
'''simple docstring'''






from typing import Optional, Tuple, Union

import tensorflow as tf

from ...activations_tf import ACTaFN
from ...file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
from ...modeling_tf_outputs import (
    TFBaseModelOutputWithNoAttention,
    TFBaseModelOutputWithPoolingAndNoAttention,
    TFSequenceClassifierOutput,
)
from ...modeling_tf_utils import TFPreTrainedModel, TFSequenceClassificationLoss, keras_serializable, unpack_inputs
from ...tf_utils import shape_list
from ...utils import logging
from .configuration_regnet import RegNetConfig


UpperCAmelCase_       :   Any   =       logging.get_logger(__name__)

# General docstring
UpperCAmelCase_       :   str   =       'RegNetConfig'

# Base docstring
UpperCAmelCase_       :   Tuple   =       'facebook/regnet-y-040'
UpperCAmelCase_       :   Optional[int]   =       [1, 1088, 7, 7]

# Image classification docstring
UpperCAmelCase_       :   Dict   =       'facebook/regnet-y-040'
UpperCAmelCase_       :   Tuple   =       'tabby, tabby cat'

UpperCAmelCase_       :   Optional[Any]   =       [
    'facebook/regnet-y-040',
    # See all regnet models at https://huggingface.co/models?filter=regnet
]

class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Any,__A      :       int,__A      :       int = 3,__A      :       int = 1,__A      :       int = 1,__A      :       Optional[str] = "relu",**__A      :       str,):
          super().__init__(**__A     )
          # The padding and conv has been verified in
          # https://colab.research.google.com/gist/sayakpaul/854bc10eeaf21c9ee2119e0b9f3841a7/scratchpad.ipynb
          _lowerCamelCase				:   Optional[int]								=   tf.keras.layers.ZeroPaddingaD(padding=kernel_size // 2     )
          _lowerCamelCase				:   str								=   tf.keras.layers.ConvaD(
              filters=__A,kernel_size=__A,strides=__A,padding="VALID",groups=__A,use_bias=__A,name="convolution",)
          _lowerCamelCase				:   List[str]								=   tf.keras.layers.BatchNormalization(epsilon=1e-5,momentum=0.9,name="normalization"     )
          _lowerCamelCase				:   Tuple								=   ACTaFN[activation] if activation is not None else tf.identity



    def   lowerCamelCase_  (	self      :       Any,__A      :       Optional[int]     ):
          _lowerCamelCase				:   int								=   self.convolution(self.padding(__A     )     )
          _lowerCamelCase				:   List[str]								=   self.normalization(__A     )
          _lowerCamelCase				:   Union[str, Any]								=   self.activation(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Tuple,__A      :       RegNetConfig,**__A      :       List[Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   Tuple								=   config.num_channels
          _lowerCamelCase				:   int								=   TFRegNetConvLayer(
              out_channels=config.embedding_size,kernel_size=3,stride=2,activation=config.hidden_act,name="embedder",)



    def   lowerCamelCase_  (	self      :       Dict,__A      :       str     ):
          _lowerCamelCase				:   List[str]								=   shape_list(__A     )[1]
          if tf.executing_eagerly() and num_channels != self.num_channels:
                raise ValueError(
                    "Make sure that the channel dimension of the pixel values match with the one set in the configuration."     )

          # When running on CPU, `tf.keras.layers.Conv2D` doesn't support `NCHW` format.
          # So change the input format from `NCHW` to `NHWC`.
          # shape = (batch_size, in_height, in_width, in_channels=num_channels)
          _lowerCamelCase				:   str								=   tf.transpose(__A,perm=(0, 2, 3, 1)     )
          _lowerCamelCase				:   Optional[int]								=   self.embedder(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Union[str, Any],__A      :       int,__A      :       int = 2,**__A      :       List[str]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   List[str]								=   tf.keras.layers.ConvaD(
              filters=__A,kernel_size=1,strides=__A,use_bias=__A,name="convolution"     )
          _lowerCamelCase				:   int								=   tf.keras.layers.BatchNormalization(epsilon=1e-5,momentum=0.9,name="normalization"     )



    def   lowerCamelCase_  (	self      :       str,__A      :       tf.Tensor,__A      :       bool = False     ):
          return self.normalization(self.convolution(__A     ),training=__A     )


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Union[str, Any],__A      :       int,__A      :       int,**__A      :       Optional[Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   List[Any]								=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=__A,name="pooler"     )
          _lowerCamelCase				:   Optional[Any]								=   [
              tf.keras.layers.ConvaD(filters=__A,kernel_size=1,activation="relu",name="attention.0"     ),
              tf.keras.layers.ConvaD(filters=__A,kernel_size=1,activation="sigmoid",name="attention.2"     ),
          ]



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       int     ):
          # [batch_size, h, w, num_channels] -> [batch_size, 1, 1, num_channels]
          _lowerCamelCase				:   Union[str, Any]								=   self.pooler(__A     )
          for layer_module in self.attention:
                _lowerCamelCase				:   List[Any]								=   layer_module(__A     )
          _lowerCamelCase				:   List[str]								=   hidden_state * pooled
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Any,__A      :       RegNetConfig,__A      :       int,__A      :       int,__A      :       int = 1,**__A      :       str     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   Dict								=   in_channels != out_channels or stride != 1
          _lowerCamelCase				:   Dict								=   max(1,out_channels // config.groups_width     )
          _lowerCamelCase				:   Any								=   (
              TFRegNetShortCut(__A,stride=__A,name="shortcut"     )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear",name="shortcut"     )
          )
          # `self.layers` instead of `self.layer` because that is a reserved argument.
          _lowerCamelCase				:   Optional[int]								=   [
              TFRegNetConvLayer(__A,kernel_size=1,activation=config.hidden_act,name="layer.0"     ),
              TFRegNetConvLayer(
                  __A,stride=__A,groups=__A,activation=config.hidden_act,name="layer.1"     ),
              TFRegNetConvLayer(__A,kernel_size=1,activation=__A,name="layer.2"     ),
          ]
          _lowerCamelCase				:   Union[str, Any]								=   ACTaFN[config.hidden_act]



    def   lowerCamelCase_  (	self      :       Tuple,__A      :       Optional[int]     ):
          _lowerCamelCase				:   List[str]								=   hidden_state
          for layer_module in self.layers:
                _lowerCamelCase				:   int								=   layer_module(__A     )
          _lowerCamelCase				:   int								=   self.shortcut(__A     )
          hidden_state += residual
          _lowerCamelCase				:   int								=   self.activation(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       Union[str, Any],__A      :       RegNetConfig,__A      :       int,__A      :       int,__A      :       int = 1,**__A      :       List[str]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   str								=   in_channels != out_channels or stride != 1
          _lowerCamelCase				:   str								=   max(1,out_channels // config.groups_width     )
          _lowerCamelCase				:   Optional[Any]								=   (
              TFRegNetShortCut(__A,stride=__A,name="shortcut"     )
              if should_apply_shortcut
              else tf.keras.layers.Activation("linear",name="shortcut"     )
          )
          _lowerCamelCase				:   Dict								=   [
              TFRegNetConvLayer(__A,kernel_size=1,activation=config.hidden_act,name="layer.0"     ),
              TFRegNetConvLayer(
                  __A,stride=__A,groups=__A,activation=config.hidden_act,name="layer.1"     ),
              TFRegNetSELayer(__A,reduced_channels=int(round(in_channels / 4     )     ),name="layer.2"     ),
              TFRegNetConvLayer(__A,kernel_size=1,activation=__A,name="layer.3"     ),
          ]
          _lowerCamelCase				:   Any								=   ACTaFN[config.hidden_act]



    def   lowerCamelCase_  (	self      :       str,__A      :       str     ):
          _lowerCamelCase				:   Any								=   hidden_state
          for layer_module in self.layers:
                _lowerCamelCase				:   Optional[int]								=   layer_module(__A     )
          _lowerCamelCase				:   Optional[Any]								=   self.shortcut(__A     )
          hidden_state += residual
          _lowerCamelCase				:   Optional[int]								=   self.activation(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       str,__A      :       RegNetConfig,__A      :       int,__A      :       int,__A      :       int = 2,__A      :       int = 2,**__A      :       Dict     ):
          super().__init__(**__A     )

          _lowerCamelCase				:   List[str]								=   TFRegNetXLayer if config.layer_type == "x" else TFRegNetYLayer
          _lowerCamelCase				:   int								=   [
              # downsampling is done in the first layer with stride of 2
              layer(__A,__A,__A,stride=__A,name="layers.0"     ),
              *[layer(__A,__A,__A,name=f'layers.{i+1}'     ) for i in range(depth - 1     )],
          ]



    def   lowerCamelCase_  (	self      :       Dict,__A      :       Dict     ):
          for layer_module in self.layers:
                _lowerCamelCase				:   str								=   layer_module(__A     )
          return hidden_state


class  UpperCAmelCase__						(       tf.keras.layers.Layer ):



    def __init__(	self      :       str,__A      :       RegNetConfig,**__A      :       Union[str, Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   Union[str, Any]								=   []
          # based on `downsample_in_first_stage`, the first layer of the first stage may or may not downsample the input
          self.stages.append(
              TFRegNetStage(
                  __A,config.embedding_size,config.hidden_sizes[0],stride=2 if config.downsample_in_first_stage else 1,depth=config.depths[0],name="stages.0",)     )
          _lowerCamelCase				:   str								=   zip(config.hidden_sizes,config.hidden_sizes[1:]     )
          for i, ((in_channels, out_channels), depth) in enumerate(zip(__A,config.depths[1:]     )     ):
                self.stages.append(TFRegNetStage(__A,__A,__A,depth=__A,name=f'stages.{i+1}'     )     )



    def   lowerCamelCase_  (	self      :       Optional[int],__A      :       tf.Tensor,__A      :       bool = False,__A      :       bool = True     ):
          _lowerCamelCase				:   Dict								=   () if output_hidden_states else None

          for stage_module in self.stages:
                if output_hidden_states:
                      _lowerCamelCase				:   str								=   hidden_states + (hidden_state,)

                _lowerCamelCase				:   List[Any]								=   stage_module(__A     )

          if output_hidden_states:
                _lowerCamelCase				:   List[str]								=   hidden_states + (hidden_state,)

          if not return_dict:
                return tuple(v for v in [hidden_state, hidden_states] if v is not None     )

          return TFBaseModelOutputWithNoAttention(last_hidden_state=__A,hidden_states=__A     )


@keras_serializable
class  UpperCAmelCase__						(       tf.keras.layers.Layer ):
    lowerCAmelCase_				    =		RegNetConfig



    def __init__(	self      :       Dict,__A      :       List[str],**__A      :       Union[str, Any]     ):
          super().__init__(**__A     )
          _lowerCamelCase				:   List[Any]								=   config
          _lowerCamelCase				:   str								=   TFRegNetEmbeddings(__A,name="embedder"     )
          _lowerCamelCase				:   str								=   TFRegNetEncoder(__A,name="encoder"     )
          _lowerCamelCase				:   Optional[Any]								=   tf.keras.layers.GlobalAveragePoolingaD(keepdims=__A,name="pooler"     )



    @unpack_inputs
    def   lowerCamelCase_  (	self      :       Union[str, Any],__A      :       tf.Tensor,__A      :       Optional[bool] = None,__A      :       Optional[bool] = None,__A      :       bool = False,):
          _lowerCamelCase				:   str								=   (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _lowerCamelCase				:   Optional[int]								=   return_dict if return_dict is not None else self.config.use_return_dict

          _lowerCamelCase				:   Union[str, Any]								=   self.embedder(__A,training=__A     )

          _lowerCamelCase				:   Any								=   self.encoder(
              __A,output_hidden_states=__A,return_dict=__A,training=__A     )

          _lowerCamelCase				:   Union[str, Any]								=   encoder_outputs[0]
          _lowerCamelCase				:   str								=   self.pooler(__A     )

          # Change to NCHW output format have uniformity in the modules
          _lowerCamelCase				:   Optional[Any]								=   tf.transpose(__A,perm=(0, 3, 1, 2)     )
          _lowerCamelCase				:   str								=   tf.transpose(__A,perm=(0, 3, 1, 2)     )

          # Change the other hidden state outputs to NCHW as well
          if output_hidden_states:
                _lowerCamelCase				:   Tuple								=   tuple([tf.transpose(__A,perm=(0, 3, 1, 2)     ) for h in encoder_outputs[1]]     )

          if not return_dict:
                return (last_hidden_state, pooled_output) + encoder_outputs[1:]

          return TFBaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=__A,pooler_output=__A,hidden_states=hidden_states if output_hidden_states else encoder_outputs.hidden_states,)


class  UpperCAmelCase__						(       A ):
    lowerCAmelCase_				    =		RegNetConfig
    lowerCAmelCase_				    =		'regnet'
    lowerCAmelCase_				    =		'pixel_values'



    @property
    def   lowerCamelCase_  (	self      :       List[str]     ):
          return {"pixel_values": tf.TensorSpec(shape=(None, self.config.num_channels, 2_2_4, 2_2_4),dtype=tf.floataa     )}


UpperCAmelCase_       :   str   =       R'\n    Parameters:\n    This model is a Tensorflow\n    [tf.keras.layers.Layer](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Layer) sub-class. Use it as a\n    regular Tensorflow Module and refer to the Tensorflow documentation for all matter related to general usage and\n    behavior.\n        config ([`RegNetConfig`]): Model configuration class with all the parameters of the model.\n            Initializing with a config file does not load the weights associated with the model, only the\n            configuration. Check out the [`~TFPreTrainedModel.from_pretrained`] method to load the model weights.\n'

UpperCAmelCase_       :   int   =       R'\n    Args:\n        pixel_values (`tf.Tensor` of shape `(batch_size, num_channels, height, width)`):\n            Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See\n            [`ConveNextImageProcessor.__call__`] for details.\n        output_hidden_states (`bool`, *optional*):\n            Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for\n            more detail.\n        return_dict (`bool`, *optional*):\n            Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple.\n'

@add_start_docstrings(
    'The bare RegNet model outputting raw features without any specific head on top.'						,	A						,	)
class  UpperCAmelCase__						(       A ):



    def __init__(	self      :       Optional[Any],__A      :       RegNetConfig,*__A      :       int,**__A      :       Optional[Any]     ):
          super().__init__(__A,*__A,**__A     )
          _lowerCamelCase				:   Union[str, Any]								=   TFRegNetMainLayer(__A,name="regnet"     )



    @unpack_inputs
    @add_start_docstrings_to_model_forward(__A     )
    @add_code_sample_docstrings(
        checkpoint=_CHECKPOINT_FOR_DOC,output_type=__A,config_class=_CONFIG_FOR_DOC,modality="vision",expected_output=_EXPECTED_OUTPUT_SHAPE,)
    def   lowerCamelCase_  (	self      :       str,__A      :       tf.Tensor,__A      :       Optional[bool] = None,__A      :       Optional[bool] = None,__A      :       Optional[int]=False,):
          _lowerCamelCase				:   str								=   (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _lowerCamelCase				:   List[Any]								=   return_dict if return_dict is not None else self.config.use_return_dict

          _lowerCamelCase				:   str								=   self.regnet(
              pixel_values=__A,output_hidden_states=__A,return_dict=__A,training=__A,)
          if not return_dict:
                return (outputs[0],) + outputs[1:]

          return TFBaseModelOutputWithPoolingAndNoAttention(
              last_hidden_state=outputs.last_hidden_state,pooler_output=outputs.pooler_output,hidden_states=outputs.hidden_states,)


@add_start_docstrings(
    '\n    RegNet Model with an image classification head on top (a linear layer on top of the pooled features), e.g. for\n    ImageNet.\n    '						,	A						,	)
class  UpperCAmelCase__						(       A						,	A ):



    def __init__(	self      :       Union[str, Any],__A      :       RegNetConfig,*__A      :       Optional[int],**__A      :       Tuple     ):
          super().__init__(__A,*__A,**__A     )
          _lowerCamelCase				:   List[Any]								=   config.num_labels
          _lowerCamelCase				:   Union[str, Any]								=   TFRegNetMainLayer(__A,name="regnet"     )
          # classification head
          _lowerCamelCase				:   Dict								=   [
              tf.keras.layers.Flatten(),
              tf.keras.layers.Dense(config.num_labels,name="classifier.1"     ) if config.num_labels > 0 else tf.identity,
          ]



    @unpack_inputs
    @add_start_docstrings_to_model_forward(__A     )
    @add_code_sample_docstrings(
        checkpoint=_IMAGE_CLASS_CHECKPOINT,output_type=__A,config_class=_CONFIG_FOR_DOC,expected_output=_IMAGE_CLASS_EXPECTED_OUTPUT,)
    def   lowerCamelCase_  (	self      :       Optional[Any],__A      :       tf.Tensor = None,__A      :       tf.Tensor = None,__A      :       bool = None,__A      :       bool = None,__A      :       Union[str, Any]=False,):
          _lowerCamelCase				:   Dict								=   (
              output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
          )
          _lowerCamelCase				:   Optional[Any]								=   return_dict if return_dict is not None else self.config.use_return_dict

          _lowerCamelCase				:   Tuple								=   self.regnet(
              __A,output_hidden_states=__A,return_dict=__A,training=__A     )

          _lowerCamelCase				:   Optional[int]								=   outputs.pooler_output if return_dict else outputs[1]

          _lowerCamelCase				:   int								=   self.classifier[0](__A     )
          _lowerCamelCase				:   Optional[int]								=   self.classifier[1](__A     )

          _lowerCamelCase				:   List[str]								=   None if labels is None else self.hf_compute_loss(labels=__A,logits=__A     )

          if not return_dict:
                _lowerCamelCase				:   List[str]								=   (logits,) + outputs[2:]
                return ((loss,) + output) if loss is not None else output

          return TFSequenceClassifierOutput(loss=__A,logits=__A,hidden_states=outputs.hidden_states     )

'''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


UpperCAmelCase_       :   Any   =       re.compile(R'\s+')







def 		A_       (	_lowerCAmelCase   :    List[str]							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    Union[str, Any]							):



      """simple docstring"""




      _lowerCamelCase				:   Union[str, Any]								=   [len(_lowerCAmelCase							) for line in example["content"].splitlines()]
      return {"line_mean": np.mean(_lowerCAmelCase							), "line_max": max(_lowerCAmelCase							)}







def 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   Union[str, Any]								=   np.mean([c.isalnum() for c in example["content"]]							)
      return {"alpha_frac": alpha_frac}







def 		A_       (	_lowerCAmelCase   :    str				,	_lowerCAmelCase   :    Tuple							):



      """simple docstring"""




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







def 		A_       (	_lowerCAmelCase   :    Optional[int]				,	_lowerCAmelCase   :    Dict=5							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[int]								=   ["auto-generated", "autogenerated", "automatically generated"]
      _lowerCamelCase				:   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 		A_       (	_lowerCAmelCase   :    List[str]				,	_lowerCAmelCase   :    Optional[Any]=5				,	_lowerCAmelCase   :    List[Any]=0.0_5							):



      """simple docstring"""




      _lowerCamelCase				:   Union[str, Any]								=   ["unit tests", "test file", "configuration file"]
      _lowerCamelCase				:   int								=   example["content"].splitlines()
      _lowerCamelCase				:   int								=   0
      _lowerCamelCase				:   Optional[Any]								=   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				:   Union[str, Any]								=   example["content"].count("\n"							)
      _lowerCamelCase				:   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 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




      _lowerCamelCase				:   str								=   ["def ", "class ", "for ", "while "]
      _lowerCamelCase				:   List[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 		A_       (	_lowerCAmelCase   :    Dict				,	_lowerCAmelCase   :    int=4							):



      """simple docstring"""




      _lowerCamelCase				:   List[Any]								=   example["content"].splitlines()
      _lowerCamelCase				:   Union[str, Any]								=   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   :    List[str]							):



      """simple docstring"""




      _lowerCamelCase				:   List[Any]								=   tokenizer(example["content"]				,	truncation=_lowerCAmelCase							)["input_ids"]
      _lowerCamelCase				:   str								=   len(example["content"]							) / len(_lowerCAmelCase							)
      return {"ratio": ratio}







def 		A_       (	_lowerCAmelCase   :    Optional[Any]							):



      """simple docstring"""




      _lowerCamelCase				:   Optional[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 		A_       (	_lowerCAmelCase   :    Union[str, Any]				,	_lowerCAmelCase   :    Optional[Any]				,	_lowerCAmelCase   :    str							):



      """simple docstring"""




      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 		A_       (	_lowerCAmelCase   :    int							):



      """simple docstring"""




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

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

# Run preprocessing
UpperCAmelCase_       :   Tuple   =       time.time()
UpperCAmelCase_       :   Optional[int]   =       ds.map(preprocess, num_proc=args.num_workers)
print(f'''Time to preprocess dataset: {time.time()-t_start:.2f}''')

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

# Deduplicate data and apply heuristics
UpperCAmelCase_       :   Optional[int]   =       time.time()
UpperCAmelCase_       :   int   =       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_       :   Tuple   =       time.time()
 UpperCAmelCase_,   UpperCAmelCase_       :   List[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
UpperCAmelCase_       :   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)

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

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