
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 					_SCREAMING_SNAKE_CASE  :
      def __init__(			self		,	lowerCamelCase		,	lowerCamelCase=13		,	lowerCamelCase=7		,	lowerCamelCase=6		,	lowerCamelCase=17		,	lowerCamelCase=23		,	lowerCamelCase=11		,	lowerCamelCase=True		,	):
          snake_case__						=				parent
          snake_case__						=				batch_size
          snake_case__						=				seq_length
          snake_case__						=				act_dim
          snake_case__						=				state_dim
          snake_case__						=				hidden_size
          snake_case__						=				max_length
          snake_case__						=				is_training
      def A_			(			self ):
          snake_case__						=				floats_tensor((self.batch_size, self.seq_length, self.state_dim) )
          snake_case__						=				floats_tensor((self.batch_size, self.seq_length, self.act_dim) )
          snake_case__						=				floats_tensor((self.batch_size, self.seq_length, 1) )
          snake_case__						=				floats_tensor((self.batch_size, self.seq_length, 1) )
          snake_case__						=				ids_tensor((self.batch_size, self.seq_length)		,	vocab_size=10_00 )
          snake_case__						=				random_attention_mask((self.batch_size, self.seq_length) )

          snake_case__						=				self.get_config()

          return (
              config,
              states,
              actions,
              rewards,
              returns_to_go,
              timesteps,
              attention_mask,
          )
      def A_			(			self ):
          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 A_			(			self		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	):
          snake_case__						=				DecisionTransformerModel(config=lowerCamelCase )
          model.to(lowerCamelCase )
          model.eval()
          snake_case__						=				model(lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase		,	lowerCamelCase )

          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 A_			(			self ):
          snake_case__						=				self.prepare_config_and_inputs()
          (
              (
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					(
              snake_case__
          )						,					
          )						=				config_and_inputs
          snake_case__						=				{
              "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 					_SCREAMING_SNAKE_CASE  (      __UpperCamelCase       ,					__UpperCamelCase       ,					__UpperCamelCase       ,					unittest.TestCase ):
      _A						:		Optional[Any]    	=     (DecisionTransformerModel,) if is_torch_available() else ()
      _A						:		List[Any]    	=     ()
      _A						:		Any    	=     {'feature-extraction': DecisionTransformerModel} if is_torch_available() else {}

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

      # Ignoring of a failing tests from ModelTesterMixin, as the model does not implement these features
      _A						:		Dict    	=     False
      _A						:		Dict    	=     False
      _A						:		Any    	=     False
      _A						:		Dict    	=     False
      _A						:		str    	=     False
      _A						:		Any    	=     False
      _A						:		Any    	=     False
      _A						:		Any    	=     False
      _A						:		str    	=     False
      def A_			(			self ):
          snake_case__						=				DecisionTransformerModelTester(self )
          snake_case__						=				ConfigTester(self		,	config_class=lowerCamelCase		,	hidden_size=37 )
      def A_			(			self ):
          self.config_tester.run_common_tests()
      def A_			(			self ):
          snake_case__						=				self.model_tester.prepare_config_and_inputs()
          self.model_tester.create_and_check_model(*lowerCamelCase )
      @slow
      def A_			(			self ):
          for model_name in DECISION_TRANSFORMER_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
              snake_case__						=				DecisionTransformerModel.from_pretrained(lowerCamelCase )
              self.assertIsNotNone(lowerCamelCase )



      def A_			(			self ):
          snake_case__						,					snake_case__						=				self.model_tester.prepare_config_and_inputs_for_common()

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

              snake_case__						=				[
                  "states",
                  "actions",
                  "rewards",
                  "returns_to_go",
                  "timesteps",
                  "attention_mask",
              ]

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





@require_torch
class 					_SCREAMING_SNAKE_CASE  (      unittest.TestCase ):
      @slow
      def A_			(			self ):
          snake_case__						=				2  # number of steps of autoregressive prediction we will perform
          snake_case__						=				10  # defined by the RL environment, may be normalized
          snake_case__						=				DecisionTransformerModel.from_pretrained("edbeeching/decision-transformer-gym-hopper-expert" )
          snake_case__						=				model.to(lowerCamelCase )
          snake_case__						=				model.config
          torch.manual_seed(0 )
          snake_case__						=				torch.randn(1		,	1		,	config.state_dim ).to(device=lowerCamelCase		,	dtype=torch.floataa )  # env.reset()

          snake_case__						=				torch.tensor(
              [[0.2_4_2_7_9_3, -0.2_8_6_9_3_0_7_4, 0.8_7_4_2_6_1_3], [0.6_7_8_1_5_2_7_4, -0.0_8_1_0_1_0_8_5, -0.1_2_9_5_2_1_4_7]]		,	device=lowerCamelCase )

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

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

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

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

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

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





import numpy as np
from cva import COLOR_BGR2GRAY, CV_8UC3, cvtColor, filteraD, imread, imshow, waitKey





def 	SCREAMING_SNAKE_CASE__    (      __lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase    ,		__lowerCAmelCase							):

    # prepare kernel
    # the kernel size have to be odd
    if (ksize % 2) == 0:
        snake_case__						=				ksize + 1
    snake_case__						=				np.zeros((ksize, ksize)    ,		dtype=np.floataa							)

    # each value
    for y in range(__lowerCAmelCase							):
        for x in range(__lowerCAmelCase							):
            # distance from center
            snake_case__						=				x - ksize // 2
            snake_case__						=				y - ksize // 2

            # degree to radiant
            snake_case__						=				theta / 180 * np.pi
            snake_case__						=				np.cos(_theta							)
            snake_case__						=				np.sin(_theta							)

            # get kernel x
            snake_case__						=				cos_theta * px + sin_theta * py

            # get kernel y
            snake_case__						=				-sin_theta * px + cos_theta * py

            # fill kernel
            snake_case__						=				np.exp(
                -(_x**2 + gamma**2 * _y**2) / (2 * sigma**2)							) * np.cos(2 * np.pi * _x / lambd + psi							)

    return gabor


if __name__ == "__main__":
     import doctest

     doctest.testmod()
     # read original image
     __magic_name__            =  imread('''../image_data/lena.jpg''')
     # turn image in gray scale value
     __magic_name__            =  cvtColor(img, COLOR_BGR2GRAY)

     # Apply multiple Kernel to detect edges
     __magic_name__            =  np.zeros(gray.shape[:2])
     for theta in [0, 30, 60, 90, 120, 150]:
          __magic_name__            =  gabor_filter_kernel(10, 8, theta, 10, 0, 0)
          out += filteraD(gray, CV_8UC3, kernel_aa)
     __magic_name__            =  out / out.max() * 255
     __magic_name__            =  out.astype(np.uinta)

     imshow('''Original''', gray)
     imshow('''Gabor filter with 20x20 mask and 6 directions''', out)

     waitKey(0)



