'''simple docstring'''





import unittest

import numpy as np

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
    import torch

if is_vision_available():
    from PIL import Image

    from transformers import LevitImageProcessor




class        __A      ( unittest.TestCase	):



    def __init__(self    :    str   ,			__a    :    str   ,			__a    :    Optional[int]=7   ,			__a    :    List[str]=3   ,			__a    :    Any=18   ,			__a    :    List[str]=30   ,			__a    :    Dict=400   ,			__a    :    Optional[Any]=True   ,			__a    :    Optional[Any]=None   ,			__a    :    int=True   ,			__a    :    int=None   ,			__a    :    List[str]=True   ,			__a    :    Union[str, Any]=[0.5, 0.5, 0.5]   ,			__a    :    Any=[0.5, 0.5, 0.5]   ,			):
         UpperCAmelCase_           =   size if size is not None else {"shortest_edge": 18}
         UpperCAmelCase_           =   crop_size if crop_size is not None else {"height": 18, "width": 18}
         UpperCAmelCase_           =   parent
         UpperCAmelCase_           =   batch_size
         UpperCAmelCase_           =   num_channels
         UpperCAmelCase_           =   image_size
         UpperCAmelCase_           =   min_resolution
         UpperCAmelCase_           =   max_resolution
         UpperCAmelCase_           =   do_resize
         UpperCAmelCase_           =   size
         UpperCAmelCase_           =   do_center_crop
         UpperCAmelCase_           =   crop_size
         UpperCAmelCase_           =   do_normalize
         UpperCAmelCase_           =   image_mean
         UpperCAmelCase_           =   image_std






    def 					_lowercase						(self    :    int ):
         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "do_center_crop": self.do_center_crop,
             "size": self.size,
             "crop_size": self.crop_size,
         }




@require_torch
@require_vision
class        __A      ( UpperCamelCase__		,	unittest.TestCase	):
    a__       :						Any          =  LevitImageProcessor if is_vision_available() else None



    def 					_lowercase						(self    :    int ):
         UpperCAmelCase_           =   LevitImageProcessingTester(self )



    @property
    def 					_lowercase						(self    :    Any ):
         return self.image_processor_tester.prepare_image_processor_dict()



    def 					_lowercase						(self    :    Union[str, Any] ):
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         self.assertTrue(hasattr(__a   ,			"image_mean" ) )
         self.assertTrue(hasattr(__a   ,			"image_std" ) )
         self.assertTrue(hasattr(__a   ,			"do_normalize" ) )
         self.assertTrue(hasattr(__a   ,			"do_resize" ) )
         self.assertTrue(hasattr(__a   ,			"do_center_crop" ) )
         self.assertTrue(hasattr(__a   ,			"size" ) )



    def 					_lowercase						(self    :    Dict ):
         UpperCAmelCase_           =   self.image_processing_class.from_dict(self.image_processor_dict )
         self.assertEqual(image_processor.size   ,			{"shortest_edge": 18} )
         self.assertEqual(image_processor.crop_size   ,			{"height": 18, "width": 18} )

         UpperCAmelCase_           =   self.image_processing_class.from_dict(self.image_processor_dict   ,			size=42   ,			crop_size=84 )
         self.assertEqual(image_processor.size   ,			{"shortest_edge": 42} )
         self.assertEqual(image_processor.crop_size   ,			{"height": 84, "width": 84} )



    def 					_lowercase						(self    :    int ):
         pass



    def 					_lowercase						(self    :    Optional[Any] ):
         # Initialize image_processing
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         # create random PIL images
         UpperCAmelCase_           =   prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__a )
         for image in image_inputs:
              self.assertIsInstance(__a   ,			Image.Image )

         # Test not batched input
         UpperCAmelCase_           =   image_processing(image_inputs[0]   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCAmelCase_           =   image_processing(__a   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)



    def 					_lowercase						(self    :    Dict ):
         # Initialize image_processing
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         # create random numpy tensors
         UpperCAmelCase_           =   prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__a   ,			numpify=__a )
         for image in image_inputs:
              self.assertIsInstance(__a   ,			np.ndarray )

         # Test not batched input
         UpperCAmelCase_           =   image_processing(image_inputs[0]   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCAmelCase_           =   image_processing(__a   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)






    def 					_lowercase						(self    :    int ):
         # Initialize image_processing
         UpperCAmelCase_           =   self.image_processing_class(**self.image_processor_dict )
         # create random PyTorch tensors
         UpperCAmelCase_           =   prepare_image_inputs(self.image_processor_tester   ,			equal_resolution=__a   ,			torchify=__a )
         for image in image_inputs:
              self.assertIsInstance(__a   ,			torch.Tensor )

         # Test not batched input
         UpperCAmelCase_           =   image_processing(image_inputs[0]   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 1,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)

         # Test batched
         UpperCAmelCase_           =   image_processing(__a   ,			return_tensors="pt" ).pixel_values
         self.assertEqual(
             encoded_images.shape   ,			(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 self.image_processor_tester.crop_size["height"],
                 self.image_processor_tester.crop_size["width"],
             )   ,			)








from ...configuration_utils import PretrainedConfig
from ...utils import logging


_snake_case             =     logging.get_logger(__name__)

_snake_case             =     {
    '''edbeeching/decision-transformer-gym-hopper-medium''': (
        '''https://huggingface.co/edbeeching/decision-transformer-gym-hopper-medium/resolve/main/config.json'''
    ),
    # See all DecisionTransformer models at https://huggingface.co/models?filter=decision_transformer
}
class 	UpperCAmelCase_					(					UpperCamelCase    ):

   '''simple docstring'''




   __A :      str     		=		"decision_transformer"
   __A :      Union[str, Any]     		=		["past_key_values"]
   __A :      Optional[int]     		=		{
       "max_position_embeddings": "n_positions",
       "num_attention_heads": "n_head",
       "num_hidden_layers": "n_layer",
   }






   def __init__(		self						,				__A=17						,				__A=4						,				__A=128						,				__A=4096						,				__A=True						,				__A=1						,				__A=1024						,				__A=3						,				__A=1						,				__A=None						,				__A="relu"						,				__A=0.1						,				__A=0.1						,				__A=0.1						,				__A=1e-5						,				__A=0.02						,				__A=True						,				__A=True						,				__A=5_0256						,				__A=5_0256						,				__A=False						,				__A=False						,				**__A						,				):






     """simple docstring"""





     lowerCamelCase :					List[str]    =				state_dim
     lowerCamelCase :					Tuple    =				act_dim
     lowerCamelCase :					List[str]    =				hidden_size
     lowerCamelCase :					Optional[Any]    =				max_ep_len
     lowerCamelCase :					Union[str, Any]    =				action_tanh
     lowerCamelCase :					int    =				vocab_size
     lowerCamelCase :					List[Any]    =				n_positions
     lowerCamelCase :					Dict    =				n_layer
     lowerCamelCase :					int    =				n_head
     lowerCamelCase :					List[Any]    =				n_inner
     lowerCamelCase :					Any    =				activation_function
     lowerCamelCase :					Optional[int]    =				resid_pdrop
     lowerCamelCase :					str    =				embd_pdrop
     lowerCamelCase :					Tuple    =				attn_pdrop
     lowerCamelCase :					List[Any]    =				layer_norm_epsilon
     lowerCamelCase :					Dict    =				initializer_range
     lowerCamelCase :					Optional[int]    =				scale_attn_weights
     lowerCamelCase :					List[Any]    =				use_cache
     lowerCamelCase :					Tuple    =				scale_attn_by_inverse_layer_idx
     lowerCamelCase :					Optional[int]    =				reorder_and_upcast_attn

     lowerCamelCase :					Dict    =				bos_token_id
     lowerCamelCase :					Any    =				eos_token_id

     super().__init__(bos_token_id=__A						,				eos_token_id=__A						,				**__A )



