






import unittest

import numpy as np

from diffusers import OnnxStableDiffusionInpaintPipelineLegacy
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    load_numpy,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)


if is_onnx_available():
	import onnxruntime as ort



@nightly
@require_onnxruntime
@require_torch_gpu
class 							SCREAMING_SNAKE_CASE__		(       unittest.TestCase		):





					"""simple docstring"""






					@property
					def  _lowercase  (      self  :						int ):
						return (
						    "CUDAExecutionProvider",
						    {
						        "gpu_mem_limit": "15000000000",  # 15GB
						        "arena_extend_strategy": "kSameAsRequested",
						    },
						)



					@property
					def  _lowercase  (      self  :						List[str] ):
						snake_case__				:   Tuple     =						ort.SessionOptions()
						snake_case__				:   Optional[Any]     =						False
						return options






					def  _lowercase  (      self  :						str ):
						snake_case__				:   List[str]     =						load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo.png" )
						snake_case__				:   Tuple     =						load_image(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/overture-creations-5sI6fQgYIuo_mask.png" )
						snake_case__				:   Tuple     =						load_numpy(
						    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
						    "/in_paint/red_cat_sitting_on_a_park_bench_onnx.npy" )

						# using the PNDM scheduler by default
						snake_case__				:   str     =						OnnxStableDiffusionInpaintPipelineLegacy.from_pretrained(
						    "CompVis/stable-diffusion-v1-4"							,	revision="onnx"							,	safety_checker=__A							,	feature_extractor=__A							,	provider=self.gpu_provider							,	sess_options=self.gpu_options							,	)
						pipe.set_progress_bar_config(disable=__A )

						snake_case__				:   Any     =						"A red cat sitting on a park bench"

						snake_case__				:   int     =						np.random.RandomState(0 )
						snake_case__				:   Tuple     =						pipe(
						    prompt=__A							,	image=__A							,	mask_image=__A							,	strength=0.7_5							,	guidance_scale=7.5							,	num_inference_steps=1_5							,	generator=__A							,	output_type="np"							,	)

						snake_case__				:   str     =						output.images[0]

						assert image.shape == (5_1_2, 5_1_2, 3)
						assert np.abs(expected_image - image ).max() < 1e-2









# Lint as: python3
import sys
from collections.abc import Mapping
from typing import TYPE_CHECKING, Dict, Optional

import numpy as np
import pyarrow as pa

from .. import config
from ..utils.logging import get_logger
from ..utils.py_utils import map_nested
from .formatting import TensorFormatter


if TYPE_CHECKING:
	import jax
	import jaxlib

__lowerCamelCase      :    Optional[int]   		=   get_logger()

__lowerCamelCase      :    Optional[dict]   		=   None



class 							SCREAMING_SNAKE_CASE__		(       TensorFormatter[Mapping, "jax.Array", Mapping]		):





					"""simple docstring"""






					def __init__(      self  :						Optional[Any]							,	__A  :						Dict=None							,	__A  :						List[str]=None							,	**__A  :						str ):
						super().__init__(features=__A )
						import jax
						from jaxlib.xla_client import Device

						if isinstance(__A							,	__A ):
							raise ValueError(
							    f'''Expected {device} to be a `str` not {type(__A )}, as `jaxlib.xla_extension.Device` '''
							    "is not serializable neither with `pickle` nor with `dill`. Instead you can surround "
							    "the device with `str()` to get its string identifier that will be internally mapped "
							    "to the actual `jaxlib.xla_extension.Device`." )
						snake_case__				:   List[Any]     =						device if isinstance(__A							,	__A ) else str(jax.devices()[0] )
						# using global variable since `jaxlib.xla_extension.Device` is not serializable neither
						# with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   Any     =						self._map_devices_to_str()
						if self.device not in list(DEVICE_MAPPING.keys() ):
							logger.warning(
							    f'''Device with string identifier {self.device} not listed among the available '''
							    f'''devices: {list(DEVICE_MAPPING.keys() )}, so falling back to the default '''
							    f'''device: {str(jax.devices()[0] )}.''' )
							snake_case__				:   str     =						str(jax.devices()[0] )
						snake_case__				:   str     =						jnp_array_kwargs



					@staticmethod
					def  _lowercase  (      ):
						import jax

						return {str(__A ): device for device in jax.devices()}



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						str ):
						import jax
						import jax.numpy as jnp

						if isinstance(__A							,	__A ) and column:
							if all(
							    isinstance(__A							,	jax.Array ) and x.shape == column[0].shape and x.dtype == column[0].dtype for x in column ):
								return jnp.stack(__A							,	axis=0 )
						return column



					def  _lowercase  (      self  :						int							,	__A  :						Tuple ):
						import jax
						import jax.numpy as jnp

						if isinstance(__A							,	(str, bytes, type(__A )) ):
							return value
						elif isinstance(__A							,	(np.character, np.ndarray) ) and np.issubdtype(value.dtype							,	np.character ):
							return value.tolist()

						snake_case__				:   Optional[int]     =						{}

						if isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.integer ):
							# the default int precision depends on the jax config
							# see https://jax.readthedocs.io/en/latest/notebooks/Common_Gotchas_in_JAX.html#double-64bit-precision
							if jax.config.jax_enable_xaa:
								snake_case__				:   Any     =						{"dtype": jnp.intaa}
							else:
								snake_case__				:   Tuple     =						{"dtype": jnp.intaa}
						elif isinstance(__A							,	(np.number, np.ndarray) ) and np.issubdtype(value.dtype							,	np.floating ):
							snake_case__				:   str     =						{"dtype": jnp.floataa}
						elif config.PIL_AVAILABLE and "PIL" in sys.modules:
							import PIL.Image

							if isinstance(__A							,	PIL.Image.Image ):
								snake_case__				:   Optional[Any]     =						np.asarray(__A )

        # using global variable since `jaxlib.xla_extension.Device` is not serializable neither
        # with `pickle` nor with `dill`, so we need to use a global variable instead
						global DEVICE_MAPPING
						if DEVICE_MAPPING is None:
							snake_case__				:   int     =						self._map_devices_to_str()

						with jax.default_device(DEVICE_MAPPING[self.device] ):
							# calling jnp.array on a np.ndarray does copy the data
							# see https://github.com/google/jax/issues/4486
							return jnp.array(__A							,	**{**default_dtype, **self.jnp_array_kwargs} )



					def  _lowercase  (      self  :						Union[str, Any]							,	__A  :						Optional[int] ):
						import jax

						# support for torch, tf, jax etc.
						if config.TORCH_AVAILABLE and "torch" in sys.modules:
							import torch

							if isinstance(__A							,	torch.Tensor ):
								return self._tensorize(data_struct.detach().cpu().numpy()[()] )
						if hasattr(__A							,	"__array__" ) and not isinstance(__A							,	jax.Array ):
							snake_case__				:   Union[str, Any]     =						data_struct.__array__()
						# support for nested types like struct of list of struct
						if isinstance(__A							,	np.ndarray ):
							if data_struct.dtype == object:  # jax arrays cannot be instantied from an array of objects
								return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
						elif isinstance(__A							,	(list, tuple) ):
							return self._consolidate([self.recursive_tensorize(__A ) for substruct in data_struct] )
						return self._tensorize(__A )



					def  _lowercase  (      self  :						Tuple							,	__A  :						dict ):
						return map_nested(self._recursive_tensorize							,	__A							,	map_list=__A )



					def  _lowercase  (      self  :						Optional[int]							,	__A  :						pa.Table ):
						snake_case__				:   int     =						self.numpy_arrow_extractor().extract_row(__A )
						snake_case__				:   Tuple     =						self.python_features_decoder.decode_row(__A )
						return self.recursive_tensorize(__A )



					def  _lowercase  (      self  :						Optional[Any]							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_column(__A )
						snake_case__				:   Optional[int]     =						self.python_features_decoder.decode_column(__A							,	pa_table.column_names[0] )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						snake_case__				:   Dict     =						self._consolidate(__A )
						return column






					def  _lowercase  (      self  :						str							,	__A  :						pa.Table ):
						snake_case__				:   Any     =						self.numpy_arrow_extractor().extract_batch(__A )
						snake_case__				:   int     =						self.python_features_decoder.decode_batch(__A )
						snake_case__				:   List[Any]     =						self.recursive_tensorize(__A )
						for column_name in batch:
							snake_case__				:   Any     =						self._consolidate(batch[column_name] )
						return batch



