'''simple docstring'''

import random
import unittest

import numpy as np
import torch

from diffusers import (
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionUpscalePipeline,
    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   snake_case					(					lowercase    ,		unittest.TestCase    ):





  """simple docstring"""




  _lowerCamelCase						=						"""ssube/stable-diffusion-x4-upscaler-onnx"""






  def 						snake_case	(					self    ,						UpperCamelCase=0  ):







   """simple docstring"""
   lowerCamelCase_  				=						floats_tensor((1, 3, 128, 128)    ,						rng=random.Random(UpperCamelCase  )  )
   lowerCamelCase_  				=						torch.manual_seed(UpperCamelCase  )
   lowerCamelCase_  				=						{
       "prompt": "A painting of a squirrel eating a burger",
       "image": image,
       "generator": generator,
       "num_inference_steps": 3,
       "guidance_scale": 7.5,
       "output_type": "numpy",
   }
   return inputs






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider="CPUExecutionProvider"  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						self.get_dummy_inputs()
   lowerCamelCase_  				=						pipe(**UpperCamelCase  ).images
   lowerCamelCase_  				=						image[0, -3:, -3:, -1].flatten()

   # started as 128, should now be 512
   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase_  				=						np.array(
       [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]  )
   assert np.abs(image_slice - expected_slice  ).max() < 1e-1






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider="CPUExecutionProvider"  )
   lowerCamelCase_  				=						PNDMScheduler.from_config(pipe.scheduler.config    ,						skip_prk_steps=UpperCamelCase  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						self.get_dummy_inputs()
   lowerCamelCase_  				=						pipe(**UpperCamelCase  ).images
   lowerCamelCase_  				=						image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase_  				=						np.array(
       [0.6_898_892, 0.59_240_556, 0.52_499_527, 0.58_866_215, 0.52_258_235, 0.52_572_715, 0.62_414_473, 0.6_174_387, 0.6_214_964]  )
   assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider="CPUExecutionProvider"  )
   lowerCamelCase_  				=						DPMSolverMultistepScheduler.from_config(pipe.scheduler.config  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						self.get_dummy_inputs()
   lowerCamelCase_  				=						pipe(**UpperCamelCase  ).images
   lowerCamelCase_  				=						image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase_  				=						np.array(
       [0.7_659_278, 0.76_437_664, 0.75_579_107, 0.7_691_116, 0.77_666_986, 0.7_727_672, 0.7_758_664, 0.7_812_226, 0.76_942_515]  )

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






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider="CPUExecutionProvider"  )
   lowerCamelCase_  				=						EulerDiscreteScheduler.from_config(pipe.scheduler.config  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						self.get_dummy_inputs()
   lowerCamelCase_  				=						pipe(**UpperCamelCase  ).images
   lowerCamelCase_  				=						image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase_  				=						np.array(
       [0.6_974_782, 0.68_902_093, 0.70_135_885, 0.7_583_618, 0.7_804_545, 0.7_854_912, 0.78_667_426, 0.78_743_863, 0.78_070_223]  )
   assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1e-1







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint    ,						provider="CPUExecutionProvider"  )
   lowerCamelCase_  				=						EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config  )
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						self.get_dummy_inputs()
   lowerCamelCase_  				=						pipe(**UpperCamelCase  ).images
   lowerCamelCase_  				=						image[0, -3:, -3:, -1]

   assert image.shape == (1, 512, 512, 3)
   lowerCamelCase_  				=						np.array(
       [0.77_424_496, 0.773_601, 0.7_645_288, 0.7_769_598, 0.7_772_739, 0.7_738_688, 0.78_187_233, 0.77_879_584, 0.767_043]  )

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





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





  """simple docstring"""




  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   return (
       "CUDAExecutionProvider",
       {
           "gpu_mem_limit": "15000000000",  # 15GB
           "arena_extend_strategy": "kSameAsRequested",
       },
   )






  @property
  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						ort.SessionOptions()
   lowerCamelCase_  				=						False
   return options






  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/img2img/sketch-mountains-input.jpg"  )
   lowerCamelCase_  				=						init_image.resize((128, 128)  )
   # using the PNDM scheduler by default
   lowerCamelCase_  				=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
       "ssube/stable-diffusion-x4-upscaler-onnx"    ,						provider=self.gpu_provider    ,						sess_options=self.gpu_options    ,						)
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						"A fantasy landscape, trending on artstation"

   lowerCamelCase_  				=						torch.manual_seed(0  )
   lowerCamelCase_  				=						pipe(
       prompt=UpperCamelCase    ,						image=UpperCamelCase    ,						guidance_scale=7.5    ,						num_inference_steps=10    ,						generator=UpperCamelCase    ,						output_type="np"    ,						)
   lowerCamelCase_  				=						output.images
   lowerCamelCase_  				=						images[0, 255:258, 383:386, -1]

   assert images.shape == (1, 512, 512, 3)
   lowerCamelCase_  				=						np.array([0.4_883, 0.4_947, 0.4_980, 0.4_975, 0.4_982, 0.4_980, 0.5_000, 0.5_006, 0.4_972]  )
   # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







  def 						snake_case	(					self  ):







   """simple docstring"""
   lowerCamelCase_  				=						load_image(
       "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
       "/img2img/sketch-mountains-input.jpg"  )
   lowerCamelCase_  				=						init_image.resize((128, 128)  )
   lowerCamelCase_  				=						LMSDiscreteScheduler.from_pretrained(
       "ssube/stable-diffusion-x4-upscaler-onnx"    ,						subfolder="scheduler"  )
   lowerCamelCase_  				=						OnnxStableDiffusionUpscalePipeline.from_pretrained(
       "ssube/stable-diffusion-x4-upscaler-onnx"    ,						scheduler=UpperCamelCase    ,						provider=self.gpu_provider    ,						sess_options=self.gpu_options    ,						)
   pipe.set_progress_bar_config(disable=UpperCamelCase  )

   lowerCamelCase_  				=						"A fantasy landscape, trending on artstation"

   lowerCamelCase_  				=						torch.manual_seed(0  )
   lowerCamelCase_  				=						pipe(
       prompt=UpperCamelCase    ,						image=UpperCamelCase    ,						guidance_scale=7.5    ,						num_inference_steps=20    ,						generator=UpperCamelCase    ,						output_type="np"    ,						)
   lowerCamelCase_  				=						output.images
   lowerCamelCase_  				=						images[0, 255:258, 383:386, -1]

   assert images.shape == (1, 512, 512, 3)
   lowerCamelCase_  				=						np.array(
       [0.50_173_753, 0.50_223_356, 0.502_039, 0.50_233_036, 0.5_023_725, 0.5_022_601, 0.5_018_758, 0.50_234_085, 0.50_241_566]  )
   # TODO: lower the tolerance after finding the cause of onnxruntime reproducibility issues

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







'''simple docstring'''

import fire
from torch.utils.data import DataLoader
from tqdm import tqdm

from transformers import AutoTokenizer
from utils import SeqaSeqDataset, pickle_save





def __snake_case      (   UpperCAmelCase_		:		List[str]      ,       UpperCAmelCase_		:		Optional[int]      ,       UpperCAmelCase_		:		Optional[int]=1024      ,       UpperCAmelCase_		:		int=1024      ,       UpperCAmelCase_		:		Optional[Any]=False      ,       **UpperCAmelCase_		:		Optional[Any]	):
 lowerCamelCase_  				=						AutoTokenizer.from_pretrained(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="train"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						tok.pad_token_id

 def get_lens(UpperCAmelCase_		:		str	):
  lowerCamelCase_  				=						tqdm(
      DataLoader(UpperCAmelCase_      ,       batch_size=512      ,       num_workers=8      ,       shuffle=UpperCAmelCase_      ,       collate_fn=ds.collate_fn	)      ,       desc=str(ds.len_file	)      ,       )
  lowerCamelCase_  				=						[]
  for batch in dl:
   lowerCamelCase_  				=						batch["input_ids"].ne(UpperCAmelCase_	).sum(1	).tolist()
   lowerCamelCase_  				=						batch["labels"].ne(UpperCAmelCase_	).sum(1	).tolist()
   if consider_target:
    for src, tgt in zip(UpperCAmelCase_      ,       UpperCAmelCase_	):
     max_lens.append(max(UpperCAmelCase_      ,       UpperCAmelCase_	)	)
   else:
    max_lens.extend(UpperCAmelCase_	)
  return max_lens

 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 lowerCamelCase_  				=						SeqaSeqDataset(UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       UpperCAmelCase_      ,       type_path="val"      ,       **UpperCAmelCase_	)
 lowerCamelCase_  				=						get_lens(UpperCAmelCase_	)
 pickle_save(UpperCAmelCase_      ,       train_ds.len_file	)
 pickle_save(UpperCAmelCase_      ,       val_ds.len_file	)


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



