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


lowercase						:      int									= logging.get_logger(__name__)

lowercase						:      Union[str, Any]									= {
    '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 				lowerCamelCase__							(       __lowercase):




	'''simple docstring'''




	_A			=  'decision_transformer'
	_A			=  ['past_key_values']
	_A			=  {
	    'max_position_embeddings': 'n_positions',
	    'num_attention_heads': 'n_head',
	    'num_hidden_layers': 'n_layer',
	}







	def __init__(      self	:Optional[int]  ,     a	:Tuple=1_7  ,     a	:Optional[Any]=4  ,     a	:Optional[int]=1_2_8  ,     a	:Union[str, Any]=4_0_9_6  ,     a	:Union[str, Any]=True  ,     a	:List[Any]=1  ,     a	:Optional[Any]=1_0_2_4  ,     a	:Any=3  ,     a	:List[str]=1  ,     a	:List[Any]=None  ,     a	:Optional[int]="relu"  ,     a	:str=0.1  ,     a	:int=0.1  ,     a	:Any=0.1  ,     a	:Union[str, Any]=1E-5  ,     a	:str=0.02  ,     a	:List[str]=True  ,     a	:str=True  ,     a	:Dict=5_0_2_5_6  ,     a	:Optional[Any]=5_0_2_5_6  ,     a	:List[Any]=False  ,     a	:List[str]=False  ,     **a	:Dict  ,     )	->				List[Any]:
								__UpperCamelCase			:     int         =      state_dim
								__UpperCamelCase			:     str         =      act_dim
								__UpperCamelCase			:     str         =      hidden_size
								__UpperCamelCase			:     List[str]         =      max_ep_len
								__UpperCamelCase			:     List[str]         =      action_tanh
								__UpperCamelCase			:     Optional[Any]         =      vocab_size
								__UpperCamelCase			:     Any         =      n_positions
								__UpperCamelCase			:     List[str]         =      n_layer
								__UpperCamelCase			:     Tuple         =      n_head
								__UpperCamelCase			:     int         =      n_inner
								__UpperCamelCase			:     Any         =      activation_function
								__UpperCamelCase			:     Union[str, Any]         =      resid_pdrop
								__UpperCamelCase			:     Optional[int]         =      embd_pdrop
								__UpperCamelCase			:     Optional[int]         =      attn_pdrop
								__UpperCamelCase			:     Tuple         =      layer_norm_epsilon
								__UpperCamelCase			:     int         =      initializer_range
								__UpperCamelCase			:     int         =      scale_attn_weights
								__UpperCamelCase			:     str         =      use_cache
								__UpperCamelCase			:     int         =      scale_attn_by_inverse_layer_idx
								__UpperCamelCase			:     Any         =      reorder_and_upcast_attn

								__UpperCamelCase			:     Optional[Any]         =      bos_token_id
								__UpperCamelCase			:     Union[str, Any]         =      eos_token_id

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

import hashlib
import unittest

from transformers import MODEL_FOR_DEPTH_ESTIMATION_MAPPING, is_torch_available, is_vision_available
from transformers.pipelines import DepthEstimationPipeline, pipeline
from transformers.testing_utils import (
    is_pipeline_test,
    nested_simplify,
    require_tf,
    require_timm,
    require_torch,
    require_vision,
    slow,
)

from .test_pipelines_common import ANY


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image
else:




		class 				lowerCamelCase__							:




			'''simple docstring'''




			@staticmethod
			def      _lowerCamelCase       (      *a	:Union[str, Any]  ,     **a	:int		)	->				str:
										pass




def 			_SCREAMING_SNAKE_CASE      (	_lowerCamelCase				:    Image)      ->					str:






							'''simple docstring'''





							__UpperCamelCase			:     Optional[int]         =      hashlib.mda(image.tobytes())
							return m.hexdigest()




@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 				lowerCamelCase__							(       unittest.TestCase):




	'''simple docstring'''




	_A			=  MODEL_FOR_DEPTH_ESTIMATION_MAPPING







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:Optional[Any]  ,     a	:Optional[int]		)	->				Optional[Any]:
								__UpperCamelCase			:     Any         =      DepthEstimationPipeline(model=a  ,     image_processor=a		)
								return depth_estimator, [
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								    "./tests/fixtures/tests_samples/COCO/000000039769.png",
								]







	def      _lowerCamelCase       (      self	:Union[str, Any]  ,     a	:Optional[Any]  ,     a	:List[Any]		)	->				Dict:
								__UpperCamelCase			:     str         =      depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"		)
								self.assertEqual({"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)}  ,     a		)
								import datasets

								__UpperCamelCase			:     List[Any]         =      datasets.load_dataset("hf-internal-testing/fixtures_image_utils"  ,     "image"  ,     split="test"		)
								__UpperCamelCase			:     int         =      depth_estimator(
								    [
								        Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png"		),
								        "http://images.cocodataset.org/val2017/000000039769.jpg",
								        # RGBA
								        dataset[0]["file"],
								        # LA
								        dataset[1]["file"],
								        # L
								        dataset[2]["file"],
								    ]		)
								self.assertEqual(
								    [
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								        {"predicted_depth": ANY(torch.Tensor		), "depth": ANY(Image.Image		)},
								    ]  ,     a  ,     )







	@require_tf
	@unittest.skip("Depth estimation is not implemented in TF"		)
	def      _lowerCamelCase       (      self	:str		)	->				Optional[Any]:
								pass







	@slow
	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[Any]:
								__UpperCamelCase			:     Dict         =      "Intel/dpt-large"
								__UpperCamelCase			:     int         =      pipeline("depth-estimation"  ,     model=a		)
								__UpperCamelCase			:     Optional[int]         =      depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"		)
								__UpperCamelCase			:     Optional[int]         =      hashimage(outputs["depth"]		)

								# This seems flaky.
								# self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
								self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()		)  ,     29.304		)
								self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()		)  ,     2.662		)







	@require_torch
	def      _lowerCamelCase       (      self	:List[Any]		)	->				List[str]:
								# This is highly irregular to have no small tests.
								self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"		)