
'''simple docstring'''

import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
     import jax
     import jax.numpy as jnp
     from flax.jax_utils import replicate
     from flax.training.common_utils import shard




@slow
@require_flax
class    lowerCAmelCase       (     unittest.TestCase  ):


 def    _A	(    self  : str    ):
  '''simple docstring'''

  super().tearDown()
  gc.collect()


 def    _A	(    self  : int    ):
  '''simple docstring'''

  lowerCAmelCase__		,     lowerCAmelCase__     :   int     		=	FlaxStableDiffusionPipeline.from_pretrained(
      "stabilityai/stable-diffusion-2"			,							revision="bf16"			,							dtype=jnp.bfloataa			,							)

  lowerCAmelCase__     :   List[str]     		=	"A painting of a squirrel eating a burger"
  lowerCAmelCase__     :   Any     		=	jax.device_count()
  lowerCAmelCase__     :   Optional[Any]     		=	num_samples * [prompt]
  lowerCAmelCase__     :   Dict     		=	sd_pipe.prepare_inputs(a__    )

  lowerCAmelCase__     :   Dict     		=	replicate(a__    )
  lowerCAmelCase__     :   List[str]     		=	shard(a__    )

  lowerCAmelCase__     :   Union[str, Any]     		=	jax.random.PRNGKey(0    )
  lowerCAmelCase__     :   Optional[Any]     		=	jax.random.split(a__			,							jax.device_count()    )

  lowerCAmelCase__     :   List[Any]     		=	sd_pipe(a__			,							a__			,							a__			,							num_inference_steps=25			,							jit=a__    )[0]
  assert images.shape == (jax.device_count(), 1, 768, 768, 3)

  lowerCAmelCase__     :   Dict     		=	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
  lowerCAmelCase__     :   Optional[int]     		=	images[0, 253:256, 253:256, -1]

  lowerCAmelCase__     :   str     		=	jnp.asarray(jax.device_get(image_slice.flatten()    )    )
  lowerCAmelCase__     :   List[Any]     		=	jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.45508, 0.4512]    )
  print(F'''output_slice: {output_slice}'''    )
  assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2


 def    _A	(    self  : Optional[Any]    ):
  '''simple docstring'''

  lowerCAmelCase__     :   Optional[int]     		=	"stabilityai/stable-diffusion-2"
  lowerCAmelCase__		,     lowerCAmelCase__     :   Union[str, Any]     		=	FlaxDPMSolverMultistepScheduler.from_pretrained(a__			,							subfolder="scheduler"    )
  lowerCAmelCase__		,     lowerCAmelCase__     :   Tuple     		=	FlaxStableDiffusionPipeline.from_pretrained(
      a__			,							scheduler=a__			,							revision="bf16"			,							dtype=jnp.bfloataa			,							)
  lowerCAmelCase__     :   Any     		=	scheduler_params

  lowerCAmelCase__     :   Any     		=	"A painting of a squirrel eating a burger"
  lowerCAmelCase__     :   Any     		=	jax.device_count()
  lowerCAmelCase__     :   Tuple     		=	num_samples * [prompt]
  lowerCAmelCase__     :   Any     		=	sd_pipe.prepare_inputs(a__    )

  lowerCAmelCase__     :   str     		=	replicate(a__    )
  lowerCAmelCase__     :   List[Any]     		=	shard(a__    )

  lowerCAmelCase__     :   Any     		=	jax.random.PRNGKey(0    )
  lowerCAmelCase__     :   List[str]     		=	jax.random.split(a__			,							jax.device_count()    )

  lowerCAmelCase__     :   Any     		=	sd_pipe(a__			,							a__			,							a__			,							num_inference_steps=25			,							jit=a__    )[0]
  assert images.shape == (jax.device_count(), 1, 768, 768, 3)

  lowerCAmelCase__     :   Optional[Any]     		=	images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]    )
  lowerCAmelCase__     :   Dict     		=	images[0, 253:256, 253:256, -1]

  lowerCAmelCase__     :   Dict     		=	jnp.asarray(jax.device_get(image_slice.flatten()    )    )
  lowerCAmelCase__     :   int     		=	jnp.array([0.4336, 0.42969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]    )
  print(F'''output_slice: {output_slice}'''    )
  assert jnp.abs(output_slice - expected_slice    ).max() < 1e-2







'''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"    )





