

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 SCREAMING_SNAKE_CASE      :
     """simple docstring"""






     @staticmethod
     def 			SCREAMING_SNAKE_CASE       (     *lowerCAmelCase					:       Any			,				**lowerCAmelCase					:       str       )				->      Any:






        """simple docstring"""







        pass





def       snake_case_ (__A	:		Tuple	)     ->      Optional[int]:
   __lowerCAmelCase    :     str          =		hashlib.mda(image.tobytes()	)
   return m.hexdigest()



@is_pipeline_test
@require_vision
@require_timm
@require_torch
class SCREAMING_SNAKE_CASE      (    unittest.TestCase					):
  """simple docstring"""






  lowerCamelCase       :     int		 =MODEL_FOR_DEPTH_ESTIMATION_MAPPING







  def 			SCREAMING_SNAKE_CASE       (     self					:       Tuple			,				lowerCAmelCase					:       int			,				lowerCAmelCase					:       Any			,				lowerCAmelCase					:       Any       )				->      Union[str, Any]:






     """simple docstring"""







     __lowerCAmelCase    :     List[Any]          =		DepthEstimationPipeline(model=lowerCAmelCase			,				image_processor=lowerCAmelCase       )
     return depth_estimator, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]







  def 			SCREAMING_SNAKE_CASE       (     self					:       int			,				lowerCAmelCase					:       List[str]			,				lowerCAmelCase					:       List[Any]       )				->      str:






     """simple docstring"""







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

     __lowerCAmelCase    :     Tuple          =		datasets.load_dataset("""hf-internal-testing/fixtures_image_utils"""			,				"""image"""			,				split="""test"""       )
     __lowerCAmelCase    :     List[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       )},
         ]			,				lowerCAmelCase			,				)







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






     """simple docstring"""







     pass







  @slow
  @require_torch
  def 			SCREAMING_SNAKE_CASE       (     self					:       Optional[Any]       )				->      Tuple:






     """simple docstring"""







     __lowerCAmelCase    :     Tuple          =		"Intel/dpt-large"
     __lowerCAmelCase    :     Optional[int]          =		pipeline("""depth-estimation"""			,				model=lowerCAmelCase       )
     __lowerCAmelCase    :     str          =		depth_estimator("""http://images.cocodataset.org/val2017/000000039769.jpg"""       )
     __lowerCAmelCase    :     Union[str, 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 			SCREAMING_SNAKE_CASE       (     self					:       Tuple       )				->      Union[str, Any]:






     """simple docstring"""







     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


a_      =     logging.get_logger(__name__)

a_      =     {
    'junnyu/roformer_chinese_small': 'https://huggingface.co/junnyu/roformer_chinese_small/resolve/main/config.json',
    'junnyu/roformer_chinese_base': 'https://huggingface.co/junnyu/roformer_chinese_base/resolve/main/config.json',
    'junnyu/roformer_chinese_char_small': (
        'https://huggingface.co/junnyu/roformer_chinese_char_small/resolve/main/config.json'
    ),
    'junnyu/roformer_chinese_char_base': (
        'https://huggingface.co/junnyu/roformer_chinese_char_base/resolve/main/config.json'
    ),
    'junnyu/roformer_small_discriminator': (
        'https://huggingface.co/junnyu/roformer_small_discriminator/resolve/main/config.json'
    ),
    'junnyu/roformer_small_generator': (
        'https://huggingface.co/junnyu/roformer_small_generator/resolve/main/config.json'
    ),
    # See all RoFormer models at https://huggingface.co/models?filter=roformer
}






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			lowerCamelCase__							     ='roformer'
			def __init__(      self				:			Dict   ,   a				:			Any=5_0000   ,   a				:			List[Any]=None   ,   a				:			str=768   ,   a				:			str=12   ,   a				:			Tuple=12   ,   a				:			Optional[Any]=3072   ,   a				:			List[str]="gelu"   ,   a				:			List[Any]=0.1   ,   a				:			Union[str, Any]=0.1   ,   a				:			Tuple=1536   ,   a				:			List[str]=2   ,   a				:			Tuple=0.02   ,   a				:			Any=1e-12   ,   a				:			Optional[int]=0   ,   a				:			Union[str, Any]=False   ,   a				:			int=True   ,   **a				:			str   ,   )				->							int:

				"""simple docstring"""

				super().__init__(pad_token_id=a   ,   **a						)

				SCREAMING_SNAKE_CASE :   str					  =							vocab_size
				SCREAMING_SNAKE_CASE :   int					  =							hidden_size if embedding_size is None else embedding_size
				SCREAMING_SNAKE_CASE :   List[str]					  =							hidden_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							num_hidden_layers
				SCREAMING_SNAKE_CASE :   int					  =							num_attention_heads
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_act
				SCREAMING_SNAKE_CASE :   int					  =							intermediate_size
				SCREAMING_SNAKE_CASE :   Tuple					  =							hidden_dropout_prob
				SCREAMING_SNAKE_CASE :   int					  =							attention_probs_dropout_prob
				SCREAMING_SNAKE_CASE :   Optional[int]					  =							max_position_embeddings
				SCREAMING_SNAKE_CASE :   Any					  =							type_vocab_size
				SCREAMING_SNAKE_CASE :   Union[str, Any]					  =							initializer_range
				SCREAMING_SNAKE_CASE :   List[str]					  =							layer_norm_eps
				SCREAMING_SNAKE_CASE :   List[str]					  =							rotary_value
				SCREAMING_SNAKE_CASE :   int					  =							use_cache






class _UpperCamelCase				(				__A ):
			'''simple docstring'''







			@property
			def 				__UpperCamelCase			(      self				:			Tuple						)				->							Mapping[str, Mapping[int, str]]:

				"""simple docstring"""

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