






import gc
import unittest

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

from diffusers import (
    AutoencoderKL,
    DDIMScheduler,
    EulerAncestralDiscreteScheduler,
    LMSDiscreteScheduler,
    PNDMScheduler,
    StableDiffusionPanoramaPipeline,
    UNetaDConditionModel,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu, skip_mps

from ..pipeline_params import TEXT_TO_IMAGE_BATCH_PARAMS, TEXT_TO_IMAGE_IMAGE_PARAMS, TEXT_TO_IMAGE_PARAMS
from ..test_pipelines_common import PipelineLatentTesterMixin, PipelineTesterMixin


enable_full_determinism()



@skip_mps
class 				__UpperCAmelCase		(_UpperCAmelCase	,_UpperCAmelCase	,unittest.TestCase   ):
			__snake_case    :				str									=							StableDiffusionPanoramaPipeline
			__snake_case    :				Optional[int]									=							TEXT_TO_IMAGE_PARAMS
			__snake_case    :				Union[str, Any]									=							TEXT_TO_IMAGE_BATCH_PARAMS
			__snake_case    :				Optional[Any]									=							TEXT_TO_IMAGE_IMAGE_PARAMS
			__snake_case    :				List[str]									=							TEXT_TO_IMAGE_IMAGE_PARAMS







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  UNetaDConditionModel(
									    block_out_channels=(32, 64)				,     layers_per_block=1				,     sample_size=32				,     in_channels=4				,     out_channels=4				,     down_block_types=("""DownBlock2D""", """CrossAttnDownBlock2D""")				,     up_block_types=("""CrossAttnUpBlock2D""", """UpBlock2D""")				,     cross_attention_dim=32				,     )
									_SCREAMING_SNAKE_CASE     				=  DDIMScheduler()
									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  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				,     )
									torch.manual_seed(0					)
									_SCREAMING_SNAKE_CASE     				=  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=1_000				,     )
									_SCREAMING_SNAKE_CASE     				=  CLIPTextModel(lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  CLIPTokenizer.from_pretrained("""hf-internal-testing/tiny-random-clip"""					)

									_SCREAMING_SNAKE_CASE     				=  {
									    """unet""": unet,
									    """scheduler""": scheduler,
									    """vae""": vae,
									    """text_encoder""": text_encoder,
									    """tokenizer""": tokenizer,
									    """safety_checker""": None,
									    """feature_extractor""": None,
									}
									return components







			def 							UpperCamelCase				(     self:  int				,     UpperCAmelCase_:  Optional[int]				,     UpperCAmelCase_:  List[Any]=0					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  {
									    """prompt""": """a photo of the dolomites""",
									    """generator""": generator,
									    # Setting height and width to None to prevent OOMs on CPU.
									    """height""": None,
									    """width""": None,
									    """num_inference_steps""": 1,
									    """guidance_scale""": 6.0,
									    """output_type""": """numpy""",
									}
									return inputs







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_components()
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline(**lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(lowercase_					)
									sd_pipe.set_progress_bar_config(disable=lowercase_					)

									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_inputs(lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(**lowercase_					).images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]
									assert image.shape == (1, 64, 64, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array([0.61_86, 0.53_74, 0.49_15, 0.41_35, 0.41_14, 0.45_63, 0.51_28, 0.49_77, 0.47_57]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  List[Any]					):







									'''simple docstring'''




									super().test_inference_batch_consistent(batch_sizes=[1, 2]					)







			def 							UpperCamelCase				(     self:  List[str]					):







									'''simple docstring'''




									super().test_inference_batch_single_identical(batch_size=2				,     expected_max_diff=3.25E-3					)







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_components()
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline(**lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(lowercase_					)
									sd_pipe.set_progress_bar_config(disable=lowercase_					)

									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_inputs(lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  """french fries"""
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(**lowercase_				,     negative_prompt=lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  Dict					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_components()
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline(**lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(lowercase_					)
									sd_pipe.set_progress_bar_config(disable=lowercase_					)

									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_inputs(lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe(**lowercase_				,     view_batch_size=2					)
									_SCREAMING_SNAKE_CASE     				=  output.images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1]

									assert image.shape == (1, 64, 64, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array([0.61_87, 0.53_75, 0.49_15, 0.41_36, 0.41_14, 0.45_63, 0.51_28, 0.49_76, 0.47_57]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_components()
									_SCREAMING_SNAKE_CASE     				=  EulerAncestralDiscreteScheduler(
									    beta_start=0.0_00_85				,     beta_end=0.0_12				,     beta_schedule="""scaled_linear"""					)
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline(**lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(lowercase_					)
									sd_pipe.set_progress_bar_config(disable=lowercase_					)

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

									assert image.shape == (1, 64, 64, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array([0.40_24, 0.65_10, 0.49_01, 0.53_78, 0.58_13, 0.56_22, 0.47_95, 0.44_67, 0.49_52]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """cpu"""  # ensure determinism for the device-dependent torch.Generator
									_SCREAMING_SNAKE_CASE     				=  self.get_dummy_components()
									_SCREAMING_SNAKE_CASE     				=  PNDMScheduler(
									    beta_start=0.0_00_85				,     beta_end=0.0_12				,     beta_schedule="""scaled_linear"""				,     skip_prk_steps=lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline(**lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  sd_pipe.to(lowercase_					)
									sd_pipe.set_progress_bar_config(disable=lowercase_					)

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

									assert image.shape == (1, 64, 64, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array([0.63_91, 0.62_91, 0.48_61, 0.51_34, 0.55_52, 0.45_78, 0.50_32, 0.50_23, 0.45_39]					)

									assert np.abs(image_slice.flatten() - expected_slice					).max() < 1E-2



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







			def 							UpperCamelCase				(     self:  Optional[int]					):







									'''simple docstring'''




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







			def 							UpperCamelCase				(     self:  List[Any]				,     UpperCAmelCase_:  Any=0					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  torch.manual_seed(lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  {
									    """prompt""": """a photo of the dolomites""",
									    """generator""": generator,
									    """num_inference_steps""": 3,
									    """guidance_scale""": 7.5,
									    """output_type""": """numpy""",
									}
									return inputs







			def 							UpperCamelCase				(     self:  int					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  """stabilityai/stable-diffusion-2-base"""
									_SCREAMING_SNAKE_CASE     				=  DDIMScheduler.from_pretrained(lowercase_				,     subfolder="""scheduler"""					)
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline.from_pretrained(lowercase_				,     scheduler=lowercase_				,     safety_checker=lowercase_					)
									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									_SCREAMING_SNAKE_CASE     				=  self.get_inputs()
									_SCREAMING_SNAKE_CASE     				=  pipe(**lowercase_					).images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 2_048, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [
									        0.36_96_83_92,
									        0.27_02_53_72,
									        0.32_44_67_66,
									        0.28_37_93_87,
									        0.36_36_32_74,
									        0.30_73_33_47,
									        0.27_10_00_27,
									        0.27_05_41_25,
									        0.25_53_60_96,
									    ]					)

									assert np.abs(expected_slice - image_slice					).max() < 1E-2







			def 							UpperCamelCase				(     self:  Tuple					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline.from_pretrained(
									    """stabilityai/stable-diffusion-2-base"""				,     safety_checker=lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  LMSDiscreteScheduler.from_config(pipe.scheduler.config					)
									pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									_SCREAMING_SNAKE_CASE     				=  self.get_inputs()
									_SCREAMING_SNAKE_CASE     				=  pipe(**lowercase_					).images
									_SCREAMING_SNAKE_CASE     				=  image[0, -3:, -3:, -1].flatten()

									assert image.shape == (1, 512, 2_048, 3)

									_SCREAMING_SNAKE_CASE     				=  np.array(
									    [
									        [
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									            0.0,
									        ]
									    ]					)

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







			def 							UpperCamelCase				(     self:  Any					):







									'''simple docstring'''




									_SCREAMING_SNAKE_CASE     				=  0

									def callback_fn(UpperCAmelCase_:  int				,     UpperCAmelCase_:  int				,     UpperCAmelCase_:  torch.FloatTensor					) -> None:
															_SCREAMING_SNAKE_CASE     				=  True
															nonlocal number_of_steps
															number_of_steps += 1
															if step == 1:
																					_SCREAMING_SNAKE_CASE     				=  latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					_SCREAMING_SNAKE_CASE     				=  latents[0, -3:, -3:, -1]

																					_SCREAMING_SNAKE_CASE     				=  np.array(
																					    [
																					        0.18_68_18_69,
																					        0.33_90_78_16,
																					        0.5_36_12_76,
																					        0.14_43_28_65,
																					        -0.02_85_66_11,
																					        -0.73_94_11_23,
																					        0.23_39_79_87,
																					        0.47_32_26_82,
																					        -0.37_82_31_64,
																					    ]					)
																					assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2
															elif step == 2:
																					_SCREAMING_SNAKE_CASE     				=  latents.detach().cpu().numpy()
																					assert latents.shape == (1, 4, 64, 256)
																					_SCREAMING_SNAKE_CASE     				=  latents[0, -3:, -3:, -1]

																					_SCREAMING_SNAKE_CASE     				=  np.array(
																					    [
																					        0.18_53_96_45,
																					        0.33_98_72_48,
																					        0.5_37_85_59,
																					        0.14_43_71_42,
																					        -0.02_45_52_61,
																					        -0.7_33_83_17,
																					        0.23_99_07_55,
																					        0.47_35_62_72,
																					        -0.3_78_65_05,
																					    ]					)

																					assert np.abs(latents_slice.flatten() - expected_slice					).max() < 5E-2

									_SCREAMING_SNAKE_CASE     				=  False

									_SCREAMING_SNAKE_CASE     				=  """stabilityai/stable-diffusion-2-base"""
									_SCREAMING_SNAKE_CASE     				=  DDIMScheduler.from_pretrained(lowercase_				,     subfolder="""scheduler"""					)
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline.from_pretrained(lowercase_				,     scheduler=lowercase_				,     safety_checker=lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing()

									_SCREAMING_SNAKE_CASE     				=  self.get_inputs()
									pipe(**lowercase_				,     callback=lowercase_				,     callback_steps=1					)
									assert callback_fn.has_been_called
									assert number_of_steps == 3







			def 							UpperCamelCase				(     self:  Optional[Any]					):







									'''simple docstring'''




									torch.cuda.empty_cache()
									torch.cuda.reset_max_memory_allocated()
									torch.cuda.reset_peak_memory_stats()

									_SCREAMING_SNAKE_CASE     				=  """stabilityai/stable-diffusion-2-base"""
									_SCREAMING_SNAKE_CASE     				=  DDIMScheduler.from_pretrained(lowercase_				,     subfolder="""scheduler"""					)
									_SCREAMING_SNAKE_CASE     				=  StableDiffusionPanoramaPipeline.from_pretrained(lowercase_				,     scheduler=lowercase_				,     safety_checker=lowercase_					)
									_SCREAMING_SNAKE_CASE     				=  pipe.to(lowercase_					)
									pipe.set_progress_bar_config(disable=lowercase_					)
									pipe.enable_attention_slicing(1					)
									pipe.enable_sequential_cpu_offload()

									_SCREAMING_SNAKE_CASE     				=  self.get_inputs()
									_SCREAMING_SNAKE_CASE     				=  pipe(**lowercase_					)

									_SCREAMING_SNAKE_CASE     				=  torch.cuda.max_memory_allocated()
									# make sure that less than 5.2 GB is allocated
									assert mem_bytes < 5.5 * 10**9









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


UpperCamelCase			    =     re.compile(R'''\s+''')

def 	__lowerCamelCase      (		snake_case__		)							->  List[str]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__		)							->  Tuple:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__	,snake_case__=5		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""auto-generated""", """autogenerated""", """automatically generated"""]
						_SCREAMING_SNAKE_CASE     				=  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 	__lowerCamelCase      (		snake_case__	,snake_case__=5	,snake_case__=0.05		)							->  Tuple:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""unit tests""", """test file""", """configuration file"""]
						_SCREAMING_SNAKE_CASE     				=  example["""content"""].splitlines()
						_SCREAMING_SNAKE_CASE     				=  0
						_SCREAMING_SNAKE_CASE     				=  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
						_SCREAMING_SNAKE_CASE     				=  example["""content"""].count("""\n"""		)
						_SCREAMING_SNAKE_CASE     				=  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 	__lowerCamelCase      (		snake_case__		)							->  str:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  ["""def """, """class """, """for """, """while """]
						_SCREAMING_SNAKE_CASE     				=  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 	__lowerCamelCase      (		snake_case__	,snake_case__=4		)							->  Union[str, Any]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  Optional[Any]:






						"""simple docstring"""





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

def 	__lowerCamelCase      (		snake_case__		)							->  int:






						"""simple docstring"""





						_SCREAMING_SNAKE_CASE     				=  {}
						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 	__lowerCamelCase      (		snake_case__	,snake_case__	,snake_case__		)							->  int:






						"""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 	__lowerCamelCase      (		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
UpperCamelCase			    =     HfArgumentParser(PreprocessingArguments)
UpperCamelCase			    =     parser.parse_args()
if args.num_workers is None:
		UpperCamelCase			    =     multiprocessing.cpu_count()
UpperCamelCase			    =     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
UpperCamelCase			    =     time.time()
UpperCamelCase			    =     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:
		UpperCamelCase			    =     time.time()
		UpperCamelCase    ,   UpperCamelCase			    =     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
UpperCamelCase			    =     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)

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

UpperCamelCase			    =     time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
		UpperCamelCase			    =     str(data_dir / f"file-{file_number+1:012}.json")
		UpperCamelCase			    =     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}")

