
from collections import OrderedDict
from typing import Mapping

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


__lowerCAmelCase     :	Tuple       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	Tuple       			=  {
    'facebook/xlm-roberta-xl': 'https://huggingface.co/facebook/xlm-roberta-xl/resolve/main/config.json',
    'facebook/xlm-roberta-xxl': 'https://huggingface.co/facebook/xlm-roberta-xxl/resolve/main/config.json',
    # See all XLM-RoBERTa-XL models at https://huggingface.co/models?filter=xlm-roberta-xl
}




class 			UpperCAmelCase_     (					UpperCamelCase_	):


  '''simple docstring'''







  a__       			=						"""xlm-roberta-xl"""
  def __init__(					self     :					List[str]     ,				UpperCamelCase__     :					Union[str, Any]=25_0880     ,				UpperCamelCase__     :					Tuple=2560     ,				UpperCamelCase__     :					List[Any]=36     ,				UpperCamelCase__     :					Optional[Any]=32     ,				UpperCamelCase__     :					Optional[Any]=1_0240     ,				UpperCamelCase__     :					List[str]="gelu"     ,				UpperCamelCase__     :					Dict=0.1     ,				UpperCamelCase__     :					Optional[int]=0.1     ,				UpperCamelCase__     :					Dict=514     ,				UpperCamelCase__     :					Tuple=1     ,				UpperCamelCase__     :					List[Any]=0.02     ,				UpperCamelCase__     :					Optional[Any]=1E-05     ,				UpperCamelCase__     :					Any=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					Union[str, Any]=2     ,				UpperCamelCase__     :					int="absolute"     ,				UpperCamelCase__     :					Union[str, Any]=True     ,				UpperCamelCase__     :					List[Any]=None     ,				**UpperCamelCase__     :					Optional[int]     ,				)     ->      List[str]:







    """simple docstring"""






    super().__init__(pad_token_id=a__     ,				bos_token_id=a__     ,				eos_token_id=a__     ,				**a__       )
    __magic_name__								=			vocab_size
    __magic_name__								=			hidden_size
    __magic_name__								=			num_hidden_layers
    __magic_name__								=			num_attention_heads
    __magic_name__								=			hidden_act
    __magic_name__								=			intermediate_size
    __magic_name__								=			hidden_dropout_prob
    __magic_name__								=			attention_probs_dropout_prob
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			type_vocab_size
    __magic_name__								=			initializer_range
    __magic_name__								=			layer_norm_eps
    __magic_name__								=			position_embedding_type
    __magic_name__								=			use_cache
    __magic_name__								=			classifier_dropout







class 			UpperCAmelCase_     (					UpperCamelCase_	):


  '''simple docstring'''







  @property
  def 				_lowercase				(					self     :					Dict       )     ->      Optional[Any]:







    """simple docstring"""






    if self.task == "multiple-choice":
      __magic_name__								=			{0: "batch", 1: "choice", 2: "sequence"}
    else:
      __magic_name__								=			{0: "batch", 1: "sequence"}
    return OrderedDict(
        [
            ("""input_ids""", dynamic_axis),
            ("""attention_mask""", dynamic_axis),
        ]       )



'''simple docstring'''

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       :


      @staticmethod
      def    _A	(    *a__  : str			,							**a__  : List[str]    ):
       '''simple docstring'''

       pass


def        UpperCAmelCase_ (  lowerCamelCase_			):







 """simple docstring"""


 lowerCAmelCase__     :   List[str]     		=	hashlib.mda(image.tobytes()			)
 return m.hexdigest()







@is_pipeline_test
@require_vision
@require_timm
@require_torch
class    lowerCAmelCase       (     unittest.TestCase  ):
 A_				:				List[Any]	=			MODEL_FOR_DEPTH_ESTIMATION_MAPPING


 def    _A	(    self  : List[Any]			,							a__  : int			,							a__  : Dict			,							a__  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   List[str]     		=	DepthEstimationPipeline(model=a__			,							image_processor=a__    )
  return depth_estimator, [
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
      "./tests/fixtures/tests_samples/COCO/000000039769.png",
  ]


 def    _A	(    self  : int			,							a__  : Optional[int]			,							a__  : List[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Any     		=	depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"    )
  self.assertEqual({"predicted_depth": ANY(torch.Tensor    ), "depth": ANY(Image.Image    )}			,							a__    )
  import datasets

  lowerCAmelCase__     :   List[str]     		=	datasets.load_dataset("hf-internal-testing/fixtures_image_utils"			,							"image"			,							split="test"    )
  lowerCAmelCase__     :   List[str]     		=	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    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  pass


 @slow
 @require_torch
 def    _A	(    self  : Any    ):
  '''simple docstring'''

  lowerCAmelCase__     :   str     		=	"Intel/dpt-large"
  lowerCAmelCase__     :   Dict     		=	pipeline("depth-estimation"			,							model=a__    )
  lowerCAmelCase__     :   Any     		=	depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"    )
  lowerCAmelCase__     :   Any     		=	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    _A	(    self  : Any    ):
  '''simple docstring'''

  self.skipTest("There is not hf-internal-testing tiny model for either GLPN nor DPT"    )





