




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






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[Any]:
      return {"hash": hashlib.mda(re.sub(__SCREAMING_SNAKE_CASE				,      ''				,      example['content']		).encode('utf-8'		)		).hexdigest()}






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   Tuple	      =    [len(__SCREAMING_SNAKE_CASE		) for line in example['content'].splitlines()]
      return {"line_mean": np.mean(__SCREAMING_SNAKE_CASE		), "line_max": max(__SCREAMING_SNAKE_CASE		)}






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[Any]:
      snake_case__       :   Optional[Any]	      =    np.mean([c.isalnum() for c in example['content']]		)
      return {"alpha_frac": alpha_frac}






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE		)    ->   int:
      if example["hash"] in uniques:
            uniques.remove(example['hash']		)
            return True
      else:
            return False






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=5		)    ->   Union[str, Any]:
      snake_case__       :   str	      =    ['auto-generated', 'autogenerated', 'automatically generated']
      snake_case__       :   str	      =    example['content'].splitlines()
      for _, line in zip(range(__SCREAMING_SNAKE_CASE		)				,      __SCREAMING_SNAKE_CASE		):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"autogenerated": True}
      else:
            return {"autogenerated": False}






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=5				,      __SCREAMING_SNAKE_CASE=0.05		)    ->   Tuple:
      snake_case__       :   List[str]	      =    ['unit tests', 'test file', 'configuration file']
      snake_case__       :   Union[str, Any]	      =    example['content'].splitlines()
      snake_case__       :   List[str]	      =    0
      snake_case__       :   Any	      =    0
      # first test
      for _, line in zip(range(__SCREAMING_SNAKE_CASE		)				,      __SCREAMING_SNAKE_CASE		):
            for keyword in keywords:
                  if keyword in line.lower():
                        return {"config_or_test": True}
    # second test
      snake_case__       :   List[Any]	      =    example['content'].count('\n'		)
      snake_case__       :   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__      (		__SCREAMING_SNAKE_CASE		)    ->   Tuple:
      snake_case__       :   List[str]	      =    ['def ', 'class ', 'for ', 'while ']
      snake_case__       :   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 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE=4		)    ->   str:
      snake_case__       :   Union[str, Any]	      =    example['content'].splitlines()
      snake_case__       :   Dict	      =    0
      for line in lines:
            counter += line.lower().count('='		)
            if counter > minimum:
                  return {"has_few_assignments": False}
      return {"has_few_assignments": True}






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   List[Any]:
      snake_case__       :   str	      =    tokenizer(example['content']				,      truncation=__SCREAMING_SNAKE_CASE		)['input_ids']
      snake_case__       :   Optional[int]	      =    len(example['content']		) / len(__SCREAMING_SNAKE_CASE		)
      return {"ratio": ratio}






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   str	      =    {}
      results.update(get_hash(__SCREAMING_SNAKE_CASE		)		)
      results.update(line_stats(__SCREAMING_SNAKE_CASE		)		)
      results.update(alpha_stats(__SCREAMING_SNAKE_CASE		)		)
      results.update(char_token_ratio(__SCREAMING_SNAKE_CASE		)		)
      results.update(is_autogenerated(__SCREAMING_SNAKE_CASE		)		)
      results.update(is_config_or_test(__SCREAMING_SNAKE_CASE		)		)
      results.update(has_no_keywords(__SCREAMING_SNAKE_CASE		)		)
      results.update(has_few_assignments(__SCREAMING_SNAKE_CASE		)		)
      return results






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


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

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

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

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

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

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






# Copyright 2021 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import json
import os

from ...utils.constants import SAGEMAKER_PARALLEL_EC2_INSTANCES, TORCH_DYNAMO_MODES
from ...utils.dataclasses import ComputeEnvironment, SageMakerDistributedType
from ...utils.imports import is_botoa_available
from .config_args import SageMakerConfig
from .config_utils import (
    DYNAMO_BACKENDS,
    _ask_field,
    _ask_options,
    _convert_dynamo_backend,
    _convert_mixed_precision,
    _convert_sagemaker_distributed_mode,
    _convert_yes_no_to_bool,
)


if is_botoa_available():
      import botoa  # noqa: F401






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Dict:
      snake_case__       :   Optional[int]	      =    botoa.client('iam'		)

      snake_case__       :   Any	      =    {
          'Version': '2012-10-17',
          'Statement': [
              {'Effect': 'Allow', 'Principal': {'Service': 'sagemaker.amazonaws.com'}, 'Action': 'sts:AssumeRole'}
          ],
      }
      try:
            # create the role, associated with the chosen trust policy
            iam_client.create_role(
                RoleName=__SCREAMING_SNAKE_CASE				,      AssumeRolePolicyDocument=json.dumps(__SCREAMING_SNAKE_CASE				,      indent=2		)		)
            snake_case__       :   str	      =    {
                'Version': '2012-10-17',
                'Statement': [
                    {
                        'Effect': 'Allow',
                        'Action': [
                            'sagemaker:*',
                            'ecr:GetDownloadUrlForLayer',
                            'ecr:BatchGetImage',
                            'ecr:BatchCheckLayerAvailability',
                            'ecr:GetAuthorizationToken',
                            'cloudwatch:PutMetricData',
                            'cloudwatch:GetMetricData',
                            'cloudwatch:GetMetricStatistics',
                            'cloudwatch:ListMetrics',
                            'logs:CreateLogGroup',
                            'logs:CreateLogStream',
                            'logs:DescribeLogStreams',
                            'logs:PutLogEvents',
                            'logs:GetLogEvents',
                            's3:CreateBucket',
                            's3:ListBucket',
                            's3:GetBucketLocation',
                            's3:GetObject',
                            's3:PutObject',
                        ],
                        'Resource': '*',
                    }
                ],
            }
            # attach policy to role
            iam_client.put_role_policy(
                RoleName=__SCREAMING_SNAKE_CASE				,      PolicyName=f"{role_name}_policy_permission"				,      PolicyDocument=json.dumps(__SCREAMING_SNAKE_CASE				,      indent=2		)				,      )
      except iam_client.exceptions.EntityAlreadyExistsException:
            print(f"role {role_name} already exists. Using existing one"		)






def 		UpperCamelCase__      (		__SCREAMING_SNAKE_CASE		)    ->   Optional[int]:
      snake_case__       :   Tuple	      =    botoa.client('iam'		)
      return iam_client.get_role(RoleName=__SCREAMING_SNAKE_CASE		)["Role"]["Arn"]






def 		UpperCamelCase__      (		)    ->   Tuple:
      snake_case__       :   Dict	      =    _ask_options(
          'How do you want to authorize?'				,      ['AWS Profile', 'Credentials (AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY) ']				,      __SCREAMING_SNAKE_CASE				,      )
      snake_case__       :   int	      =    None
      if credentials_configuration == 0:
            snake_case__       :   Any	      =    _ask_field('Enter your AWS Profile name: [default] '				,      default='default'		)
            snake_case__       :   Union[str, Any]	      =    aws_profile
      else:
            print(
                'Note you will need to provide AWS_ACCESS_KEY_ID and AWS_SECRET_ACCESS_KEY when you launch you training script with,'
                '`accelerate launch --aws_access_key_id XXX --aws_secret_access_key YYY`'		)
            snake_case__       :   Dict	      =    _ask_field('AWS Access Key ID: '		)
            snake_case__       :   Tuple	      =    aws_access_key_id

            snake_case__       :   int	      =    _ask_field('AWS Secret Access Key: '		)
            snake_case__       :   Optional[int]	      =    aws_secret_access_key

      snake_case__       :   str	      =    _ask_field('Enter your AWS Region: [us-east-1]'				,      default='us-east-1'		)
      snake_case__       :   Dict	      =    aws_region

      snake_case__       :   Optional[int]	      =    _ask_options(
          'Do you already have an IAM Role for executing Amazon SageMaker Training Jobs?'				,      ['Provide IAM Role name', 'Create new IAM role using credentials']				,      __SCREAMING_SNAKE_CASE				,      )
      if role_management == 0:
            snake_case__       :   Optional[int]	      =    _ask_field('Enter your IAM role name: '		)
      else:
            snake_case__       :   Dict	      =    'accelerate_sagemaker_execution_role'
            print(f"Accelerate will create an iam role \"{iam_role_name}\" using the provided credentials"		)
            _create_iam_role_for_sagemaker(__SCREAMING_SNAKE_CASE		)

      snake_case__       :   List[str]	      =    _ask_field(
          'Do you want to use custom Docker image? [yes/NO]: '				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
      snake_case__       :   Dict	      =    None
      if is_custom_docker_image:
            snake_case__       :   Optional[int]	      =    _ask_field('Enter your Docker image: '				,      lambda __SCREAMING_SNAKE_CASE		: str(__SCREAMING_SNAKE_CASE		).lower()		)

      snake_case__       :   int	      =    _ask_field(
          'Do you want to provide SageMaker input channels with data locations? [yes/NO]: '				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
      snake_case__       :   Dict	      =    None
      if is_sagemaker_inputs_enabled:
            snake_case__       :   Optional[int]	      =    _ask_field(
                'Enter the path to the SageMaker inputs TSV file with columns (channel_name, data_location): '				,      lambda __SCREAMING_SNAKE_CASE		: str(__SCREAMING_SNAKE_CASE		).lower()				,      )

      snake_case__       :   str	      =    _ask_field(
          'Do you want to enable SageMaker metrics? [yes/NO]: '				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
      snake_case__       :   Optional[int]	      =    None
      if is_sagemaker_metrics_enabled:
            snake_case__       :   int	      =    _ask_field(
                'Enter the path to the SageMaker metrics TSV file with columns (metric_name, metric_regex): '				,      lambda __SCREAMING_SNAKE_CASE		: str(__SCREAMING_SNAKE_CASE		).lower()				,      )

      snake_case__       :   List[str]	      =    _ask_options(
          'What is the distributed mode?'				,      ['No distributed training', 'Data parallelism']				,      _convert_sagemaker_distributed_mode				,      )
      snake_case__       :   Optional[int]	      =    {}
      snake_case__       :   str	      =    _ask_field(
          'Do you wish to optimize your script with torch dynamo?[yes/NO]:'				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
      if use_dynamo:
            snake_case__       :   List[str]	      =    'dynamo_'
            snake_case__       :   Tuple	      =    _ask_options(
                'Which dynamo backend would you like to use?'				,      [x.lower() for x in DYNAMO_BACKENDS]				,      _convert_dynamo_backend				,      default=2				,      )
            snake_case__       :   Optional[int]	      =    _ask_field(
                'Do you want to customize the defaults sent to torch.compile? [yes/NO]: '				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )

            if use_custom_options:
                  snake_case__       :   int	      =    _ask_options(
                      'Which mode do you want to use?'				,      __SCREAMING_SNAKE_CASE				,      lambda __SCREAMING_SNAKE_CASE		: TORCH_DYNAMO_MODES[int(__SCREAMING_SNAKE_CASE		)]				,      default='default'				,      )
                  snake_case__       :   Dict	      =    _ask_field(
                      'Do you want the fullgraph mode or it is ok to break model into several subgraphs? [yes/NO]: '				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
                  snake_case__       :   List[Any]	      =    _ask_field(
                      'Do you want to enable dynamic shape tracing? [yes/NO]: '				,      _convert_yes_no_to_bool				,      default=__SCREAMING_SNAKE_CASE				,      error_message='Please enter yes or no.'				,      )
      snake_case__       :   int	      =    'Which EC2 instance type you want to use for your training?'
      if distributed_type != SageMakerDistributedType.NO:
            snake_case__       :   Tuple	      =    _ask_options(
                __SCREAMING_SNAKE_CASE				,      __SCREAMING_SNAKE_CASE				,      lambda __SCREAMING_SNAKE_CASE		: SAGEMAKER_PARALLEL_EC2_INSTANCES[int(__SCREAMING_SNAKE_CASE		)]		)
      else:
            eca_instance_query += "? [ml.p3.2xlarge]:"
            snake_case__       :   Dict	      =    _ask_field(__SCREAMING_SNAKE_CASE				,      lambda __SCREAMING_SNAKE_CASE		: str(__SCREAMING_SNAKE_CASE		).lower()				,      default='ml.p3.2xlarge'		)

      snake_case__       :   Optional[Any]	      =    1
      if distributed_type in (SageMakerDistributedType.DATA_PARALLEL, SageMakerDistributedType.MODEL_PARALLEL):
            snake_case__       :   Union[str, Any]	      =    _ask_field(
                'How many machines do you want use? [1]: '				,      __SCREAMING_SNAKE_CASE				,      default=1				,      )

      snake_case__       :   Optional[int]	      =    _ask_options(
          'Do you wish to use FP16 or BF16 (mixed precision)?'				,      ['no', 'fp16', 'bf16', 'fp8']				,      _convert_mixed_precision				,      )

      if use_dynamo and mixed_precision == "no":
            print(
                'Torch dynamo used without mixed precision requires TF32 to be efficient. Accelerate will enable it by default when launching your scripts.'		)

      return SageMakerConfig(
          image_uri=__SCREAMING_SNAKE_CASE				,      compute_environment=ComputeEnvironment.AMAZON_SAGEMAKER				,      distributed_type=__SCREAMING_SNAKE_CASE				,      use_cpu=__SCREAMING_SNAKE_CASE				,      dynamo_config=__SCREAMING_SNAKE_CASE				,      eca_instance_type=__SCREAMING_SNAKE_CASE				,      profile=__SCREAMING_SNAKE_CASE				,      region=__SCREAMING_SNAKE_CASE				,      iam_role_name=__SCREAMING_SNAKE_CASE				,      mixed_precision=__SCREAMING_SNAKE_CASE				,      num_machines=__SCREAMING_SNAKE_CASE				,      sagemaker_inputs_file=__SCREAMING_SNAKE_CASE				,      sagemaker_metrics_file=__SCREAMING_SNAKE_CASE				,      )
