=					DPTModel(config=__lowercase				)
										model.to(__lowercase				)
										model.eval()
										lowerCAmelCase_			:   Dict     					=					model(__lowercase				)
										self.parent.assertEqual(result.last_hidden_state.shape ,    (self.batch_size, self.seq_length, self.hidden_size)				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     str:
										lowerCAmelCase_			:   Union[str, Any]     					=					self.num_labels
										lowerCAmelCase_			:   Optional[Any]     					=					DPTForDepthEstimation(__lowercase				)
										model.to(__lowercase				)
										model.eval()
										lowerCAmelCase_			:   Tuple     					=					model(__lowercase				)
										self.parent.assertEqual(result.predicted_depth.shape ,    (self.batch_size, self.image_size, self.image_size)				)




						def     lowercase_			(					self ,    __lowercase ,    __lowercase ,    __lowercase				)       ->     Tuple:
										lowerCAmelCase_			:   Dict     					=					self.num_labels
										lowerCAmelCase_			:   Tuple     					=					DPTForSemanticSegmentation(__lowercase				)
										model.to(__lowercase				)
										model.eval()
										lowerCAmelCase_			:   List[Any]     					=					model(__lowercase ,    labels=__lowercase				)
										self.parent.assertEqual(
										    result.logits.shape ,    (self.batch_size, self.num_labels, self.image_size, self.image_size)				)




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					self.prepare_config_and_inputs()
										lowerCAmelCase_			:   Tuple     					=					config_and_inputs
										lowerCAmelCase_			:   Dict     					=					{'''pixel_values''': pixel_values}
										return config, inputs_dict







@require_torch
class 					snake_case__(				A__,    A__,    unittest.TestCase				):







						'''simple docstring'''




						SCREAMING_SNAKE_CASE__   :	Optional[int]       =				(DPTModel, DPTForDepthEstimation, DPTForSemanticSegmentation) if is_torch_available() else ()
						SCREAMING_SNAKE_CASE__   :	Dict       =				(
						    {
						        """depth-estimation""": DPTForDepthEstimation,
						        """feature-extraction""": DPTModel,
						        """image-segmentation""": DPTForSemanticSegmentation,
						    }
						    if is_torch_available()
						    else {}
						)

						SCREAMING_SNAKE_CASE__   :	Any       =				False
						SCREAMING_SNAKE_CASE__   :	int       =				False
						SCREAMING_SNAKE_CASE__   :	int       =				False




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   List[str]     					=					DPTModelTester(self				)
										lowerCAmelCase_			:   Any     					=					ConfigTester(self ,    config_class=__lowercase ,    has_text_modality=__lowercase ,    hidden_size=3_7				)




						def     lowercase_			(					self				)       ->     Tuple:
										self.config_tester.run_common_tests()




						@unittest.skip(reason='''DPT does not use inputs_embeds'''				)
						def     lowercase_			(					self				)       ->     Any:
										pass




						def     lowercase_			(					self				)       ->     Dict:
										lowerCAmelCase_			:   Any     					=					self.model_tester.prepare_config_and_inputs_for_common()

										for model_class in self.all_model_classes:
														lowerCAmelCase_			:   Optional[int]     					=					model_class(__lowercase				)
														self.assertIsInstance(model.get_input_embeddings() ,    (nn.Module)				)
														lowerCAmelCase_			:   str     					=					model.get_output_embeddings()
														self.assertTrue(x is None or isinstance(__lowercase ,    nn.Linear				)				)




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   Optional[int]     					=					self.model_tester.prepare_config_and_inputs_for_common()

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

														lowerCAmelCase_			:   int     					=					['''pixel_values''']
														self.assertListEqual(arg_names[:1] ,    __lowercase				)




						def     lowercase_			(					self				)       ->     int:
										lowerCAmelCase_			:   Any     					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_model(*__lowercase				)




						def     lowercase_			(					self				)       ->     Dict:
										lowerCAmelCase_			:   Dict     					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_depth_estimation(*__lowercase				)




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										lowerCAmelCase_			:   str     					=					self.model_tester.prepare_config_and_inputs()
										self.model_tester.create_and_check_for_semantic_segmentation(*__lowercase				)




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

										for model_class in self.all_model_classes:
														if model_class.__name__ == "DPTForDepthEstimation":
																		continue

														lowerCAmelCase_			:   Dict     					=					self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase_			:   int     					=					True

														if model_class in get_values(__lowercase				):
																		continue

														lowerCAmelCase_			:   Any     					=					model_class(__lowercase				)
														model.to(__lowercase				)
														model.train()
														lowerCAmelCase_			:   Any     					=					self._prepare_for_class(__lowercase ,    __lowercase ,    return_labels=__lowercase				)
														lowerCAmelCase_			:   List[str]     					=					model(**__lowercase				).loss
														loss.backward()




						def     lowercase_			(					self				)       ->     Tuple:

										for model_class in self.all_model_classes:
														if model_class.__name__ == "DPTForDepthEstimation":
																		continue

														lowerCAmelCase_			:   str     					=					self.model_tester.prepare_config_and_inputs_for_common()
														lowerCAmelCase_			:   List[str]     					=					False
														lowerCAmelCase_			:   int     					=					True

														if model_class in get_values(__lowercase				) or not model_class.supports_gradient_checkpointing:
																		continue
														lowerCAmelCase_			:   Dict     					=					model_class(__lowercase				)
														model.to(__lowercase				)
														model.gradient_checkpointing_enable()
														model.train()
														lowerCAmelCase_			:   Optional[Any]     					=					self._prepare_for_class(__lowercase ,    __lowercase ,    return_labels=__lowercase				)
														lowerCAmelCase_			:   str     					=					model(**__lowercase				).loss
														loss.backward()




						def     lowercase_			(					self				)       ->     Optional[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					self.model_tester.prepare_config_and_inputs_for_common()

										lowerCAmelCase_			:   List[str]     					=					_config_zero_init(__lowercase				)
										for model_class in self.all_model_classes:
														lowerCAmelCase_			:   Optional[int]     					=					model_class(config=__lowercase				)
														# Skip the check for the backbone
														lowerCAmelCase_			:   str     					=					[]
														for name, module in model.named_modules():
																		if module.__class__.__name__ == "DPTViTHybridEmbeddings":
																						lowerCAmelCase_			:   Dict     					=					[f"""{name}.{key}""" for key in module.state_dict().keys()]
																						break

														for name, param in model.named_parameters():
																		if param.requires_grad:
																						if name in backbone_params:
																										continue
																						self.assertIn(
																						    ((param.data.mean() * 1e9).round() / 1e9).item() ,    [0.0, 1.0] ,    msg=f"""Parameter {name} of model {model_class} seems not properly initialized""" ,    )




						@unittest.skip('''Will be fixed soon by reducing the size of the model used for common tests.'''				)
						def     lowercase_			(					self				)       ->     List[str]:
										pass




						@slow
						def     lowercase_			(					self				)       ->     Dict:

										for model_name in DPT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
														lowerCAmelCase_			:   Tuple     					=					DPTModel.from_pretrained(__lowercase				)
														self.assertIsNotNone(__lowercase				)






def       lowerCAmelCase			(		)->			Union[str, Any]:
				lowerCAmelCase_			:   Union[str, Any]     					=					Image.open('''./tests/fixtures/tests_samples/COCO/000000039769.png'''			)
				return image







@require_torch
@require_vision
@slow
class 					snake_case__(				unittest.TestCase				):







						'''simple docstring'''




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										lowerCAmelCase_			:   Union[str, Any]     					=					DPTImageProcessor.from_pretrained('''Intel/dpt-large'''				)
										lowerCAmelCase_			:   Optional[Any]     					=					DPTForDepthEstimation.from_pretrained('''Intel/dpt-large'''				).to(__lowercase				)

										lowerCAmelCase_			:   int     					=					prepare_img()
										lowerCAmelCase_			:   List[str]     					=					image_processor(images=__lowercase ,    return_tensors='''pt'''				).to(__lowercase				)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase_			:   str     					=					model(**__lowercase				)
														lowerCAmelCase_			:   Optional[int]     					=					outputs.predicted_depth

										# verify the predicted depth
										lowerCAmelCase_			:   Any     					=					torch.Size((1, 3_8_4, 3_8_4)				)
										self.assertEqual(predicted_depth.shape ,    __lowercase				)

										lowerCAmelCase_			:   Any     					=					torch.tensor(
										    [[6.31_99, 6.36_29, 6.41_48], [6.38_50, 6.36_15, 6.41_66], [6.35_19, 6.31_76, 6.35_75]]				).to(__lowercase				)

										self.assertTrue(torch.allclose(outputs.predicted_depth[0, :3, :3] ,    __lowercase ,    atol=1e-4				)				)




						def     lowercase_			(					self				)       ->     Optional[Any]:
										lowerCAmelCase_			:   Optional[Any]     					=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''				)
										lowerCAmelCase_			:   List[Any]     					=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''				).to(__lowercase				)

										lowerCAmelCase_			:   Dict     					=					prepare_img()
										lowerCAmelCase_			:   int     					=					image_processor(images=__lowercase ,    return_tensors='''pt'''				).to(__lowercase				)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase_			:   Optional[Any]     					=					model(**__lowercase				)

										# verify the logits
										lowerCAmelCase_			:   Any     					=					torch.Size((1, 1_5_0, 4_8_0, 4_8_0)				)
										self.assertEqual(outputs.logits.shape ,    __lowercase				)

										lowerCAmelCase_			:   Dict     					=					torch.tensor(
										    [[4.04_80, 4.24_20, 4.43_60], [4.31_24, 4.56_93, 4.82_61], [4.57_68, 4.89_65, 5.21_63]]				).to(__lowercase				)

										self.assertTrue(torch.allclose(outputs.logits[0, 0, :3, :3] ,    __lowercase ,    atol=1e-4				)				)




						def     lowercase_			(					self				)       ->     Union[str, Any]:
										lowerCAmelCase_			:   Any     					=					DPTImageProcessor.from_pretrained('''Intel/dpt-large-ade'''				)
										lowerCAmelCase_			:   Union[str, Any]     					=					DPTForSemanticSegmentation.from_pretrained('''Intel/dpt-large-ade'''				).to(__lowercase				)

										lowerCAmelCase_			:   Optional[Any]     					=					prepare_img()
										lowerCAmelCase_			:   Any     					=					image_processor(images=__lowercase ,    return_tensors='''pt'''				).to(__lowercase				)

										# forward pass
										with torch.no_grad():
														lowerCAmelCase_			:   Optional[Any]     					=					model(**__lowercase				)

										lowerCAmelCase_			:   List[Any]     					=					outputs.logits.detach().cpu()

										lowerCAmelCase_			:   List[str]     					=					image_processor.post_process_semantic_segmentation(outputs=__lowercase ,    target_sizes=[(5_0_0, 3_0_0)]				)
										lowerCAmelCase_			:   str     					=					torch.Size((5_0_0, 3_0_0)				)
										self.assertEqual(segmentation[0].shape ,    __lowercase				)

										lowerCAmelCase_			:   Optional[Any]     					=					image_processor.post_process_semantic_segmentation(outputs=__lowercase				)
										lowerCAmelCase_			:   Tuple     					=					torch.Size((4_8_0, 4_8_0)				)
										self.assertEqual(segmentation[0].shape ,    __lowercase				)
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       a__      (					a  ,   a  ,   a  ,   a  ,   a  ,   a							)		->	int:
       # we don't care about the past rewards in this model

       A_							:       Dict	      =				states.reshape(1  ,   -1  ,   model.config.state_dim							)
       A_							:       Dict	      =				actions.reshape(1  ,   -1  ,   model.config.act_dim							)
       A_							:       Dict	      =				returns_to_go.reshape(1  ,   -1  ,   1							)
       A_							:       Tuple	      =				timesteps.reshape(1  ,   -1							)

       if model.config.max_length is not None:
              A_							:       int	      =				states[:, -model.config.max_length :]
              A_							:       Optional[int]	      =				actions[:, -model.config.max_length :]
              A_							:       Any	      =				returns_to_go[:, -model.config.max_length :]
              A_							:       Tuple	      =				timesteps[:, -model.config.max_length :]

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

       A_      ,							A_      ,							A_							:       List[str]	      =				model(
           states=a  ,   actions=a  ,   rewards=a  ,   returns_to_go=a  ,   timesteps=a  ,   attention_mask=a  ,   return_dict=a  ,   )

       return action_preds[0, -1]


# build the environment

_lowerCAmelCase        =       gym.make('Hopper-v3')
_lowerCAmelCase        =       env.observation_space.shape[0]
_lowerCAmelCase        =       env.action_space.shape[0]
_lowerCAmelCase        =       1_0_0_0
_lowerCAmelCase        =       'cuda'
_lowerCAmelCase        =       1000.0  # normalization for rewards/returns
_lowerCAmelCase        =       3_6_0_0 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_lowerCAmelCase        =       np.array(
    [
        1.31_1279,
        -0.0846_9521,
        -0.538_2719,
        -0.0720_1576,
        0.0493_2366,
        2.106_6856,
        -0.1501_7354,
        0.0087_8345,
        -0.284_8186,
        -0.1854_0096,
        -0.2846_1286,
    ]
)
_lowerCAmelCase        =       np.array(
    [
        0.1779_0751,
        0.0544_4621,
        0.2129_7139,
        0.1453_0419,
        0.612_4444,
        0.8517_4465,
        1.451_5252,
        0.675_1696,
        1.53_6239,
        1.616_0746,
        5.607_2536,
    ]
)
_lowerCAmelCase        =       torch.from_numpy(state_mean).to(device=device)
_lowerCAmelCase        =       torch.from_numpy(state_std).to(device=device)

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

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

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

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

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

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

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


