
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


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




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




def    __UpperCamelCase     (				lowerCAmelCase__							:				Any  ):
 __a :  Dict						=  [len(lowerCAmelCase__  ) for line in example['''content'''].splitlines()]
 return {"line_mean": np.mean(lowerCAmelCase__  ), "line_max": max(lowerCAmelCase__  )}




def    __UpperCamelCase     (				lowerCAmelCase__							:				int  ):
 __a :  List[Any]						=  np.mean([c.isalnum() for c in example['''content''']]  )
 return {"alpha_frac": alpha_frac}




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ,   lowerCAmelCase__							:				List[Any]  ):
 if example["hash"] in uniques:
  uniques.remove(example['''hash''']  )
  return True
 else:
  return False




def    __UpperCamelCase     (				lowerCAmelCase__							:				Optional[Any]  ,   lowerCAmelCase__							:				List[str]=5  ):
 __a :  Union[str, Any]						=  ['''auto-generated''', '''autogenerated''', '''automatically generated''']
 __a :  Dict						=  example['''content'''].splitlines()
 for _, line in zip(range(lowerCAmelCase__  )  ,   lowerCAmelCase__  ):
  for keyword in keywords:
   if keyword in line.lower():
    return {"autogenerated": True}
 else:
  return {"autogenerated": False}




def    __UpperCamelCase     (				lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				List[Any]=5  ,   lowerCAmelCase__							:				int=0.05  ):
 __a :  List[Any]						=  ['''unit tests''', '''test file''', '''configuration file''']
 __a :  Optional[int]						=  example['''content'''].splitlines()
 __a :  Union[str, Any]						=  0
 __a :  str						=  0
 # first test
 for _, line in zip(range(lowerCAmelCase__  )  ,   lowerCAmelCase__  ):
  for keyword in keywords:
   if keyword in line.lower():
    return {"config_or_test": True}
    # second test
 __a :  List[Any]						=  example['''content'''].count('''\n'''  )
 __a :  Optional[Any]						=  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     (				lowerCAmelCase__							:				int  ):
 __a :  List[Any]						=  ['''def ''', '''class ''', '''for ''', '''while ''']
 __a :  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     (				lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Any=4  ):
 __a :  Any						=  example['''content'''].splitlines()
 __a :  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     (				lowerCAmelCase__							:				Tuple  ):
 __a :  List[str]						=  tokenizer(example['''content''']  ,   truncation=lowerCAmelCase__  )['''input_ids''']
 __a :  str						=  len(example['''content''']  ) / len(lowerCAmelCase__  )
 return {"ratio": ratio}




def    __UpperCamelCase     (				lowerCAmelCase__							:				List[str]  ):
 __a :  Tuple						=  {}
 results.update(get_hash(lowerCAmelCase__  )  )
 results.update(line_stats(lowerCAmelCase__  )  )
 results.update(alpha_stats(lowerCAmelCase__  )  )
 results.update(char_token_ratio(lowerCAmelCase__  )  )
 results.update(is_autogenerated(lowerCAmelCase__  )  )
 results.update(is_config_or_test(lowerCAmelCase__  )  )
 results.update(has_no_keywords(lowerCAmelCase__  )  )
 results.update(has_few_assignments(lowerCAmelCase__  )  )
 return results




def    __UpperCamelCase     (				lowerCAmelCase__							:				Dict  ,   lowerCAmelCase__							:				Tuple  ,   lowerCAmelCase__							:				Any  ):
 if not check_uniques(lowerCAmelCase__  ,   lowerCAmelCase__  ):
  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     (				lowerCAmelCase__							:				List[str]  ):
 with open(lowerCAmelCase__  ,   '''rb'''  ) as f_in:
  with gzip.open(str(lowerCAmelCase__  ) + '''.gz'''  ,   '''wb'''  ,   compresslevel=6  ) as f_out:
   shutil.copyfileobj(lowerCAmelCase__  ,   lowerCAmelCase__  )
 os.unlink(lowerCAmelCase__  )


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

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

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

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

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

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

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


import gc
import unittest

from diffusers import FlaxDPMSolverMultistepScheduler, FlaxStableDiffusionPipeline
from diffusers.utils import is_flax_available, slow
from diffusers.utils.testing_utils import require_flax


if is_flax_available():
      import jax
      import jax.numpy as jnp
      from flax.jax_utils import replicate
      from flax.training.common_utils import shard



@slow
@require_flax
class 		UpperCamelCase__  (       unittest.TestCase    ):
   def       lowerCAmelCase			(self :					Optional[Any]   ):
    # clean up the VRAM after each test
    super().tearDown()
    gc.collect()
   def       lowerCAmelCase			(self :					int   ):
    __a				,       __a :  Optional[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        '''stabilityai/stable-diffusion-2'''					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)

    __a :  Any						=  '''A painting of a squirrel eating a burger'''
    __a :  Dict						=  jax.device_count()
    __a :  Optional[int]						=  num_samples * [prompt]
    __a :  Optional[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Optional[Any]						=  replicate(snake_case_   )
    __a :  Optional[int]						=  shard(snake_case_   )

    __a :  int						=  jax.random.PRNGKey(0   )
    __a :  str						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  int						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  Optional[int]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  List[Any]						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  Dict						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  str						=  jnp.array([0.4238, 0.4414, 0.4395, 0.4453, 0.4629, 0.4590, 0.4531, 0.4_5508, 0.4512]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2







   def       lowerCAmelCase			(self :					Tuple   ):
    __a :  Optional[Any]						=  '''stabilityai/stable-diffusion-2'''
    __a				,       __a :  Dict						=  FlaxDPMSolverMultistepScheduler.from_pretrained(snake_case_					,				subfolder='''scheduler'''   )
    __a				,       __a :  List[Any]						=  FlaxStableDiffusionPipeline.from_pretrained(
        snake_case_					,				scheduler=snake_case_					,				revision='''bf16'''					,				dtype=jnp.bfloataa					,				)
    __a :  Union[str, Any]						=  scheduler_params

    __a :  List[Any]						=  '''A painting of a squirrel eating a burger'''
    __a :  Any						=  jax.device_count()
    __a :  Any						=  num_samples * [prompt]
    __a :  List[Any]						=  sd_pipe.prepare_inputs(snake_case_   )

    __a :  Tuple						=  replicate(snake_case_   )
    __a :  Dict						=  shard(snake_case_   )

    __a :  Dict						=  jax.random.PRNGKey(0   )
    __a :  Dict						=  jax.random.split(snake_case_					,				jax.device_count()   )

    __a :  str						=  sd_pipe(snake_case_					,				snake_case_					,				snake_case_					,				num_inference_steps=2_5					,				jit=snake_case_   )[0]
    assert images.shape == (jax.device_count(), 1, 7_6_8, 7_6_8, 3)

    __a :  List[Any]						=  images.reshape((images.shape[0] * images.shape[1],) + images.shape[-3:]   )
    __a :  Any						=  images[0, 2_5_3:2_5_6, 2_5_3:2_5_6, -1]

    __a :  List[Any]						=  jnp.asarray(jax.device_get(image_slice.flatten()   )   )
    __a :  List[Any]						=  jnp.array([0.4336, 0.4_2969, 0.4453, 0.4199, 0.4297, 0.4531, 0.4434, 0.4434, 0.4297]   )
    print(f"output_slice: {output_slice}"   )
    assert jnp.abs(output_slice - expected_slice   ).max() < 1E-2
