





'''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   UpperCAmelCase      :
   @staticmethod
   def 	UpperCAmelCase_				(							*lowercase_ :List[Any]		,			**lowercase_ :int				)->				Union[str, Any]:
       pass





def 		UpperCamelCase							(				_lowerCamelCase   :   Image			):
    A__							=     hashlib.mda(image.tobytes()			)
    return m.hexdigest()






@is_pipeline_test
@require_vision
@require_timm
@require_torch
class   UpperCAmelCase      (			unittest.TestCase							):
 __lowercase  						=							MODEL_FOR_DEPTH_ESTIMATION_MAPPING
 def 	UpperCAmelCase_				(							self :str		,			lowercase_ :Dict		,			lowercase_ :List[str]		,			lowercase_ :Union[str, Any]				)->				Tuple:
     A__							=     DepthEstimationPipeline(model=lowercase_		,			image_processor=lowercase_				)
     return depth_estimator, [
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
         "./tests/fixtures/tests_samples/COCO/000000039769.png",
     ]
 def 	UpperCAmelCase_				(							self :Any		,			lowercase_ :Union[str, Any]		,			lowercase_ :str				)->				List[str]:
     A__							=     depth_estimator("./tests/fixtures/tests_samples/COCO/000000039769.png"				)
     self.assertEqual({"predicted_depth": ANY(torch.Tensor				), "depth": ANY(Image.Image				)}		,			lowercase_				)
     import datasets

     A__							=     datasets.load_dataset("hf-internal-testing/fixtures_image_utils"		,			"image"		,			split="test"				)
     A__							=     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				)},
         ]		,			lowercase_		,			)
 @require_tf
 @unittest.skip("Depth estimation is not implemented in TF"				)
 def 	UpperCAmelCase_				(							self :Dict				)->				Optional[Any]:
     pass
 @slow
 @require_torch
 def 	UpperCAmelCase_				(							self :Union[str, Any]				)->				Any:
     A__							=     "Intel/dpt-large"
     A__							=     pipeline("depth-estimation"		,			model=lowercase_				)
     A__							=     depth_estimator("http://images.cocodataset.org/val2017/000000039769.jpg"				)
     A__							=     hashimage(outputs["depth"]				)

     # This seems flaky.
     # self.assertEqual(outputs["depth"], "1a39394e282e9f3b0741a90b9f108977")
     self.assertEqual(nested_simplify(outputs["predicted_depth"].max().item()				)		,			2_9.3_0_4				)
     self.assertEqual(nested_simplify(outputs["predicted_depth"].min().item()				)		,			2.6_6_2				)



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










'''simple docstring'''

import json
import os
import tempfile

import datasets
from utils import generate_example_dataset, get_duration


__lowerCAmelCase				:      int						      =5_0000
__lowerCAmelCase				:      Union[str, Any]						      =5000

__lowerCAmelCase      ,	__lowerCAmelCase				:      Tuple						      =os.path.split(__file__)
__lowerCAmelCase				:      int						      =os.path.join(RESULTS_BASEPATH, "results", RESULTS_FILENAME.replace(".py", ".json"))





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   int			):
    for i in range(_lowerCamelCase			):
        A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   Any			):
    for i in range(0		,			len(_lowerCamelCase			)		,			_lowerCamelCase			):
        A__							=     dataset[i : i + batch_size]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[Any]		,			_lowerCamelCase   :   List[str]			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(_lowerCamelCase			):
            A__							=     dataset[i]





@get_duration
def 		UpperCamelCase							(				_lowerCamelCase   :   datasets.Dataset		,			_lowerCamelCase   :   Optional[int]		,			_lowerCamelCase   :   int		,			_lowerCamelCase   :   Dict			):
    with dataset.formatted_as(type=_lowerCamelCase			):
        for i in range(0		,			_lowerCamelCase		,			_lowerCamelCase			):
            A__							=     dataset[i : i + batch_size]





def 		UpperCamelCase							(				):
    A__							=     {"num examples": SPEED_TEST_N_EXAMPLES}
    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted, {"type": "pandas", "length": SMALL_TEST}),
        (read_formatted, {"type": "torch", "length": SMALL_TEST}),
        (read_formatted, {"type": "tensorflow", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]

    A__							=     [
        (read, {"length": SMALL_TEST}),
        (read, {"length": SPEED_TEST_N_EXAMPLES}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 1_00}),
        (read_batch, {"length": SPEED_TEST_N_EXAMPLES, "batch_size": 10_00}),
        (read_formatted, {"type": "numpy", "length": SMALL_TEST}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10}),
        (read_formatted_batch, {"type": "numpy", "length": SMALL_TEST, "batch_size": 10_00}),
    ]
    with tempfile.TemporaryDirectory() as tmp_dir:
        print("generating dataset"			)
        A__							=     datasets.Features(
            {"list": datasets.Sequence(datasets.Value("float32"			)			), "numbers": datasets.Value("float32"			)}			)
        A__							=     generate_example_dataset(
            os.path.join(_lowerCamelCase		,			"dataset.arrow"			)		,			_lowerCamelCase		,			num_examples=_lowerCamelCase		,			seq_shapes={"list": (1_00,)}		,			)
        print("first set of iterations"			)
        for func, kwargs in functions:
            print(func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(_lowerCamelCase		,			**_lowerCamelCase			)

        print("shuffling dataset"			)
        A__							=     dataset.shuffle()
        print("Second set of iterations (after shuffling"			)
        for func, kwargs in functions_shuffled:
            print("shuffled "		,			func.__name__		,			str(_lowerCamelCase			)			)
            A__							=     func(
                _lowerCamelCase		,			**_lowerCamelCase			)

    with open(_lowerCamelCase		,			"wb"			) as f:
        f.write(json.dumps(_lowerCamelCase			).encode("utf-8"			)			)


if __name__ == "__main__":  # useful to run the profiler
  benchmark_iterating()



