





"""simple docstring"""




import inspect
import unittest

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

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


if is_torch_available():
   import torch

   from transformers import MODEL_MAPPING, PoolFormerConfig, PoolFormerForImageClassification, PoolFormerModel
   from transformers.models.poolformer.modeling_poolformer import POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
   from PIL import Image

   from transformers import PoolFormerImageProcessor

class 					_UpperCAmelCase    (  UpperCAmelCase__						):



      '''simple docstring'''

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Tuple									=    self.config_class(**self.inputs_dict    )
        self.parent.assertTrue(hasattr(snake_case_   , 'hidden_sizes'    )    )
        self.parent.assertTrue(hasattr(snake_case_   , 'num_encoder_blocks'    )    )


class 					_UpperCAmelCase    :



      '''simple docstring'''

      def __init__(	self   , snake_case_   , snake_case_=1_3   , snake_case_=6_4   , snake_case_=3   , snake_case_=4   , snake_case_=[2, 2, 2, 2]   , snake_case_=[8, 4, 2, 1]   , snake_case_=[1_6, 3_2, 6_4, 1_2_8]   , snake_case_=[1, 4, 8, 1_6]   , snake_case_=False   , snake_case_=True   , snake_case_="gelu"   , snake_case_=0.1   , snake_case_=0.02   , snake_case_=3   , snake_case_=None   , ):

        """simple docstring"""

        A_    :			Any									=    parent
        A_    :			Optional[Any]									=    batch_size
        A_    :			List[str]									=    image_size
        A_    :			Optional[int]									=    num_channels
        A_    :			Tuple									=    num_encoder_blocks
        A_    :			Tuple									=    sr_ratios
        A_    :			Optional[int]									=    depths
        A_    :			Dict									=    hidden_sizes
        A_    :			str									=    downsampling_rates
        A_    :			Dict									=    is_training
        A_    :			Union[str, Any]									=    use_labels
        A_    :			List[str]									=    hidden_act
        A_    :			Any									=    hidden_dropout_prob
        A_    :			int									=    initializer_range
        A_    :			List[str]									=    num_labels
        A_    :			str									=    scope

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size]    )

        A_    :			Optional[int]									=    None
        if self.use_labels:
          A_    :			Tuple									=    ids_tensor([self.batch_size, self.image_size, self.image_size]   , self.num_labels    )

        A_    :			Union[str, Any]									=    PoolFormerConfig(
            image_size=self.image_size   , num_channels=self.num_channels   , num_encoder_blocks=self.num_encoder_blocks   , depths=self.depths   , hidden_sizes=self.hidden_sizes   , hidden_act=self.hidden_act   , hidden_dropout_prob=self.hidden_dropout_prob   , initializer_range=self.initializer_range   , )

        return config, pixel_values, labels

      def    lowerCamelCase_     (	self   , snake_case_   , snake_case_   , snake_case_    ):

        """simple docstring"""

        A_    :			Any									=    PoolFormerModel(config=snake_case_    )
        model.to(snake_case_    )
        model.eval()
        A_    :			Dict									=    model(snake_case_    )
        A_    :			Union[str, Any]									=    self.image_size // 32.0
        self.parent.assertEqual(
            result.last_hidden_state.shape   , (self.batch_size, self.hidden_sizes[-1], expected_height, expected_width)    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Union[str, Any]									=    self.prepare_config_and_inputs()
        A_		,			A_		,			A_    :			Optional[int]									=    config_and_inputs
        A_    :			Any									=    {'pixel_values': pixel_values}
        return config, inputs_dict


@require_torch
class 					_UpperCAmelCase    (  UpperCAmelCase__     ,  UpperCAmelCase__     ,  unittest.TestCase						):



      '''simple docstring'''
      lowercase_ :					int             =  (PoolFormerModel, PoolFormerForImageClassification) if is_torch_available() else ()
      lowercase_ :					Any             =  (
          {"""feature-extraction""": PoolFormerModel, """image-classification""": PoolFormerForImageClassification}
          if is_torch_available()
          else {}
      )

      lowercase_ :					Union[str, Any]             =  False
      lowercase_ :					Optional[Any]             =  False
      lowercase_ :					List[Any]             =  False
      lowercase_ :					Any             =  False
      lowercase_ :					List[Any]             =  False

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			Optional[int]									=    PoolFormerModelTester(self    )
        A_    :			Optional[Any]									=    PoolFormerConfigTester(self   , config_class=snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        self.config_tester.run_common_tests()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			List[str]									=    self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*snake_case_    )

      @unittest.skip('PoolFormer does not use inputs_embeds'    )
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      @unittest.skip('PoolFormer does not have get_input_embeddings method and get_output_embeddings methods'    )
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        pass

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        def check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    ):
          A_    :			Dict									=    model_class(snake_case_    )
          model.to(snake_case_    )
          model.eval()

          with torch.no_grad():
            A_    :			str									=    model(**self._prepare_for_class(snake_case_   , snake_case_    )    )

          A_    :			List[Any]									=    outputs.hidden_states

          A_    :			str									=    self.model_tester.num_encoder_blocks
          self.assertEqual(len(snake_case_    )   , snake_case_    )

          # verify the first hidden states (first block)
          self.assertListEqual(
              list(hidden_states[0].shape[-3:]    )   , [
                  self.model_tester.hidden_sizes[0],
                  self.model_tester.image_size // 4,
                  self.model_tester.image_size // 4,
              ]   , )

        A_		,			A_    :			Tuple									=    self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
          A_    :			List[str]									=    True
          check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    )

          # check that output_hidden_states also work using config
          del inputs_dict["output_hidden_states"]
          A_    :			Optional[int]									=    True

          check_hidden_states_output(snake_case_   , snake_case_   , snake_case_    )

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        if not self.model_tester.is_training:
          return

        A_		,			A_    :			str									=    self.model_tester.prepare_config_and_inputs_for_common()
        A_    :			str									=    True

        for model_class in self.all_model_classes:
          if model_class in get_values(snake_case_    ):
            continue
          A_    :			List[str]									=    model_class(snake_case_    )
          model.to(snake_case_    )
          model.train()
          A_    :			Any									=    self._prepare_for_class(snake_case_   , snake_case_   , return_labels=snake_case_    )
          A_    :			List[str]									=    model(**snake_case_    ).loss
          loss.backward()

      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_		,			A_    :			List[str]									=    self.model_tester.prepare_config_and_inputs_for_common()

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

          A_    :			List[Any]									=    ['pixel_values']
          self.assertListEqual(arg_names[:1]   , snake_case_    )

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        for model_name in POOLFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
          A_    :			Any									=    PoolFormerModel.from_pretrained(snake_case_    )
          self.assertIsNotNone(snake_case_    )





def 		UpperCAmelCase__						(	):






  """simple docstring"""





  A_    :			List[str]									=    Image.open('./tests/fixtures/tests_samples/COCO/000000039769.png'   )
  return image





@require_torch
class 					_UpperCAmelCase    (  unittest.TestCase						):



      '''simple docstring'''

      @slow
      def    lowerCamelCase_     (	self    ):

        """simple docstring"""

        A_    :			int									=    PoolFormerImageProcessor()
        A_    :			List[Any]									=    PoolFormerForImageClassification.from_pretrained('sail/poolformer_s12'    ).to(snake_case_    )

        A_    :			List[str]									=    image_processor(images=prepare_img()   , return_tensors='pt'    ).to(snake_case_    )

        # forward pass
        with torch.no_grad():
          A_    :			Any									=    model(**snake_case_    )

        # verify the logits
        A_    :			Union[str, Any]									=    torch.Size((1, 1_0_0_0)    )
        self.assertEqual(outputs.logits.shape   , snake_case_    )

        A_    :			List[Any]									=    torch.tensor([-0.61_13, 0.16_85, -0.04_92]    ).to(snake_case_    )
        self.assertTrue(torch.allclose(outputs.logits[0, :3]   , snake_case_   , atol=1E-4    )    )







"""simple docstring"""



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     lowerCamelCase_					(	__A	:		Tuple      ,						__A	:		Optional[int]      ,						__A	:		str      ,						__A	:		Optional[Any]      ,						__A	:		Optional[int]      ,						__A	:		Union[str, Any]      ):
					# we don't care about the past rewards in this model

					lowerCamelCase__					=states.reshape(1      ,						-1      ,						model.config.state_dim      )
					lowerCamelCase__					=actions.reshape(1      ,						-1      ,						model.config.act_dim      )
					lowerCamelCase__					=returns_to_go.reshape(1      ,						-1      ,						1      )
					lowerCamelCase__					=timesteps.reshape(1      ,						-1      )

					if model.config.max_length is not None:
										lowerCamelCase__					=states[:, -model.config.max_length :]
										lowerCamelCase__					=actions[:, -model.config.max_length :]
										lowerCamelCase__					=returns_to_go[:, -model.config.max_length :]
										lowerCamelCase__					=timesteps[:, -model.config.max_length :]

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

					lowerCamelCase__					,  lowerCamelCase__					,  lowerCamelCase__					=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

_snake_case							:   List[str]	  =       gym.make("Hopper-v3")
_snake_case							:   Optional[int]	  =       env.observation_space.shape[0]
_snake_case							:   Tuple	  =       env.action_space.shape[0]
_snake_case							:   Union[str, Any]	  =       1_000
_snake_case							:   Dict	  =       "cuda"
_snake_case							:   Dict	  =       1000.0  # normalization for rewards/returns
_snake_case							:   Optional[int]	  =       3_600 / scale  # evaluation conditioning targets, 3600 is reasonable from the paper LINK
_snake_case							:   Optional[Any]	  =       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,
    ]
)
_snake_case							:   Tuple	  =       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,
    ]
)
_snake_case							:   int	  =       torch.from_numpy(state_mean).to(device=device)
_snake_case							:   int	  =       torch.from_numpy(state_std).to(device=device)

# Create the decision transformer model
_snake_case							:   List[Any]	  =       DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-medium")
_snake_case							:   List[str]	  =       model.to(device)
model.eval()

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

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

												_snake_case							:   List[str]	  =       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),
												)
												_snake_case							:   List[str]	  =       action
												_snake_case							:   Optional[int]	  =       action.detach().cpu().numpy()

												_snake_case  ,    _snake_case  ,    _snake_case  ,    _snake_case							:   List[str]	  =       env.step(action)

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

												_snake_case							:   List[str]	  =       target_return[0, -1] - (reward / scale)
												_snake_case							:   Optional[int]	  =       torch.cat([target_return, pred_return.reshape(1, 1)], dim=1)
												_snake_case							:   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