



"""simple docstring"""







import unittest
from typing import Dict, List, Optional, Union

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 BridgeTowerImageProcessor






class      UpperCAmelCase__		(      unittest.TestCase     ):



  def __init__( self  :    Optional[int]      ,							snake_case  :    List[Any]      ,							snake_case  :    bool = True      ,							snake_case  :    Dict[str, int] = None      ,							snake_case  :    int = 32      ,							snake_case  :    bool = True      ,							snake_case  :    Union[int, float] = 1 / 255      ,							snake_case  :    bool = True      ,							snake_case  :    bool = True      ,							snake_case  :    Optional[Union[float, List[float]]] = [0.48145466, 0.4578275, 0.40821073]      ,							snake_case  :    Optional[Union[float, List[float]]] = [0.26862954, 0.26130258, 0.27577711]      ,							snake_case  :    bool = True      ,							snake_case  :    Union[str, Any]=7      ,							snake_case  :    Tuple=30      ,							snake_case  :    Union[str, Any]=400      ,							snake_case  :    Optional[Any]=3      ,							)			->						Union[str, Any]:






         '''simple docstring'''

         A         =					parent
         A         =					do_resize
         A         =					size if size is not None else {'shortest_edge': 288}
         A         =					size_divisor
         A         =					do_rescale
         A         =					rescale_factor
         A         =					do_normalize
         A         =					do_center_crop
         A         =					image_mean
         A         =					image_std
         A         =					do_pad
         A         =					batch_size
         A         =					num_channels
         A         =					min_resolution
         A         =					max_resolution



  def 					A_		( self  :    int				)			->						List[Any]:






         '''simple docstring'''

         return {
             "image_mean": self.image_mean,
             "image_std": self.image_std,
             "do_normalize": self.do_normalize,
             "do_resize": self.do_resize,
             "size": self.size,
             "size_divisor": self.size_divisor,
         }



  def 					A_		( self  :    int      ,							snake_case  :    str      ,							snake_case  :    Union[str, Any]=False				)			->						List[Any]:






         '''simple docstring'''

         if not batched:
                A         =					self.size['shortest_edge']
                A         =					image_inputs[0]
                if isinstance(snake_case      ,							Image.Image				):
                       A      ,  A         =					image.size
                else:
                       A      ,  A         =					image.shape[1], image.shape[2]
                A         =					size / min(snake_case      ,							snake_case				)
                if h < w:
                       A      ,  A         =					size, scale * w
                else:
                       A      ,  A         =					scale * h, size

                A         =					int((1_333 / 800) * size				)
                if max(snake_case      ,							snake_case				) > max_size:
                       A         =					max_size / max(snake_case      ,							snake_case				)
                       A         =					newh * scale
                       A         =					neww * scale

                A      ,  A         =					int(newh + 0.5				), int(neww + 0.5				)
                A      ,  A         =					(
                    newh // self.size_divisor * self.size_divisor,
                    neww // self.size_divisor * self.size_divisor,
                )

         else:
                A         =					[]
                for image in image_inputs:
                       A      ,  A         =					self.get_expected_values([image]				)
                       expected_values.append((expected_height, expected_width)				)
                A         =					max(snake_case      ,							key=lambda snake_case				: item[0]				)[0]
                A         =					max(snake_case      ,							key=lambda snake_case				: item[1]				)[1]

         return expected_height, expected_width






@require_torch
@require_vision
class      UpperCAmelCase__		(      UpperCamelCase   ,unittest.TestCase     ):
  lowerCAmelCase_       :	Tuple 							=     BridgeTowerImageProcessor if is_vision_available() else None



  def 					A_		( self  :    Dict				)			->						Dict:






         '''simple docstring'''

         A         =					BridgeTowerImageProcessingTester(self				)



  @property
  def 					A_		( self  :    Any				)			->						List[str]:






         '''simple docstring'''

         return self.image_processor_tester.prepare_image_processor_dict()



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






         '''simple docstring'''

         A         =					self.image_processing_class(**self.image_processor_dict				)
         self.assertTrue(hasattr(snake_case      ,							'image_mean'				)				)
         self.assertTrue(hasattr(snake_case      ,							'image_std'				)				)
         self.assertTrue(hasattr(snake_case      ,							'do_normalize'				)				)
         self.assertTrue(hasattr(snake_case      ,							'do_resize'				)				)
         self.assertTrue(hasattr(snake_case      ,							'size'				)				)
         self.assertTrue(hasattr(snake_case      ,							'size_divisor'				)				)



  def 					A_		( self  :    Any				)			->						Optional[int]:






         '''simple docstring'''

         pass



  def 					A_		( self  :    Tuple				)			->						Dict:






         '''simple docstring'''

         A         =					self.image_processing_class(**self.image_processor_dict				)
         # create random PIL images
         A         =					prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=snake_case				)
         for image in image_inputs:
                self.assertIsInstance(snake_case      ,							Image.Image				)

         # Test not batched input
         A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

         A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)
         self.assertEqual(
             encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

         # Test batched
         A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

         A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)
         self.assertEqual(
             encoded_images.shape      ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,							)



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






         '''simple docstring'''

         A         =					self.image_processing_class(**self.image_processor_dict				)
         # create random numpy tensors
         A         =					prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=snake_case      ,							numpify=snake_case				)
         for image in image_inputs:
                self.assertIsInstance(snake_case      ,							np.ndarray				)

         # Test not batched input
         A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

         A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)
         self.assertEqual(
             encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

         # Test batched
         A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

         A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)
         self.assertEqual(
             encoded_images.shape      ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,							)



  def 					A_		( self  :    Union[str, Any]				)			->						Optional[int]:






         '''simple docstring'''

         A         =					self.image_processing_class(**self.image_processor_dict				)
         # create random PyTorch tensors
         A         =					prepare_image_inputs(self.image_processor_tester      ,							equal_resolution=snake_case      ,							torchify=snake_case				)
         for image in image_inputs:
                self.assertIsInstance(snake_case      ,							torch.Tensor				)

         # Test not batched input
         A         =					image_processing(image_inputs[0]      ,							return_tensors='pt'				).pixel_values

         A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case				)
         self.assertEqual(
             encoded_images.shape      ,							(1, self.image_processor_tester.num_channels, expected_height, expected_width)      ,							)

         # Test batched
         A         =					image_processing(snake_case      ,							return_tensors='pt'				).pixel_values

         A      ,  A         =					self.image_processor_tester.get_expected_values(snake_case      ,							batched=snake_case				)
         self.assertEqual(
             encoded_images.shape      ,							(
                 self.image_processor_tester.batch_size,
                 self.image_processor_tester.num_channels,
                 expected_height,
                 expected_width,
             )      ,							)








"""simple docstring"""







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


A    =			logging.get_logger(__name__)

A    =			{
    '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     ):
		lowerCAmelCase_       :	Tuple 							=     """decision_transformer"""
		lowerCAmelCase_       :	Tuple 							=     ["""past_key_values"""]
		lowerCAmelCase_       :	Dict 							=     {
		    """max_position_embeddings""": """n_positions""",
		    """num_attention_heads""": """n_head""",
		    """num_hidden_layers""": """n_layer""",
		}



		def __init__( self  :    Optional[Any]      ,							snake_case  :    int=17      ,							snake_case  :    int=4      ,							snake_case  :    Union[str, Any]=128      ,							snake_case  :    Optional[Any]=4_096      ,							snake_case  :    List[Any]=True      ,							snake_case  :    str=1      ,							snake_case  :    str=1_024      ,							snake_case  :    List[str]=3      ,							snake_case  :    str=1      ,							snake_case  :    Any=None      ,							snake_case  :    Optional[int]="relu"      ,							snake_case  :    List[Any]=0.1      ,							snake_case  :    str=0.1      ,							snake_case  :    Tuple=0.1      ,							snake_case  :    Union[str, Any]=1E-5      ,							snake_case  :    Optional[int]=0.02      ,							snake_case  :    List[Any]=True      ,							snake_case  :    Tuple=True      ,							snake_case  :    Optional[Any]=50_256      ,							snake_case  :    List[Any]=50_256      ,							snake_case  :    List[str]=False      ,							snake_case  :    Any=False      ,							**snake_case  :    List[str]      ,							)			->						List[Any]:






									'''simple docstring'''

									A         =					state_dim
									A         =					act_dim
									A         =					hidden_size
									A         =					max_ep_len
									A         =					action_tanh
									A         =					vocab_size
									A         =					n_positions
									A         =					n_layer
									A         =					n_head
									A         =					n_inner
									A         =					activation_function
									A         =					resid_pdrop
									A         =					embd_pdrop
									A         =					attn_pdrop
									A         =					layer_norm_epsilon
									A         =					initializer_range
									A         =					scale_attn_weights
									A         =					use_cache
									A         =					scale_attn_by_inverse_layer_idx
									A         =					reorder_and_upcast_attn

									A         =					bos_token_id
									A         =					eos_token_id

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



