
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 							A__		:
            @staticmethod
            def 						UpperCamelCase__							(      *__magic_name__    ,  **__magic_name__      ):
             pass





def     _a					(						lowerCamelCase							):
 lowerCamelCase		:	Union[str, Any]							=      hashlib.mda(image.tobytes()							)
 return m.hexdigest()





@is_pipeline_test
@require_vision
@require_timm
@require_torch
class 							A__		(	unittest.TestCase):
      _UpperCAmelCase				:    Any           =			MODEL_FOR_DEPTH_ESTIMATION_MAPPING
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	int							=      DepthEstimationPipeline(model=__magic_name__    ,  image_processor=__magic_name__      )
       return depth_estimator, [
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
           "./tests/fixtures/tests_samples/COCO/000000039769.png",
       ]
      def 						UpperCamelCase__							(      self    ,  __magic_name__    ,  __magic_name__      ):
       lowerCamelCase		:	str							=      depth_estimator("""./tests/fixtures/tests_samples/COCO/000000039769.png"""      )
       self.assertEqual({"""predicted_depth""": ANY(torch.Tensor      ), """depth""": ANY(Image.Image      )}    ,  __magic_name__      )
       import datasets

       lowerCamelCase		:	int							=      datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""    ,  """image"""    ,  split="""test"""      )
       lowerCamelCase		:	Optional[Any]							=      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      )},
           ]    ,  __magic_name__    ,  )
      @require_tf
      @unittest.skip("""Depth estimation is not implemented in TF"""      )
      def 						UpperCamelCase__							(      self      ):
       pass
      @slow
      @require_torch
      def 						UpperCamelCase__							(      self      ):
       lowerCamelCase		:	int							=      """Intel/dpt-large"""
       lowerCamelCase		:	Dict							=      pipeline("""depth-estimation"""    ,  model=__magic_name__      )
       lowerCamelCase		:	Tuple							=      depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""      )
       lowerCamelCase		:	Optional[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 						UpperCamelCase__							(      self      ):
       # This is highly irregular to have no small tests.
       self.skipTest("""There is not hf-internal-testing tiny model for either GLPN nor DPT"""      )






from collections import OrderedDict
from typing import Mapping

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


_lowerCamelCase					   =logging.get_logger(__name__)

_lowerCamelCase					   ={
    """camembert-base""": """https://huggingface.co/camembert-base/resolve/main/config.json""",
    """umberto-commoncrawl-cased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1/resolve/main/config.json"""
    ),
    """umberto-wikipedia-uncased-v1""": (
        """https://huggingface.co/Musixmatch/umberto-wikipedia-uncased-v1/resolve/main/config.json"""
    ),
}





class 							A__		(	__SCREAMING_SNAKE_CASE):
      _UpperCAmelCase				:    Tuple           =			"""camembert"""
      def __init__(      self    ,  __magic_name__=3_0_5_2_2    ,  __magic_name__=7_6_8    ,  __magic_name__=1_2    ,  __magic_name__=1_2    ,  __magic_name__=3_0_7_2    ,  __magic_name__="gelu"    ,  __magic_name__=0.1    ,  __magic_name__=0.1    ,  __magic_name__=5_1_2    ,  __magic_name__=2    ,  __magic_name__=0.02    ,  __magic_name__=1e-12    ,  __magic_name__=1    ,  __magic_name__=0    ,  __magic_name__=2    ,  __magic_name__="absolute"    ,  __magic_name__=True    ,  __magic_name__=None    ,  **__magic_name__    ,  ):
       super().__init__(pad_token_id=__magic_name__    ,  bos_token_id=__magic_name__    ,  eos_token_id=__magic_name__    ,  **__magic_name__      )

       lowerCamelCase		:	int							=      vocab_size
       lowerCamelCase		:	int							=      hidden_size
       lowerCamelCase		:	int							=      num_hidden_layers
       lowerCamelCase		:	int							=      num_attention_heads
       lowerCamelCase		:	Optional[int]							=      hidden_act
       lowerCamelCase		:	List[Any]							=      intermediate_size
       lowerCamelCase		:	Tuple							=      hidden_dropout_prob
       lowerCamelCase		:	Optional[int]							=      attention_probs_dropout_prob
       lowerCamelCase		:	Optional[int]							=      max_position_embeddings
       lowerCamelCase		:	str							=      type_vocab_size
       lowerCamelCase		:	Optional[Any]							=      initializer_range
       lowerCamelCase		:	int							=      layer_norm_eps
       lowerCamelCase		:	Any							=      position_embedding_type
       lowerCamelCase		:	Optional[int]							=      use_cache
       lowerCamelCase		:	Union[str, Any]							=      classifier_dropout





class 							A__		(	__SCREAMING_SNAKE_CASE):
      @property
      def 						UpperCamelCase__							(      self      ):
       if self.task == "multiple-choice":
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """choice""", 2: """sequence"""}
       else:
        lowerCamelCase		:	List[str]							=      {0: """batch""", 1: """sequence"""}
       return OrderedDict(
           [
               ("""input_ids""", dynamic_axis),
               ("""attention_mask""", dynamic_axis),
           ]      )




