
import os
import sys
import unittest


lowercase						:      int									= os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__))))
sys.path.append(os.path.join(git_repo_path, 'utils'))

import check_dummies  # noqa: E402
from check_dummies import create_dummy_files, create_dummy_object, find_backend, read_init  # noqa: E402


# Align TRANSFORMERS_PATH in check_dummies with the current path
lowercase						:      Optional[Any]									= os.path.join(git_repo_path, 'src', 'transformers')

lowercase						:      Optional[int]									= '''
{0} = None
'''

lowercase						:      Optional[int]									= '''
class {0}(metaclass=DummyObject):
    _backends = {1}

    def __init__(self, *args, **kwargs):
        requires_backends(self, {1})
'''


lowercase						:      int									= '''
def {0}(*args, **kwargs):
    requires_backends({0}, {1})
'''




class 				lowerCamelCase__							(       unittest.TestCase):




 '''simple docstring'''




 def      _lowerCamelCase       (      self	:Optional[Any]		)	->				str:
        __UpperCamelCase			:     Union[str, Any]         =      find_backend("    _import_structure[\"models.albert\"].append(\"AlbertTokenizerFast\")"		)
        self.assertIsNone(_A		)

        __UpperCamelCase			:     Optional[int]         =      find_backend("    if not is_tokenizers_available():"		)
        self.assertEqual(_A  ,     "tokenizers"		)

        __UpperCamelCase			:     int         =      find_backend("    if not is_tensorflow_text_available():"		)
        self.assertEqual(_A  ,     "tensorflow_text"		)

        __UpperCamelCase			:     str         =      find_backend("    if not (is_sentencepiece_available() and is_tokenizers_available()):"		)
        self.assertEqual(_A  ,     "sentencepiece_and_tokenizers"		)

        __UpperCamelCase			:     Optional[Any]         =      find_backend(
            "    if not (is_sentencepiece_available() and is_tensorflow_text_available()):"		)
        self.assertEqual(_A  ,     "sentencepiece_and_tensorflow_text"		)

        __UpperCamelCase			:     Tuple         =      find_backend(
            "    if not (is_sentencepiece_available() and is_tokenizers_available() and is_vision_available()):"		)
        self.assertEqual(_A  ,     "sentencepiece_and_tokenizers_and_vision"		)







 def      _lowerCamelCase       (      self	:Optional[int]		)	->				Union[str, Any]:
        __UpperCamelCase			:     Dict         =      read_init()
        # We don't assert on the exact list of keys to allow for smooth grow of backend-specific objects
        self.assertIn("torch"  ,     _A		)
        self.assertIn("tensorflow_text"  ,     _A		)
        self.assertIn("sentencepiece_and_tokenizers"  ,     _A		)

        # Likewise, we can't assert on the exact content of a key
        self.assertIn("BertModel"  ,     objects["torch"]		)
        self.assertIn("TFBertModel"  ,     objects["tf"]		)
        self.assertIn("FlaxBertModel"  ,     objects["flax"]		)
        self.assertIn("BertModel"  ,     objects["torch"]		)
        self.assertIn("TFBertTokenizer"  ,     objects["tensorflow_text"]		)
        self.assertIn("convert_slow_tokenizer"  ,     objects["sentencepiece_and_tokenizers"]		)







 def      _lowerCamelCase       (      self	:List[str]		)	->				Optional[Any]:
        __UpperCamelCase			:     Optional[int]         =      create_dummy_object("CONSTANT"  ,     "\'torch\'"		)
        self.assertEqual(_A  ,     "\nCONSTANT = None\n"		)

        __UpperCamelCase			:     Optional[int]         =      create_dummy_object("function"  ,     "\'torch\'"		)
        self.assertEqual(
            _A  ,     "\ndef function(*args, **kwargs):\n    requires_backends(function, \'torch\')\n"		)

        __UpperCamelCase			:     List[Any]         =      '\nclass FakeClass(metaclass=DummyObject):\n    _backends = \'torch\'\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, \'torch\')\n'
        __UpperCamelCase			:     Any         =      create_dummy_object("FakeClass"  ,     "\'torch\'"		)
        self.assertEqual(_A  ,     _A		)







 def      _lowerCamelCase       (      self	:Tuple		)	->				Dict:
        __UpperCamelCase			:     Tuple         =      '# This file is autogenerated by the command `make fix-copies`, do not edit.\nfrom ..utils import DummyObject, requires_backends\n\n\nCONSTANT = None\n\n\ndef function(*args, **kwargs):\n    requires_backends(function, ["torch"])\n\n\nclass FakeClass(metaclass=DummyObject):\n    _backends = ["torch"]\n\n    def __init__(self, *args, **kwargs):\n        requires_backends(self, ["torch"])\n'
        __UpperCamelCase			:     List[str]         =      create_dummy_files({"torch": ["CONSTANT", "function", "FakeClass"]}		)
        self.assertEqual(dummy_files["torch"]  ,     _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"		)