
from pathlib import Path

import fire
from tqdm import tqdm







def     A__   (  lowerCamelCase="ro"       ,      lowerCamelCase="en"       ,      lowerCamelCase="wmt16"       ,      lowerCamelCase=None  )					->		None:
				try:
								import datasets
				except (ModuleNotFoundError, ImportError):
								raise ImportError("""run pip install datasets"""  )
				UpperCamelCase_:							Tuple		=  F'''{src_lang}-{tgt_lang}'''
				print(F'''Converting {dataset}-{pair}'''  )
				UpperCamelCase_:							Any		=  datasets.load_dataset(lowerCamelCase       ,      lowerCamelCase  )
				if save_dir is None:
								UpperCamelCase_:							Any		=  F'''{dataset}-{pair}'''
				UpperCamelCase_:							Optional[Any]		=  Path(lowerCamelCase  )
				save_dir.mkdir(exist_ok=lowerCamelCase  )

				for split in ds.keys():
								print(F'''Splitting {split} with {ds[split].num_rows} records'''  )

								# to save to val.source, val.target like summary datasets
								UpperCamelCase_:							int		=  """val""" if split == """validation""" else split
								UpperCamelCase_:							int		=  save_dir.joinpath(F'''{fn}.source'''  )
								UpperCamelCase_:							str		=  save_dir.joinpath(F'''{fn}.target'''  )
								UpperCamelCase_:							int		=  src_path.open("""w+"""  )
								UpperCamelCase_:							Union[str, Any]		=  tgt_path.open("""w+"""  )

								# reader is the bottleneck so writing one record at a time doesn't slow things down
								for x in tqdm(ds[split]  ):
												UpperCamelCase_:							List[Any]		=  x["""translation"""]
												src_fp.write(ex[src_lang] + """\n"""  )
												tgt_fp.write(ex[tgt_lang] + """\n"""  )

				print(F'''Saved {dataset} dataset to {save_dir}'''  )


if __name__ == "__main__":
							fire.Fire(download_wmt_dataset)








import random
import unittest

import numpy as np

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionImgaImgPipeline,
    PNDMScheduler,
)
from diffusers.utils import floats_tensor
from diffusers.utils.testing_utils import (
    is_onnx_available,
    load_image,
    nightly,
    require_onnxruntime,
    require_torch_gpu,
)

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
							import onnxruntime as ort

class     _UpperCamelCase			(   _A       ,    unittest.TestCase					):






			'''simple docstring'''

			__UpperCamelCase :  Optional[Any]    =	"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""

			def  lowerCAmelCase__		(    self	:    List[str]  ,		snake_case_	:    Optional[int]=0 ):
							UpperCamelCase_:							str		=  floats_tensor((1, 3, 128, 128)  ,		rng=random.Random(snake_case_ ) )
							UpperCamelCase_:							Optional[int]		=  np.random.RandomState(snake_case_ )
							UpperCamelCase_:							Dict		=  {
							    """prompt""": """A painting of a squirrel eating a burger""",
							    """image""": image,
							    """generator""": generator,
							    """num_inference_steps""": 3,
							    """strength""": 0.75,
							    """guidance_scale""": 7.5,
							    """output_type""": """numpy""",
							}
							return inputs

			def  lowerCAmelCase__		(    self	:    Any ):
							UpperCamelCase_:							Any		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="""CPUExecutionProvider""" )
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							str		=  self.get_dummy_inputs()
							UpperCamelCase_:							Any		=  pipe(**snake_case_ ).images
							UpperCamelCase_:							Any		=  image[0, -3:, -3:, -1].flatten()

							assert image.shape == (1, 128, 128, 3)
							UpperCamelCase_:							str		=  np.array([0.6_9643, 0.5_8484, 0.5_0314, 0.5_8760, 0.5_5368, 0.5_9643, 0.5_1529, 0.4_1217, 0.4_9087] )
							assert np.abs(image_slice - expected_slice ).max() < 1e-1

			def  lowerCAmelCase__		(    self	:    List[str] ):
							UpperCamelCase_:							int		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="""CPUExecutionProvider""" )
							UpperCamelCase_:							Tuple		=  PNDMScheduler.from_config(pipe.scheduler.config  ,		skip_prk_steps=snake_case_ )
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							str		=  self.get_dummy_inputs()
							UpperCamelCase_:							Dict		=  pipe(**snake_case_ ).images
							UpperCamelCase_:							Optional[Any]		=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							UpperCamelCase_:							Any		=  np.array([0.6_1737, 0.5_4642, 0.5_3183, 0.5_4465, 0.5_2742, 0.6_0525, 0.4_9969, 0.4_0655, 0.4_8154] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1

			def  lowerCAmelCase__		(    self	:    str ):
							UpperCamelCase_:							str		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="""CPUExecutionProvider""" )
							UpperCamelCase_:							List[Any]		=  LMSDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=snake_case_ )

							# warmup pass to apply optimizations
							UpperCamelCase_:							Union[str, Any]		=  pipe(**self.get_dummy_inputs() )

							UpperCamelCase_:							Tuple		=  self.get_dummy_inputs()
							UpperCamelCase_:							Optional[int]		=  pipe(**snake_case_ ).images
							UpperCamelCase_:							str		=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							UpperCamelCase_:							Optional[int]		=  np.array([0.5_2761, 0.5_9977, 0.4_9033, 0.4_9619, 0.5_4282, 0.5_0311, 0.4_7600, 0.4_0918, 0.4_5203] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1

			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							List[str]		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="""CPUExecutionProvider""" )
							UpperCamelCase_:							Optional[int]		=  EulerDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							Union[str, Any]		=  self.get_dummy_inputs()
							UpperCamelCase_:							List[Any]		=  pipe(**snake_case_ ).images
							UpperCamelCase_:							List[Any]		=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							UpperCamelCase_:							List[str]		=  np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1

			def  lowerCAmelCase__		(    self	:    Optional[int] ):
							UpperCamelCase_:							List[Any]		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="""CPUExecutionProvider""" )
							UpperCamelCase_:							List[Any]		=  EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							List[str]		=  self.get_dummy_inputs()
							UpperCamelCase_:							Any		=  pipe(**snake_case_ ).images
							UpperCamelCase_:							Any		=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							UpperCamelCase_:							Tuple		=  np.array([0.5_2911, 0.6_0004, 0.4_9229, 0.4_9805, 0.5_4502, 0.5_0680, 0.4_7777, 0.4_1028, 0.4_5304] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1




			def  lowerCAmelCase__		(    self	:    Union[str, Any] ):
							UpperCamelCase_:							Optional[Any]		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(self.hub_checkpoint  ,		provider="""CPUExecutionProvider""" )
							UpperCamelCase_:							str		=  DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							List[Any]		=  self.get_dummy_inputs()
							UpperCamelCase_:							Optional[int]		=  pipe(**snake_case_ ).images
							UpperCamelCase_:							Dict		=  image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							UpperCamelCase_:							Tuple		=  np.array([0.6_5331, 0.5_8277, 0.4_8204, 0.5_6059, 0.5_3665, 0.5_6235, 0.5_0969, 0.4_0009, 0.4_6552] )

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 1e-1




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






			'''simple docstring'''

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

			@property
			def  lowerCAmelCase__		(    self	:    List[Any] ):
							UpperCamelCase_:							Dict		=  ort.SessionOptions()
							UpperCamelCase_:							Optional[int]		=  False
							return options

			def  lowerCAmelCase__		(    self	:    Tuple ):
							UpperCamelCase_:							Optional[int]		=  load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg""" )
							UpperCamelCase_:							Union[str, Any]		=  init_image.resize((768, 512) )
							# using the PNDM scheduler by default
							UpperCamelCase_:							int		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    """CompVis/stable-diffusion-v1-4"""  ,		revision="""onnx"""  ,		safety_checker=snake_case_  ,		feature_extractor=snake_case_  ,		provider=self.gpu_provider  ,		sess_options=self.gpu_options  ,		)
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							Dict		=  """A fantasy landscape, trending on artstation"""

							UpperCamelCase_:							List[str]		=  np.random.RandomState(0 )
							UpperCamelCase_:							List[str]		=  pipe(
							    prompt=snake_case_  ,		image=snake_case_  ,		strength=0.75  ,		guidance_scale=7.5  ,		num_inference_steps=10  ,		generator=snake_case_  ,		output_type="""np"""  ,		)
							UpperCamelCase_:							List[str]		=  output.images
							UpperCamelCase_:							Optional[Any]		=  images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							UpperCamelCase_:							Tuple		=  np.array([0.4909, 0.5059, 0.5372, 0.4623, 0.4876, 0.5049, 0.4820, 0.4956, 0.5019] )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2




			def  lowerCAmelCase__		(    self	:    Optional[Any] ):
							UpperCamelCase_:							Union[str, Any]		=  load_image(
							    """https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"""
							    """/img2img/sketch-mountains-input.jpg""" )
							UpperCamelCase_:							List[Any]		=  init_image.resize((768, 512) )
							UpperCamelCase_:							Union[str, Any]		=  LMSDiscreteScheduler.from_pretrained(
							    """runwayml/stable-diffusion-v1-5"""  ,		subfolder="""scheduler"""  ,		revision="""onnx""" )
							UpperCamelCase_:							Dict		=  OnnxStableDiffusionImgaImgPipeline.from_pretrained(
							    """runwayml/stable-diffusion-v1-5"""  ,		revision="""onnx"""  ,		scheduler=snake_case_  ,		safety_checker=snake_case_  ,		feature_extractor=snake_case_  ,		provider=self.gpu_provider  ,		sess_options=self.gpu_options  ,		)
							pipe.set_progress_bar_config(disable=snake_case_ )

							UpperCamelCase_:							Any		=  """A fantasy landscape, trending on artstation"""

							UpperCamelCase_:							Dict		=  np.random.RandomState(0 )
							UpperCamelCase_:							Union[str, Any]		=  pipe(
							    prompt=snake_case_  ,		image=snake_case_  ,		strength=0.75  ,		guidance_scale=7.5  ,		num_inference_steps=20  ,		generator=snake_case_  ,		output_type="""np"""  ,		)
							UpperCamelCase_:							Optional[int]		=  output.images
							UpperCamelCase_:							Dict		=  images[0, 255:258, 383:386, -1]

							assert images.shape == (1, 512, 768, 3)
							UpperCamelCase_:							Optional[int]		=  np.array([0.8043, 0.926, 0.9581, 0.8119, 0.8954, 0.913, 0.7209, 0.7463, 0.7431] )
							# TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

							assert np.abs(image_slice.flatten() - expected_slice ).max() < 2e-2






