






"""simple docstring"""



import gc
import random
import tempfile
import unittest

import numpy as np
import torch
from PIL import Image
from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer

from diffusers import (
    AutoencoderKL,
    DDIMInverseScheduler,
    DDIMScheduler,
    DPMSolverMultistepInverseScheduler,
    DPMSolverMultistepScheduler,
    StableDiffusionDiffEditPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import load_image, slow
from diffusers.utils.testing_utils import enable_full_determinism, floats_tensor, require_torch_gpu, torch_device

from ..pipeline_params import TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS, TEXT_GUIDED_IMAGE_INPAINTING_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()







class    lowercase__			(							__a,   __a,   unittest.TestCase   ):
			_UpperCAmelCase		:Union[str, Any]   			= StableDiffusionDiffEditPipeline
			_UpperCAmelCase		:int   			= TEXT_GUIDED_IMAGE_INPAINTING_PARAMS - {"height", "width", "image"} | {"image_latents"}
			_UpperCAmelCase		:Optional[int]   			= TEXT_GUIDED_IMAGE_INPAINTING_BATCH_PARAMS - {"image"} | {"image_latents"}
			_UpperCAmelCase		:int   			= frozenset(
			    []   )  # TO-DO: update image_params once pipeline is refactored with VaeImageProcessor.preprocess
			_UpperCAmelCase		:List[str]   			= frozenset([]   )


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										torch.manual_seed(0					)
										lowerCamelCase_       :							Dict									=UNetaDConditionModel(
										    block_out_channels=(32, 64)   ,       layers_per_block=2   ,       sample_size=32   ,       in_channels=4   ,       out_channels=4   ,       down_block_types=("DownBlock2D", "CrossAttnDownBlock2D")   ,       up_block_types=("CrossAttnUpBlock2D", "UpBlock2D")   ,       cross_attention_dim=32   ,       attention_head_dim=(2, 4)   ,       use_linear_projection=a_   ,       )
										lowerCamelCase_       :							str									=DDIMScheduler(
										    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule="scaled_linear"   ,       clip_sample=a_   ,       set_alpha_to_one=a_   ,       )
										lowerCamelCase_       :							str									=DDIMInverseScheduler(
										    beta_start=0.00_085   ,       beta_end=0.012   ,       beta_schedule="scaled_linear"   ,       clip_sample=a_   ,       set_alpha_to_zero=a_   ,       )
										torch.manual_seed(0					)
										lowerCamelCase_       :							Optional[Any]									=AutoencoderKL(
										    block_out_channels=[32, 64]   ,       in_channels=3   ,       out_channels=3   ,       down_block_types=["DownEncoderBlock2D", "DownEncoderBlock2D"]   ,       up_block_types=["UpDecoderBlock2D", "UpDecoderBlock2D"]   ,       latent_channels=4   ,       sample_size=128   ,       )
										torch.manual_seed(0					)
										lowerCamelCase_       :							List[str]									=CLIPTextConfig(
										    bos_token_id=0   ,       eos_token_id=2   ,       hidden_size=32   ,       intermediate_size=37   ,       layer_norm_eps=1E-05   ,       num_attention_heads=4   ,       num_hidden_layers=5   ,       pad_token_id=1   ,       vocab_size=1000   ,       hidden_act="gelu"   ,       projection_dim=512   ,       )
										lowerCamelCase_       :							Optional[int]									=CLIPTextModel(a_					)
										lowerCamelCase_       :							int									=CLIPTokenizer.from_pretrained("hf-internal-testing/tiny-random-clip"					)

										lowerCamelCase_       :							str									={
										    """unet""": unet,
										    """scheduler""": scheduler,
										    """inverse_scheduler""": inverse_scheduler,
										    """vae""": vae,
										    """text_encoder""": text_encoder,
										    """tokenizer""": tokenizer,
										    """safety_checker""": None,
										    """feature_extractor""": None,
										}

										return components


			def 	UpperCAmelCase__						(      self     :  Optional[Any]   ,       snake_case__     :  Any   ,       snake_case__     :  Any=0					):
										lowerCamelCase_       :							List[str]									=floats_tensor((1, 16, 16)   ,       rng=random.Random(a_					)					).to(a_					)
										lowerCamelCase_       :							Dict									=floats_tensor((1, 2, 4, 16, 16)   ,       rng=random.Random(a_					)					).to(a_					)
										if str(a_					).startswith("mps"					):
																	lowerCamelCase_       :							Optional[int]									=torch.manual_seed(a_					)
										else:
																	lowerCamelCase_       :							List[Any]									=torch.Generator(device=a_					).manual_seed(a_					)
										lowerCamelCase_       :							Dict									={
										    """prompt""": """a dog and a newt""",
										    """mask_image""": mask,
										    """image_latents""": latents,
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs


			def 	UpperCAmelCase__						(      self     :  Any   ,       snake_case__     :  Dict   ,       snake_case__     :  List[Any]=0					):
										lowerCamelCase_       :							str									=floats_tensor((1, 3, 32, 32)   ,       rng=random.Random(a_					)					).to(a_					)
										lowerCamelCase_       :							str									=image.cpu().permute(0   ,       2   ,       3   ,       1					)[0]
										lowerCamelCase_       :							Dict									=Image.fromarray(np.uinta(a_					)					).convert("RGB"					)
										if str(a_					).startswith("mps"					):
																	lowerCamelCase_       :							str									=torch.manual_seed(a_					)
										else:
																	lowerCamelCase_       :							Dict									=torch.Generator(device=a_					).manual_seed(a_					)
										lowerCamelCase_       :							int									={
										    """image""": image,
										    """source_prompt""": """a cat and a frog""",
										    """target_prompt""": """a dog and a newt""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """num_maps_per_mask""": 2,
										    """mask_encode_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """output_type""": """numpy""",
										}

										return inputs


			def 	UpperCAmelCase__						(      self     :  Any   ,       snake_case__     :  int   ,       snake_case__     :  Optional[Any]=0					):
										lowerCamelCase_       :							Tuple									=floats_tensor((1, 3, 32, 32)   ,       rng=random.Random(a_					)					).to(a_					)
										lowerCamelCase_       :							Optional[Any]									=image.cpu().permute(0   ,       2   ,       3   ,       1					)[0]
										lowerCamelCase_       :							List[str]									=Image.fromarray(np.uinta(a_					)					).convert("RGB"					)
										if str(a_					).startswith("mps"					):
																	lowerCamelCase_       :							int									=torch.manual_seed(a_					)
										else:
																	lowerCamelCase_       :							Union[str, Any]									=torch.Generator(device=a_					).manual_seed(a_					)
										lowerCamelCase_       :							Dict									={
										    """image""": image,
										    """prompt""": """a cat and a frog""",
										    """generator""": generator,
										    """num_inference_steps""": 2,
										    """inpaint_strength""": 1.0,
										    """guidance_scale""": 6.0,
										    """decode_latents""": True,
										    """output_type""": """numpy""",
										}
										return inputs


			def 	UpperCAmelCase__						(      self     :  str					):



										if not hasattr(self.pipeline_class   ,       "_optional_components"					):
																	return

										lowerCamelCase_       :							Optional[int]									=self.get_dummy_components()
										lowerCamelCase_       :							Optional[int]									=self.pipeline_class(**a_					)
										pipe.to(a_					)
										pipe.set_progress_bar_config(disable=a_					)

										# set all optional components to None and update pipeline config accordingly
										for optional_component in pipe._optional_components:
																	setattr(a_   ,       a_   ,       a_					)
										pipe.register_modules(**{optional_component: None for optional_component in pipe._optional_components}					)

										lowerCamelCase_       :							Optional[int]									=self.get_dummy_inputs(a_					)
										lowerCamelCase_       :							Optional[Any]									=pipe(**a_					)[0]

										with tempfile.TemporaryDirectory() as tmpdir:
																	pipe.save_pretrained(a_					)
																	lowerCamelCase_       :							int									=self.pipeline_class.from_pretrained(a_					)
																	pipe_loaded.to(a_					)
																	pipe_loaded.set_progress_bar_config(disable=a_					)

										for optional_component in pipe._optional_components:
																	self.assertTrue(
																	    getattr(a_   ,       a_					) is None   ,       F"""`{optional_component}` did not stay set to None after loading."""   ,       )

										lowerCamelCase_       :							Tuple									=self.get_dummy_inputs(a_					)
										lowerCamelCase_       :							Tuple									=pipe_loaded(**a_					)[0]

										lowerCamelCase_       :							List[str]									=np.abs(output - output_loaded					).max()
										self.assertLess(a_   ,       1E-4					)


			def 	UpperCAmelCase__						(      self     :  Any					):
										lowerCamelCase_       :							List[str]									="""cpu"""

										lowerCamelCase_       :							Optional[int]									=self.get_dummy_components()
										lowerCamelCase_       :							Dict									=self.pipeline_class(**a_					)
										pipe.to(a_					)
										pipe.set_progress_bar_config(disable=a_					)

										lowerCamelCase_       :							int									=self.get_dummy_mask_inputs(a_					)
										lowerCamelCase_       :							Union[str, Any]									=pipe.generate_mask(**a_					)
										lowerCamelCase_       :							Union[str, Any]									=mask[0, -3:, -3:]

										self.assertEqual(mask.shape   ,       (1, 16, 16)					)
										lowerCamelCase_       :							Tuple									=np.array([0] * 9					)
										lowerCamelCase_       :							str									=np.abs(mask_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(a_   ,       1E-3					)
										self.assertEqual(mask[0, -3, -4]   ,       0					)


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							int									="""cpu"""

										lowerCamelCase_       :							Union[str, Any]									=self.get_dummy_components()
										lowerCamelCase_       :							Union[str, Any]									=self.pipeline_class(**a_					)
										pipe.to(a_					)
										pipe.set_progress_bar_config(disable=a_					)

										lowerCamelCase_       :							List[str]									=self.get_dummy_inversion_inputs(a_					)
										lowerCamelCase_       :							str									=pipe.invert(**a_					).images
										lowerCamelCase_       :							List[str]									=image[0, -1, -3:, -3:]

										self.assertEqual(image.shape   ,       (2, 32, 32, 3)					)
										lowerCamelCase_       :							List[Any]									=np.array(
										    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]   ,       )
										lowerCamelCase_       :							Optional[Any]									=np.abs(image_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(a_   ,       1E-3					)


			def 	UpperCAmelCase__						(      self     :  Any					):
										super().test_inference_batch_single_identical(expected_max_diff=5E-3					)


			def 	UpperCAmelCase__						(      self     :  Optional[int]					):
										lowerCamelCase_       :							Any									="""cpu"""

										lowerCamelCase_       :							List[Any]									=self.get_dummy_components()

										lowerCamelCase_       :							str									={"""beta_start""": 0.00_085, """beta_end""": 0.012, """beta_schedule""": """scaled_linear"""}
										lowerCamelCase_       :							Dict									=DPMSolverMultistepScheduler(**a_					)
										lowerCamelCase_       :							Any									=DPMSolverMultistepInverseScheduler(**a_					)

										lowerCamelCase_       :							Optional[Any]									=self.pipeline_class(**a_					)
										pipe.to(a_					)
										pipe.set_progress_bar_config(disable=a_					)

										lowerCamelCase_       :							List[Any]									=self.get_dummy_inversion_inputs(a_					)
										lowerCamelCase_       :							Union[str, Any]									=pipe.invert(**a_					).images
										lowerCamelCase_       :							List[str]									=image[0, -1, -3:, -3:]

										self.assertEqual(image.shape   ,       (2, 32, 32, 3)					)
										lowerCamelCase_       :							int									=np.array(
										    [0.5_150, 0.5_134, 0.5_043, 0.5_376, 0.4_694, 0.51_050, 0.5_015, 0.4_407, 0.4_799]   ,       )
										lowerCamelCase_       :							Tuple									=np.abs(image_slice.flatten() - expected_slice					).max()
										self.assertLessEqual(a_   ,       1E-3					)







@require_torch_gpu
@slow
class    lowercase__			(							unittest.TestCase   ):


			def 	UpperCAmelCase__						(      self     :  Tuple					):
										super().tearDown()
										gc.collect()
										torch.cuda.empty_cache()


			@classmethod
			def 	UpperCAmelCase__						(      cls     :  Dict					):
										lowerCamelCase_       :							Dict									=load_image(
										    "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main/diffedit/fruit.png"					)

										lowerCamelCase_       :							Optional[Any]									=raw_image.convert("RGB"					).resize((768, 768)					)

										lowerCamelCase_       :							Optional[Any]									=raw_image


			def 	UpperCAmelCase__						(      self     :  Optional[Any]					):
										lowerCamelCase_       :							Tuple									=torch.manual_seed(0					)

										lowerCamelCase_       :							Optional[int]									=StableDiffusionDiffEditPipeline.from_pretrained(
										    "stabilityai/stable-diffusion-2-1"   ,       safety_checker=a_   ,       torch_dtype=torch.floataa					)
										lowerCamelCase_       :							Any									=DDIMScheduler.from_config(pipe.scheduler.config					)
										lowerCamelCase_       :							Optional[Any]									=DDIMInverseScheduler.from_config(pipe.scheduler.config					)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=a_					)

										lowerCamelCase_       :							Optional[Any]									="""a bowl of fruit"""
										lowerCamelCase_       :							Union[str, Any]									="""a bowl of pears"""

										lowerCamelCase_       :							List[str]									=pipe.generate_mask(
										    image=self.raw_image   ,       source_prompt=a_   ,       target_prompt=a_   ,       generator=a_   ,       )

										lowerCamelCase_       :							Optional[Any]									=pipe.invert(
										    prompt=a_   ,       image=self.raw_image   ,       inpaint_strength=0.7   ,       generator=a_					).latents

										lowerCamelCase_       :							Dict									=pipe(
										    prompt=a_   ,       mask_image=a_   ,       image_latents=a_   ,       generator=a_   ,       negative_prompt=a_   ,       inpaint_strength=0.7   ,       output_type="numpy"   ,       ).images[0]

										lowerCamelCase_       :							Union[str, Any]									=(
										    np.array(
										        load_image(
										            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										            "/diffedit/pears.png"					).resize((768, 768)					)					)
										    / 255
										)
										assert np.abs((expected_image - image).max()					) < 5E-1


			def 	UpperCAmelCase__						(      self     :  List[str]					):
										lowerCamelCase_       :							Tuple									=torch.manual_seed(0					)

										lowerCamelCase_       :							List[Any]									=StableDiffusionDiffEditPipeline.from_pretrained(
										    "stabilityai/stable-diffusion-2-1"   ,       safety_checker=a_   ,       torch_dtype=torch.floataa					)
										lowerCamelCase_       :							Dict									=DPMSolverMultistepScheduler.from_config(pipe.scheduler.config					)
										lowerCamelCase_       :							Any									=DPMSolverMultistepInverseScheduler.from_config(pipe.scheduler.config					)
										pipe.enable_model_cpu_offload()
										pipe.set_progress_bar_config(disable=a_					)

										lowerCamelCase_       :							Tuple									="""a bowl of fruit"""
										lowerCamelCase_       :							Optional[Any]									="""a bowl of pears"""

										lowerCamelCase_       :							Any									=pipe.generate_mask(
										    image=self.raw_image   ,       source_prompt=a_   ,       target_prompt=a_   ,       generator=a_   ,       )

										lowerCamelCase_       :							Optional[int]									=pipe.invert(
										    prompt=a_   ,       image=self.raw_image   ,       inpaint_strength=0.7   ,       generator=a_   ,       num_inference_steps=25   ,       ).latents

										lowerCamelCase_       :							Optional[Any]									=pipe(
										    prompt=a_   ,       mask_image=a_   ,       image_latents=a_   ,       generator=a_   ,       negative_prompt=a_   ,       inpaint_strength=0.7   ,       num_inference_steps=25   ,       output_type="numpy"   ,       ).images[0]

										lowerCamelCase_       :							str									=(
										    np.array(
										        load_image(
										            "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"
										            "/diffedit/pears.png"					).resize((768, 768)					)					)
										    / 255
										)
										assert np.abs((expected_image - image).max()					) < 5E-1









"""simple docstring"""


import gzip
import hashlib
import json
import multiprocessing
import os
import re
import shutil
import time
from pathlib import Path

import numpy as np
from arguments import PreprocessingArguments
from datasets import load_dataset
from minhash_deduplication import deduplicate_dataset

from transformers import AutoTokenizer, HfArgumentParser


A_						=				re.compile(r'''\s+''')


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  return {"hash": hashlib.mda(re.sub(snake_case__		,   """"""		,   example["""content"""]    ).encode("""utf-8"""    )    ).hexdigest()}


def 					UpperCAmelCase__       (snake_case__							:					Dict    ):
  """simple docstring"""

  _snake_case							:		Any								=  [len(snake_case__    ) for line in example["""content"""].splitlines()]
  return {"line_mean": np.mean(snake_case__    ), "line_max": max(snake_case__    )}


def 					UpperCAmelCase__       (snake_case__							:					List[Any]    ):
  """simple docstring"""

  _snake_case							:		Tuple								=  np.mean([c.isalnum() for c in example["""content"""]]    )
  return {"alpha_frac": alpha_frac}


def 					UpperCAmelCase__       (snake_case__							:					List[Any]		,   snake_case__							:					List[Any]    ):
  """simple docstring"""

  if example["hash"] in uniques:
    uniques.remove(example["""hash"""]    )
    return True
  else:
    return False


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]		,   snake_case__							:					List[str]=5    ):
  """simple docstring"""

  _snake_case							:		Any								=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
  _snake_case							:		Tuple								=  example["""content"""].splitlines()
  for _, line in zip(range(snake_case__    )		,   snake_case__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}


def 					UpperCAmelCase__       (snake_case__							:					Any		,   snake_case__							:					Union[str, Any]=5		,   snake_case__							:					Any=0.05    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  ["""unit tests""", """test file""", """configuration file"""]
  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		Dict								=  0
  _snake_case							:		str								=  0
  # first test
  for _, line in zip(range(snake_case__    )		,   snake_case__    ):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  _snake_case							:		Optional[int]								=  example["""content"""].count("""\n"""    )
  _snake_case							:		Tuple								=  int(coeff * nlines    )
  for line in lines:
    count_config += line.lower().count("""config"""    )
    count_test += line.lower().count("""test"""    )
    if count_config > threshold or count_test > threshold:
      return {"config_or_test": True}
  return {"config_or_test": False}


def 					UpperCAmelCase__       (snake_case__							:					str    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  ["""def """, """class """, """for """, """while """]
  _snake_case							:		str								=  example["""content"""].splitlines()
  for line in lines:
    for keyword in keywords:
      if keyword in line.lower():
        return {"has_no_keywords": False}
  return {"has_no_keywords": True}


def 					UpperCAmelCase__       (snake_case__							:					List[str]		,   snake_case__							:					List[str]=4    ):
  """simple docstring"""

  _snake_case							:		List[Any]								=  example["""content"""].splitlines()
  _snake_case							:		str								=  0
  for line in lines:
    counter += line.lower().count("""="""    )
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}


def 					UpperCAmelCase__       (snake_case__							:					List[str]    ):
  """simple docstring"""

  _snake_case							:		Optional[Any]								=  tokenizer(example["""content"""]		,   truncation=snake_case__    )["""input_ids"""]
  _snake_case							:		Optional[Any]								=  len(example["""content"""]    ) / len(snake_case__    )
  return {"ratio": ratio}


def 					UpperCAmelCase__       (snake_case__							:					Optional[int]    ):
  """simple docstring"""

  _snake_case							:		Optional[int]								=  {}
  results.update(get_hash(snake_case__    )    )
  results.update(line_stats(snake_case__    )    )
  results.update(alpha_stats(snake_case__    )    )
  results.update(char_token_ratio(snake_case__    )    )
  results.update(is_autogenerated(snake_case__    )    )
  results.update(is_config_or_test(snake_case__    )    )
  results.update(has_no_keywords(snake_case__    )    )
  results.update(has_few_assignments(snake_case__    )    )
  return results


def 					UpperCAmelCase__       (snake_case__							:					Tuple		,   snake_case__							:					List[Any]		,   snake_case__							:					List[str]    ):
  """simple docstring"""

  if not check_uniques(snake_case__		,   snake_case__    ):
    return False
  elif example["autogenerated"]:
    return False
  elif example["line_max"] > args.line_max:
    return False
  elif example["line_mean"] > args.line_mean:
    return False
  elif example["alpha_frac"] < args.alpha_frac:
    return False
  elif example["ratio"] < args.min_token_ratio:
    return False
  elif example["config_or_test"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_no_keywords"] and np.random.rand() <= args.filter_proba:
    return False
  elif example["has_few_assignments"]:
    return False
  else:
    return True


def 					UpperCAmelCase__       (snake_case__							:					Optional[Any]    ):
  """simple docstring"""

  with open(snake_case__		,   """rb"""    ) as f_in:
    with gzip.open(str(snake_case__    ) + """.gz"""		,   """wb"""		,   compresslevel=6    ) as f_out:
      shutil.copyfileobj(snake_case__		,   snake_case__    )
  os.unlink(snake_case__    )


# Settings
A_						=				HfArgumentParser(PreprocessingArguments)
A_						=				parser.parse_args()
if args.num_workers is None:
     A_						=				multiprocessing.cpu_count()
A_						=				AutoTokenizer.from_pretrained(args.tokenizer_dir)

# Load dataset
A_						=				time.time()
A_						=				load_dataset(args.dataset_name, split='''train''')
print(F'''Time to load dataset: {time.time()-t_start:.2f}''')

# Run preprocessing
A_						=				time.time()
A_						=				ds.map(preprocess, num_proc=args.num_workers)
print(F'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

# Deduplicate hashes
A_						=				set(ds.unique('''hash'''))
A_						=				len(uniques) / len(ds)
print(F'''Fraction of duplicates: {1-frac:.2%}''')

# Deduplicate data and apply heuristics
A_						=				time.time()
A_						=				ds.filter(filter, fn_kwargs={'''uniques''': uniques, '''args''': args})
print(F'''Time to filter dataset: {time.time()-t_start:.2f}''')
print(F'''Size of filtered dataset: {len(ds_filter)}''')

# Deduplicate with minhash and jaccard similarity
if args.near_deduplication:
     A_						=				time.time()
     A_	,    A_						=				deduplicate_dataset(ds_filter, args.jaccard_threshold)
     print(F'''Time to deduplicate dataset: {time.time()-t_start:.2f}''')
     print(F'''Size of deduplicate dataset: {len(ds_filter)}''')

# Save data in batches of samples_per_file
A_						=				Path(args.output_dir)
output_dir.mkdir(exist_ok=True)

# save duplicate_clusters in the output_dir as artifacts
# not sure it is the right place the save it
if args.near_deduplication:
     with open(output_dir / '''duplicate_clusters.json''', '''w''') as f:
          json.dump(duplicate_clusters, f)

A_						=				output_dir / '''data'''
data_dir.mkdir(exist_ok=True)

A_						=				time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
     A_						=				str(data_dir / F'''file-{file_number+1:012}.json''')
     A_						=				min(len(ds_filter), index + args.samples_per_file)
     ds_filter.select(list(range(index, end_index))).to_json(file_path)
     compress_file(file_path)






print(F'''Time to save dataset: {time.time()-t_start:.2f}''')


