"""simple docstring"""







import gc
import unittest

import numpy as np
import torch

from diffusers import RePaintPipeline, RePaintScheduler, UNetaDModel
from diffusers.utils.testing_utils import (
    enable_full_determinism,
    load_image,
    load_numpy,
    nightly,
    require_torch_gpu,
    skip_mps,
    torch_device,
)

from ..pipeline_params import IMAGE_INPAINTING_BATCH_PARAMS, IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineTesterMixin


enable_full_determinism()






class     _SCREAMING_SNAKE_CASE       ( UpperCAmelCase						,							unittest.TestCase							):



			'''simple docstring'''



			__UpperCAmelCase  :       str						=						RePaintPipeline
			__UpperCAmelCase  :       Any						=						IMAGE_INPAINTING_PARAMS - {"width", "height", "guidance_scale"}
			__UpperCAmelCase  :       List[str]						=						PipelineTesterMixin.required_optional_params - {
			    "latents",
			    "num_images_per_prompt",
			    "callback",
			    "callback_steps",
			}
			__UpperCAmelCase  :       Any						=						IMAGE_INPAINTING_BATCH_PARAMS
			__UpperCAmelCase  :       int						=						False







			def       _A							(  self  ):


						'''simple docstring'''

						torch.manual_seed(0  )
						torch.manual_seed(0  )
						__lowerCamelCase								=				UNetaDModel(
						    block_out_channels=(32, 64)   ,							layers_per_block=2   ,							sample_size=32   ,							in_channels=3   ,							out_channels=3   ,							down_block_types=('DownBlock2D', 'AttnDownBlock2D')   ,							up_block_types=('AttnUpBlock2D', 'UpBlock2D')   ,							)
						__lowerCamelCase								=				RePaintScheduler()
						__lowerCamelCase								=				{'unet': unet, 'scheduler': scheduler}
						return components







			def       _A							(  self   ,							_SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE=0  ):


						'''simple docstring'''

						if str(_SCREAMING_SNAKE_CASE  ).startswith('mps'  ):
									__lowerCamelCase								=				torch.manual_seed(_SCREAMING_SNAKE_CASE  )
						else:
									__lowerCamelCase								=				torch.Generator(device=_SCREAMING_SNAKE_CASE  ).manual_seed(_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				np.random.RandomState(_SCREAMING_SNAKE_CASE  ).standard_normal((1, 3, 32, 32)  )
						__lowerCamelCase								=				torch.from_numpy(_SCREAMING_SNAKE_CASE  ).to(device=_SCREAMING_SNAKE_CASE   ,							dtype=torch.floataa  )
						__lowerCamelCase								=				(image > 0).to(device=_SCREAMING_SNAKE_CASE   ,							dtype=torch.floataa  )
						__lowerCamelCase								=				{
						    'image': image,
						    'mask_image': mask,
						    'generator': generator,
						    'num_inference_steps': 5,
						    'eta': 0.0,
						    'jump_length': 2,
						    'jump_n_sample': 2,
						    'output_type': 'numpy',
						}
						return inputs







			def       _A							(  self  ):


						'''simple docstring'''

						__lowerCamelCase								=				'cpu'  # ensure determinism for the device-dependent torch.Generator
						__lowerCamelCase								=				self.get_dummy_components()
						__lowerCamelCase								=				RePaintPipeline(**_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				sd_pipe.to(_SCREAMING_SNAKE_CASE  )
						sd_pipe.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )

						__lowerCamelCase								=				self.get_dummy_inputs(_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				sd_pipe(**_SCREAMING_SNAKE_CASE  ).images
						__lowerCamelCase								=				image[0, -3:, -3:, -1]

						assert image.shape == (1, 32, 32, 3)
						__lowerCamelCase								=				np.array([1.0000, 0.5426, 0.5497, 0.2200, 1.0000, 1.0000, 0.5623, 1.0000, 0.6274]  )

						assert np.abs(image_slice.flatten() - expected_slice  ).max() < 1E-3







			@skip_mps
			def       _A							(  self  ):


						'''simple docstring'''

						return super().test_save_load_local()







			@unittest.skip('non-deterministic pipeline'  )
			def       _A							(  self  ):


						'''simple docstring'''

						return super().test_inference_batch_single_identical()







			@skip_mps
			def       _A							(  self  ):


						'''simple docstring'''

						return super().test_dict_tuple_outputs_equivalent()







			@skip_mps
			def       _A							(  self  ):


						'''simple docstring'''

						return super().test_save_load_optional_components()







			@skip_mps
			def       _A							(  self  ):


						'''simple docstring'''

						return super().test_attention_slicing_forward_pass()






@nightly
@require_torch_gpu
class     _SCREAMING_SNAKE_CASE       ( unittest.TestCase							):



			'''simple docstring'''



			def       _A							(  self  ):


						'''simple docstring'''

						super().tearDown()
						gc.collect()
						torch.cuda.empty_cache()







			def       _A							(  self  ):


						'''simple docstring'''

						__lowerCamelCase								=				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
						    'repaint/celeba_hq_256.png'  )
						__lowerCamelCase								=				load_image(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/repaint/mask_256.png'  )
						__lowerCamelCase								=				load_numpy(
						    'https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/'
						    'repaint/celeba_hq_256_result.npy'  )

						__lowerCamelCase								=				'google/ddpm-ema-celebahq-256'
						__lowerCamelCase								=				UNetaDModel.from_pretrained(_SCREAMING_SNAKE_CASE  )
						__lowerCamelCase								=				RePaintScheduler.from_pretrained(_SCREAMING_SNAKE_CASE  )

						__lowerCamelCase								=				RePaintPipeline(unet=_SCREAMING_SNAKE_CASE   ,							scheduler=_SCREAMING_SNAKE_CASE  ).to(_SCREAMING_SNAKE_CASE  )
						repaint.set_progress_bar_config(disable=_SCREAMING_SNAKE_CASE  )
						repaint.enable_attention_slicing()

						__lowerCamelCase								=				torch.manual_seed(0  )
						__lowerCamelCase								=				repaint(
						    _SCREAMING_SNAKE_CASE   ,							_SCREAMING_SNAKE_CASE   ,							num_inference_steps=250   ,							eta=0.0   ,							jump_length=10   ,							jump_n_sample=10   ,							generator=_SCREAMING_SNAKE_CASE   ,							output_type='np'   ,							)
						__lowerCamelCase								=				output.images[0]

						assert image.shape == (256, 256, 3)
						assert np.abs(expected_image - image  ).mean() < 1E-2







"""simple docstring"""







import sys

from transformers import AutoTokenizer


_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[1]
_SCREAMING_SNAKE_CASE  :		List[str]        =      sys.argv[2]
_SCREAMING_SNAKE_CASE  :		Tuple        =      int(sys.argv[3])

_SCREAMING_SNAKE_CASE  :		List[str]        =      0

_SCREAMING_SNAKE_CASE  :		Optional[int]        =      AutoTokenizer.from_pretrained(model_name_or_path)
max_len -= tokenizer.num_special_tokens_to_add()

with open(dataset, '''rt''') as f_p:
     for line in f_p:
          _SCREAMING_SNAKE_CASE  :		List[Any]        =      line.rstrip()

          if not line:
               print(line)
               _SCREAMING_SNAKE_CASE  :		Union[str, Any]        =      0
               continue

          _SCREAMING_SNAKE_CASE  :		Dict        =      line.split()[0]

          _SCREAMING_SNAKE_CASE  :		Tuple        =      len(tokenizer.tokenize(token))

          # Token contains strange control characters like \x96 or \x95
          # Just filter out the complete line
          if current_subwords_len == 0:
               continue

          if (subword_len_counter + current_subwords_len) > max_len:
               print('''''')
               print(line)
               _SCREAMING_SNAKE_CASE  :		List[Any]        =      current_subwords_len
               continue

          subword_len_counter += current_subwords_len

          print(line)






