



'''simple docstring'''
import tempfile
import unittest

import numpy as np

from diffusers import (
    DDIMScheduler,
    DPMSolverMultistepScheduler,
    EulerAncestralDiscreteScheduler,
    EulerDiscreteScheduler,
    LMSDiscreteScheduler,
    OnnxStableDiffusionPipeline,
    PNDMScheduler,
)
from diffusers.utils.testing_utils import is_onnx_available, nightly, require_onnxruntime, require_torch_gpu

from ..test_pipelines_onnx_common import OnnxPipelineTesterMixin


if is_onnx_available():
					import onnxruntime as ort






class 							lowercase__	(							lowercase						,					unittest.TestCase   ):
	lowercase__  				=			"""hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline"""





	def 	UpperCamelCase_  ( self					:				Optional[int] ,lowerCamelCase__					:				Optional[int]=0 ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   np.random.RandomState(lowerCamelCase__ )
							_UpperCamelCase					:		Optional[Any] 				=   {
							    'prompt': 'A painting of a squirrel eating a burger',
							    'generator': generator,
							    'num_inference_steps': 2,
							    'guidance_scale': 7.5,
							    'output_type': 'numpy',
							}
							return inputs





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		Tuple 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Optional[Any] 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		Optional[int] 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							_UpperCamelCase					:		str 				=   np.array([0.6_5_0_7_2, 0.5_8_4_9_2, 0.4_8_2_1_9, 0.5_5_5_2_1, 0.5_3_1_8_0, 0.5_5_9_3_9, 0.5_0_6_9_7, 0.3_9_8_0_0, 0.4_6_4_5_5] )

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





	def 	UpperCamelCase_  ( self					:				str ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		Dict 				=   PNDMScheduler.from_config(pipe.scheduler.config ,skip_prk_steps=lowerCamelCase__ )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Dict 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		str 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		str 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							_UpperCamelCase					:		Dict 				=   np.array([0.6_5_8_6_3, 0.5_9_4_2_5, 0.4_9_3_2_6, 0.5_6_3_1_3, 0.5_3_8_7_5, 0.5_6_6_2_7, 0.5_1_0_6_5, 0.3_9_7_7_7, 0.4_6_3_3_0] )

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





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		List[Any] 				=   LMSDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

							assert image.shape == (1, 128, 128, 3)
							_UpperCamelCase					:		List[str] 				=   np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )

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





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		int 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		str 				=   EulerDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Union[str, Any] 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Union[str, Any] 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		Optional[int] 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							_UpperCamelCase					:		Dict 				=   np.array([0.5_3_7_5_5, 0.6_0_7_8_6, 0.4_7_4_0_2, 0.4_9_4_8_8, 0.5_1_8_6_9, 0.4_9_8_1_9, 0.4_7_9_8_5, 0.3_8_9_5_7, 0.4_4_2_7_9] )

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





	def 	UpperCamelCase_  ( self					:				Optional[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		Tuple 				=   EulerAncestralDiscreteScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

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

							assert image.shape == (1, 128, 128, 3)
							_UpperCamelCase					:		List[Any] 				=   np.array([0.5_3_8_1_7, 0.6_0_8_1_2, 0.4_7_3_8_4, 0.4_9_5_3_0, 0.5_1_8_9_4, 0.4_9_8_1_4, 0.4_7_9_8_4, 0.3_8_9_5_8, 0.4_4_2_7_1] )

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





	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							_UpperCamelCase					:		List[Any] 				=   DPMSolverMultistepScheduler.from_config(pipe.scheduler.config )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Optional[int] 				=   pipe(**lowerCamelCase__ ).images
							_UpperCamelCase					:		Optional[Any] 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 128, 128, 3)
							_UpperCamelCase					:		Dict 				=   np.array([0.5_3_8_9_5, 0.6_0_8_0_8, 0.4_7_9_3_3, 0.4_9_6_0_8, 0.5_1_8_8_6, 0.4_9_9_5_0, 0.4_8_0_5_3, 0.3_8_9_5_7, 0.4_4_2_0_0] )

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





	def 	UpperCamelCase_  ( self					:				List[str] ):







							'''simple docstring'''
							_UpperCamelCase					:		str 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Optional[Any] 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		int 				=   3 * [inputs['prompt']]

							# forward
							_UpperCamelCase					:		Union[str, Any] 				=   pipe(**lowerCamelCase__ )
							_UpperCamelCase					:		Optional[Any] 				=   output.images[0, -3:, -3:, -1]

							_UpperCamelCase					:		Dict 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Optional[int] 				=   3 * [inputs.pop('prompt' )]

							_UpperCamelCase					:		Tuple 				=   pipe.tokenizer(
							    lowerCamelCase__ ,padding='max_length' ,max_length=pipe.tokenizer.model_max_length ,truncation=lowerCamelCase__ ,return_tensors='np' ,)
							_UpperCamelCase					:		Dict 				=   text_inputs['input_ids']

							_UpperCamelCase					:		Optional[Any] 				=   pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0]

							_UpperCamelCase					:		Tuple 				=   prompt_embeds

							# forward
							_UpperCamelCase					:		Dict 				=   pipe(**lowerCamelCase__ )
							_UpperCamelCase					:		Union[str, Any] 				=   output.images[0, -3:, -3:, -1]

							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4





	def 	UpperCamelCase_  ( self					:				Any ):







							'''simple docstring'''
							_UpperCamelCase					:		List[str] 				=   OnnxStableDiffusionPipeline.from_pretrained(self.hub_checkpoint ,provider='CPUExecutionProvider' )
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		int 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Dict 				=   3 * ['this is a negative prompt']
							_UpperCamelCase					:		Optional[int] 				=   negative_prompt
							_UpperCamelCase					:		Tuple 				=   3 * [inputs['prompt']]

							# forward
							_UpperCamelCase					:		Tuple 				=   pipe(**lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   output.images[0, -3:, -3:, -1]

							_UpperCamelCase					:		List[Any] 				=   self.get_dummy_inputs()
							_UpperCamelCase					:		Optional[int] 				=   3 * [inputs.pop('prompt' )]

							_UpperCamelCase					:		List[str] 				=   []
							for p in [prompt, negative_prompt]:
													_UpperCamelCase					:		int 				=   pipe.tokenizer(
													    lowerCamelCase__ ,padding='max_length' ,max_length=pipe.tokenizer.model_max_length ,truncation=lowerCamelCase__ ,return_tensors='np' ,)
													_UpperCamelCase					:		Optional[Any] 				=   text_inputs['input_ids']

													embeds.append(pipe.text_encoder(input_ids=text_inputs.astype(np.intaa ) )[0] )

							_UpperCamelCase				,			_UpperCamelCase					:		Optional[Any] 				=   embeds

							# forward
							_UpperCamelCase					:		Dict 				=   pipe(**lowerCamelCase__ )
							_UpperCamelCase					:		int 				=   output.images[0, -3:, -3:, -1]

							assert np.abs(image_slice_a.flatten() - image_slice_a.flatten() ).max() < 1E-4






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





	@property
	def 	UpperCamelCase_  ( self					:				List[Any] ):







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





	@property
	def 	UpperCamelCase_  ( self					:				Dict ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   ort.SessionOptions()
							_UpperCamelCase					:		str 				=   False
							return options





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							# using the PNDM scheduler by default
							_UpperCamelCase					:		Optional[Any] 				=   OnnxStableDiffusionPipeline.from_pretrained(
							    'CompVis/stable-diffusion-v1-4' ,revision='onnx' ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		List[Any] 				=   'A painting of a squirrel eating a burger'
							np.random.seed(0 )
							_UpperCamelCase					:		int 				=   sd_pipe([prompt] ,guidance_scale=6.0 ,num_inference_steps=10 ,output_type='np' )
							_UpperCamelCase					:		List[Any] 				=   output.images

							_UpperCamelCase					:		List[Any] 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		Tuple 				=   np.array([0.0_4_5_2, 0.0_3_9_0, 0.0_0_8_7, 0.0_3_5_0, 0.0_6_1_7, 0.0_3_6_4, 0.0_5_4_4, 0.0_5_2_3, 0.0_7_2_0] )

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





	def 	UpperCamelCase_  ( self					:				Optional[int] ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   DDIMScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5' ,subfolder='scheduler' ,revision='onnx' )
							_UpperCamelCase					:		List[Any] 				=   OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5' ,revision='onnx' ,scheduler=lowerCamelCase__ ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Any 				=   'open neural network exchange'
							_UpperCamelCase					:		List[Any] 				=   np.random.RandomState(0 )
							_UpperCamelCase					:		List[Any] 				=   sd_pipe([prompt] ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=lowerCamelCase__ ,output_type='np' )
							_UpperCamelCase					:		Optional[Any] 				=   output.images
							_UpperCamelCase					:		List[Any] 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		Optional[int] 				=   np.array([0.2_8_6_7, 0.1_9_7_4, 0.1_4_8_1, 0.7_2_9_4, 0.7_2_5_1, 0.6_6_6_7, 0.4_1_9_4, 0.5_6_4_2, 0.6_4_8_6] )

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





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Any 				=   LMSDiscreteScheduler.from_pretrained(
							    'runwayml/stable-diffusion-v1-5' ,subfolder='scheduler' ,revision='onnx' )
							_UpperCamelCase					:		Union[str, Any] 				=   OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5' ,revision='onnx' ,scheduler=lowerCamelCase__ ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
							sd_pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		List[Any] 				=   'open neural network exchange'
							_UpperCamelCase					:		Dict 				=   np.random.RandomState(0 )
							_UpperCamelCase					:		Union[str, Any] 				=   sd_pipe([prompt] ,guidance_scale=7.5 ,num_inference_steps=10 ,generator=lowerCamelCase__ ,output_type='np' )
							_UpperCamelCase					:		Union[str, Any] 				=   output.images
							_UpperCamelCase					:		Tuple 				=   image[0, -3:, -3:, -1]

							assert image.shape == (1, 512, 512, 3)
							_UpperCamelCase					:		Optional[Any] 				=   np.array([0.2_3_0_6, 0.1_9_5_9, 0.1_5_9_3, 0.6_5_4_9, 0.6_3_9_4, 0.5_4_0_8, 0.5_0_6_5, 0.6_0_1_0, 0.6_1_6_1] )

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





	def 	UpperCamelCase_  ( self					:				List[Any] ):







							'''simple docstring'''
							_UpperCamelCase					:		Dict 				=   0

							def test_callback_fn(lowerCamelCase__					:				int ,lowerCamelCase__					:				int ,lowerCamelCase__					:				np.ndarray ) -> None:
													_UpperCamelCase					:		Union[str, Any] 				=   True
													nonlocal number_of_steps
													number_of_steps += 1
													if step == 0:
																			assert latents.shape == (1, 4, 64, 64)
																			_UpperCamelCase					:		Any 				=   latents[0, -3:, -3:, -1]
																			_UpperCamelCase					:		Dict 				=   np.array(
																			    [-0.6_7_7_2, -0.3_8_3_5, -1.2_4_5_6, 0.1_9_0_5, -1.0_9_7_4, 0.6_9_6_7, -1.9_3_5_3, 0.0_1_7_8, 1.0_1_6_7] )

																			assert np.abs(latents_slice.flatten() - expected_slice ).max() < 1E-3
													elif step == 5:
																			assert latents.shape == (1, 4, 64, 64)
																			_UpperCamelCase					:		Union[str, Any] 				=   latents[0, -3:, -3:, -1]
																			_UpperCamelCase					:		int 				=   np.array(
																			    [-0.3_3_5_1, 0.2_2_4_1, -0.1_8_3_7, -0.2_3_2_5, -0.6_5_7_7, 0.3_3_9_3, -0.0_2_4_1, 0.5_8_9_9, 1.3_8_7_5] )

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

							_UpperCamelCase					:		Dict 				=   False

							_UpperCamelCase					:		List[Any] 				=   OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5' ,revision='onnx' ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
							pipe.set_progress_bar_config(disable=lowerCamelCase__ )

							_UpperCamelCase					:		Tuple 				=   'Andromeda galaxy in a bottle'

							_UpperCamelCase					:		Optional[Any] 				=   np.random.RandomState(0 )
							pipe(
							    prompt=lowerCamelCase__ ,num_inference_steps=5 ,guidance_scale=7.5 ,generator=lowerCamelCase__ ,callback=lowerCamelCase__ ,callback_steps=1 ,)
							assert test_callback_fn.has_been_called
							assert number_of_steps == 6





	def 	UpperCamelCase_  ( self					:				Tuple ):







							'''simple docstring'''
							_UpperCamelCase					:		List[Any] 				=   OnnxStableDiffusionPipeline.from_pretrained(
							    'runwayml/stable-diffusion-v1-5' ,revision='onnx' ,safety_checker=lowerCamelCase__ ,feature_extractor=lowerCamelCase__ ,provider=self.gpu_provider ,sess_options=self.gpu_options ,)
							assert isinstance(lowerCamelCase__ ,lowerCamelCase__ )
							assert pipe.safety_checker is None

							_UpperCamelCase					:		List[Any] 				=   pipe('example prompt' ,num_inference_steps=2 ).images[0]
							assert image is not None

							# check that there's no error when saving a pipeline with one of the models being None
							with tempfile.TemporaryDirectory() as tmpdirname:
													pipe.save_pretrained(lowerCamelCase__ )
													_UpperCamelCase					:		Tuple 				=   OnnxStableDiffusionPipeline.from_pretrained(lowerCamelCase__ )

							# sanity check that the pipeline still works
							assert pipe.safety_checker is None
							_UpperCamelCase					:		Union[str, Any] 				=   pipe('example prompt' ,num_inference_steps=2 ).images[0]
							assert image is not None









"""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


lowerCamelCase_     :						Any						=					re.compile(r'\s+')





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[str]										=  [len(_UpperCAmelCase					) for line in example['content'].splitlines()]
  return {"line_mean": np.mean(_UpperCAmelCase					), "line_max": max(_UpperCAmelCase					)}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5					):






  """simple docstring"""





  A_  :       Optional[int]										=  ['auto-generated', 'autogenerated', 'automatically generated']
  A_  :       List[str]										=  example['content'].splitlines()
  for _, line in zip(range(_UpperCAmelCase					) ,					_UpperCAmelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"autogenerated": True}
  else:
    return {"autogenerated": False}





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase=5 ,					_UpperCAmelCase=0.05					):






  """simple docstring"""





  A_  :       Any										=  ['unit tests', 'test file', 'configuration file']
  A_  :       Dict										=  example['content'].splitlines()
  A_  :       List[Any]										=  0
  A_  :       str										=  0
  # first test
  for _, line in zip(range(_UpperCAmelCase					) ,					_UpperCAmelCase					):
    for keyword in keywords:
      if keyword in line.lower():
        return {"config_or_test": True}
    # second test
  A_  :       Tuple										=  example['content'].count('\n'					)
  A_  :       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__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       List[Any]										=  ['def ', 'class ', 'for ', 'while ']
  A_  :       Tuple										=  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__		(     _UpperCAmelCase ,					_UpperCAmelCase=4					):






  """simple docstring"""





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





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Optional[Any]										=  tokenizer(example['content'] ,					truncation=_UpperCAmelCase					)['input_ids']
  A_  :       Dict										=  len(example['content']					) / len(_UpperCAmelCase					)
  return {"ratio": ratio}





def 						UpperCAmelCase__		(     _UpperCAmelCase					):






  """simple docstring"""





  A_  :       Any										=  {}
  results.update(get_hash(_UpperCAmelCase					)					)
  results.update(line_stats(_UpperCAmelCase					)					)
  results.update(alpha_stats(_UpperCAmelCase					)					)
  results.update(char_token_ratio(_UpperCAmelCase					)					)
  results.update(is_autogenerated(_UpperCAmelCase					)					)
  results.update(is_config_or_test(_UpperCAmelCase					)					)
  results.update(has_no_keywords(_UpperCAmelCase					)					)
  results.update(has_few_assignments(_UpperCAmelCase					)					)
  return results





def 						UpperCAmelCase__		(     _UpperCAmelCase ,					_UpperCAmelCase ,					_UpperCAmelCase					):






  """simple docstring"""





  if not check_uniques(_UpperCAmelCase ,					_UpperCAmelCase					):
    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__		(     _UpperCAmelCase					):






  """simple docstring"""





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


# Settings
lowerCamelCase_     :						Optional[int]						=					HfArgumentParser(PreprocessingArguments)
lowerCamelCase_     :						Optional[Any]						=					parser.parse_args()
if args.num_workers is None:
   lowerCamelCase_     :						int						=					multiprocessing.cpu_count()
lowerCamelCase_     :						Tuple						=					AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

# Run preprocessing
lowerCamelCase_     :						List[str]						=					time.time()
lowerCamelCase_     :						Optional[int]						=					ds.map(preprocess, num_proc=args.num_workers)
print(F"Time to preprocess dataset: {time.time()-t_start:.2f}")

# Deduplicate hashes
lowerCamelCase_     :						int						=					set(ds.unique('hash'))
lowerCamelCase_     :						Union[str, Any]						=					len(uniques) / len(ds)
print(F"Fraction of duplicates: {1-frac:.2%}")

# Deduplicate data and apply heuristics
lowerCamelCase_     :						Optional[int]						=					time.time()
lowerCamelCase_     :						Tuple						=					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:
   lowerCamelCase_     :						Union[str, Any]						=					time.time()
   lowerCamelCase_					,					lowerCamelCase_     :						str						=					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
lowerCamelCase_     :						Tuple						=					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)

lowerCamelCase_     :						Optional[Any]						=					output_dir / 'data'
data_dir.mkdir(exist_ok=True)

lowerCamelCase_     :						List[str]						=					time.time()
for file_number, index in enumerate(range(0, len(ds_filter), args.samples_per_file)):
   lowerCamelCase_     :						Optional[int]						=					str(data_dir / F"file-{file_number+1:012}.json")
   lowerCamelCase_     :						List[str]						=					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}")