"""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   :	Optional[Any] 					=     re.compile(r'\s+')





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str			) ->   Dict:
  return {"hash": hashlib.mda(re.sub(_lowerCamelCase    ,""""""    ,example["""content"""]			).encode("""utf-8"""			)			).hexdigest()}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Any			) ->   Optional[int]:
  _lowerCAmelCase		: Optional[int]						    =  [len(_lowerCamelCase			) for line in example["""content"""].splitlines()]
  return {"line_mean": np.mean(_lowerCamelCase			), "line_max": max(_lowerCamelCase			)}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]			) ->   Any:
  _lowerCAmelCase		: Tuple						    =  np.mean([c.isalnum() for c in example["""content"""]]			)
  return {"alpha_frac": alpha_frac}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: str    ,_lowerCamelCase		: Any			) ->   str:
  if example["hash"] in uniques:
    uniques.remove(example["""hash"""]			)
    return True
  else:
    return False





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict    ,_lowerCamelCase		: Any=5			) ->   int:
  _lowerCAmelCase		: str						    =  ["""auto-generated""", """autogenerated""", """automatically generated"""]
  _lowerCAmelCase		: List[str]						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]    ,_lowerCamelCase		: Any=5    ,_lowerCamelCase		: Any=0.05			) ->   Dict:
  _lowerCAmelCase		: Tuple						    =  ["""unit tests""", """test file""", """configuration file"""]
  _lowerCAmelCase		: Any						    =  example["""content"""].splitlines()
  _lowerCAmelCase		: List[Any]						    =  0
  _lowerCAmelCase		: List[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
  _lowerCAmelCase		: Tuple						    =  example["""content"""].count("""\n"""			)
  _lowerCAmelCase		: 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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[int]			) ->   int:
  _lowerCAmelCase		: Any						    =  ["""def """, """class """, """for """, """while """]
  _lowerCAmelCase		: Optional[int]						    =  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict    ,_lowerCamelCase		: Union[str, Any]=4			) ->   str:
  _lowerCAmelCase		: Any						    =  example["""content"""].splitlines()
  _lowerCAmelCase		: Tuple						    =  0
  for line in lines:
    counter += line.lower().count("""="""			)
    if counter > minimum:
      return {"has_few_assignments": False}
  return {"has_few_assignments": True}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Union[str, Any]			) ->   Optional[int]:
  _lowerCAmelCase		: Optional[int]						    =  tokenizer(example["""content"""]    ,truncation=_lowerCamelCase			)["""input_ids"""]
  _lowerCAmelCase		: int						    =  len(example["""content"""]			) / len(_lowerCamelCase			)
  return {"ratio": ratio}





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: int			) ->   Dict:
  _lowerCAmelCase		: List[Any]						    =  {}
  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: str    ,_lowerCamelCase		: int			) ->   Tuple:
  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 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Dict			) ->   Union[str, Any]:
  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
_a   :	Dict 					=     HfArgumentParser(PreprocessingArguments)
_a   :	Dict 					=     parser.parse_args()
if args.num_workers is None:
     _a   :	str 					=     multiprocessing.cpu_count()
_a   :	str 					=     AutoTokenizer.from_pretrained(args.tokenizer_dir)

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

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

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

# Deduplicate data and apply heuristics
_a   :	Dict 					=     time.time()
_a   :	Dict 					=     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   :	Optional[int] 					=     time.time()
     _a					,     _a   :	Union[str, Any] 					=     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   :	List[str] 					=     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   :	Dict 					=     output_dir / 'data'
data_dir.mkdir(exist_ok=True)

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




from typing import List, Optional, Union

import numpy as np
import PIL
import torch
from PIL import Image

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


_a   :	Union[str, Any] 					=     logging.get_logger(__name__)  # pylint: disable=invalid-name

_a   :	int 					=     '\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Img2ImgPipeline, KandinskyV22PriorPipeline\n        >>> from diffusers.utils import load_image\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-prior", torch_dtype=torch.float16\n        ... )\n        >>> pipe_prior.to("cuda")\n\n        >>> prompt = "A red cartoon frog, 4k"\n        >>> image_emb, zero_image_emb = pipe_prior(prompt, return_dict=False)\n\n        >>> pipe = KandinskyV22Img2ImgPipeline.from_pretrained(\n        ...     "kandinsky-community/kandinsky-2-2-decoder", torch_dtype=torch.float16\n        ... )\n        >>> pipe.to("cuda")\n\n        >>> init_image = load_image(\n        ...     "https://huggingface.co/datasets/hf-internal-testing/diffusers-images/resolve/main"\n        ...     "/kandinsky/frog.png"\n        ... )\n\n        >>> image = pipe(\n        ...     image=init_image,\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=100,\n        ...     strength=0.2,\n        ... ).images\n\n        >>> image[0].save("red_frog.png")\n        ```\n'





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Optional[Any]    ,_lowerCamelCase		: Dict    ,_lowerCamelCase		: Dict=8			) ->   Any:
  _lowerCAmelCase		: List[Any]						    =  height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  _lowerCAmelCase		: Optional[Any]						    =  width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





def 							SCREAMING_SNAKE_CASE  (    _lowerCamelCase		: Tuple    ,_lowerCamelCase		: Any=512    ,_lowerCamelCase		: Dict=512			) ->   List[Any]:
  _lowerCAmelCase		: Any						    =  pil_image.resize((w, h)    ,resample=Image.BICUBIC    ,reducing_gap=1			)
  _lowerCAmelCase		: Dict						    =  np.array(pil_image.convert("""RGB"""			)			)
  _lowerCAmelCase		: List[str]						    =  arr.astype(np.floataa			) / 1_27.5 - 1
  _lowerCAmelCase		: int						    =  np.transpose(_lowerCamelCase    ,[2, 0, 1]			)
  _lowerCAmelCase		: Optional[Any]						    =  torch.from_numpy(_lowerCamelCase			).unsqueeze(0			)
  return image






class 		__A							(				SCREAMING_SNAKE_CASE_	):






     def __init__(	self       ,  a__       ,  a__       ,  a__       ,  ):
       super().__init__()

       self.register_modules(
           unet=a__       ,  scheduler=a__       ,  movq=a__       ,  )
       _lowerCAmelCase		: Optional[Any]						    =  2 ** (len(self.movq.config.block_out_channels						) - 1)






     def 					__A						(	self       ,  a__       ,  a__       ,  a__						):
       # get the original timestep using init_timestep
       _lowerCAmelCase		: Optional[Any]						    =  min(int(num_inference_steps * strength						)       ,  a__						)

       _lowerCAmelCase		: List[Any]						    =  max(num_inference_steps - init_timestep       ,  0						)
       _lowerCAmelCase		: Dict						    =  self.scheduler.timesteps[t_start:]

       return timesteps, num_inference_steps - t_start






     def 					__A						(	self       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__       ,  a__=None						):
       if not isinstance(a__       ,  (torch.Tensor, PIL.Image.Image, list)						):
         raise ValueError(
             F"`image` has to be of type `torch.Tensor`, `PIL.Image.Image` or list but is {type(a__						)}"						)

       _lowerCAmelCase		: Union[str, Any]						    =  image.to(device=a__       ,  dtype=a__						)

       _lowerCAmelCase		: int						    =  batch_size * num_images_per_prompt

       if image.shape[1] == 4:
         _lowerCAmelCase		: int						    =  image

       else:
         if isinstance(a__       ,  a__						) and len(a__						) != batch_size:
           raise ValueError(
               F"You have passed a list of generators of length {len(a__						)}, but requested an effective batch"
               F" size of {batch_size}. Make sure the batch size matches the length of the generators."						)

         elif isinstance(a__       ,  a__						):
           _lowerCAmelCase		: Optional[int]						    =  [
               self.movq.encode(image[i : i + 1]						).latent_dist.sample(generator[i]						) for i in range(a__						)
           ]
           _lowerCAmelCase		: Optional[int]						    =  torch.cat(a__       ,  dim=0						)
         else:
           _lowerCAmelCase		: List[Any]						    =  self.movq.encode(a__						).latent_dist.sample(a__						)

         _lowerCAmelCase		: Dict						    =  self.movq.config.scaling_factor * init_latents

       _lowerCAmelCase		: str						    =  torch.cat([init_latents]       ,  dim=0						)

       _lowerCAmelCase		: Dict						    =  init_latents.shape
       _lowerCAmelCase		: str						    =  randn_tensor(a__       ,  generator=a__       ,  device=a__       ,  dtype=a__						)

       # get latents
       _lowerCAmelCase		: Optional[Any]						    =  self.scheduler.add_noise(a__       ,  a__       ,  a__						)

       _lowerCAmelCase		: int						    =  init_latents

       return latents






     def 					__A						(	self       ,  a__=0						):
       if is_accelerate_available():
         from accelerate import cpu_offload
       else:
         raise ImportError("""Please install accelerate via `pip install accelerate`"""						)

       _lowerCAmelCase		: str						    =  torch.device(F"cuda:{gpu_id}"						)

       _lowerCAmelCase		: int						    =  [
           self.unet,
           self.movq,
       ]
       for cpu_offloaded_model in models:
         if cpu_offloaded_model is not None:
           cpu_offload(a__       ,  a__						)






     def 					__A						(	self       ,  a__=0						):
       if is_accelerate_available() and is_accelerate_version(""">="""       ,  """0.17.0.dev0"""						):
         from accelerate import cpu_offload_with_hook
       else:
         raise ImportError("""`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher."""						)

       _lowerCAmelCase		: Optional[int]						    =  torch.device(F"cuda:{gpu_id}"						)

       if self.device.type != "cpu":
         self.to("""cpu"""       ,  silence_dtype_warnings=a__						)
         torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

       _lowerCAmelCase		: List[str]						    =  None
       for cpu_offloaded_model in [self.unet, self.movq]:
         _lowerCAmelCase    ,			_lowerCAmelCase		: str						    =  cpu_offload_with_hook(a__       ,  a__       ,  prev_module_hook=a__						)

       # We'll offload the last model manually.
       _lowerCAmelCase		: Tuple						    =  hook






     @property
     # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
     def 					__A						(	self						):
       if not hasattr(self.unet       ,  """_hf_hook"""						):
         return self.device
       for module in self.unet.modules():
         if (
             hasattr(a__       ,  """_hf_hook"""						)
             and hasattr(module._hf_hook       ,  """execution_device"""						)
             and module._hf_hook.execution_device is not None
         ):
           return torch.device(module._hf_hook.execution_device						)
       return self.device






     @torch.no_grad()
     @replace_example_docstring(a__						)
     def __call__(	self       ,  a__       ,  a__       ,  a__       ,  a__ = 512       ,  a__ = 512       ,  a__ = 100       ,  a__ = 4.0       ,  a__ = 0.3       ,  a__ = 1       ,  a__ = None       ,  a__ = "pil"       ,  a__ = True       ,  ):
       _lowerCAmelCase		: Dict						    =  self._execution_device

       _lowerCAmelCase		: Optional[Any]						    =  guidance_scale > 1.0

       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Dict						    =  torch.cat(a__       ,  dim=0						)
       _lowerCAmelCase		: Dict						    =  image_embeds.shape[0]
       if isinstance(a__       ,  a__						):
         _lowerCAmelCase		: List[Any]						    =  torch.cat(a__       ,  dim=0						)

       if do_classifier_free_guidance:
         _lowerCAmelCase		: int						    =  image_embeds.repeat_interleave(a__       ,  dim=0						)
         _lowerCAmelCase		: Any						    =  negative_image_embeds.repeat_interleave(a__       ,  dim=0						)

       _lowerCAmelCase		: Optional[int]						    =  torch.cat([negative_image_embeds, image_embeds]       ,  dim=0						).to(dtype=self.unet.dtype       ,  device=a__						)

       if not isinstance(a__       ,  a__						):
         _lowerCAmelCase		: Any						    =  [image]
       if not all(isinstance(a__       ,  (PIL.Image.Image, torch.Tensor)						) for i in image						):
         raise ValueError(
             F"Input is in incorrect format: {[type(a__						) for i in image]}. Currently, we only support  PIL image and pytorch tensor"						)

       _lowerCAmelCase		: Tuple						    =  torch.cat([prepare_image(a__       ,  a__       ,  a__						) for i in image]       ,  dim=0						)
       _lowerCAmelCase		: Union[str, Any]						    =  image.to(dtype=image_embeds.dtype       ,  device=a__						)

       _lowerCAmelCase		: Union[str, Any]						    =  self.movq.encode(a__						)["""latents"""]
       _lowerCAmelCase		: Tuple						    =  latents.repeat_interleave(a__       ,  dim=0						)
       self.scheduler.set_timesteps(a__       ,  device=a__						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Union[str, Any]						    =  self.get_timesteps(a__       ,  a__       ,  a__						)
       _lowerCAmelCase		: Union[str, Any]						    =  timesteps[:1].repeat(batch_size * num_images_per_prompt						)
       _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  downscale_height_and_width(a__       ,  a__       ,  self.movq_scale_factor						)
       _lowerCAmelCase		: List[str]						    =  self.prepare_latents(
           a__       ,  a__       ,  a__       ,  a__       ,  image_embeds.dtype       ,  a__       ,  a__						)
       for i, t in enumerate(self.progress_bar(a__						)						):
         # expand the latents if we are doing classifier free guidance
         _lowerCAmelCase		: List[str]						    =  torch.cat([latents] * 2						) if do_classifier_free_guidance else latents

         _lowerCAmelCase		: int						    =  {"""image_embeds""": image_embeds}
         _lowerCAmelCase		: List[str]						    =  self.unet(
             sample=a__       ,  timestep=a__       ,  encoder_hidden_states=a__       ,  added_cond_kwargs=a__       ,  return_dict=a__       ,  )[0]

         if do_classifier_free_guidance:
           _lowerCAmelCase    ,			_lowerCAmelCase		: Optional[int]						    =  noise_pred.split(latents.shape[1]       ,  dim=1						)
           _lowerCAmelCase    ,			_lowerCAmelCase		: List[Any]						    =  noise_pred.chunk(2						)
           _lowerCAmelCase    ,			_lowerCAmelCase		: Tuple						    =  variance_pred.chunk(2						)
           _lowerCAmelCase		: List[str]						    =  noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
           _lowerCAmelCase		: List[str]						    =  torch.cat([noise_pred, variance_pred_text]       ,  dim=1						)

         if not (
             hasattr(self.scheduler.config       ,  """variance_type"""						)
             and self.scheduler.config.variance_type in ["learned", "learned_range"]
         ):
           _lowerCAmelCase    ,			_lowerCAmelCase		: Dict						    =  noise_pred.split(latents.shape[1]       ,  dim=1						)

         # compute the previous noisy sample x_t -> x_t-1
         _lowerCAmelCase		: List[str]						    =  self.scheduler.step(
             a__       ,  a__       ,  a__       ,  generator=a__       ,  )[0]

       # post-processing
       _lowerCAmelCase		: int						    =  self.movq.decode(a__       ,  force_not_quantize=a__						)["""sample"""]

       if output_type not in ["pt", "np", "pil"]:
         raise ValueError(F"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"						)

       if output_type in ["np", "pil"]:
         _lowerCAmelCase		: List[Any]						    =  image * 0.5 + 0.5
         _lowerCAmelCase		: Any						    =  image.clamp(0       ,  1						)
         _lowerCAmelCase		: Tuple						    =  image.cpu().permute(0       ,  2       ,  3       ,  1						).float().numpy()

       if output_type == "pil":
         _lowerCAmelCase		: List[str]						    =  self.numpy_to_pil(a__						)

       if not return_dict:
         return (image,)

       return ImagePipelineOutput(images=a__						)




