



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=5					):



							"""simple docstring"""



							__lowercase       	=['auto-generated', 'autogenerated', 'automatically generated']
							__lowercase       	=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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=5  ,   _lowerCAmelCase=0.05					):



							"""simple docstring"""



							__lowercase       	=['unit tests', 'test file', 'configuration file']
							__lowercase       	=example['content'].splitlines()
							__lowercase       	=0
							__lowercase       	=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
							__lowercase       	=example['content'].count('\n'					)
							__lowercase       	=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   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=['def ', 'class ', 'for ', 'while ']
							__lowercase       	=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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase=4					):



							"""simple docstring"""



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



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	=tokenizer(example['content']  ,   truncation=__snake_case					)['input_ids']
							__lowercase       	=len(example['content']					) / len(__snake_case					)
							return {"ratio": ratio}



def   _A       (   _lowerCAmelCase					):



							"""simple docstring"""



							__lowercase       	={}
							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   _A       (   _lowerCAmelCase  ,   _lowerCAmelCase  ,   _lowerCAmelCase					):



							"""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   _A       (   _lowerCAmelCase					):



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

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

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

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

# Deduplicate data and apply heuristics
lowerCamelCase	 =       time.time()
lowerCamelCase	 =       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	 =       time.time()
		lowerCamelCase ,	lowerCamelCase	 =       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	 =       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	 =       output_dir / """data"""
data_dir.mkdir(exist_ok=True)

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








'''simple docstring'''



import gc
import unittest

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

from diffusers import AltDiffusionPipeline, AutoencoderKL, DDIMScheduler, PNDMScheduler, UNetaDConditionModel
from diffusers.pipelines.alt_diffusion.modeling_roberta_series import (
    RobertaSeriesConfig,
    RobertaSeriesModelWithTransformation,
)
from diffusers.utils import slow, torch_device
from diffusers.utils.testing_utils import enable_full_determinism, require_torch_gpu

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


enable_full_determinism()





class _UpperCamelCase		(     A      ,       A      ,       A      ,       unittest.TestCase		):





				'''simple docstring'''





				lowerCAmelCase__					    =    AltDiffusionPipeline
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_BATCH_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_IMAGE_PARAMS
				lowerCAmelCase__					    =    TEXT_TO_IMAGE_IMAGE_PARAMS

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											torch.manual_seed(0)
											__lowercase       	=UNetaDConditionModel(
											    block_out_channels=(3_2, 6_4)  ,       layers_per_block=2  ,       sample_size=3_2  ,       in_channels=4  ,       out_channels=4  ,       down_block_types=('DownBlock2D', 'CrossAttnDownBlock2D')  ,       up_block_types=('CrossAttnUpBlock2D', 'UpBlock2D')  ,       cross_attention_dim=3_2  ,       )
											__lowercase       	=DDIMScheduler(
											    beta_start=0.0_0085  ,       beta_end=0.012  ,       beta_schedule='scaled_linear'  ,       clip_sample=_lowerCAmelCase  ,       set_alpha_to_one=_lowerCAmelCase  ,       )
											torch.manual_seed(0)
											__lowercase       	=AutoencoderKL(
											    block_out_channels=[3_2, 6_4]  ,       in_channels=3  ,       out_channels=3  ,       down_block_types=['DownEncoderBlock2D', 'DownEncoderBlock2D']  ,       up_block_types=['UpDecoderBlock2D', 'UpDecoderBlock2D']  ,       latent_channels=4  ,       )

											# TODO: address the non-deterministic text encoder (fails for save-load tests)
											# torch.manual_seed(0)
											# text_encoder_config = RobertaSeriesConfig(
											#     hidden_size=32,
											#     project_dim=32,
											#     intermediate_size=37,
											#     layer_norm_eps=1e-05,
											#     num_attention_heads=4,
											#     num_hidden_layers=5,
											#     vocab_size=5002,
											# )
											# text_encoder = RobertaSeriesModelWithTransformation(text_encoder_config)

											torch.manual_seed(0)
											__lowercase       	=CLIPTextConfig(
											    bos_token_id=0  ,       eos_token_id=2  ,       hidden_size=3_2  ,       projection_dim=3_2  ,       intermediate_size=3_7  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       pad_token_id=1  ,       vocab_size=5_0_0_2  ,       )
											__lowercase       	=CLIPTextModel(_lowerCAmelCase)

											__lowercase       	=XLMRobertaTokenizer.from_pretrained('hf-internal-testing/tiny-xlm-roberta')
											__lowercase       	=7_7

											__lowercase       	={
											    'unet': unet,
											    'scheduler': scheduler,
											    'vae': vae,
											    'text_encoder': text_encoder,
											    'tokenizer': tokenizer,
											    'safety_checker': None,
											    'feature_extractor': None,
											}
											return components

				def 				__lowerCamelCase     (							self  :  List[Any]  ,       _lowerCAmelCase  :  Union[str, Any]  ,       _lowerCAmelCase  :  Dict=0):







											'''simple docstring'''

											if str(_lowerCAmelCase).startswith('mps'):
																		__lowercase       	=torch.manual_seed(_lowerCAmelCase)
											else:
																		__lowercase       	=torch.Generator(device=_lowerCAmelCase).manual_seed(_lowerCAmelCase)
											__lowercase       	={
											    'prompt': 'A painting of a squirrel eating a burger',
											    'generator': generator,
											    'num_inference_steps': 2,
											    'guidance_scale': 6.0,
											    'output_type': 'numpy',
											}
											return inputs

				def 				__lowerCamelCase     (							self  :  List[str]):







											'''simple docstring'''

											super().test_attention_slicing_forward_pass(expected_max_diff=3e-3)

				def 				__lowerCamelCase     (							self  :  Optional[int]):







											'''simple docstring'''

											super().test_inference_batch_single_identical(expected_max_diff=3e-3)

				def 				__lowerCamelCase     (							self  :  Optional[Any]):







											'''simple docstring'''

											__lowercase       	='cpu'  # ensure determinism for the device-dependent torch.Generator

											__lowercase       	=self.get_dummy_components()
											torch.manual_seed(0)
											__lowercase       	=RobertaSeriesConfig(
											    hidden_size=3_2  ,       project_dim=3_2  ,       intermediate_size=3_7  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       vocab_size=5_0_0_2  ,       )
											# TODO: remove after fixing the non-deterministic text encoder
											__lowercase       	=RobertaSeriesModelWithTransformation(_lowerCAmelCase)
											__lowercase       	=text_encoder

											__lowercase       	=AltDiffusionPipeline(**_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=self.get_dummy_inputs(_lowerCAmelCase)
											__lowercase       	='A photo of an astronaut'
											__lowercase       	=alt_pipe(**_lowerCAmelCase)
											__lowercase       	=output.images
											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__lowercase       	=np.array(
											    [0.574_8162, 0.6044_7145, 0.4882_1217, 0.5010_0636, 0.543_1185, 0.4576_3683, 0.4965_7696, 0.4813_2733, 0.4757_3093])

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

				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

											__lowercase       	='cpu'  # ensure determinism for the device-dependent torch.Generator

											__lowercase       	=self.get_dummy_components()
											__lowercase       	=PNDMScheduler(skip_prk_steps=_lowerCAmelCase)
											torch.manual_seed(0)
											__lowercase       	=RobertaSeriesConfig(
											    hidden_size=3_2  ,       project_dim=3_2  ,       intermediate_size=3_7  ,       layer_norm_eps=1e-05  ,       num_attention_heads=4  ,       num_hidden_layers=5  ,       vocab_size=5_0_0_2  ,       )
											# TODO: remove after fixing the non-deterministic text encoder
											__lowercase       	=RobertaSeriesModelWithTransformation(_lowerCAmelCase)
											__lowercase       	=text_encoder
											__lowercase       	=AltDiffusionPipeline(**_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	=self.get_dummy_inputs(_lowerCAmelCase)
											__lowercase       	=alt_pipe(**_lowerCAmelCase)
											__lowercase       	=output.images
											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 6_4, 6_4, 3)
											__lowercase       	=np.array(
											    [0.5160_5093, 0.570_7241, 0.4736_5507, 0.5057_8886, 0.563_3877, 0.464_2503, 0.518_2081, 0.4876_3484, 0.4908_4237])

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





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





				'''simple docstring'''





				def 				__lowerCamelCase     (							self  :  str):







											'''simple docstring'''

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

				def 				__lowerCamelCase     (							self  :  Union[str, Any]):







											'''simple docstring'''

											__lowercase       	=AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,       safety_checker=_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	='A painting of a squirrel eating a burger'
											__lowercase       	=torch.manual_seed(0)
											__lowercase       	=alt_pipe([prompt]  ,       generator=_lowerCAmelCase  ,       guidance_scale=6.0  ,       num_inference_steps=2_0  ,       output_type='np')

											__lowercase       	=output.images

											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowercase       	=np.array([0.1010, 0.0800, 0.0794, 0.0885, 0.0843, 0.0762, 0.0769, 0.0729, 0.0586])

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

				def 				__lowerCamelCase     (							self  :  Dict):







											'''simple docstring'''

											__lowercase       	=DDIMScheduler.from_pretrained('BAAI/AltDiffusion'  ,       subfolder='scheduler')

											__lowercase       	=AltDiffusionPipeline.from_pretrained('BAAI/AltDiffusion'  ,       scheduler=_lowerCAmelCase  ,       safety_checker=_lowerCAmelCase)
											__lowercase       	=alt_pipe.to(_lowerCAmelCase)
											alt_pipe.set_progress_bar_config(disable=_lowerCAmelCase)

											__lowercase       	='A painting of a squirrel eating a burger'
											__lowercase       	=torch.manual_seed(0)

											__lowercase       	=alt_pipe([prompt]  ,       generator=_lowerCAmelCase  ,       num_inference_steps=2  ,       output_type='numpy')
											__lowercase       	=output.images

											__lowercase       	=image[0, -3:, -3:, -1]

											assert image.shape == (1, 5_1_2, 5_1_2, 3)
											__lowercase       	=np.array([0.4019, 0.4052, 0.3810, 0.4119, 0.3916, 0.3982, 0.4651, 0.4195, 0.5323])

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



